Progresif: Jurnal Ilmiah Komputer
type: channel
description: <img style="font-size: 0.875rem;" src="[binary]" alt="" /> Progresif: Jurnal Ilmiah Komputer e-ISSN: 2685-0877 adalah jurnal ilmiah peer-review yang diterbitkan oleh STMIK Banjarbaru. Jurnal ini merupakan jurnal kases terbuka (open access), sehingga seluruh artikel yang diterbitkan dapat diakses secara bebas. Jurnal Jutisi terbit 4 Nomor dalam 1 tahun: bulan Januari, April, Juli, dan Oktober. Naskah yang dapat diterbitkan dapat berupa naskah Hasil Riset, naskah Konseptual, dan Naskah Review (SLR) pada bidang Informatika & Komputer. Kontributor dapat berasal dari kalangan Peneliti dan Akademisi (Umum, Dosen, dan Mahasiswa). Jurnal Jutisi saat ini terdaftar di CrossRef dengan awalan Digital Object Identifier (DOI) 10.35889, juga Terindeks pada database Google Schoolar, Garuda, dan SINTA 4 (hingga Tahun 2030).
publisher: STMIK Banjarbaru
language: en-US
publication name: Progresif: Jurnal Ilmiah Komputer
issn: 0216-3284
items: Phishing URL Detection Using Hybrid CNN-BiLSTM Character-Level Deep Learning
items: Diagnosa Penyakit Jagung Menggunakan Dempster Shafer Tanaman Pangan Dan Hortikultural Kalimantan Barat
items: Deteksi Diabetes Menggunakan Analisis Citra Kuku Berbasis Vision Transformer CNN-LSTM
items: Implementasi Transfer Learning Model InceptionV3 Untuk Deteksi Penyakit Daun Jagung Berbasis Mobile
items: Rancang Bangun Sistem Informasi Sirkulasi Data Perpustakaan SMAN 2 Subang
items: Analisis Kinerja Algoritma Naïve Bayes Dalam Prediksi Tingkat Persediaan Susu Formula Pada Sektor Ritel
items: Perancangan Sistem Informasi Administrasi Kasir (Point of Sale) Berbasis Web pada Kedai Selera Rasa
items: Klasifikasi Named Entity Recognition Pada Cerita Pendek Bahasa Indonesia Menggunakan Model Indobert
items: Sistem Survei Kepuasan Masyarakat Terhadap Pelayanan Disdukcapil Banjarbaru Dengan K-Means Dan Text Mining
items: Imputasi Curah Hujan ERA5 Menggunakan Random Forest dan XGBoost di Maluku Utara
items: Penentuan Pelanggan Loyal Laundry Baik Clean Menggunakan Metode MOORA
items: Analisis Sentimen Ulasan Aplikasi Gojek Menggunakan Support Vector Machine
items: Model Aplikasi Pembelajaran Huruf Hijaiyah Di Taman Pendidikan Al-Qur'an Halaby Banjarbaru
items: Perancangan Failure-Aware Self-Healing Workflow pada Sistem Event-Driven untuk Pemulihan Proses Layanan Digital Secara Otomatis
items: Analisis Peramalan Penjualan Gorden Menggunakan Metode Single Moving Average dengan Perbandingan Periode pada Usaha Ritel Fadhila Gorden
items: Klasifikasi Teks Depresi Pada Media Sosial Menggunakan Convolutional Neural Network Dengan Fasttext Embedding
items: Implementasi Model Hybrid IndoBERT - LinearSVC untuk Deteksi Spam Judol Obfuscated pada Komentar YouTube
items: Pengukuran Dampak Interferensi Sinyal Co-Channel dan Adjacent Channel pada Kualitas Layanan Jaringan Nirkabel IEEE 802.11n
items: Rancang Bangun Aplikasi Web Preventif Computer Vision Syndrome Berbasis Monitoring Postur Leher Real-Time Dengan Metode Personalized Baseline
items: Komparasi YOLOv8 Nano dan YOLO11 Nano untuk Klasifikasi Penyakit Daun Tomat pada Perangkat Edge
items: Pengembangan Aplikasi Sistem Prediksi Kasus Demam Berdarah Dengue Berdasarkan Curah Hujan Berbasis Web
items: Rancang Bangun Sistem Pendukung Keputusan Monitoring Jaringan GPON Berbasis Rule-Based Classification
items: Analisis Sentimen Kesehatan Mental pada Media Sosial X Menggunakan BERT dengan Pendekatan XAI Berbasis SHAP dan LIME
items: Analisis Kecenderungan Depresi Remaja dalam Penggunaan Media Sosial dengan Algoritma Random Forest
items: Implementasi Sistem Inventory Coffee shop dengan Integrasi Resep dan Metode FEFO

Phishing URL Detection Using Hybrid CNN-BiLSTM Character-Level Deep Learning
type: item
description: Phishing attacks conducted through fraudulent URLs continue to become a significant cybersecurity challenge, demanding detection methods that are both rapid and reliable. This research introduces a hybrid deep learning model that integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) architectures for automated phishing URL detection. The CNN component is utilized to identify lexical and character-level patterns within URLs, whereas BiLSTM captures contextual dependencies from both forward and backward sequence directions. The proposed model was trained using 450,176 labeled URL samples with character-level tokenization, minimizing the need for manual feature extraction. Experimental evaluations demonstrated excellent performance, achieving an accuracy of 99.78%, while precision and recall consistently remained above 99% according to confusion matrix analysis. Furthermore, the trained model was implemented into a Python-based check_link() function that integrates deep learning prediction scores with rule-based analysis to categorize URLs into safe, suspicious, or dangerous classes. Nevertheless, inference testing revealed that the model is sensitive to incomplete URL formats, particularly legitimate domains lacking the www prefix. In general, the CNN-BiLSTM approach proved highly effective for large-scale phishing URL classification, although incorporating additional external features may help reduce false positive predictions. Keywords: Phising; URL; Deep learning; Convolutional Neural Network; Bidirectional Long Short-Term Memory.
creator: Alrafiful Rahman
creator: Pratiwi Rachmadi
creator: Farah Kaylila
creator: Sae Khatami
creator: Maria Stefani
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-29
publication date: 2026-07-29
volume: 22
number: 3
starting page: 617
ending page: 626
doi: 10.35889/progresif.v22i3.3753

Diagnosa Penyakit Jagung Menggunakan Dempster Shafer Tanaman Pangan Dan Hortikultural Kalimantan Barat
type: item
description: Disease attacks on corn plants can reduce productivity, requiring a system capable of rapid and accurate diagnosis. This study aims to develop a web-based expert system for diagnosing corn plant diseases using the Dempster-Shafer method. The system was built using a knowledge base obtained from experts at the Food Crops and Horticulture Service of West Kalimantan Province. The variables processed included symptom data, disease type, belief value, and control solutions. The diagnosis process was carried out by combining the belief value for each symptom using the Dempster-Shafer combination rule to generate a confidence level for the disease. Testing was conducted by comparing the system's diagnosis results with expert diagnoses on 20 test data sets. The test results showed the system achieved 90% accuracy, indicating that the Dempster-Shafer method is effective in handling symptom uncertainty and is capable of producing accurate corn plant disease diagnoses. Keyword: Corn Plants; Expert System; Dempster Shafer Abstrak Serangan penyakit pada tanaman jagung dapat menurunkan produktivitas sehingga diperlukan sistem yang mampu membantu diagnosis secara cepat dan akurat. Penelitian ini bertujuan mengembangkan sistem pakar berbasis web untuk mendiagnosis penyakit tanaman jagung menggunakan metode Dempster-Shafer. Sistem dibangun menggunakan basis pengetahuan yang diperoleh dari pakar di Dinas Tanaman Pangan dan Hortikultura Provinsi Kalimantan Barat. Variabel yang diproses meliputi data gejala, jenis penyakit, nilai belief, dan solusi pengendalian. Proses diagnosis dilakukan dengan menggabungkan nilai belief setiap gejala menggunakan aturan kombinasi Dempster-Shafer untuk menghasilkan tingkat keyakinan terhadap penyakit. Pengujian dilakukan dengan membandingkan hasil diagnosis sistem terhadap diagnosis pakar pada 20 data uji. Hasil pengujian menunjukkan sistem memperoleh akurasi sebesar 90%, sehingga metode Dempster-Shafer efektif dalam menangani ketidakpastian gejala dan…
creator: Putri Nabila Aprilia
creator: Menur Wahyu Pangestika
creator: Alda Cendekia Siregar
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-29
publication date: 2026-07-29
volume: 22
number: 3
starting page: 627
ending page: 637
doi: 10.35889/progresif.v22i3.3833

Deteksi Diabetes Menggunakan Analisis Citra Kuku Berbasis Vision Transformer CNN-LSTM
type: item
description: Diabetes mellitus is a chronic metabolic disease that requires early detection to prevent complications. However, commonly used diagnostic methods are still invasive and require laboratory testing. This study aims to develop a diabetes detection model based on nail image analysis using the Hybrid Vision Transformer–Convolutional Neural Network–Long Short-Term Memory (Vision Transformer–CNN–LSTM) method as a non-invasive approach. The study included dataset collection, image preprocessing, including resizing, normalization, segmentation, dataset partitioning, model training, and evaluation using a confusion matrix. The Vision Transformer was used to capture a global image representation, the Convolutional Neural Network extracted local features, and the Long Short-Term Memory enhanced the feature representation before the classification process. Test results showed that the model achieved 93.33% accuracy, 91.89% precision, 94.44% recall, 93.13% F1-score, and an Area Under the Curve of 0.972. These results demonstrate that the proposed model is capable of accurately detecting diabetes and has the potential to be a fast, easy, and non-invasive alternative for initial screening based on nail images. Keywords: Diabetes mellitus; nail image; Vision Transformer; Convolutional Neural Network–Long Short-Term Memory; Early detection. Abstrak Diabetes mellitus menjadi penyakit metabolik kronis yang memerlukan deteksi dini untuk mencegah terjadinya komplikasi, namun metode diagnosis yang umum digunakan masih bersifat invasif dan memerlukan pemeriksaan laboratorium. Penelitian ini bertujuan mengembangkan model deteksi diabetes berbasis analisis citra kuku menggunakan metode Hybrid Vision Transformer–Convolutional Neural Network–Long Short-Term Memory (Vision Transformer–CNN–LSTM) sebagai pendekatan noninvasif. Penelitian dilakukan melalui tahapan pengumpulan dataset, preprocessing citra berupa resize, normalisasi, segmentasi, pembagian dataset, pelatihan model, dan evaluasi meng…
creator: Annas Prasetio
creator: Sri Handayani
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-29
publication date: 2026-07-29
volume: 22
number: 3
starting page: 638
ending page: 655
doi: 10.35889/progresif.v22i3.3941

Implementasi Transfer Learning Model InceptionV3 Untuk Deteksi Penyakit Daun Jagung Berbasis Mobile
type: item
description: Maize represents a highly crucial agricultural staple within the Indonesian nation, where productivity is often affected by leaf diseases. Diseases like blight, common rust, and gray leaf spot prove hard to recognize by hand since the task demands time and risks personnel mistakes. Current research constructs a maize leaf disease classification model applying transfer learning based on InceptionV3 and evaluates the capabilities of three optimizing algorithms, specifically Adam, Stochastic Gradient Descent (SGD), and RMSProp. The dataset consists of 8,040 images collected from Kaggle and Mendeley, separated into four categories: blight, common rust, gray leaf spot, and healthy. Model training was executed using three dataset scenarios to assess the generalization ability of this suggested method. The empirical findings indicate that the Adam optimizer implemented on the merged dataset attained the highest effectiveness, reaching an exactness of 97.26%, as well as the highest precision, recall, and F1-score compared to SGD and RMSProp. The best-performing model was then converted to TensorFlow Lite and launched within a Flutter-driven Android software to help individuals with the initial spotting of maize leaf diseases. Keywords: Maize; Leaf Disease; Convolutional Neural Network (CNN); InceptionV3 Abstrak Jagung adalah salah satu komoditas pangan penting di Indonesia yang kerap mengalami kendala produksi akibat serangan penyakit pada daunnya. Penyakit seperti hawar, karat, dan bercak daun abu-abu sukar dikenali secara manual karena memerlukan waktu yang lama dan berisiko mengalami kesalahan. Studi ini mengembangkan model klasifikasi penyakit daun jagung menggunakan transfer learning berbasis InceptionV3 dengan membandingkan kinerja tiga algoritma optimasi, yaitu Adam, SGD, dan RMSProp. Dataset yang digunakan terdiri dari 8.040 citra dari Kaggle dan Mendeley yang terbagi ke dalam empat kelas, yaitu blight, common rust, gray leaf spot, dan healthy. Pelatihan dilakukan p…
creator: Dion Danianto
creator: Salamun Rohman Nudin
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-29
publication date: 2026-07-29
volume: 22
number: 3
starting page: 656
ending page: 669
doi: 10.35889/progresif.v22i3.3842

Rancang Bangun Sistem Informasi Sirkulasi Data Perpustakaan SMAN 2 Subang
type: item
description: Library administration management at Sekolah Menengah Atas Negeri 2 Subang previously relied on conventional paper-based records, which caused data inaccuracies and service inefficiencies. This study aimed to develop a web-based library data circulation information system as an integrated solution to these problems. The software development methodology applied was the sequential waterfall model with an Object Oriented Analysis and Design approach. The novelty of this research focused on the integration of a dynamic business rules engine for automated fine calculations and the provision of a real time visual analysis dashboard. Functional testing results using the blackbox testing method demonstrated an absolute success rate of 100%. System implementation successfully reduced circulation transaction time from the initial 3 to 5 minutes to less than 50 seconds. In conclusion, this information system effectively resolved administrative constraints and provided a valid decision support instrument for school management. Keywords: Library information system; Waterfall model; Data circulation; Fine automation Abstrak Pengelolaan administrasi perpustakaan di Sekolah Menengah Atas Negeri 2 Subang sebelumnya masih mengandalkan pencatatan konvensional berbasis kertas sehingga memicu ketidakakuratan data dan inefisiensi pelayanan. Penelitian ini bertujuan untuk membangun sistem informasi sirkulasi data perpustakaan berbasis web sebagai solusi terintergrasi atas permasalahan tersebut. Metode pengembangan perangkat lunak yang diterapkan adalah model waterfall dengan pendekatan Object Oriented Analysis and Design. Kebaruan penelitian ini terletak pada integrasi mesin aturan bisnis dinamis untuk otomatisasi perhitungan denda serta penyediaan dashboard analisis visual secara real time. Hasil pengujian fungsional menggunakan metode blackbox testing menunjukkan tingkat keberhasilan mutlak sebesar 100%. Implementasi sistem terbukti berhasil memangkas waktu transaksi sirkulasi dari semu…
creator: Caca Arif Herdian
creator: Maya Destriani
creator: Tazkia Salsabila Ardan
creator: Lia Kartika
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-30
publication date: 2026-07-30
volume: 22
number: 3
starting page: 670
ending page: 682
doi: 10.35889/progresif.v22i3.3861

Analisis Kinerja Algoritma Naïve Bayes Dalam Prediksi Tingkat Persediaan Susu Formula Pada Sektor Ritel
type: item
description: Formula milk inventory management at Toko Serba Ada has traditionally relied on a manual system that is often inefficient and error-prone. One of the main challenges is the mismatch between inventory levels and market demand, leading to overstocking and the risk of financial losses from product expiration. This study aims to analyze the performance of the Naïve Bayes algorithm in predicting formula milk inventory levels in the retail sector. The variables used in this study include initial stock, demand trends, product price, and sales volume. The prediction model was developed as a web-based application using PHP. Model performance was evaluated using a confusion matrix with accuracy, precision, and recall as evaluation metrics. The experimental results showed that the model achieved 71.43% accuracy, 73.76% precision, and 74.08% recall. These findings indicate that the Naïve Bayes algorithm demonstrates satisfactory decision-making capability for formula milk inventory management in the retail sector. Keywords: Naïve Bayes; Inventory Prediction; Formula Milk; Retail Sector; Inventory Management. Abstrak Selama ini, pengelolaan stok susu formula di Toko Serba Ada masih mengandalkan sistem manual yang sering kali kurang efisien dan rentan terhadap kesalahan manusia. Masalah utama yang sering muncul adalah ketidaksesuaian antara jumlah stok dan permintaan pasar sehingga menyebabkan penumpukan produk maupun risiko kerugian akibat kedaluwarsa. Penelitian ini bertujuan untuk menganalisis kinerja algoritma Naïve Bayes dalam memprediksi tingkat persediaan susu formula pada sektor ritel. Variabel yang digunakan meliputi stok awal, tren permintaan, harga, dan tingkat penjualan. Model dikembangkan dalam aplikasi berbasis web menggunakan bahasa pemrograman PHP. Evaluasi dilakukan menggunakan confusion matrix dengan parameter akurasi (accuracy), presisi (precision), dan recall. Hasil pengujian menunjukkan bahwa model menghasilkan tingkat akurasi sebesar 71,43%, presisi sebesar …
creator: Muhammad Erwan Fuqoha Suryanata
creator: Budi Rahmani
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-30
publication date: 2026-07-30
volume: 22
number: 3
starting page: 683
ending page: 693
doi: 10.35889/progresif.v22i3.3946

Perancangan Sistem Informasi Administrasi Kasir (Point of Sale) Berbasis Web pada Kedai Selera Rasa
type: item
description: Selera Rasa Restaurant still relied on manual transaction recording and stock management, resulting in data errors and delays in report preparation. This study aimed to design and develop a web-based cashier administration information system using the Laravel framework to automate sales transactions, stock management, and report generation. The system was developed using the Waterfall method. Data were collected through observation, interviews, and literature review. The system was implemented using Laravel, PHP, MySQL, and Bootstrap. System testing was conducted using the Black Box Testing method on all main features. The testing results showed that all system features functioned according to the functional requirements and produced valid results. Based on the testing results, the system was considered suitable for supporting sales transactions, stock management, and automated report generation. Keywords: Information System; Point of Sale; Laravel; Black Box Testing; Cashier Abstrak Kedai Selera Rasa masih mengandalkan pencatatan transaksi dan pengelolaan stok secara manual sehingga sering terjadi kesalahan data dan keterlambatan penyusunan laporan. Penelitian ini bertujuan merancang dan membangun sistem informasi administrasi kasir berbasis web menggunakan framework Laravel untuk mengotomatisasi proses transaksi penjualan, pengelolaan stok, dan pembuatan laporan. Metode pengembangan sistem yang digunakan adalah Waterfall. Data penelitian dikumpulkan melalui observasi, wawancara, dan studi pustaka. Sistem dibangun menggunakan Laravel, PHP, MySQL, dan Bootstrap. Pengujian sistem dilakukan menggunakan metode Black Box Testing pada seluruh fitur utama. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berfungsi sesuai dengan kebutuhan fungsional dan memperoleh hasil yang valid. Berdasarkan hasil pengujian tersebut, sistem dinyatakan layak digunakan untuk mendukung proses transaksi penjualan, pengelolaan stok, dan penyusunan laporan secara otomatis. Kata kunci: Si…
creator: Ila Nabilla
creator: Anton Zulkarnain Sianipar
creator: Ifan Junaedi
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-30
publication date: 2026-07-30
volume: 22
number: 3
starting page: 694
ending page: 706
doi: 10.35889/progresif.v22i3.3791

Klasifikasi Named Entity Recognition Pada Cerita Pendek Bahasa Indonesia Menggunakan Model Indobert
type: item
description: Entity recognition in Indonesian short stories requires a model capable of understanding narrative language context effectively. This study aimed to implement the IndoBERT model for the Named Entity Recognition (NER) task to identify Person, Location, and Organization entities. The dataset was obtained from the Majalah Bobo e-book and organized using the BIO (Begin, Inside, Outside) format. The research included pre-processing, tokenization, label encoding, token alignment, and fine-tuning using the AutoModelForTokenClassification model. Evaluation was conducted using precision, recall, and F1-score metrics. The results showed that IndoBERT achieved F1-scores of 0.95 for Person, 0.81 for Organization, and 0.75 for Location, with a weighted average F1-score of 0.90. These results indicated that IndoBERT performed entity recognition effectively on Indonesian short stories. Keywords: Named Entity Recognition; IndoBERT; Indonesian short stories; Natural Language Processing; Transformer. Abstrak Pengenalan entitas pada cerita pendek bahasa Indonesia memerlukan model yang mampu memahami konteks bahasa naratif secara efektif. Penelitian ini bertujuan menerapkan model IndoBERT pada tugas Named Entity Recognition (NER) untuk mengenali entitas Person, Location, dan Organization. Dataset diperoleh dari e-book Majalah Bobo dan disusun menggunakan format BIO (Begin, Inside, Outside). Penelitian dilakukan melalui tahapan pre-processing, tokenisasi, label encoding, token alignment, dan fine-tuning menggunakan model AutoModelForTokenClassification. Evaluasi dilakukan menggunakan metrik precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model IndoBERT memperoleh nilai F1-score sebesar 0.95 pada label Person, 0.81 pada Organization, dan 0.75 pada Location, dengan weighted average F1-score sebesar 0.90. Hasil tersebut menunjukkan bahwa IndoBERT mampu melakukan pengenalan entitas dengan baik pada cerita pendek bahasa Indonesia. Kata kunci: Named Entity Recognition; Indo…
creator: Nazifa Samsurizal
creator: Hendri Ahmadian
creator: Nurrizqa Nurrizqa
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-14
publication date: 2026-07-14
volume: 22
number: 3
starting page: 707
ending page: 716
doi: 10.35889/progresif.v22i3.3731

Sistem Survei Kepuasan Masyarakat Terhadap Pelayanan Disdukcapil Banjarbaru Dengan K-Means Dan Text Mining
type: item
description: This study develops a public satisfaction survey system for the services of the Banjarbaru City Population and Civil Registration Office (Disdukcapil), which integrates online and offline data collection. The problem encountered is the lack of utilization of collected criticism and suggestions. The purpose of this study is to identify segments of public satisfaction and explore patterns of complaints or appreciation through the application of the K-Means Clustering and Text Mining methods. The methods used in this study are K-Means Clustering to group satisfaction levels and Text Mining to analyze respondents' comments. The results show that the grouping and analysis of public comments provide deeper insights for data-driven public service improvements. This system is expected to assist in the evaluation and continuous improvement of service quality. Keywords: Survey; Population and Civil Registration Office; Banjarbaru; K-Means Clustering; Text Mining Abstrak Penelitian ini mengembangkan sistem survei kepuasan masyarakat terhadap pelayanan Dinas Kependudukan dan Pencatatan Sipil (Disdukcapil) Kota Banjarbaru, yang mengintegrasikan pengumpulan data online dan offline. Masalah yang dihadapi adalah kurangnya pemanfaatan data kritik dan saran yang dikumpulkan. Tujuan penelitian ini adalah untuk mengidentifikasi segmen kepuasan masyarakat dan menggali pola keluhan atau apresiasi melalui penerapan metode K-Means Clustering dan Text Mining. Metode yang digunakan dalam penelitian ini adalah K-Means Clustering untuk mengelompokkan tingkat kepuasan dan Text Mining untuk menganalisis komentar responden. Hasil penelitian menunjukkan bahwa pengelompokan dan analisis komentar masyarakat memberikan wawasan yang lebih dalam untuk perbaikan layanan publik yang berbasis data. Sistem ini diharapkan dapat membantu evaluasi dan peningkatan kualitas pelayanan secara berkelanjutan.
creator: Fadilah Fadilah
creator: Ahmad Naufal
creator: Bahar Bahar
creator: Muhammad Arsyad
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-14
publication date: 2026-07-14
volume: 22
number: 3
starting page: 717
ending page: 729
doi: 10.35889/progresif.v22i3.3523

Imputasi Curah Hujan ERA5 Menggunakan Random Forest dan XGBoost di Maluku Utara
type: item
description: Climate change increases hydrometeorological disaster risks in North Maluku, yet missing BMKG rainfall data and ERA5's spatial bias hinder precise risk analysis. This study applied point-based Statistical Downscaling using Random Forest (RF) and Extreme Gradient Boosting (XGBoost), with ERA5 precipitation as the single predictor, to impute missing rainfall at four stations. XGBoost achieved lower MSE and RMSE at three stations (Sultan Babullah: MSE 1017.29, RMSE 31.89, MAE 12.31, R² 0.0351; Gamar Malamo: MSE 513.96, RMSE 22.67, MAE 9.91, R² -0.0141; Oesman Sadik: MSE 163.59, RMSE 12.79, MAE 6.27, R² 0.0369), with NRMSE-based accuracy of 83.89 – 87.56%, though RF retained slightly lower MAE. RF was selected at Emalamo (MSE 163.75, RMSE 12.79, MAE 5.62, R² -0.0517, accuracy 85.78%). The models imputed 298 missing days, though the univariate predictor underestimated rainfall above 40 mm. The resulting continuous dataset offers a scientific basis for regional disaster mitigation planning. Key Word: ERA5; Imputation; Statistical Downscaling; Random Forest; XGBoost Abstrak Perubahan iklim meningkatkan risiko bencana hidrometeorologi di Maluku Utara, sementara kekosongan data curah hujan BMKG dan bias spasial ERA5 menghambat analisis risiko presisi. Penelitian ini menerapkan Statistical Downscaling berbasis titik menggunakan Random Forest (RF) dan Extreme Gradient Boosting (XGBoost), dengan presipitasi ERA5 sebagai prediktor tunggal, untuk mengimputasi kekosongan data curah hujan di empat stasiun. XGBoost menghasilkan MSE dan RMSE lebih rendah di tiga stasiun (Sultan Babullah: MSE 1017,29, RMSE 31,89, MAE 12,31, R² 0,0351; Gamar Malamo: MSE 513,96, RMSE 22,67, MAE 9,91, R² -0,0141; Oesman Sadik: MSE 163,59, RMSE 12,79, MAE 6,27, R² 0,0369), dengan akurasi berbasis NRMSE 83,89 – 87,56%, meski RF tetap mencatat MAE sedikit lebih rendah. RF terpilih di Emalamo (MSE 163,75, RMSE 12,79, MAE 5,62, R² -0,0517, akurasi 85,78%). Model berhasil mengimputasi 298 hari data kosong, mes…
creator: Balya Badar Syah
creator: Brina Miftahurrohmah
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 730
ending page: 742
doi: 10.35889/progresif.v22i3.3734

Penentuan Pelanggan Loyal Laundry Baik Clean Menggunakan Metode MOORA
type: item
description: Customer loyalty determination at Baik Clean Laundry has traditionally been based solely on the number of customer transactions. This approach creates challenges in the reward selection process, particularly when multiple customers have the same number of transactions, making it difficult to identify the most deserving customer. This study aims to implement the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method as a decision support approach to rank loyal customers more objectively and accurately. The evaluation is based on five criteria: total transaction value, visit frequency, service variety, timeliness of laundry collection, and payment punctuality. A web-based decision support system was developed to facilitate data processing and ranking. System testing was conducted by comparing manual calculations with the results generated using the MOORA method. The results indicate that the MOORA method produces a more objective customer ranking, eliminates ties in the evaluation process, and effectively identifies the customer who is most eligible to receive rewards at Baik Clean Laundry. Keywords: Loyal customers; Decision Support System; MOORA; Reward Abstrak Penentuan pelanggan loyal pada Laundry Baik Clean selama ini hanya didasarkan pada jumlah transaksi yang dilakukan oleh pelanggan. Pendekatan tersebut menimbulkan kendala dalam proses pemberian reward, terutama ketika terdapat beberapa pelanggan yang memiliki jumlah transaksi yang sama sehingga sulit menentukan pelanggan paling layak memperoleh penghargaan. Penelitian ini bertujuan untuk mengimplementasikan metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) sebagai metode pendukung keputusan dalam melakukan perangkingan pelanggan loyal lebih objektif dan akurat. Penilaian dilakukan berdasarkan lima kriteria, yaitu total nilai transaksi, frekuensi kunjungan, ragam layanan, ketepatan waktu pengambilan cucian, dan kelancaran pembayaran. Sistem pendukung keputusan dikem…
creator: Yulia Yudihartanti
creator: Khairun Nisa
creator: Taufiq Taufiq
creator: Ahmad Pahdi
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 743
ending page: 754
doi: 10.35889/progresif.v22i3.3903

Analisis Sentimen Ulasan Aplikasi Gojek Menggunakan Support Vector Machine
type: item
description: Evaluating Gojek’s service quality can be achieved by extracting valuable insights from the Google Play Store review corpus. However, the surging volume of data necessitates computational automation to substitute inefficient manual analysis. This study develops a sentiment analysis model using the Support Vector Machine (SVM) algorithm applied to 2,000 scraped reviews. The corpus undergoes comprehensive text preprocessing, rating-based polarity labeling, and lexical feature extraction via Term Frequency-Inverse Document Frequency (TF-IDF). Model validation demonstrates an impressive predictive performance, achieving an accuracy of 86.21%. The distribution of public opinion reveals a dominance of positive sentiment at 57.47%, outperforming the negative cluster at 42.53%. Empirically, the methodological fusion of TF-IDF and SVM is highly reliable as an analytical instrument to dissect consumer perceptions for future service improvements. Keywords: Sentiment Classification; Support Vector Machine; TF-IDF Weighting; Service Evaluation; Gojek. Abstrak Evaluasi kualitas layanan Gojek dapat diekstraksi dari korpus ulasan Google Play Store. Namun, lonjakan volume data menuntut otomatisasi komputasional guna mensubstitusi analisis manual yang inefisien. Penelitian ini mengonstruksi model analisis sentimen menggunakan algoritma Support Vector Machine (SVM) terhadap 2.000 entri ulasan hasil web scraping. Korpus diproses melalui tahapan prapemrosesan teks komprehensif, pelabelan polaritas berbasis rating, serta ekstraksi fitur leksikal mendayagunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Validasi arsitektur klasifikasi membuktikan performa prediktif yang impresif, dengan raihan akurasi menyentuh 86,21%. Peta distribusi opini publik mendemonstrasikan dominasi sentimen afirmatif (positif) sebesar 57,47%, mengungguli klaster negatif di angka 42,53%. Secara empiris, fusi metodologis antara pembobotan TF-IDF dan SVM terbukti sangat andal sebagai instrumen analit…
creator: Muhammad Fariz
creator: Ade Davy Wiranata
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 755
ending page: 768
doi: 10.35889/progresif.v22i3.3969

Model Aplikasi Pembelajaran Huruf Hijaiyah Di Taman Pendidikan Al-Qur'an Halaby Banjarbaru
type: item
description: Learning hijaiyah letters at Taman Pendidikan Al-Qur’an Halaby Banjarbaru previously faced challenges such as low student interest and an unconducive classroom environment due to conventional teaching methods. This study aimed to design and develop a web-based hijaiyah learning application model that enhances interactivity while supporting learning management. The method used was Research and Development with a Waterfall system development model consisting of requirements analysis, design, implementation, and testing stages. The developed application included learning materials, interactive quizzes, teacher and student data management, and learning outcome recording features. The testing results showed that all system functions operated properly, and the application improved learning outcomes for most students. The novelty of this study lies in the integration of interactive learning media and learning management systems within a single platform. Keywords: Hijaiyah Learning; Web Application; Learning Media; Quran Education Abstrak Pembelajaran huruf hijaiyah di Taman Pendidikan Al-Qur’an Halaby Banjarbaru sebelumnya menghadapi kendala berupa rendahnya minat belajar dan kurang kondusifnya suasana kelas akibat metode pembelajaran yang masih konvensional. Penelitian ini bertujuan untuk merancang dan membangun model aplikasi pembelajaran huruf hijaiyah berbasis web yang mampu meningkatkan interaktivitas serta mendukung pengelolaan pembelajaran. Metode yang digunakan adalah Research and Development dengan model pengembangan sistem Waterfall yang meliputi tahapan analisis kebutuhan, perancangan, implementasi, dan pengujian. Aplikasi yang dikembangkan dilengkapi dengan fitur materi pembelajaran, kuis interaktif, pengelolaan data guru dan peserta didik, serta pencatatan hasil belajar. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berfungsi dengan baik dan penerapan aplikasi memberikan peningkatan hasil belajar pada sebagian besar peserta didik. Kebaruan penelitian …
creator: Siti Dzakia Salsabila
creator: Khairullah Khairullah
creator: Wahyudi Ariannor
creator: Muhammad Arsyad
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 769
ending page: 778
doi: 10.35889/progresif.v22i3.3648

Perancangan Failure-Aware Self-Healing Workflow pada Sistem Event-Driven untuk Pemulihan Proses Layanan Digital Secara Otomatis
type: item
description: Digital service systems built on event-driven architecture relied on infrastructure-level self-healing, leaving workflow-layer failures handled reactively and processes abandoned mid-execution even when infrastructure remained available. This study designed a failure-aware self-healing workflow that detected, classified, and recovered digital service failures automatically based on event context, treating classification as mandatory before recovery. A prototype was built following the Prototyping Model, integrating n8n as workflow orchestrator and Apache Kafka as event broker inside Docker, with recovery governed by a JavaScript finite state machine, tested on five failure scenarios and two baseline conditions, each run for 30 iterations. The system classified all failures correctly across 150 iterations, reaching 100% recovery accuracy and a 100% success rate where fallback was available. Self-healing lowered Recovery Time by 29.90% against a no-recovery baseline, though a static-recovery baseline reached a lower time with 0% success, showing recovery speed and correctness can move in opposite directions. Keywords: Failure-Aware; Self-Healing; Event-Driven Architecture; Workflow Orchestration; Mean Time to Recovery (MTTR) Abstrak Sistem layanan digital berbasis arsitektur event-driven umumnya mengandalkan self-healing pada tingkat infrastruktur, sementara kegagalan pada lapisan orkestrasi workflow ditangani secara reaktif melalui pemantauan manual sehingga proses bisnis dapat terhenti di tengah eksekusi meskipun infrastrukturnya masih berjalan normal. Penelitian ini merancang failure-aware self-healing workflow yang mendeteksi, mengklasifikasikan, dan memulihkan kegagalan layanan digital secara otomatis berdasarkan konteks event, dengan klasifikasi sebagai tahap wajib sebelum pemulihan dijalankan. Prototipe dibangun mengikuti Prototyping Model, mengintegrasikan n8n sebagai orkestrator workflow dan Apache Kafka sebagai event broker di dalam Docker, dengan keputusan …
creator: Dimaz Ardawan
creator: Denny Kurniadi
creator: Khairi Budayawan
creator: Randi Proska Sandra
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 779
ending page: 793
doi: 10.35889/progresif.v22i3.3977

Analisis Peramalan Penjualan Gorden Menggunakan Metode Single Moving Average dengan Perbandingan Periode pada Usaha Ritel Fadhila Gorden
type: item
description: The Fadhila Gorden retail business is facing critical instability in inventory management due to unpredictable sales fluctuations. This triggers the risk of financial losses from both overstocking and stockouts. This research aims to resolve this issue by predicting future sales using the Single Moving Average (SMA) forecasting method and implementing it into a web-based interface system. The research process was conducted by comparing the performance of three observation time-span scenarios—namely 3-, 5-, and 7-month periods—using 18 months of historical data. The performance accuracy level was measured using the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) parameters. The research results indicate that the 5-period scenario produced the lowest error rate, with an MAE value of 26.75 and a MAPE of 37.64%. In conclusion, the 5-period computational model proved to be the most precise and was successfully developed into a standalone interactive application. This system effectively provides a quantitative reference for the business owner to optimize the efficiency of raw material stock purchasing plans. Keywords: Sales forecasting; Single Moving Average (SMA); Inventory management; Web-based system. Abstrak Usaha Ritel Fadhila Gorden menghadapi urgensi ketidakstabilan manajemen persediaan akibat fluktuasi penjualan yang tidak menentu. Hal ini memicu risiko kerugian dari kelebihan maupun kekosongan stok barang. Penelitian ini bertujuan untuk menyelesaikan permasalahan tersebut dengan memprediksi penjualan masa depan menggunakan metode peramalan Single Moving Average (SMA) dan mengimplementasikannya ke dalam sebuah sistem antarmuka berbasis web. Proses penelitian dilakukan dengan membandingkan kinerja tiga skenario rentang waktu pengamatan yakni 3, 5, dan 7 periode bulanan—menggunakan 18 data historis masa lalu. Pengujian tingkat akurasi kinerja diukur menggunakan parameter Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE). Hasil p…
creator: Fadhila Mutia Rahmi
creator: Ahmad Jazuli
creator: Esti Wijayanti
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 794
ending page: 806
doi: 10.35889/progresif.v22i3.3943

Klasifikasi Teks Depresi Pada Media Sosial Menggunakan Convolutional Neural Network Dengan Fasttext Embedding
type: item
description: Depression is a persistent mood disorder that significantly impacts an individual's functioning, and its prevalence is becoming increasingly concerning among adolescents (the "Strawberry Generation"). Expressions of this psychological distress have largely migrated to social media in the form of unstructured text posts laden with informal terms, abbreviations, and typos. These linguistic characteristics pose "Out-of-Vocabulary" (OOV) challenges for traditional classification models. This study proposes integrating FastText word embeddings—which utilize subword information—to enhance feature representation, combined with a Convolutional Neural Network (CNN) architecture as the primary classifier. Using the "Student Depression Text" secondary dataset from Kaggle (comprising 7,489 entries), class imbalance was addressed exclusively within the training data using the Random Oversampling technique. Experimental results demonstrate that the proposed model achieved an overall accuracy of 95%, with Precision, Recall, and F1-Score values ​​for the depression class of 90%, 80%, and 84%, respectively. The integration of CNN and pre-trained FastText proved to be a reliable and efficient solution for managing the complexities of digital language in the context of early mental health detection. Keywords: Depression; Social Media; Natural Language Processing; Convolutional Neural Network; FastText Embedding. Abstrak Depresi merupakan gangguan suasana hati persisten yang secara signifikan memengaruhi fungsi fungsional individu dan prevalensinya kian mengkhawatirkan pada kelompok remaja (Strawberry Generation). Ekspresi tekanan psikologis ini kini banyak bermigrasi ke media sosial dalam bentuk unggahan teks tidak terstruktur yang sarat akan istilah non-formal, singkatan, dan salah ketik (typo). Karakteristik bahasa tersebut memicu kendala Out-of-Vocabulary (OOV) pada model klasifikasi tradisional. Penelitian ini mengusulkan integrasi word embedding FastText berbasis informasi tingka…
creator: Azki Hamdi
creator: Mohammad Zoqi Sarwani
creator: Anang Aris Widodo
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 807
ending page: 819
doi: 10.35889/progresif.v22i3.3899

Implementasi Model Hybrid IndoBERT - LinearSVC untuk Deteksi Spam Judol Obfuscated pada Komentar YouTube
type: item
description: Online gambling (judol) spam comments on Indonesian YouTube are disguised using obfuscated text techniques, substituting non-standard Unicode characters designed to bypass string-matching moderation systems. Vocabulary-based Natural Language Processing (NLP) models such as IndoBERT risk representation degradation as tokenizers map non-standard characters to unknown ([UNK]) tokens. This study implemented a hybrid model using a Dual-Track Preprocessing architecture combining IndoBERT [CLS] vectors from normalized text with TF-IDF character N-Gram LinearSVC features from raw Unicode text via sparse matrix concatenation. Experiments on 6,000 YouTube judol comments show the hybrid model achieving Accuracy=0.9978, Precision=0.9956, Recall=1.0000, F1-Score=0.9978 on the test set, identical to standalone IndoBERT and outperforming standalone LinearSVC (F1=0.9944). The hybrid model preserved IndoBERT peak performance while fully closing the 4-comment obfuscated-spam gap missed by LinearSVC, with no performance penalty from fusion. Keywords: Character N-Gram; Gambling Spam Detection; Hybrid Model; IndoBERT; LinearSVC Abstrak Komentar spam judi online (judol) di YouTube Indonesia disamarkan menggunakan teknik obfuscated text, yakni substitusi karakter Unicode non-standar yang dirancang untuk menghindari sistem moderasi berbasis pencocokan string. Model Natural Language Processing (NLP) berbasis kosakata seperti IndoBERT berisiko mengalami degradasi representasi karena tokenizer memetakan karakter non-standar ke token tidak dikenal ([UNK]). Penelitian ini mengimplementasikan model hybrid dengan arsitektur Dual-Track Preprocessing yang menggabungkan vektor [CLS] IndoBERT dari teks ternormalisasi dan fitur TF-IDF karakter N-Gram LinearSVC dari teks Unicode mentah melalui konkatenasi sparse matrix. Eksperimen pada 6.000 sampel komentar judol YouTube menunjukkan model hybrid mencapai Accuracy=0,9978, Precision=0,9956, Recall=1,0000, F1-Score=0,9978 pada test set, identik dengan Ind…
creator: Danang Budiman Hidayat
creator: Ahmad Abdul Chamid
creator: Ahmad Jazuli
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 820
ending page: 833
doi: 10.35889/progresif.v22i3.3914

Pengukuran Dampak Interferensi Sinyal Co-Channel dan Adjacent Channel pada Kualitas Layanan Jaringan Nirkabel IEEE 802.11n
type: item
description: This research is motivated by the high number of complaints regarding IEEE 802.11n wireless network connection fluctuations at PT Ardi Karya Media due to the auto-channeling feature, which triggers overlapping in the 2.4 GHz spectrum. To address this problem, this study evaluates the impact of Co-Channel Interference (CCI) and Adjacent Channel Interference (ACI) using the Action Research method. The measurement process focuses on evaluating Quality of Service (QoS) parameters—namely throughput, delay, jitter, and packet loss—using Wireshark software across three testing scenarios: baseline condition, CCI exposure, and ACI exposure. The analysis results prove that CCI causes a drastic decline in throughput due to a high rate of data collisions, while ACI generates noise that degrades data integrity. As a technical solution, this research recommends implementing manual (static) channel management and migrating to the 5 GHz frequency band to ensure long-term connection reliability. Keywords: Action Research; Adjacent Channel; Co-Channel; Quality of Service; TIPHON Abstrak Penelitian ini dilatarbelakangi oleh tingginya keluhan fluktuasi koneksi jaringan nirkabel IEEE 802.11n di PT Ardi Karya Media akibat fitur auto-channeling yang memicu tumpang tindih pada spektrum 2.4 GHz. Untuk mengatasi permasalahan tersebut, studi ini mengevaluasi dampak Co-Channel Interference (CCI) dan Adjacent Channel Interference (ACI) menggunakan metode Action Research. Proses pengukuran difokuskan pada evaluasi parameter Quality of Service (QoS) yang meliputi throughput, delay, jitter, dan packet loss menggunakan peranti lunak Wireshark ke dalam tiga skenario pengujian: kondisi baseline, paparan CCI, dan paparan ACI. Hasil analisis membuktikan bahwa gangguan CCI menyebabkan kemerosotan throughput secara drastis akibat tingginya tabrakan data, sedangkan gangguan ACI menghasilkan noise yang mendegradasi keutuhan informasi. Sebagai solusi teknis, penelitian ini merekomendasikan penerapan manajem…
creator: Syafril Ardi Hermawan
creator: Ahmad Jazuli
creator: Esti Wijayanti
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 834
ending page: 845
doi: 10.35889/progresif.v22i3.3885

Rancang Bangun Aplikasi Web Preventif Computer Vision Syndrome Berbasis Monitoring Postur Leher Real-Time Dengan Metode Personalized Baseline
type: item
description: Prolonged use of digital devices triggers the risk of Computer Vision Syndrome (CVS) and neck posture abnormalities such as Forward Head Posture. Conventional detection systems with static thresholds often trigger false alarms due to differences in individuals' natural postures. This study aims to develop a real-time neck posture monitoring web application to prevent CVS adaptively. The system was developed using the Prototyping method based on a Progressive Web App (PWA). Visual processing is executed entirely on the client-side using MediaPipe Pose Landmarker and WebRTC, while dynamic threshold calibration utilizes the Personalized Baseline method. Monitoring data and gamification history are managed locally through Local Storage. The test results indicate that the system is capable of extracting neck coordinates, monitoring posture inclination, and providing ergonomic notifications accurately without data transmission to a server. The Personalized Baseline method proved effective in reducing the false alarm rate, making this system an adaptive, lightweight CVS preventive solution capable of ensuring user privacy. Keywords: Computer Vision Syndrome; Forward Head Posture; Local Storage; MediaPipe; Personalized Baseline. Abstrak Penggunaan perangkat digital yang berkepanjangan memicu risiko Computer Vision Syndrome (CVS) dan kelainan postur leher seperti Forward Head Posture. Sistem deteksi konvensional dengan ambang batas statis sering memicu peringatan palsu akibat perbedaan postur alami individu. Penelitian ini bertujuan mengembangkan aplikasi web pemantau postur leher real-time untuk mencegah CVS secara adaptif. Sistem dikembangkan menggunakan metode Prototyping berbasis Progressive Web App (PWA). Pemrosesan visual dieksekusi sepenuhnya di sisi klien menggunakan MediaPipe Pose Landmarker dan WebRTC, sedangkan kalibrasi ambang batas dinamis menggunakan metode Personalized Baseline. Data pemantauan dan riwayat gamifikasi dikelola secara lokal melalui Local Storage…
creator: M Rizal Saputra
creator: Khairi Budayawan
creator: Syafrijon Syafrijon
creator: Delvi Asmara
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 846
ending page: 857
doi: 10.35889/progresif.v22i3.4055

Komparasi YOLOv8 Nano dan YOLO11 Nano untuk Klasifikasi Penyakit Daun Tomat pada Perangkat Edge
type: item
description: Tomato leaf diseases pose a significant threat to horticultural productivity, while field identification methods remain highly dependent on expert observation. This study compared the performance of YOLOv8 Nano and YOLO11 Nano for tomato leaf disease classification on an edge device. A dataset of 7,200 tomato leaf images across six disease classes was used; both models were trained under identical configurations with 20 independent seed replications, exported to ONNX format, and evaluated on a Raspberry Pi 5. YOLOv8 Nano achieved a mean accuracy of 88.32% while YOLO11 Nano achieved 87.81%. The Wilcoxon Signed-Rank test confirmed that the accuracy difference was not statistically significant (W = 75.5; p = 0.2706). Nevertheless, YOLOv8 Nano demonstrated shorter mean inference time (8.430 ms vs 8.502 ms; p = 0.0064), smaller model size (5.531 MB vs 5.895 MB), and lower peak memory usage (72.31 MB vs 72.72 MB). Both models proved comparable in classification accuracy, but YOLOv8 Nano is recommended for edge deployment owing to its statistically verified efficiency advantages. Keywords: YOLOv8 Nano; YOLO11 Nano; tomato leaf disease classification; edge device; Wilcoxon Signed-Rank Abstrak Penyakit daun tomat menjadi ancaman serius bagi produktivitas hortikultura, sementara metode identifikasi visual di lapangan masih sangat bergantung pada keahlian pengamat. Penelitian ini membandingkan performa YOLOv8 Nano dan YOLO11 Nano untuk klasifikasi penyakit daun tomat pada perangkat edge. Dataset yang digunakan terdiri dari 7.200 citra daun tomat yang terbagi dalam enam kelas penyakit; kedua model dilatih menggunakan 20 variasi seed independen dengan konfigurasi identik, kemudian diekspor ke format ONNX dan diuji langsung pada Raspberry Pi 5. YOLOv8 Nano memperoleh akurasi rata-rata 88,32%, sedangkan YOLO11 Nano memperoleh 87,81%. Uji Wilcoxon Signed-Rank mengonfirmasi bahwa selisih akurasi tidak signifikan secara statistik (W = 75,5; p = 0,2706). Meski demikian, YOLOv8 Nano me…
creator: Muhammad Nur
creator: Iwan Jaya
creator: Haytsam Adzka Mawla
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 858
ending page: 870
doi: 10.35889/progresif.v22i3.3929

Pengembangan Aplikasi Sistem Prediksi Kasus Demam Berdarah Dengue Berdasarkan Curah Hujan Berbasis Web
type: item
description: Dengue Hemorrhagic Fever (DHF) is an infectious disease that poses a serious public health problem in tropical regions, with its spread strongly influenced by rainfall; however, current surveillance systems remain manual and reactive, often delaying intervention. This study aims to design and implement a rainfall-based DHF case prediction system using a Long Short-Term Memory (LSTM) algorithm tailored to each sub-district. The system was developed using the Prototyping method and integrated into a web application, with Laravel as the main interface and Flask as the prediction server. Testing was conducted through model accuracy evaluation using MAE, RMSE, and MAPE metrics compared against the ARIMA model, along with Black Box Testing of system functionality. Results show that LSTM outperformed ARIMA in 9 of 11 sub-districts (81.8%), and all Black Box test scenarios ran as designed, confirming that the system can proactively support local health decision-making. Keywords: dengue hemorrhagic fever; rainfall; LSTM; prediction; web application Abstrak Demam Berdarah Dengue (DBD) merupakan penyakit menular yang menjadi permasalahan kesehatan masyarakat serius di wilayah tropis, dengan penyebaran yang sangat dipengaruhi oleh curah hujan; namun sistem surveilans yang berjalan saat ini masih bersifat manual dan reaktif sehingga intervensi sering terlambat. Penelitian ini bertujuan merancang dan mengimplementasikan sistem prediksi kasus DBD berbasis curah hujan menggunakan algoritma Long Short-Term Memory (LSTM) yang spesifik untuk masing-masing kecamatan. Sistem dikembangkan menggunakan metode Prototyping dan diintegrasikan ke dalam aplikasi web dengan arsitektur Laravel sebagai antarmuka utama dan Flask sebagai peladen prediksi. Pengujian dilakukan melalui evaluasi akurasi model menggunakan metrik MAE, RMSE, dan MAPE yang dibandingkan dengan model ARIMA, serta pengujian Black Box terhadap fungsionalitas sistem. Hasil pengujian menunjukkan LSTM unggul pada 9 dari 11 kecamat…
creator: Billy Kelvin Tressa
creator: Syafrijon Syafrijon
creator: Khairi Budayawan
creator: Delvi Asmara
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 871
ending page: 880_
doi: 10.35889/progresif.v22i3.4065

Rancang Bangun Sistem Pendukung Keputusan Monitoring Jaringan GPON Berbasis Rule-Based Classification
type: item
description: Monitoring subscriber network devices in Internet Service Providers (ISPs) is generally conducted manually and reactively, causing the Network Operations Center (NOC) team to frequently experience delays in detecting disruptions within the GPON network. This study designs and develops a web-based Decision Support System (DSS) utilizing rule-based classification with a thresholding approach to diagnose GPON network anomalies proactively and in real-time at PT Lintas Data Prima (LDP) Bali. The system was developed using the Next.js 15, Python FastAPI, and Supabase (PostgreSQL) frameworks, integrated with the SmartOLT v2 API and Telegram Bot API. The diagnostic engine classifies the subscriber's ONT device conditions into three severity categories, namely Critical, Warning, and Normal, based on the SFP attenuation values and PPPoE status. The test results indicate that the system is capable of automatically detecting and classifying anomalies, as well as instantly sending emergency notifications via Telegram Bot to NOC technicians. This system is expected to serve as a reference for developing proactive network monitoring solutions in ISPs. Keywords: Decision Support System; GPON; Rule-Based Classification; NOC; ISP Abstrak Pemantauan perangkat jaringan pelanggan pada Internet Service Provider (ISP) umumnya dilakukan secara manual dan reaktif, sehingga tim Network Operations Center (NOC) kerap terlambat mendeteksi gangguan pada jaringan GPON. Penelitian ini merancang dan membangun Sistem Pendukung Keputusan (SPK) berbasis rule-based classification dengan pendekatan thresholding untuk mendiagnosis gangguan jaringan GPON secara proaktif dan real-time pada PT Lintas Data Prima (LDP) Bali. Sistem dikembangkan menggunakan framework Next.js 15, Python FastAPI, dan Supabase (PostgreSQL) dengan integrasi SmartOLT v2 API dan Telegram Bot API. Mesin diagnosis mengklasifikasikan kondisi perangkat ONT pelanggan ke dalam tiga kategori keparahan, yaitu Critical, Warning, dan Normal,…
creator: Rai Masita Dewi
creator: Vincent Alfian Artha
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 881
ending page: 892
doi: 10.35889/progresif.v22i3.3802

Analisis Sentimen Kesehatan Mental pada Media Sosial X Menggunakan BERT dengan Pendekatan XAI Berbasis SHAP dan LIME
type: item
description: Mental health had become an important issue widely discussed on social media, creating the need for an automated method to identify mental health conditions based on textual data. This study aimed to develop a text classification model using Bidirectional Encoder Representations from Transformers and improve the transparency of prediction results through Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations. The dataset consisted of 51,073 text records categorized into seven mental health classes. The research stages included data and text cleaning, label encoding, data splitting, tokenization, model training, evaluation, and result interpretation. The testing results showed that the model achieved an accuracy of 82% and a weighted average F1-score of 0.82. The interpretation results indicated that specific words and phrases contributed to class predictions. The findings demonstrated that the model performed text classification effectively, while both interpretation methods improved the transparency of the model’s decision-making process. Keywords: Sentiment Analysis; BERT; XAI; SHAP; LIME Abstrak Kesehatan mental telah menjadi isu penting yang banyak dibahas melalui media sosial sehingga diperlukan metode otomatis untuk mengidentifikasi kategori kondisi kesehatan mental berdasarkan teks. Penelitian ini bertujuan membangun model klasifikasi teks menggunakan Bidirectional Encoder Representations from Transformers (BERT) serta meningkatkan transparansi hasil prediksi melalui Shapley Additive Explanations (SHAP) dan Local Interpretable Model-agnostic Explanations (LIME). Dataset yang digunakan terdiri atas 51.073 teks dalam tujuh kategori kesehatan mental. Tahapan penelitian meliputi pembersihan data dan teks, pengodean label, pembagian data, tokenisasi, pelatihan model, evaluasi, serta interpretasi hasil. Hasil pengujian menunjukkan bahwa model memperoleh akurasi sebesar 82% dan nilai F1-score rata-rata tertimbang sebesar 0,82. Interpretasi…
creator: Annisa Maulana Majid
creator: Ismasari Nawangsih
creator: Karina Imelda
rights: Copyright (c) 2026
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 893
ending page: 909
doi: 10.35889/progresif.v22i3.4070

Analisis Kecenderungan Depresi Remaja dalam Penggunaan Media Sosial dengan Algoritma Random Forest
type: item
description: Depression is a major factor in Bali’s high suicide rates, particularly among social media-active adolescents. This study analyzes the effects of social media usage intensity, addiction, and sleep quality on adolescents’ mental health using the Random Forest algorithm. Quantitative research involved data exploration, preprocessing, modeling, hyperparameter tuning, and evaluation with R² and MSE. From 36 valid respondents, the Addicted Score was the strongest predictor of sleep quality and mental health (feature importance ±0.7), exceeding average daily usage (0.2–0.3). Negative correlations linked addiction to mental health (r = –0.6257) and sleep quality (r = –0.4588). Models achieved R² of 0.19 (sleep quality) and 0.38 (mental health). Findings show addictive behavior impacts more than usage duration, so prevention should focus on controlling social media addiction. Keywords: Random Forest; Regression; Depression Abstrak Depresi merupakan salah satu faktor utama yang berkontribusi terhadap tingginya angka bunuh diri di Bali, terutama pada remaja yang aktif menggunakan media sosial. Penelitian ini bertujuan menganalisis pengaruh intensitas penggunaan media sosial, kecanduan media sosial, dan kualitas tidur terhadap kesehatan mental remaja menggunakan algoritma Random Forest. Penelitian kuantitatif dilakukan melalui tahapan eksplorasi data, preprocessing, pemodelan, hyperparameter tuning, dan evaluasi menggunakan R² serta MSE. Berdasarkan 36 data responden valid, Addicted Score menjadi faktor paling dominan dalam memprediksi kualitas tidur dan kesehatan mental (feature importance ±0,7), dibandingkan rata-rata penggunaan media sosial harian (0,2–0,3). Korelasi menunjukkan hubungan negatif antara kecanduan media sosial dengan kesehatan mental (r = –0,6257) dan kualitas tidur (r = –0,4588). Model menghasilkan R² 0,19 untuk kualitas tidur dan 0,38 untuk kesehatan mental. Temuan menunjukkan bahwa perilaku adiktif lebih berpengaruh daripada durasi penggunaan, sehingga pen…
creator: I Made Adi Bhaskara
creator: I Made Surya Kumara
creator: Ni Wayan Diana Ekayani
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-07-15
publication date: 2026-07-15
volume: 22
number: 3
starting page: 910
ending page: 924
doi: 10.35889/progresif.v22i3.3257

Implementasi Sistem Inventory Coffee shop dengan Integrasi Resep dan Metode FEFO
type: item
description: Coffee shop raw material inventory requires accurate recording because ingredients have different units, storage locations, and expiration dates. This study aims to implement a coffee shop inventory system that integrates menu recipes with automatic stock deduction using the First Expired First Out (FEFO) method. The method follows applied software development stages, including requirements analysis, design, implementation, and black-box testing. The system was developed as a web/PWA application with a Node.js/Express backend and a MySQL database. The implementation includes login, dashboard, supplier, stock, batch, stock-in, stock-out, menu, recipe, report, and role-based access control modules. Black-box testing on core functional scenarios showed that the system outputs matched the expected results. The novelty of this system lies in integrating menu recipes with earliest-expiry batch selection, allowing menu transactions to be directly converted into FEFO-based raw material consumption. Keywords: Inventory system; Coffee shop; Recipe; FEFO; Raw materials. Abstrak Pengelolaan persediaan bahan baku coffee shop membutuhkan pencatatan yang akurat karena bahan memiliki satuan, lokasi, dan tanggal kedaluwarsa yang berbeda. Penelitian ini bertujuan mengimplementasikan sistem inventory coffee shop yang mengintegrasikan resep menu dengan pengurangan stok otomatis menggunakan metode First Expired First Out (FEFO). Metode yang digunakan adalah pengembangan perangkat lunak terapan melalui analisis kebutuhan, perancangan, implementasi, dan pengujian black-box. Sistem dibangun berbasis web/PWA dengan backend Node.js/Express dan basis data MySQL. Hasil implementasi mencakup modul login, dashboard, supplier, stok, batch, stok masuk, stok keluar, menu, resep, laporan, dan role-based access control. Pengujian black-box pada skenario fungsi inti menunjukkan keluaran sistem sesuai dengan hasil yang diharapkan. Kebaruan sistem terletak pada integrasi resep menu dengan pemilihan batc…
creator: Rizki Aditya Ananda
creator: Tutik Khotimah
creator: Indra Lina Putra
rights: Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
date: 2026-08-15
publication date: 2026-08-15
volume: 22
number: 3
starting page: 925
ending page: 937
doi: 10.35889/progresif.v22i3.4025