Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
type: channel
description: e-ISSN: 2685-0893
publisher: STMIK Banjarbaru
language: en-US
publication name: Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
issn: 2089-3787
items: Weighted Moving Average Berbasis Variasi Window untuk Optimasi Persediaan dan Reorder Point Fiber Optik
items: Penerapan Quantum Machine Learning Untuk Klasifikasi Ulasan Asli Dan Palsu Pada Amazon
items: Penerapan Algoritma Support Vector Machine Dalam Analisis Sentimen Terhadap Ulasan Pengguna Pada Aplikasi NewSakpole
items: Analisis Perbandingan ANN dan Hybrid QNN pada Klasifikasi Multikarakteristik Data
items: Prediksi Kadar Air Greenbeans Kopi Pra-Roasting Menggunakan Metode ANFIS
items: Perancangan Dan Implementasi Sistem IoT Untuk Monitoring Kualitas Air Menggunakan ESP8266 Dan Sensor TDS Meter
items: Evaluasi Virtual Reality terhadap Keterlibatan dan Pemahaman Anak dalam Pembelajaran
items: Analisis Perbandingan Quantum Machine Learning Dalam Klasifikasi Berita Politik Fakta Dan Hoaks
items: Analisis Threshold Intensitas Cahaya Otomasi Lampu untuk Efisiensi Energi Rumah Tangga
items: Analisis Performa Metode YOLOv5-CNN Dalam Meningkatkan Deteksi Dan Pengenalan Ras Kelinci
items: Analisis Kinerja Smart Door Hybrid Haar Cascade dan ArcFace pada Raspberry
items: Prototipe Kendali Lampu dan Pintu Garasi Menggunakan NodeMCU ESP8266 dan Blynk
items: Rancangan EduChat Berbasis WebSockets dan E2EE di Institut Pendidikan Indonesia Garut
items: Sistem Informasi Pengarsipan Dokumen Berbasis Website Pada Kantor Desa Tibubeneng
items: Implementasi Metode Queue Tree Untuk Optimalisasi Jaringan Dan Web Filtering Beserta Evaluasi Kinerja Jaringan Berdasarkan Standar Tiphon
items: Analisis Komparatif Arsitektur CNN untuk Klasifikasi Penyakit Daun Tebu Berbasis Transfer Learning
items: Sistem Pendukung Keputusan Pemilihan Internet Service Provider Menggunakan Metode TOPSIS Berbasis Web di Kota Batam
items: Analisis Spasial Jalur Pendakian Gunung Lawu via Cemoro Sewu Berbasis SIG
items: Deteksi Dini Diabetes Mellitus Tipe 2 Pada Usia Dewasa Muda Menggunakan Algoritma Decision Tree - C4.5
items: Analisis Kesuksesan Reporting Information System Menggunakan Model DeLone & McLean pada Kantor Jasa Penilai Publik
items: Implementasi Algoritma K-Means Clustering untuk Mengelompokkan Wilayah Produksi Kopi di Jawa Barat
items: Perbandingan Metode Decision Tree dan Logistic Regression dalam Klasifikasi Tingkat Obesitas Berdasarkan Gaya Hidup
items: Analisis Sentimen Isu Redominasi Rupiah Menggunakan Lexicon Based dan Naïve Bayes

Weighted Moving Average Berbasis Variasi Window untuk Optimasi Persediaan dan Reorder Point Fiber Optik
type: item
description: Poor inventory management is a primary challenge for telecommunications service providers. This study implements the weighted moving average (WMA) algorithm with window size variations to forecast demand for kabel drop core and Optical Network Terminal (ONT) at Fibertrust Madiun, integrating it with Safety Stock (SS) and Reorder Point (ROP) calculations. The data covers 63 kabel drop core transactions and 112 ONT transactions during January–December 2025, with coefficients of variation of 0.384 and 0.567 respectively. Three window sizes (3, 4, and 5 periods) were tested using MAD, MSE, and MAPE. Window 5 achieved the best accuracy with MAPE of 27.11% for kabel drop core and 47.57% for ONT, both in the sufficient category. A 95% service level provides the optimal balance between holding cost and stockout risk. ROP implementation has the potential to reduce stockout incidents by 71–79%, from 28 to 6–8 incidents per year. Keywords: Weighted moving average; Reorder point; Safety stock; Inventory management; Stockout Abstrak Pengelolaan persediaan yang tidak tertata dengan baik menjadi tantangan utama perusahaan penyedia layanan telekomunikasi. Penelitian ini mengimplementasikan algoritma weighted moving average (WMA) dengan variasi ukuran window untuk meramalkan permintaan kabel drop core dan Optical Network Terminal (ONT) di Fibertrust Madiun, serta mengintegrasikannya dengan perhitungan Safety Stock (SS) dan Reorder Point (ROP). Data mencakup 63 transaksi kabel drop core dan 112 transaksi ONT selama Januari-Desember 2025, dengan koefisien variasi masing-masing 0,384 dan 0,567. Tiga variasi ukuran window (3, 4, dan 5 periode) diuji menggunakan metrik MAD, MSE, dan MAPE. Window 5 periode mencatat akurasi terbaik dengan MAPE 27,11% untuk kabel drop core dan 47,57% untuk ONT, keduanya berkategori cukup. Service level 95% memberikan keseimbangan terbaik antara biaya penyimpanan dan risiko kehabisan stok (stockout). Penerapan ROP berpotensi menekan insiden stockout hingga 7…
creator: Ridho Fajar Fahturohman
creator: Budi Nugroho
creator: Eva Yulia Puspaningrum
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-08
publication date: 2026-04-08
volume: 15
number: 2
starting page: 502
ending page: 511
doi: 10.35889/jutisi.v15i2.3592

Penerapan Quantum Machine Learning Untuk Klasifikasi Ulasan Asli Dan Palsu Pada Amazon
type: item
description: Classifying product reviews on e-commerce platforms, both real and fake, requires a model that can effectively represent text data patterns. This study aims to compare the performance of several Quantum Machine Learning methods, namely QNN, QSVC, and Hybrid Quantum kernel and Classical SVM, in classifying Amazon product reviews. The study uses a quantitative approach with a computational experimental design. Review data is represented using TF-IDF, standardized, and reduced in dimension with Principal Component Analysis (PCA) before being transformed into quantum feature space. Performance evaluation is carried out using accuracy, precision, recall, F1-Score, and MCC metrics. The experimental results show that QNN achieved the best performance with an accuracy value of 85,63%, an F1-Score of 0.9130, and an MCC of 0.5608, while QSVC and the hybrid approach achieved an accuracy of 83.23% with an MCC of 0.4331. These results indicate that QNN has more balanced classification performance. Keywords: Quantum Neural Network; Fake Review Detection; Amazon Reviews; Natural Language Processing; Quantum Machine Learning. Abstrak Klasifikasi ulasan produk pada platform e-commerce, baik ulasan asli maupun palsu, memerlukan model yang mampu merepresentasikan pola data teks secara efektif. Penelitian ini bertujuan untuk membandingkan kinerja beberapa metode Quantum Machine Learning (QML), yaitu QNN, Quantum Support Vector (QSVC), dan Hybrid Quantum kernel and Classical SVM, dalam mengklasifikasikan ulasan produk Amazon. Penelitian menggunakan pendekatan kuantitatif dengan desain eksperimen komputasional. Data ulasan direpresentasikan menggunakan TF-IDF, distandardisasi, dan direduksi dimensinya dengan Principal Component Analysis (PCA) sebelum ditransformasikan ke ruang fitur kuantum. Evaluasi kinerja dilakukan menggunakan metrik accuracy, precision, recall, F1-Score, dan MCC. Hasil eksperimen menunjukkan bahwa QNN memperoleh kinerja terbaik dengan nilai accuracy sebesar 85,63%, F…
creator: Krisna Putri Telaumbanua
creator: Sunneng Sandino Berutu
creator: Aninda Astuti
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-08
publication date: 2026-04-08
volume: 15
number: 2
starting page: 512
ending page: 524
doi: 10.35889/jutisi.v15i2.3498

Penerapan Algoritma Support Vector Machine Dalam Analisis Sentimen Terhadap Ulasan Pengguna Pada Aplikasi NewSakpole
type: item
description: To determine the success rate of a service, it is necessary to conduct sentiment analysis to understand public opinion and the level of public satisfaction, both positive, neutral, and negative. Sentiment analysis was used to improve the quality of service on the NewSakpole application in android-based vehicle tax payments. In this study, several stages were used such as data crawling, data preprocessing, word weighting using TF-IDF (Term Frequency Inverse Document Frequency) and SVM (Support Vectore Machine) classification model for sentiment classification. By using the Confusionsion Matrix test with a data sharing of 20% for data training and 80% for data testing, the accuracy results were obtained of 81.08%. The accuracy value of each sentiment also showed a fairly good performance, with a positive sentiment of 81.11%, a neutral sentiment of 78.62%, and a negative sentiment of 83.33%. These results show that the SVM algorithm is able to provide a fairly stable classification performance in this case study. Keywords: Sentimen Analysis; Google Play Store; NewSakpole; Support Vectore Machine Abstrak Untuk mengetahui tingkat keberhasilan suatu pelayanan, perlu dilakukan analisis sentimen untuk memahami opini publik dan tingkat kepuasan publik baik positif, netral, maupun negatif. Analisis sentimen digunakan untuk meningkatkan kualitas pelayanan pada aplikasi NewSakpole dalam pembayaran pajak kendaraan yang berbasis android. Dalam penelitian ini menggunakan beberapa tahap seperti crawling data, preprocessing data, pembobotan kata menggunakan TF-IDF (Term Frequency Inverse Document Frequency) serta model klasifikasi SVM (Support Vectore Machine) untuk klasifikasi sentimen. Dengan menggnakan pengujian Confussion Matrix dengan pembagian data 20% untuk data training dan 80% untuk data testing memperoleh hasil akurasi sebesar 81,08%. Nilai akurasi pada masing-masing sentimen juga menunjukkan performa yang cukup baik, dengan sentimen positif sebesar 81,11%, sentimen netral…
creator: Renaldi Irfan Firdaus
creator: Ahmad Abdul Chamid
creator: Ahmad Jazuli
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-08
publication date: 2026-04-08
volume: 15
number: 2
starting page: 525
ending page: 536
doi: 10.35889/jutisi.v15i2.3478

Analisis Perbandingan ANN dan Hybrid QNN pada Klasifikasi Multikarakteristik Data
type: item
description: The rapid advancement of machine learning has driven the exploration of various computational models to address classification problems across datasets with diverse characteristics. This study aimed to compare the performance of Artificial Neural Network (ANN) and Hybrid Quantum Neural Network (Hybrid QNN) under controlled experimental conditions. Three benchmark datasets, namely Red Wine Quality, Banknote Authentication, and Leukemia Gene Expression, were used with a training and test data split of 80:20. The evaluation was conducted using accuracy, precision, recall, F1-score, and computational time. The results showed that on the Gene Expression Leukemia dataset, Hybrid QNN achieved an accuracy of 0.9333, significantly outperforming ANN, which obtained 0.6111. Conversely, ANN demonstrated competitive performance and higher computational efficiency on low- and medium-dimensional datasets. These findings indicate that the advantages of Hybrid QNN are contextual and strongly dependent on data characteristics. Keywords: Artificial Neural Network; Hybrid Quantum Neural Network; classification; machine learning Abstrak Perkembangan pesat pembelajaran mesin telah mendorong eksplorasi berbagai model komputasi untuk menyelesaikan permasalahan klasifikasi pada data dengan karakteristik yang beragam. Penelitian ini bertujuan untuk membandingkan kinerja Artificial Neural Network (ANN) dan Hybrid Quantum Neural Network (Hybrid QNN) dalam kondisi eksperimen yang terkontrol. Tiga dataset acuan digunakan, yaitu Red Wine Quality, Banknote Authentication, dan Gene Expression Leukemia, dengan pembagian data latih dan uji sebesar 80:20. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, F1-score, serta waktu komputasi. Hasil eksperimen menunjukkan bahwa pada dataset Gene Expression Leukemia, Hybrid QNN mencapai akurasi 0,9333, jauh lebih tinggi dibandingkan ANN sebesar 0,6111. Sebaliknya, ANN menunjukkan performa yang kompetitif dan lebih efisien pada dataset berdimen…
creator: Kharisteas Josan Sedi
creator: Sunneng Sandino Berutu
creator: Aninda Astuti
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-08
publication date: 2026-04-08
volume: 15
number: 2
starting page: 537
ending page: 547
doi: 10.35889/jutisi.v15i2.3485

Prediksi Kadar Air Greenbeans Kopi Pra-Roasting Menggunakan Metode ANFIS
type: item
description: Moisture content of green coffee beans is a critical parameter that determines quality stability during storage and the pre-roasting stage; however, conventional measurement methods are destructive and unsuitable for continuous monitoring. This study aims to develop an Internet of Things (IoT)-based moisture content prediction system using the Adaptive Neuro-Fuzzy Inference System (ANFIS). Input variables include temperature, relative humidity (RH), and capacitive sensor ADC signals, while moisture content is used as the target variable. A dataset consisting of 1032 observations was divided into training and testing sets with an 80:20 ratio. The ANFIS model employed Gaussian membership functions and an early stopping mechanism, and its performance was evaluated using MAE, RMSE, MAPE, and the coefficient of determination (R²). Experimental results achieved MAE of 0.2648, RMSE of 0.4187, MAPE of 2.077%, and R² of 0.8109 with an accuracy of 97.923%. The proposed system enables accurate, non-destructive, and real-time moisture content prediction. Keywords: Moisture content; Green beans; Coffee; ANFIS; Prediction. Abstrak Kadar air biji kopi hijau merupakan parameter penting yang menentukan stabilitas mutu selama penyimpanan hingga tahap pra-roasting, namun metode pengukuran konvensional bersifat destruktif dan tidak mendukung monitoring berkelanjutan. Penelitian ini bertujuan mengembangkan sistem prediksi kadar air berbasis Internet of Things (IoT) menggunakan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Variabel input meliputi suhu, kelembaban relatif (RH), dan sinyal ADC sensor, dengan kadar air sebagai variabel target. Dataset sebanyak 1032 data dibagi menjadi data latih dan data uji dengan rasio 80:20. Model ANFIS menggunakan fungsi keanggotaan Gaussian dan mekanisme early stopping, serta dievaluasi menggunakan MAE, RMSE, MAPE, dan koefisien determinasi (R²). Hasil pengujian menunjukkan MAE 0,2648, RMSE 0,4187, MAPE 2,077%, dan R² sebesar 0,8109 dengan akur…
creator: Muchammad Fadika Naddiyanto
creator: Mohammad Idhom
creator: Hendra Maulana
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-08
publication date: 2026-04-08
volume: 15
number: 2
starting page: 548
ending page: 559
doi: 10.35889/jutisi.v15i2.3568

Perancangan Dan Implementasi Sistem IoT Untuk Monitoring Kualitas Air Menggunakan ESP8266 Dan Sensor TDS Meter
type: item
description: Continuous monitoring of water quality plays an important role in supporting effective water resource management. This research develops a water quality monitoring system based on the Internet of Things (IoT) using an ESP8266 microcontroller integrated with a Total Dissolved Solids (TDS) sensor. The study includes hardware design, software development, system integration, and sensor accuracy evaluation by comparing the measurement results with a standard TDS meter. Testing was conducted on three water samples: bottled water, reverse osmosis (RO) gallon water, and tap water. The experimental results show measurement errors of 3.45%, 6.12%, and 6.67%, respectively. The average error obtained is 5.41%, indicating an overall system accuracy of 94.59%. In addition, the system is capable of measuring TDS values in real-time and transmitting the data to the server in a stable manner. These results indicate that the developed system is feasible for IoT-based water quality monitoring applications. Keywords: Internet of Things; Water Quality; ESP8266; TDS Sensor; Real-time Monitoring Abstrak Pemantauan kualitas air secara berkelanjutan memiliki peran penting dalam mendukung pengelolaan sumber daya air yang efektif. Penelitian ini mengembangkan sistem monitoring kualitas air berbasis Internet of Things (IoT) dengan memanfaatkan mikrokontroler ESP8266 yang terintegrasi dengan sensor Total Dissolved Solids (TDS). Metode penelitian meliputi perancangan perangkat keras, pengembangan perangkat lunak, integrasi sistem, serta evaluasi akurasi sensor melalui perbandingan hasil pengukuran dengan alat ukur TDS meter. Pengujian dilakukan pada tiga sampel air, yaitu air kemasan, air galon reverse osmosis (RO), dan air keran. Hasil pengujian menunjukkan tingkat kesalahan sebesar 3,45%, 6,12%, dan 6,67%. Rata-rata kesalahan sistem sebesar 5,41% dengan tingkat akurasi mencapai 94,59%. Sistem juga mampu melakukan pengukuran nilai TDS secara real-time dan mengirimkan data ke server secara stab…
creator: Piki Darinding
creator: Gogor Chrismass Setyawan
creator: Kristian Juri Dama Lase
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-08
publication date: 2026-04-08
volume: 15
number: 2
starting page: 560
ending page: 571
doi: 10.35889/jutisi.v15i2.3510

Evaluasi Virtual Reality terhadap Keterlibatan dan Pemahaman Anak dalam Pembelajaran
type: item
description: This study aims to evaluate the feasibility of Virtual Reality technology as a learning medium in elementary schools to address the shortcomings of conventional learning methods in increasing student engagement and enthusiasm in learning. The approach used is quantitative descriptive, by utilizing the System Usability Scale (SUS) for usability evaluation. Data collection involved administering a questionnaire to 35 students after they watched a Virtual Reality-based learning video and a brief explanation of the concept and application of the technology. The instrument used consisted of 10 questions that had undergone validity and reliability assessments. The results of the study indicate that all questionnaire items are considered valid, and the instrument shows a satisfactory level of reliability, as indicated by the Cronbach's Alpha of 0.701. The average SUS value of 78.43 is within the acceptable range, which indicates that Virtual Reality-based learning media is feasible and has the potential for sustainable development as an interactive learning tool in elementary schools. Kata kunci: Virtual Reality; Learning media; Usability; System Usability Scale; Elementary school Abstrak Penelitian ini bertujuan untuk mengevaluasi kelayakan teknologi Virtual Reality sebagai media pembelajaran di sekolah dasar untuk mengatasi kekurangan metode pembelajaran konvensional dalam meningkatkan keterlibatan dan antusiasme siswa dalam belajar. Pendekatan yang digunakan adalah deskriptif kuantitatif, dengan memanfaatkan System Usability Scale (SUS) untuk evaluasi usability. Pengumpulan data melibatkan pemberian kuesioner kepada 35 siswa setelah mereka menonton video pembelajaran berbasis Virtual Reality dan penjelasan singkat tentang konsep dan penerapan teknologi tersebut. Instrumen yang digunakan terdiri dari 10 butir pertanyaan yang telah menjalani penilaian validitas dan reliabilitas. Hasil penelitian menunjukkan bahwa semua butir kuesioner dianggap valid, dan instrumen tersebu…
creator: Desi Rosma Wati
creator: Ria Andryani
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-13
publication date: 2026-04-13
volume: 15
number: 2
starting page: 572
ending page: 584
doi: 10.35889/jutisi.v15i2.3529

Analisis Perbandingan Quantum Machine Learning Dalam Klasifikasi Berita Politik Fakta Dan Hoaks
type: item
description: This study analyzes the comparative performance of Quantum Machine Learning in classifying factual and hoax political news using three approaches, namely Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), and Hybrid Quantum Kernel with Classical SVM. News data is represented using TF-IDF and its dimensionality is reduced using Principal Component Analysis, then balanced using SMOTE. Feature transformation is carried out to the quantum domain through angle encoding, then applied to the QML model. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score. The experimental results show that QSVC has the best performance with an accuracy of 0.629 and an F1-Score of 0.735, followed by QNN and Hybrid Quantum Kernel Classical SVM. This study proves that the quantum kernel-based approach is effective in classifying medium-dimensional text, while also demonstrating the potential of Quantum Machine Learning as an alternative method for classifying factual and hoax political news. Keywords: Quantum Machine Learning; Quantum Neural Network; Quantum Support Vector Classifier; Hybrid Quantum Kernel; News Classification Abstrak Penelitian ini menganalisis perbandingan kinerja Quantum Machine Learning dalam klasifikasi berita politik fakta dan hoaks dengan menggunakan tiga pendekatan, yaitu Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), dan Hybrid Quantum Kernel dengan Classical SVM. Data berita direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan Principal Component Analysis, kemudian diseimbangkan menggunakan SMOTE. Transformasi fitur dilakukan ke domain kuantum melalui angle encoding, kemudian diterapkan pada model QML. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, dan F1-Score. Hasil eksperimen menunjukkan QSVC memiliki performa terbaik dengan accuracy 0,629 dan F1-Score 0,735, diikuti QNN dan Hybrid Quantum Kernel Classical SVM. Penelitian ini membuktikan bahwa pende…
creator: Linda Kristiani Zebua
creator: Sunneng Sandino Berutu
creator: Aninda Astuti
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-04-13
publication date: 2026-04-13
volume: 15
number: 2
starting page: 585
ending page: 595
doi: 10.35889/jutisi.v15i2.3524

Analisis Threshold Intensitas Cahaya Otomasi Lampu untuk Efisiensi Energi Rumah Tangga
type: item
description: Inefficiency in household energy consumption is frequently driven by human error in lighting system management. This study aims to analyze the effect of varying light intensity thresholds on electrical energy consumption using the Tinkercad simulation platform. An experimental method with a multi-treatment design was employed, comparing the baseline phase against three threshold variations: 30 lx, 50 lx, and 70 lx. The Tinkercad platform was utilized to simulate environmental variable control in a measurable manner, providing consistent initial data as a representation of system behavior. The energy consumption parameter was measured in Watt-hours (Wh) based on the load's operational duration, recorded via the millis() function on the microcontroller. Statistical analysis using One-Way ANOVA indicated significant differences in energy consumption across all treatment groups (p < 0.05). The research findings conclude that a 50 lx threshold represents the optimal equilibrium point, as it achieves significant energy efficiency while maintaining visual comfort standards in accordance with the SNI 6197:2011 regulation. Keywords: Energy Efficiency; Lighting Automation; Tinkercad; Lx Threshold; SNI 6197:2011. Abstrak Ketidakefisienan penggunaan energi pada sektor rumah tangga sering kali dipicu oleh faktor kelalaian manusia (human error) dalam pengelolaan sistem penerangan. Penelitian ini bertujuan untuk menganalisis pengaruh variasi ambang batas (threshold) intensitas cahaya terhadap konsumsi energi listrik melalui platform simulasi Tinkercad. Metode penelitian yang digunakan adalah eksperimental dengan desain Multi treatment yang membandingkan fase baseline terhadap tiga variasi perlakuan threshold: 30 lx, 50 lx, dan 70 lx. Platform Tinkercad digunakan untuk mensimulasikan kontrol variabel lingkungan secara terukur, sehingga diperoleh data awal yang konsisten sebagai representasi perilaku sistem. Parameter konsumsi energi diukur dalam satuan Watt-hour (Wh) berdasarkan du…
creator: Gautama Wijaya
creator: Stefanus Eko Prasetyo
creator: Yodi Salim Putra
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-07
publication date: 2026-05-07
volume: 15
number: 2
starting page: 596
ending page: 604
doi: 10.35889/jutisi.v15i2.3532

Analisis Performa Metode YOLOv5-CNN Dalam Meningkatkan Deteksi Dan Pengenalan Ras Kelinci
type: item
description: Manual identification of rabbit breeds is time-consuming and error-prone, requiring an automated system based on digital images. This study proposes the YOLOv5-CNN approach to automatically detect rabbit objects and classify their breeds. The first stage uses YOLOv5 to detect rabbits in images and generate bounding boxes. The detected images are then used as input for a Convolutional Neural Network (CNN) model for breed classification. Testing was conducted using a rabbit image dataset divided into 70% training data, 10% validation data, and 20% testing data. In the training and validation stages, the model demonstrated stable learning capabilities in recognizing visual patterns between breeds. Next, testing was conducted on 200 independent test images not used during the training process. The evaluation results showed that the YOLOv5-CNN combination system achieved 96% accuracy on the test data. These findings demonstrate that the integration of object detection and image classification in a single processing pipeline can support automatic rabbit breed identification based on digital images. Keywords: Object detection; Image classification; YOLOv5; EfficientNet-B0; Rabbit breeds Abstrak Identifikasi ras kelinci secara manual membutuhkan waktu dan rentan kesalahan, sehingga diperlukan sistem otomatis berbasis citra digital. Penelitian ini mengusulkan pendekatan YOLOv5-CNN untuk mendeteksi objek kelinci dan mengklasifikasikan rasnya secara otomatis. Tahap pertama menggunakan YOLOv5 untuk mendeteksi kelinci pada citra dan menghasilkan bounding box, kemudian citra hasil deteksi dijadikan masukan model Convolutional Neural Network (CNN) untuk klasifikasi ras. Pengujian dilakukan menggunakan dataset citra kelinci yang dibagi menjadi 70% data pelatihan, 10% data validasi, dan 20% data pengujian. Pada tahap pelatihan dan validasi, model menunjukkan kemampuan belajar yang stabil dalam mengenali pola visual antar ras. Selanjutnya, pengujian dilakukan pada 200 citra uji indep…
creator: Enjelina Citra Hulu
creator: Agustinus Rudatyo Himamunanto
creator: Jatmika Jatmika
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-07
publication date: 2026-05-07
volume: 15
number: 2
starting page: 605
ending page: 616
doi: 10.35889/jutisi.v15i2.3499

Analisis Kinerja Smart Door Hybrid Haar Cascade dan ArcFace pada Raspberry
type: item
description: Implementing biometric security systems on deep learning devices faces a major challenge in balancing identity verification accuracy with computational resource efficiency. This study presents a performance analysis of a Raspberry Pi 5-based Smartdoor system integrating the detection speed of Haar Cascade with the recognition accuracy of ArcFace. System performance was evaluated based on RAM usage, CPU load, FPS stability, and access Success Rate parameters. Empirical evaluation results indicate that integrating Deep learning ArcFace increased RAM usage by 33.7% and CPU load from 33% to 53%. However, due to the processing capacity of the Raspberry Pi 5, the system maintained stable real-time performance with an average of 18.3 FPS. In terms of security, the Hybrid method proved superior with an access success rate of 73.7%, surpassing the conventional Haar Cascade method which only reached 68.4%. This study concludes that the Hybrid method is a viable solution for home security systems, where the increased computational load is justified by a significant improvement in identity verification reliability. Keyword: Raspberry Pi 5; Smartdoor; Haar Cascade; ArcFace; Computational Performance. Abstrak Implementasi sistem keamanan biometrik pada perangkat deep learning menghadapi tantangan utama dalam menyeimbangkan akurasi verifikasi dengan efisiensi sumber daya. Penelitian ini menyajikan analisis kinerja sistem Smartdoor berbasis Raspberry Pi 5 yang mengintegrasikan kecepatan deteksi Haar Cascade dengan akurasi pengenalan wajah ArcFace. Kinerja sistem dievaluasi berdasarkan parameter penggunaan RAM, beban CPU, stabilitas FPS, dan tingkat keberhasilan akses. Hasil evaluasi empiris menunjukkan bahwa integrasi Deep learning ArcFace meningkatkan penggunaan RAM sebesar 33,7% dan beban CPU dari 33% menjadi 53%. Namun, berkat kapasitas pemrosesan Raspberry Pi 5, sistem mampu mempertahankan stabilitas kinerja real-time dengan rata-rata 18,3 FPS. Dari segi keamanan, metode Hybrid…
creator: Gautama Wijaya
creator: Stefanus Eko Prasetyo
creator: Haeruddin Haeruddin
creator: Kevin Kevin
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-07
publication date: 2026-05-07
volume: 15
number: 2
starting page: 617
ending page: 625
doi: 10.35889/jutisi.v15i2.3537

Prototipe Kendali Lampu dan Pintu Garasi Menggunakan NodeMCU ESP8266 dan Blynk
type: item
description: Garage system automation has become essential to enhance security and energy efficiency. This study aims to design and implement a prototype of a lighting and garage door control system based on the NodeMCU ESP8266 integrated with the Blynk application. The research method includes hardware design using ultrasonic and PIR sensors, as well as software development based on the Internet of Things. System testing was conducted using black-box testing to verify system functionality and delay testing to measure response time. The results show that the ultrasonic sensor can detect objects up to 20 cm, while the PIR sensor detects motion up to 53 cm, with a relatively fast and stable system response, achieving an average error rate of 3.54% for the ultrasonic sensor. The system is capable of activating lights, opening the garage door using a servo motor, and sending real-time notifications. Therefore, the developed system is proven effective in improving security and energy efficiency, as well as enabling remote control. Keywords: Automatic control; Internet of Things; NodeMCU ESP8266; PIR sensor; Ultrasonic sensor Abstrak Otomatisasi sistem garasi menjadi kebutuhan penting dalam meningkatkan aspek keamanan dan efisiensi energi. Penelitian ini bertujuan merancang dan mengimplementasikan prototipe sistem kendali lampu dan pintu garasi berbasis NodeMCU ESP8266 yang terintegrasi dengan aplikasi Blynk. Metode penelitian meliputi perancangan perangkat keras menggunakan sensor ultrasonik dan sensor PIR serta pengembangan perangkat lunak berbasis Internet of Things. Pengujian sistem dilakukan menggunakan metode black-box testing untuk memverifikasi fungsi sistem serta uji delay untuk mengukur waktu respons. Hasil pengujian menunjukkan sensor ultrasonik mampu mendeteksi objek hingga jarak 20 cm dan sensor PIR hingga 53 cm, dengan waktu respons sistem yang relatif cepat dan stabil, serta rata-rata error sebesar 3,54% pada sensor ultrasonik. Sistem mampu mengaktifkan lampu, membuka p…
creator: Ita Rusmala Dewi
creator: Febriyansyah Ramadhan
creator: Naufal Febianto
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-07
publication date: 2026-05-07
volume: 15
number: 2
starting page: 626
ending page: 634
doi: 10.35889/jutisi.v15i2.2884

Rancangan EduChat Berbasis WebSockets dan E2EE di Institut Pendidikan Indonesia Garut
type: item
description: Academic communication in higher education environments often mixes with personal interactions due to the use of general instant messaging applications, which impacts professionalism and information organization. This study aims to design and develop a dedicated academic communication platform (EduChat) for the academic community of Institut Pendidikan Indonesia (IPI) Garut. The system was developed using the Laravel framework with a REST API architecture. To meet the need for rapid communication, the system integrates the WebSocket protocol to support real-time messaging and announcements. The novelty of this system lies in the implementation of a Python-based security module that encrypts message texts and document attachments using the AES-GCM algorithm, alongside Argon2 for credential security. Based on functionality testing using the Black-box testing method, all system features, including user management, authentication, and real-time communication, successfully operated with a high level of validity. The platform has proven viable for implementation to create a structured, secure, and professional academic communication ecosystem. Keywords: Academic communication; Laravel; AES-GCM encryption; Real-time; Websocket. Abstrak Komunikasi akademik di lingkungan perguruan tinggi sering kali bercampur dengan interaksi personal akibat penggunaan aplikasi pesan instan umum, yang berdampak pada menurunnya profesionalisme dan ketertataan informasi. Penelitian ini bertujuan untuk merancang dan membangun platform komunikasi akademik khusus (EduChat) bagi sivitas akademika Institut Pendidikan Indonesia (IPI) Garut. Sistem dikembangkan menggunakan kerangka kerja Laravel dengan arsitektur REST API. Untuk memenuhi kebutuhan komunikasi cepat, sistem mengintegrasikan protokol WebSocket guna menunjang pengiriman pesan dan pengumuman secara seketika (real-time). Nilai kebaruan pada sistem ini terletak pada penerapan modul keamanan berbasis Python yang mengenkripsi teks pesan beser…
creator: Salman Al Farizi
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-07
publication date: 2026-05-07
volume: 15
number: 2
starting page: 635
ending page: 645
doi: 10.35889/jutisi.v15i2.3600

Sistem Informasi Pengarsipan Dokumen Berbasis Website Pada Kantor Desa Tibubeneng
type: item
description: The development of information technology encourages village government institutions to improve efficiency in administrative management and public services. The Tibubeneng Village Office still implements a manual document archiving system, which causes various problems such as slow document retrieval, risk of document loss, and data duplication. Based on observations and questionnaire results, most employees experience difficulties in managing and retrieving documents, indicating the need for a computerized system. This research aims to design and develop a website-based document archiving information system that integrates archive management and administrative services into a single digital platform. The system development process includes requirements analysis, system design using Data Flow Diagram (DFD) and Entity Relationship Diagram (ERD), database development, and implementation using PHP, MySQL, and Bootstrap. System testing was conducted using the Black Box Testing method, and the results show that all features function according to user requirements. This system improves the efficiency of archive management, accelerates document retrieval, and supports more effective and professional village administrative services. Keywords: Information System; Document Archiving; Website; Village Administration. Abstrak Perkembangan teknologi informasi mendorong instansi pemerintahan desa untuk meningkatkan efisiensi dalam pengelolaan administrasi dan pelayanan kepada masyarakat. Kantor Desa Tibubeneng masih menerapkan sistem pengarsipan dokumen secara manual yang menyebabkan berbagai kendala, seperti lamanya proses pencarian arsip, risiko kehilangan dokumen, serta terjadinya duplikasi data. Berdasarkan hasil observasi dan penyebaran kuesioner, sebagian besar pegawai mengalami kesulitan dalam pengelolaan dan pencarian dokumen sehingga diperlukan sistem yang terkomputerisasi. Penelitian ini bertujuan untuk merancang dan membangun distem informasi pengarsipan dokumen berbas…
creator: Ni Made Ayu Juli Astari
creator: Pande Putu Gede Putra Pertama
creator: Putu Winda Enjelina
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-08
publication date: 2026-05-08
volume: 15
number: 2
starting page: 646
ending page: 655
doi: 10.35889/jutisi.v15i2.3621

Implementasi Metode Queue Tree Untuk Optimalisasi Jaringan Dan Web Filtering Beserta Evaluasi Kinerja Jaringan Berdasarkan Standar Tiphon
type: item
description: Internet connection instability at SMP Negeri 4 Pangkalpinang often occurs during peak hours, even though the bandwidth capacity reaches 200 Mbps. This problem is triggered by the absence of a locked data traffic management system and students' free access to non-educational activities, such as online games and streaming. Therefore, this research is important to design bandwidth management using the Queue Tree method with an allocation of 15 Mbps and a Web Filtering security system. Performance evaluation is carried out quantitatively through a Quality of Service (QoS) test using the Wireshark application measured based on the TIPHON standard. The results of load testing on 5 clients show good network quality, with an average throughput of 71.33% (Good), delay of 165.00 ms (Good), packet loss of 0.096% (Very Good), and jitter of 55.00 ms (Good). In conclusion, the combination of the Queue Tree and Web Filtering methods has proven crucial in distributing bandwidth fairly and blocking irrelevant access, resulting in optimal, stable, and effective network performance supporting educational activities. Keywords: Bandwidth Management; Queue Tree; Web Filtering; Quality of Service; TIPHON. Abstrak Ketidakstabilan koneksi internet di SMP Negeri 4 Pangkalpinang sering terjadi pada jam sibuk meski kapasitas bandwidth mencapai 200 Mbps. Permasalahan ini dipicu oleh ketiadaan sistem manajemen lalu lintas data terpusat dan akses bebas siswa pada aktivitas non-edukatif, seperti game online dan streaming. Oleh karena itu, penelitian ini penting dilakukan untuk merancang manajemen bandwidth menggunakan metode Queue Tree dengan alokasi 15 Mbps serta sistem keamanan Web Filtering. Evaluasi kinerja dilakukan secara kuantitatif melalui uji Quality of Service (QoS) menggunakan aplikasi Wireshark yang diukur berdasarkan standar TIPHON. Hasil pengujian beban pada 5 client menunjukkan kualitas jaringan yang baik, dengan rata-rata throughput 71,33% (Bagus), delay 165,00 ms (Bagus), packet …
creator: Purnawiranda Purnawiranda
creator: Devi Irawan
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-12
publication date: 2026-05-12
volume: 15
number: 2
starting page: 656
ending page: 665
doi: 10.35889/jutisi.v15i2.3642

Analisis Komparatif Arsitektur CNN untuk Klasifikasi Penyakit Daun Tebu Berbasis Transfer Learning
type: item
description: Classifying sugarcane leaf diseases is critical in modern cultivation, because symptoms are often difficult to identify accurately through visual inspection alone. This study compares the performance of three Convolutional Neural Network (CNN) architectures Xception, EfficientNetB0, and ResNet50 using transfer learning on a dataset of 2,521 sugarcane leaf images grouped into five disease classes. A preprocessing stage, including image resizing, was applied to all samples. The data were then split into 80% training, 10% validation, and 10% testing sets. Each model was trained with the same training configuration to ensure a fair comparison, with consistent hyperparameters across experiments. Testing results indicate that EfficientNetB0 achieved the most stable performance with 99.5% accuracy, followed by ResNet50 at 98.2%, whereas Xception yielded the lowest performance due to training instability. These findings suggest that CNN architectures optimized via network-scaling efficiency better handle visual variability in sugarcane leaf disease images. Keywords: Sugarcane Leaf Disease; Transfer Learning; Xception; EfficientNetB0; ResNet50. Abstrak Klasifikasi penyakit daun tebu sangat penting dalam praktik budidaya modern, karena gejala penyakit sering kali sulit diidentifikasi secara akurat hanya melalui pengamatan visual. Studi ini mengkomparasi kinerja tiga arsitektur Convolutional Neural Network (CNN), yaitu Xception, EfficientNetB0, dan ResNet50, menggunakan pendekatan transfer learning pada dataset 2.521 citra daun tebu yang dibagi ke dalam lima kelas penyakit. Tahap pra proses yang mencakup penyesuaian ukuran diterapkan pada seluruh citra. Selanjutnya, data dibagi menjadi 80% data pelatihan, 10% data validasi, dan 10% data pengujian. Setiap model dilatih menggunakan konfigurasi pelatihan yang seragam untuk memastikan perbandingan yang adil. Hasil pengujian menunjukkan bahwa EfficientNetB0 memiliki performa paling stabil dengan akurasi 99,5%, diikuti oleh ResNet50…
creator: Fakhrur Rofiq
creator: Achmad Noercholis
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-13
publication date: 2026-05-13
volume: 15
number: 2
starting page: 666
ending page: 678
doi: 10.35889/jutisi.v15i2.3431

Sistem Pendukung Keputusan Pemilihan Internet Service Provider Menggunakan Metode TOPSIS Berbasis Web di Kota Batam
type: item
description: Pemilihan Internet Service Provider (ISP) yang tepat menjadi tantangan kompleks bagi konsumen dan pelaku usaha di Kota Batam, mengingat banyaknya pilihan penyedia layanan dengan karakteristik beragam meliputi kecepatan, harga, kualitas layanan, dan jangkauan wilayah. Penelitian ini mengembangkan sistem pendukung keputusan berbasis web yang mengimplementasikan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk membantu proses seleksi ISP secara objektif dan terstruktur. Sistem dirancang menggunakan arsitektur React.js dengan integrasi algoritma TOPSIS yang mampu memproses enam kriteria penilaian: kecepatan download, kecepatan upload, harga, kualitas layanan, jangkauan wilayah, dan stabilitas koneksi. Data penelitian mencakup enam provider ISP utama di Batam dengan pengujian dilakukan melalui dua skenario berbeda: kasus single-ISP dan multi-ISP. Hasil validasi matematis menunjukkan akurasi perhitungan sistem mencapai toleransi error di bawah 0,001 dibandingkan dengan perhitungan manual menggunakan Microsoft Excel. Sistem berhasil menghasilkan rekomendasi ISP dengan nilai preferensi tertinggi mencapai 92% untuk kasus multi-ISP dan menangani edge case single-ISP dengan mekanisme fallback yang memberikan nilai preferensi 100%. Implementasi sistem ini terbukti efektif dalam menyediakan rekomendasi yang objektif, transparan, dan dapat diandalkan untuk mendukung pengambilan keputusan pemilihan ISP di Kota Batam.
creator: Rita Rianggun
creator: Yonhendri Yonhendri
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-13
publication date: 2026-05-13
volume: 15
number: 2
starting page: 679
ending page: 693
doi: 10.35889/jutisi.v15i2.3487

Analisis Spasial Jalur Pendakian Gunung Lawu via Cemoro Sewu Berbasis SIG
type: item
description: This study analyzes the spatial characteristics of the Mount Lawu hiking trail via Cemoro Sewu based on Geographic Information Systems (GIS) by utilizing National Digital Elevation Model (DEMNAS) data. The methods used include the collection of trail and hiking post point data, spatial processing using QGIS, overlay with DEMNAS, as well as elevation profile extraction and slope analysis. The results of the study show that the hiking trail has an elevation range from approximately 1,913 meters to approximately 3,229 meters above sea level with a total elevation gain of around 1,316 meters. Elevation profile analysis shows a gradual increase pattern, with the most significant segment being from Post 1 to Post 2, which has the highest elevation gain. In addition, slope analysis results show that the trail is dominated by moderate to steep slope classes (approximately 23°–30°), especially in the middle sections up to near the summit. The spatial information produced in the form of route maps, elevation profiles, and slope distribution is able to provide a quantitative picture of the difficulty level of the route. Keywords: Geographic information system; Demnas; Hiking trails; Elevation profile; Mount Lawu Abstrak Penelitian ini menganalisis karakteristik spasial jalur pendakian Gunung Lawu via Cemoro Sewu berbasis Sistem Informasi Geografis (SIG) dengan memanfaatkan data Digital Elevation Model Nasional (DEMNAS). Metode yang digunakan meliputi pengumpulan data jalur dan titik pos pendakian, pengolahan spasial menggunakan QGIS, overlay dengan DEMNAS, serta ekstraksi profil elevasi dan analisis kemiringan lereng. Hasil penelitian menunjukkan bahwa jalur pendakian memiliki rentang elevasi dari ±1.913 mdpl hingga ±3.229 mdpl dengan total kenaikan elevasi sekitar ±1.316 meter. Analisis profil elevasi menunjukkan pola kenaikan bertahap dengan segmen paling signifikan berada pada Pos 1–Pos 2 sebagai bagian dengan kenaikan elevasi tertinggi. Selain itu, hasil analisis kemiringa…
creator: Andin Ayu Oksilia Ramadhani
creator: Nur Ariesanto Ramdhan
creator: Bambang Irawan
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-13
publication date: 2026-05-13
volume: 15
number: 2
starting page: 694
ending page: 707
doi: 10.35889/jutisi.v15i2.3583

Deteksi Dini Diabetes Mellitus Tipe 2 Pada Usia Dewasa Muda Menggunakan Algoritma Decision Tree - C4.5
type: item
description: The shift in the epidemiological pattern of metabolic diseases is indicated by the increasing incidence of Type 2 Diabetes Mellitus (T2DM) in young individuals. The increasing incidence of glucose metabolism disorders is largely due to changes in modern lifestyles, such as high calorie intake and lack of physical activity. Early detection during productive age is crucial to prevent long-term problems. Based on clinical data, this study created a model for early detection of type 2 diabetes using the Decision Tree C4.5 algorithm. The dataset was filtered for people aged between 20 and 40 years after being obtained from Kaggle. The study phase included data pre-processing, data splitting for testing and training, and model development using entropy and information gain. Accuracy, precision, recall, F1-score, and ROC-AUC were used to evaluate the model. The accuracy was 92.4% and the ROC-AUC was 0.938. The completed model can be used as a data-driven interpretive health screening tool. Keywords: Early diagnosis; Decision Tree algorithm; C4.5; Classification model Abstrak Pergeseran pola epidemiologi penyakit metabolik ditunjukkan oleh meningkatnya kejadian Diabetes Melitus Tipe 2 (T2DM) pada individu muda. Meningkatnya kejadian gangguan metabolisme glukosa sebagian besar disebabkan oleh perubahan gaya hidup modern, seperti pola asupan kalori tinggi dan kurangnya aktivitas fisik. Deteksi dini pada usia produktif sangat penting untuk mencegah masalah jangka panjang. Berdasarkan data klinis, penelitian ini menciptakan model deteksi dini diabetes tipe 2 menggunakan algoritma Decision Tree C4.5. Dataset difilter untuk orang berusia antara 20 dan 40 tahun setelah diperoleh dari Kaggle. Fase studi meliputi pra-pemrosesan data, pemisahan data untuk pengujian dan pelatihan, serta pengembangan model menggunakan entropi dan perolehan informasi. Akurasi, presisi, recall, F1-score, dan ROC-AUC digunakan untuk menilai model tersebut. Akurasinya adalah 92,4% dan ROC-AUC adalah 0,938.…
creator: SOFIA DARFINDA DALIMAJUN
creator: Azwar Riza Habibi
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-14
publication date: 2026-05-14
volume: 15
number: 2
starting page: 708
ending page: 716
doi: 10.35889/jutisi.v15i2.3578

Analisis Kesuksesan Reporting Information System Menggunakan Model DeLone & McLean pada Kantor Jasa Penilai Publik
type: item
description: This study examines the extent to which the Reporting Information System (RIS) implemented at the Batam branch of a public appraisal firm has achieved its intended success, evaluated through a simplified version of the DeLone & McLean IS Success Model. A sequential explanatory mixed-method design was adopted, engaging all 16 active system users as participants through a census-based approach, yielding a complete response rate. The study investigates how System Quality and Information Quality shape User Satisfaction, and how satisfaction subsequently translates into Individual Impact, combining regression analysis with in-depth thematic interviews. Findings reveal that all constructs were perceived favorably, with mean scores consistently exceeding 3.9. Information Quality emerged as the dominant predictor of User Satisfaction, which in turn strongly accounted for Individual Impact (R² = 0.82 and 0.62, respectively). Although System Quality exhibited a positive directional effect, its contribution fell short of statistical significance, attributable to the limited sample size and shared variance between predictors. Qualitative evidence reinforced the primacy of Information Quality, reflecting the output-oriented and compliance-critical nature of the system context. To optimize overall system performance, the study recommends addressing server stability issues, restructuring automated report templates, and introducing role-specific interface customization. Keywords: Information Systems; System Success; DeLone & McLean; Information Quality; User Satisfaction. Abstrak Penelitian ini bertujuan mengevaluasi sejauh mana kesuksesan Sistem Informasi RIS yang diimplementasikan pada kantor cabang Batam sebuah perusahaan jasa penilai publik, menggunakan pendekatan model DeLone & McLean yang telah diadaptasi sesuai konteks sistem. Desain penelitian yang digunakan adalah sequential explanatory dengan kombinasi metode kuantitatif dan kualitatif, melibatkan seluruh 16 pengguna akti…
creator: erwan erwan
creator: Ahmad Zulfan
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-14
publication date: 2026-05-14
volume: 15
number: 2
starting page: 717
ending page: 732
doi: 10.35889/jutisi.v15i2.3593

Implementasi Algoritma K-Means Clustering untuk Mengelompokkan Wilayah Produksi Kopi di Jawa Barat
type: item
description: Regional disparities in Arabica coffee output across West Java Province underscore the necessity of evidence-based analytical approaches to systematically map production distribution. This study applies K-means clustering to segment Arabica coffee-producing districts and cities based on their production volumes. The analytical pipeline encompasses data preprocessing, cluster modeling, performance evaluation, and result visualization. Three distinct production tiers emerged from the analysis: low, moderate, and high. A silhouette score of 0.883 confirmed excellent cluster cohesion and separation quality. The novelty of this research lies in its territory-level, data-driven segmentation approach paired with interpretable visualization to inform regional policy. The findings reveal pronounced inter-regional production disparities and offer an empirical foundation for crafting targeted, data-informed, and sustainable development strategies to improve the equity and competitiveness of Arabica coffee production at the regional level. Keywords: K-Means clustering; Arabica coffee; Coffee production; Regional clustering; Data analysis Abstrak Kesenjangan tingkat produksi kopi Arabika antar daerah di Provinsi Jawa Barat mengindikasikan perlunya pendekatan analitis berbasis data untuk memetakan pola sebaran produksi secara sistematis. Penelitian ini menerapkan metode K-means clustering guna mengelompokkan daerah penghasil kopi Arabika berdasarkan volume produksinya. Rangkaian analisis mencakup pra-pemrosesan data, pemodelan cluster, evaluasi performa, dan visualisasi hasil. Tiga kelompok wilayah berhasil terbentuk, yakni kategori produksi rendah, menengah, dan tinggi. Nilai silhouette score sebesar 0,883 mengonfirmasi kualitas pengelompokan yang sangat baik dengan separasi antar cluster yang tegas. Kebaruan penelitian terletak pada penerapan segmentasi berbasis wilayah yang bersifat data-driven serta penyajian visual yang informatif sebagai fondasi pengambilan kebijakan. Temua…
creator: Jingga Pratama
creator: Baenil Huda
creator: Shofa Shofiah Hilabi
creator: Elvina Novalia
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-14
publication date: 2026-05-14
volume: 15
number: 2
starting page: 733
ending page: 745
doi: 10.35889/jutisi.v15i2.3622

Perbandingan Metode Decision Tree dan Logistic Regression dalam Klasifikasi Tingkat Obesitas Berdasarkan Gaya Hidup
type: item
description: This study compares two classification algorithms, Decision Tree and Logistic Regression, to predict obesity levels based on individual lifestyle patterns. The initial dataset consisted of 2,212 data points with 17 attributes, which were then narrowed down to 499 data points with the nine most relevant attributes. After a cleaning process, 498 valid data points were obtained, including demographic information and daily habits, which were then used in the modelling process. To objectively evaluate model performance, a stratified 5-fold cross-validation method was used, along with testing on separate test data. The evaluation results showed that Logistic Regression consistently performed better, with an average accuracy of 0.8755 and an F1-score of 0.8576. In contrast, Decision Tree achieved an accuracy of 0.7851 and an F1-score of 0.7704. The test data also showed a similar pattern, with Decision Tree achieving an accuracy of 0.75 and an F1-score of 0.7534, while Logistic Regression achieved an accuracy of 0.88 and an F1-score of 0.8664. Overall, the results showed that logistic regression performed more consistently and reliably in classifying obesity levels, suggesting that this method may be a superior method for supporting analysis in the healthcare industry. Kata Kunci: Classification; Obesity; Lifestyle; Decision Tree; Logistic Regression Abstrak Studi ini membandingkan dua algoritma klasifikasi, Decision Tree dan Logistic Regression, untuk memprediksi tingkat obesitas berdasarkan pola gaya hidup individu. Dataset awal terdiri dari 2.212 data dengan 17 atribut, yang kemudian diseleksi menjadi 499 data dengan 9 atribut yang paling relevan. Setelah melalui proses pembersihan, diperoleh 498 data valid yang mencakup informasi demografis dan kebiasaan sehari-hari, yang selanjutnya digunakan dalam proses pemodelan. Untuk mengevaluasi kinerja model secara objektif, digunakan metode stratified 5-fold cross-validation serta pengujian pada data uji terpisah. Hasil evalua…
creator: Yunisia Rosari Bere
creator: Fadhli Almu’iini Ahda
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-14
publication date: 2026-05-14
volume: 15
number: 2
starting page: 746
ending page: 756
doi: 10.35889/jutisi.v15i2.3591

Analisis Sentimen Isu Redominasi Rupiah Menggunakan Lexicon Based dan Naïve Bayes
type: item
description: Rupiah redenomination has resurfaced as a strategic issue in Indonesian monetary policy following the announcement of the roadmap by Minister of Finance Purbaya Yudhi Sadewa in 2025. This study aims to analyze public sentiment towards the redenomination issue through a hybrid approach: lexicon based (LBA) based on InSet Lexicon enriched with 217 contextual phrases (“bunker money”, “DPR ketar-ketir”), combined with Naive Bayes based on TF-IDF representation. The primary dataset of 1,087 public comments from YouTube (Isu_Ekonomi_Redominasi.csv) was processed using Sastrawi (stopword removal and stemming). The results show a dominance of positive sentiment (58.3%), driven by the justice frame narrative (redenomination as a tool for exposing corrupt assets), while negative (24.1%) and neutral (17.6%) sentiment reflect concerns over inflation risks and transition confusion. The MNB model achieved an average accuracy of 86.3% in 10-fold cross-validation. The findings reveal that public support is not merely monetary, but rather an expression of collective aspirations for state transparency and accountability. This research demonstrates the effectiveness of a hybrid lexicon-enhanced approach for domain-specific sentiment analysis in Indonesian, while also providing evidence-based policy insights for inclusive policy design. Keywords: Public Sentiment; Twitter Media; Naive Bayes; Visualization Abstrak Redenominasi rupiah kembali mencuat sebagai isu strategis dalam kebijakan moneter Indonesia pasca pengumuman roadmap oleh Menteri Keuangan Purbaya Yudhi Sadewa pada 2025. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap isu redenominasi melalui pendekatan lexicon based berbasis InSet Lexicon yang diperkaya dengan 217 frasa kontekstual (misal: “uang bunker”, “DPR ketar-ketir”), dikombinasikan dengan Naive Bayes berbasis representasi TF-IDF. Dataset primer berupa 1.087 komentar publik dari YouTube (Isu_Ekonomi_Redominasi.csv) diproses menggunakan Sastrawi (stop…
creator: Muhammad Fadli
creator: Ival Sanjaya
creator: Muhammad Surono
creator: Ryan Randy Suryono
rights: Copyright (c) 2026 Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
date: 2026-05-17
publication date: 2026-05-17
volume: 15
number: 2
starting page: 757
ending page: 766
doi: 10.35889/jutisi.v15i2.3436