e-ISSN: 2685-0877
Nextcloud Based Personal Cloud Storage Implementation on Raspberry-Pi 4 for Lightweight Cloud Infrastructure
Cloud computing has emerged as a prevalent technique for data storage and management via internet-based platforms, eliminating the need for local storage devices. Nonetheless, public cloud services like Google Drive and Dropbox have issues related to data privacy, security, and user control over the foundational infrastructure. This study advocates for the deployment of a private cloud storage system utilizing Nextcloud on a Raspberry Pi 4 as a streamlined and economical infrastructure option. The Raspberry Pi is chosen for its compact dimensions, little power usage, and cost-effectiveness relative to traditional servers. The system is constructed on Ubuntu Server, utilizing Apache as the web server, MySQL as the database management system, and PHP to facilitate the Nextcloud application. The research process encompasses system requirements analysis, system design, implementation, and thorough testing. The findings indicate that the system effectively delivers critical cloud storage capabilities, encompassing file upload, download, sharing, data synchronization, and user access management. The performance assessment reveals that the system functions stably, responsively, and reliably for small to medium-scale storage requirements, while ensuring a sufficient degree of security. Keywords: Cloud computing; Cloud storage; Raspberry-pi; Nextcloud; Lightweight cloud infrastructure; Abstrak Cloud computing menjadi teknologi yang banyak diadopsi untuk penyimpanan dan pengelolaan data melalui internet tanpa bergantung pada perangkat lokal. Namun, layanan cloud publik seperti Google Drive dan Dropbox menimbulkan kekhawatiran terkait privasi, keamanan, serta kendali pengguna terhadap data dan infrastruktur. Penelitian ini mengusulkan implementasi sistem cloud pribadi berbasis Nextcloud pada Raspberry Pi 4 sebagai solusi infrastruktur ringan dan berbiaya rendah. Raspberry Pi dipilih karena ukurannya ringkas, hemat daya, serta ekonomis dibandingkan server konvensional. Sistem dibangun menggunakan Ubuntu Server dengan Apache sebagai web server, MySQL sebagai database, serta PHP untuk mendukung aplikasi. Metodologi penelitian meliputi analisis kebutuhan, perancangan sistem, implementasi, dan pengujian sistem secara menyeluruh dan terstruktur. Hasil menunjukkan sistem mampu menyediakan fitur unggah, unduh, berbagi file, sinkronisasi data, serta manajemen akses pengguna secara efektif dan efisien. Pengujian kinerja menunjukkan sistem berjalan stabil, responsif, dan andal untuk kebutuhan penyimpanan skala kecil hingga menengah secara optimal dan berkelanjutan dengan tingkat keamanan yang memadai. Kata kunci: Komputasi awan; Penyimpanan awan; Raspberry-pi; Nextcloud; Infrastruktur awan ringan
Copyright (c) 2026 Progresif: Jurnal Ilmiah Komputer
Deteksi Serangan DDoS pada Trafik IoT Menggunakan Random Forest dengan Dataset CICIoT2023
As the number of Internet of Things (IoT) devices continues to grow, these devices become increasingly vulnerable to Distributed Denial of Service (DDoS) attacks. However, their limited computational capacity makes it difficult to implement conventional security mechanisms. This study proposes a model for detecting DDoS attacks using Random Forest, trained using the CICIoT2023 dataset, which consists of 46 flow-based features collected from 105 real-world IoT devices. The preprocessing stage includes binary classification, normalization using StandardScaler, and handling class imbalance through a combination of 1:10 undersampling and class weighting. Evaluation on 1,154,684 test samples shows excellent performance, achieving 99.99% accuracy, 100% precision, 99.99% recall, and 99.99% F1-score. To ensure reliability, six validation checks are conducted, including overfitting analysis, cross-validation. The results confirm that the model can generalize well beyond the training data. Most attack types are detected perfectly, although application-layer attacks such as DDoS-SlowLoris remain more challenging. Overall, Random Forest proves to be an effective and relatively lightweight approach for DDoS detection in IoT environments. Keywords: DDoS; Random Forest; IoT; CICIoT2023; Machine Learning Abstrak Pertumbuhan jumlah perangkat IoT menyebabkan peningkatan risiko terhadap berbagai ancaman keamanan terhadap serangan Distributed Denial of Service (DDoS). Namun, keterbatasan kapasitas komputasi pada perangkat IoT menyulitkan penerapan mekanisme keamanan konvensional. Penelitian ini mengusulkan model deteksi DDoS berbasis Random Forest yang dilatih menggunakan dataset CICIoT2023, yang terdiri dari 46 fitur berbasis flow yang dikumpulkan dari 105 perangkat IoT nyata. Tahap preprocessing meliputi klasifikasi biner, normalisasi menggunakan StandardScaler, serta penanganan ketidakseimbangan kelas melalui kombinasi undersampling (1:10) dan class weighting. Hasil evaluasi pada 1.154.684 data uji menunjukkan performa yang sangat tinggi, dengan accuracy sebesar 99,99%, precision 100%, recall 99,99%, dan F1-score 99,99%. Untuk memastikan keandalan model, dilakukan enam pengujian validasi, termasuk analisis overfitting, cross-validation. Hasil penelitian mengonfirmasi bahwa model mampu melakukan generalisasi dengan baik terhadap data di luar data pelatihan. Sebagian besar jenis serangan berhasil dideteksi secara sempurna, meskipun serangan pada lapisan aplikasi seperti DDoS-SlowLoris masih menjadi tantangan. Secara keseluruhan, Random Forest terbukti sebagai pendekatan yang efektif dan relatif ringan untuk deteksi DDoS pada lingkungan IoT Kata kunci: DDoS; Random Forest; IoT; CICIoT2023; Machine Learning
Analisis Sentimen Terhadap Program Makan Bergizi Gratis Menggunakan Metode Logistic Regression
This study aims to analyze public sentiment toward the Free Nutritious Meal Program (MBG), a policy implemented by the Indonesian government to improve the nutritional quality of students. The data used consist of 1,440 tweets collected through a scraping process from the X/Twitter platform. The data processing stages include preprocessing steps such as case folding, cleaning, tokenizing, stopword removal, and stemming using the Sastrawi library. Furthermore, the text data are transformed into numerical representations using the TF-IDF method and classified using the Logistic Regression algorithm. To enhance the model's performance, the SMOTE technique is applied to address data imbalance, along with GridSearchCV for parameter optimization. The results indicate that the Logistic Regression model achieves excellent performance, with an Accuracy of 98.96%, Precision of 99.14%, Recall of 98.10%, and an F1-Score of 98.61%. This study is expected to provide an objective overview of public perception and serve as a reference for policy evaluation and decision-making. Keywords: Sentiment Analysis; Free Nutritious Meal Program; Logistic Regression; Text mining; NLP. Abstrak Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap Program Makan Bergizi Gratis (MBG) yang merupakan kebijakan pemerintah Indonesia dalam meningkatkan kualitas gizi peserta didik. Data yang digunakan berupa 1.440 tweet yang diperoleh melalui proses scraping dari platform X/Twitter. Tahapan pengolahan data meliputi preprocessing yang terdiri dari case folding, cleaning, tokenizing, stopword removal, dan stemming menggunakan library Sastrawi. Selanjutnya, data teks diubah menjadi representasi numerik menggunakan metode TF-IDF dan diklasifikasikan menggunakan algoritma Logistic Regression. Untuk meningkatkan performa model, diterapkan teknik SMOTE dalam mengatasi ketidakseimbangan data serta GridSearchCV untuk optimasi parameter. Hasil penelitian menunjukkan bahwa model Logistic Regression memiliki kinerja yang sangat baik dengan akurasi sebesar 98,96%, Precision 99,14%, Recall 98,10%, dan F1-Score 98,61%. Penelitian ini diharapkan dapat memberikan gambaran objektif mengenai persepsi masyarakat serta menjadi bahan evaluasi dalam pengambilan kebijakan. Kata kunci: Analisis Sentimen; Makan Bergizi Gratis; Logistic Regression; Text mining; NLP.
Analisis Klasifikasi Risiko Penyakit Jantung Menggunakan Metode Random Forest
Heart disease is one of the leading causes of death worldwide, making early detection crucial to reduce the risk of complications and mortality. The advancement of machine learning technology enables fast and accurate analysis of medical data to support the diagnostic process. This study aims to develop a classification model for heart disease risk using the Random Forest algorithm. The dataset used is the Heart Disease Dataset from Kaggle, consisting of 1,025 patient records with 14 medical attributes, such as age, gender, blood pressure, cholesterol level, and maximum heart rate. The methodology applied is CRISP-DM, which includes Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Model Evaluation is conducted using a confusion matrix, cross-validation, and ROC-AUC. The results show that the Random Forest algorithm achieves a high Accuracy of 99.96% and a cross-validation score of 0.996. The variables chest pain, ca, and thalach are identified as the most influential factors in the prediction. Keywords: Heart Disease; Random Forest; Machine learning; Classification; CRISP-DM Abstrak Penyakit jantung merupakan salah satu penyebab utama kematian di dunia sehingga deteksi dini sangat penting untuk mengurangi risiko komplikasi dan kematian. Perkembangan teknologi machine learning memungkinkan analisis data medis secara cepat dan akurat dalam membantu proses diagnosis. Penelitian ini bertujuan membangun model klasifikasi risiko penyakit jantung menggunakan Algoritma Random Forest. Dataset yang digunakan adalah Heart Disease Dataset dari Kaggle yang terdiri dari 1025 data pasien dengan 14 atribut medis, seperti usia, jenis kelamin, tekanan darah, kadar kolesterol, dan detak jantung maksimum. Metode yang digunakan adalah CRISP-DM meliputi Data Understanding, Data Preparation, Modeling, Evaluation, dan Deployment. Evaluasi model dilakukan menggunakan confusion matrix, cross validation, dan ROC-AUC. Hasil penelitian menunjukkan bahwa Random Forest menghasilkan akurasi tinggi dengan nilai 99,96% serta cross validation sebesar 0,996. Variabel chest pain, ca, dan thalach menjadi faktor paling berpengaruh dalam prediksi. Kata kunci: Penyakit jantung; Random Forest; Machine learning; Klasifikasi; CRISP-DM.
Analisis Trade-Off Arsitektur Multi-Tenant Sebagai Dasar Justifikasi Strategi Basis Data SaaS
Designing a database architecture within a multi-tenant software as a service environment brings forth strategic challenges related to balancing cost efficiency, data separation, and scalability. The aim of this study is to provide a foundational framework for such systems by analyzing the trade-offs among three distinct database architecture models. By employing a case study that involves the deployment of a web-based system, the descriptive qualitative methodology utilizes data collection techniques such as document reviews, system observations, and artifact analyses. The evaluation results indicate that no single model stands out as the best, as each one presents its own set of competing advantages and disadvantages. The innovation in this research lies in the creation of a conceptual compromise model intended to enhance decision-making in database design. The choice of approach should be tailored to meet the specific requirements of the system and its operational context. Keywords: SaaS; Multi-tenant; Trade-off analysis; Database architecture; Shared schema. Abstrak Merancang arsitektur basis data dalam lingkungan perangkat lunak sebagai layanan multi-tenant menghadirkan tantangan strategis terkait dengan keseimbangan antara efisiensi biaya, pemisahan data, dan skalabilitas. Tujuan dari penelitian ini adalah untuk menyediakan suatu rencana dasar untuk sistem tersebut dengan membandingkan pertukaran di antara tiga jenis arsitektur basis data yang berbeda. Dengan menggunakan studi kasus yang melibatkan penyebaran sistem berbasis web, metodologi kualitatif deskriptif menerapkan teknik pengumpulan data yang mencakup tinjauan dokumen, pengamatan sistem, dan analisis artefak. Hasil evaluasi menunjukkan bahwa tidak ada satu model yang benar-benar menonjol karena masing-masing memiliki kelebihan dan kekurangan yang saling bertentangan. Inovasi dalam penelitian ini terletak pada pengembangan model kompromi konseptual yang bertujuan untuk meningkatkan pengambilan keputusan dalam desain basis data. Pemilihan pendekatan harus disesuaikan dengan kebutuhan spesifik dari sistem dan lingkungan kerjanya. Kata kunci: Perangkat lunak sebagai layanan; Multi-tenant; Analisis trade-off; Arsitektur basis data; Skema basis data
Perancangan Sistem Informasi Manajemen Desa Berbasis Web Pada Kantor Desa Medan Krio
Currently, the Medan Krio Village Office, Sunggal District, Deli Serdang Regency is still running information services and manual correspondence administration. This research aims to design a web-based management information system for the Medan Krio Village Office which includes information about the village, correspondence services that can be accessed by the public online, as well as population data management. The method used in this research is Waterfall which includes five stages, namely Requirements, Dsign, Implementation, Testing, and Operation, with system testing using Black Box Testing. This system development uses programming languages such as CSS, JavaScript, HTML, and PHP, and is connected to the MySQL database. It is hoped that with this web-based information system, administrative activities at the Medan Krio Village Office can run more effectively and make it easier for the public to access information and submit request for correspondence online. Keywords: Village Management Information System; Website; Waterfalls. Abstrak Pada Saat ini Kantor Desa Medan Krio Kecamatan Sunggal Kabupaten Deli Serdang masih menjalankan pelayanan informasi dan administrasi surat menyurat secara manual. Pengolahan data terkait penduduk juga masih dilakukan dengan cara manual. Penelitian ini bertujuan untuk merancang sistem informasi manajemen berbasis web untuk Kantor Desa Medan Krio yang mencakup informasi tentang desa, layanan surat menyurat yang dapat diakses masyarakat secara online, serta pengelolaan data penduduk. Metode yang digunakan dalam penelitian ini adalah Waterfall yang meliputi lima tahap, yaitu Requirement, Design, Implementation, Testing, dan Operation, dengan pengujian sistem menggunakan Black Box Testing. Pengembangan sistem ini menggunakan bahasa pemprograman seperti CSS, JavaScript, HTML, dan PHP, serta terhubung dengan database MySQL. Berdasarkan hasil uji fungsional, sistem dinilai telah berjalan dengan baik dalam mengelola sentralisasi basis data, pemprosesan dan pengiriman surat, serta mendukung penyebaran informasi secara terpusat melalui fitur artikel sesuai dengan kebutuhan yang telah dirangcang. Kata Kunci: Sistem Informasi Manajemen Desa; Website; Waterfall.
Metode Hybrid SVR-GWO Untuk Prediksi Harga Saham PT. Aneka Tambang Tbk
Fluctuating and unpredictable stock price movements pose a challenge for investors in their decision-making. This study aims to apply and analyze the performance of a hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) model in predicting the stock price of PT Aneka Tambang Tbk. The data used consists of daily stock prices from September 11, 2020, to September 11, 2025, totaling 1,202 data points, with a division of 70% training data and 30% testing data. The research stages include pre-processing, basic SVR modeling, and parameter optimization using GWO. The evaluation was carried out using RMSE, MAE, and MAPE. The results show that GWO optimization improved the model's performance from RMSE 99.78, MAE 55.70, and MAPE 2.61% to RMSE 77.27, MAE 48.97, and MAPE 2.37%. Thus, the SVR–GWO model is capable of improving the accuracy of stock price predictions and has the potential to support investment decision-making. Keyword: Grey Wolf Optimizer; Machine Learning; Prediction; Stock Price; Support Vector Re-gression Abstrak Pergerakan harga saham yang fluktuatif dan sulit diprediksi menjadi tantangan bagi investor dalam pengambilan keputusan. Penelitian ini bertujuan menerapkan dan menganalisis kinerja model hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) dalam memprediksi harga saham PT Aneka Tambang Tbk. Data yang digunakan berupa harga saham harian periode 11 September 2020 hingga 11 September 2025 sebanyak 1202 data, dengan pembagian 70% data pelatihan dan 30% data pengujian. Tahapan penelitian meliputi pre-processing, pemodelan SVR dasar, serta optimasi parameter menggunakan GWO. Evaluasi dilakukan menggunakan RMSE, MAE, dan MAPE. Hasil menunjukkan bahwa optimasi GWO meningkatkan kinerja model dari RMSE 99.78, MAE 55.70, dan MAPE 2.61% menjadi RMSE 77.27, MAE 48.97, dan MAPE 2.37%. Dengan demikian, model SVR–GWO mampu meningkatkan akurasi prediksi harga saham dan berpotensi mendukung pengambilan keputusan investasi. Kata Kunci: Grey Wolf Optimizer; Harga Saham; Machine Learning; Prediksi; Support Vector Regression
Analisis Sentimen Ulasan Hotel Zuri Express Lippo Cikarang Menggunakan Algoritma Naive Bayes
The hospitality industry relies heavily on customer satisfaction, which is often reflected through reviews on online travel applications. Hotel Zuri Express Lippo Cikarang requires an automated sentiment monitoring system to enhance its service quality. This study aims to implement the Naive Bayes Classifier algorithm to classify customer review sentiments into positive and negative categories. The research dataset consists of 1,637 reviews obtained via web scraping from the google maps. The research methodology includes text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, and N-Gram (Bigram) feature modeling. The experimental results demonstrate that the model achieves an accuracy rate of 91.16%, with a precision of 0.91 and a recall of 0.99 for the majority class. Keyword analysis using Word clouds identified cleanliness and staff service as the primary factors driving customer satisfaction. Despite challenges in detecting negative sentiments within an imbalanced dataset, the Naive Bayes algorithm proved to be reliable and efficient for automated sentiment analysis, providing a robust tool to support fast and accurate management decision-making. Keywords: Sentiment Analysis; Customer Reviews; Hotel Zuri Express; Naive Bayes; Text mining. Abstrak Industri perhotelan sangat bergantung pada kepuasan pelanggan yang tercermin melalui ulasan aplikasi travel online. Hotel Zuri Express Lippo Cikarang memerlukan sistem otomatis untuk memantau sentimen guna meningkatkan kualitas layanan. Penelitian ini bertujuan mengimplementasikan algoritma Naive Bayes Classifier dalam mengklasifikasikan sentimen ulasan pelanggan ke kategori positif dan negatif. Data penelitian mencakup 1.637 ulasan hasil web scraping dari google maps. Tahapan penelitian meliputi preprocessing teks, pembobotan kata Term Frequency-Inverse Document Frequency (TF-IDF), serta pemodelan fitur N-Gram (Bigram). Hasil pengujian menunjukkan model menghasilkan tingkat akurasi sebesar 91,16%, dengan nilai presisi 0,91 dan recall 0,99 pada kelas mayoritas. Analisis kata kunci melalui Word cloud mengidentifikasi aspek kebersihan dan pelayanan staf sebagai faktor utama kepuasan pelanggan. Meskipun terdapat tantangan dalam mendeteksi sentimen negatif pada dataset tidak seimbang, algoritma Naive Bayes terbukti handal dan efisien dalam melakukan analisis sentimen otomatis untuk mendukung pengambilan keputusan manajemen hotel secara cepat dan akurat. Kata kunci: Analisis Sentimen; Ulasan Pelanggan; Hotel Zuri Express; Naive Bayes; Text mining.
Sistem Informasi E-Voting Berbasis Blockchain Untuk Meningkatkan Transparansi Data
Elections that are held in person are vulnerable to fraud, for example, adding votes, miscounting votes and voting more than once, which will be very detrimental to the parties involved, both interested parties and voters. Based on the results of data collection obtained from observations and interviews, an application is needed that allows the above fraud to be avoided. This research develops a blockchain-based E-Voting application to reduce fraud that occurs and to increase transparency of election results data. System development uses the Waterfall method, while system testing uses black boxes. Test results show that by using the blockchain model, voters can only use their NIK once to enter E-Voting to give their voting rights, and refuse to enter their NIK a second time. Blockchain functions to store NIK and lock it so that there is no data duplication so that fraud can be resolved. Keywords: Application; E-Voting; Blockchain; Election Abstrak Pemilu yang dilakukan secara langsung berpotensi akan kecurangan, seperti penambahan suara, salah menghitung suara dan mencoblos lebih dari satu kali ini akan sangat merugikan pihak terkait baik pihak yang berkepentingan maupun pihak pemilih. Berdasarkan hasil observasi dan wawancara, dibutuhkan aplikasi yang memungkinkan kecurangan dapat dihindari. Penelitian ini mengembangkan aplikasi E-Voting berbasis blockchain untuk mengurangi kecurangan yang terjadi, sehingga dapat meningkatkan transparansi data hasil pemilu. Pengembangan sistem menggunakan metode Waterfall, sedangkan pengujian sistem menggunakan blackbox. Hasil pengujian menunjukkan dengan penggunaan model blockchain, pemilih hanya dapat menggunakan NIK satu kali masuk ke E-Voting untuk memberikan hak pilihnya, dan menolak bilamana memasukkan NIK keduia kalinya. Blockchain berfungsi untuk menyimpan NIK dan menguncinya agar tidak ada kerangkapan data sehingga kecurangan dapat teratasi. Kata Kunci: Apilkasi; E-Voting; Blockchain; Pemilu
Pengembangan Sistem Web Survei Indeks Kepuasan Masyarakat Pada BAPPERIDA Kota Pangkalpinang
This research evaluates the deployment of a web-centered platform designed to assess community satisfaction regarding public services provided by Bapperida in Pangkalpinang City. Prior to this, the survey mechanism relied on traditional methods and lacked digital integration, which led to operational bottlenecks in data handling and constrained data presentation. To address these challenges, this study focuses on designing and deploying an online survey application aimed at streamlining data gathering, processing, and reporting workflows. The methodology employs a Research and Development (R&D) approach using the Waterfall model, encompassing requirements analysis, system architecture design, implementation, and rigorous testing. The developed platform incorporates core functionalities, including digital questionnaire submission, database administration, and automated report generation. The outcomes demonstrate that the proposed system significantly enhances accessibility, operational speed, and accuracy within survey data administration. Furthermore, User Acceptance Testing (UAT) results revealed high user satisfaction scores, confirming that the system is fully viable for supporting public service quality evaluations. Keywords: Community Satisfaction Indeks; Web-Based Survey; Public Service; Information System; User Satisfaction Abstrak Evaluasi terhadap penerapan sistem digital berbasis web dilakukan dalam penelitian ini untuk mengukur sejauh mana kepuasan masyarakat terhadap pelayanan publik pada Bapperida Kota Pangkalpinang. Masalah utama bersumber dari mekanisme survei terdahulu yang masih bersifat manual dan belum terintegrasi secara digital, memicu keterlambatan pemrosesan data serta penyampaian informasi yang terbatas. Oleh karena itu, studi ini diarahkan untuk membangun sekaligus menerapkan sebuah platform survei online yang mampu mengoptimalkan tahapan pengumpulan, manajemen, hingga pelaporan data secara efisien. Pendekatan yang digunakan mengadopsi metode Research and Development (R&D) melalui kerangka kerja Waterfall, yang meliputi fase analisis kebutuhan, perancangan arsitektur, pengodean, hingga pengujian sistem. Aplikasi ini mengintegrasikan fitur-fitur utama seperti pengisian kuesioner daring, manajemen basis data, serta pembuatan laporan otomatis. Temuan penelitian menunjukkan bahwa implementasi platform ini berhasil mendongkrak aspek keteraksesan, efisiensi operasional, dan validitas pengelolaan data hasil survei. Berdasarkan hasil validasi via User Acceptance Testing (UAT), sistem memperoleh respons kepuasan yang sangat positif dari pengguna, menandakan bahwa teknologi ini sangat layak diimplementasikan demi mendukung program evaluasi mutu pelayanan publik. Kata kunci: Indeks Kepuasan Masyarakat; Survei Berbasis Web; Pelayanan Publik; Sistem Informasi; Kepuasan Pengguna
Rancang Bangun Robot Penghindar Rintangan Berbasis Raspberry Pi Pico Dan Sensor Time of Flight
This research addresses the collision risks in remote-controlled robots caused by communication latency and human operator response limitations. The primary focus of this study is the development of a lightweight autonomous safety override mechanism using a resource-constrained microcontroller, the Raspberry Pi Pico W. The system integrates the VL53L0X Time of Flight sensor for high-precision obstacle detection and the KY-032 infrared sensor for cliff protection. The methodology involves designing a hybrid control architecture that combines web-based instructions with real-time safety logic. Experimental results demonstrate that the ToF sensor achieves an accuracy rate of 98.01%, surpassing the reliability of conventional ultrasonic sensors. The cliff protection system shows a 100% success rate in performing automatic avoidance maneuvers without relying on operator input. This research proves that integrating ToF sensors and override mechanisms on low-power platforms significantly enhances navigation safety for Internet of Things (IoT)-based robots. Keywords: Internet of Things; Raspberry Pi Pico W; KY-032 sensor; Time of Flight sensor; autonomous safety. Abstrak Penelitian ini mengatasi permasalahan risiko tabrakan pada robot kendali jarak jauh yang disebabkan oleh latensi komunikasi dan keterbatasan respons operator manusia. Fokus utama penelitian adalah pengembangan mekanisme keselamatan otonom (autonomous safety override) yang ringan (lightweight) menggunakan mikrokontroler berbasis sumber daya terbatas, Raspberry Pi Pico W. Sistem ini mengintegrasikan sensor Time of Flight VL53L0X untuk deteksi rintangan presisi tinggi dan sensor inframerah KY-032 sebagai proteksi terhadap area curam (jurang). Metodologi yang digunakan mencakup perancangan arsitektur hybrid control yang menggabungkan instruksi web-based dengan logika proteksi real-time. Hasil pengujian menunjukkan sensor ToF mencapai tingkat akurasi 98,01%, melampaui keandalan sensor ultrasonik konvensional. Sistem proteksi jurang menunjukkan tingkat keberhasilan 100% dalam melakukan manuver penghindaran otomatis tanpa bergantung pada instruksi operator. Penelitian ini membuktikan bahwa integrasi sensor ToF dan mekanisme override pada platform hemat daya mampu memberikan jaminan keamanan navigasi yang signifikan bagi robot berbasis Internet of Things (IoT). Kata kunci: Internet of Things; Raspberry Pi Pico W; sensor KY-032; sensor Time of Flight
Systematic Literature Review: GPU, TPU, FPGA dalam Akselerasi AI
The increasing complexity of artificial intelligence (AI) models has raised the demand for efficient hardware accelerators. A key challenge is selecting an accelerator that aligns with application needs, as mismatches can affect energy efficiency, inference speed, and system scalability. GPU, TPU, and FPGA are the most commonly used accelerators in AI deployment, each with specific advantages and limitations. This study aims to systematically evaluate the utilization of these three accelerators across various AI domains. A Systematic Literature Review (SLR) was conducted using the Kitchenham framework, analyzing 20 scientific articles. Results show that GPUs are used in 90% of studies, TPUs in 50%, and FPGAs in 70%. In terms of energy efficiency, FPGAs are superior in 78% of the relevant articles, while GPUs dominate inference performance in 85% of cases. This study concludes that selecting an AI accelerator should be guided by power efficiency, system architecture, and domain-specific requirements. The findings offer practical implications for graduate students in selecting appropriate accelerators that align with their research topics, experimental goals, and resource constraints in AI-driven thesis projects. Keywords: Artificial Intelligence Accelerator; Graphics Processing Unit; Tensor Processing Unit; Field Programmable Gate Array; Systematic Literature Review Abstrak Perkembangan model kecerdasan buatan (AI) yang semakin kompleks menimbulkan kebutuhan akan akselerator perangkat keras yang efisien. Masalah utama yang sering dihadapi adalah pemilihan akselerator yang tidak sesuai dengan kebutuhan aplikasi, yang berdampak pada efisiensi daya, kecepatan inferensi, dan skalabilitas sistem. GPU, TPU, dan FPGA merupakan tiga jenis akselerator yang paling banyak digunakan dalam implementasi AI, Penelitian ini bertujuan mengevaluasi pemanfaatan ketiga akselerator dalam berbagai domain AI menggunakan Systematic Literature Review (SLR) berbasis pendekatan Kitchenham. Sebanyak 20 artikel diseleksi dari lima basis data ilmiah terkemuka. Hasil menunjukkan GPU digunakan dalam 90% studi, FPGA dalam 70%, dan TPU dalam 50%. FPGA unggul dalam efisiensi energi (78% studi), sementara GPU dominan dalam performa inferensi (85% kasus). Penelitian menyimpulkan pemilihan akselerator AI harus mempertimbangkan efisiensi daya, arsitektur sistem, dan kebutuhan domain. Temuan ini memberikan panduan praktis bagi mahasiswa magister dalam memilih akselerator sesuai topik, tujuan eksperimen, dan keterbatasan sumber daya. Kata Kunci: Akselerator Artificial Intelligence; Graphics Processing Unit; Tensor Processing Unit; Field Programmable Gate Array; Tinjauan Sistematik
Pengembangan Sistem Informasi Prediksi Risiko Dropout Mahasiswa Berbasis Web Menggunakan Algoritme CatBoost
The problem addressed was the limited use of dropout prediction models, which had generally focused on algorithm performance and had not been integrated into an academic monitoring system design.This study aimed to develop a web-based prototype information system for predicting student dropout risk using the CatBoost algorithm to support academic monitoring. The development method used was Prototyping, while the prediction model was built from the Predict Students Dropout and Academic Success dataset, which was reduced to 3,399 records with two classes, namely dropout and graduate. The results showed that the developed prototype included manual prediction, CSV import, prediction monitoring, and role-based reporting features. The CatBoost model achieved 90.29% accuracy, 85.33% precision, 88.76% recall, and 87.01% F1-score. These findings indicated that the prototype had the potential to serve as a basis for developing an early detection system for students at risk of dropout. Keywords: Academic monitoring; CatBoost; Dropout prediction; System prototype; Web-based information system Abstrak Permasalahan yang diangkat adalah keterbatasan pemanfaatan model prediksi dropout yang umumnya masih berfokus pada performa algoritme dan belum terintegrasi ke dalam rancangan sistem pemantauan akademik. Penelitian ini bertujuan mengembangkan prototype sistem informasi prediksi risiko dropout mahasiswa berbasis web menggunakan algoritme CatBoost untuk mendukung pemantauan akademik. Metode pengembangan yang digunakan adalah Prototyping, sedangkan model prediksi dibangun dari dataset Predict Students Dropout and Academic Success yang diseleksi menjadi 3.399 data dengan dua kelas, yaitu dropout dan graduate. Hasil penelitian menunjukkan bahwa prototype yang dikembangkan memuat fitur prediksi manual, impor file CSV, monitoring hasil prediksi, dan laporan berbasis peran pengguna. Model CatBoost memperoleh accuracy 90,29%, precision 85,33%, recall 88,76%, dan F1-score 87,01%. Temuan ini menunjukkan bahwa prototype tersebut berpotensi menjadi dasar pengembangan sistem deteksi dini mahasiswa berisiko dropout. Kata kunci: Pemantauan akademik
Pengembangan Framework Monolithic-SPA Hybrid Berbasis Mobile Web Menggunakan CodeIgniter 3 dan Angular
This study aimed to develop a hybrid framework integrating CodeIgniter 3 as the backend and Angular as the frontend based on a Single Page Application architecture. The problem addressed was the limited scalability and poor separation of concerns found in conventional monolithic web architectures. The proposed solution employed a RESTful architecture approach, in which CodeIgniter 3 acted as a service provider and Angular managed the user interface layer. The research methodology consisted of requirements analysis, hybrid system architecture design, implementation, and functional testing. The results indicated that the developed hybrid framework improved system modularity, maintainability, and user experience through more responsive page rendering. The novelty of this study lay in the structured implementation of a hybrid framework concept combining CodeIgniter 3 and Angular within an integrated development framework. Keywords: Hybrid framework; CodeIgniter 3; Angular; SPA; RESTful API Abstrak Penelitian ini bertujuan mengembangkan framework Monolithic-SPA Hybrid yang mengintegrasikan CodeIgniter 3 sebagai backend dan Angular sebagai frontend. Pendekatan ini menyatukan hasil kompilasi Angular ke dalam direktori aset CodeIgniter untuk menciptakan satu kesatuan sistem yang efisien namun tetap mempertahankan pemisahan tanggung jawab pada arsitektur web konvensional monolitik. Pendekatan yang digunakan adalah pengembangan perangkat lunak dengan arsitektur RESTful, di mana CodeIgniter 3 berfungsi sebagai penyedia layanan aplikasi dan Angular sebagai pengelola antarmuka pengguna. Metodologi penelitian meliputi analisis kebutuhan, perancangan arsitektur sistem hybrid, implementasi, serta pengujian fungsional sistem. Hasil penelitian menunjukkan bahwa framework hybrid yang dikembangkan mampu meningkatkan modularitas, kemudahan pemeliharaan, serta pengalaman pengguna melalui pemuatan halaman yang lebih responsif. Unsur kebaruan penelitian ini terletak pada penerapan konsep hybrid secara terstruktur pada CodeIgniter 3 dan Angular dalam satu kerangka kerja terpadu. Kata kunci: Framework hybrid; CodeIgniter 3; Angular; SPA; RESTful API
Pengembangan Sistem Informasi Deteksi Dini Penyakit Ginjal Kronis Berbasis Web dengan TabNet
Chronic kidney disease is a global health problem that requires early detection to prevent the progression of more serious conditions. This study aims to design and implement a web-based early detection information system for chronic kidney disease integrated with a prediction model. The system was developed using the Prototyping method with TabNet as the classification model and Class-Conditional Conformal Prediction (CCP) to provide prediction confidence information. The study used secondary dummy data representing clinical attributes and risk factors for chronic kidney disease. Data were processed using median imputation, soft class weighting, and train–validation–calibration–test splitting. Prediction labels were determined using a CKD probability threshold of 0.75. Evaluation results showed accuracy of 0.8614, precision of 0.9448, recall of 0.9013, and F1-score of 0.9226. CCP enables the system to display a prediction set, making early detection results more informative and structured. Keywords: Chronic kidney disease; Conformal prediction; Early detection; Information systems; TabNet algorithm Abstrak Penyakit ginjal kronis merupakan masalah kesehatan global yang memerlukan deteksi dini untuk mencegah perkembangan kondisi yang lebih serius. Penelitian ini bertujuan merancang dan mengimplementasikan sistem informasi deteksi dini penyakit ginjal kronis berbasis web yang terintegrasi dengan model prediksi. Sistem dikembangkan menggunakan metode Prototyping dengan TabNet sebagai model klasifikasi dan Class-Conditional Conformal Prediction (CCP) untuk menyajikan informasi keyakinan prediksi. Data penelitian menggunakan data sekunder berbentuk data dummy yang merepresentasikan atribut klinis dan faktor risiko penyakit ginjal kronis. Data diproses menggunakan median imputation, soft class weighting, dan pembagian train–validation–calibration–test. Label prediksi ditentukan berdasarkan threshold probabilitas CKD sebesar 0,75. Hasil pengujian menunjukkan accuracy 0,8614, precision 0,9448, recall 0,9013, dan F1-score 0,9226. Penerapan CCP memungkinkan sistem menampilkan prediction set, sehingga hasil deteksi dini menjadi lebih informatif dan terstruktur. Kata kunci: penyakit ginjal kronis; conformal prediction; deteksi dini; sistem informasi; algoritma TabNet
Implementation of MobileNetV2 Transfer Learning for Image-Based Classification of Cocoa Fruit Diseases
Cocoa is one of the high-value agricultural commodities in Indonesia; however, its productivity continues to decline due to the increasing prevalence of plant diseases, particularly Black Pod Disease and attacks by Helopeltis spp. Traditional disease detection, which is generally performed manually by farmers, is often inefficient and prone to errors, thereby highlighting the need for an intelligent and technology-assisted early diagnosis system. This study aims to develop a disease classification model for cocoa plants using a Convolutional Neural Network (CNN) based on the MobileNet-V2 architecture, which is recognized for its computational efficiency and strong performance in image analysis. The dataset consisted of 300 images divided into three categories: healthy cocoa pods, Black Pod Disease, and damage caused by Helopeltis. Following the Pareto principle, 80% of the data were allocated for training and 20% for testing. The model was trained for 10 epochs with a batch size of 32 and was supported by data augmentation to improve data variability. Experimental results demonstrated a significant improvement in performance, with the highest validation accuracy of 93.75% achieved at the seventh epoch. The confusion matrix further confirmed that the model classified each category with a high level of precision. These findings indicate that MobileNet-V2 is an effective approach for automatic cocoa disease detection and has strong potential to assist farmers in improving disease management practices in the field. Key words: Convolutional Neural Network; MobileNet-V2; Cocoa Disease Classification; Helopeltis spp.; Deep Learning
Analisis dan Visualisasi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma K-Means Clustering dan Silhoutte Score Berdasarkan Asal Daerah
Academic achievement of graduates serves as a key indicator of higher education quality and graduates’ readiness to enter the workforce. This study examines the Christian Religious Education Study Program at the State Christian Institute of Ambon (IAKN Ambon) during the 2010–2020 period, focusing on the influence of students’ district of origin on graduation rates and Grade Point Average (GPA). Employing a descriptive quantitative approach based on secondary data, the analysis was conducted using descriptive statistics and the K-Means clustering method, with the optimal number of clusters evaluated through the silhouette score. The findings reveal a concentration of students from several major districts, including Central Maluku, Ambon City, and West Seram Regency. Four performance clusters were identified: high-performing, moderate-performing, challenging, and low-performing/no graduates. An Average Silhouette Score of 0.77 indicates good cluster quality and strong separation among groups. The study highlights disparities in academic achievement based on students’ geographical origins, suggesting the need for more targeted interventions and stronger collaboration with students’ home districts to improve academic success and graduation outcomes. Keywords: Academic Performance; District of Origin; Graduation Rate; K-Means Clustering; Silhouette Score Abstrak Prestasi akademik lulusan menjadi indikator utama mutu pendidikan tinggi dan kesiapan lulusan menghadapi dunia kerja. Penelitian ini mengkaji Program Studi Pendidikan Agama Kristen (PAK) Institut Agama Kristen Negeri Ambon periode 2010–2020, dengan fokus pada pengaruh asal kabupaten terhadap tingkat kelulusan dan IPK. Menggunakan pendekatan kuantitatif deskriptif berbasis data sekunder, analisis dilakukan melalui statistik deskriptif dan metode K-Means clustering, dengan evaluasi jumlah klaster menggunakan silhouette score. Hasil menunjukkan konsentrasi mahasiswa pada beberapa kabupaten utama seperti Maluku Tengah, Kota Ambon, dan Seram Bagian Barat. Ditemukan empat klaster kinerja, yaitu tinggi, moderat, menantang, dan rendah/tanpa lulusan. Nilai Average Silhouette Score sebesar 0,77 menunjukkan kualitas klaster yang baik. Studi ini mengungkap adanya perbedaan capaian akademik berdasarkan asal daerah, sehingga diperlukan intervensi yang lebih terarah serta kerja sama dengan daerah asal mahasiswa untuk meningkatkan keberhasilan studi. Kata kunci: Asal Kabupaten; Clustering: K-Means; Silhoutte Score; Tingkat Kelulusan
Peran Artificial Intelligence Dalam Mendorong Inovasi Dunia Bisnis Untuk Mencapai Keunggulan Yang Kompetitif
The development of Artificial Intelligence (AI) has revolutionized various aspects of the business sector, particularly in driving innovation and creating competitive advantage. This study aims to analyze the contribution of AI to business innovation through a Systematic Literature Review (SLR) approach. The SLR approach was employed to identify, review, evaluate, and synthesize relevant literature related to the research topic. The findings indicate that AI plays an important role in transforming business processes, supporting data-driven decision-making, improving operational efficiency, and strengthening marketing strategies and customer service. In addition, AI enables organizations to become more adaptive and innovative through predictive capabilities and advanced automation. Nevertheless, AI implementation also raises ethical challenges, technological dependence, and the need for adequate organizational capabilities. This study provides both theoretical and practical implications for business practitioners, academics, and policymakers in optimizing the role of AI as an enabler of innovation and the creation of competitive value. Keywords: Artificial Intelligence; Business innovation; Competitive advantage; SLR; Digital transformation Abstrak Perkembangan Artificial Intelligence (AI) telah merevolusi berbagai aspek dalam dunia bisnis, terutama dalam mendorong inovasi dan menciptakan keunggulan kompetitif. Tujuan dari Penelitian ini bertujuan menganalisis kontribusi AI terhadap inovasi bisnis melalui pendekatan Systematic Literature Review (SLR). Pendekatan SLR diterapkan untuk mengidentifikasi, menelaah, mengevaluasi, serta menyusun sintesis dari berbagai literatur yang relevan dengan judul penelitian. Hasil kajian menunjukkan bahwa AI berperan penting dalam hal transformasi mengenai proses sebuah bisnis, serta pengambilan keputusan berbasis data, peningkatan efisiensi operasional, serta penguatan strategi pemasaran dan layanan pelanggan. Selain itu, AI juga mendorong organisasi untuk lebih adaptif dan inovatif melalui kemampuan prediktif dan otomatisasi yang canggih. Namun, implementasi AI juga menimbulkan tantangan etis, ketergantungan teknologi, dan kebutuhan kapabilitas organisasi yang memadai. Studi ini memberikan implikasi teoretis dan praktis bagi pelaku bisnis, akademisi, dan pembuat kebijakan dalam mengoptimalkan peran AI sebagai enabler inovasi dan pencipta nilai kompetitif. Kata kunci: Artificial Intelligence; Inovasi bisnis; Keunggulan kompetitif; SLR; Transformasi digital