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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 43 Documents
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Search results for , issue "Vol. 13 No. 1 (2026): Februari 2026" : 43 Documents clear
Edge AI Berbasis Computer Vision Untuk Meningkatkan Efektivitas Sistem Deteksi Pemilahan Sampah Real-Time Integrasi YOLOv8, Raspberry Pi 5 dan SEE Syaifuddin; Ifriandi Labolo; Nuranissa D. Paemo; Abdul Malik I Buna
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9298

Abstract

Waste management in Indonesia is still characterized by a high volume of improperly managed waste and low source-level segregation, causing recyclable materials to mix with other waste streams and reducing their recovery value. This situation calls for a sorting system that is effective, fast, and affordable, while also providing real-time operational information to support on site decision-making. This study presents an integrated computer vision approach using YOLOv8 deployed on a Raspberry Pi 5 with a Camera Module 3, connected to a real-time information system via Server-Sent Events (SSE) for monitoring and analytics. The methodology includes constructing a labeled dataset in YOLO TXT format, training a YOLOv8n model, deploying edge inference, and developing a backend API to receive detection outputs and stream them to a dashboard in real time. The system is evaluated using mean Average Precision (mAP), precision–recall, frames per second (FPS), and end-to-end latency from the camera to the dashboard. The prototype achieves an mAP@0.5 of 98.5% with precision–recall above 97%, an average throughput of 8.3 FPS at 640×640 resolution, and a median SSE communication latency of 0.5–0.6 ms, demonstrating the feasibility of a cost-effective solution for automated waste sorting. The system also provides logging, operational statistics, an offline queue, and an idempotency mechanism to support reliable operation in real-world deployments.
Penerapan BERT dalam Memetakan Opini Pengguna Instagram Terhadap Program Makan Bergizi Gratis Septiana, Rika Septiana; Ibrahim, Ali; Lestari, Endang; Utama, Yadi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9397

Abstract

The Free Lunch Program is a new government initiative designed to improve students’ nutritional intake and enhance their focus during school activities. Its implementation has gained significant public attention, generating a variety of reactions across social media platforms, particularly Instagram. This study aims to examine public sentiment toward the program’s rollout in South Sumatra by collecting user comments from posts using the hashtag #mbgsumsel. The collected comments were processed through several stages, including data cleaning, text pre-processing, dataset partitioning, fine-tuning and model evaluation. The BERT model was employed due to its strong capability in capturing contextual meaning within text, making it more effective than conventional classification approaches. Experimental results indicate that the model achieved an akurasi of 88% in classifying sentiments. The test dataset consisted of 751 positive comments, 233 neutral comments, and 257 negative comments. Overall, this study provides a quantitative overview of how the public perceives the Free Lunch Program based on their social media expressions.
Analisis Kepatuhan Belanja Dana BOS Melalui Klasterisasi K-Means++: Studi Kasus Pada Satuan Pendidikan Tingkat Menengah Pertama Jayyid Jiddan; Ilka Zufria
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9424

Abstract

The Ministry of Education, Culture, Research, and Technology Regulation Number 18 of 2022 mandates all educational units to procure goods and services using School Operational Assistance (BOS) funds through the SIPLah platform. This policy is a strategic government step to enhance transparency, accountability, and efficiency in the management of national education funds. However, field implementation still faces significant challenges characterized by low platform adoption rates, where the majority of schools tend to prioritize conventional manual procurement prone to administrative irregularity risks. This study aims to analyze the compliance level of BOS fund spending through spending pattern clustering to identify SIPLah adoption characteristics and formulate targeted policy recommendations. The method used is the K-Means++ algorithm, selected for its superiority in optimal centroid initialization compared to standard K-Means, resulting in more accurate clusters with fast convergence. Research data sourced from the 2024 School Activity Plan and Budget (RKAS) of 40 State Junior High School (SMPN) samples selected using Slovin's formula with a 10% error rate. The clustering process produced three distinct compliance characteristic groups: Cluster 1 (Low Compliance) dominating with 30 schools (75%) in the SIPLah usage range of 0-5%, Cluster 2 (Medium Compliance) comprising 8 schools (20%) in the range of >5-15%, and Cluster 3 (High Compliance) comprising only 2 schools (5%) in the range of >15%. Validation using the Silhouette Coefficient yielded a score of 0.687, indicating a well-formed cluster structure. These findings provide a significant contribution to the education office as a basis for policy evaluation, where schools in Cluster 1 require infrastructure audits and intensive assistance, while Cluster 3 can serve as a best practice model in school financial governance.
Analisis Sentimen Masyarakat Indonesia terhadap Keterlibatan Bill Gates dalam Program Vaksin TBC di Media Sosial X Menggunakan SVM Fahraini, Fakhita; Sriani, Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9431

Abstract

The involvement of Bill Gates in the development program of the tuberculosis (TB) vaccine has generated diverse responses among the Indonesian public, particularly as expressed through social media platform X, making it an important issue to examine since public sentiment may influence public trust in health programs and the success of TB prevention efforts. This study aims to analyze public sentiment in Indonesia toward Bill Gates’ involvement in the TB vaccine program based on social media data from platform X using the Support Vector Machine (SVM) algorithm. The research data consist of 784 Indonesian-language tweets collected through web scraping techniques using keywords related to the TB vaccine issue and Bill Gates. The collected data then underwent several text preprocessing stages, including data cleaning, case folding, tokenization, word normalization, stopword removal, and stemming to improve text quality and consistency. Feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the dataset was split into 80% training data and 20% testing data. The classification model was built using an SVM algorithm with a linear kernel to optimally separate positive and negative sentiment classes. The experimental results show that the SVM model combined with TF-IDF achieved an accuracy of 96.8%, with a precision of 98.3%, a recall of 79.2%, and an F1-score of 86.0%. Sentiment distribution analysis indicates that the majority of tweets were dominated by negative sentiment, while positive sentiment appeared in a smaller proportion. The main contribution of this study lies in the application of social media–based sentiment analysis specifically to the TB vaccine issue associated with a global public figure in the Indonesian context, as well as in demonstrating that the combination of SVM and TF-IDF is effective in accurately classifying public opinion. These findings are expected to serve as a data-driven reference for government institutions and public health stakeholders in designing more effective public communication strategies.
Optimized Fault Prediction in Power Distribution Transformers Using Grey Wolf Optimizer-Based SVM and MLP Models Rosena Shintabella; Silaban, Meyer Mega Eklesia; Basir, Muhammad Ichsan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9435

Abstract

Distribution transformers are critical components of power distribution systems, and their reliability directly affects the continuity and quality of electrical energy supply. However, early-stage transformer faults are difficult to detect because their operational characteristics often closely resemble normal operating conditions, which can lead to undetected degradation and unexpected failures. This study aims to improve the accuracy and robustness of fault prediction in distribution transformers by proposing a hybrid approach that integrates the Grey Wolf Optimizer (GWO) with Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models. The main contribution of this research is a direct and systematic performance comparison between baseline machine learning models and their GWO-optimized counterparts, highlighting the effectiveness of metaheuristic optimization in enhancing classification performance. GWO is employed to optimize key model parameters, enabling improved convergence behavior, higher classification accuracy, and better generalization capability. The proposed models are evaluated under four transformer operating conditions, namely Light Load Imbalance, Light Overload, Normal, and Normal High Temperature, which represent practical scenarios in power distribution networks. Model performance is assessed using standard classification metrics, including Accuracy, Precision, Recall, and F1-Score. Experimental results show that the baseline SVM achieved an accuracy of 68%, while the baseline MLP reached 87% accuracy. After GWO-based optimization, the SVM–GWO model demonstrated a significant improvement, achieving 92% accuracy, whereas the MLP–GWO model produced the best overall performance, achieving 93% accuracy, precision, recall, and F1-score. These findings confirm that GWO-based optimization substantially enhances transformer fault prediction performance and demonstrates the strong potential of the proposed hybrid models for real-time monitoring and preventive maintenance of power distribution transformers.
Analisis Performa WireGuard dan OpenVPN pada VPN Perbankan Berbasis MikroTik menggunakan ICMP, iPerf3, dan Mann–Whitney Muhajir, Fadhil; Ema Utami
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9438

Abstract

The banking industry necessitates secure and reliable site-to-site connectivity between data centres and branch offices to support transactions, data replication, and monitoring that is sensitive to latency, jitter, and packet loss. In practice, VPN gateways often operate concurrently with routing and security functions, leading to specific issues characterised by a trade-off between network service quality and processing overhead on gateway devices. Given that OpenVPN remains widely used while WireGuard is increasingly adopted as a more streamlined protocol, there is a need for measurable evidence to determine the most suitable protocol in a banking environment based on MikroTik routers. This study aims to compare the performance of WireGuard and OpenVPN across three dimensions: connection quality via ICMP Ping (minimum/average/maximum round-trip time, jitter, packet loss), transfer capacity using iPerf3 in TCP/UDP scenarios (download and upload), and processing efficiency through monitoring of CPU and memory usage. Data will be summarised using descriptive statistics and tested with the Mann–Whitney U test under equivalent test conditions. The results indicate that WireGuard provides better delay stability (lower average/maximum round-trip time and jitter) and more efficient CPU utilisation compared to OpenVPN, whereas throughput is context-dependent, with WireGuard generally excelling in UDP scenarios and OpenVPN performing better in certain TCP situations. The contribution of this research lies in the replicable testing methodology and quantitative evidence that serves as a foundation for recommending VPN protocol selection for banking based on network performance, resource efficiency, and service continuity.
Penerapan Logika Fuzzy Tsukamoto Sebagai Sistem Pendukung Keputusan Penentuan Mata Kuliah Pilihan Mahasiswa Ilmu Komputer XYZ Muhammad Reza Alhafiz; Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9453

Abstract

The selection of elective courses poses a challenge for Computer Science students at XYZ University because it influences competency development, while objective decision-making guidance remains limited. This study aims to develop a web-based decision support system to recommend specialization elective courses using the Fuzzy Tsukamoto method. Data were collected through questionnaires from students in semesters five to seven and processed into four input variables: Robotics, Mathematics, Programming, and Analysis. Each variable was modeled into three fuzzy sets (Weak, Moderate, Strong) using trapezoidal membership functions and processed through IF–THEN rule-based inference with a total of 162 rules. Output values were obtained through weighted average defuzzification to generate course recommendations. System testing was conducted by comparing system outputs with manual calculations and evaluated using the Mean Absolute Percentage Error (MAPE). The results showed a MAPE value of approximately ±0.1096%, indicating that the implementation of the Tsukamoto method in the system is consistent with manual calculations. This study contributes to providing a structured and objective decision support system to assist students in determining elective courses based on their competencies.
Implementasi Metode Forward Chaining untuk Rekomendasi Jurusan Perguruan Tinggi Berdasarkan Minat dan Bakat MA XYZ Shofa Allaisya; Aditya Akbar Riadi; Rizkysari Meimaharani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9463

Abstract

Choosing a college major is a crucial decision for final year students as it impacts their academic success and future career paths. However, the process of selecting a major is often carried out without objectively considering students' interests and talents, which can lead to mismatches in the learning process. This study aims to develop an expert system-based college major recommendation system using the Forward Chaining method to analyze students' interests and talents. Interest and talent data are obtained through questionnaires filled out by students independently through the system, then used as the initial basis for the conclusion-making process. The knowledge base is structured in the form of IF–THEN rules that link interest and talent characteristics with specific majors and their respective weights. The inference process is carried out by matching existing facts with available rules to produce a suitability score for each major. The results of the study show that the system is able to provide logical and structured major recommendations according to students' interest and talent profiles. The results of system testing on student data indicate that the system is able to produce logical and consistent major recommendations. Functional testing using the Black Box Testing method shows a success rate of 100%, indicating that all system functions run according to the specified requirements.
Optimasi Hyperparameter Optuna Pada Model mT5 Untuk Penerjemahan Angkola-Indonesia Harahap, Awal Ridho; Hanafi, Hanafi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9465

Abstract

This research aims to address the challenges of preserving the Angkola language in the digital era, which are exacerbated by the lack of an adequate digital data corpus, by developing an accurate and efficient automatic Angkola-to-Indonesian machine translation system. The proposed method focuses on a fine-tuning approach for the Multilingual Text-to-Text Transfer Transformer (mT5-base) model using an Angkola-Indonesian text data corpus.The initial dataset, consisting of Angkola-Indonesian sentence pairs, was cleaned, resulting in 28,775 sentence pairs used for training. The data was subsequently split into 70% training data (20,142 lines), 15% validation data (4,316 lines), and 15% test data (4,317 lines). Intelligent model performance optimization was conducted using Optuna Hyperparameter Tuning to find the best hyperparameter combination. Optuna's objective function was designed to maximize a composite score based on the BLEU and chrF metrics from the validation evaluation results. The optimization process yielded the best Trial (Trial 50) with key hyperparameters: learning rate = 0.0004316 and num beams = 4. The best model obtained from the fine-tuning process was then evaluated on a separate Test dataset. The final evaluation on the test data using standard translation metrics demonstrated excellent performance, achieving a BLEU score of 73.84 and a chrF score of 83.34. Overall, this research successfully implemented hyperparameter optimization using Optuna for the mT5 model, resulting in an Angkola-to-Indonesian translation model that exhibits high accuracy and more efficient performance. These results provide a tangible contribution to the preservation of the Angkola language by offering a modern and accurate translation tool.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Kopi Menggunakan Data Sensor Electronic Nose dan Tongue Dwi Issadari Hastuti; Mula Agung Barata; Ifnu Wisma Dwi Prastya
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9349

Abstract

Coffee is a leading Indonesian commodity with a diversity of aromas and flavors influenced by variety and region of origin. However, the process of identifying and classifying coffee types is still often carried out conventionally through sensory testing, which is subjective, time-consuming, and dependent on panelist expertise. This situation encourages the need for a more objective and consistent automated approach based on sensor technology and machine learning. This study aims to compare the performance of several machine learning algorithms, namely Logistic Regression, Support Vector Classifier (SVC), and Random Forest, in classifying Indonesian coffee types using multisensor Electronic Nose and Electronic Tongue data. The data used comes from gas, temperature, and pH sensors with a total of 1,503 samples representing ten coffee classes. The preprocessing stage includes data cleaning using the Interquartile Range (IQR) method to remove outliers and noise reduction using the Moving Average method. The results show that the application of data cleaning and noise reduction significantly improves the performance of all classification models. Among the algorithms tested, Random Forest showed the most stable and superior performance in classifying coffee types. These findings confirm that the combination of appropriate data preprocessing and appropriate algorithm selection plays a crucial role in improving the accuracy of machine learning-based coffee classification systems.

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