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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 1,135 Documents
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Perbandingan Kinerja Model Forecasting Nilai Perdagangan Komoditas HS pada Evaluasi Time-Based Ridwan Dwi Irawan; Marta Ardiyanto; Ringgo Ismoyo Buwono; Faulinda Ely Nastiti
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9843

Abstract

Global economic uncertainty, trade-regime shifts, and supply-chain disruptions have made export-import trade-value forecasting increasingly complex. This study compares the performance of Random Forest, Extra Trees Regression, and SARIMA in predicting monthly trade values of HS commodities in the apparel and footwear sector, covering HS 61, HS 62, HS 63, and HS 64. The dataset was obtained from Indonesia?s Central Bureau of Statistics (BPS) and organized as a monthly time series using a leakage-safe workflow through a time-based train-validation-test split. The modeling stage employed 19 predictive features consisting of historical, local statistical, calendar-seasonal, and exogenous variables, and each model was tuned on the validation set before being evaluated on the holdout test. Performance was assessed using MAE, RMSE, MAPE, sMAPE, and wMAPE, with MAPE as the primary ranking metric. The main contribution of this study lies in providing a fair and replicable comparison of three forecasting models under a time-based evaluation protocol for HS commodity trade data, making the model selection results more representative of real implementation settings. The results show that Random Forest achieved the best MAPE at 22.7746%, slightly outperforming Extra Trees Regression at 22.9469%, while SARIMA recorded 28.9794%. These findings indicate that tree-based ensemble models are more adaptive to volatile trade data, whereas SARIMA remains relevant as a statistical baseline for structured seasonal patterns.
Implementasi Business Intelligence untuk Visualisasi Data Akademik Menggunakan Power BI Dalam Meningkatkan Kualitas Layanan Akademik: - Vera Wijaya; Tongam E. Panggabean
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9844

Abstract

Improving the quality of higher education requires the effective, integrated, and measurable management and analysis of students’ academic data. However, many higher education institutions still face challenges in collecting, integrating, and analyzing academic data efficiently. Consequently, the monitoring of academic performance has not yet been conducted in a standardized manner and has not fully supported the achievement of additional performance indicators. In this context, the implementation of Business Intelligence (BI) is highly relevant, as it provides a systematic platform for managing students’ academic performance data and presenting it through informative visualizations. This study aims to implement a Business Intelligence system using Power BI architecture to integrate and manage students’ academic performance data obtained from various academic sources. The data used in this study consisted of 80 student records, including grades, semester grade point average, cumulative grade point average, academic status, study program, and cohort year for the 2023 - 2024 period. The data integration process was carried out through the stages of extraction, transformation, and loading into the Power BI model. The implementation results show that the system was able to process 98% of the available academic data and generate three interactive dashboards, including visualizations of GPA trends, student attendance distribution, and student grade distribution. Based on the testing results, the use of the Power BI dashboard improved the efficiency of the academic reporting process from three days to one hour, representing an increase of 93.1%. In addition, the level of conformity between the information displayed on the dashboard and the source data achieved an accuracy of 97.5%, indicating that the system can support information validity in the academic decision-making process. The implementation of Business Intelligence contributes to improving academic data management, accelerating the monitoring of student achievement, assisting stakeholders in interpreting data more effectively, and supporting the enhancement of academic service quality and higher education performance evaluation.
Internet of Things-Based Water Quality Monitoring and Automatic Feeding for Catfish Ponds Using Mamdani Fuzzy Logic Muhammad Gian Azzra Ramadhan; Ahmad Taqwa; Mohammad Fadhli
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9857

Abstract

Manual water-quality monitoring and schedule-based feeding may delay responses to unsuitable pond conditions and lead to inappropriate feed dispensing in catfish cultivation. This study developed an Internet of Things (IoT)-based monitoring and automatic feeding system that evaluates water temperature and pH using Mamdani fuzzy inference. The prototype integrates an ESP32 microcontroller, temperature and pH sensors, an ultrasonic sensor for feed-level monitoring, and a servo motor for feed dispensing. Temperature and pH measurements are classified into five linguistic categories and evaluated through a 25-rule base to produce a binary Feed ON or Feed OFF decision. Sensor validation yielded accuracies of 95.52% for temperature measurement, 95.87% for pH measurement, and 95.97% for ultrasonic distance measurement. Validation using five representative input combinations showed that all program outputs matched the expected rule-base decisions, resulting in 100% decision conformity for the tested cases. During seven days of field monitoring, all 21 evaluations conducted at 09:00, 15:00, and 21:00 produced Feed ON because the paired temperature and pH measurements satisfied the minimum rule-base requirements. Web and mobile dashboards displayed sensor measurements, feed availability, device status, operating mode, and feeding activity in real time. The main contribution is a compact and interpretable mechanism that performs condition-based feeding decisions locally on the ESP32 while supporting remote supervision through IoT interfaces. The results indicate adequate sensor performance and consistent fuzzy decision implementation under the tested conditions.
Systematic Literature Review: Application of Deep Learning in Tuberculosis Diagnosis Using Chest X-Ray Images – A Focus on Models, Challenges, and Research Opportunities Janera Almasahni; Nurgiyatna
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9880

Abstract

Tuberculosis (TB) is an infectious disease with a high mortality rate worldwide. Deep learning offers promising opportunities for automated TB diagnosis from chest X-ray (CXR) images. This systematic literature review (SLR), conducted following PRISMA 2020 guidelines, analyzes 66 articles from Scopus (2021–2026) to examine deep learning models, datasets, evaluation methods, challenges, and research opportunities. Findings reveal that CNN models remain dominant (42.42%), followed by hybrid CNN-Transformer models (37.88%), while public datasets are most frequently used (63.64%). Key challenges include dataset limitations, poor generalization, computational complexity, and lack of interpretability. This review contributes a comprehensive taxonomy of deep learning architectures for TB detection, identifies emerging trends toward hybrid and ensemble approaches, and provides actionable recommendations for future research, including federated learning, explainable AI, and clinical integration. These findings offer valuable guidance for researchers and practitioners developing reliable AI-based TB diagnostic systems.
Informasi Evaluasi Tata Kelola Teknologi Informasi Pada Klinik Menggunakan Framework Cobit 5 Meisyah Nadila Mukti; Raissa Amanda Putri; Aninda Muliani Harahap
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9897

Abstract

The rapid advancement of information technology has driven healthcare facilities to optimize the use of information systems to support operations and data management. The UINSU Medan Primary Outpatient Clinic has been using the eClinic system; however, a standardized evaluation of information technology governance has not been systematically established. This study aims to design and implement a web-based information system for evaluating IT governance using the COBIT 5 framework. The method employed is quantitative descriptive with an evaluative approach through the Process Assessment Model (PAM) in the Evaluate, Direct, and Monitor (EDM) domain, covering processes EDM01, EDM02, and EDM05. Data were collected via a web-based questionnaire from five respondents consisting of administrators, doctors, and nurses. Black-box testing confirmed that all system features function as intended. Evaluation results indicate that EDM02 and EDM05 achieved the target Capability Level 3, while EDM01 only reached Level 2, with a gap of +1 level due to the unmet Process Definition attribute (PA 3.1). Improvement recommendations focus on standardizing and documenting IT governance operational procedures at the clinic.

Page 114 of 114 | Total Record : 1135

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