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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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Implementasi Website Pendataan Dan Monitoring Kelompok Tani Menggunakan Algoritma Decision Tree Ruth junia Sinaga; Sondy Campvid Kumajas; Glenn David Paulus Maramis
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.v13i3.9723

Abstract

This research focuses on developing a web-based information system for data management and monitoring of farmer groups in Minahasa Regency. The system aims to enhance the efficiency, accuracy, and transparency of agricultural data collection and supervision processes. Utilizing technologies such as Laravel framework and Decision Tree algorithms, the system provides real-time data visualization, prediction of assistance needs, and facilitates farmer participation in reporting activities, applying for aid, and accessing training online. The Decision Tree algorithm is employed to generate predictive insights for future aid requirements based on historical data, supporting more informed decision-making by the relevant authorities. The implementation of this system is expected to streamline administrative tasks, improve data accuracy, and promote good governance within the agricultural sector. Overall, this system contributes to advancing digital transformation in agricultural management, enabling better planning, resource allocation, and service delivery for farmers in Minahasa.
Evaluasi Komparatif Algoritma Machine Learning dalam Analisis Sentimen Program Makan Bergizi Gratis di Media Sosial X Angelina Putri Ariani; Norhikmah; Yoga Pristyanto
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.v13i3.9741

Abstract

The analysis of public opinion on social media platforms through sentiment analysis plays a crucial role in understanding how the public responds to government policies, including the Free Nutritious Meal Program (MBG). However, the imbalanced nature of social media data and the use of informal language such as sarcasm pose challenges in the sentiment classification process. Therefore, this study aims to examine public perceptions of the MBG program on platform X while also evaluating the effectiveness of several machine learning algorithms in categorizing sentiment. The dataset used in this study consists of 2,000 comments collected between February and April 2026. The data were labeled using a lexicon-based approach and processed through preprocessing and feature extraction using TF-IDF. The classification process was carried out using six algorithms: Naïve Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The results show that Random Forest achieved the highest accuracy, reaching 92%, supported by a cross-validation score of 89%, indicating strong model stability. Based on the classification results, public sentiment is predominantly neutral at 66.3%, followed by negative sentiment at 22.6% and positive sentiment at 11.1%. These findings suggest that public opinion toward the MBG program tends to be neutral, with a stronger inclination toward criticism than support. Furthermore, the results highlight the importance of selecting appropriate algorithms to improve the accuracy of sentiment analysis on complex and imbalanced textual data
Analisis Komparasi Algoritma XGBoost dan Logistic Regression Berbasis Explainable AI (SHAP) untuk Deteksi Hipertensi Risky Radison Nasution; Kurniabudi; Dodo Zaenal Abidin
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.v13i3.9748

Abstract

Hypertension represents a significant global health challenge due to its frequently asymptomatic nature until serious complications arise. This study aims to conduct an in-depth comparison between XGBoost and Logistic Regression algorithms for the early detection of hypertension, with a specific focus on explainability utilizing the SHAP Permutation Explainer method. The primary issue addressed is the tendency of previous research to manipulate datasets through oversampling techniques, which risks altering the natural distribution of clinical features, alongside the presence of "explainer bias" resulting from the use of non-uniform explanation methods. This research utilizes original data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) to ensure clinically reliable results. The results demonstrate that the XGBoost model consistently outperforms Logistic Regression across all evaluation metrics, with XGBoost achieving an AUC of 0.805 and an accuracy of 73.4%, compared to Logistic Regression's AUC of 0.802 and accuracy of 73.1%. SHAP-based interpretation reveals that BMI and Age are the most dominant predictors in both models. The primary contribution of this study is the provision of a predictive framework leveraging the original data distribution, yielding more objective predictions compared to synthetic manipulation, thereby serving as a credible reference for medical practitioners to understand hypertension risk factors in a transparent and accountable manner.
Comparison of Logistic Regression and Random Forest Performance in Student Dropout Prediction based on Multi Source Data Sartika Lina Mulani Sitio; Sunardi; Abdul Fadlil
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

Abstract

The high rate of student dropouts is one of the important challenges in higher education because it can affect academic quality, learning effectiveness, and the performance of educational institutions. This condition encourages the need for a prediction system that is able to identify students at risk of dropouts early so that preventive measures can be taken appropriately. This study aims to compare the performance of Logistic Regression and Random Forest algorithms in predicting student dropout based on multi-source. The dataset consists of 4,424 student data with 34 attributes covering academic, demographic, socioeconomic, and academic administration aspects. The research stages include data preprocessing, target transformation into binary classification, feature scaling, data sharing using an 80:20 scheme, and handling class imbalances using the Synthetic Minority Oversampling Technique (SMOTE). Furthermore, a modeling process was carried out using Logistic Regression and Random Forest algorithms to predict the risk of student dropout. Model evaluation was carried out using accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristic (AUC-ROC). The results showed that Random Forest performed better than Logistic Regression with an accuracy of 0.884, precision of 0.842, recall of 0.785, F1-score of 0.812, and AUC-ROC of 0.930. Meanwhile, Logistic Regression obtained an accuracy of 0.871, precision of 0.780, recall of 0.835, F1-score of 0.806, and AUC-ROC of 0.928. These results show that Random Forest is more effective in handling complex relationships in multi-source data for student dropout predictions
Perbandingan Random Forest dan Gradient Boosting pada Prediksi Hasil Belajar Siswa Sopiyan Apandi; Muhammad Bahrein
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.v13i3.9776

Abstract

Student learning outcomes are important indicators for evaluating the success of the learning process and can be used to support data-driven academic decision-making. This study aims to compare the performance of the Random Forest and Gradient Boosting algorithms in predicting student learning outcomes. The study employed a quantitative approach with a comparative experimental design using the student dataset containing 500 student records and 11 attributes. The target variable was passed, while several attributes such as gender, age, study_hours_per_week, attendance_rate, parent_education, internet_access, extracurricular, and previous_score were used as predictors. The data preprocessing stage included data cleaning, missing value handling, categorical data transformation, feature selection, and train-test splitting with an 80:20 ratio. Model evaluation was conducted using confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC, supported by 10-fold cross validation. The results showed that Random Forest slightly outperformed Gradient Boosting on most evaluation metrics. On the test data, Random Forest achieved an accuracy of 87.00%, precision of 90.63%, recall of 89.23%, F1-score of 89.92%, and ROC-AUC of 93.27%, while Gradient Boosting obtained an accuracy of 86.00%, precision of 90.48%, recall of 87.69%, F1-score of 89.06%, and ROC-AUC of 93.10%. These findings indicate that both algorithms performed well in predicting student learning outcomes, with Random Forest showing more stable performance on the dataset used.
Analisis Sinyal Fisiologis Frekuensi Tinggi untuk Ekstraksi Fitur Kebugaran Berbasis HRV dan Signal Processing Iwan Giri Waluyo; Sunardi; Abdul Fadlil
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

Abstract

Heart Rate Variability (HRV) is a non-invasive biomarker that reflects the activity of the autonomic nervous system and is closely related to physical fitness and recovery. This study aims to analyze physiological signals based on frequency domain components to extract fitness-related features using signal processing techniques. However, previous studies have shown limitations in utilizing comprehensive HRV frequency analysis for fitness evaluation, thus motivating this study to focus on frequency-based physiological interpretation. Electrocardiogram (ECG) signals were obtained from the PhysioNet database and processed through filtering, R peak detection, and RR interval extraction. Frequency domain analysis was performed using Power Spectral Density (PSD) to obtain spectral features, including Low Frequency (LF) (0.04–0.15 Hz), High Frequency (HF) (0.15–0.40 Hz), and LF/HF ratio. The results showed that the LF component exhibited a dominant peak around 0.1 Hz with values ranging from 0.008–0.009 s²/Hz, while the HF component ranged from 0.002–0.003 s²/Hz and had a broader distribution. An LF/HF ratio greater than 2 indicated a predominance of sympathetic activity. These findings suggest that HRV energy distribution is concentrated in the low-frequency band, reflecting a stable physiological state that has not yet reached optimal recovery. This study demonstrates that frequency-based HRV analysis using signal processing provides meaningful physiological insights for fitness evaluation without relying on machine learning models
Klasifikasi Kematangan Buah Kelapa Sawit Dengan SimCLR Berbasis HSV Controlled Color Augmentasi Sulasmi Harahap; Khairi Ibnutama; Zaimah Panjaitan
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.v13i3.9794

Abstract

Self-supervised learning with the SimCLR method requires appropriate data augmentation as the foundation for contrastive representation learning. In the task of classifying oil palm fruit maturity, color is the main discriminative feature, so the standard RGB-based ColorJitter augmentation has the potential to distort critical color information. This study proposes an augmentation framework based on the HSV color space with eight parameter configurations (C1–C8) determined in a data-driven manner through Eta-squared (η²) analysis, designed to preserve the semantic integrity of maturity color while generating varied positive pairs. Experiments were conducted on 1,016 oil palm fruit images across three classes (Unripe, Ripe, Overripe) using ResNet-18 as the SimCLR encoder with a linear evaluation protocol. The results show that all HSV configurations outperform the Baseline (82.22%), with HSV_C1 being the best configuration, achieving a test accuracy of 94.44% and Macro F1-Score of 94.46% (+12.22 pp). The η² analysis confirms that the Hue component is the most discriminative feature between classes, so constraining it during augmentation proves essential for the quality of the resulting representations. The proposed framework consistently improves SimCLR performance in color-based classification domains with minimal labeled data requirements.
Sistem Monitoring Kualitas Udara Ruangan Berbasis IoT Dengan Algoritma Fuzzy Logic Andreas Tandana; Achmad Ridwan; Tan Della Angelica; Caryn Evelyn Tannesia; Rivanco Winson
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.v13i3.9796

Abstract

Indoor air quality is often not monitored directly, although exposure to pollutant gases, fine particles, unsuitable temperature, and humidity may increase the potential for respiratory health problems. The main problem is that air monitoring devices are commonly expensive, difficult to access, and present technical data that is not easy for users to understand. This study aims to design an Internet of Things (IoT)-based indoor air quality monitoring system that processes sensor data using Fuzzy Logic to produce air quality risk categories rather than disease diagnosis. The system uses an ESP32 DevKit v1, DHT22, MQ-135, and GP2Y1010AU0F sensors, then sends data in real-time through the MQTT protocol to a Mosquitto broker and Python Bridge Server for visualization on a web dashboard. The testing results show that the DHT22 sensor has a maximum error of 0.90% for temperature and 2.17% for humidity, the MQ-135 can distinguish normal to dangerous air conditions, the GP2Y1010AU0F detects PM2.5 up to 89.3 ug/m3, and MQTT communication remains stable with latency below 200 ms. Fuzzy Logic testing produces scores of 15-91 that map clean, moderate, unhealthy, and dangerous air scenarios. The contribution of this study is a low-cost air monitoring prototype that presents real-time air quality information in an understandable form and can support early prevention actions against poor air exposure.
Smart Virtual Guide: Chatbot Cerdas Sistem Informasi Goa Terawang dengan Analisis User Adoption Rosvika Dwi Umanisti; Aditya Akbar Riadi; Rizkysari Meimaharani
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.v13i3.9807

Abstract

Most local tourist destinations in Indonesia still rely on static information systems that are unable to provide real-time responses to visitors, including the Terawang Ecopark Cave in Blora Regency, Central Java. This research developed an intelligent chatbot called Smart Virtual Guide based on the Large Language Model (LLM) which is integrated into the information system of Goa Terawang Ecopark, as well as analyzing the level of user acceptance using the Technology Acceptance Model (TAM). Different from previous tourism chatbot research which is generally based on conventional NLP and only supports one language, the novelty of this research lies in the integration of LLM with the support of Indonesian and Javanese at the same time in local tourist destinations. The development of the system uses the Waterfall method with functional testing through Black Box Testing and data collection from 30 respondents using a Likert scale questionnaire. The results of the Black Box Testing test showed that all features were valid, while the TAM results obtained an average percentage of 80.95% (Good category) on the PEOU, PU, BI, and ATU variables. These findings prove that LLM-based approaches with regional language support are effective in improving the quality of digital tourism information services that are more interactive, inclusive, and adaptive.
Analisis Pengaruh Kadar Amonia terhadap Biota pada Sistem Akuaponik Berbasis IoT Menggunakan Sensor Fusion dan K-Means Cindy Pakpahan; Erna Budhiarti Nababan; Baihaqi Siregar; Opim Salim Sitompul; Hayatunnufus
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.v13i3.9827

Abstract

Aquaponic systems integrate fish and plant cultivation in a single production cycle, but their success depends heavily on water quality  particularly ammonia levels, which can be harmful to living organisms if left unmonitored. This study develops an IoT-based monitoring system using an ESP32 microcontroller equipped with eight sensors (pH, water temperature, air temperature, humidity, DO, EC, TDS, and ammonia) integrated through a sensor fusion approach. Sensor data were processed using mean imputation and Z-score normalization, then analyzed with Pearson Correlation for feature selection and K-Means clustering for anomaly detection in an aquaponic system cultivating Channa striata and Amaranthus sp. Results show that ammonia correlates most strongly with pH (r = 0.50), while correlations with other parameters were relatively low. K-Means successfully distinguished normal from anomalous conditions automatically, and biological testing confirmed that optimal growth occurred at ammonia levels below 1 mg/L. Compared to single-parameter monitoring systems, this multivariate approach provides a more comprehensive picture of environmental conditions and offers a foundation for developing smart, efficient, and sustainable aquaponic systems.

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