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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 1,099 Documents
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Sistem Pakar Berbasis Web untuk Diagnosis Stunting pada Balita Menggunakan Metode Naïve Bayes Cesilia, Yolinda; Nurdin, Nurdin; Cut Agusniar
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9130

Abstract

Stunting is a health problem caused by chronic malnutrition that affects children's physical growth and cognitive development. This condition has become a serious concern because it impacts the quality of human resources in the future. This study aims to develop an expert system for diagnosing Stunting using the Naïve Bayes method to assist healthcare workers in the early detection of at-risk toddlers. The research data were obtained from Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 170 training data and 30 testing data. The system was developed using the Python programming language with the Flask framework and SQLite database. The input variables consisted of seven symptoms (G01–G07), including age, weight, height, gender, and other supporting factors. The testing results showed that the Naïve Bayes method achieved an accuracy of 86.66%, with 26 out of 30 test data correctly classified according to expert diagnoses. This system can be used as a decision-support tool for healthcare workers to accelerate diagnosis and improve the effectiveness of Stunting management, particularly in areas with limited healthcare resources. 
Algoritma Apriori dan Visualisasi Heatmap GIS untuk Evaluasi Ketimpangan Distribusi Bantuan Sosial Senung, Bachtiar; Satriadi D. Ali; Abdul Malik I. Buna; Nuranissa D. Paemo
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9154

Abstract

This study integrates the Apriori algorithm and local spatial analytics to assess inequality in social assistance distribution in Gorontalo Province, Indonesia, covering six regencies/cities. The administrative Beneficiary Master List (BNBA) dataset was standardized for association rule mining to identify co-beneficiary patterns across major social assistance schemes, namely PKH, BPNT, BST, and BPUM. In parallel, the data were aggregated at the sub-district level to construct an inequality score based on Principal Component Analysis (PCA) of beneficiary proportions, which was then analyzed using Local Moran’s I (LISA) and Getis–Ord Gi*. The Apriori analysis of the province-wide dataset produced 64 association rules, 74 frequent itemsets, and 38 unique items. The results indicate strong co-beneficiary relationships among BPNT, BST, BPUM, and PKH, with confidence values ranging from approximately 0.60 to 0.95 and lift values exceeding 10. Spatial analysis shows that five of the six regencies/cities exhibit significant positive spatial autocorrelation (p < 0.10), with particularly strong clustering in Pohuwato (I = 0.9681) and North Gorontalo (I = 0.8331), while Gorontalo Regency shows no statistically significant pattern. LISA cluster maps further identify high-high (HH) clusters in parts of Boalemo and North Gorontalo, as well as low-low (LL) and high-low (HL) areas relevant for policy refinement. These findings suggest that integrating Apriori and local spatial analytics provides an effective operational approach for improving targeting accuracy, reducing overlap in assistance allocation, and identifying areas at risk of under-coverage.
Integrasi Faktor Iklim dan Lingkungan untuk Prediksi Risiko DBD di Kota Palembang Menggunakan Pendekatan GeoAI Berbasis LSTM Tia Arlin Dita; Ali Ibrahim
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9407

Abstract

Dengue Hemorrhagic Fever (DHF) remains a significant vector-borne disease threat to public health in Palembang. This study aims to analyze the environmental and demographic factors influencing DHF risk and predict risk trends using a GeoAI approach. Four primary variables land surface temperature, rainfall, population density, and residential area were integrated to develop a DHF risk index for the 2020 - 2025 period. The analysis reveals that the risk index consistently falls within the high category across all regions, showing a gradual upward trend from 0.517 in 2020 to 0.527 in 2025. To project future risks, a Long Short-Term Memory (LSTM) model was employed. Model evaluation demonstrated robust performance with a Mean Squared Error (MSE) of 0.0028, a Root Mean Squared Error (RMSE) of 0.052, and a Mean Absolute Error (MAE) of 0.031, indicating low error rates and stable predictive capability. Prediction results suggest that DHF risk is expected to continue increasing through 2029, particularly in sub-districts with high population density and expanding residential areas. This research provides a scientific contribution by developing a predictive model that is more adaptive and precise than conventional statistical approaches. Through the integration of artificial intelligence and spatial data (GeoAI), this model effectively captures non-linear patterns and spatio temporal dynamics, serving as a sustainable early warning system.
Analisis Prediksi Resiko Perceraian Menggunakan Algoritma Random Forest dengan Optimasi Hyperparameter Random Search Nisa, Indah Khoirun; Vikri, Muhammad Jauhar; Diansyah, Denny nur
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9529

Abstract

Divorce is a social problem that continues to increase and has negative impacts on the psychological, social, and economic conditions of individuals and families. This study aims to build a divorce risk prediction model using the Random Forest algorithm with hyperparameter optimization using the Random Search method. The dataset was obtained from the Kaggle platform with 170 samples and 54 psychological-behavioral attributes of couples. The research stages included data preprocessing, dataset splitting (80:20), baseline model development, hyperparameter optimization with Random Search, and evaluation using accuracy, precision, recall, and AUC-ROC metrics. The results showed that the model achieved 94.12% accuracy on the test data with 97% recall that minimizes false negative risk. Hyperparameter optimization successfully improved the model's internal stability with a cross-validation average of 98.57%, although the test accuracy was equivalent to the baseline model. A gap of 4.45% between validation and test accuracy indicates potential overfitting, which is common in small datasets. Feature importance analysis revealed five dominant psychological factors: willingness to compromise, effective communication, conflict resolution, alignment of life values, and forgiveness ability. This research contributes to the development of an early detection system for divorce risk based on machine learning and provides an empirical basis for more targeted counseling interventions
Analisis Faktor Keberhasilan Penjualan Kerajinan Tangan menggunakan Decision Tree dengan Optimasi Grid Search Septiana, Nailus Saidah Anindia; Vikri, Muhammad Jauhar; Sa’ida, Ita Aristia
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9534

Abstract

This study is motivated by the limited ability of handicraft Micro, Small, and Medium Enterprises (MSMEs) to analyze the key factors influencing sales success on e-commerce platforms, despite the availability of historical transaction data. Previous studies generally applied classification algorithms without systematic hyperparameter optimization, potentially leading to suboptimal models and overfitting issues. To address this gap, this research proposes the implementation of a Decision Tree algorithm optimized using Grid search Cross-validation. The dataset was obtained from the Brazilian e-commerce platform (Olist Dataset), specifically the ‘artes’ category as a proxy for handicraft products, with an 80:20 split for training and testing data. The optimization process explored 576 parameter combinations to determine the best configuration. The optimized model achieved an accuracy of 97.61% with a simplified tree structure (max_depth=None), enhancing interpretability. Feature importance analysis product_height_cm as the most dominant factor (64.23%), followed by product_height_cm, product_width_cm, Freight_value, product_weight_g, and price. These findings demonstrate that the combination of Decision Tree and Grid search effectively produces an accurate and interpretable predictive model, providing strategic decision-making support for handicraft MSMEs in digital marketplaces.
Prediction of Burnout Syndrome Risk in University Students Using the C5.0 Algorithm Rahmadani, Noni Fauzia; Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9550

Abstract

Burnout among university students is a serious issue that can reduce learning motivation, academic performance, and mental health. Approximately 25–30% of students experience burnout symptoms, which negatively affect concentration and academic productivity. Early detection is still limited due to the lack of accurate data analysis. This study aims to predict the risk level of student burnout using the C5.0 algorithm as a classification method capable of handling both categorical and numerical data. The research data were obtained from 306 students at Universitas Islam Negeri Sumatera Utara through an online questionnaire based on the Maslach Burnout Inventory–Student Survey (MBI-SS). The data were processed through cleaning, encoding, and splitting into training and testing sets using Python. The results show that the model achieves excellent classification performance, with an accuracy of 99.25% on the training set (precision 99.72%, recall 99.45%) and 97% on the testing set (precision 100%, recall 96%). The model also identifies the most influential attributes contributing to burnout, such as stress level and emotional exhaustion. The main contribution of this study is the development of an accurate and interpretable machine learning-based model for predicting student burnout risk. These findings provide practical implications for educational institutions in supporting early detection and designing data-driven preventive interventions, such as counseling services and stress management programs.
Clustering YouTube Comments on Mental Health in Indonesia Using the K-Means Algorithm Nugroho, Agung; Putri, Raissa Amanda
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9552

Abstract

This study aims to analyze mental health expressions in Indonesian-language YouTube comments using a text mining approach and the K-Means clustering algorithm. The increasing use of social media as a platform for expressing psychological conditions has resulted in large volumes of unstructured textual data that are difficult to analyze manually. Therefore, this study applies text preprocessing techniques, including case folding, tokenization, stopword removal, and stemming, followed by Term Frequency–Inverse Document Frequency (TF-IDF) weighting to transform textual data into numerical representations. The clustering process is performed using the K-Means algorithm, and the optimal number of clusters is determined using the Elbow Method and Silhouette Coefficient. The results show that the optimal number of clusters is k = 3, with the highest Silhouette Coefficient value indicating good cluster quality. A total of 2,411 YouTube comments were successfully grouped into three clusters, representing different types of mental health expressions, namely complaint expressions, personal experience narratives, and general responses. This study contributes by providing a social media comment clustering model to analyze mental health expressions in the Indonesian digital context. The results demonstrate that the K-Means algorithm can effectively identify meaningful patterns in large-scale textual data without requiring labeled datasets, making it useful for supporting data-driven mental health analysis.
Sistem Deteksi Dan Monitoring Jendela Rumah Berbasis Sensor Magnetik Dengan Logika Fuzzy Mamdani Ariansyah, Rino; Halim Hasugian, Abdul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9563

Abstract

Home security is often disrupted by false alarms because conventional systems rely solely on binary logic that does not consider the context of time and the magnitude of window opening. This study designs and implements an Internet of Things (IoT)-based window detection and monitoring system that integrates an MC 38 magnetic sensor and a HY SRF05 ultrasonic sensor, with inference processing using Mamdani Fuzzy Logic on a NodeMCU ESP8266 microcontroller. The system is equipped with a DS3231 RTC module and an NTP synchronization mechanism to maintain timeliness, and provides adaptive responses through LED indicators, buzzer sound patterns, Telegram notifications, and a Flutter-based mobile application. The research objective is to produce contextual alarm decisions (Safe, Alert, Danger) to reduce false alarms without sacrificing response speed. The main contribution is the implementation of a time-aware multi-sensor approach and edge processing so that the system is able to assess the level of urgency based on the physical status of the window, the distance of damage, and the time of the incident. Testing was carried out in tightly closed scenarios, small edits during the day, wide edits at night, and disturbances due to wind or vibration. Test results showed a resolution accuracy of 93.85%, an average ultrasonic measurement error of 0.63% (a difference of <0.5 cm at the test distance), and an average notification latency to the app and Telegram of around 5 seconds. These findings demonstrate that the integration of redundant sensors with fuzzy inference improves intrusion detection evidence in smart home windows
Pengaruh Inkonsistensi Switching Light-Dark Mode Antar Aplikasi Terhadap Mental Workload dan Kinerja Pengguna Smartphone Putri, Layla Ayu Mustika; Nurlifa, Alfian; Suryanto, Andik Adi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9579

Abstract

Visual inconsistency in user interface contexts, such as visual theme changes during mode switching (from Light to Dark Mode), is often considered an optional aesthetic element; however, its impact on user cognitive load and performance has not been widely researched. This study aims to analyze the effect of mode switching on subjective cognitive load, task completion time efficiency, and user performance accuracy in an interface interaction context. The method used in this experiment was a Quasi-Experimental Design with a posttest-only control group type, involving 40 respondents divided into two groups: static theme condition and mode switching condition. Subjective cognitive load was measured using the NASA-TLX instrument, while objective performance was evaluated through Task Completion Time and Error Rate. The results of this study indicate that the treatment group experienced an increase in cognitive load, with an average WWL score of 55.23, compared to the control group at 42.50. The frustration dimension was the highest, at 28.0, indicating emotional pressure due to interface inconsistency. Objectively, the application of mode switching slowed down task completion time by 3.65 seconds and increased the error rate by up to two times. Statistical test results also showed differences in all research variables, with a p-value < 0.005 and an effect size value on cognitive load of 0.816 (Large Effect). These findings lead to the conclusion that visual stability is an important factor in interface design, necessary for maintaining navigation efficiency, increasing user comfort, and minimizing interaction errors.
Predict Goods Demand Using the XGBoost Method Based on Sales Historical Data Badriah Nursakinah; Nurhalimah; Yuda Samudra
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9584

Abstract

Predicting the demand for goods is an important aspect of inventory management and operational planning because inaccurate predictions can lead to overstock or shortages of goods. This study aims to predict the demand for goods using the Extreme Gradient Boosting (XGBoost) algorithm based on historical sales data. The dataset used contains information on the transaction date, number of sales, stock, price, and time index, which is then processed through the preprocessing and feature engineering stages, including the formation of temporal features and sales lag features. Data sharing is carried out using a time series split approach to maintain the chronological order of the data. The XGBoost model is optimized using GridSearchCV with the TimeSeriesSplit validation scheme. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results showed that the model produced an MAE score of 54.13 and an RMSE of 77.60, while a SMAPE score of 43.13% showed an acceptable relative error rate in highly fluctuating sales data. Feature importance analysis shows that previous period (lag_1) sales and weekly patterns are the most dominant factors in demand predictions. These results prove that XGBoost is effectively used for historical data-driven demand prediction of goods and has the potential to support inventory management decision-making.

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