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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 1,069 Documents
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Studi Komparatif Model Machine Learning untuk Klasifikasi Penyakit Jantung dengan SMOTE pada Data Imbalanced Sijabat, Glen Fierre; Prabowo, Wahyu Aji Eko
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.v13i1.9485

Abstract

This study examines the application of the Synthetic Minority Over-sampling Technique (SMOTE) for heart disease classification using four machine learning algorithms, namely Logistic Regression, Random Forest, LightGBM, and XGBoost, based on the Heart Disease UCI dataset consisting of 920 medical records with 16 clinical features. The original severity labels (0–4) are converted into two classes, namely not sick (0) and sick (1–4), to better align with binary decision-making needs in clinical screening. The experiments are conducted in two scenarios: (1) training models on the original data without handling class imbalance and (2) training models with SMOTE applied only to the training data within a pipeline, accompanied by hyperparameter tuning using k-fold cross-validation. Model performance is evaluated using accuracy, precision, recall, F1-score, AUC-ROC, as well as confusion matrix analysis to examine misclassifications, particularly false negatives in the sick class. In the scenario without SMOTE, the best model, Logistic Regression, achieves an accuracy of 84.78%, recall of 84.31%, F1-score of 86.00%, and AUC-ROC of 91.95%, although the number of false negatives remains relatively high. After applying SMOTE, there is an increase in recall and F1-score for the positive class across all models, with the best performance obtained by Random Forest with SMOTE, which achieves an accuracy of 86.96%, recall of 87.25%, F1-score of 88.12%, and AUC-ROC of 93.34%. These findings indicate that the combination of SMOTE and hyperparameter optimization can produce a more balanced and reliable heart disease classification model that is potentially useful as a clinical decision support system in healthcare services.
Evaluasi Komparatif Random Forest, XGBoost, LightGBM, dan K-Nearest Neighbors untuk Prediksi Cuaca di Kota Semarang Maulana Wahyu Ibrahim; Prabowo, Wahyu Aji Eko
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.v13i1.9488

Abstract

Accurate weather predictions play an important role in assisting strategic decisions in various fields, from agriculture to disaster management. However, there is a fundamental challenge in creating automatic prediction models, namely the nature of meteorological datasets, which are often imbalanced in class distribution. This phenomenon causes conventional machine learning algorithms to favor the dominant class and be less capable of detecting the rare class (rain), as seen in the low sensitivity values. This study aims to overcome this bias problem and improve the accuracy of daily rainfall classification using a comparative approach with four algorithms: Random Forest, K-Nearest Neighbor (KNN), LightGBM, and XGBoost. As the main method to overcome data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to generate new samples in the underrepresented class. Model performance was evaluated comprehensively using a confusion matrix, One-vs-Rest (OvR) strategy, and conventional evaluation metrics. The results of the experiments on the baseline model showed a failure to detect the minority class with very low Recall and F1-Score values (< 0.30). The application of SMOTE was proven to significantly improve Recall and F1-Score compared to the SMOTE. LightGBM using SMOTE was recorded as the most superior model that successfully balanced all evaluation metrics.
Analisis Sentimen Komentar Cyberbullying Terhadap Fenomena Flexing di Tiktok Menggunakan Artificial Neural Network Lailatul Qodriyah; Ifnu Wisma Dwi Prastya; Guruh Purbo Dirgontoro
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.v13i1.9494

Abstract

The rising trend of flexing on TikTok has created a dynamic digital space that often triggers varied user reactions, including subtle forms of cyberbullying. This study aims to analyze public sentiment toward flexing content and evaluate the performance of the Artificial Neural Network (ANN) algorithm in classifying user comments. A total of 4,013 comments were collected through a scraping process on the TikTok account of Miechel Halim and automatically labeled using a lexicon-based approach. The comments were then pre-processed and transformed into Term Frequency–Inverse Document Frequency (TF-IDF) representations before being split into training and testing datasets with an 80:20 ratio. The ANN model was trained under two scenarios before and after the application of the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Experimental results show that the initial model achieved an accuracy of 89.79%, which increased to 90.04% after SMOTE, accompanied by an improvement in the recall of the negative class. These findings indicate that ANN is effective for sentiment classification of TikTok comments, although informal language patterns and highly imbalanced labels remain challenges in identifying negative or potentially harmful remarks related to cyberbullying.
Implementasi Arsitektur Keamanan Terintegrasi IDS, WAF Dan FIM Berbasis Wazuh Pada Platform Open Jurnal System Mengacu Pada NIST Cybersecurity Framework Kornelius, William Carey; Adytia, Pitrasacha; Pukeng, Ahmad Fahrijal
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.v13i1.9500

Abstract

The utilization of Open Journal Systems (OJS) as a scientific publishing platform faces significant security threats, including SQL Injection, Cross-Site Scripting (XSS), and webshell injection, which may compromise data integrity and service availability. This study aims to design and evaluate an integrated security architecture based on Wazuh through the implementation of an Intrusion Detection System (IDS), Web Application Firewall (WAF), and File Integrity Monitoring (FIM) using the NIST Cybersecurity Framework approach. The research methodology includes vulnerability identification across 11 journals in 7 universities, the development of a defense-in-depth architecture, and controlled penetration testing based on OWASP Top 10 scenarios. Testing results from 30 attack scenarios demonstrate a 100% detection rate for SQL Injection and webshell injection, and an 80% detection rate for XSS attacks. The system successfully blocks malicious requests with 403 Forbidden responses and generates real-time alerts through centralized log correlation in Wazuh. However, potential false positives were observed in several generic security rules, indicating the need for rule fine-tuning to align with OJS traffic characteristics. Overall, the integrated security approach measurably enhances threat detection and incident response capabilities.
Sistem Presensi Mahasiswa Berbasis Pengenalan Wajah Real-Time dengan Deteksi Anti-Spoofing Menggunakan YOLOv8 dan ArcFace Fajar Satria; Defry Hamdhana; Lidya Rosnita
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.v13i1.9502

Abstract

Student attendance recording is an important aspect in supporting discipline and administrative order in academic environments. Manual attendance methods still have several limitations, such as potential fraud and inefficient recapitulation processes. This study aims to develop a real-time face recognition-based student attendance system by implementing the YOLOv8 algorithm for face detection and ArcFace for identity recognition, complemented with an anti-spoofing feature to prevent fraudulent attempts. The system is designed to detect faces directly, recognize registered student identities, and record attendance automatically. The main contribution of this study lies in the integration of YOLOv8-based face detection, ArcFace-based face recognition, and an anti-spoofing mechanism into a single unified real-time attendance system. Experimental results show that the system successfully recognizes all registered students with a 100% success rate. The YOLOv8 anti-spoofing model demonstrates excellent performance in distinguishing real and fake faces, achieving an mAP@0.5 value of 0.995 and an F1-score close to 1. The system is also able to record attendance time in real time according to the actual time and present attendance data systematically. Based on these results, the developed real-time face recognition attendance system is accurate, secure, and feasible to be implemented as an attendance solution in academic environments
Klasifikasi Penyakit Daun Mangga Menggunakan CNN Berbasis Transfer Learning Dengan Model Arsitektur VGG16, DenseNet121, dan InceptionV3 Purnomo, Zaky Dwi; Prabowo, Wahyu Aji Eko
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.v13i1.9504

Abstract

Mango is one of the important fruits in Indonesia, but its production is often disrupted by leaf diseases and pests that are difficult to detect early. Manual disease recognition methods usually depend on observers and are not always accurate. This study aims to create an automated system to classify mango leaf diseases, using deep learning techniques based on the Convolutional Neural Network (CNN) algorithm. This study also compares three models, namely VGG16, DenseNet121, and InceptionV3, by applying the transfer learning method. The dataset used consists of 4,000 images divided evenly into 8 categories, consisting of 7 types of diseases (Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mold) and 1 category of healthy plants. Evaluation was carried out using the 5-Fold Cross-Validation method to ensure valid results. The results show that all three models are able to provide an accuracy of more than 90%. The VGG16 model showed the best and most stable performance, with an accuracy of 93.25%, a Precision of 0.93, a Recall of 0.93, an F1-Score of 0.93, and an AUC-ROC of 0.98. Meanwhile, InceptionV3 achieved an accuracy of 92.38% and DenseNet121 reached 91.25%. Therefore, VGG16 is recommended as the primary model due to its better ability to extract texture features and accurately recognize mango leaf diseases. VGG16 architecture is able to outperform complex models in efficiently extracting mango leaf texture features, making it very potential to be used as a basis for real-time plant disease diagnosis applications for farmers
A Optimization of Sales Strategies and Inventory Forecasting for Processed Banana Products Utilizing the Conceptual Framework of Economic Efficiency and Accounting Precision Based on Simple Moving Average Zulham Sitorus; Lia Nazliana Nasution; Rahima Br Purba; Amnisuhaila Abarahan; Rowiyah Asengbaramae; Feby Wulandari Sembirinng; Mhd Ihsan Abidi
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.v13i1.9505

Abstract

Fluctuations in demand for processed banana products often lead to inaccurate inventory planning at the MSME scale, resulting in decreased operational efficiency and potential accounting inaccuracies in inventory valuation and the calculation of Cost of Goods Sold (COGS). The calculation of raw material stock forecasting for 2024-2025 produces the following predicted values: 124 bunches of bananas, 80 pieces of chocolate, 81 kg of cooking oil, and 42 kg of granulated sugar. This simple, fast, and accurate forecasting process enables producers to more accurately predict product demand, ultimately reducing the risk of overstocking or shortages. This study aims to optimize sales strategies and inventory forecasting for processed banana products through a conceptual framework that integrates economic efficiency. The method used is the Simple Moving Average (SMA) to forecast inventory needs based on historical sales data at the BananaChips MSME, by testing several variations of the forecasting period to obtain the most stable and representative results. Overall, the recapitulation results show that the Cooking Oil raw material has the highest forecasting accuracy, with the lowest MAPE of 1.81% (MAD 1.50, MSE 5.20). Meanwhile, Granulated Sugar raw material recorded the highest MAPE value of 5.08% (MAD 2.25, MSE 9.73), followed by Chocolate (MAPE 2.43%) and Banana (MAPE 2.18%). The implementation results show an increase in stock management efficiency of up to 20% and a 15% decrease in excess raw materials. These findings indicate that integrating SMA forecasting with an economic efficiency framework and accounting accuracy can improve the quality of inventory and sales decision-making, thereby strengthening the profitability and sustainability of the banana-processed product business at the Bananachips MSME
Studi Komparatif Algoritma Random Forest dan Logistic Regression dalam Analisis Sentimen Ulasan Aplikasi E-Wallet Dana Diah Fitriani; Afril Efan Pajri; Aprillia Dwi Ardianti
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.v13i1.9517

Abstract

The increasing use of digital wallets in Indonesia has led to a growing number of user opinions expressed, including on the DANA platform in the Play Store. These reviews reflect users' experiences and satisfaction levels, necessitating sentiment analysis to comprehend public opinions about the application’s service quality. The study conducts an analytical comparison between Random Forest and Logistic Regression methods in classification sentiments for DANA application. Data was obtained through scraping techniques, resulting in 2,068 reviews after the cleaning process. The analysis stages include text preprocessing, labeling based on review scores, weighting using TF-IDF, and modeling with both algorithms. The evaluation results demonstrate that Random Forest obtains an accuracy of 86.23%, while Logistic Regression obtains an accuracy of 84.54%. Both models are capable of classifying positive sentiments well but are less optimal in detecting negative sentiments. Random Forest shows higher performance compared to Logistic Regression within the task in sentiment analysis for DANA app reviews. Thus, we can conclude that using the random forest algorithm is able to produce accurate sentiment analysis and can act as a basis for making decisions in further research
Perbandingan Metode Euclidean dan Manhattan Distance dalam Implementasi Algoritma K-Means dan K-Medoid pada Pengelompokkan Faktor Dominan Perceraian di Kabupaten Bojonegoro Salma, Elok Salma Nabila; Ifnu Wisma Dwi Prastya; Ita Aristia Sa’ida
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.v13i1.9520

Abstract

The divorce rate in Bojonegoro Regency continues to increase, driven by various social factors such as constant disputes, economic pressure, and household disharmony. Consequently, an analysis is required to map dominant and non-dominant factors more effectively. This study aims to group the factors causing divorce in Bojonegoro Regency for the 2021–2023 period and determine the most optimal clustering method. The research utilizes K-Means and K-Medoids algorithms with Euclidean and Manhattan distance metrics applied to both raw data and data normalized using the Min–Max Scaler, evaluated via the Silhouette Score. The results indicate that data normalization improves cluster quality, and K-Means with Manhattan distance on normalized data achieves the best performance, yielding a Silhouette Score of 0.849547. Cluster displacement analysis reveals that the grouping patterns remain relatively consistent across years, with "constant disputes" consistently emerging as the dominant factor, while other factors remain in the non-dominant cluster with similar patterns. This study demonstrates that K-Means with Manhattan distance on normalized data is more effective for clustering divorce factors. These findings can serve as a methodological foundation for the local government in formulating data-driven social policies and interventions.
Evaluasi Kinerja Algoritma AES-128 dan SPECK-128/128 pada Sistem Smart Door Lock Berbasis IoT Yunita Sari; Galura Muhammad Suranegara
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.v13i1.9524

Abstract

The Internet of Things (IoT) is increasingly being used in smart home systems, one of which is the Smart Door Lock. However, data communication on IoT devices is vulnerable to network attacks such as sniffing and man-in-the-middle, especially since many devices are still designed with weak security mechanisms. The main issue studied in this research is how to protect RFID data from being easily intercepted, while maintaining efficiency on devices with limited resources. The proposed solution is the implementation of AES-128 and SPECK-128/128 cryptographic algorithms on the MQTT communication protocol using ESP8266 devices. The research was conducted experimentally with 50 measurements for each algorithm, covering encryption and decryption time parameters, memory usage, and encryption effectiveness against sniffing. The results show that AES-128 has stable performance but requires more execution time and memory because its algorithm is complex with many stages. In contrast, SPECK-128/128 is faster and more memory-efficient thanks to its simple ARX-based structure, although its consistency is slightly lower. Sniffing analysis shows that both algorithms are capable of encrypting all packets with 100% security, so that no plaintext data can be read. Thus, this study confirms that there is a trade-off between security and efficiency

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