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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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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.
Penerapan Data Mining Menggunakan Teknik Classification Untuk Melihat Potensi Kepatuhan Wajib Pajak Badan Anuqman Fitriadi; Popalia, Qamarullah; Wibowo, Arief
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.v13i1.9354

Abstract

The application of data mining using classification techniques has significant potential to assist tax authorities in identifying and mapping the compliance levels of corporate taxpayers. This study aims to develop a corporate taxpayer compliance classification model using the Naive Bayes algorithm based on the ratio of Annual Tax Return (SPT) filing and the ratio of tax payments. The data used consist of aggregated data from Tax Service Offices (Kantor Pelayanan Pajak/KPP) for the 2022–2024 period obtained from the Directorate General of Taxes. The research stages follow the Knowledge Discovery in Databases (KDD) methodology, which includes data selection, preprocessing, transformation, modeling, and evaluation. The experimental results indicate that the Naive Bayes model is able to classify compliance levels with an accuracy of 100%, precision of 1.00, recall of 1.00, and an F1-score of 1.00. These findings suggest that the SPT filing ratio is the dominant factor in determining corporate taxpayer compliance. The proposed model can be utilized as a decision support system to assist tax authorities in determining supervision and guidance priorities for corporate taxpayers
Analisis Pola Dan Prediksi Churn: Hybrid Segmentasi SOM+K-Means Dan Klasifikasi Machine Learning Rahmadhani, Rizka; Ermatita
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.v13i1.9409

Abstract

Customer churn is a significant challenge in the banking industry, which can have a substantial impact on the profitability and long-term sustainability. Customer churn management is typically addressed using binary classification approaches, which often fail to provide the depth needed to understand customer characteristics. This study proposes addressing churn through customer segmentation as an preliminary step before classification, offering a clearer and deeper understanding of each segment’s characteristics. The research combines Self-Organizing Map (SOM) and K-Means clustering to create interpretable segments. The SOM+K-Means model is used for segmentation and visual mapping, which helps identify customer groups at risk of churn and the key features influencing these risks. Cluster labels are then used as features for classification using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). In the classification phase, the Synthetic Minority Oversampling Technique (SMOTE) and GridSearchCV are applied to address class imbalance and optimize model parameters. XGB outperformed the other models with an accuracy of 85% and an AUC score of 85%. These results highlight that customer segmentation with SOM+K-Means enables more effective churn management strategies, while XGB proves to be a strong model for churn prediction. This research contributes to the application of clustering and machine learning classification techniques in churn analysis within the banking industry, offering a pathway to better customer retention strategies and lower churn rates.
Pemodelan Distribusi Waktu Kedatangan Dalam Teori Antrian Dengan Pendekatan Simulasi Monte Carlo Hevlie Winda Nazry S; Firahmi Rizky; Fithria Ulfah; Budi Antoro
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.v13i1.9411

Abstract

The study of queuing systems has an important role in the development of applied mathematics, especially in probability theory and stochastic processes. Classical models such as M/M/1 generally assume a Poisson arrival process so that the inter-arrival time is exponential, but in real service systems the arrival pattern is often non-Poisson with excessive variance and long tails of the distribution. This research proposes Monte Carlo simulation-based inter-arrival time distribution modeling in two scenarios: constant arrival rate (Scenario 1) and variable (Scenario 2). The interarrival data from the simulation results were analyzed using descriptive statistics and validated with the Kolmogorov–Smirnov (K–S) goodness of fit and Chi square tests for four candidate distributions: Exponential, Gamma, Weibull, and Lognormal. Descriptively, Scenario 1 has a mean of 1.9790 and a variance of 1.3238, while Scenario 2 has a mean of 2.0076 and a variance of 2.4025 and higher skewness and kurtosis. The K–S test results show that the exponential distribution is rejected in Scenario 1 (D = 0.1708; p < 0.001) and Scenario 2 (D = 0.0906; p = 0.0135). In Scenario 1, the Gamma distribution provided the best fit (K–S D = 0.0265; p=0.9808; Chi square = 19.8667; p = 0.2811). In Scenario 2, the Lognormal distribution was the most appropriate (K–S D = 0.0230; p = 0.9963; Chi square = 7.3333; p = 0.9788). These findings confirm that the exponential Poisson assumption is not always representative and that choosing a validated arrival distribution (Gamma/Lognormal) can increase the accuracy of queuing system analysis in both stable and dynamic conditions.
Perbandingan Performa Algoritma Random Forest dan XGBoost dalam Memprediksi Hujan di Area Gunung Ungaran Arizal Irsyad Imanullah; Ahmad Zainul Fanani
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.v13i1.9416

Abstract

Hiking activities in Mount Ungaran are frequently hindered by extreme and unpredictable weather changes, which potentially endanger the safety of hikers. One of the primary challenges in developing an automated rainfall prediction model for this region is the class imbalance in historical meteorological data, where the number of non-rainy days significantly dominates rainfall events. This condition often causes machine learning models to become biased toward the majority class, leading to a failure in detecting actual rainfall events (false negatives). This study aims to address this issue through a comparative analysis of the performance of two popular ensemble algorithms, namely Random Forest and Extreme Gradient Boosting (XGBoost). Specifically, this research investigates the impact of applying the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data distribution in order to enhance minority class detection accuracy. Using the ERA5 reanalysis daily dataset for the 2019–2023 period with input variables including temperature, humidity, air pressure, and wind speed, the models were trained and validated using a time-based split method with an 80:20 ratio. Performance evaluation was conducted comprehensively using accuracy, precision, recall, and F1-score metrics. The results provide strong empirical evidence that the application of SMOTE yields the most optimal impact on the XGBoost algorithm. The combined XGBoost-SMOTE model successfully achieved the best performance with an accuracy of 80.50% and an F1-score of 83.23%, outperforming the Random Forest model which remained at an accuracy of 78.21%. In conclusion, the integration of boosting methods with data resampling techniques proves to be highly effective in improving rainfall prediction reliability in regions with complex topography.
Analisis Perbandingan Seleksi Fitur dalam Memprediksi Kelulusan Mahasiswa dengan Menngunakan Artificial Neural Network M. Khoirul Risqi; Dwi Prastya, Ifnu Wisma; Barata, Mula Agung
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.v13i1.9420

Abstract

Student attrition presents a major challenge in higher education due to its direct impact on academic quality and institutional graduation rates. Detecting students who are likely to withdraw at an early stage is therefore essential to ensure that timely interventions can be made. This study investigates how three distinct feature selection techniques—Chi-Square, Information Gain, and ANOVA—affect the performance of Artificial Neural Networks (ANN) in classifying student outcomes. The data used in the experiment were drawn from academic and administrative records, which had been standardized through Min-Max normalization. The results demonstrate that each method contributes positively, with classification accuracies ranging from 88.71% to 91.37%. Information Gain emerged as the most effective approach, yielding the highest accuracy at 91.37% and a recall score of 97.29%, largely due to its capability to reduce entropy and isolate the most informative variables. ANOVA also performed consistently well with 90.82% accuracy, while Chi-Square was comparatively less effective, potentially due to its reliance on categorical variables that may not capture predictive nuances. These findings emphasize the strategic importance of applying robust feature selection to improve ANN-based prediction models. Ultimately, this research supports the design of data-driven systems aimed at reducing student dropout rates and strengthening academic retention strategies across higher education institutions.

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