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INDONESIA
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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Implemetasi HHKBRM Pada Model Rekomendasi Hibrida untuk Perencanaan Kompetensi SDM Rumah Sakit Fredy Sitinjak; Alva Hendi Muhammad; Sri Ngudi Wahyuni
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.v13i3.9720

Abstract

The development of human resources (HR) competencies in hospitals has become increasingly important in the era of digital healthcare transformation, which requires healthcare professionals to possess adaptive and data-driven capabilities. However, competency planning still faces several challenges, including limited training data (data sparsity), high variability in competency needs, and the limitations of conventional recommendation systems that are not yet capable of providing personalized and contextual recommendations. This study aims to develop a hybrid recommendation model, namely the Hierarchical Hybrid Knowledge-Based Recommendation Model (HHKBRM), to support more effective competency planning for hospital HR. This research adopts a quantitative experimental approach using secondary data, including HR profiles, training data, training histories, organizational data, and competency standards. The proposed model integrates knowledge-based, content-based filtering, and collaborative filtering approaches, supported by a competency level categorization technique. The analytical process includes data preprocessing, semantic mapping using term weighting and similarity measurement, and hybrid score computation to generate relevant training recommendations. The evaluation results indicate that the model achieves a Precision@10 of 0,054, Recall@10 of 0,300, NDCG@10 of 0,299, Hit Rate@10 of 0,300, and Diversity@10 of 0,849. These results demonstrate that the model is capable of providing relevant recommendations with good ranking quality and high diversity. Overall, the proposed model is effective in supporting data-driven competency planning for hospital human resources by balancing recommendation relevance and diversity.
Analisis Pengaruh Preprocessing Regex dan Cosine Similarity terhadap Performa IndoBERT dalam Klasifikasi Berita Hoaks Berbahasa Indonesia Minggar P. D. Ramadhan; Zainal Abidin; Mochamad Imamudin
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.v13i3.9730

Abstract

The rapid growth of online media has significantly improved access to information, but it has also accelerated the spread of misinformation and hoax news. In hoax detection research, datasets are commonly derived from fact-checking platforms, which typically contain structured components such as claims, narratives, and clarification statements explicitly indicating that certain information is false. The presence of such clarification sentences has the potential to cause bias, a condition in which the model learns text patterns that explicitly indicate the label, thereby reducing the model's ability to fully understand the content of the news. This study aims to analyze the impact of preprocessing techniques based on regular expression (regex) and cosine similarity on the performance of the IndoBERT model for Indonesian hoax news classification. Both approaches are employed to identify and handle clarification sentences, enabling the model to focus more on contextual and semantic understanding of the news content. Experimental results show that the cosine similarity-based preprocessing outperforms the regex-based approach, achieving accuracy, precision, recall, and F1-score of 92.8%. In comparison, the regex-based method obtains an accuracy of 90.7%, precision of 91.3%, recall of 90.7%, and F1-score of 90.6%. These findings indicate that the semantic-based approach is more effective in handling linguistic variability and reducing potential bias caused by explicit clarification patterns. Overall, this study highlights the importance of appropriate preprocessing strategies in improving classification performance and provides insights into the impact of clarification statements in fact-checking datasets on transformer-based hoax detection models
Penerapan Algoritma Genetika Pada Aplikasi Optimasi Penentuan Kelompok KKM Reguler UIN Maliki Berbasis Web Adi Novendra Putra; Nurizal Dwi Priandani
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.v13i3.9736

Abstract

A genetic algorithm was implemented in a web-based application to optimize the formation of Regular KKM groups at UIN Maulana Malik Ibrahim Malang. The main contribution of this study was reflected in the formulation of constraint rules that were aligned with the requirements of KKM group assignment, so that a fitness function different from those used in previous studies was established [15]. Group formation was carried out by considering four constraints, namely the presence of at least one HTQ member in each group, a low ratio of duplicated majors within a group, a gender proportion aligned with the data distribution, and an even number of members across groups. In addition, the algorithm was integrated into a web-based application so that the group formation process was not only optimized, but also supported by a more interactive system with a high level of usability. The system interface was developed using Laravel on the front-end side. The computational process was executed using Python on the back-end side. The relatively long computation time of the genetic algorithm was handled by applying a flagging-process mechanism in the database so that request timeouts could be avoided. Parameter testing was conducted on Popsize, Generation, Crossover Rate, and Mutation  Rate to obtain the best configuration. The test results showed that the best solution was produced at the configuration of Popsize 70, Generation 400, Crossover Rate 0.5, and Mutation  Rate 0.5, with a average fitness value of 0.983684211. The evaluation results showed that the number of groups fulfilling all criteria was increased from 82 groups to 177 groups after optimization. Thus, a more optimal, structured, and institutionally appropriate KKM group formation was achieved through the implementation of an interactive web-based system using a genetic algorithm.
Evaluating Unsupervised Clustering for Credit Card Fraud Detection Under Extreme Class Imbalance Tiara Azizah; Abdussalam
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.v13i3.9747

Abstract

The exponential rise in digital payments has elevated the difficulty of identifying fraudulent credit card activities, especially considering the extreme class imbalance inherent in financial records, where illicit actions typically represent a minuscule fraction of overall traffic. This research aims to assess the efficacy of unsupervised machine learning techniques for anomaly recognition within a public, anonymized dataset. The proposed methodology establishes K-Means clustering as a foundational baseline to understand broader structural patterns. Subsequently, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is deployed as the principal mechanism to isolate dense anomalous regions. To enhance processing speed and determine optimal hyperparameters, specifically epsilon and minimum points, initial tuning occurs on a representative data sample, followed by a comprehensive evaluation across the entire dataset. System performance is systematically evaluated through confusion matrix metrics, prioritizing the accurate classification of minority fraud cases. Experimental outcomes reveal that the DBSCAN algorithm attains an 88.5% detection rate for illegitimate transactions, substantially exceeding the 42.3% threshold achieved by the baseline model. Nevertheless, this heightened sensitivity introduces a trade-off, generating a 10.2% false-positive rate regarding legitimate operations. Ultimately, the density-based approach proves robust for isolating rare fraudulent behaviors in massive data environments, demonstrating substantial viability for practical deployment despite the slight increase in false alarms
Analisis Sentimen Ulasan Mobile Banking Bank Kalbar pada Google Play Store Menggunakan IndoBert Hasrul Rahman; Hanafi
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.v13i3.9779

Abstract

User reviews on the Google Play Store can serve as an important data source for understanding public perceptions of mobile banking services. At the time this paper was written, the application’s average user review score was 3.2, indicating a poor rating. Therefore, the bank needs to further examine the factors that could improve this rating in order to minimize reputational risk, since most users who intend to install an application tend to check its rating first before deciding whether to install it. In general, a rating considered very good is at least 4.5. This study aims to analyze user review sentiment toward the Bank Kalbar Mobile Banking application using a natural language processing approach with IndoBERT, along with two comparison models: TF-IDF with Logistic Regression and RNN BiLSTM. The dataset consists of 2,465 reviews classified into three sentiment classes: positive, negative, and neutral. The data distribution shows class imbalance, with 1,445 positive reviews, 894 negative reviews, and 126 neutral reviews. The data were split using a stratified method into 70% training data and 30% testing data. The research stages included text cleaning, data splitting, model training, and evaluation using accuracy, macro-F1, precision, recall, and a confusion matrix. The experimental results show that the TF-IDF + Logistic Regression model achieved the best performance, with an accuracy of 0.8581 and a macro-F1 score of 0.6777. The RNN BiLSTM model obtained an accuracy of 0.8311 and a macro-F1 score of 0.6777, while IndoBERT achieved an accuracy of 0.8041 and a macro-F1 score of 0.6774. Although IndoBERT did not achieve the highest accuracy, it demonstrated better capability in identifying the neutral class, as indicated by a recall score of 0.6316. These findings indicate that, for a relatively small and imbalanced dataset, a classical TF-IDF-based model can still deliver competitive performance compared with deep learning and transformer-based models.
Implementation Of Attention Mechanism And Explainable Ai For Skin Lesion Classification Using CNN Ilham Nur Fajri; Aditya Dwiputro Wicaksono; Lisda Lisda
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.v13i3.9781

Abstract

Skin lesions are critical dermatological indicators that require early detection to prevent severe outcomes such as melanoma. Traditional Convolutional Neural Network (CNN) architectures employed for categorizing these lesions frequently encounter significant hurdles, notably disproportionate class distributions and a lack of transparency, functioning essentially as opaque "black boxes" during inferential processes. To mitigate these limitations, the current research deploys a ResNet-50 framework augmented by a Convolutional Block Attention Module (CBAM) to refine spatial and channel feature prioritization, alongside the integration of Gradient-Weighted Class Activation Mapping (Grad-CAM) to yield interpretable visualizations. The empirical analysis utilized the HAM10000 repository, incorporating a preprocessing pipeline that encompassed spatial resizing, pixel normalization, and data augmentation, subsequently trained via a bipartite transfer learning methodology. Quantitative metrics reveal that the CBAM-integrated architecture elevates the baseline global accuracy from 82.00% to 86.83%, while simultaneously augmenting the Macro F1-Score from 68.00% to 77.00%.  Qualitative evaluation using Grad-CAM shows sharper and more localized heatmaps, indicating that the attention mechanism successfully guides the model to focus on clinically relevant lesion areas. These findings suggest that combining attention mechanisms with explainable AI not only enhances classification performance but also provides visual transparency, supporting clinical interpretation. This approach is expected to improve trust and reliability in automated skin lesion classification systems
Prediksi Persetujuan Pinjaman Bank Ritel Menggunakan CatBoost dengan Optimasi Hyperparameter Berbasis Optuna dan Analisis Interpretabilitas SHAP Dwi Martantiningsih; Andy Haryoko; Amaludin Arifia
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.v13i3.9790

Abstract

The banking sector faces significant challenges in accurately classifying loan applications, particularly for datasets dominated by categorical features with imbalanced class distribution (71%:29%). This study proposes the application of CatBoost (Categorical Boosting) with Optuna-based hyperparameter optimization a Bayesian optimization framework using Tree-structured Parzen Estimator (TPE) for bank loan approval prediction. Two class imbalance handling scenarios are comparatively evaluated: SMOTE and CatBoost built-in class_weight. Experiments are conducted on a dataset of 381 samples with 15 active features (12 original features and 3 engineered features) using 5-fold stratified cross-validation. Results show LightGBM achieves the best overall performance with Accuracy 93.42%, Precision 94.83%, Recall 96.49%, F1-Score 95.65%, ROC-AUC 0.9215, and MCC 0.8220. CatBoost (SMOTE) achieves competitive performance with AUC-CV 0.9030 and F1 94.02%. SHAP (SHapley Additive exPlanations) analysis identifies Credit_History as the dominant feature (mean|SHAP|=3.2225), followed by ApplicantIncome (0.6896) and Property_Area_Semiurban (0.5222). This study contributes as the first investigation integrating CatBoost+Optuna+XAI-SHAP in retail bank loan approval prediction with dominant categorical features, while providing systematic comparison against LightGBM, XGBoost, and Random Forest.
Perbandingan Moving Average dan Exponential Smoothing untuk Prediksi Harga Saham BBRI pada Dataset 2019–2026 Wahyu Dedy Setiyawan; Andy Haryoko; Amaludin Arifia
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.v13i3.9791

Abstract

This study compares four time series forecasting methods Simple Moving Average (SMA), Double Moving Average (DMA), Single Exponential Smoothing (SES), and Double Exponential Smoothing (DES/Holt) for predicting the closing stock price of BBRI.JK. The dataset comprises 1,768 daily observations spanning January 2019 to December 2026, split into training (80%) and testing (20%) sets. Each method's parameters were optimized via grid search minimizing MAPE, then evaluated across three metrics: MAPE, MAE, and RMSE. SES (α = 0.9) emerged as the best-performing model, achieving a MAPE of 0.3763%, MAE of IDR 14.93, and RMSE of IDR 24.31 substantially outperforming SMA (3.1591%), DMA (2.7561%), and DES (3.6973%). These findings offer methodological guidance for researchers and practical insight for investors operating in emerging market equities with near weak-form efficiency.
Forecasting Volume Penumpang Harian KRL Yogyakarta–Solo Menggunakan SARIMA, LSTM, dan SARIMA-LSTM Marta Ardiyanto; Ridwan Dwi Irawan; Esti Dwi Rahmawati
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.v13i3.9798

Abstract

Passenger volume forecasting in public transportation is an important aspect of supporting data-driven operational decision-making. The Yogyakarta–Solo Commuter Rail is a strategic public transportation mode that supports interregional mobility. Fluctuations in passenger volume influenced by daily patterns, weekends, national holidays, and collective leave periods require forecasting models capable of capturing seasonal patterns and possible nonlinear changes in time series data. This study aims to compare the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and hybrid SARIMA-LSTM models in forecasting the daily passenger volume of the Yogyakarta–Solo Commuter Rail. The dataset consists of daily passenger volume data from January to December 2025. The research stages include data preprocessing, calendar-based feature engineering, chronological training and testing data splitting, SARIMA modeling, LSTM modeling, residual modeling using LSTM, and performance evaluation using MAE, RMSE, and MAPE. The results show that the SARIMA model obtained an MAE of 2644.81, an RMSE of 3299.78, and a MAPE of 10.56%. The LSTM model obtained an MAE of 1977.50, an RMSE of 2528.75, and a MAPE of 7.28%. Meanwhile, the hybrid SARIMA-LSTM model achieved an MAE of 2634.78, an RMSE of 3294.24, and a MAPE of 10.52%. Based on these results, the LSTM model achieved the best forecasting performance compared to SARIMA and hybrid SARIMA-LSTM. The hybrid SARIMA-LSTM model provided only a slight improvement over SARIMA but did not outperform the LSTM model. These findings indicate that forecasting model selection should consider dataset characteristics, residual patterns, and the adequacy of historical data. Future research is recommended to use longer historical data and incorporate relevant external variables to improve forecasting accuracy.
Implementasi Algoritma C4.5 Untuk Klasifikasi Menu Favorit Pelanggan Pada UMKM Warung Mie Aceh Seafood: - Helma Tiara Syifa; Dicky Nofriansyah; Fifin Sonata
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 rapidly growing culinary sector requires Micro, Small, and Medium Enterprises (MSMEs) to implement targeted operational strategies. The main problem often faced by the management of the Warung Mie Aceh Seafood MSME is the inability to accurately predict the level of menu popularity based on external factors. This directly impacts the inefficiency of managing wet raw material (seafood) inventory due to its perishable nature. Therefore, this study aims to implement data mining technology using the C4.5 algorithm to predictively classify customers' favorite menus. As a solution to these problems, this study processed 586 historical sales transaction data records from July to September 2025. Unlike previous studies, the novelty of this research lies in integrating external environmental variables, namely weather conditions and day types, along with menu type attributes into a single multidimensional decision tree model evaluated using the 10-fold cross-validation method. The test results show that the C4.5 algorithm classification model has proven to be excellent, achieving an accuracy rate of 96.08% and a class precision value of 100% for the "Very Favorite" and "Not Favorite" classes. Practically, extracting rules from this model provides a specific contribution as a decision support system; for instance, guiding management to reduce cold beverage stocks during heavy rain, or maximize the 'Snacks' menu inventory on weekends, enabling the MSME to avoid material losses while efficiently minimizing the risk of lost potential revenue.

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