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ILKOMNIKA: Journal of Computer Science and Applied Informatics
Published by Universitas Nahdlatul Ulama Blitar
ISSN : -     EISSN : 27152731     DOI : https://doi.org/10.28926/ilkomnika
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
ILKOMNIKA: Journal of Computer and Applied Informatics is is a peer reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of computer science and applied informatics which covers five (5) majors areas of research that includes 1) Informatics Engineering and Its Application 2) Computer Science 3) Software Engineering 4) Computer Engineering 5) Information System. This journal is published 3 issues a year, in April, August, and December.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 244 Documents
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Integrating Intelligent Consumer Behavior into Procedure Analysis for Campus Business Information System Development Sugiarto, Sugiarto; Atmaja, Pratama Wirya; Novembrianto, Rizka; Kurniati, Ely; Widnyani, Ni Made
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.866

Abstract

This study develops SIPUS, a campus business information system for the Business Management Unit (BPU), by integrating procedure analysis with intelligent consumer behavior considerations to improve both operational correctness and user-facing service experience. The research applies a requirements-driven approach in which current procedures are documented, key procedural issues are identified (e.g., fragmented coordination, unclear responsibility boundaries, inconsistent schedule handling, non-explicit handoffs, and incomplete decision gates), and the findings are translated into traceable functional requirements and user-oriented acceptance criteria. SIPUS implementation is organized across five BPU service domains, including facility rentals and food stall participation, and emphasizes standardized workflow control points such as availability validation, conflict detection, closure status handling, approval routing where applicable, and payment verification. System validation combines stakeholder review of procedure–requirement–implementation alignment with user-oriented evaluation using CSUQ. CSUQ results show consistently high ratings on a 1–7 scale, with an overall mean of 5.79 (82.7% of the maximum score) and low dispersion (SD = 0.12), indicating favorable perceived usability and interaction quality. Overall, the results demonstrate that incorporating consumer-oriented evidence during requirements formulation strengthens alignment between operational procedures, implemented system behavior, and user expectations in campus service delivery.
Development of a Driver Drowsiness Detection System Using YOLO, EAR, and MAR for Driving Performance Evaluation Mahfudi, Isa; Rasyid, Muhammad Akida Jabbar; Adzikirani, Adzikirani; Kusumawardani, Mila; Soelistianto, Farida Arinie; Wijayanti, Rieke Adriati; Novianti, Atik
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.878

Abstract

Driver fatigue and drowsiness are among the major factors causing traffic accidents, leading to decreased alertness and slower reaction time. This study aims to develop a computer vision-based driver drowsiness detection system by integrating the You Only Look Once (YOLO), Eye Aspect Ratio (EAR), and Mouth Aspect Ratio (MAR) methods, as well as to evaluate driving performance quantitatively. The system is designed to operate in real time using a camera as the visual data source, which is processed through face detection, facial landmark extraction, and the calculation of EAR and MAR parameters as indicators of eye condition and yawning activity. The experimental results show that the system is capable of classifying driver conditions into three categories: normal (alert), drowsy, and microsleep, with an average F1-score of 0.92. In addition, this study proposes the Alertness Index (AI) as a composite indicator calculated based on drowsiness frequency, eye closure duration, and yawning intensity to represent the driver’s alertness level more comprehensively. The system is also able to operate in real time with stable performance, supporting implementation in real-world conditions. The results demonstrate that the integration of YOLO, EAR, and MAR not only improves detection accuracy but also enables objective and data-driven driving performance evaluation. This system can be applied as a driver monitoring solution to improve road safety.
Microservices Architecture Reconstructed from Monolithic Moodle using Graph-Based Deep Learning Jannah, Maughfirotul; Yaqin, Muhammad Ainul
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.883

Abstract

This research aims to reconstruct the microservices architecture of the Moodle monolithic system using the Graph-Based Deep Learning approach, especially the Graph Convolutional Network (GCN). The study focused on analyzing dependencies in 13,841 PHP files from the Moodle repository version MOODLE_403_STABLE. The main problem is the high complexity of dependencies in Moodle's monolithic architecture, which makes it difficult for traditional clustering methods to automatically determine the right service boundaries. The results showed that the Wide GCN configuration (with a wider layer) converted the negative modularity (-0.29007) to positive (0.11676) in the global population and achieved the highest modularity value of 0.24794 in the assessment module. The study's main finding is that the use of Wide GCN consistently improves cohesion by up to tenfold compared to monolithic conditions, although there is a trade-off in the form of increased coupling. The contributions of this study include: (1) empirical evidence regarding the advantages of GNN in systems with dense dependencies, (2) systematic comparisons between wide and standard configurations, and (3) simultaneous integration of four architecture quality metrics.
Robustness Evaluation of Gradient Boosting Models Against Unseen Attacks on the ToN-IoT Dataset Hakim, Albi Akhsanul; Aditiawan, Firza Prima; Junaidi, Achmad
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.886

Abstract

Machine learning-based Intrusion Detection Systems (IDS) often achieve strong performance on known attack distributions but may degrade when encountering unseen attacks. This study evaluates the robustness of two gradient boosting models, XGBoost and LightGBM, under unseen attack distribution shifts using the ToN-IoT dataset. A controlled attack-exclusion strategy was applied, where selected attack types were excluded from training and evaluated only during testing. Unlike conventional unseen-attack evaluations that primarily report performance degradation, this study further investigates attack-specific degradation through feature-level distribution similarity analysis. Feature importance analysis was used to identify key traffic features, while Jensen-Shannon Divergence (JS Divergence) quantified distribution similarity between attack and normal traffic. Model robustness was assessed using Recall, Macro-F1, PR-AUC, False Negatives (FN), and False Negative Rate (FNR) across five random seeds. The results show that performance degradation varied substantially across attack types. Both models maintained near-baseline performance for unseen Scanning attacks, whereas unseen DDoS and especially MITM attacks produced larger increases in FNR and greater performance degradation. Correlation analysis indicated that the proto feature exhibited the strongest relationship between distribution similarity and detection errors, with lower JS Divergence generally associated with higher FNR. These findings suggest that robustness degradation depends not only on attack novelty but also on the similarity between attack and normal traffic distributions, providing additional insight into attack-specific robustness behavior in IDS models.
Optimization of Sauvola Thresholding Parameters for Braille Dot Detection: A Comparative Study with Niblack Method Prihantono, Silvanus; Anggraeny, Fetty Tri; Sihananto, Andreas Nugroho
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.887

Abstract

Braille is a tactile writing system used to assist individuals with visual impairments. While Braille paper is a common medium, it is highly vulnerable to physical degradation, making automated optical recognition challenging. Despite effectiveness of local thresholding for degraded documents, grid search parameter analysis and configurations for Braille dot detection remain largely unexplored, leading to suboptimal detection performance. This study aims to systematically grid search Sauvola parameter analysis binarization parameters for Braille dot detection prior to Circle Hough Transform, encompassing a comparative evaluation against Niblack and Otsu baseline methods. To eliminate evaluation bias, dataset of 38 manually annotated 640x640 pixel images was strictly partitioned into a 10-image tuning set and a 28-image test set. Parameter grid search identified an optimal spatial boundary at window size=13. Mathematically aligning with maximum topographical footprint of a Braille cell and sensitivity parameter of k=0.050. In isolated test set, Sauvola achieved a superior Mean F1-Score of 0.7916, significantly outperforming Niblack of 0.7088, global Otsu thresholding of 0.4513, and CHT-only baseline of 0.7392. Our results suggest that normalization factor may contribute to observed performance differences. However, its individual effect was not isolated in present experiments.
Website Quality Analysis of Glints on User Satisfaction Using WebQual 4.0 and Perceived Trust Azizah, Aulia Wafiq; Dewangga, Danang Ary; Arifah, Ika Diyah Candra
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.893

Abstract

A high bounce rate of 60.03% on the Glints job portal signals potential user experience flaws, revealing a critical gap between high traffic volume and actual user engagement. Therefore, this research investigates the direct impact of usability, information quality, service quality, and perceived trust on user satisfaction. We applied a quantitative approach based on the WebQual 4.0 model, collecting survey data from 200 purposively sampled users. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis confirms that these four exogenous factors positively and significantly enhance satisfaction. The combined framework is highly accurate, predicting 83.3% of the dependent variable. Notably, service quality had the most significant influence, with a path coefficient of 0.285 (t = 4.037, p < 0.001). The key contribution of this study is the addition of perceived trust to the WebQual 4.0 framework, which also proved vital regarding job legitimacy and data security. Consequently, future platform optimizations should focus on responsive services, rigorous job screening, and clear privacy standards.
Analysis of Operational Cost Anomalies for Coal Getting and Overburden Removal in the Mining Industry Using Machine Learning Pranata, Okta Robian; Raharjo, Agus Budi
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.894

Abstract

Operational costs in coal getting (CG) and overburden removal (OBR) represent the largest expenditure in coal mining, yet systematic anomaly detection remains limited. This study develops an integrated machine learning pipeline using 1,508 daily observations from a coal mining company in Indonesia (July-December 2025), integrating five operational data sources. Anomaly detection combines Isolation Forest, Local Outlier Factor, ECOD, COPOD, HBOS, and an Autoencoder under a non-circular evaluation design in which detection features and the weak ground-truth label are strictly independent. All six baselines show weak discriminative power against this weak label (AUC 0.47-0.59), with the Autoencoder narrowly the strongest (AUC=0.590). A continuous Ensemble Score, thresholded at the 95th percentile, isolates 76 candidate anomalies (5.0%) for typology classification and full-population expert validation. Validating all 76 candidates plus a 30-observation Normal control sample (106 total, blind labeling) yields Precision=0.82, Recall=1.00, F1=0.90, and Cohen's Kappa=0.71 (substantial agreement); critically, AUC against this expert label reaches 0.91, confirming that the Ensemble Score's ranking ability is genuine even though its AUC against the weak proxy label is not. Root-cause analysis identifies breakdown hours, downtime ratio, and lost time - not unit cost - as the dominant discriminators of anomalous days. Hauling costs dominate at 65.3% of total operational expenditure. A material-split regression model reduces prediction error (MAPE) from 23.80% to a weighted 16.77%, with SHAP analysis identifying operational efficiency as the primary cost driver. Seven evidence-based managerial recommendations are proposed for predictive-preventive cost management.
ZCR-Based Suspicious Sound-Event Monitoring for Examination Supervision Using Android Smartphones Adzikirani, Adzikirani; Ardiansyah, Rizky; Rasyid, Abdul; Pratama, Adevian Fairuz
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.904

Abstract

This study presents a functional prototype of an Android-based classroom noise-mapping system that uses smartphone microphones, Zero Crossing Rate (ZCR), and Firebase Realtime Database. Two integrated applications were developed: a student-side sensing client and a lecturer monitoring dashboard. Functional testing covered three source conditions (human speech, a dropped pen, and table movement) at three source desks. Each event was observed at seven desk positions, producing 63 sensor-location records from nine source events. The active source position was detected in all nine events. Source-classification accuracy was 77.8% (7/9): human speech and dropped-pen events were correctly classified in all trials, while only one of three table-movement events was classified as an object and two were returned as unclear. The results demonstrate real-time synchronization and functional spatial monitoring, while also showing sensitivity to sound propagation and overlapping acoustic patterns. Because the documented prototype uses an uncalibrated level indicator and its appendix retains a simulated audio-buffer routine, the findings should be interpreted as functional prototype validation rather than calibrated sound-level or field-performance validation.
Deep Learning Modeling for Subseasonal to Seasonal Rainfall Prediction Lumalessil, Ferry Lodewik; Kustiyo, Aziz; Buono, Agus; Faqih, Akhmad
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.922

Abstract

Rainfall variability has a major impact on the agricultural sector because conditions that are too wet or too dry can increase the risk of flooding, drought, crop disturbances, and changes in the planting calendar. Subseasonal to Seasonal (S2S) forecasting is important because it is located between short-term weather forecasts and seasonal predictions, so that it can provide early information for decision-making in the agriculture, water management, and disaster mitigation sectors. This study aims to develop a deep learning-based S2S rainfall prediction model using the CNN–LSTM and CNN–GRU hybrid architecture by utilizing ECMWF atmospheric variables, as well as CHIRPS rainfall as predictors. CNNs are used to extract spatial features, while LSTM and GRU model temporal dynamics. The model generates predictions at a lead time of 0–45 or up to 46 days ahead for each ECMWF release. The results of the evaluation showed spatial variation in performance on the island of Java. CNN–LSTM showed the best performance, especially in the central Java region with an RMSE of 3.286, a correlation of 0.944, a BSS of 0.414, and a CRPSS of 0.775. CNN-GRU also showed good performance in the central to southern regions with an RMSE of 2.939, a correlation of 0.954, a BSS of 0.406, and a CRPSS of 0.631. In general, CNN-LSTM provides a more stable performance, especially in probabilistic evaluations, while the western Java region still shows greater prediction challenges.
Extractive Summarization of Indonesian Sirah Nabawiyah Texts Using a Hybrid TextRank Algorithm Davissyah, Asfa; Supriyono, Supriyono; Lestari, Tri Mukti; Octadaniswara, Daffa Andika; Najib, Jihan
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.931

Abstract

Sirah Nabawiyah is a religious narrative text that contains numerous events, figures, locations, and chronological relationships. These characteristics mean that summarisation methods relying solely on word frequency or lexical similarity may overlook historically significant sentences. This study proposes Hybrid TextRank for the extractive summarisation of Indonesian-language Sirah Nabawiyah texts. The proposed method combines TF-IDF and BM25 similarities as graph edge weights and incorporates sentence position and historical entity density into the PageRank personalisation vector. The dataset comprises 312 subchapters from two parts of the Sirah Nabawiyah book. Quantitative evaluation was conducted on 157 subchapters from Part 1, which include human reference summaries, while 155 subchapters from Part 2 were used as a supporting corpus. Each method selected three sentences and was evaluated using ROUGE-1, ROUGE-2, ROUGE-L, and intra-summary similarity. Hybrid TextRank achieved F1 scores of 0.3353 for ROUGE-1, 0.1178 for ROUGE-2, and 0.2264 for ROUGE-L. Compared with standard TF-IDF-based TextRank, these results represent improvements of 6.28%, 12.73%, and 8.59%, respectively. Hybrid TextRank achieved the highest ROUGE-2 score, although Max-TFIDF slightly outperformed it on ROUGE-1 and ROUGE-L. These findings indicate that personalisation based on narrative structure and historical entities helps preserve important word pairs, while sentence selection still requires further optimisation to improve unigram coverage and overall sentence ordering.

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