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Journal of Information System Exploration and Research
Published by shm publisher
ISSN : 29641160     EISSN : 29636361     DOI : https://doi.org/10.52465/joiser.xxxx
Core Subject :
Journal of Information System Exploration and Research is a journal that publishes and disseminates scientific research papers on information systems to a wide audience particularly within the information system society
Arjuna Subject : -
Articles 25 Documents
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Risk-Aware Multi-Capacity Routing for Shift-Feasible Cosmetic Distribution Ayub Prasetyo; Firda Amalia; Sarifuddin Madenda; Ernastuti; Murni
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.62

Abstract

Urban cosmetic distribution requires routing decisions that satisfy delivery time windows, vehicle capacity, and working-hour constraints. Conventional routing models often rely on deterministic travel-time assumptions and single-capacity limits, which mayobscure lateness risk and volume overload. This study proposes a risk-aware multi-capacity routing framework for shift-feasible cosmetic distribution. Customer and depot coordinates were used to estimate distances with the Haversine formula, adjusted by acircuity factor and converted into travel time. The model applies split-delivery preprocessing for oversized records, enforces weight and volume capacities, and distinguishes delivery trips from vehicle requirements within a 09:00–17:00 shift.Three routing models were evaluated: deterministic single-capacity, distance-based multi-capacity, and risk-aware multi-capacity routing. Results show that oversized records were split, eliminating unserved and infeasible customers. The single-capacity model produced multiple over-volume cases, while the multi-capacity model removed capacity violations without significantly affecting lateness or resource needs. The risk-aware model reduced lateness and late customers but increased travel time, delivery trips, and vehicle requirements. Sensitivity analysis confirmed stable conclusions across key assumptions. These findings highlight improved delivery reliability alongside trade-offs between service performance and resource usage.
Integration of the K-Fold Cross-Validation Algorithm for the Classification of Pronator Teres EMG Signals Anjar Setiawan; Fauzan Dika; Hayadi Hamuda
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.70

Abstract

Individuals with disabilities often require mobility assistance, and conventional joystick-controlled wheelchairs are ineffective for users with upper-limb impairments. Electromyography (EMG) signals offer a promising alternative for wheelchair navigation by translating muscle activity into control commands. This study aims to classify four left-hand movements using EMG signals and an Artificial Neural Network (ANN). Root Mean Square (RMS) and Mean Frequency (MF) features were extracted and used as ANN inputs. The results show that each movement produces distinct RMS and MF patterns; however, these features alone are insufficient for optimal classification. The best ANN model, consisting of four hidden layers and 320 neurons, achieved 77.5% accuracy, 77.9% precision, and 77.5% sensitivity. These findings demonstrate the potential of ANN for EMG-based hand movement recognition, while suggesting that additional features and larger datasets may further improve classification performance.
Development of a Data Analytics-Based Decision Support System for Optimizing Public Transportation Services Maria Atik Sunarti Ekowati Maria; Nurul Hidayat
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.73

Abstract

Public transport is essential for sustainable urban mobility, but challenges such as limited capacity, operational inefficiency, and service performance remain. This study develops a Decision Support System (DSS) that integrates operational data, passenger preferences, and performance indicators to provide recommendations for decision-making. The study uses passenger surveys, system performance analysis, and data analysis and prediction techniques. The DSS incorporates information visualization, performance monitoring, and scenario analysis to help decision-makers anticipate potential outcomes and make informed choices. The results demonstrate improvements in service performance, with passenger satisfaction increasing from 76% to 95%, while prediction accuracy exceeded 90%. These findings indicate that the DSS can support more effective decision-making and resource allocation while improving transparency and accountability in public transport services. The system also aligns with efforts to promote technology-driven urban development. Overall, integrating data and decision-support technologies can improve public transport operations, support better resource utilization, and contribute to more sustainable and inclusive urban mobility.
SentinelKEM: Securing AI Model Distribution Using Hybrid Cryptography Singgih Naufal; Eko Hari Rachmawanto; Mohamed Doheir
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.76

Abstract

Deep neural network (DNN) models are valuable intellectual property that can be copied or redistributed without authorization during distribution. Existing protection methods mainly rely on watermarking, which verifies ownership but does not secure model files during transfer. This study proposes SentinelKEM, a hybrid cryptographic framework that combines AES-256-GCM for authenticated encryption, Kyber/ML-KEM-768 with HKDF-derived key encryption keys for post-quantum key protection, Ed25519 digital signatures for ownership verification, and scrypt for passphrase-based private-key security. Encrypted models and cryptographic metadata are packaged into a secure ZIP archive, while private keys are distributed separately. During decryption, SHA-256 integrity verification and Ed25519 authentication are performed before model restoration. SentinelKEM was implemented as a Python Streamlit application and evaluated on various AI model formats, including .h5, .pt, .pth, .pkl, and .joblib. Experimental results showed successful encryption and decryption, reliable ownership authentication, effective tamper detection, and a constant cryptographic overhead of approximately 4.5 KB regardless of model size. Unlike watermarking, SentinelKEM protects AI models before recipient access through post-quantum key encapsulation and authenticated encryption, providing practical and robust security for AI model distribution.
Explainable Ensemble Learning for Identifying Digital Citizenship Drivers Among Generative AI Users: A SHAP-Based Behavioral Analysis Muchammad Thoha; Andy Prasetyo Utomo; Zainur Romadhon
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.79

Abstract

The increasing integration of Generative Artificial Intelligence (Generative AI) into higher education has transformed how university students learn and interact within digital environments. However, the psychological and behavioral factors influencing responsible Digital Citizenship behavior among Generative AI users remain insufficiently understood. Existing studies have largely relied on descriptive, regression-based, or structural modeling approaches that may overlook complex nonlinear behavioral relationships. This study proposes an explainable ensemble learning framework to identify the dominant drivers of Digital Citizenship among university students using Generative AI technologies. A national-scale dataset consisting of 2,008 students from 41 universities across 22 provinces in Indonesia was analyzed using Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). Model performance was evaluated using R², MAE, RMSE, and cross-validation procedures. To improve transparency and interpretability, SHAP (Shapley Additive Explanations) was employed to examine feature importance and nonlinear behavioral effects. The results showed that XGBoost achieved the highest predictive performance. SHAP analysis consistently identified technology anxiety, self-efficacy, and attitude toward behavior as the most influential predictors of Digital Citizenship. The findings further revealed nonlinear effects of technology anxiety on responsible AI participation. This study contributes to explainable educational analytics by providing interpretable insights into responsible AI behavior and supporting evidence-based AI governance and digital education policy in higher education.
Machine Learning-Based Obesity Prediction and Feature Importance Analysis Using Random Forest Anjelina Dwi Puspita; Nelly Astuti Hasibuan; Kevin Boy Sinaga; Krisna Apta Jaya Zendrato; Muhammad Alief Rahman Susanto
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.82

Abstract

Obesity prevalence continues to rise both globally and in Indonesia, yet most predictive studies rely on datasets from Western populations or clinical settings, leaving a gap in machine-learning-based evidence from Southeast Asian university communities. This study applies Random Forest to survey data from 300 respondents in Medan, Indonesia, to identify dominant determinants of Body Mass Index (BMI) category and to evaluate predictive reliability under class imbalance and limited sample size. After cleaning, 297 valid records remained; twelve behavioral and demographic features were used as model inputs. Beyond a conventional 80:20 train-test split, this study applied SMOTE to address minority-class imbalance and 10-fold stratified cross-validation to assess stability. The 80:20 split yielded 83.33% accuracy (weighted F1 = 0.82); 10-fold cross-validation produced a more conservative mean accuracy of 77.05% (SD = 6.37%), confirming that single-split evaluation overstated performance. SMOTE improved minority-class (Obesity) recall from 0.67 to 0.78 without reducing overall accuracy. Age emerged as the dominant predictor (importance = 0.25), followed by meal frequency, physical activity, and vegetable consumption frequency. These findings support Random Forest as a viable obesity-risk screening tool in resource-limited, questionnaire-based settings, while highlighting the need for imbalance-aware evaluation on modest, real-world health-survey samples. 
Comparison of Shallow and Deep Learning for Indonesian Clickbait Headline Classification Muhammad Noer Attalah Dzahkwan; Majid Rahardi
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.85

Abstract

Clickbait is an increasingly prevalent phenomenon in Indonesian online news media, where headlines are crafted to attract clicks without accurately reflecting article content. This study proposes and compares eight classification models: three shallow learning algorithms — Naive Bayes, Support Vector Machine (SVM), and Logistic Regression — and five transformer-based deep learning models: IndoBERT-p1, IndoBERT-p2, XLM-RoBERTa, mBERT, and DistilBERT. The dataset used is CLICK-ID, consisting of 15,000 labeled headlines from 12 Indonesian news portals, expanded to 25,138 samples via semi-supervised pseudo labeling with a confidence threshold of 0.85. All deep learning models were trained with Focal Loss (α=0.25, γ=2.0) to address class imbalance and Automatic Mixed Precision (AMP) for GPU efficiency. Results show that IndoBERT-p1, IndoBERT-p2, XLM-RoBERTa, mBERT, and DistilBERT achieve comparable performance, with macro F1-scores ranging from 86.57% to 88.54%. Among shallow learning models, SVM performs best with 83.51% F1-score. An average ensemble of all five transformer models achieves the best overall performance at 90.14% accuracy and 89.00% F1-score, outperforming every individual model. This study contributes to Indonesian clickbait detection research by demonstrating that ensemble aggregation of diverse transformer architectures yields more reliable performance than reliance on any single model.
Comparative Analysis of Data Normalization Effects on RFMBased Customer Segmentation Using K-Means and DBSCAN Nabilah Zahra; Tyas Arum; Zerafica Patriawan; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim; Yusuf Enril Fathurrohman
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.86

Abstract

Customer segmentation is widely used to analyze customer transaction patterns and support effective business strategies. However, previous studies have reported inconsistent findings regarding the impact of data normalization on clustering quality across different datasets and algorithms. This study investigates the effect of data normalization on RFM-based customer segmentation using K-Means and DBSCAN. Two transaction datasets, Online Retail II and TransJakarta, were analyzed under three preprocessing scenarios: no normalization, Min-Max normalization, and Z-Score normalization. Clustering performance was evaluated using the Silhouette Score and Davies–Bouldin Index (DBI). For the Online Retail II dataset, K-Means achieved the best performance without normalization (Silhouette Score = 0.9845), while DBSCAN produced valid clusters only after Z-Score normalization. For the TransJakarta dataset, both algorithms performed best without normalization, whereas DBSCAN identified up to 20 clusters and noise points. These findings highlight that the effectiveness of normalization depends on dataset characteristics and the clustering algorithm used.
Investigating Mobile Legends Top Up Fraud on Instagram Using the National Institute of Justice Method Eko Aribowo; Auliana
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.88

Abstract

The rapid growth of digital technology has increased the occurrence of cybercrime, including fraud through social media platforms. This study investigates a Mobile Legends top-up fraud case carried out via Instagram using the National Institute of Justice (NIJ) digital forensic framework. The NIJ method consists of five stages: identification, collection, examination, analysis, and reporting. The primary data source was an Instagram archive in JSON format obtained through the Download Your Information feature and examined using Oxygen Forensic Detective. Evidence integrity was verified using FTK Imager through hash verification, while Metadata2Go was utilized for metadata analysis. The investigation successfully recovered 51 digital artifacts, comprising 1 Instagram account profile, 3 promotional posts, 2 testimonial highlights, 19 Direct Message (DM) communication threads, and 15 digital transfer receipts, identifying 15 distinct fraud victims. Hash verification yielded 100% match accuracy (MD5 and SHA-1), validating that the evidence remained unaltered. The findings revealed a fraud pattern characterized by payment acceptance without service fulfillment, followed by communication termination. Furthermore, metadata analysis and hash verification confirmed that the digital evidence was authentic, unaltered, and forensically reliable. These results demonstrate that Instagram archive data can serve as valid digital evidence in social media fraud investigations.
Analyzing the Impact of Class Imbalance Handling on Explainable Fake Job Posting Detection Using XGBoost and SHAP Budi Prasetiyo; Hadiyanto; Budi Warsito
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.91

Abstract

Fake job postings on online recruitment platforms can cause job seekers to suffer financial losses and identity theft. The detection task for such fraudulent postings has a core challenge: datasets suffer from severe class imbalance, where fake postings account for only a tiny fraction of the total data. Most previous studies only focus on models’ classification performance, and rarely discuss the impact of class imbalance processing on feature attribution and model interpretability. This study adopts the XGBoost and SHAP methods to conduct detection research. The framework built for this study first completes text preprocessing, then extracts hybrid features by combining TF-IDF and metadata attributes, and evaluates four class imbalance processing strategies in total: Baseline, SMOTE, Borderline-SMOTE, and ADASYN. Experimental results show that compared with the baseline model, oversampling methods improve the detection performance for the minority class. ADASYN delivers the best performance, with corresponding scores of 79.23% for Recall, 81.91% for F1-score, and 88.70% for G-Mean. SHAP analysis finds that the model’s feature attribution pattern changes, with its attention shifting to fraud-related features, while hascompanylogo consistently remains the feature with the highest influence. This study confirms that class imbalance processing affects both classification performance and model interpretability

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