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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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Performance Comparison of Naive Bayes and Support Vector Machine Algorithms in Sentiment Analysis of TIX ID Application Reviews Using VADER Automatic Labeling Zenia Kumala Rizka; Jumanto Unjung; Zaki Zaini
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.3

Abstract

This study compares the sentiment classification performance of Naive Bayes and Support Vector Machine (SVM). It uses 28247 user reviews for the Google Play Store app TIX ID collected from Kaggle. The reviews were first translated into English, then their sentiment was labeled using VADER. After completing text preprocessing, feature extraction via TF-IDF combined with 1-gram and 2-gram features, and class balancing through random oversampling, test results show that SVM achieved an accuracy of 93.45% and an F1-score of 93.78%, which outperforms Naive Bayes’ respective scores of 90.90% accuracy and 91.72% F1-score. Experiments in this study found that the Support Vector Machine (SVM) outperformed Naive Bayes across all three evaluation metrics: precision, recall, and F1-score. This verifies that the approach consisting of VADER annotation, TF-IDF feature extraction, and SVM can effectively conduct sentiment analysis on mobile application reviews, and meets the needs of the industry.
From Propagation to Detection: A Unified SEIR-Based Simulation and Deep Learning Framework for IoT Malware in Interactive Mobile Environments Dwi Ely Kurniawan; Ahmadi Irmansyah Lubis; Noper Ardi; Antoni Haikal
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.5

Abstract

The rapid growth of Internet of Thing (IoT) devices in highly connected mobile environments has increases the risk of malware propagation. Existing studies mainly focus on either malware propagation modeling or malware detection, leaving a gap between understanding malware spread and accurately identifying attacks. This study proposes SEIR-DLFNet, a unified framework integrating an extended Susceptible-Exposed-Infected-Recovered (SEIR) model with a hybrid Transformer BiLSTM network. The SEIR model captures device-to-device communication, mobility, partial immunity loss, and quarantine mechanisms to generate synthetic traffic that augments the CICIoT2023 and CIC IoT-DIAD 2024 datasets. Experimental  results show that SEIR-DLFNet achieves 99.31% accuracy, 99.28% F1-score, and 99.44% AUC-ROC across seven attack categories. SEIR-based synthetic data augmentation improves detection accuracy by 2.71 percentage points compared with using empirical data alone. Furthermore, zero-shot evaluation on a previously unseen polymorphic Mirai variant achieves an F1-score of 94.17%, outperforming the strongest baseline by 6.84 percentage points. These result demonstrate that integrating epidemic-based malware propagation modeling with deep learning enhances both malware detection performance and generalization to emerging IoT threats.
Evaluation of the Implementation of Digital Forensic Readiness at the Managerial Level (a Study of Policy, Competencies, Risks, and Incidents) Tri Rochmadi; Abdul Fadlil; Imam Riadi; Heni Inayatul Arifah; Dadang Heksaputra
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.6

Abstract

Indonesia’s rapid digital transformation has improved efficiency, accuracy, performance, and accessibility across sectors, but has increased the complexity of cyberattacks. A 2023 BSSN report recorded more than 603 million cyberattacks, highlighting the need for stronger cybersecurity resilience. Digital Forensic Readiness (DFR) is essential to ensure organizations identify, preserve, and manage digital evidence during incidents. This study evaluates DFR in universities using the COBIT 2019 framework. A convergent mixed-methods design involved 10 IT managers or department heads from private universities in Yogyakarta. Questionnaire responses were converted into capability indices from 0 to 5 using weighted frequencies, while interview data were analyzed thematically and integrated with quantitative findings. The results show most processes at capability level 3, although capability varied across institutions and components. Interviews revealed several reported capabilities were based on operational practices not formalized through written policies, standardized procedures, competencies, or evidence-preservation mechanisms. These findings emphasize interpreting capability scores alongside qualitative evidence and conducting context-specific improvements.
Temperature, Humidity, and Weather Prediction Using Random Forest and LSTM for Food Crop Cultivation Optimization in Tegal Sarwo Edi; Aji Supriyanto
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.v4i3.10

Abstract

The agricultural sector in the Tegal region faces uncertain climate fluctuations that directly impact food crop productivity. A crucial indicator for determining plant environmental comfort is the Temperature Humidity Index (THI). This research aims to develop a hybrid model capable of predicting and classifying future THI values to support precision decision-making for farmers. The methodology utilized historical climate data including Temperature humidity, rainfall, sunshine, and wind speed from the Tegal Maritime Meteorology Station spanning a 10-year period (2016-2025). A Long Short-Term Memory (LSTM) model was applied to forecast future THI values, while a Random Forest (RF) model was utilized to classify plant stress categories. Model performance was evaluated using Root Mean Square Error (RMSE), Accuracy, and F1-score. The results indicate that Tegal experiences comfortable (24 ≤ THI < 27), moderately comfortable (27 ≤ THI < 30), and uncomfortable (THI ≥ 30) conditions, particularly during dry and transitional seasons. The LSTM model achieved a 98.42% prediction accuracy, and the RF model reached a 99.59% classification accuracy. In conclusion, this highly accurate model can serve as an early warning system, providing farmers with actionable recommendations for optimal planting schedules and stress mitigation.
Clustering Analysis of Temperature Humidity Index (THI) andSupporting Weather for Food Crop Cultivation in Tegal Willy Yudha Perdana; Aji Supriyanto
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.v4i2.11

Abstract

The food crop agriculture sector faces serious challenges due to global climate change that disrupts the stability of conventional production systems. The Temperature Humidity Index (THI) is a crucial indicator for measuring environmental thermal comfort, which, combined with supporting weather parameters, can map the risk of crop failure. This study aims to analyze the clustering of THI and weather variables in the Tegal area using a Machine Learning approach. The dataset used is daily historical weather data for 10 years (2016–2025) from BMKG, including temperature (T), relative humidity (RH), solar radiation (SR), wind speed (WS), and rainfall (RF). The method includes preprocessing, normalization, THI calculation, and clustering using K-Means and DBSCAN. K-Means identified agro-climate vulnerability zones: Optimal, Alert, and Critical for food crop growth. DBSCAN effectively detected dominant cluster patterns and outliers of extreme weather anomalies. Internal evaluation shows K-Means performs better, with a Silhouette score of 0.4057 and Davies-Bouldin Index of 0.8391, compared to DBSCAN with 0.3957 and 2.7432. The results are expected to support farmers and policymakers in determining adaptive cropping patterns and mitigating climate change impacts in Tegal.
A Multi-Level Clustering Framework for Provincial EducationalFacility Equalization in Indonesia Antika Zahrotul Kamalia; Zaenur Rozikin; Hemdani Rahendra Herlianto; Hendra Arya Syaputra; Asep Arwan Sulaeman; Choiriyatun Nisa Latansa
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.13

Abstract

Equitable distribution of educational facilities is crucial for development planning, as regional disparities in facility availability can constrain access to education. This study identifies priority areas for school-facility equalization in Indonesia based on 2024 data covering 38 provinces and village/urban-ward-based facility availability by education level. Unlike single-stage clustering studies, this study combines macro-prioritization and micro-level need profiling to identify which provinces should be prioritized but also which education levels require attention. The analysis includes log1p transformation, standardization, optimal cluster selection using Elbow and Silhouette criteria, and the application of Level-1 and Level-2 K-Means clustering. The Level-1 results produce three priority groups: High Priority, Medium Priority, and Low Priority, with the optimal structure at K=3. The Level-2 analysis within the high-priority group is most stable at K2=2, distinguishing provinces dominated by primary and lower-secondary facility shares from those with a more balanced composition and relatively higher tertiary share. The Silhouette values indicate that the selected clusters provide reasonably separated groupings. The proposed framework provides a data-driven priority map and level-specific need profiles. The results can support staged infrastructure planning and differentiated interventions across provincial priority groups to strengthen educational facility equalization in Indonesia.
Differentiating Hantavirus Pulmonary Syndrome (HPS) andHemorrhagic Fever with Renal Syndrome (HFRS) Using a Stacking Ensemble Kartika Imam Santoso; Andri Triyono; Rahmawati; Yuwanti
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.v4i2.15

Abstract

Distinguishing Hantavirus Pulmonary Syndrome (HPS) from Hemorrhagic Fever with Renal Syndrome (HFRS) becomes difficult once organ-specific manifestations emerge, while delayed laboratory confirmation can hinder timely clinical decisions. A major limitation is the absence of a publicly available structured dataset describing HPS/HFRS symptom profiles. As a methodological first step, this study compiled a synthetic dataset reflecting documented clinical patterns from the literature to evaluate a stacking ensemble using XGBoost and LightGBM as base learners with Logistic Regression as the meta-learner. Using 8,000 synthetic records and 22 symptom features, preprocessing included binary encoding, SMOTE applied only within cross-validation folds, and 5-fold stratified cross-validation with grid-search hyperparameter tuning. The proposed model achieved 94.87% accuracy, 95.12% precision, 94.61% recall, 94.86% F1-score, 95.52% specificity, an MCC of 0.891, and an AUC-ROC of 0.9821, outperforming both individual base learners. SHAP analysis identified cough, tachycardia, and pulmonary edema as the strongest HPS indicators, whereas proteinuria, facial flushing, and conjunctival injection were the strongest HFRS indicators, consistent with reported organ-specific manifestations. Although limited to synthetic data, the proposed framework demonstrates methodological potential for HPS/HFRS differentiation and provides a foundation for future validation using prospective clinical datasets.
Ransomware Detection Model Using Deep Learning WithEnsemble Technique Approach Candra Aditya Wardana; Dwi Pebrianti
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.v4i2.19

Abstract

Ransomware attacks are increasingly prevalent, posing significant cybersecurity challenges. According to the National Cyber and Crypto Agency (BSSN), ransomware incidents surged from 69,853 in 2021 and 69,854 in 2022 to 1,011,209 in 2023. As ransomware variants continue to evolve, effective detection methods are crucial. This research proposes an ensemble deep learning model integrating Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance ransomware detection accuracy. In this framework, DNN captures complex patterns, CNN analyses static features, and RNN processes sequential data. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing, producing a balanced 50:50 distribution between ransomware and non-ransomware samples. The study uses the UGRansome dataset comprising 149,043 samples (71.44% positive, 28.56% negative) with 13 features. Experimental results demonstrate that the ensemble model significantly outperforms individual models, achieving an accuracy of 98.85%, precision of 98.51%, recall of 98.68%, and an F1-score of 98.59%. These findings highlight the effectiveness of ensemble learning in improving ransomware detection performance.
Approaching the Human Ceiling in Sleep Staging: A Controlled Comparison of Feature-Engineered and Deep Architectures Rand Nabeel Dawood
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.20

Abstract

Polysomnography (PSG) is the clinical gold standard for sleep assessment, yet manual epoch-by-epoch scoring is labor-intensive and subject to substantial inter-rater variability, which limits its use in large-scale, home-based, and routine clinical practice and makes reliable automated scoring an urgent need. This study suggests and compares an automated sleep-staging model based on two different supervised pipelines: a conventional feature-engineered Random Forest and a state-of-the-art end-to-end Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network. Based on the recordings of the Sleep-EDF Expanded database, we preprocessed the multichannel signals (EEG and EOG) into 30-second epochs, which were classified into five stages (Wake, N1, N2, N3 and REM). Our findings show that the feature-engineered baseline is very competitive with 95.49% accuracy, but the CNN-LSTM model performs better (96.44% accuracy) and has a much higher capability of classifying the transitional N1 stage which is harder. Because these results are obtained from a single overnight recording with a within-subject train/test split, they are best interpreted as a controlled proof-of-concept upper bound rather than as evidence of clinical-grade or human-level performance; cross-subject (leave-one-subject-out) validation on multiple recordings is required before any such claim can be made.
Personality Recognition Based on Palmistry Using DeepLearning YOLOv5 and YOLO-NAS Dwi Rusjayanthi; Darma Putra; Made Sudarma; Oka Sudana; I Made Sunia Raharja
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.27

Abstract

As an intangible cultural heritage and traditional belief system, palmistry has been conventionally utilized as a medium to understand human personality. However, its interpretation remains subjective and manual, while previous digital studies have been restricted to single-object recognition. This study aims to develop an automated multi-object recognition system for palmistry features, which include palmar lines, mounts, fingers, and hand types, by employing YOLOv5 (anchor-based) and YOLO-NAS (anchor-free) architectures under the constraint of a small-scale dataset. The research phases encompass data selection integrated with data augmentation, bounding box annotation, data splitting, as well as model training and testing evaluated using Mean Average Precision (mAP), precision, and recall metrics. Experimental results demonstrate that YOLOv5 outperforms YOLO-NAS under limited data conditions, achieving a precision of 0.800 and an mAP of 0.846, compared to YOLO-NAS which yields a precision of 0.104 and an mAP of 0.603. Conversely, YOLO-NAS records a higher recall of 0.894. The imbalance between the recall and precision values in YOLO-NAS is primarily influenced by the limited training samples and the implementation of int8 quantization techniques. This study contributes by establishing the efficiency boundaries of deep learning architectures for the digitalization of cultural heritage based on limited datasets.

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