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JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 870 Documents
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Implementasi Algoritma Decision Tree dan Ensemble Learning Berdasarkan Spesifikasiperangkat Keras Untuk Klasifikasi Harga Laptop Lian Galang Prayoga; Rachmad Sanuri
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10250

Abstract

The complexity of hardware specifications in laptops often makes it difficult for consumers to estimate price suitability in the digital market. This study aims to implement a laptop price classification system by comparing the performance of the baseline Decision Tree algorithm and the Ensemble Learning method (Random Forest) utilizing the Knowledge Discovery in Databases (KDD) approach. Contrary to the initial hypothesis that the ensemble model would provide significant performance improvements, the evaluation results revealed a paradoxical finding. Both classification models produced identically exact performance with an accuracy rate of 73.39% and an F1-Score of 73.41%. Technical analysis indicates that this anomaly is caused by the narrow dimension of deterministic features in the dataset, where RAM capacity and CPU architecture attributes dictate the decision boundaries absolutely, rendering the addition of hundreds of decision trees in the Random Forest computationally redundant. The results of this study contribute theoretical insights regarding the efficiency limitations of ensemble algorithms on low-dimensional datasets, while proving that a single Decision Tree is optimal and computationally efficient enough to be implemented as an inference engine in laptop price recommendation systems.
Clustering Pola Penggunaan Energi pada Smart Home Menggunakan DBSCAN Berbasis Data Time Series Sensor Arif Susilo; Asep Arwan Sulaeman; Nur Suci Rahayu
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10519

Abstract

The increasing adoption of Internet of Things (IoT) devices in smart homes has generated continuous and complex energy consumption data, requiring effective clustering techniques to identify household energy usage patterns. This study aims to cluster household energy consumption patterns using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm based on sensor time-series data. The study utilized the Smart Home Energy Consumption Dataset, consisting of approximately 90,000 observations with six main variables: Energy Consumption, Peak Hours Usage, Household Size, Average Temperature, Has AC, and Weekday. The research workflow included feature selection, data cleaning, data normalization using StandardScaler, parameter determination through the K-Distance Graph, DBSCAN clustering, and clustering evaluation using the Silhouette Score. Experimental results indicated that the optimal parameters were ε = 0.38 and MinPts = 5, producing 89 clusters, 541 noise observations (0.60%), and a Silhouette Score of 0.0690. Cluster characteristic analysis revealed that energy consumption, peak-hour energy usage, air conditioner ownership, household size, and ambient temperature were the primary factors distinguishing household energy usage patterns. The findings demonstrate that DBSCAN effectively identifies household energy consumption patterns while detecting outliers without requiring the number of clusters to be predefined, making it a promising approach for supporting intelligent energy management systems in smart home environments.
Segmentasi Perilaku Pemustaka Menggunakan DBSCAN untuk Optimalisasi Layanan Perpustakaan Digital Candra Naya; Ermanto Ermanto; Unggul Prima Dhani
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10533

Abstract

The rapid growth of digital libraries has generated increasingly large borrowing transaction data, creating the need for analytical techniques to understand user behavior patterns and support data-driven library management. This study aims to cluster library users based on their borrowing behavior using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify user segments according to their borrowing activity. The study employed the Book-Crossing Dataset, consisting of 278,858 rating transactions, 271,379 user records, and 271,360 book records. The research methodology included Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature standardization using StandardScaler, ε parameter selection through the K-Distance Graph, DBSCAN clustering, cluster evaluation using the Silhouette Score, and visualization using Principal Component Analysis (PCA). The experimental results indicate that ε = 0.5 and MinPts = 5 produced three clusters, with 244 users identified as noise. The obtained Silhouette Score of 0.5996 demonstrates a reasonably good clustering quality. Furthermore, the resulting clusters successfully represent users with low, moderate, and very high borrowing activities, providing valuable insights for developing personalized library services, improving book recommendation systems, supporting collection development, and facilitating data-driven decision-making to enhance the overall quality of digital library services.
Prediksi Indeks Pembangunan Manusia Menggunakan Support Vector Regression dengan Optimasi Particle Swarm Optimization Arif Siswandi; Arif Susilo; Rizki Muhammad Mukti
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10535

Abstract

The Human Development Index (HDI) is a key indicator for measuring regional development performance and serves as an essential reference for evidence-based policy formulation. Accurate HDI prediction is crucial to support effective development planning and decision-making. This study aims to develop an HDI prediction model using Support Vector Regression (SVR) optimized with Particle Swarm Optimization (PSO) to improve prediction accuracy. The dataset was obtained from Statistics Indonesia (BPS), covering 38 provinces during the 2015–2025 period with a total of 421 observations. The research process consisted of data preprocessing, Min-Max Scaling normalization, an 80:20 train-test split, SVR model development, parameter optimization using PSO, and performance evaluation based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results show that the baseline SVR model achieved an MAE of 0.069187, RMSE of 0.093548, and R² of 0.492454. After PSO optimization, the model performance improved, achieving an MAE of 0.060864, RMSE of 0.084224, and R² of 0.588583. These findings demonstrate that PSO effectively enhances the predictive performance of SVR by identifying optimal parameter combinations. The main contribution of this study is the development and validation of an optimized SVR-PSO framework for HDI prediction using multi-provincial socioeconomic data in Indonesia, providing a more accurate machine learning-based approach to support data-driven human development planning and policy formulation.
Sistem Rekomendasi Produk Sparepart Motor Menggunakan Metode Knowledge Based Recommendation Vony Nur Alizah; Anang Aris Widodo; Nanda Martyan Anggadimas
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10601

Abstract

The rapid growth of e-commerce has led to an increasing number of motorcycle spare part products available across various online marketplaces. This condition often makes it difficult for users to select products that match their vehicle type, needs, and budget. This study aims to develop a motorcycle spare parts recommendation system using the Knowledge-Based Recommendation method to assist users in obtaining suitable product recommendations based on their stated preferences. The system was developed using the Python programming language on the Google Colab platform and utilized a dataset consisting of 100 motorcycle spare part products with attributes including product name, motorcycle compatibility, product category, price, and rating. The recommendation process was carried out by matching user preferences with product attributes, after which each product was scored using a weighted calculation with compatibility weighted at 45%, product category at 30%, price at 15%, and rating at 10%. The system was evaluated using 10 testing scenarios by assessing the Top-1 recommendation. The evaluation results showed that all testing scenarios successfully generated recommendations that matched user requirements, achieving an accuracy of 100%. The findings indicate that the implementation of the Knowledge-Based Recommendation method, combined with a weighted attribute mechanism based on motorcycle compatibility, product category, price, and rating, is capable of producing recommendations that align with user preferences without requiring users' purchase history or rating history. Furthermore, the proposed method was found to be effective for motorcycle spare parts recommendation systems and has the potential to assist users in selecting appropriate products more quickly and accurately.
Implementasi MobileNetV2 Pada Aplikasi Forensik Android Untuk Deteksi Citra AI-generated dengan Ketahanan Terhadap Transformasi Citra Aryanahta Putra; Resmi Darni; Dony Novaliendry; Vikri Aulia
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10666

Abstract

The development of Generative Artificial Intelligence has produced realistic synthetic images that are difficult to distinguish from authentic images through visual inspection. This study aims to implement an Android-based mobile digital forensics application for detecting AI-generate d images using MobileNetV2 converted to TensorFlow Lite for on-device inference. A quantitative-experimental approach used 2,000 images, consisting of 1,000 authentic and 1,000 AI-generate d images. The authentic class comprised 500 smartphone photographs and 500 GenImage samples, while the AI-generate d class included Stable Diffusion v1.4, Stable Diffusion v1.5, Wukong, and Midjourney images. The dataset was divided into 1,000 training, 500 validation, and 500 testing images. Training data were used for model development, validation data for model selection and threshold determination, and testing data only for final evaluation. Robustness testing used copies of 500 testing images without retraining. Under normal conditions with a threshold of 0.680, the model achieved 85.40% accuracy, 85.53% precision, 85.40% recall, and an 85.39% F1-score. JPEG q=65 compression produced 86.00% accuracy. The largest degradation occurred with 112 × 112 resizing combined with JPEG q=65, resulting in 62.40% accuracy, a decrease of 23.00 percentage points, and a 56.66% F1-score. The application performed local inference, displayed prediction labels and confidence scores, and stored detection history. This study contributes an on-device detection application and robustness evaluation using a consistent test subset, positioning the system as an initial detection aid rather than a final forensic verification tool.
Prediksi Cuaca Harian Menggunakan Algoritma Long Short-Term Memory (LSTM) Berdasarkan Data Meteorologi Tahun 2025 Ika Novianti; Fathir Fathir; Irma Eryanti Putri
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10689

Abstract

Daily weather prediction is crucial in supporting decision-making in the agriculture, transportation, disaster mitigation, and community activities influenced by atmospheric conditions. The weather in Bima City is dynamic, requiring a predictive model capable of learning sequential meteorological data patterns. This study aims to build a daily average temperature prediction model using the Long Short-Term Memory (LSTM) algorithm based on 2025 meteorological data. The contribution of this study is to develop an LSTM-based daily average temperature prediction model using multivariate meteorological data from Bima City combined with pre-processing steps in the form of missing value handling using moving averages, MinMaxScaler normalization, and time series data formation using a 30-day sliding window. This study also provides an initial evaluation of the application of LSTM to local meteorological data from Bima City, which has been studied only limitedly, as a basis for developing a deep learning-based weather prediction system. The variables used include minimum temperature (TN), maximum temperature (TX), average temperature (TAVG), average air humidity (RH_AVG), rainfall (RR), sunshine duration (SS), and average wind speed (FF_AVG). The test results show that the model produces a Root Mean Square Error (RMSE) value of 0.7242 and is able to follow the daily temperature change pattern in the actual data. The prediction results on the test data also show that most of the predicted values ​​have a relatively small difference compared to the actual values, so the model is able to describe the daily temperature change pattern quite well. Based on the predicted weather parameters, the model is able to provide information about daily weather conditions, namely sunny, cloudy, and rainy, according to the values ​​of rainfall, air humidity, and sunshine duration produced. This predicted information is expected to help the community as an initial picture of future weather conditions so that it can support the planning of various daily activities. However, the results of this study are still limited to one prediction method and have not been compared with other methods. Therefore, further research can conduct comparisons with other algorithms to improve the accuracy of weather predictions in Bima City.
Prediksi Penyakit Jantung Berbasis Random Forest dengan GridSearchCV dan SHAP Menggunakan Dataset Publik UCI Cleveland Anggi Setiyawan; Sulistiyasni Sulistiyasni; Muhammad Akbar Setiawan
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10703

Abstract

Cardiovascular disease remains the leading cause of death globally, with 19.2 million fatalities recorded in 2023, making the development of automated data-driven prediction systems an urgent necessity. This study develops a heart disease prediction model based on Random Forest optimized using Grid Search Cross-Validation (GridSearchCV) and supplemented with SHapley Additive exPlanations (SHAP) analysis for clinical interpretability on the UCI Cleveland Heart Disease dataset (297 samples, 13 clinical features). Three methodological contributions are implemented: (1) a reproducible preprocessing pipeline with post-split StandardScaler to prevent data leakage, (2) deterministic and exhaustive hyperparameter search using GridSearchCV with 216 combinations and Stratified 10-Fold Cross Validation, and (3) SHAP analysis at the global level based on training data and at the local level based on test data to produce clinically interpretable predictions. The optimal hyperparameter configuration obtained is n_estimators = 100, max_depth = None, min_samples_leaf = 4, min_samples_split = 2, and max_features = 'sqrt'. The Tuned RF model achieves an AUC-ROC of 0.9453 and CV AUC of 0.9010, outperforming the RF Baseline (AUC-ROC 0.9414; CV AUC 0.8804) on key discrimination metrics with greater stability. SHAP analysis identifies cp (mean |SHAP| = 0.1031), thal (0.0976), and ca (0.0805) as the three most influential clinical features, consistent with established cardiological diagnostic indicators. The integration of GridSearchCV and SHAP produces a model that is not only accurate but also transparent in supporting medical decision-making.
Implementasi One-Dimensional Convolutional Neural Network untuk Klasifikasi Kualitas Air Depot Reverse Osmosis secara Real-Time berbasis Internet of Things Filemon V Makaenas; Wayan G Y Sukarya; Toban T Pairunan; Oldi M Lambonan
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10712

Abstract

Water quality monitoring in refill reverse osmosis (RO)-based drinking water depots in Indonesia requires continuous surveillance because membrane performance degradation due to fouling can occur gradually without visual indication, potentially endangering consumer health. This study aims to implement and evaluate a One-Dimensional Convolutional Neural Network (1D-CNN) architecture for real-time water quality classification on an IoT-equipped RO filtration system at Politeknik Negeri Manado. The system simultaneously reads eight sensor parameters at two measurement points (inlet and outlet), comprising turbidity, pH, Total Dissolved Solids (TDS), and temperature. A synthetic dataset of 5,000 samples representing operational condition variations is classified into three classes (Normal, Warning, Danger) based on thresholds from Indonesian Ministry of Health Regulation No. 2/2023. Preprocessing includes Min-Max Scaling and a sliding window technique (size 10 timesteps) to construct three-dimensional input tensors. A compact 1D-CNN model with only ~3,000 parameters is trained using the Adam optimizer with early stopping to prevent overfitting. The main contributions include a Dual-Protection mechanism integrating deterministic regulation-based rules with CNN inference, and an Explainability Engine generating textual diagnostics in Indonesian for field operators. Evaluation results demonstrate 99.80% accuracy with only 1 misclassifications from 500 actual test samples, proving the proposed approach effective for automated, accurate, and interpretable real-time water quality monitoring in RO depots.
Rancang Bangun Sistem Informasi Pengaduan Fasilitas Publik pada Dinas PUPR Kabupaten Bima Menggunakan Prototyping dan Black Box Testing Nur Anisa; Syarifuddin Syarifuddin; Hilyatul Mustafidah
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10714

Abstract

The management of complaints regarding public facility damage at the Public Works and Spatial Planning Agency (Dinas PUPR) of Bima Regency currently faces challenges, specifically regarding the lack of optimal integration across the processes of report submission, verification, disposition, follow-up, and monitoring. This situation results in an unstructured complaint management process and hinders the monitoring of report handling progress. This research aims to design and develop a web-based Public Facility Complaint Information System capable of supporting structured complaint management aligned with the workflow of the Bima Regency PUPR Agency. The system development method employed is Prototyping, which allows for the evaluation and refinement of the prototype based on user feedback. System design utilizes the Unified Modeling Language (UML), encompassing Use Case Diagrams, Activity Diagrams, Entity Relationship Diagrams (ERD), and user interface design. The system is implemented using the Laravel framework and a MySQL database. System testing was conducted using the Black Box Testing method across 12 test scenarios covering core system functions—ranging from user management, complaint submission, verification, disposition, follow-up, and monitoring to report generation. Test results indicate that all 12 scenarios were executed successfully, yielding the expected outcomes with a 100% success rate. The contribution of this research is the creation of a complaint information system that integrates report submission via Village Operators, verification and disposition by the General Admin, follow-up by Division Admins, and monitoring by the Head of the PUPR Agency. Consequently, the designed and developed system facilitates a more structured, well-documented, effective, and easily monitored process for managing public facility complaints.

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