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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Household Clustering in West Java Based on Stunting Risk Factors Using K-Modes and K-Prototypes Algorithms Yusran, Muhammad; Nuradilla, Siti; Putri, Mega Ramatika; Fitrianto, Anwar; Yudhianto, Rachmat Bintang
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11508

Abstract

Stunting remains one of Indonesia’s most persistent public health challenges, with West Java contributing the highest number of cases due to its large population and regional disparities in household welfare. Identifying household groups vulnerable to stunting is essential for designing targeted interventions that integrate nutrition, sanitation, and socio-economic development. This study introduces a data-driven clustering framework using the K-Modes and K-Prototypes algorithms to classify 22,161 households in West Java based on 26 indicators from the March 2024 National Socioeconomic Survey (SUSENAS), encompassing food security, sanitation, drinking water access, economic conditions, social assistance, and demographics. The K-Modes algorithm was applied to categorical data, while K-Prototypes integrated numerical and categorical variables, with parameter optimization performed using a grid search and the Elbow method. Clustering performance was evaluated through the Silhouette Score, Calinski–Harabasz Index, and Davies–Bouldin Index, followed by a bootstrapped stability analysis employing the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Results show that K-Prototypes outperformed K-Modes, yielding a higher Silhouette Score (0.6681 compared to 0.2922), higher CH Index (13,890.6 compared to 3,976.1), and lower DBI (0.4607 compared to 1.5274), indicating superior compactness and separation. Stability testing confirmed strong robustness, with mean ARI = 0.959 and mean NMI = 0.932 across 50 bootstrap replications. The optimal five-cluster structure identified distinct socioeconomic groups, with the highest stunting risk found among households with low income, limited housing space, inadequate sanitation, and more children under five. The findings highlight the effectiveness of K-Prototypes in modeling mixed-type data and support the design of evidence-based, regionally adaptive stunting reduction strategies aligned with Presidential Regulation No. 72/2021 on the Acceleration of Stunting Reduction.
Transfer Learning Analysis on Tuberculosis Classification Using MobileNetV2 Architecture Based on Chest X-Ray Images Latupono, Ali Samsul; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11510

Abstract

Tuberculosis(TBC) remains a major global health issue, with millions of new cases reported annually. Early and accurate diagnosis is essential, but manual interpretation of chest X-ray(CXR) images is limited by subjectivity and resource constrains. This study applies the MobileNetV2 architecture using transfer learning to classify tuberculosis from CXR images. The publicly available Tuberculosis Chest X-ray dataset containing 4200 images was divided into training (70%), validation (15%), and testing (15%). The pretrained MobileNetV2 model on ImageNet was used as the base network, with additional classification layers and training through the Adam optimizer and early stopping. The model achieved a validation accuracy above 99.84% after the second epoch maintained stable performance. Once the test set, model reached 99.84% accuracy, with precision 99.53% and recall 99.90% for the tuberculosis class. The result demonstrate that the transfer learning with MobileNetV2 provides a fast, efficient, and highly accurate method for tuberculosis detection. This model show potential for integration into Computer-Aided Diagnosis (CAD) system in low resource clinical settings.
Data Streaming Pipeline Model Using DBSTREAM-Based Online Machine Learning for E-Commerce User Segmentation Musababa, Muhammad Adin; Fachrie, Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11522

Abstract

The rapid development of information technology has driven major transformations in the digital business sector, particularly e-commerce. Consumers who shop at e-commerce sites generally have different characteristics, behaviors, and needs. Analyzing the behavior of each consumer is difficult to do manually, requiring an automation system that can help identify consumer behavior patterns adaptively. However, most customer segmentation approaches still rely on batch learning methods based on static data, making them unable to quickly adapt to changes in user behavior. This study aims to design a streaming data pipeline based on Online Machine Learning (OML) integrated with the Density-Based Clustering for Data Streams (DBSTREAM) algorithm to produce adaptive e-commerce user segmentation. The system was developed using Python with RabbitMQ as a real-time data stream simulator, MongoDB for storing results, and Streamlit as a visualization interface. The clustering process was performed incrementally using DBSTREAM, then stabilized through Hierarchical Agglomerative Clustering (HAC) to avoid over-segmentation. Evaluation using the Silhouette Coefficient and Davies-Bouldin Index (DBI) shows that the optimal model for the cluster threshold is in the range of 0.6 to 0.8 and for the fading factor is 0.0005 or even smaller, such as 0.0003. The evaluation results obtained a Silhouette value of -0.1125 and a DBI of 0.2796. These results prove that DBSTREAM-based OML integration is capable of forming consumer behavior segmentation efficiently and adaptively to continuous and real-time changes in streaming data.
Vision Transformer for Pneumonia Classification with Grad-CAM Explainability Darmawan, Immanuel Julius; Supriyanto, Catur
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11532

Abstract

Pneumonia is still one of the main causes of death around the world, especially in kids and older people. To lower the death rate, early and accurate diagnosis is very important. Chest X-ray (CXR) imaging is widely used for this purpose, but manual reading of CXR images can be time-consuming and may lead to differences in interpretation between observers. To address this problem, this study presents a pneumonia classification model based on the Vision Transformer (ViT) architecture combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to make the model’s decisions more interpretable. The model was trained on a publicly available CXR dataset with 5,863 images that were split into Normal and Pneumonia classes, using a 70:15:15 split for training, validation, and testing. The ViT model achieves an accuracy of 96.41% on the test set and a high recall for pneumonia cases, while class weighted loss helps to maintain more balanced predictions between the two classes. The Area Under the Curve (AUC) of 0.975 indicates strong discrimination between pneumonia-positive and normal samples. Grad-CAM visualizations, supported by a randomization test and occlusion analysis, provide an initial qualitative view of the lung regions that influence the model’s predictions and often overlap with radiologically plausible areas. However, the heatmaps have not been formally evaluated by radiologists, and the correspondence between highlighted regions and pneumonia consolidation patterns has not yet been quantitatively validated. Therefore, the proposed ViT Grad-CAM framework should be regarded as an exploratory step toward explainable pneumonia classification on chest X-rays rather than a system that is ready for clinical deployment.
Sentiment Analysis of US-China Tariffs using IndoBERT and Economic Impact on Indonesia Khaqqi, Fitriyana Nuril; Alfat, Lathifah; Nurhaida, Ida
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11544

Abstract

The US-China trade war has influenced public perception due to its potential economic impact on developing countries like Indonesia. This study analyses Indonesian sentiment towards the tariff policies and their correlation with economic indicators. The dataset consisted of 38,739 social media comments collected through web scraping. The data were processed through data cleaning, case folding, stopword removal, normalization, and stemming. Each comment was labeled as positive, negative, and neutral. The dataset was split into 80% training and 20% testing sets, followed by an oversampling process to balance the class distribution. The data is fine-tuned using the IndoBERT model with the Python programming language. The model achieved its highest performance with an accuracy of 93.03%, precision of 93.42%, recall of 93.03%, and F1-score of 92.94%. Spearman correlation revealed a weak to moderate positive and significant correlation (ρ = 0.434, p-value < 0.05) between public sentiment and global soybean prices. These findings underscore the effectiveness of combining a deep learning model like IndoBERT with statistical analysis to link digital discourse to tangible economic indicators, highlighting the method's potential as a data-driven tool for policy evaluation.
Two-Stage Maritime Anomaly Detection: Unsupervised Outlier Filtering and Optimized Bidirectional LSTM on Southeast Asian AIS Data Afaf Firmansyah, Daffa; Astuti, Erna Zuni
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11545

Abstract

This paper presents a two-stage framework for detecting anomalous vessel trajectories in Automatic Identification System (AIS) data from Southeast Asian waters, addressing challenges of high traffic density, diverse vessel behaviors, and severe class imbalance. The primary objective is to minimize missed threats while maintaining manageable false alarm rates in security-critical maritime surveillance systems. The research employs a hybrid approach combining unsupervised and supervised learning methods. In the first stage, DBSCAN and Isolation Forest algorithms filter noise and generate high-confidence outlier labels from 15,542 real-world vessel trajectories. Comparative analysis demonstrates substantial agreement between methods with Cohen's Kappa of 0.688 and 55.3% anomaly overlap, indicating complementary detection capabilities that enhance filtering robustness. In the second stage, a Bidirectional Long Short-Term Memory model is optimized through systematic hyperparameter tuning across 48 configurations, covering sequence length, network architecture, dropout rate, learning rate, and sampling strategies. Comprehensive baseline evaluation validates BiLSTM's suitability for security applications, achieving 15.41% F1-score improvement over unidirectional LSTM and 33% fewer false negatives compared to Bidirectional GRU alternative. The optimized BiLSTM attains F1-score of 0.5709 with precision 0.5444 and recall 0.6000, exhibiting 90.03% specificity for normal vessels and 76.17% sensitivity for anomalies. The model misses only 23.8% of threats while maintaining 9.97% false alarm rate, providing balanced performance suitable for human-verified security-critical maritime surveillance in Southeast Asian waters.
A Comparative Study of Machine Learning and Deep Learning Models for Heart Disease Classification Simanjuntak, Martina Sances; Robet, Robet; Hoki, Leony
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11546

Abstract

Heart disease remains one of the leading causes of mortality worldwide, necessitating accurate early detection. This study aims to compare the performance of several Machine Learning (ML) and Deep Learning (DL) algorithms in heart disease classification using the Heart Disease dataset with 918 samples. The methods tested included Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), and Deep Neural Network (DNN). Preprocessing included feature normalization, data splitting (80:20), and simple hyperparameter tuning for parameter-sensitive models. Evaluations were conducted using accuracy, precision, recall, F1-score, AUC, and confusion matrix analysis to identify error patterns. The results showed that SVM and DNN achieved the highest accuracies of 91.3% and 92.1%, respectively. However, DNN has higher computational costs and risks of overfitting on small datasets. These findings confirm that traditional ML models such as SVM remain highly competitive on tabular medical data.
Binary Classification for Predicting the Investment Trends of The Younger Generation Based on Machine Learning Oktana, Weka Brilliant Jaya; Sanjaya, Ucta Pradema
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11549

Abstract

This computational study examines investment behavior patterns among a specialized cohort of 115 final year and thesis writing university students, implementing sophisticated feature engineering to transform categorical survey responses into quantifiable financial metrics. The research methodology leverages this unique dataset where respondents' advanced academic standing provides particularly relevant insights into near-term investment decisions. Experimental outcomes reveal distinct algorithmic performance patterns: Random Forest achieved 69.6% accuracy in multi-class classification with weighted averages of 0.662 precision, 0.696 recall, and 0.678 F1-score, while Logistic Regression demonstrated superior binary classification capability with 82.6% accuracy, supported by 0.818 precision, 0.826 recall, and 0.814 F1-score (weighted averages). The hybrid architecture integrating machine learning with business rules achieved peak performance of 85.2% accuracy, successfully balancing predictive power with operational interpretability. These findings underscore how strategically engineered features combined with a carefully selected respondent pool can effectively decode complex financial behaviors, providing financial institutions with actionable frameworks for developing targeted investment solutions for the graduate student demographic while advancing methodological approaches for specialized survey data in fintech applications.
Optimization of Early Diagnosis Prediction Models for Acute Respiratory Infections (ARI) in Children Using Decision Tree, Random Forest, and Resampling Techniques Firdaus, Caesario Gumilang; Rohmani, Asih; Suharnawi, Suharnawi
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11558

Abstract

Acute Respiratory Tract Infections (ARI) are the leading cause of childhood morbidity in Indonesia, with challenges in early detection due to limited medical personnel and diagnostic data imbalance, where LRTI cases are far fewer than URTI cases. This study developed and optimized an ARI classification prediction model (URTI and LRTI) based on machine learning with resampling techniques to address imbalance. An explanatory quantitative design was used with secondary data from the Mijen Community Health Center, Semarang (2020–2025, 12.177 valid data), with preprocessing including outlier handling (Winsorizing, IQR), stratified split (70:30), and RobustScaler on the training data. Three resampling techniques (SMOTE, ADASYN, SMOTE-ENN) were applied, then tested using Decision Tree and Random Forest with GridSearchCV and 5-fold cross-validation, focusing on Recall and AUC-PR evaluation for minority classes. The results showed that Random Forest with SMOTE-ENN provided the best performance, increasing the LRTI recall from 0.02 to 0.37 and F1-macro to 0.54, while Decision Tree with SMOTE-ENN produced the highest AUC-PR of 0.31. Despite this significant improvement, a recall of 0.37 is still low for clinical applications because the risk of false negatives remains high, potentially delaying patient treatment Future implementation requires the integration of clinical symptom data (e.g., respiratory rate) to achieve clinically acceptable sensitivity. These findings confirm that resampling can improve model capabilities, but additional feature exploration is needed to achieve adequate diagnostic sensitivity in the context of healthcare analytics.
Comparative Analysis of EfficientNet-B0 and ViT-B16 for Multiclass Classification of Green Coffee Beans Syaputra, Muh. Rezky; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11563

Abstract

Green coffee bean classification plays an important role in the coffee supply chain, as bean quality has a direct impact on the taste and final quality of the product. The USK-Coffee dataset, which consists of four bean object classes defect, longberry, peaberry, and premium, is photographed under varied lighting conditions and capture angles, thus challenging the accuracy of conventional visual models. Although lightweight CNN models have been used, not many studies have directly compared transformer-based architectures (ViT-B16) and modern efficient CNNs (EfficientNet-B0) for green coffee bean classification under real conditions. With transfer learning strategy, image augmentation (resize, flip, rotation, color jitter, random crop), and normalization, we evaluate the performance of both models on the dataset. ViT-B16 achieved 85% accuracy on the test data (F1-score 0.85), with a fast batch inference latency of 0.0074 seconds per batch. EfficientNet-B0 achieved 87% accuracy (F1-score 0.87), with a slower batch latency (0.0106 seconds per batch). However, EfficientNet-B0 is significantly faster for single image inference (real-time) (0.035 seconds) compared to ViT-B16 (0.426 seconds). This trade-off higher accuracy/faster single inference on EfficientNet-B0 vs. faster batch processing on ViT-B16 shows that both are feasible for edge computing-based classification systems.

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