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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 805 Documents
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Comparison of Naïve Bayes and Support Vector Machine for Sentiment Classification of Acne Skincare Reviews Arindika, Alti; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11869

Abstract

The increasing popularity of skincare products for acne-prone skin had led to a surge in online consumer reviews, which are characterized by informal language, domain-specific terminology, and imbalanced sentiment distribution, posing challenges for sentiment classification tasks. This study aims not only to compare the performance but also to analyze the generalization behavior of two popular machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM), for sentiment classification of skincare product reviews specifically targeting acne-prone skin. A comprehensive methodology was employed, including thorough text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) with n-gram representation, and data balancing through Synthetic Minority Over-sampling Technique (SMOTE). The study utilized a dataset of 4,004 labeled reviews categorized into positive and negative sentiments. The models were evaluated using stratified 5-Fold cross-validation to ensure robust and fair assessment. Results indicate that Naïve Bayes slightly outperforms SVM on the testing set, achieving the highest accuracy of 91.14% compared to 90.64% for SVM. While SVM demonstrated higher performance during training, its testing performance suggested a tendency toward overfitting, whereas Naïve Bayes exhibited more stable generalization on unseen data. Further qualitative insight analysis revealed that product effectiveness and user experience are the primary drivers of consumer sentiment, while competitive analysis highlighted distinct brand perception patterns across skincare categories. These findings indicate that simpler probabilistic models such as Naïve Bayes can provide robust and reliable performance for sentiment analysis in specialized and imbalanced skincare review datasets.
Analysis of Gradient Boosted Trees Algorithm in Breast Cancer Classification Suryaputri, Cantika Okzen; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11875

Abstract

Early and accurate classification of breast cancer is essential to support clinical diagnostic processes and improve patient outcomes. This study proposes a comprehensive machine learning pipeline based on Gradient Boosted Tree algorithms to classify breast tumors into benign and malignant categories. The proposed framework integrates several preprocessing stages, including outlier handling using the Local Outlier Factor (LOF), feature normalization with StandardScaler, class imbalance handling using SMOTE, and feature selection through ANOVA-based SelectKBest. Five ensemble learning models—XGBoost, LightGBM, CatBoost, HistGradientBoosting, and GradientBoosting—were trained and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The experimental results show that all models achieved strong and comparable classification performance. Among them, CatBoost obtained the highest ROC-AUC value of 0.9960, along with an accuracy of 0.9649, precision of 0.9750, recall of 0.9286, and F1-score of 0.9512. Statistical evaluation using the DeLong test indicated that the differences in ROC-AUC among the evaluated models were not statistically significant (p > 0.05), suggesting similar discriminative capabilities across models. To enhance model interpretability, SHAP (SHapley Additive exPlanations) was applied to the CatBoost model as a representative classifier. The results show that features related to nuclear size and shape, such as radius, area, perimeter, and concavity, contributed most significantly to malignant predictions. This study demonstrates that the integration of robust preprocessing techniques, Gradient Boosted Tree models, and explainable machine learning provides an accurate and interpretable approach for breast cancer classification. However, the evaluation was conducted on a single public dataset without external validation, and further studies using independent and real-world datasets are required before clinical deployment.
Dynamics and Control of Human Papillomavirus (HPV) Infection Using an SVITR Compartmental Model MATONDO MANANGA, Herman; Lea-Irène, Milolo Kanumuambidi; Patience, Pokuaa Gambrah; Junior, Mukinayi Kanumuambidi; Marcial, Nguemfouo; Peter, Kasende Mundeke; Benjamin, Consolant Majegeza
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11876

Abstract

Human papillomavirus (HPV) remains a significant public health concern due to its high transmissibility and associated health risks. This study underscores the pivotal role of vaccination in reducing HPV transmission, while also highlighting the limitations of relying solely on vaccination for infection control. In this study, we present a deterministic compartmental model to investigate the transmission dynamics of Human Papillomavirus (HPV). The model stratifies the population into five compartments: susceptible individuals S(t), Vaccinated individuals V(t), HPV Infected individuals I(t), treated HPV-infected individuals T(t) and recovered individuals R(t). We establish the existence and uniqueness of the model solution and also examine the existence of disease-free and endemic equilibrium and analyze their stability properties. Numerical simulations were performed to explore the temporal evolution of the compartments, assess the sensitivity of key parameters, and investigated the behaviour of the basic reproduction number R_0. Our findings were that a comprehensive strategy, incorporating both preventive vaccination and therapeutic management, is essential for achieving sustainable control of HPV spread. Strengthening these measures, alongside reducing transmission through demographic interventions, offers the best way for long-term management of the infection. These results provide insights into the impact of vaccination and treatment strategies on HPV transmission and highlight critical factors for public health.
Comparative Analysis of MobileNetV3 and EfficientNetv2B0 in BISINDO Hand Sign Recognition Using MediaPipe Landmarks Fadzli, Alief Khairul; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11878

Abstract

Sign language is a vital communication medium for individuals with hearing and speech impairments. In Indonesia, more than 2.6 million people experience hearing disabilities, most of whom rely on Bahasa Isyarat Indonesia BISINDO for daily interaction. However, limited public understanding and the scarcity of professional interpreters continue to hinder inclusive communication. Recent advancements in computer vision and deep learning have enabled camera-based sign language recognition systems that are more affordable and practical compared to sensor-glove solutions. this study presents a comparative analysis between EfficientNetV2-B0 and MobileNetV3-Large in recognizing BISINDO hand sign alphabets using MediaPipe landmarks. The dataset was derived from BISINDO video recordings, from which hand landmarks were extracted using MediaPipe Hands and subsequently converted into two-dimensional skeletal images. In total, 10,309 skeletal images representing BISINDO alphabets A–Z were generated and used for model training and evaluation. Both models were trained under identical configurations using TensorFlow. The results show that MobileNetV3-Large achieved 89.67% test accuracy and an F1-score of 89.76%, while EfficientNetV2-B0 obtains 95.98% test accuracy and an F1-score of 95.93%. These findings highlight the trade-off between the higher classification accuracy of EfficientNetV2-B0 and the superior computational efficiency of MobileNetV3-Large. This research contributes to the development of lightweight, high-performance BISINDO recognition systems for assistive communication applications.
Transformer-Based Models for Electronic Health Records and Omics in Healthcare: A Systematic Literature Review Machemedze, Joshua; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11893

Abstract

Electronic Health Records (EHRs) have become central to modern healthcare. The emergence of transformer-based models has profoundly influenced how EHRs are used for modelling complex, longitudinal data. Integration with omics technologies improves the precision of disease identification and risk assessment during modelling. While several reviews have examined transformers in healthcare broadly, a systematic synthesis focused on their architectural design, empirical performance and integration of EHRs with omics data remains limited. This study presents a systematic literature review of transformer-based models applied to electronic health records (EHRs) and omics data, and of their integration into healthcare. Following PRISMA guidelines, peer-reviewed studies were retrieved from IEEE Xplore, ACM Digital Library, PubMed, and ScienceDirect, resulting in 14 eligible empirical studies published between 2020 and 2025. The review analyses transformer architectures, submodules, application domains, comparative performance, interpretability mechanisms, and limitations. Findings indicate that architectural design drives task-specific advantages in disease prediction, phenotyping, medication recommendation, and omics analysis. The integration of self-attention with deep learning, temporal modelling, and a pre-trained biomedical transformer improves performance. However, most studies remain centred on EHR, with limited empirical integration of omics data. Persistent challenges include limited generalisability, high computational cost, data quality issues, and insufficient interpretability for clinical deployment. The primary contribution of this review lies in synthesising architectural trends and methodological gaps. By consolidating current evidence, the study provides clear directions for the development of explainable, generalisable, and multimodal transformer-based systems in precision healthcare.
Analysis of BRIsat Investment Success from Financial and Nonfinancial Perspectives Adyasari, I Gusti Ayu Agung Wiwin; Dwi Putri, I Gst. Agung Pramesti; Dewi, Eka Grana Aristyana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11895

Abstract

Investment in information technology within the banking sector requires not only financial viability but also public acceptance, particularly for state-owned enterprises (SOEs) that carry both commercial and social mandates. This study aims to evaluate the success of PT Bank Rakyat Indonesia (Persero) Tbk.’s BRIsat satellite investment from both financial and nonfinancial perspectives. A Cost–Benefit Analysis (CBA) was employed to assess financial feasibility using BRI’s publicly available financial statements from 2014 to 2024, while sentiment analysis using the Naive Bayes algorithm was conducted to examine public perception based on social media data from platform X covering the period 2016–2024. The financial analysis indicates that the BRIsat investment is financially feasible, with a Return on Investment (ROI) of 2.58%, a Payback Period of 6.2 years, a positive Net Present Value (NPV) of IDR 166,161,960, and a Benefit Cost Ratio (BCR) of 184.9, suggesting that every IDR 1 invested generates IDR 184.9 in economic benefits. From the nonfinancial perspective, sentiment analysis of 10,066 valid tweets reveals that 55.90% of public sentiment is negative (5,627 tweets), while 44.10% is positive (4,439 tweets), with the Naive Bayes model achieving an accuracy of 96.76%. Positive sentiment is primarily associated with keywords such as “successful,” “fast,” and “service,” reflecting appreciation for BRIsat as a strategic innovation, whereas negative sentiment is dominated by terms such as “error,” “failed,” and “disruption,” indicating persistent technical issues in digital banking services. These findings highlight a clear contradiction between the strong financial performance of the BRIsat investment and the predominantly negative public perception of service quality. The study implies that the success of large-scale technology investments in SOEs cannot be assessed solely through financial metrics, but must be accompanied by continuous improvements in operational reliability and digital service quality to ensure sustainable value creation and public trust.
Optimizing XGBoost for Heart Disease Risk Classification Using Optuna and Random Search on the Behavioral Risk Factor Surveillance System (BRFSS) 2023 Dataset Dzaky, Muhammad; Kuncoro, Adam Prayogo; Riyanto, Riyanto
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11897

Abstract

Heart disease is a critical public health issue in Indonesia, contributing to approximately 1,5 million deaths annually. Although machine learning methods, particularly Extreme Gradient Boosting (XGBoost), have demonstrated strong performance in medical classification tasks, their optimization on large-scale and highly imbalanced health datasets remains underexplored. This study optimizes XGBoost for heart disease risk classification using the Behavioral Risk Factor Surveillance System (BRFSS) 2023 dataset, consisting of 290.156 samples after preprocessing. Two hyperparameter optimization approaches, Optuna and Random Search, are evaluated across three class imbalance handling techniques, namely class weighting, SMOTE, and Random Undersampling (RUS). Model evaluation focuses on AUC and recall to prioritize sensitivity in identifying individuals at risk. The results show that the OptunaRUS and RandomWeight models achieve the most stable performance, with OptunaRUS attaining an AUC of 83,06% and a recall of 75,69% on the test dataset. Feature importance analysis indicates that age range and hypertension are the most influential predictors. These findings confirm that hyperparameter optimization on large-scale health data improves model discriminative capability and generalization, while selective sampling strategies such as RUS provide more stable performance than generative methods in high-dimensional datasets.
Transformer-based Models for Cardiovascular Disease Predictions from Electronic Health Records: A Systematic Review Chikumo, Onayi Theresa; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11899

Abstract

This systematic literature review (SLR) analyses 16 studies published between 2020 and 2025 that applied transformer-based or other machine learning models to predict cardiovascular disease (CVD) using electronic health records (EHRs). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review ensures transparency in the identification, screening, and quality appraisal of eligible studies. The key findings reveal a rapid shift from traditional machine learning models, such as Random Forest, toward transformer architectures like the Bidirectional Encoder Representation from Transformers for Electronic Health Record (BEHRT) and its variants. These models demonstrate a superior discrimination (Area Under Curve:0.84 to 0.93) due to their capacity to model long-term temporal dependencies. Explainable AI (XAI) tools, such as attention visualisation, were frequently employed, yet clinical interpretability and integration into decision support remain underexplored. The review also highlights opportunities in federated and privacy-preserving learning, multimodal data fusion, and hybrid architectures that integrate transformers with traditional machine learning methods. This review addresses a gap in the past literature by being the first SLR to compare transformer variants for the prediction of CVDs. Other SLRs examined general CVD risk models, but the present SLR analyses interpretability, external validation and methodological limitations to transformer models. The findings of the recent SLR reported challenges that include data-shift limitations, model-poor population generalisation and their limitations to clinical adoption, which highlights the need for more evaluation protocols and clinicians’ interpretability frameworks.
Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework Mugambiwa , Tarisai; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11900

Abstract

Multi agent retrieval augmented generation (RAG) systems are increasingly explored as advanced architectures for clinical decision support combining information retrieval, reasoning and verification through coordinated agent interactions. This study systematically reviews applications of agentic and multi agent RAG in clinical decision support systems (CDSS) and synthesizes an integrative conceptual framework linking technical design to technology adoption considerations. Following PRISMA guidelines, searches were conducted from PubMed, IEEE Xplore and ScienceDirect using structured Boolean strings combining terms for multi agent architectures, RAG and CDSS.The search yielded 12 studies published between 2020 and 2025 that met the inclusion criteria. The review synthesises evidence on multi agent role configurations retrieval and reasoning strategies, verification mechanisms and reported clinical contexts. Across studies, dominant challenges include data and corpus limitations retrieval quality dependency, limited clinical validation and computational overhead, alongside governance concerns such as privacy, bias and accountability. Building on the synthesis, we propose a four-agent CDSS framework retriever, reasoner, verifier, safety and map its deployment determinants to Technology Acceptance Model constructs perceived usefulness, perceived ease of use, trust and diffusion of Innovations attributes. The review concludes with design-oriented recommendations for safer, explainable, and adoption-ready multi-agent RAG CDSS, particularly for low-resource contexts.
Network-Informed Optimal Control via Graph Neural Networks: A Framework with Application to Tax Enforcement Nguemfouo, Marcial; Bossale, Pierre Raymond
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11909

Abstract

This paper introduces a novel framework integrating multiplex network theory, machine learning, and optimal control to optimize tax revenue dynamics in the Democratic Republic of Congo (DRC). We model the Congolese economy as a multiplex network where economic sectors represent interdependent layers. Using machine learning techniques on empirical tax data (2000-2024), we reconstruct network topology and identify systemic sectors. Our network informed optimal control approach demonstrates potential revenue increases of 25-35% with 30-40% volatility reduction. The framework provides actionable insights for the upcoming transition to Corporate Income Tax (CIT) and offers a replicable methodology for developing economies.

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