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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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Optimizing Bankruptcy Prediction on Imbalanced Data using XGBoost with Random Oversampling and Chi-Square Suyatno, Revalina; Udayanti, Erika Devi; Dewi, Ika Novita
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.v10i1.11841

Abstract

In the midst of modern financial dynamics, the ability to predict corporate bankruptcy holds strategic significance, as it directly affects economic stability and investor confidence. However, the development of a reliable predictive model is often hindered by the complex nature of financial data, particularly the class imbalance between bankrupt and non-bankrupt companies. This imbalance causes models to become biased toward the majority class, thereby reducing their sensitivity in detecting bankruptcy cases which are, in fact, the most critical for financial decision-making. This research aims to construct a more balanced and sensitive bankruptcy prediction model by specifically addressing the issue of data imbalance. The proposed approach integrates the Random Oversampling (ROS) technique to equalize class distribution, Chi-Square feature selection to identify the most informative financial variables, and the Extreme Gradient Boosting (XGBoost) algorithm as the core predictive model. The dataset used is the UCI Taiwanese Bankruptcy Prediction dataset, consisting of 6,819 observations and 96 financial ratio variables. Experimental results show that the Chi-Square method successfully identified 20 influential variables, including Per Share Net Profit Before, Debt Ratio, and ROA(B) Before Interest and Depreciation After Tax. The proposed XGBoost model achieved an overall accuracy of 0.9648 and an F1-score of 0.4286, demonstrating superior performance. These findings confirm that the combination of ROS, Chi-Square, and XGBoost effectively enhances data balance and prediction sensitivity for the bankruptcy class. This research is expected to serve as a foundation for developing financial decision-support systems capable of providing early warnings of potential corporate bankruptcy.
Implementation of SSL-Vision Transformer (ViT) for Multi-Lung Disease Classification on X-Ray Images Baasith, Rafi Haqul; Sasongko, Theopilus Bayu; Hadinegoro, Arifiyanto; Saputro, Uyock Anggoro
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.v10i1.11844

Abstract

Chest X-ray imaging is one of the most widely used modalities for lung disease screening; however, manual interpretation remains challenging due to overlapping pathological patterns and the frequent presence of multiple coexisting abnormalities. In recent years, Vision Transformer (ViT) models have demonstrated strong potential for medical image analysis by capturing global contextual relationships. Nevertheless, their performance is highly dependent on large-scale labeled datasets, which are costly and difficult to obtain in clinical settings. To address this limitation, this study proposes a Self-Supervised Learning Vision Transformer (SSL-ViT) framework for multi-label lung disease classification using the CheXpert-v1.0-small dataset. The proposed approach leverages self-supervised pretraining to learn robust and transferable visual representations from unlabeled chest X-ray images prior to supervised fine-tuning. A total of twelve clinically relevant thoracic disease labels are retained, while non-disease labels are excluded to enhance interpretability and reduce confounding effects. Experimental results demonstrate that SSL-ViT achieves a high recall of 0.73 and a peak AUC of 0.75 on the test set, indicating strong sensitivity in detecting pathological cases. Compared to the baseline ViT model, SSL-ViT exhibits a recall-oriented performance profile that is particularly suitable for screening applications, where minimizing false negatives is critical. Furthermore, Grad-CAM visualizations confirm that the model focuses on anatomically meaningful lung regions, supporting its clinical relevance. These findings suggest that SSL-enhanced Vision Transformers provide a robust and effective solution for multi-label chest X-ray screening tasks.
Explainable Transformer and Machine Learning Models in Predicting Tuberculosis Treatment Outcomes. A Systematic Review Sibanda, Shumirai; 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.v10i1.11846

Abstract

Tuberculosis (TB) remains a major health challenge, and predicting treatment outcomes continues to be difficult in real-world settings. Recent advances in Artificial Intelligence (AI), particularly transformer-based models, have shown promise in modelling longitudinal, multimodal, and heterogeneous TB data. However, their clinical adoption is constrained by limited interpretability, fairness concerns, and deployment challenges. This study presents a systematic literature review of explainable transformer and machine learning models used for TB prognosis. Following PRISMA guidelines, searches across ACM, IEEE Xplore, PubMed, and ScienceDirect identified 17 peer-reviewed studies published between 2020 and 2025 that met the inclusion criteria. The review synthesises evidence on predictive performance, explainability techniques, and deployment considerations. Findings indicate that transformer-based and deep learning models generally outperform conventional machine learning approaches on longitudinal and multimodal data. In contrast, traditional models remain competitive for tabular clinical datasets. Explainability approaches are dominated by feature importance methods and SHAP, with limited use of intrinsic transformer interpretability mechanisms. Persistent challenges include data scarcity, limited generalisability, computational overhead, insufficient evaluation of fairness, and weak alignment with real-world TB care workflows. Building on these findings, the study proposes the Explainable Transformer Adoption Model for TB Prognosis (ETAMTB) as a conceptual clinical adoption framework integrating multimodal transformers, explainability layers, clinician-facing interfaces, and deployment enablers. Overall, the review concludes that effective AI adoption in TB care requires balancing predictive performance, interpretability, and equity, and that explainable transformers should currently be viewed as promising but largely experimental tools rather than deployment-ready solutions.
Analysis of Public Sentiment Towards the Free Nutritious Meals Program on TikTok Social Media Using the K-Nearest Neighbor Algorithm Nugroho, Ivan; Mujiyono, Sri
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.v10i1.11849

Abstract

The Free Nutritious Meals Program is currently one of the most talked about public policies, generating a wide range of responses from the public. One of the most active discussion forums is the social media platform TikTok, given that it has a large number of users and a relaxed and informal style of language. This study aims to examine public sentiment toward the MBG program through TikTok user comments, while also testing the performance of the K-Nearest Neighbor (KNN) algorithm in classifying sentiment as positive or negative. Research data was collected by crawling comments on several TikTok videos discussing Free Nutritious Meals during the period from September to November 2025. A total of 1,000 comments were obtained and then processed through data cleaning stages, such as data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. To convert the text into numerical form, the Term Frequency–Inverse Document Frequency (TF-IDF) method was used. Meanwhile, sentiment labeling was done manually to maintain the quality of the training data. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score indicators. The test results showed that the best accuracy rate, which was 70.50%, was obtained at a K value of 4. From the sentiment analysis conducted, negative comments were found to outnumber positive sentiments. The criticism that emerged generally related to food quality and safety, inequality in program distribution, and a lack of transparency in information provided to the public. This study shows that the KNN algorithm is quite capable of being used for sentiment analysis on TikTok comment data, although it still has limitations in understanding the variety of informal language often used by users. Therefore, the results of this study are expected to provide public opinion-based input for policymakers, as well as a foundation for the development of sentiment analysis methods that are more suited to the characteristics of social media in future studies.
Calibration and Applied Statistical Modeling Using Logistic Regression on the UCI Heart Disease Dataset Cahyono, Andi; Ameriza, Inkha; Gunadi, Gunadi; As Syifa, Ervira Dwiaprini; Alqudah, Mashal Kasem
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.v10i1.11853

Abstract

Accurate and well-calibrated heart disease risk prediction is essential for supporting medical decision-making. This study analyzes Logistic Regression as an applied statistical model for heart disease prediction using the UCI Heart Disease dataset. Beyond discrimination metrics, we explicitly focus on probability reliability by evaluating calibration through the Brier score, calibration slope, and intercept, and by quantifying the impact of post-hoc calibration (isotonic regression and Platt scaling) on both calibration and discrimination. Model validation was conducted using stratified 5-fold cross-validation with AUROC, AUPRC, accuracy, and F1-score as evaluation metrics. The results show that Logistic Regression achieved competitive performance (AUROC 0.903; AUPRC 0.911; Accuracy 0.822; F1-score 0.835) with well-calibrated probability estimates relative to Random Forest and Gradient Boosting under the evaluated setting. Feature importance analysis using permutation methods identified chest pain type, number of major vessels (ca), ST depression (oldpeak), and exercise-induced angina (exang) as key predictors consistent with clinical literature. These findings indicate that simple applied statistical modeling, when paired with rigorous calibration assessment, can provide interpretable risk estimates that are more suitable for threshold-based decision support in early heart disease screening.
Classification of Facial Acne Types Based on Self-Supervised Learning using DINOv2 Chardaputeri, Gantari; Thenata, Angelina Pramana
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.v10i1.11856

Abstract

Acne is a common inflammatory skin condition that can affect an individual’s psychological well-being and overall quality of life. The inability to independently recognize specific types of acne often leads to the use of inappropriate skincare products. This situation highlights the need for an image-based classification system that can provide accurate visual identification. The self-supervised learning method Distillation with NO Labels, version 2 (DINOv2), is employed as a feature extractor to classify four types of acne—Acne fulminans, Acne nodules, Papules, and Pustules—using the “skin-90” dataset. The fine-tuning process is conducted through a Parameter-Efficient Fine-Tuning (PEFT) approach using Low-Rank Adaptation (LoRA) to adjust the model’s visual representations to the acne domain without updating all parameters in full, followed by integration with a classification head. The results show that the model achieves an accuracy of 90.70%, with precision, recall, and F1-score values of 90.64%, 90.68%, and 90.57%, respectively. The findings suggest that the proposed architectural design and training configuration are suitable for capturing relevant visual patterns of acne, while further validation is required to assess robustness across more diverse data distributions.
Analysis of Gradient Boosting Algorithms with Optuna Optimization and SHAP Interpretation for Phishing Website Detection Abu Bakar, Rahmat Fauzi; 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.v10i1.11857

Abstract

Phishing remains a persistent cybersecurity threat, evolving rapidly to bypass traditional blacklist-based detection systems. Machine Learning (ML) approaches offer a promising solution, yet finding the optimal balance between detection accuracy and model interpretability remains a challenge. This study aims to evaluate and optimize the performance of three state-of-the-art Gradient Boosting algorithms—XGBoost, LightGBM, and CatBoost—for phishing website detection. The research utilizes the UCI Phishing Websites dataset consisting of 11,055 instances. The novelty of this study lies in the implementation of the Optuna framework with the Tree-structured Parzen Estimator (TPE) for automated hyperparameter optimization and the application of SHAP (Shapley Additive Explanations) interaction values to interpret the "black-box" models. The experimental results demonstrate that the LightGBM model, optimized via Optuna, achieved the highest performance with an F1-Score of 0.9798, outperforming the baseline model (0.9713) by 0.87%. Furthermore, SHAP analysis identified 'SSLfinal_State' as the most critical determinant for distinguishing phishing sites. This study confirms that optimizing modern boosting algorithms significantly enhances phishing detection capabilities while providing necessary explainability for cybersecurity analysts.
Implementation of LSTM for Gold Price Prediction in Indonesia Sibannang, Maria Oktaviani Giska; Wijayakusuma, I Gusti Ngurah Lanang
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.v10i1.11860

Abstract

Gold is a significant investment instrument that serves as a safe-haven asset; nevertheless, its price dynamics are inherently nonlinear and highly volatile due to the influence of various economic factors. This study aims to develop a predictive model for daily gold prices denominated in Indonesian Rupiah. The proposed methodology employs a Long Short-Term Memory (LSTM) neural network architecture. Historical gold price data covering the period from January 1, 2015, to October 1, 2025, were obtained from investing.com. The dataset underwent a preprocessing phase, which included normalization using the MinMaxScaler and the construction of input sequences with a sliding window of 60 time steps. The implemented LSTM model consists of two stacked layers, each comprising 16 units, and is equipped with a dropout rate of 0.2 as well as an early stopping mechanism to improve generalization and prevent overfitting. The evaluation results demonstrate that the proposed model achieved a Mean Absolute Percentage Error (MAPE) of 5.08% and an accuracy of 94.92%, with a Mean Squared Error (MSE) of 0.00203. Furthermore, the visualization of prediction outcomes confirms the model’s capability to effectively capture actual price fluctuations, including during periods of heightened market volatility. Overall, these findings indicate that a relatively simple LSTM architecture is effective for forecasting gold price movements in the Indonesian market. The results of this study provide a robust foundation for the future development of more sophisticated predictive systems and potential real-time applications.
Anomaly-Based DDoS Detection Using Improved Deep Support Vector Data Description (Deep SVDD) and Multi-Model Ensemble Approach Imran, Bahtiar; Samsumar , Lalu Delsi; Subki, Ahmad; Wahyuni, Wenti Ayu; Muahidin, Zumratul; Karim, Muh Nasirudin; Yani, Ahmad; M. Zulpahmi
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.v10i1.11863

Abstract

Distributed Denial-of-Service (DDoS) attacks remain a critical threat to network infrastructure, demanding robust and efficient detection mechanisms. This study proposes an enhanced Deep Support Vector Data Description (Deep SVDD) model for unsupervised DDoS detection using the UNSW-NB15 dataset. The approach leverages a deep encoder architecture with batch normalization and dropout to learn compact latent representations of normal traffic, minimizing the hypersphere volume enclosing benign flows. Only normal samples are used during training, adhering to the unsupervised anomaly detection paradigm. The model is evaluated against five established baselines—Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, Autoencoder, and a simple ensemble—using AUC, F1-score, and recall as primary metrics. Experimental results demonstrate that Deep SVDD significantly outperforms all baselines, achieving superior class separation, high detection sensitivity, and computational efficiency (0.0004 GFLOPs). Notably, while LOF exhibited a deceptively high F1-score, its AUC near 0.5 revealed poor discriminative capability, highlighting the risk of relying on single metrics. The ensemble approach failed to improve performance, underscoring the limitation of naive score averaging when weak detectors are included. Visualization of score distributions and ROC curves further confirms Deep SVDD’s ability to effectively distinguish DDoS from benign traffic. These findings affirm that representation learning in latent space offers a more reliable foundation for anomaly detection than traditional distance-, density-, or reconstruction-based methods. The proposed model presents a promising solution for real-time, low-overhead intrusion detection systems in modern network environments. Future work will explore adaptive ensembles, self-supervised pretraining, and deployment on edge devices.
A Fine-Tuned Transfer Learning Vision Transformer Framework for Lungs X-Ray Image Classification Wijayakusuma, I Gusti Ngurah Lanang; Sudarma, Made; Dian Astutik, Ni Putu
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.v10i1.11865

Abstract

Lung diseases constitute a significant source of morbidity and therefore require diagnostic frameworks that provide both high accuracy and operational efficiency. This study proposes the development of a Vision Transformer (ViT)-based classification model for lung X-ray images, employing transfer learning and fine-tuning techniques to improve detection performance across five disease categories. Experimental results demonstrate stable and effective model convergence, as reflected by the consistent decrease in loss metrics throughout the learning process. Evaluation on an independent test dataset shows that the proposed approach achieves an accuracy of 0.958, indicating strong and balanced generalization performance. Further analysis using a confusion matrix reveals that the ViT model is capable of recognizing subtle and complex radiographic patterns with low misclassification rates, particularly achieving high recall for major pathological classes, which is critical for minimizing false negatives in clinical screening scenarios. Overall, this study demonstrates that the application of transfer learning with fine-tuning on a Vision Transformer architecture yields competitive performance for multi-class lung X-ray classification when trained on a balanced dataset. These findings are consistent with prior evidence highlighting the effectiveness of ViT in capturing global contextual information in medical imaging tasks.

Page 75 of 81 | Total Record : 805

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