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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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Lightweight BiLSTM-Attention Model Using GloVe for Multi-Class Mental Health Classification on Reddit Branwen, Devin; Emigawaty, Emigawaty
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10157

Abstract

Mental health issues such as depression, stress, anxiety, and personality disorders are increasingly prevalent, particularly within online communities. This study proposes a lightweight and efficient multi-class classification framework to identify five mental health conditions using Reddit user-generated posts. While previous studies predominantly rely on conventional CNNs or standard machine learning techniques for binary classification, our work introduces a novel Bidirectional Long Short-Term Memory (BiLSTM) model integrated with an attention mechanism. The architecture is further enhanced by synonym-based data augmentation using the WordNet lexical database, which improves semantic diversity and enhances model robustness, particularly for underrepresented classes. Unlike prior works that focus narrowly on binary classification or employ transformer-based models with high computational demands, our model offers a lightweight, high-performance architecture optimized for multi-class detection and real-world deployment. Experimental results demonstrate that the proposed model achieves a peak validation accuracy of 95.02%, along with precision 95.08%, recall 95.02%, and F1-scores of 95.03%. These findings support the advancement of efficient AI-driven diagnostic systems in mental health analytics and lay the groundwork for future integration into mobile or resource-constrained platforms.
Python-Based Linear Regression Modeling of Liquidity and Profitability Ratios as Determinants of Firm Value in Commercial Banks in Indonesia Febriyanti, Zahra; Karismariyanti, Magdalena; Sukmawati, Fitri
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10248

Abstract

The decline in Indonesian banking stocks in early 2024 was caused by foreign capital outflows. A decrease in foreign investment can reduce banks' ability to maintain their capital structure and intensify liquidity pressures. The primary measure for assessing company value is firm value, which can attract investors and indicate potential returns for shareholders. This study aims to analyze the influence of liquidity and profitability on firm value. The sample consists of 55 firm-year observations from the banking sector listed on the Indonesia Stock Exchange (IDX) ranging from 2019 to 2023, obtained using a purposive sampling technique. Data processed with Python using Multiple Linear Regression shows that liquidity has a significant and positive effect on firm value. Similarly, profitability also has a positive and significant effect on firm value. The results of the F-test indicate that liquidity and profitability have a simultaneous influence on firm value. The model demonstrates an outstanding prediction rate, with an R-squared value of 99.8%. The model These findings contribute valuable insights for stakeholders and investors, suggesting that by assessing the strength of liquidity and profitability in financial statements, they can enhance their profit potential.
Utilizing IndoBERT and BERTopic to Explore Public Opinion on BPS Instagram Posts Anugrah, Ahmad Farhan; Agatha, Rendy Dwi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10327

Abstract

This study aims to analyze public sentiment and topics of opinion toward the Central Statistics Agency (BPS) through comments on the Instagram account @bps_statistics. A total of 3,075 comments collected from January 1 to July 24, 2025, were analyzed using the IndoBERT model for sentiment classification and BERTopic for topic modeling. The IndoBERT model was developed using a semi-supervised learning approach, achieving an 88% classification accuracy with high precision and recall across all sentiment categories. The analysis results show that neutral comments dominate (52.78%), followed by negative comments (31.54%) and positive comments (15.69%). Topic modeling on negative sentiment revealed two main issues: distrust of poverty data and preference for international institution indicators such as the World Bank. Positive sentiment reflects appreciation for the quality of statistical data and moral support for BPS. Neutral comments mostly contain informative discussions about socioeconomic conditions and access to digital services. These findings emphasize the importance of improving BPS public communication, particularly in bridging the gap in public perception of official data. The social media-based approach has proven effective as a complement to formal surveys in capturing public opinion in a broad and dynamic manner.
Addition of Non-Skin Classes in Skin Type Classification Using EfficientNet-B0 Architecture Sani, Haitsam Muftin; Wardhana, Ajie Kusuma
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10335

Abstract

Skin type classification is an essential process in dermatology and skincare, aiming to categorize skin conditions such as dry, normal, and oily. However, image-based skin classification models often struggle when confronted with non-skin objects like clothing, background, or hair that are not accounted for in standard datasets. This study proposes a novel approach by integrating a nonskin class into a skin type classification model based on the EfficientNet-B0 architecture. The dataset used consists of images categorized into four classes: dry, normal, oily, and nonskin. The model was trained using transfer learning and optimized through techniques such as data augmentation, learning rate scheduling, and early stopping. The final evaluation achieved an accuracy of 91%, with the nonskin class showing perfect precision and recall. These results demonstrate that incorporating nonskin data can significantly enhance model robustness and accuracy. This research contributes a practical method for improving the reliability of skin classification systems, especially in real-world environments.
Pap Smear Image Classification for Cervical Cancer Prediction with Transfer Learning on ResNet101 Architecture Dewi, Sila Cahya; Rumini, Rumini
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10343

Abstract

Early detection of cervical cancer remains a pivotal strategy to improve clinical outcomes and mitigate mortality associated with this disease. This study introduces a robust deep learning framework employing the ResNet101 architecture to facilitate the automated classification of cervical cell images derived from Pap smear examinations. By leveraging transfer learning, the pre-trained ResNet101 model was fine-tuned to extract salient morphological features critical for distinguishing among diverse cervical cell categories. A comprehensive dataset of labeled Pap smear images, systematically expanded through augmentation techniques, was utilized to enhance model generalizability. The proposed approach achieved a remarkable classification accuracy of 99.6%, highlighting its effectiveness in reliably differentiating between normal and abnormal cellular structures. These findings substantiate the promise of deep residual networks coupled with transfer learning as a powerful tool in advancing computer-aided diagnostic systems, thereby reinforcing early screening initiatives for cervical cancer.
Comparison of Light Gradient Boosting Machine, eXtreme Gradient Boosting, and CatBoost with Balancing and Hyperparameter Tuning for Hypertension Risk Prediction on Clinical Dataset Murtiningsih, Dewi Ayu; Sari, Bety Wulan; Fajri, Ika Nur
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10400

Abstract

Hypertension is a long-lasting condition that is highly prevalent and significantly contributes to cardiovascular issues, making early identification a crucial preventive action. This research evaluates the efficacy of three boosting algorithms, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and CatBoost in forecasting hypertension risk. A publicly accessible dataset consisting of 4,363 samples was employed, followed by data preprocessing, feature selection through a voting method that integrates Boruta, Recursive Feature Elimination (RFE), and SelectKBest, as well as addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) and ADASYN (Adaptive Synthetic Sampling Approach). The models were additionally fine-tuned through hyperparameter optimization using GridSearchCV and Repeated Stratified K-Fold Cross Validation. The evaluation results demonstrate that all three algorithms exhibited strong predictive capabilities, with CatBoost leading the way, achieving an accuracy of 0.992, precision of 0.992, recall of 0.992, F1-score of 0.992, and ROC-AUC of 0.9987. Analyzing the confusion matrix further validated that CatBoost had the lowest number of misclassifications when compared to XGBoost and LGBM. Additionally, the use of SHapley Additive exPlanations (SHAP) for model interpretability highlighted that the key factors influencing the prediction of hypertension risk are blood pressure, body mass index (BMI), overall physical activity, waist circumference, triglyceride levels, age, and LDL cholesterol levels, aligning with established medical knowledge. To facilitate real-world use, the top-performing model was implemented into a user-friendly website interface, allowing users to predict their hypertension risk interactively. These findings illustrate that boosting algorithms, especially CatBoost, offer an accurate, dependable, and interpretable machine learning method for creating hypertension risk prediction systems.
Experimental Evaluation of Wazuh-Grafana Integration for Real-Time Cyber Threat Detection in Resource-Constrained Environments Sutanto, Achmad; Rakhman, Arif
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10404

Abstract

This research evaluates the performance of integrating Wazuh, an open-source Security Information and Event Management (SIEM) platform, with Grafana, a real-time visualization tool, for cyber threat detection in resource-constrained environments. The objective is to assess detection accuracy, false positive rates, response times, and system efficiency under controlled experimental conditions. The testbed consisted of two virtual private servers (4 vCPUs, 4–8 GB RAM, 38–50 GB storage) and employed the CIC-IDS2017 dataset as a benchmark for simulating three representative attacks: brute-force, malware injection, and webshell exploitation. The results showed that the integrated system achieved 100% detection accuracy with 0% false positives across 30 trials, with an average total detection time of 3033 ms. Resource utilization remained low, with CPU usage below 35% and memory consumption under 25%, confirming feasibility for mid-range servers typical of small institutions. While these results underscore the system’s efficiency, the findings must be interpreted within the limitations of a laboratory environment where predefined signatures were used. Performance in real-world networks with diverse traffic and unknown threats may differ, and further validation is required. This study makes two key contributions: (1) it provides the first structured quantitative benchmark of Wazuh-Grafana integration in constrained environments using a standardized dataset, and (2) it offers practical recommendations for small and medium-sized institutions, including minimum system requirements and guidelines for dashboard configuration. These findings reinforce the role of open-source solutions as affordable, adaptive, and effective alternatives to commercial SIEM systems, particularly for organizations with limited cybersecurity budgets.
Optimizing Decision Tree and Random Forest with Grid Search and SMOTE for Malware Classification on IoT Network Traffic Siroj, Muhammad Nurus; Zyen, Akhmad Khanif; Wibowo, Gentur Wahyu Nyipto
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10542

Abstract

The rapid growth of the Internet of Things (IoT) has increased the risk of malware attacks, posing serious threats especially to micro, small, and medium enterprises (MSMEs) that often lack sufficient cybersecurity resources. This study aims to optimize Decision Tree (DT) and Random Forest (RF) classifiers using Grid Search, while addressing the class imbalance problem through the Synthetic Minority Oversampling Technique (SMOTE). The Security Attacks Malware IoT Networks dataset with five classes (Benign, Malware, DDoS, Brute Force, Scanning) was used and divided into training and testing sets with stratified 80:20 split. Experimental results show that DT achieved 67.3% accuracy with a macro F1-score of 42.9%, while RF achieved 70.7% accuracy but a very low macro F1-score of 21.4%, indicating bias toward the majority class despite balancing. Boosting methods provided stronger baselines, with XGBoost reaching 87.0% accuracy and 66.7% F1-score, while LightGBM achieved 85.6% accuracy and 64.4% F1-score. ROC curves and confusion matrices confirmed that boosting methods were more balanced in recognizing minority classes. In terms of efficiency, DT required the shortest training time (8 seconds), while LightGBM provided the best trade-off between accuracy and computational cost (26 seconds). Paired t-tests further confirmed that performance differences between DT and RF were not significant, while boosting methods significantly outperformed RF. Overall, optimizing DT and RF with Grid Search and SMOTE enhances their performance, but boosting methods remain more robust for malware detection in IoT traffic. These findings provide practical insights for MSMEs in balancing accuracy and efficiency when deploying intrusion detection systems.
Sentiment Analysis of the Film "JUMBO" on Twitter Using the Naive Bayes Method and Support Vector Machine (SVM) with a Text Mining Approach Widodo, Tegar Robi; Fajri, Ika Nur; Sari, Bety Wulan
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10557

Abstract

This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector Machine (SVM) methods. The data were gathered through a crawling process on Twitter, yielding 2,011 tweets, which were then processed through several pre-processing steps, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Subsequently, the data were transformed into numerical representations using TF-IDF, followed by sentiment labeling into positive, negative, and neutral categories. For the Naive Bayes method, training and evaluation were conducted using 5-fold Cross Validation. The results showed that the Naive Bayes model achieved an accuracy of 80.60%, precision of 73.83%, recall of 73.50%, and an F1-score of 69.98%. Meanwhile, the SVM method obtained an accuracy of 75.87%, precision of 76.36%, recall of 62.45%, and an F1-score of 65.64%. Compared to the baseline random classifier, which only achieved an accuracy of 32.47%, both primary methods significantly outperformed it in classifying film review sentiments. The analysis also indicates that the F1-score is lower than the accuracy due to the imbalanced data distribution, with a considerably higher number of positive reviews. This study also presents visualizations of sentiment distribution and word clouds to provide a clearer understanding of audience opinions. The results demonstrate that the Naive Bayes method performs well and has potential for use in sentiment analysis of films on social media platforms. These findings are expected to provide valuable insights for the creative industry, particularly in evaluating audience responses and improving the quality of future film productions.
IoT-based Soil Nutrient Monitoring and Control Using Fuzzy Logic and Multi-Modal Sensor Integration Hakis, Andi Wahyunita; Arda, Abdul Latief; Jalil, Abdul
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10575

Abstract

The decline in soil quality due to inappropriate agricultural practices has become one of the main factors contributing to reduced agricultural productivity. The primary focus of this research is on monitoring and controlling soil nutrient quality, particularly in clay soil used for chili cultivation. This study aims to develop an Internet of Things (IoT)-based monitoring system integrated with multi-modal sensors and fuzzy logic algorithms. The system is designed to support precision agriculture by enabling automated decision-making based on real-time environmental data. The research uses an experimental approach, involving the design of a system based on the ESP32 microcontroller, sensor data processing using the Mamdani fuzzy algorithm, and integration with the Blynk platform for remote monitoring and control. The system responds to changes in environmental conditions to determine optimal timing for irrigation and liquid nutrient application adaptively. The test results show that the system achieved a classification accuracy of 84% and an average F1-score of 88.5%, indicating its effectiveness in handling continuous and uncertain sensor data. Evaluation of the fuzzy logic performance revealed a 75.8% success rate in irrigation control and 99.8% accuracy in nutrient delivery, demonstrating the system’s ability to respond accurately and efficiently to actual soil and environmental conditions. With its stable, adaptive, and resource-efficient performance, this system has the potential to become a practical solution for automating irrigation and fertilization processes in support of technology-driven and sustainable agriculture.

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