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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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Benchmarking Oversampling Strategies to Enhance the Performance of Machine Learning Algorithms in Hypertension Classification Maulia, Aenur Hakim; Salam, Abu
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.v10i1.11917

Abstract

This study benchmarks the effectiveness of three oversampling techniques, namely SMOTE, Random Oversampling (ROS), and ADASYN, in enhancing machine learning performance for multiclass hypertension classification. Using key physiological features and four optimized algorithms Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, and Artificial Neural Networks, model performance was assessed using accuracy, F1-macro, and ROC AUC metrics. The experimental results indicate that the combination of SMOTE and Linear Discriminant Analysis (LDA) yields the highest overall performance, achieving an accuracy of 0.9773 and an F1-macro score of 0.9848. Logistic Regression demonstrates optimal results when paired with ROS, also reaching an accuracy of 0.9773. Artificial Neural Networks show the most substantial performance improvement under ADASYN, particularly reflected in higher F1-macro values. Although Support Vector Machine is less sensitive to oversampling interventions, it achieves a strong ROC AUC score of 0.9776 when trained using SMOTE. Overall, the findings confirm that oversampling techniques significantly improve classification performance in multilevel hypertension prediction, with SMOTE combined with LDA emerging as the most effective configuration.
English English Karimah, Sofia Rizkal; Udayanti, Erika Devi
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.v10i1.11921

Abstract

This research aims to compare the performance of the Apriori and FP-Growth algorithms in the process of data mining association patterns in the online sales transaction data of a bookstore. The dataset used consists of 74.090 transactions resulting from data cleaning from the period January-June 2025. The analysis was conducted through the stages of data collection, followed by data preparation consisting of data cleaning and data transformation, and then continued to the modeling stage of the two algorithms. The results of the experiment show that Apriori tends to be faster on small-scale datasets with simple transaction patterns, while FP-Growth has more stable memory usage and shows more efficient processing time on parameters that analyze larger data. Both algorithms produce identical numbers and contents of association rules for each parameter variation, indicating that the significant difference lies in performance efficiency, and not in the knowledge patterns produced. Rules with the highest lift values, such as the association between the books "Rumah Kaca" and "Jejak Langkah" (lift: 183,306 & confidence 0,903) and between the books "Namaku Alam" and "Pulang" (lift: 34,062 & confidence: 0,51) indicate strong purchasing patterns between titles with the same author and theme. These findings have the potential to support cross-selling strategies and product recommendations in online sales systems. This research is still limited to a relatively small and homogeneous dataset, so further using a broader data coverage is recommended to test the algorithm's performance more comprehensively.
Suicidal Ideation Detection in Social Media using Optimized CNN-BiLSTM Architecture Putri Novitasari, Hestiana; Soeleman, M. Arief; Rosita Sari, Sifa Ayu; Maida, Mamay
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.v10i1.11926

Abstract

This research aims to develop an optimized hybrid deep learning model for detecting suicidal ideation from social media text. The growing volume of online discussions, particularly on platforms such as Reddit, provides valuable signals for early identification of individuals at risk; however, the linguistic characteristics of user-generated content are highly diverse and often noisy. To address this challenge, this study proposes an Optimized CNN-BiLSTM architecture enhanced with a dropout rate of 0.6 and a strategic training approach utilizing Early Stopping (patience=3) and a Learning Rate Scheduler (ReduceLROnPlateau) to prevent local minima and ensure convergence stability. The dataset used consists of 232,074 text entries with a balanced class distribution (50% suicide, 50% non-suicide) to ensure the validity of evaluation metrics and eliminate majority class bias. Experimental results demonstrate that the optimized model achieves an accuracy of 94.96%, precision of 95.70%, recall of 94.15%, and an F1-score of 94.92%, indicating a significant improvement over the baseline CNN-BiLSTM and single BiLSTM models. Furthermore, interpretability analysis via keyword visualization (Word Cloud) validates that the model effectively captures semantically relevant emotional expressions of despair. These findings suggest that the optimized hybrid architecture provides a robust and operationally viable approach for supporting real-time early-warning systems on social media platforms to facilitate timely mental health interventions.
Improving the Accuracy of Obesity Classification Using a Stacking Classifier on Imbalanced Data with SMOTE Sari, Sifa; Soeleman, M.Arief; Maida, Mamay; Novitasari, Hestiana Putri
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.v10i1.11928

Abstract

Overweight continues to be a prevalent public health problem related to lifestyle behavior, eating behaviour and physical activity. The aim of this work is to develop a generalized and robust machine learning model having a high accuracy for categorizing obesity-level. The study applies to the Obesity Dataset with 1610 members and some preprocessing methods such selected data cleaning, categorical attributes transformation, train/test data set split and class imbalance under utilization of SMOTE approach. The modeling process is based on two base learners namely an optimized Random Forest and Gaussian Naïve Bayes that are fused by Stacking Classifier while using Logistic Regression as the meta-model. Experimental results show that the performance of stacking is the best where it obtains an accuracy rate of 86.34%, outperforming each single model. The analysis also reveals enhancements of various classification measures: stacking can indeed model complex non-linear dependencies between instances as well as simple linear ones. In general, the results serve to demonstrate that stacking-based ensemble learning is a strong solution for predicting obesity level and holds promise against early risk detection in preventive health care systems.
Comparison of K-Nearest Neighbor, Naïve Bayes, and C4.5 Algorithms for Predicting Academic Stress Risk in Students Based on Psychological Survey Data Widya Pertiwi, Nur Annisa; Wibisono, Iwan Setiawan
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.v10i1.11932

Abstract

Academic stress is a psychological problem experienced by many students and can have an impact on learning achievement, mental health, and quality of life. This study aims to compare the performance of the K-Nearest Neighbor (KNN), Naïve Bayes, and C4.5 (Decision Tree) algorithms in predicting the level of academic stress risk in students based on psychological survey data. Data were obtained from 700 active students at Ngudi Waluyo University through a questionnaire covering physiological, psychological, and behavioral aspects, with a total of 15 indicators using a Likert scale. The data then underwent pre-processing, labeling, standardization, and division into training and test data with a ratio of 80:20. The evaluation was conducted using the Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and AUC-ROC metrics. The results showed that the Naïve Bayes algorithm performed best with an accuracy of 93.26%, precision of 93.35%, recall of 92.26%, and F1-score of 92.80%. The KNN algorithm was in second place with an accuracy of 91.43%, while the C4.5 algorithm had the lowest performance with an accuracy of 80.60%. Based on these results, Naïve Bayes is recommended as the most optimal algorithm for predicting academic stress risk in students using psychological survey data. This study is expected to assist educational institutions in identifying students at risk of stress early on and supporting the development of more effective prevention strategies.
Evaluating Image Recognition Accuracy in Explicit Content Detection: A Comparative Study with Indonesian Perceptions Fahmi, Rauhil; Utama, Deni; Pratama, Muhammad Ridho Kurniawan
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.v10i1.11934

Abstract

This study evaluates image recognition accuracy in explicit content detection by using the Indonesian social context as a comparative reference. Google Vision SafeSearch is employed as a representative automated image recognition system widely used in online content moderation. Although such systems provide efficiency in detecting adult, violent, or racy content, challenges arise when their detection outputs must align with more conservative cultural and religious norms, such as those in Indonesia. A quantitative descriptive-comparative method was applied by testing six representative images based on SafeSearch explicit content categories (adult, racy, violence, medical, and spoof) and comparing the automated detections with Indonesian respondents’ perceptions collected through a Likert-scale questionnaire. Statistical analysis shows a significant difference between the system’s explicit content classifications and human perceptions, with respondents consistently rating explicitness higher than Google Vision API. Despite this difference, a strong Spearman rank correlation indicates that Google Vision SafeSearch is consistent in ranking explicit content levels, although still limited in capturing emotional intensity and cultural sensitivity. These findings highlight how Indonesian social and cultural norms shape the perception of explicit imagery, emphasizing the need for image recognition systems that incorporate local contextual factors.
AI-YOLO Based Smart Laboratory Security for Automated Face Recognition and Suspicious Activity Detection Hamzidah, Nurul Khaerani; Syahrir, Syahrir; Jariyah, Ainun; Da Costa, Carlos Agunar; Saenab, Sitti; Muin, Dul Arafat; Ichzan As, Nur
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.v10i1.11936

Abstract

Ensuring laboratory security is a critical consideration within campus environments to effectively prevent theft and suspicious activities. Traditional CCTV systems predominantly rely on manual monitoring, resulting in delayed responses to incidents. This research seeks to develop and implement an Artificial Intelligence (AI)-based laboratory security system, integrating three primary models: YOLOv5 for human object detection, Face Recognition for individual identification, and Media Pipe Pose for real-time analysis of suspicious movements. The system is designed as a Flask-based monitoring website, which displays activity logs, detected individual identities, and automated notifications based on image processing results on a Raspberry Pi connected to CCTV cameras. The research methodology employs an applied experimental approach, encompassing stages such as system design, face dataset collection, data encoding utilizing the Face Recognition Library, and performance evaluation under two lighting conditions (bright and dark) and three distance variations. The test results indicate that the Face Recognition method operates optimally at a distance of 2 meters in bright lighting conditions, achieving an accuracy of up to 92%. However, its performance declines at distances exceeding 3 meters and under low-light conditions. Conversely, MediaPipe Pose exhibits high stability, with an average accuracy of 94% in bright conditions and 89% in dark conditions, and is capable of transmitting real-time notifications for activities such as lifting objects or placing hands into pockets. The AI-based laboratory security system developed has demonstrated effectiveness, adaptability, and responsiveness in the automatic detection of identities and suspicious activities. The integration of YOLO v5, Face Recognition, and MediaPipe Pose models offers an intelligent and efficient security solution that facilitates the implementation of the Smart Campus concept within educational environments.
Comparative Study of LSTM and GRU Accuracy in Predicting BBRI Stock Closing Price Akbari, Rifqy Willy; Prayogo, Adam; Jahir, Abdul
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.v10i1.11938

Abstract

Stock price forecasting plays an important role in supporting investment decision-making in volatile financial markets. This study compares the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting the closing price of PT Bank Rakyat Indonesia (BBRI.JK) stock using daily closing price data from Yahoo Finance for the period November 2, 2020, to October 30, 2025. The research methodology includes data collection, preprocessing, model development, and evaluation. The results show that the GRU model outperforms LSTM in prediction accuracy, achieving an RMSE of 90.14, MAPE of 1.86%, and MAE of 68.89, while LSTM records an RMSE of 111.00, MAPE of 2.37%, and MAE of 87.55. In terms of computational efficiency, LSTM requires less training time (343.57 seconds) compared to GRU (471.98 seconds). The Diebold–Mariano test yields a DM statistic of 1.9949 with a p-value of 0.0461, indicating a statistically significant difference in predictive accuracy, where GRU produces lower prediction errors. This study provides empirical insights into the trade-off between accuracy and computational efficiency of deep learning models for stock price forecasting.
Analysis of the Determinants of Pelni Mobile Adoption Failure in Manokwari: A Hybrid Diffusion of Innovation and Theory of Planned Behaviour Approach Bonai, Yubelina Meilia; Sumendap, Andreas Leonardo; Sanglise, Marlinda
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.v10i1.11959

Abstract

The adoption of digital services like Pelni Mobile in developing regions faces complex challenges. Despite offering ease of access, its adoption rate in Manokwari Regency remains low. Previous studies have not extensively explored typical barriers such as resistance to change, perceived financial costs, inconvenience, and ease of access. This study analyzes the factors behind Pelni Mobile's adoption failure by integrating the DOI and TPB approaches. Data were collected via online questionnaires from 435 participants and analyzed using SEM-PLS. Findings show that Perceived Financial Cost (P=0.000), Resistance to Change (P=0.000), and Inconvenience (P=0.000) have a significant negative influence on Behavioral Intention to Use. This means perceived costs, resistance to change, and inconvenience can reduce usage interest. Conversely, Perceived Ubiquity (P=0.000) has a significant positive influence on usage intention, and Behavioral Intention to Use positively influences Use Behavior, indicating that ease of access can encourage adoption.The implications highlight the need for strategies to reduce financial barriers, improve accessibility, employ educational approaches to address resistance, and enhance user experience. For developers and policymakers, these results serve as a guide for designing more inclusive digital services tailored to the characteristics of developing communities, particularly in contexts similar to Manokwari. Generalizing the findings to other regions must consider local social, economic, and cultural differences.
Leveraging Convolutional Neural Networks and Random Forests for Advanced Sentiment Classification of Social Media Responses on Public Services Rohalia, Alya; Rifkha Rahmika, Afiyah
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.v10i1.11965

Abstract

In the digital era, social media has become a significant channel for citizens to express their opinions on government services. In Indonesia, particularly in the context of municipal issues, understanding public sentiment is essential to improving public service delivery. This study analyzes user comments from Facebook, Instagram, Twitter, and YouTube to capture public responses toward local government performance. Departing from previous studies that typically employ binary or three-level classifications, this research implements a five-category sentiment scheme: Very Good, Good, Fair, Poor, and Very Poor. A hybrid model combining a Convolutional Neural Network (CNN) for feature extraction and a Random Forest (RF) classifier is proposed to address this multi-class task. The model achieves 87% accuracy, outperforming the individual CNN and RF models. The results demonstrate the potential of social media–based sentiment analysis to enhance public service quality in Indonesia.

Page 77 of 81 | Total Record : 805

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