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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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Smart Glove Design to Improve Accessibility Communication for the Deaf Amanda, Janeri; Destya, Senie; Koprawi, Muhammad
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.v10i1.12110

Abstract

Deaf people rely on hand gestures as their primary means of communication; however, communication barriers often arise when surrounding individuals do not understand sign language. This study presents the design and evaluation of an Internet of Things (IoT)-based smart glove to improve communication accessibility for deaf individuals. The proposed system utilizes multiple MPU6050 motion sensors integrated with an Arduino Nano to detect finger and hand movements. Gesture recognition is implemented using a rule-based approach with predefined threshold values, enabling real-time detection without the need for training data. System performance was evaluated through response time and recognition accuracy measurements, as well as qualitative observations related to system stability and usability. Experimental results show response times ranging from 146–147 ms, indicating a fast and stable system. Recognition accuracy varies between 70% and 85%, depending on gesture complexity and finger movement patterns. Although the accuracy is moderate compared to machine learning-based approaches, the proposed system offers advantages in computational efficiency, simplicity, and ease of implementation. These findings demonstrate the potential of the smart glove as a practical assistive communication device, while also highlighting opportunities for further development through improved gesture modeling and user-centered evaluation.
Public Sentiment Analysis on Demonstration Actions Using IndoBERT Based on Transfer Learning Tentriajaya, I Dewa Ayu Pradnya Pratiwi; Agustina, Ni Putu Dina; 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.v10i1.12116

Abstract

Sentiment analysis based on language modeling plays a crucial role in mapping public perception of socio-political dynamics in Indonesia. This study aims to evaluate public sentiment toward the House of Representatives of the Republic of Indonesia (DPR RI) in response to the August 2025 demonstrations using the IndoBERT model based on transfer learning. The dataset comprises 1,815 Indonesian-language opinion texts classified into positive and negative sentiments. Due to a substantial class imbalance dominated by negative opinions, a hybrid sampling strategy combining oversampling and undersampling was employed to obtain a balanced dataset of 650 samples per class. The research methodology included text preprocessing, an 80:20 training–testing split, and fine-tuning the IndoBERT-base-p1 model. Experimental results indicate that the proposed model achieves robust and balanced performance, with an overall accuracy of 85%. Precision and F1-score for both sentiment classes reached 0.85, while recall values were 0.86 for negative sentiment and 0.85 for positive sentiment, demonstrating the model’s ability to identify both classes effectively without bias toward the majority class. Despite the dominance of negative sentiment in the original dataset, the application of data balancing techniques successfully mitigated class imbalance effects, enabling fair and proportional sentiment classification. These findings confirm that the IndoBERT-based transfer learning approach is effective in capturing public sentiment related to mass demonstrations and can provide valuable, data-driven insights for policymakers in understanding societal concerns in the digital era.
Interpretable Ensemble Models for Lifestyle-Based Sleep Disorder Prediction Rahardian, Farhan; Rakasiwi, Sindhu
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.v10i1.12125

Abstract

Sleep disorders are a major global health concern that affect cognitive performance, mental well-being, and long-term physiological health. Conventional diagnostic methods such as polysomnography are time-consuming and resource-intensive, limiting their use for large-scale early detection. Therefore, machine learning offers a practical alternative for predictive and data-driven sleep disorder analysis. This study compares the performance of four ensemble learning algorithms Random Forest, Gradient Boosting, AdaBoost, and XGBoost in predicting sleep disorders based on lifestyle and physiological factors using the Sleep Health and Lifestyle dataset consisting of 374 samples and three classes: insomnia, none, and sleep apnea. The research workflow includes data preprocessing, feature encoding, dataset splitting (70:30), and hyperparameter optimization using Grid Search with 5-fold Cross Validation to improve model stability and generalization given the limited dataset size. Model evaluation is conducted using accuracy, precision, recall, and F1-score calculated with a macro-average approach to ensure fair multi-class performance assessment. The results show that AdaBoost and XGBoost achieve the highest test accuracy of 90.27%, while Random Forest and Gradient Boosting obtain 89.38%. Performance differences among models are relatively small (±1%) but indicate consistent predictive behavior. Feature importance analysis identifies BMI category and systolic blood pressure as the most influential predictors, followed by occupation and physical activity level, highlighting the relevance of lifestyle and physiological factors in sleep disorder risk. Overall, this study demonstrates that ensemble learning models provide reliable predictive performance and interpretable insights to support early detection of sleep disorders based on lifestyle patterns.
Association Rule Mining for Truck Body Damage Pattern Analysis Using Apriori and CRISP-DM Dendy, Livanty Efatania; Tanamal, Rinabi
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.v10i1.12134

Abstract

This study investigates damage patterns in truck body components by applying the Apriori association rule mining algorithm within the CRISP-DM framework. The analysis is based on 281 historical repair records from CV Lestari’s fleet throughout 2024. The dataset encompasses 14 attributes, including vehicle types, route categories, body materials, and load conditions. To ensure the robustness of the generated rules, parameter tuning was conducted using a grid search approach, resulting in minimum support and confidence thresholds of 15% and 60%, respectively. A total of 50 association rules were derived, with several rules demonstrating high confidence values and lift values above 1.1, indicating meaningful and non-random correlations. Notably, structural frame damage is strongly associated with mountainous routes and heavy loads, while door and hinge damage tends to occur in aluminum box-bodied trucks operating under medium loads. These patterns were aligned with practical insights from field technicians and further contextualized through technical recommendations, such as reinforcing high-stress points and adjusting inspection schedules for high-risk configurations. The findings support the formulation of predictive maintenance strategies, enabling companies to transition from reactive repairs to proactive, data-driven decision-making. By integrating rule-based insights into maintenance planning, the study contributes to reducing unexpected failures, optimizing inspection frequency, and enhancing overall fleet reliability.
Sentiment Analysis of President Prabowo's Performance on Twitter (X) with a Comparative Study of SVM, XGBoost, and AdaBoost Maruf, Anang; Pajri, Afril Efan; Liana, 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.v10i1.12138

Abstract

This study was conducted to understand how Twitter (X) users respond to President Prabowo's performance through machine learning-based sentiment analysis. Data was collected using a dataset crawling approach, then processed through a series of pre-processing stages such as cleansing, case folding, tokenisation, stopword removal, and stemming before being converted into a numerical representation with TF-IDF. The class imbalance problem was addressed by applying SMOTE so that the model could learn more evenly. Three classification algorithms, SVM, XGBoost, and AdaBoost, were tested with the help of GridSearchCV to obtain the best parameter configuration. The research evaluation showed that the XGBoost algorithm was able to provide the best performance with an accuracy of 0.8443, followed by the SVM algorithm with an RBF kernel, which achieved an accuracy of 0.8135. The AdaBoost algorithm came in third with an accuracy of 0.7868. These findings indicate that the boosting approach, especially XGBoost, is better able to handle complex language patterns and high-dimensional text data characteristics. Overall, this study provides an overview of public opinion trends on social media and can be used as a reference for the development of sentiment analysis models in future research.
Indonesian Gold Price Forecasting Using Simple and Stacked LSTM with Expanding Window Lambang, Rahmat Tegar Patriot Hari; Prastya, Ifnu Wisma Dwi; Barata, Mula Agung Barata
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.v10i1.12148

Abstract

This study investigates the performance of two deep learning architectures, namely Simple LSTM and Stacked LSTM, for Indonesian gold price forecasting, with a particular focus on evaluating the effect of optimizer selection and learning rate configurations. An experimental framework is implemented using daily Indonesian gold price data from 2021 to 2024. Model performance is assessed using five-fold expanding window time series cross-validation to ensure robustness and avoid data leakage. Four adaptive training optimizers (Adam, Nadam, Adamax, and RMSprop) are evaluated across three learning-rate settings as part of a systematic sensitivity analysis of training hyperparameters. The results indicate that the Simple LSTM consistently outperforms the Stacked LSTM. The best performance is achieved by the Simple LSTM using the Adam optimizer with a learning rate of 0.01, yielding an RMSE of 9.235, MAE of 7.060, and MAPE of 0.71%. These findings demonstrate that simpler architectures combined with appropriate training configurations can provide superior forecasting accuracy for volatile financial time series.
Comparative Analysis of BERT and LSTM Models for Sentiment Classification of Mobile Game User Reviews Indriyatmoko, Toto; Rahardi, Majid; Utama, Hastari; Frobenius, Arvin Claudy
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.v10i1.12149

Abstract

Sentiment classification of user reviews for mobile games that rely on direct advertising (direct ads) is crucial for understanding player perceptions and improving user experience. This study aims to compare the performance of two deep learning architectures, Long Short-Term Memory (LSTM) and multilingual Bidirectional Encoder Representations from Transformers (BERT) in classifying sentiment in reviews into three categories, positive, negative, and neutral. The dataset used consists of reviews from games employing direct ads, which underwent rule-based labeling and text preprocessing. The LSTM model was built from scratch using a custom embedding layer, while the multilingual BERT model was fine-tuned using a transfer learning approach. Evaluation was conducted based on accuracy, precision, recall, and F1-score metrics. Experimental results show that multilingual BERT achieves superior validation loss compared to LSTM (0.37 vs. 0.44). BERT also outperforms LSTM significantly in terms of F1-score and its ability to understand multilingual linguistic context. However, LSTM demonstrates advantages in computational efficiency and training speed. These findings offer practical recommendations for developers in selecting an appropriate sentiment analysis model based on accuracy requirements and resource availability.
The Integration of AHP and Rank Order Centroid in a Decision Support System for Selecting Social Media for MSMEs Putri, Mayang Anglingsari
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.v10i1.12153

Abstract

This study aims to develop a Decision Support System (DSS) to assist Micro, Small, and Medium Enterprises (MSMEs) in selecting the most optimal social media platform for promotional activities. In the digital era, choosing an appropriate platform is a critical factor in enhancing marketing effectiveness; however, many MSMEs face challenges in making informed decisions due to limited analytic capabilities and resources. To address this issue, the proposed system integrates the Analytic Hierarchy Process (AHP) to determine the relative importance of decision criteria and the Rank Order Centroid (ROC) method to assign weights to the alternatives. The evaluation criteria include audience reach, cost efficiency, and user engagement, which are considered essential factors in digital marketing strategies for MSMEs. The results indicate that Instagram achieved the highest score of 0.208 and is recommended as the most suitable social media platform for MSME promotion. TikTok ranked second with a score of 0.082, followed by Facebook with a score of 0.041. Furthermore, user validation testing demonstrates that the system is well accepted by MSME practitioners, as it provides recommendations that are accurate, structured, and easy to use.This research contributes by offering a technology-based decision-making solution that enhances the effectiveness of digital marketing strategies for MSMEs. The developed DSS serves as a practical and relevant tool to support promotional decision-making and to address the challenges of social media utilization in today’s competitive digital landscape.
Identification and Classification of Cracks in Traditional Pottery from West Sumatra Using Digital Image Processing Mahessya, Raja Ayu; Yenila, Firna
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.v10i1.12156

Abstract

Cracks in traditional West Sumatran pottery are a major challenge in preserving this cultural heritage. With age and the manual manufacturing process, pottery becomes highly susceptible to physical damage, particularly cracks on the surface and internal structure. These cracks not only affect the functional and aesthetic value but also reduce the cultural and economic value of the pottery. Therefore, an accurate early identification system is crucial to ensure the survival and preservation of this culture. This study developed a digital image processing-based system to detect and classify cracks in traditional pottery. The system integrates image preprocessing, including cropping, resizing, grayscale conversion, contrast stretching, and histogram equalization to improve image quality and highlight thin and irregular cracks. Image segmentation was performed using the Multi-Threshold Otsu method to separate cracks from the background, while classification was performed using a convolutional neural network (CNN). Experimental results show that this system is able to achieve an accuracy of 94.8%, precision of 93.5%, recall of 92.3%, and F1-score of 92.9%, indicating the system's ability to accurately detect cracks. Comparisons with other segmentation and classification methods are needed to provide a more comprehensive picture of the effectiveness of this approach. The implementation of this system is expected to support the preservation of traditional Minangkabau pottery through digitalization, provide an ornament database that can be accessed by researchers, artists, and the general public, and assist in more efficient cultural documentation and archiving.
Software Development for Swimmer Performance Prediction System Based on Physical Characteristics using XGBoost Tanzil, Surya Pratama; Tinaliah, Tinaliah; Widi, Anugerah
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.v10i1.12176

Abstract

Swimmer performance assessment in Indonesia still largely depends on coaches’ intuition, which may lead to subjective decisions and inconsistencies in training program planning, particularly in environments where frequent changes in coaches and sports administrators occur. The lack of structured and data-driven performance assessment tools further limits the continuity and objectivity of athlete development. This study aims to develop a web-based system capable of predicting swimmers’ performance potential by estimating race times based on physical characteristics using the XGBoost model. The proposed system is designed to support coaches in identifying athlete performance potential in a more objective and data-driven manner. Model evaluation results indicate that the XGBoost model achieved an R² value of 0.9190, demonstrating a very high level of prediction accuracy, with an average prediction time of 7.036 seconds. Software testing results confirm that the system operates as intended and is able to present prediction outputs in the form of estimated swimming time, performance percentage, and performance classification into four categories: Very High, High, Medium, and Low. Furthermore, usability evaluation using the USE method yielded excellent results, with an average score of 88.16%.

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