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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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Image Processing and Object Detection in the Indonesian Sign System (SIBI) for Hearing-Impaired Communication Faroek, Dewi Astria; Yusuf, Muhammad; Haris, Haris
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.v10i1.11395

Abstract

Communication is a fundamental human need, yet individuals with hearing impairments continue to face barriers due to limited access to sign language translation technologies. In Indonesia, the adoption of such technologies remains low, particularly in regions such as Sorong, Southwest Papua, creating a communication gap between the Deaf community and the general public. This study develops a web-based detection system for 36 classes of the Indonesian Sign System (SIBI) using the YOLOv5 algorithm. The dataset consists of 5,682 images of SIBI hand poses with variations in lighting and background, divided into 4,970 training images (87%), 376 validation images (7%), and 335 test images (6%). All data were processed through labeling, preprocessing, augmentation, balancing, and model training. The training was conducted for 150 epochs, and the evaluation results show that YOLOv5 is capable of detecting SIBI signs with significant accuracy. Performance evaluation using a confusion matrix achieved a detection accuracy of 95%, supported by stable precision and recall values and real-time inference performance on common web browsers. Usability testing with 20 respondents indicated satisfaction levels above 72.8%, demonstrating that the system is practical and easy to use. This research presents a validated real-time, web-based SIBI detection system that supports inclusive computer vision applications and enhances accessibility in public services such as education, healthcare, and administrative environments.
Analysis of the Impact of Lateral Stock Transfers in Distribution Network with a Central Warehouse and Two Storage Points Dambo Punga; Mabela Makengo Rostin; MATONDO MANANGA, Herman; Muhala Luhepa Blaise; Boono Yaba Benjamin
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.v10i1.11408

Abstract

Our study, Analysis of the impact of lateral Transfers in a stock distribution system with a central warehouse and two stocking points, aims to analyze the effet of lateral stock transfers between the two stocking points on minimizing the total inventory management cost system, retailers manage their inventories according to the (R,S) policy. This study also examines the service level and the stockout rate resulting from the implementation of lateral stock transfers. Each point i (i=1,2) manages its inventory independently in order to meet the consomer demand yi. Each stocking point has a maximun inventory level Si , when customer demand is less than or equal to the reorder point si , an order of quantity Qi= Si-si is placed with the central warehouse. This quantity is delivered after a known lead time Li. If the delivery lead time is too long, stocking point i may request a lateral transfer of quantity Xji from stocking point j, which has excess inventory, in order to avoid a stockout. The originality of this publication stems from the implementation of a numerical application using MATLAB, which allowed us to conduct this analysis.
Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest Febriani, Wulandari; Mambang, Mambang; Prastya, Septyan Eka; Sabella, Billy; Marleny, Finki Dona
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.v10i1.11415

Abstract

Adolescent exposure to violent content on social media has emerged as a critical issue due to its potential impact on mental health and cyberpsychological well-being. This study aims to classify multiple cyberpsychological impacts experienced by adolescents as a result of exposure to violent content on social media using a multi-label machine learning approach. A quantitative method was employed using self-reported data collected from 550 Indonesian adolescents aged 12–18 years through an online questionnaire. Psychological impacts were measured using adapted instruments from the Depression Anxiety Stress Scales (DASS-21) and cyberpsychology scales, then transformed into multi-label targets. Support Vector Machine (SVM) and Random Forest algorithms were implemented using a One-vs-Rest strategy. Model performance was evaluated using Hamming Loss, precision, recall, and Macro F1-score. The results indicate that SVM outperformed Random Forest with a Hamming Loss of 23.16% and a Macro F1-score of 0.42, particularly in predicting dominant labels such as anxiety and decreased self-confidence. However, both models showed limited performance in predicting minority labels such as depression and academic decline due to data imbalance. These findings highlight the importance of handling imbalanced data in cyberpsychology-based machine learning research and demonstrate the potential of multi-label classification in representing the complexity of psychological impacts of digital violence on adolescents.
A Fuzzy C-Means–Based Clustering Model for Analyzing TOEFL Prediction Scores in Higher Education Gulo, Filipus Mei Tri Boy; Hidayat, Rahmad; Hendrawaty, Hendrawaty; Hidayat, Rahmat Isma; Fasya, Muhammad Heikal; Syifaurrahman, Syifaurrahman; Ananda, Dea Syafira
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.v10i1.11468

Abstract

In the era of digital transformation, the application of data mining in academic data management has become an important requirement for improving the quality of education. One crucial aspect is English proficiency. One of the tools for measuring English proficiency is the Test of English as a Foreign Language (TOEFL) Prediction test, which is administered at every university, including the State Polytechnic of Lhokseumawe. The management of TOEFL Prediction scores can utilize data mining as a basis for more in-depth learning analysis, as well as evaluation material. This study aims to design and develop a model for grouping the TOEFL scores of students at State Polytechnic of Lhokseumawe by applying the Fuzzy C-Means (FCM) algorithm. The research methods included observation and interviews, data collection and pre-processing, cluster model design, web-based system development, and system testing. Evaluation was conducted through Black Box and White Box testing for the system, as well as cluster quality validation using the Xie-Beni Index (XB) and Partition Coefficient. The results showed that the pre-test dataset of first-year students (651 data) produced three clusters with an XB value of 0.623, while the dataset of final-year students (826 data) produced six clusters with an XB value of 0.181. The developed model proved to be able to map students' English language abilities in a more structured manner and could be used as a basis for academic planning and skill improvement.
Comparative Analysis of Foot Sole Classification Models: Evaluating Logistic Regression, SVM, and Random Forest Purba, Trie Dinda Maharani; Yuadi, Imam
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.v10i1.11550

Abstract

Accurate sole classification and types can aid applications in healthcare, sports, and biometrics such as diagnosis of high arch or flat foot disease, as well as in improved design of custom orthotics and enhanced gait analysis to improve sports performance. When applied to large-scale datasets, traditional methods for foot sole classification are inefficient as they are often manual, time-consuming and prone to human error. Machine learning has the ability to significantly improve accuracy and efficiency in automating this process. The proposed method uses Logistic Regression model compared to Support Vector Machines (SVM), and Random Forest using Orange Data Mining. The performance of these algorithms changes depending on the complexity of the data and model parameters. There are three types of feet that will be processed in this image analytics namely normal arch, flat foot and high arch. The pre-trained models used are Inception V3, VGG-19 and SqueezeNet. Logistic Regression model showed the best overall performance with superior parameter values such as AUC of 0.973, Classification Accuracy (CA) of 0.933, and MCC of 0.902, and demonstrated reliability and balance between precision and recall.
Aspect-Based Sentiment Analysis of Tourist Attractions in Labuanbajo Using the Transformer Model as a Recommendation for Improving Service Quality Nur, Sadikah; Sahibu, Supriadi; Razak, Mashur
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.v10i1.11565

Abstract

Labuan Bajo as super-priority destinations experience improvement visit in a number of year lastly, however quality service Not yet fully fulfil expectation tourists . Study This analyze perception traveler through Aspect-Based Sentiment Analysis (ABSA) approach using the IndoBERT model. A total of 2,564 reviews multilingual from Google Maps and TripAdvisor processed through translation, pre-processing, extraction aspects, sentiment labels automatic, and model training. Four aspects analyzed based on framework SERVQUAL theory and Tourism Destination Quality: attractions, amenities, accessibility, and price. Model evaluation was conducted using precision, recall, and F1-score per aspect. The results show performance best The amenity and attraction aspects obtained the highest and most consistent scores across all metrics (around 0.83–0.88), indicating that reviews for these two aspects were more explicit and easily mapped by the model. In contrast, the access and price aspects showed lower scores (around 0.65–0.72), indicating linguistic challenges such as implicit aspects, variations in the context of the travel experience, and figurative complaints . The study This give recommendation policy connected data based direct with model findings. Limitations such as translation noise, biased datasets dominated by review positive-neutral, and not existence baseline comparison also discussed. These results confirm that ABSA approach can help stakeholder’s policy, however Still need improvement through other models such as IndoBERTweet, mBERT, or IndoBART.
Comprehensive Comparison of TF-IDF and Word2Vec in Product Sentiment Classification Using Machine Learning Models Sinaga, Asra Gretya; Robet, Robet; Pribadi, Octara
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.v10i1.11582

Abstract

Sentiment analysis supports data-driven decisions by turning product reviews into reliable polarity labels. We compare four text representations, TF-IDF, TF-IDF reduced via SVD, Word2Vec (trained from scratch), and a hybrid TF-IDF(SVD-300). Word2Vec, for sentiment classification of Indonesian Shopee product reviews from Kaggle (~2.5k texts). After normalization (with optional emoji handling and Indonesian stemming), ratings are mapped to binary sentiment (≤2 negative, ≥4 positive; 3 discarded). Each representation is evaluated with Logistic Regression, Support Vector Machines (linear/RBF), Naive Bayes, and Random Forest under stratified 5-fold cross-validation. TF-IDF with Logistic Regression (C=1.0) yields the best results (F1-macro = 0.816 ± 0.026; Accuracy = 0.816 ± 0.026), with LinearSVC as a strong runner-up. Word2Vec (scratch) performs lower, consistent with limited data being insufficient to learn stable embeddings, while the hybrid representation offers only modest gains over Word2Vec and does not surpass TF-IDF. These findings indicate that TF-IDF is the most reliable and consistent representation for small, short-text review datasets, and they underscore the impact of feature design on downstream classification performance.
Indobert-Based Sentiment Analysis of Political Discourse on Platform X: The Case Of Prabowo-Gibran Administration Sidauruk, Vanesa Estetika; Herowati, Wise
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.v10i1.11586

Abstract

The 2024 Indonesian presidential election inaugurated the Prabowo Subianto–Gibran Rakabuming Raka administration, whose early performance has been widely discussed on digital social networks, particularly X (Twitter). This study evaluates public sentiment toward the administration's performance up to June 30, 2025 using an IndoBERT-based text classification approach. A total of 2,612 public posts were collected via web scraping and processed through text preprocessing steps (noise removal, slang correction, normalization, and lemmatization). The data were labeled into three sentiment classes (positive, neutral, and negative) and split into training, validation, and test sets (2,092 / 418 / 105). The fine-tuned IndoBERT model achieved an overall test accuracy of 0.78, with the highest F1-score on the negative class (0.82), followed by neutral (0.76) and positive (0.75). The confusion matrix indicates that neutral posts are more frequently confused with positive posts, suggesting that neutral sentiment remains harder to separate in politically nuanced and noisy social-media text. This study also compares IndoBERT with a traditional baseline (TF-IDF + SVM using polynomial kernel). Results show that IndoBERT (78% accuracy) significantly outperforms SVM (72.19%), particularly in detecting negative sentiment (F1: 0.82 vs 0.72), demonstrating superior contextual understanding of politically nuanced text. This work also highlights methodological and ethical considerations for political opinion mining, including representativeness limits of X users and privacy-preserving handling of public posts. Future work should expand the dataset, address class imbalance, and explore additional transformer-based architectures to strengthen generalizability and benchmarking.
EDCST-Rain: Enhanced Density-Aware Cross-Scale Transformer for Robust Object Classification Under Diverse Rainfall Conditions OSHASHA, Fiston; Djungu Ahuka, Saint Jean; Mwamba Kande, Franklin; Simboni Simboni, Tege; Biaba Kuya, Jirince; Muka Kabeya, Arsene; Tietia Ndengo , Tresor; Dumbi Kabangu , Dieu merci
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.v10i1.11590

Abstract

Rain degradation significantly impairs object classification systems, causing accuracy drops of 40-60% under severe conditions and limiting autonomous vehicle deployment. While preprocessing approaches attempt deraining before classification, they suffer from error propagation and computational overhead. This paper introduces EDCST-Rain, an Enhanced Density-Aware Cross-Scale Transformer specifically designed for robust classification under diverse rain conditions. The architecture consists of five integrated components: a Rain Density Encoding Module that captures rain streak density, accumulation, and orientation; a Swin-Tiny Backbone for hierarchical feature extraction; and three rain-specific mechanisms: directional attention modules adapting to rain streak orientation, accumulation-aware processing handling lens droplet distortions, and adaptive cross-scale fusion integrating multi-resolution information. We develop a comprehensive physics-based rain simulation framework covering four rain types (drizzle, moderate, heavy, storm) and implement a curriculum learning strategy that progressively introduces rain complexity during training. Extensive experiments on CIFAR-10 demonstrate that EDCST-Rain achieves 83.1% clean accuracy while maintaining 71.8% under severe rain (86.4% retention), representing a 10-percentage-point improvement over state-of-the-art methods. With 15.8 million parameters and a 14.3 ms GPU inference time, enabling real-time operation, EDCST-Rain provides a practical, weather-robust perception framework suitable for autonomous systems operating under adverse weather conditions.
Addressing Extreme Class Imbalance in Multilingual Complaint Classification Using XLM-RoBERTa Ariyanto, Muhammad; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Gamayanto, Indra; Naufal, Muhammad; Winarno, Sri; Iswahyudi
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.v10i1.11606

Abstract

Government complaint management systems often suffer from extreme class imbalance, where a few public service categories accumulate most reports while many others remain under-represented. This research examines whether simple class weighting can improve fairness in multilingual transformer models for automatic routing of Indonesian citizen complaints on the LaporGub Central Java e-governance platform. The dataset comprises 53,877 Indonesian-language complaints spanning 18 service categories with an imbalance ratio of about 227:1 between the largest and smallest classes. After cleaning and deduplication, we stratify the data into training, validation, and test sets. We compare three approaches: (i) a linear support vector machine (SVM) with term frequency inverse document frequency (TF-IDF) unigram and bigram and class-balanced weights, (ii) a cross-lingual RoBERTa (XLM-RoBERTa-base) model without class weighting, and (iii) an XLM-RoBERTa-base model with a class-weighted cross-entropy loss. Fairness is operationalised as equal importance for categories and quantified primarily using the macro-averaged F1-score (Macro-F1), complemented by per-class F1, weighted F1, and accuracy. The unweighted XLM-RoBERTa model outperforms the SVM baseline in Macro-F1 (0.610 vs 0.561). The class-weighted variant attains similar Macro-F1 (0.608) while redistributing performance towards minority categories. Analysis shows that class weighting is most beneficial for categories with a few hundred to several thousand samples, whereas extremely rare categories with fewer than 200 complaints remain difficult for all models and require additional data-centric interventions. These findings demonstrate that multilingual transformer architectures combined with simple class weighting can provide a more balanced backbone for automated complaint routing in Indonesian e-government, particularly for low- and medium-frequency service categories.

Page 71 of 81 | Total Record : 805

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