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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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Sentiment Analysis of Public Comments on X Social Media Related to Israeli Product Boycotts Using The Long Short-Term Memory (LSTM) Method Panggabean, Pitra Rahmadani; Asrianda, Asrianda; Aidilof, Hafizh Al-Kausar
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9458

Abstract

The boycott of Israeli products is a widely discussed issue on social media, particularly on X. This study aims to analyze public sentiment regarding the boycott using the Long Short-Term Memory (LSTM) method. Data was collected via the X API, resulting in 800 comments after cleaning and removing duplicates from initially 980 crawled datasets. LSTM was chosen for this analysis due to its superior ability to process sequential data like text and effectively capture long-term dependencies in natural language, which is crucial for accurate sentiment classification. Data was processed through preprocessing steps, sentiment labeling, and Term Frequency-Inverse Document Frequency (TF-IDF) weighting before being fed into the LSTM model. Sentiment was classified into three categories: positive, negative, and neutral. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the LSTM model achieved an accuracy of 80.62%, with negative sentiment dominating, followed by neutral and positive. This study demonstrates that the LSTM method effectively classifies public sentiment and can be applied to inform public policy decisions, map public opinion trends, and monitor responses to foreign policy issues related to the Israeli-Palestinian conflict.
A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools Saputri, Rifa Andriani; Asrianda, Asrianda; Rosnita, Lidya
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9460

Abstract

The rapid advancement of information technology has transformed education by providing tools to accurately predict students' academic performance. This study aims to develop a system for predicting academic achievement using the Random Forest algorithm, with a case study at SMAN 1 Aceh Barat Daya and SMAN 3 Aceh Barat Daya. Data from 632 student report cards for grades X and XI in the second semester of the 2023/2024 academic year were used, covering subjects such as Mathematics, Indonesian Language, and others, divided into 80% training data (506 samples) and 20% test data (136 samples). The research methodology involved data preprocessing, training the Random Forest model using entropy and information gain to construct decision trees, and performance evaluation using metrics such as accuracy, precision, and recall. The implementation resulted in a web-based application using Python and Flask, featuring an interactive interface and decision tree visualization. Testing on 136 test samples achieved an accuracy of 87.40%, with 111 correct predictions, 16 false positives, and 0 false negatives, demonstrating the model's reliability in identifying high-achieving students without missing potential. This research is expected to assist schools in identifying outstanding students, making data-driven decisions, and designing more effective educational strategies.
Sentiment Analysis of Youtube and Gotube Reviews on Google Play Using the Support Vector Machine (SVM) Method in Indonesia Putri, Sri Raihan; Asrianda, Asrianda; Rosnita, Lidya
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9461

Abstract

This research, titled Sentiment Analysis of YouTube and GoTube Reviews on Google Play Using the Support Vector Machine (SVM) Method in Indonesia, analyzes user perceptions of YouTube and GoTube based on Google Play reviews. The study is motivated by the growing popularity of video streaming apps in Indonesia and the limited sentiment analysis research on these platforms. The research collects 1,600 reviews (800 per app) from 2023-2024 using Python’s Scrapy library. The data is split 70% for training and 30% for testing, undergoing text preprocessing (tokenization, stop word removal, stemming), TF-IDF weighting, and SVM classification with an RBF kernel. Evaluation metrics include accuracy, precision, recall, and F1-score, with PCA used for visualization. Results show 94.50% accuracy overall, 97.01% for YouTube, and 92.66% for GoTube. GoTube has higher positive sentiment (385 of 400 test reviews) than YouTube (345 of 400) but lower negative sentiment (15 vs. 55). However, the model exhibits a positive class bias due to data imbalance. The study concludes that SVM effectively detects positive sentiment, but balancing data and exploring non-linear methods could improve negative sentiment detection.
Comparative Analysis of CNN, Transformers, and Traditional ML for Classifying Online Gambling Spam Comments in Indonesian Manullang, Martin Clinton Tosima; Rakhman, Arkham Zahri; Tantriawan, Hartanto; Setiawan, Andika
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9468

Abstract

The rise of user-generated content on social media and live-streaming platforms has intensified the spread of spam, particularly online gambling (Judi Online) promotions, which remain prevalent in Indonesian comment sections. This study investigates the effectiveness of various machine learning (ML) and deep learning (DL) approaches in classifying such spam content in Bahasa Indonesia. We compare five models: Support Vector Machine (SVM), Random Forest (RF), a CNN-based model, IndoBERT, and a custom lightweight transformer model named Wordformer. While IndoBERT achieves the highest performance across all metrics, it comes with high computational demands. Wordformer, in contrast, delivers a strong balance between accuracy and efficiency, outperforming traditional models while being significantly more lightweight than IndoBERT. Wordformer achieved 0.9975 accuracy and macro F1-score, surpassing SVM (0.9578) and Random Forest (0.9729), while maintaining a significantly smaller model size and fewer multiply-add operations. An extensive ablation study further explores the architectural and training design choices that influence Wordformer’s performance. The findings suggest that lightweight transformer models can offer practical, scalable solutions for spam detection in low-resource language settings without the need for large pretrained backbones.
Analysis of docker container Implementation in SIEM infrastructure Ardi, Noper; Lubis, Ahmadi Irmansyah; Ikhwan Ash Shafa Arrafi
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9476

Abstract

It is known that configuring system information and event management (SIEM) infrastructure using conventional virtualization still provides essential functions. However, if a problem occurs such as a configuration error during the staging process or application service failure, the recovery process from the error requires quite a long time. This research aims to explore and analyze the implementation of container technology in the SIEM Infrastructure using the Wazuh platform. The analysis focuses on a Docker-based architecture running Wazuh's core components: the wazuh-indexer, wazuh-manager, and wazuh-dashboard, each in its own container. This approach is evaluated to see how containerization affects SIEM effectiveness and efficiency, particularly in resource utilization and fault recovery. Performance testing carried out on systems using Docker Containers shows lower Memory and CPU usage compared to Conventional Virtualization. The results demonstrate that Docker not only enhances resource efficiency but also improves system resilience, directly impacting SIEM operational functionality.
Pemanfaatan Machine Learning untuk Menganalisis Praktik ESG: Studi Kasus pada Bank Berkinerja Unggul di Indonesia Tirkaamiana, Dean; Basuki, Satrio Samudro Aji
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9480

Abstract

This research related to the discover ESG practices by Top Performing Banks in Indonesia based on their Return on Asset (ROA) and Net Interest Margin (NIM). Due to the shifting the interest of the investor and the world condtion. Recently, investors have shown a growing inclination to incorporate ESG (Environmental, Social, and Governance) considerations into their decision-making processes. This trend is driven by a heightened awareness of environmental risks, particularly those linked to sustainability. In response to climate change, there is also a growing public interest in adopting more sustainable practices. Due to the broad and sometimes ambiguous definition of ESG, identifying the specific practices that should be implemented by banks remains challenging. Research on frameworks to guide these actions is still limited. For instance, explores the role of knowledge management in facilitating the integration of ESG factors. This research aims to create an ESG knowledge discovery framework with Natural Language Process (NLP) based on DBSCAN results of several companies which have good financial performance such as Return on Asset (ROA) and Net Interest Margin (NIM). The result will be analyzed by NLP, especially TF-IDF (Term Frequency - Inverse Document Frequency) and visualized by Word Cloud. The priority of Top Performing Bank in Indonesia related to the environment indicator conduct the activity which focus on carbon, green, and water. For social indicator, the main focus are employee, training, and financial. Lastly, for governance indicator, they are most prominent activity to the security, data, and policy. Overall, leading banks in Indonesia tend to prioritize environmental aspects in their operations. Companies with strong environmental initiatives have been shown to positively impact their earnings and financial performance.
A Improving House Price Clustering Results with K-means through the Implementation of One-hot Encoding Pre-processing Technique Maulani, Vicka Rizqi; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9481

Abstract

Basic human needs include a house that serves as a place to live and a shelter from everything. In Indonesia, owning a house is still a challenging aspect due to its high price. Information on house prices is needed for prospective buyers or consumers, so that buyers can adjust their needs and finances, and for producers or sellers it is used as a way to determine the segmentation of targeted market groups. House prices are influenced by several factors including, building area, number of bedrooms, number of bathrooms, location, condition and the presence of a garage. This research aims to improve the quality of house price clustering with K-means and the application of one-hot encoding in the data pre-processing process in representing categorical data. The dataset used has two types of data, namely numeric and categorical. The cluster evaluation is based on the silhouette score matrix and the determination of k is based on the elbow graph. The results showed an increase in the silhouette score value after applying one-hot encoding 0.15 which was previously 0.09, with the number of k = 3. The 0.15 matrix result is relatively low, which is caused by the overlap of house price values in the dataset, but it has been shown that one-hot encoding can represent categorical data well in the data pre-processing process so that the data can be processed with the k-means algorithm.
Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews Heti Aprilianti; Khothibul Umam; Maya Rini Handayani
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9482

Abstract

This study aims to evaluate and compare the performance of three machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naïve Bayes—for sentiment classification of user reviews on the NU Online application in the Google Play Store. NU Online is a religious digital platform providing Islamic content such as articles, prayers, and worship schedules. A total of 1,500 user reviews were collected using web scraping, and 1,491 were retained after data cleaning. Preprocessing steps included punctuation removal, case folding, normalization, stopword removal, stemming, and tokenization. Sentiment labels (positive or negative) were automatically assigned using a lexicon-based approach. The performance of the models was assessed using accuracy, precision, recall, and F1-score, calculated via confusion matrix with a training-testing data split. The results show that the SVM with a linear kernel achieved the best accuracy (81.6%), followed by Naïve Bayes (73.2%) and K-NN (66.9%). These findings indicate that SVM is the most effective algorithm in this context, providing practical contributions for developers of the NU Online digital religious platform and contributing to research in Indonesian natural language processing.
Implementation of FP-Growth Algorithms for Promo Package Determination in a Scooter Motorcycle Workshop Business I Gusti Ngurah Bagus Picessa Kresna Mandala; I Ketut Adi Purnawan; I Made Agus Dwi Suarjaya
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9499

Abstract

This study applies the FP-Growth algorithm to design bundled promotions for a scooter motorcycle accessory store and workshop in Denpasar, Bali. FP-Growth was chosen for its efficiency in mining frequent itemsets without generating candidate sets. From 23,381 transaction records (January-August 2024), the algorithm identified 16 association rules using a minimum support of 1% and confidence of 50%. These rules were selected based on lift values and product relevance. One notable example is the association between "BAUT TITANIUM GR5 M10 X 60" and "BAUT TITANIUM GR5 M8X50", which had a lift of 47.814, indicating a very strong co-purchase relationship. These high-lift combinations present valuable opportunities for bundling and targeted point-of-sale offers. The algorithm performed efficiently, with a runtime of just 0.1354 seconds and 402.6 MB of memory usage. Bundles based on these associations were presented to customers, and feedback was collected through a Customer Satisfaction (CSAT) survey involving 56 recent buyers. The survey yielded a high CSAT score of 83.93%, demonstrating customer satisfaction with the bundles’ relevance and appeal. These results confirm that FP-Growth can effectively inform promotional strategies by identifying strong product pairings that align with actual purchasing behavior. Strategically promoting such bundles not only enhances customer experience but also encourages multi-item purchases. This data-driven bundling approach is practical and profitable for medium-sized retail businesses, ultimately supporting the goal of increasing the Average Order Value.
Multiclass Sentiment Analysis of Electric Vehicle Incentive Policies Using IndoBERT and DeBERTa Algorithms Muhammad Bayu Nugroho; Akhmad Khanif Zyen; Nur Aeni Widiastuti
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9511

Abstract

The electric vehicle (EV) incentive policy in Indonesia has generated various public reactions, particularly on social media platforms. This study aims to classify public sentiment using the IndoBERT and DeBERTa transformer models. A total of 6,758 comments were collected from YouTube, filtered, preprocessed, and labeled into three sentiment categories: positive, negative, and neutral. From this, 1,711 clean data points were used and analyzed in two phases: before and after applying the Random Oversampling technique to address class imbalance. Model performance was evaluated using accuracy, precision, recall, F1-score, and training time. In the initial phase, IndoBERT achieved 96% accuracy with 603.71 seconds of training time, while DeBERTa reached 93% in 439.19 seconds. After balancing and applying 5-Fold Cross Validation, IndoBERT maintained 96% accuracy with balanced metric distribution, while DeBERTa recorded 93% accuracy. IndoBERT performed better in recognizing neutral sentiment, whereas DeBERTa was more time-efficient. These results highlight the effectiveness of local transformer models and data balancing techniques in improving sentiment classification performance on imbalanced datasets.

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