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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Teknik Informatika (JUTIF) PARAHITA : Jurnal Pengabdian kepada Masyarakat Sevanam: Jurnal Pengabdian Masyarakat AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Pengabdian Masyarakat Inovasi Indonesia
Sasongko, Theopilus Bayu
Informatics, Faculty Of Computer Science, Universitas Amikom Yogyakarta, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences

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Journal : journal of applied informatics and computing

 1 
Sentiment Analysis of Sirekap Application Users Using the Support Vector Machine Algorithm Setyanto, Joko; Sasongko, Theopilus Bayu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7772

Abstract

In the current era of digitalization, various activities are conducted using technology to aid their execution, including the democratic process scheduled for February 2024. The Komisi Pemilihan Umum (KPU) is utilizing a mobile-based application called Sirekap. During the previous presidential and vice-presidential elections, there were many pros and cons regarding the Sirekap application. A significant number of negative reviews were expressed by the public towards this application. This study employs the SVM algorithm to perform sentiment analysis of Sirekap application users. Before building the model, several steps were undertaken, including data labeling, data preprocessing, splitting the dataset into training and testing data, and performing transformations using Count Vectorizer. Evaluation of the SVM model results shows quite good performance with an accuracy of 81%. For the negative class, the precision and recall values are 87% and 85%, respectively, while for the positive class, the precision and recall values only reach 66% and 70%, indicating a need for improvement in the model's identification of the positive class. Five-fold cross-validation was performed with an average cross-validation score of 79.6% and a standard deviation of 2.14%, indicating the model's consistency across various training data subsets. These findings suggest that the SVM model can effectively perform text classification tasks. Based on the negative word cloud, it can be concluded that the Sirekap application still has many shortcomings affecting the democratic process in February 2024.
Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks Arnandito, Seno; Sasongko, Theopilus Bayu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7927

Abstract

This study compares the performance of EfficientNetB7 and MobileNetV2 in classifying herbal plant species using Convolutional Neural Networks (CNNs). The primary objective was to automatically identify herbal plant species with high accuracy. Based on the evaluation results, both EfficientNetB7 and MobileNetV2 achieved approximately 98% accuracy in recognizing herbal plant species. While both models demonstrated excellent performance in precision, recall, and F1-score for most plant species, EfficientNetB7 showed a slight edge in some evaluation metrics. These findings provide valuable insights into the potential implementation of CNN architectures in automatic plant recognition applications, particularly for developing widely applicable web-based systems for herbal plant identification.
Comparison of LSTM Model Performance with Classical Regression in Predicting Gaming Laptop Prices in Indonesia Dewantoro, Agus; Sasongko, Theopilus Bayu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8137

Abstract

The demand for gaming laptops has surged in the digital era, appealing to both professional gamers and the general public. Gaming laptops come equipped with advanced features such as powerful graphics, fast processors, and sleek designs, offering a portable solution for gaming enthusiasts. However, the price of gaming laptops varies due to factors like brand, hardware specifications, screen size, and additional features. Accurately predicting these prices can help consumers make informed purchasing decisions and assist manufacturers in setting competitive prices. This research proposes the use of the Long Short-Term Memory (LSTM) algorithm to predict gaming laptop prices, comparing its performance with classic regression algorithms such as Linear Regression and Multi-layer Perceptron. Utilizing a comprehensive dataset of gaming laptop prices and specifications in Indonesia, this study employs robust pre-processing and model optimization techniques. The results show that the LSTM model achieves a Root Mean Squared Error (RMSE) of 0.09011, a Mean Squared Error (MSE) of 0.00812, and an R² Score of 0.90016. In comparison, the Linear Regression model has an RMSE of 0.09075, an MSE of 0.00823, and an R² Score of 0.89873, while the Multi-layer Perceptron model has an RMSE of 0.09891, an MSE of 0.00978, and an R² Score of 0.87971. These results indicate that the Long Short-Term Memory algorithm outperforms other classic regression algorithms in this case. This study highlights the potential of LSTM in developing a robust price prediction model for gaming laptops, particularly in the Indonesian market, providing valuable insights for both consumers and manufacturers.
Sentiment Analysis on BRImo Application Reviews Using IndoBERT Simarmata, Asyer Aprinando Pratama; Sasongko, Theopilus Bayu
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.v9i3.8162

Abstract

The advancement of information technology has significantly impacted various sectors, including digital banking. BRImo, a mobile banking application from Bank Rakyat Indonesia (BRI), has been widely used, generating numerous user reviews that reflect their experiences. This study applies IndoBERT, a transformer-based model specifically designed for the Indonesian language, to analyze sentiment in BRImo user reviews. IndoBERT excels in handling the unique characteristics of the Indonesian language, such as informal and mixed-language usage. The dataset was collected from Kaggle and processed through labeling, data balancing, and splitting into 80% training, 10% validation, and 10% testing data. The IndoBERT model was evaluated using a confusion matrix and achieved 90% accuracy, with F1-scores of 0.89 for negative, 0.91 for neutral, and 0.90 for positive sentiments. Sentiment analysis results indicate that a significant portion of negative reviews highlight issues related to login difficulties, transaction failures, and slow customer service response times. These insights can help BRI enhance application reliability and customer support efficiency. This study demonstrates that IndoBERT is effective in sentiment analysis for Indonesian text and can be utilized to enhance BRImo services by providing deeper insights into user feedback.
Implementation of SSL-Vision Transformer (ViT) for Multi-Lung Disease Classification on X-Ray Images Baasith, Rafi Haqul; Sasongko, Theopilus Bayu; Hadinegoro, Arifiyanto; Saputro, Uyock Anggoro
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.v10i1.11844

Abstract

Chest X-ray imaging is one of the most widely used modalities for lung disease screening; however, manual interpretation remains challenging due to overlapping pathological patterns and the frequent presence of multiple coexisting abnormalities. In recent years, Vision Transformer (ViT) models have demonstrated strong potential for medical image analysis by capturing global contextual relationships. Nevertheless, their performance is highly dependent on large-scale labeled datasets, which are costly and difficult to obtain in clinical settings. To address this limitation, this study proposes a Self-Supervised Learning Vision Transformer (SSL-ViT) framework for multi-label lung disease classification using the CheXpert-v1.0-small dataset. The proposed approach leverages self-supervised pretraining to learn robust and transferable visual representations from unlabeled chest X-ray images prior to supervised fine-tuning. A total of twelve clinically relevant thoracic disease labels are retained, while non-disease labels are excluded to enhance interpretability and reduce confounding effects. Experimental results demonstrate that SSL-ViT achieves a high recall of 0.73 and a peak AUC of 0.75 on the test set, indicating strong sensitivity in detecting pathological cases. Compared to the baseline ViT model, SSL-ViT exhibits a recall-oriented performance profile that is particularly suitable for screening applications, where minimizing false negatives is critical. Furthermore, Grad-CAM visualizations confirm that the model focuses on anatomically meaningful lung regions, supporting its clinical relevance. These findings suggest that SSL-enhanced Vision Transformers provide a robust and effective solution for multi-label chest X-ray screening tasks.
Co-Authors Abd Mizwar A. Rahim Abd Mizwar A. Rahim Abdul Mizwar A. Rahim AGIT AMRULLAH Agus Fatkhurohman Agus Fatkhurohman Alifqi Radjavani Amrullah, Yusuf Amri Anas Fikri Hanif Anas Fikri Hanif Arif Dwi Laksito Arifin, Oki Arifiyanto Hadinegoro, Arifiyanto Arnandito, Seno Baasith, Rafi Haqul Dewantoro, Agus Erfina Nurussa’adah Fajar, Rizqi Al Fikri, Muhammad Ainul Ginanjar Wiro Sasmito Hannan Asrawi Haryoko, Haryoko Hasbi Huda, Amirudin Khorul Joshua Agung Nurcahyo Joshua Agung Nurcahyo Khairul Rohman Kusuma, Satya Tegar Muhammad Fikri Hidayatullah Muhammad Taqiyuddin MUhammad Yusrizal Ningrum, Fauzia Anis Sekar Radjavani, Alifqi Rahim, Abd. Mizwar A. Restu Bumi Apit Rivi Neritarani Rivi Neritarani Rosyida, Anistya Safitri, Anisa Ika Saputro, Uyock Anggoro Satya Tegar Kusuma Senli Yusdiantoro Yusdiantoro Setyanto, Joko Simarmata, Asyer Aprinando Pratama Subagyo, Ageng Ramdhan Yusdiantoro, Senli Yusdiantoro