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JUITA : Jurnal Informatika
Published by Universitas Muhammadiyah Purwokerto
ISSN : 20869398     EISSN : 25798901     DOI : 10.30595/JUITA
Core Subject : Science,
Computer Science & IT
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 400 Documents
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Performance Analysis of Deep Learning Architectures in Classifying Fake and Real Images Arya Faisal Akbar; Putu Desiana Wulaning Ayu; Dandy Pramana Hostiadi
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25790

Abstract

The advancements in artificial intelligence (AI) have significantly enhanced image manipulation capabilities, yet they also raise concerns regarding the proliferation of synthetic images. This study investigates the impact of Dynamic Dropout in optimizing deep learning models, including ResNet-101, DenseNet-201, VGG-19, and AlexNet, for classifying real and synthetic images using the CIFAKE and Real and Fake Face datasets. Dynamic Dropout was applied with a progressively increasing rate from 20 percent to 50 percent to enhance training stability and generalization. The results indicate that the optimal configuration consisting of 15 epochs, the Adam optimizer, and Dynamic Dropout consistently outperformed Static Dropout across all models. DenseNet-201 with Dynamic Dropout achieved the highest accuracy of 97.42%, with a precision of 97.33%, recall of 97.58%, and an F1-score of 97.45%. ResNet-101 and VGG-19 exhibited enhanced training stability, while AlexNet proved efficient for lightweight datasets. The Adam optimizer outperformed Nadam, offering greater stability in deeper architectures. Additionally, the 15th epoch was identified as the optimal training duration, balancing accuracy and overfitting mitigation. These findings underscore the importance of selecting optimal training configurations to enhance deep learning performance. Future research should explore adaptive dropout strategies, assess scalability on diverse datasets, and validate these techniques in real-world applications such as digital forensics and AI-generated content detection
Enhanced OCR Recognition for Madurese Text Documents: A Genetic Algorithm Approach with Tesseract 5.5 Muhammad Nazir Arifin; Muhammad Umar Mansyur; Ali Rahman; Nindian Puspa Dewi; Fauzan Prasetyo Eka Putra
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25794

Abstract

Character Recognition (OCR) for the Madurese language using Genetic Algorithms (GA). The study addresses the challenges in processing Madurese text documents by implementing a nine-step image preprocessing workflow optimized through GA. Our methodology combines rescaling, grayscale conversion, adaptive thresholding, deskewing, median blur, Otsu thresholding, border removal, contrast enhancement, and noise reduction, with the sequence determined by GA optimization. The system utilizes Tesseract 5.5 OCR engine configured with Vietnamese language model parameters to accommodate Maderese writing characteristics. Experiments conducted on a dataset of 500 images demonstrated significant improvements in recognition accuracy. The GA-optimized preprocessing sequence achieved a 24.32% Word Error Rate (WER) and 7.47% Character Error Rate (CER), marking substantial improvements over the baseline Tesseract implementation. Further optimization through language model selection, particularly using the Occitan (OCI) model, yielded 100% accuracy in specific test cases. The research also explored various fitness function configurations, with a 0.7:0.3 WER-to-CER ratio proving most effective. These results demonstrate the potential of GA optimization in enhancing OCR performance for regional languages with unique characteristics, contributing to the broader field of document digitization and language preservation
Analysis of LMS Implementation Success Based on Information System Success and End-User Computing Satisfaction Models Rahmat Pambudi; Berlilana Berlilana; Giat Karyono
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25963

Abstract

A learning Management System (LMS) is critical because it can help improve learning quality and efficiency. Therefore, organizations need to evaluate their performance and utilization. The objective of this study is to evaluate the success of the LMS Onclass implementation using Combination the Information System Success Model (ISSM) and End User Computing Satisfaction (EUCS). This study's data collection approach was a quantitative survey. The population consisted of all students in the Faculty of Engineering and Science at the Muhammadiyah University of Purwokerto, with a total of 1022 using the Onclass. The researcher used data from active students in semesters 1, 3 and 5 with a total of 534 data. The data collection technique was conducted non-test through a questionnaire to assess the success of LMS implementation based on the ISSM and EUCS models. After collecting the data, the next step is to process it using the SEM-PLS multivariate analysis technique, which includes validating the measurement model as well as the structural model. The study's findings revealed that the respondents' data were validated and reliable. The test was then performed, and it was discovered that all hypotheses were accepted, indicating that the factors had a positive and significant influence
Sentiment Analysis of Pro-Israel Product Boycott Action Using IndoBERT Method on Unbalanced Data Auliana Rizky Puspita Dewi; Slamet Riyadi; Cahya Darmajati; Nor Ashidi Mat Isa; Annisa Divayu Andriyani
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25976

Abstract

A boycott was an act taken to stop the purchase or use of a particular product or service as a form of public protest against a particular company or group committing a deviation. The Israeli-Palestinian conflict, which had been ongoing since 1948, peaked in October 2023 and had claimed more than 35,000 Palestinian lives. This conflict generated a wide range of public opinions in Indonesia, which were expressed through social media, especially Twitter. Thus, the sentiment analysis of public reactions on Twitter became important to understand the reactions and perspectives of society towards the boycott of Pro-Israel products. This study used the IndoBERT method, which was a variant of the BERT method specifically designed to understand Indonesian. Although many studies had applied the IndoBERT method for sentiment analysis and text classification in Indonesian, none had used the IndoBERT method along with data balancing techniques to analyze Indonesian sentiments regarding the boycott of Pro-Israel products on Twitter. Therefore, this study aimed to develop a sentiment analysis model using the IndoBERT method with more data to examine sentiments related to the boycott of Pro-Israel products on Twitter using imbalanced data, as well as to evaluate the effect of balancing methods using under sampling and oversampling on the model’s accuracy and performance. The methods used included data crawling, data preprocessing, labeling with a Lexicon-Based approach, data balancing, and data splitting. The IndoBERT model was trained with 20 epochs, a batch size of 16, and a learning rate of 2e-5. The results of the study showed that the model with balanced data using the oversampling method achieved an accuracy of 97% and an F1-Score of 97%, which was better compared to the model with imbalanced data and the undersampling method. Thus, data balancing using the oversampling method proved to be effective in improving accuracy in sentiment analysis. This research made a significant contribution to understanding the behavior of Indonesian society towards a product boycott supporting Israel and suggested further exploration in parameter optimization and evaluation with larger and more diverse data, as well as further development of data balancing methods to improve the generalization and capabilities of the model
Comparison Analysis of Hierarchical Clustering and K-Means Methods in Grouping Provinces in Indonesia Based on Dengue Hemorrhagic Fever (DHF) Cases Alfidha Rahmah; Nida Faoziatun Khusna; Safril Ahmadi Sanmas; Syifa Aulia; Shinta Amaria; Fatkhurokhman Fauzi
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26131

Abstract

Indonesia, a tropical country, experiences climate variations that influence the spread of infectious diseases, including Dengue Hemorrhagic Fever (DHF). The increase in DHF cases necessitates clustering provinces based on their vulnerability to design effective mitigation strategies. This study compares two clustering methods: Hierarchical Clustering and K-Means Clustering. Within the hierarchical clustering analysis, five linkage methods were evaluated: Average Linkage, Complete Linkage, Single Linkage, Ward’s Method, and Centroid Linkage. The best linkage method was identified using the cophenetic correlation coefficient, indicating that Average Linkage produced the most representative cluster structure, resulting in three distinct groups. For the K-Means method, the optimal number of clusters was determined using the Silhouette Coefficient, which also indicated three clusters. Clustering performance evaluation revealed that Average Linkage outperformed K-Means, with a higher Silhouette Score of 0.552. The resulting clusters categorized provinces into three risk groups: high-risk areas (e.g., DKI Jakarta), moderate-risk areas (e.g., West Java and East Java), and low-risk areas, comprising the remaining provinces in Indonesia
Sentiment Analysis of X Users Toward Electric Motorcycles Using SVM and BERT Algorithms Calvin Adiwinata; Afiyati Afiyati
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26152

Abstract

This study presents a comparative analysis of Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis on electric motorcycles in Indonesia using data from the social media platform X, formerly known as Twitter. The dataset of 128,711 tweets collected between 2015 and 2024 was refined through systematic preprocessing, reducing the corpus to 38,954 entries after data cleaning, tokenization, and feature selection. The objective was to evaluate algorithm performance in classifying public sentiment, with metrics including accuracy, precision, recall, and computational efficiency. Results showed that SVM achieved higher overall accuracy 89.74% with strong precision for positive sentiment 91%, while BERT, specifically the IndoBERT variant, demonstrated superior recall for negative sentiment 91% despite slightly lower accuracy 87.90%, effectively capturing nuanced contextual language, such as sarcasm, informal expressions, and emotionally ambiguous statements that require deeper semantic understanding beyond literal word meanings. Computational analysis revealed that SVM required approximately 53 minutes of CPU training, compared to BERT’s 3.3 hours on GPU. The study suggests that SVM is optimal for rapid, resource-constrained applications, whereas BERT excels in detailed contextual analysis. These findings guide stakeholders in selecting algorithms based on analytical priorities, such as monitoring public reception or addressing consumer concerns
Evaluating LSTM Performance on Multivariate Time Series with One-Class SVM Outlier Detection Ragita Anillya Putri; Sugiyarto Surono; Aris Thobirin
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26160

Abstract

Weekly sales forecasting plays a crucial role in retail business planning and inventory management.This study evaluates the prediction performance of a Long Short-Term Memory (LSTM) model for weekly sales forecasting after data preprocessing using standardization and outlier detection with One-Class Support Vector Machine (OCSVM) method. The independent variables used include temperature, fuel price, holidays, Consumer Price Index (CPI), and unemployment rate, with weekly sales as the target variable. The dataset is preprocessed using StandardScaler and OCSVM to detect and remove outliers before model training. The evaluation shows that the LSTM model on the clean data achieves an MSE of 0.03, an RMSE of 0.18, and an MAE of 0.11. The LSTM model demonstrates good forecasting performance when trained on cleaned data without outliers. This study provides practical insights into applying data preprocessing with OCSVM to improve the consistency of prediction models in retail time series analysis.
Transformer-Based Detection Model for Number Recognition on Electric kWh Meters Leni Fitriani; Ahmad Sanusi; Rita Rismala; Dewi Tresnawati
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26161

Abstract

Manual recording of analog kWh meters frequently results in user complaints due to discrepancies between recorded and actual electricity usage. These issues stem from the continued reliance on manual data collection. This study proposes a model that automatically detects and extracts numerical values from kWh electricity meters using the Detection Transformer (DETR) for object detection and EasyOCR for optical character recognition (OCR). The model was developed using the Machine Learning Life Cycle (MLLC) methodology, comprising data acquisition, preprocessing, modeling, evaluation, and deployment. Evaluation using the Mean Average Precision (mAP) metric yielded a score of 96.83%, demonstrating high object detection accuracy. The trained model was integrated into a simple web application built with the Flask framework. While the model performed well on high-quality images, its effectiveness declined on low-quality images, such as blurry or distant captures. This study highlights the potential of DETR for object detection and OCR-based text extraction in analog meter reading, while also identifying challenges in handling suboptimal image conditions for future improvements
Performance Evaluation of ARIMA and GRU Models for Forecasting Chili Price in East Jawa Windi Pangesti; Nabila Syukri; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26445

Abstract

Time series forecasting plays a crucial role in predicting future conditions based on historical data, particularly in the food sector, which is highly susceptible to price fluctuations. This study compares two approaches: the conventional ARIMA method and the deep learning method GRU, to forecast the price of red chillies in East Java. East Java was chosen because it is the largest national producer of chilies, thus the stability of its prices has a broad impact. The research results indicate that the GRU model outperforms the ARIMA model with a MAPE value of 19.80% compared to a MAPE of 27.63% for the latter. The benefit of this research is to contribute to the literature on developing agricultural commodity price forecasting models as a basis for enhancing food security policies and stabilizing commodity prices, particularly in East Java Province, Indonesia
Analisis Komparasi Model BERT dan Model DISTILBERT Pada Klasifikasi Struktur Judul Berita Clickbait Online Berbahasa Indonesia Rananggana Trustha Dewangga; Budi Prasetiyo
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.26479

Abstract

Clickbait uses sensational or misleading headlines to attract readers, which can degrade information quality in online news. This study presents a comparative evaluation of BERT and DistilBERT for detecting clickbait headline structures in the Indonesian language using the CLICK-ID dataset. The approach examines how class imbalance influences performance by training models on multiple dataset variants created through oversampling, undersampling, and data augmentation. Inputs are tokenized with model specific tokenizers and evaluated with accuracy, precision, recall, and F1-score. Confusion matrices are used to interpret error patterns across classes. Experimental results show that DistilBERT trained on an oversampled dataset achieves 94% for accuracy, precision, recall, and F1-score, while BERT on the same oversampled setting reaches 93%. Models trained on unbalanced data yield the lowest recall and F1 for the clickbait class, confirming the adverse effect of skewed distributions. Augmented and undersampled variants produce slightly lower but competitive results in the 92% to 93% range. Error analysis shows that DistilBERT reduces missed clickbait while maintaining a similar level of false positives, producing more balanced behavior across classes. These results outperform prior CLICK-ID studies and highlight the advantage of transformer architectures combined with effective class balancing for Indonesian clickbait detection.

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