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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 16 Documents
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Search results for , issue "JUITA Vol. 13 Issue 3, November 2025" : 16 Documents clear
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.
A Comparative Study of K-Means and KNN Imputation for Handling Missing Data in Scholarship Applicant Datasets Muhammad Muhammad; Tole Sutikno; Imam Riadi
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.v13i3.26502

Abstract

Handling missing values is a key issue in data processing, especially in financial records of prospective scholarship recipients where precision is vital for effective decision making. This research aims to analyze the effectiveness of two commonly used imputation methods, namely K-Nearest Neighbors (KNN) and K-Means, in filling missing values across key attributes such as Semester, Grade Point Average (GPA), number of dependents, number of credits, and parental income. Performance evaluation was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results indicate that KNN generally provides more stable and accurate imputations, particularly for attributes with homogeneous distributions such as Semester and GPA, while K-Means demonstrates competitive performance on attributes with higher variability, provided that the number of clusters is optimally defined. Nonetheless, K-Means tends to be more sensitive to increasing proportions of missing data. These findings underscore the importance of selecting imputation methods that align with attribute distribution characteristics and the extent of missing data in order to develop reliable predictive models, as observed in scenarios with 15% and 25% missing data. The findings can also serve as a reference for developing more accurate scholarship selection processes in the presence of incomplete financial data.
Development of ATOBAT: an Android-BasedMedicinal Plant Application Using Google Sites andMIT App Inventor Zalfa Nur Awali; Sugeng Supriyanto; Anwar Sodik; Muhammad Husnul Khuluq; Herniyatun
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.v13i3.26583

Abstract

Indonesia, as a "Live Laboratory," has abundant biological wealth, especially in traditional medicinal plants. The knowledge of these plants has been passed down through generations within communities. However, modernization and societal shifts have contributed to a decline in public understanding of the benefits of medicinal plants. In Pagebangan Village, while 55 types of medicinal plants are recognized, many inhabitants lack detailed knowledge about the specific advantages of each plant. To address this critical knowledge gap, an Android application named ATOBAT was developed. This application was created using Google Sites and MIT App Inventor, both of which do not require advanced programming skills. The primary aim of ATOBAT is to enhance residents' understanding of the medicinal plants available in their environment. This research employs an experimental design utilizing the Research and Development (R&D) methodology, which encompasses the design, development, and evaluation of the application. The evaluation process included validation by media experts and feasibility testing with 92 respondents from the Pagebangan Village community, using purposive sampling and a Likert scale questionnaire. The results indicated a feasibility score of 74% from media experts and 85% from users, confirming that the ATOBAT application effectively enhances community understanding of medicinal plants and their benefits.
Forensic Analysis of UAV DJI MINI 3 and Non-rooted RC-N1 Android Smartphone Using DRF Framework Muhammad Yusuf Halim; Ahmad Luthfi
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.v13i3.26598

Abstract

The increasing use of Unmanned Aerial Vehicles across various sectors also raises potential misuse, such as unauthorized surveillance and airspace violations. This research aims to analyze digital artefacts from UAV DJI Mini 3 and Android Smartphone Controller (DJI RC-N1) using the DRF Field forensic framework. Data acquisition was performed through static and dynamic methods on the UAV and both physical and logical methods on the smartphone, without rooting the device. The analysis reveals that dynamic acquisition on the UAV provides geotagged EXIF images, including latitude, longitude, and altitude information. Meanwhile, flight log data were not found on the UAV but were successfully retrieved from the smartphone via logical acquisition by identifying the DJI Fly application package (dji.go.v5). The extracted flight logs were then processed into .kmz format using Phantomhelp.com and visualized through Google Earth to confirm airspace violations. This study highlights that all forensic acquisition was conducted without rooting, ensuring device integrity and legal admissibility.
A Comparative Evaluation of Drone Detection Models on Aerial Imageryacross Varying Training Epochs Astika Ayuningtyas; Imam Riadi; Anton Yudhana
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.v13i3.26618

Abstract

Drone detection in aerial imagery has become increasingly important in security, surveillance, and military applications. This study aims to evaluate the performance of a deep learning model in detecting drone images by varying the number of training epochs (10, 20, and 50 epochs). A drone image dataset was used to train and test the model, with performance evaluated using precision, recall, mAP@0.5, and mAP@0.5:0.95 metrics. The experimental results indicate that increasing the number of epochs significantly enhances model performance. At 10 epochs, the model achieved a precision of 0.905, recall of 0.857, mAP@0.5 of 0.904, and mAP@0.5:0.95 of 0.455. At 20 epochs, recall improved to 0.879, and mAP@0.5:0.95 increased to 0.476. The best performance was observed at 50 epochs, with a precision of 0.918, recall of 0.886, mAP@0.5 of 0.920, and mAP@0.5:0.95 of 0.494. These findings demonstrate that increasing the number of training epochs not only improves detection accuracy but also enhances the model's generalization capability. The study concludes that training for 50 epochs is the optimal configuration for achieving the best performance in drone image detection, despite requiring longer training time. These results provide practical recommendations for implementing deep learning models in real-world drone detection applications.
Optimizing Sentiment Classification of E-Commerce Product Reviews: A Comparative Study of Naïve Bayes and SVM with SMO Riki; Sonya Eliesse Dameria; Aditiya Hermawan; Junaedi; Yusuf Kurnia
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.v13i3.26642

Abstract

The rapid growth of e-commerce has led to a surge in user-generated product reviews, making manual sentiment analysis impractical. This study explores automated sentiment classification using two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM) that is optimized with Sequential Minimal Optimization (SMO). The dataset comprises 2,000 Shopee product reviews that are labeled as positive, neutral, or negative. The study focuses on assessing the effectiveness of these algorithms in classifying product reviews, especially in the diverse and high-volume data that is typically on e-commerce environments. Empirical evaluation shows that Naïve Bayes achieves 68% accuracy, while SVM with SMO attains 79%. Additionally, the study evaluates other important performance metrics, such as precision, recall, and F1-score. This study show that SVM with SMO outperforms Naïve Bayes in accurately classifying product reviews. These findings highlight the superior capability of SVM with SMO in handling complex sentiment data, thereby offering a more robust foundation for automated review classification. This research provides insights into selecting suitable classifiers for improving customer experience and strategic decision-making in digital commerce.
Optimalisasi Random Forest untuk Sentimen Pilkada Jawa Timur dengan Chi-Square dan Mutual Information Rahma Putri Widyaiswari; Anisa Dzulkarnain; Alqis Rausanfita
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.v13i3.26778

Abstract

The rise of social media has transformed the way people express opinions, including in political contexts. In the 2024 East Java Gubernatorial Election, social media platform X became a major outlet for public sentiment toward the governor and deputy governor candidates. This study aims to analyse public sentiment toward three candidate pairs by categorizing the data into three sentiment classes: positive, negative, and neutral. Feature selection was conducted by combining Term Frequency-Inverse Document Frequency (TF-IDF) with Chi-Square and Mutual Information (MI) methods to improve feature quality. The Random Forest algorithm was employed as the primary classification model. In addition, several other algorithms were tested for comparison. The results indicate that the TF-IDF and Chi-Square combination with Random Forest achieved the highest accuracy of 82.07%. These findings highlight the importance of feature selection in improving model performance for sentiment classification. The study provides insights into public opinion that can serve as a reference for strategic decision-making in the political and public sectors.
Enhancing Cybersecurity: Design of an Automated Penetration Testing Framework for Common Vulnerabilities and Exposures (CVE) Nur Rohman Rosyid; Anni Karimatul Fauziyyah; Yoan Navie Ananda
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.v13i3.26938

Abstract

The progression of digital transformation has increased cybersecurity concerns, primarily due to the growing prevalence of system vulnerabilities. Penetration testing (pentesting) is an essential technique for identifying and assessing vulnerabilities; however, conventional methods are labor-intensive and heavily reliant on expert participation. This study proposes the development of an automated penetration testing framework that utilizes Common Vulnerabilities and Exposures (CVE) to enhance efficiency and reduce reliance on manual processes. The framework utilizes software engineering design patterns, namely the Template Method and Abstract Factory, to guarantee modularity, scalability, and maintainability. The implementation and evaluation reveal the system's capacity to reliably perform CVE-based penetration testing activities with consistent performance across multiple iterations. Comparative testing demonstrates that the suggested framework attains superior consistency in execution time and resource utilization compared to monolithic solutions. In conclusion, the established methodology offers a dependable basis for automated CVE-based security evaluations and facilitates continuous adaptation to forthcoming cybersecurity issues.
Deep Learning-Based Sea Level Forecasting Using Informer in Cilacap, Indonesia Reva Rivandi Salim; Didit Adytia
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.v13i3.27176

Abstract

Sea level forecasting is very important for coastal risk management and operational planning, especially in regions vulnerable to frequent tidal flooding events. Tidal Harmonic Analysis (THA) and other traditional methods can effectively reconstruct tidal components but typically overlook non-tidal influences such as meteorological variability and ocean swell. This study mitigates these limitations by proposing the Informer model, a Transformer-based deep learning architecture for long-range sequence forecasting, to predict sea levels using 11 months of hourly observational data (December 2023 – October 2024) from Cilacap, a tropical coastal region in Indonesia. A new preprocessing pipeline is introduced, integrating THA-based tidal reconstruction with interpolation techniques to handle missing data. Forecasting performance is evaluated across multiple prediction horizons (1, 3, 5, 7, and 14 days) and compared against XGBoost, LSTM, and the standard Transformer. The results show that Informer does better than the other models, especially over longer horizons. It has the lowest RMSE (0.091), the lowest MAPE (2.14%), and the highest correlation coefficient (0.98) on the 14-day forecast. In this study, we focused on the Informer’s capability for long horizon from sea level data for providing a reliable solution for sea level prediction. This results show that the model is applicable for integration into early warning systems.
Real-Time Detection of Outdoor Parking Space Availability Using YOLOv8 Rafi Ardinata Riskiansyah; Yohanes Setiawan; Farah Zakiyah Rahmanti
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.v13i3.27278

Abstract

Finding an empty parking spot in open areas, particularly in busy locations such as shopping centers, remains a significant challenge. This study proposes a real-time system for detecting outdoor parking space availability using the YOLOv8 algorithm, selected for its speed and accuracy in object detection. The dataset consists of 131 annotated images, expanded through three augmentation techniques (rotation, shearing, and flipping) to increase variability. Model training was performed with multiple hyperparameter configurations and evaluated using precision, recall, F1-score, accuracy, and mAP@50. The best configuration, obtained with the Adam optimizer, achieved 96.74% precision, 99.06% recall, 99.17% mAP@50, and 77.91% accuracy. While the system performed effectively and responsively in real-time daytime scenarios, a key limitation is its reduced performance under nighttime conditions due to low visibility and image noise.This research contributes by demonstrating YOLOv8’s potential to improve real-time detection of parking spaces, particularly through handling occlusions and lighting variations, which remain challenges in outdoor environments.

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