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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 338 Documents
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Analysis for Detecting Banana Leaf Disease Using the CNN Method Helmawati, Nita; Utami, Ema
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24514

Abstract

Banana farmers face major challenges due to banana leaf diseases such as Cordana, Pestalotiopsis and Sigatoka, which severely affect the quality and quantity of the crop. Early detection of these diseases is particularly challenging as the initial symptoms are often similar to other disorders. To solve this problem, fast and accurate automated detection is needed to help farmers effectively identify diseases on banana leaves. This research focuses on developing a banana leaf disease detection model using Convolutional Neural Network (CNN) method with MobileNetV2 architecture. The dataset used consists of 937 images of both infected and healthy banana leaves. These images were collected under various lighting conditions and viewing angles to simulate real field situations. The dataset was divided into 70% for training, 20% for validation, and 10% for testing, to ensure robust model evaluation. The CNN model was trained to recognize important visual features on banana leaves that indicate disease infection. The results showed that the model was able to detect banana leaf diseases with an accuracy of 90.62%, indicating high effectiveness. This accuracy confirms the potential of CNN in significantly improving the disease detection process on banana plants. This research is expected to help farmers identify diseases more quickly and accurately, thereby minimizing yield losses and increasing productivity. In addition, this research provides valuable insights into the application of technology in agriculture, particularly in plant disease detection which opens up opportunities for further advancements in this sector.
Subject Independent Emotion Recognition Using Electroencephalogram Signals with Continuous Capsule Network Method Sujaya, Made Agus Panji; Wirawan, I Made Agus; Indrawan, Gede
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24583

Abstract

Emotions play an essential role in human reasoning. Researchers have made various efforts to improve emotion classification methods. Based on the several emotion classification methods studied in previous studies, the Continuous Capsule Network method produced the highest accuracy compared to other classification methods. This method can maintain spatial information from electroencephalogram signals so they are not reduced. However, this method has only been tested subject-dependently. Based on this study, the Continuous Capsule Network method will be applied to classify emotions in the Faculty of Engineering and Vocational Studies, Universitas Pendidikan Ganesha students. The number of participants involved in this study was 17 people (10 men and 7 women). Through six subjectindependent test scenarios, the Continuous Capsule Network method produced accuracy, precision, recall, and F1 scores of 99.31%, 99.34%, 99.20%, and 99.27, respectively. At the same time, the loss value was 0.88%. In addition, the Continuous Capsule Network method produced an average training and validation time of 401.17 seconds and an average testing time of 4.67 seconds for the six test scenarios.
ElderCare Monitor Application Design: Elderly Health Monitoring based on Family and Community Health Center Collaboration Tanti, Wa Ode; Kusumadewi, Sri; Kurniawan, Rahadian
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24680

Abstract

The elderly are a vulnerable group that is susceptible to various degenerative diseases. The lack of information and understanding of the elderly causes a high rate of disease complications. This study aims to design an ElderCare Monitor application that is easy to use and tailored to the needs of users. The application caters to a variety of users, including elderly families, the elderly themselves, the pre-elderly, and health center officers. Health workers at the health center and the elderly's family collaborate to independently monitor the health of the elderly and pre-elderly. The system is designed using the design thinking method. The design thinking method involves five stages: empathy, define, ideate, prototyping, and testing. The results of the study showed an effectiveness rate of 95.24% for the elderly and families and 95.83% for health center officers. The average SUS score for families and the elderly was 74.17, while for medical personnel, it was 77.5. Therefore, the ElderCare application received the "Good" usability category. This study contains numerous components that may serve as encouragement. In particular, it is imperative that we consistently improve the UI/UX of ElderCare by routinely gathering user feedback and incorporating features that can more effectively support the elderly.
Comparison of Multinomial, Bernoulli, and Gaussian Naïve Bayes for Complaint Classification in Pro Denpasar Application Mahendra, Ida Bagus; Sunarya, I Made Gede; Wirawan, I Made Agus
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24828

Abstract

Pelayanan Rakyat Online Denpasaror PRO Denpasar is a Denpasar City Government application intended as a public service mall to support Denpasar to become a smart city. This application was built since 2014 and is actively used in channeling public complaints so optimization is continuously needed to increase the efficiency of application use. Application optimization is carried out by developing decision support tools to determine complaint categories that are still done manually. The application of the properartificial intelligence method can be used as a solution in classifying complaint categories to become a decision support tool for operators. This study compares three classification methods including multinomial naïve bayes, bernoulli naïve bayes and gaussian naïve bayes by applying TF-IDF feature extraction to determine the best complaint category classification method. Based on eight comparison scenario results by applying a comparison of 25%, 50%, 75% and 100% of complaint descriptions with 5-fold cross validation and 10-fold cross validation, it was found that the multinomial naïve Bayes method provided the best result in seven combined comparisons involving the test parameters accuracy, precision, recall, f1-score and processing time.
Performance Analysis of Resampling Techniques for Overcoming Data Imbalance in Multiclass Classification Larasati, Anggit; Surono, Sugiyarto; Thobirin, Aris; Dewi, Deshinta Arrova
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.25270

Abstract

In the digital era, the development of modern technology has brought significant transformation to the medical world. The main objective of this research is to identify the performance of deep learning models in classifying kidney disease. By integrating the Convolutional Neural Network model, the performance of the classification process can be analyzed effectively and efficiently. However, data imbalance dramatically affects the performance evaluation of a model, requiring data resampling techniques. This research applies two resampling techniques, bootstrap-based random oversampling and random undersampling, to training data and adds data augmentation to increase image variations to prevent model overfitting. The architecture uses MobileNetV2, which compares hyperparameter fine-tuning in three optimizers. This research shows that the performance of MobileNetV2, which implements the bootstrap-based random oversampling technique, has the highest accuracy compared to random undersampling and no resampling methods. The oversampling technique with the RMSprop optimizer produced the highest accuracy, namely 95%. With precision, recall, and F-1 score, respectively, 0.93, 0.95, 0.94. The accuracy of oversampling with the Adam and Nadam optimizer is 94%. So, the contribution of this research is by applying bootstrap-based oversampling techniques and adding data augmentation to produce good model performance to be used to classify medical images.
Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach Mustafidah, Hindayati; Gunadi, Ilham; Purbomartono, Cahyono; Suwarsito, Suwarsito; Zuliarso, Eri
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.26031

Abstract

Gourami is an economically significant fish in the aquaculture sector due to its high market demand and relatively stable price. However, it is also challenging to cultivate, with disease outbreaks being one of the primary difficulties. Early diagnosis of gourami fish diseases requires expertise from fish health specialists, who are often difficult to find due to their limited availability. With advancements in artificial intelligence-based technology, this study developed an expert system to diagnose gourami fish diseases based on observed symptoms. The system employs the Certainty Factor (CF) approach to estimate the likelihood of a particular disease affecting the fish. The Certainty Factor approach utilizes a knowledge base derived from expert knowledge to address uncertainty in diagnosis. The certainty factor weights are determined based on confidence levels from both experts and users to generate an accurate diagnosis. This expert system was developed using data from 20 types of gourami fish diseases and 38 associated symptoms. The system successfully identified diseases with a certain level of confidence and provided appropriate treatment recommendations based on the confidence level obtained. By implementing this expert system, the risk of disease outbreaks can be minimized, thereby improving efficiency and productivity in gourami fish farming while helping maintain fish health and reducing economic losses caused by disease.
Combination of VGG19 (Encoder) and U-Net (Decoder) for Colorectal Polyp Segmentation Image Sutiyaningsih, Nuri; Ayu, Putu Desiana Wulaning; Huizen, Roy Rudolf
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Health involves the proper function of the body and organs, with colon polyps being a common issue. Doctors often face challenges in segmenting medical images, especially endoscopic images for polyp detection. The complexity and variation in the appearance of polyps make accurate identification challenging, and the subjective manual segmentation process can result in misdiagnosis or delayed treatment.  This study examines the effectiveness of the combination of U-Net decoder model architecture and VGG19 encoder in segmentation of colon polyp images.  This study uses a public dataset, namely Kvasir-Seg with a total of 1000 images of colon polyps.  An innovative approach using VGG19 as encoder and U-Net as decoder improves colorectal polyp segmentation, achieving high performance with a Loss of 0.05, Accuracy 0.95, Precision 0.96, Recall 0.92, IoU 0.89, and Dice 0.94. Using optimal parameters such as Nadam Optimizer, 5 Fold Cross Validation, Learning Rate 0.0001, and 25 Epochs significantly improved performance, increasing the Dice Coefficient to 0.92 and IoU to 0.86 compared to previous studies.   This study concludes that the proposed architecture is reliable for colon polyp segmentation. Future work should explore attention mechanisms or transformer-based models to enhance accuracy and efficiency.
Classification Brain Tumor in HyperparameterOptimization of VGG-16 Model and Data Augmentation Analysis Ayu, Putu Desiana Wulaning; Dharma, I Gede Teguh Satya; Wijaya, I Wayan Rizky; Gunawan, Made Agus Oka; Apriyanthi, Ni Putu Eka; Dhewanty, Civica Moehaimin
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This Advancements in computational technology have driven the development of Deep Learning, particularly Convolutional Neural Networks (CNN), in the classification and recognition of digital images. This research focuses on the classification of MRI brain tumor images using the VGG-16 architecture. The primary challenges include gradient vanishing and overfitting due to a small dataset. The objective of the study is to evaluate the performance of the model with various data augmentation techniques and to assess the impact of different dataset compositions (90:10 and 70:30) for training and testing. Two model configurations are used: Model A with 4096 neurons and Model B with 128 and 64 neurons in the first two Dense layers, respectively. The tested augmentation techniques include rotation, flip, Zoom , and their combinations. The results indicate that rotation and Zoom augmentations provide the best performance for both models and dataset compositions. Model A (90:10) achieved an accuracy of 96% with rotation and 92% with Zoom, while Model B (90:10) achieved 94% with rotation and 98% with Zoom. For the 70:30 composition, Model A achieved 94% (rotation) and 90% (Zoom ), while Model B achieved 95% (rotation) and 96% (Zoom ). This research provides valuable insights into optimizing VGG-16 architecture for brain tumor classification using limited datasets.
Mobile Forensic Investigation of E-Commerce Fraud Using DFRWS Method and Perceptual Hashing Prambudi, Rizal; Riadi, Imam; Murinto, Murinto
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Social media platforms have enabled real-time communication and broad user interaction, but they are often exploited for cybercrime. One such vulnerable medium is e-commerce applications, which facilitate transactions and store sensitive user data. This study investigates digital evidence in a simulated fraud case involving an e-commerce application by applying mobile forensic techniques guided by the Digital Forensic Research Workshop framework. The investigation focused on recovering user accounts, text messages, images, and videos from an Android smartphone. Two forensic tools Oxygen Forensic Detective and MOBILedit Forensic Express were used for data extraction and analysis. To improve the reliability of visual evidence, the study incorporated perceptual hashing and wavelet hashing techniques to validate compressed image files. The results showed that Oxygen Forensic Detective recovered 71.4% of digital evidence, while MOBILedit achieved 57%. Although both tools successfully recovered multimedia files, Oxygen performed better in extracting text messages. These findings demonstrate the effectiveness of mobile forensic methods in identifying and validating digital evidence in e-commerce fraud cases. Moreover, integrating the DFRWS methodology with perceptual hashing significantly improves the interpretation of manipulated or compressed images, thus enhancing the evidentiary value for legal proceedings.
Interactive 3D Rendering of the Human Heart on Mobile Web Using WebGL and Three.js Sunardi, Sunardi; Herman, Herman; Astianingrum, Krisna
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The advancement of web-based 3D visualization technology has created new opportunities for interactive medical learning, particularly in anatomy education. The existing rendering techniques for the mobile web still face challenges due to limitations of cellular and mobile device capacity This study focuses on optimizing real-time rendering of an interactive 3D heart model for mobile web platforms using WebGL and Three.js. Several optimization techniques were applied, including Draco compression, polygon reduction, and the GLB file format, to achieve high rendering performance while maintaining anatomical accuracy. Performance testing was conducted on three device tiers—low-, mid-, and high-end—under different network conditions. Key metrics such as frame rate, loading time, and memory usage were systematically measured. The optimized system achieved stable rendering at 58–60 FPS with a reduced loading time from 6.2 seconds to 1.4 seconds, demonstrating strong scalability and responsiveness. From an educational perspective, this interactive 3D heart model enables medical students, trainees, and patients to dynamically explore cardiac anatomy, improving their spatial understanding of complex structures without requiring high-end VR hardware. The novelty of this work lies in its optimization pipeline tailored for mobile web, making real-time anatomical visualization lightweight and accessible. Future research will involve larger user studies to evaluate educational effectiveness.

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