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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 316 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.

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