cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hindayati Mustafidah
Contact Email
jurnal.juita@gmail.com
Phone
+6285842817313
Journal Mail Official
jurnal.juita@gmail.com
Editorial Address
Gedung Fakultas Teknik dan Sains Universitas Muhammadiyah Purwokerto Jl. K.H. Ahmad Dahlan, Dukuh Waluh, Kembaran, Banyumas, Central Java, Indonesia
Location
Kab. banyumas,
Jawa tengah
INDONESIA
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 22 Documents
 1   2   3 
Search results for , issue "JUITA Vol. 14 Issue 1, March 2026" : 22 Documents clear
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 | DOI: 10.30595/juita.v14i1.25783

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 | DOI: 10.30595/juita.v14i1.27675

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 | DOI: 10.30595/juita.v14i1.27690

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 | DOI: 10.30595/juita.v14i1.27793

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.
Analisis Kinerja SVM dan BERT dalam Memprediksi Ketersediaan Layanan Pencegahan Stunting di Indonesia Putra, Bayu Anugerah; Handayani, Fitri; Fatma, Yulia; Hendra, Jhidan Daelvin
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 | DOI: 10.30595/juita.v14i1.27950

Abstract

Stunting, defined as being too short for one's age with a Height-for-Age Z-score (HAZ) below –2 SD according to WHO, remains a serious public health problem in Indonesia. This study predicts the availability of stunting prevention services at the village level using machine learning. Data from 25,800 villages were categorized into Complete (9,245), Partial (13,609), and Not Available (2,946), showing class imbalance. Two algorithms were evaluated: Support Vector Machine (SVM) with TF-IDF and SMOTE for class balancing, and Bidirectional Encoder Representations from Transformers (BERT) using IndoBERT with class-weighted loss. Evaluation metrics included accuracy, precision, recall, F1-score, and computation time. Results show BERT achieved 92% accuracy with consistent performance across classes (cross-validation 91.55%, SD 0.0024), effectively capturing contextual meaning in narrative text. SVM reached 83% test accuracy with fast computation (±1 min 42 s) and remained robust for imbalanced data. Both models performed well, but minority-class recognition remains challenging. These findings highlight the complementary strengths of SVM and BERT, providing data-driven insights to support policy decisions and improve targeting of stunting prevention services at the village level.
Leafy AI: Integrating MobileNetV2 and TensorFlow Lite into a Flutter-Based Application for Real-Time Ornamental Plant Recognition Setyawan, Haris; Zulkarnain, Nur Zareen; Fikri, Abian Ayatullah
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 | DOI: 10.30595/juita.v14i1.28141

Abstract

Operating artificial intelligence on smartphones attracted interest in various applications, but in practice, device capacity limited AI capabilities. Limited processing power, restricted memory capacity, and unstable network connectivity could make AI models difficult to use outside lab environments. In this work, we describe Leafy AI, a mobile application that identifies ornamental plants designed to work fully on the device. The classifier is based on MobileNetV2 and trained with transfer learning using 67,200 images from 112 plant categories. Images were resized to 224 × 224 pixels and normalized before training. After training, the model was converted into TensorFlow Lite format and integrated within a Flutter application. A lightweight service layer manages preprocessing and inference so that the interface remains simple for the user. Evaluation using 13,440 test images achieved a top-one accuracy of 0.89. A smaller field experiment involving 226 photos captured under real-world conditions resulted in lower accuracy, primarily due to variations in lighting and background. Nevertheless, the system remained reliable in offline mode. The findings show that recognition of ornamental plants can be carried out on ordinary smartphones and that further improvements are possible through augmentation, domain adaptation, quantization, and hardware acceleration.
Evaluation Municipal E-Health Service Quality: An E-GovQual and IPA Assesment of Surabaya’s Public Healthcare Platform Bisma, Rahadian; Prameisty, Dewi Rara
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 | DOI: 10.30595/juita.v14i1.28292

Abstract

The Surabaya City E-Health service is one of the digital public services used for online queue registration for community health centers and hospitals owned by the Surabaya City government. Over the past three years, the number of individuals using the service has risen, but during implementation, service users still face several obstacles. The obstacles users encounter certainly affect their experience using the service. Therefore, it is important to evaluate the quality of Surabaya City's E-Health service using the E-GovQual and Importance-Performance Analysis (IPA) methods. Data collection was conducted using questionnaires distributed to users, and 400 respondents who met the research sample criteria were obtained. The analysis using E-GovQual found the lowest gap level for the RL6 indicator at -0.17 and the highest level of conformity at 96%. The quadrant mapping results on the IPA diagram identified six indicators that were highlighted for improvement: TR1, TR2, TR3, TR4, RL2, and CS2. Several improvement recommendations were provided in accordance with the priority improvement indicators, including those related to the security of service users' personal data. In contrast to earlier E-GovQual–IPA research that mainly evaluates general e-government portals, this study delivers an in-depth assessment of a municipal public healthcare digital service and presents targeted strategic suggestions derived from quadrant prioritization. The results emphasize the essential importance of trust-related factors, especially data protection and system dependability, in enhancing public trust in local e-health services
Using Text-Based Interactive Games as a Tool for Studying Decision Making Patterns Tan, Tony; Wibowo, Tony; Walvinson, Riyaldi
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 | DOI: 10.30595/juita.v14i1.28331

Abstract

Decision making is a key factor in student success and wellbeing. Yet little is known about how interactive games foster reflection on daily habits. This study examined the influence of the text-based game A Day at Home on university students’ free-time activities, satisfaction, and intentions to change habits. A qualitative descriptive design was applied with 29 participants aged 18–25 who completed pre- and post-game questionnaires. Data were analyzed thematically and supported by descriptive statistics. The findings show that most students (52 %) reported consistency between their real-life and in-game free-time activities, while 35 % noted differences. Satisfaction with free-time use remained unchanged for 65.5 % of students but decreased for 34.5 %, indicating greater self-awareness and critical evaluation of current routines. Nearly half (48.3 %) expressed new or stronger intentions to adopt healthier behaviors, such as exercising more or reducing gaming. These results suggest that although the game did not alter habits for the majority, it provided a reflective tool that encouraged students to assess their routines and consider alternatives. The study concludes that text-based interactive games offer a promising approach to support reflection, motivate behavioral change, and enhance student wellbeing in higher education.
English English Anwar, Muhammad Hariz Faizul; Anhari, Nizam Avif; Wicaksono, Galih Wasis; Hidayah, Nur Putri
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 | DOI: 10.30595/juita.v14i1.28345

Abstract

This paper presents Legal-Case LLM, an open-source, fine-tuned language model tailored for Indonesian human-trafficking jurisprudence. General-purpose large language models exhibit high fluency but risk factual hallucination and limited jurisprudential fidelity when applied to legal texts. The objective is to develop a reproducible model that improves factual recall, legal terminology use, and jurisprudential alignment for Indonesian trafficking cases. Methods: We assembled a curated corpus of 400 court decisions from the Direktori Putusan Mahkamah Agung, extracted structured metadata and summaries, and generated question–answer pairs via large models followed by multi-stage cleaning and expert validation. We fine-tuned open models from the LLaMA family variants using parameter-efficient techniques (LoRA), evaluated with automatic metrics (ROUGE, BLEU, BERTScore, BARTScore), and a focused qualitative audit. Results: The fine-tuned model demonstrates marked improvements in content recall and semantic alignment versus zero-shot baselines, produces more jurisprudentially aligned phrasing (accurate use of terms such as amar putusan, Majelis Hakim, and percobaan), and reduces hallucination propensity in statute-related outputs. Conclusion and impact: Legal-Case LLM offers a reproducible, transparent tool to assist legal practitioners and researchers in Indonesia, while emphasising human-in-the-loop verification and citation-matching to ensure legal reliability and ethical deployment.
English English Pratama, Farriel Arrianta Akbar; Arief, Muhammad Eka Nur; Nastiti, Vinna Rahmayanti Setyaning
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 | DOI: 10.30595/juita.v14i1.28346

Abstract

The exponential growth of scientific literature poses a significant challenge for manually identifying thematic trends, necessitating automated analysis methods. This study aims to determine an optimal topic modeling pipeline by conducting a comparative analysis to maximize the coherence of topics extracted from scientific research. Three distinct pipelines were implemented and evaluated on a corpus of 20,972 scientific article abstracts. These included a custom pipeline combining SBERT, UMAP, and HDBSCAN; a second configuration using RoBERTa, PCA, and KMeans; and a third using the integrated BERTopic model. Performance evaluation, quantitatively benchmarked using the C_v coherence score, revealed that the integrated BERTopic model achieved the highest score of 0.7012. This result significantly surpassed the custom SBERT-based pipeline and the RoBERTa-based pipeline, which scored 0.6079 and 0.4756, respectively. The findings demonstrate that an integrated, purpose-built model like BERTopic is superior for generating highly coherent and interpretable thematic structures from scientific text. This research provides empirical guidance for researchers, benchmarking how integrated models offer a more robust solution for large-scale literature analysis compared to modular pipeline designs.

Page 1 of 3 | Total Record : 22

 1   2   3 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue JUITA Vol. 14 Issue 1, March 2026 JUITA Vol. 13 Issue 1, March 2025 JUITA Vol. 12 No. 2, November 2024 JUITA Vol. 12 No. 1, May 2024 JUITA Vol. 11 No. 2, November 2023 JUITA Vol. 11 No. 1, May 2023 JUITA Vol. 10 No. 2, November 2022 JUITA Vol. 10 No. 1, May 2022 JUITA Vol. 9 No. 2, November 2021 JUITA Vol. 9 No. 1, May 2021 JUITA Vol. 8 Nomor 2, November 2020 JUITA Vol. 8 Nomor 1, Mei 2020 JUITA VoL. 7 Nomor 2, November 2019 JUITA VoL. 7 Nomor 1, Mei 2019 JUITA Vol. 6 Nomor 2, November 2018 JUITA Vol. 6 Nomor 1, Mei 2018 JUITA Vol. 5 Nomor 2, November 2017 JUITA Vol. 5 Nomor 1, Mei 2017 JUITA Vol. 4 Nomor 2, November 2016 JUITA Vol. 4 Nomor 1, Mei 2016 JUITA Vol. 3, Nomor 4 Nopember 2015 JUITA Vol. 3 Nomor 3 Mei 2015 JUITA Vol. 3 Nomor 2, Nopember 2014 JUITA Vol. 3 Nomor 1, Mei 2014 JUITA Vol. 2 Nomor 4, Nopember 2013 JUITA Vol. 2 Nomor 3, Mei 2013 JUITA Vol. 2 Nomor 2, Nopember 2012 JUITA Vol. 2 Nomor 1, Mei 2012 JUITA Vol. 1 Nomor 4, Nopember 2011 JUITA Vol. 1 Nomor 3, Mei 2011 JUITA Vol. 1 Nomor 2, Nopember 2010 JUITA Vol. 1 Nomor 1, Mei 2010 More Issue