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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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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

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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

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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

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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

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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

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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

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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.
Evaluation of CNN Architectures for Kidney Stone Classification in Ultrasound Image Zuriati, Zuriati; Sriyanto, Sriyanto; Supriyatna, Agiska Ria; Qomariyah, Nurul; Afifah, Dian Ayu; Zarnelly, Zarnelly
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

Kidney stone diagnosis requires fast and reliable evaluation, yet ultrasound interpretation still largely depends on clinical expertise. This study evaluates four Convolutional Neural Network (CNN) architectures, VGG16, ResNet50, MobileNetV2, and EfficientNetB0 for classifying kidney ultrasound images into Normal and Stone categories. Using a public dataset of 9,416 images, the models were assessed in terms of predictive performance and computational efficiency. MobileNetV2 achieved perfect classification performance, recording 100% accuracy, precision, recall, and F1-score, while maintaining the smallest parameter size (≈3.6M) and fastest training time (~44 s/epoch). VGG16 and ResNet50 also delivered near perfect accuracy (99.79% and 99.89%) with full recall for Stone cases. In contrast, EfficientNetB0 failed to generalize, yielding only 51.62% accuracy due to severe misclassification of Normal images. These results demonstrate that MobileNetV2 provides the most reliable and efficient solution for ultrasound based kidney stone classification, highlighting its strong potential for practical clinical deployment.
SmartHealth a Web-Based Platform for Adolescent Health Education with User Testing Rosmalina; Waluyo, Sutiyono; Solihat Holida, Siti; Nistrina, Khilda; Nopiani, Devia Fitri
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

Adolescents in Indonesia continue to face barriers in accessing accurate, engaging, and relevant health education, while digital health literacy remains uneven. This study aims to develop and evaluate SmartHealth, a web-based adolescent health education platform, using an integrated theoretical framework combining the Technology Acceptance Model, Health Belief Model, Theory of Planned Behavior, and the Digital Health Literacy Model. The research employs a Research and Development approach that integrates the Borg and Gall model with the Waterfall software development method across four stages: need assessment, product design, system development, and limited field testing. A total of 60 high school students participated in usability testing using the System Usability Scale. The results show that SmartHealth achieved a mean usability score of 78.4, indicating good user acceptance, ease of use, and suitability for adolescent needs. The platform successfully provides accessible digital health content, interactive features, and online consultation support. This study concludes that SmartHealth is feasible, usable, and contextually relevant as a digital health education platform for Indonesian adolescents.
A Multispectral YOLOv8-Based System for Real-Time Object Detection and Distance Estimation in Blind Navigation Utami, Ema; Syahrudin, Erwin; Hartanto, Anggit Dwi; Raharjo, Suwanto
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

Developing reliable real-time navigation systems for visually impaired individuals remains challenging, particularly in dynamic and low-light environments. This study proposes an integrated framework combining YOLOv8, OpenCV-based monocular distance estimation, and RGB–NIR multispectral imaging to enhance detection robustness and distance awareness. A dataset of 1,700 annotated images collected from diverse indoor and outdoor environments was used for training and evaluation using preprocessing techniques such as resizing, normalization, and data augmentation. System performance was evaluated using Precision, Recall, F1-Score, mean Average Precision (mAP), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Frames Per Second (FPS). Experimental results show that YOLOv8x achieved the best performance with an F1-Score of 0.91, mAP@50 of 0.74, MAE of 0.15 m, RMSE of 0.20 m, and a processing speed of 22 FPS. Multispectral RGB–NIR integration further improved low-light performance, increasing the F1-Score from 0.83 to 0.89 and reducing MAE from 0.28 m to 0.19 m with only a minor reduction in speed. These findings demonstrate that the proposed system provides an effective balance between accuracy and real-time performance for assistive navigation applications.
A Hybrid Case-Based Reasoning Framework Using KNN, Word2Vec, and Cosine Similarity for Employee Attrition Analysis Siregar, Akhmad Arif Faisal; Utami, Ema; Sari, Tika Novita
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

Employee attrition prediction remains a longstanding challenge in human resource analytics, as organizations increasingly depend on computational decision-support systems that are transparent, consistent, and operationally accountable. Conventional methods that rely solely on numerical attributes are restricted in their ability to accurately capture the structural and contextual relationships inherent in categorical and text-based employee descriptors. To overcome this limitation, the current study investigates a hybrid Case-Based Reasoning (CBR) retrieval framework that combines K-Nearest Neighbors (KNN) with Word2Vec embeddings derived from the dataset's limited textual attributes, specifically Department, Gender, EducationField, MaritalStatus, and OverTime. Eight experimental configurations were assessed to examine the impact of alternative similarity metrics and diverse feature representations. The optimal configuration of KNN, enhanced with Word2Vec embeddings and cosine similarity, attained an accuracy of 0.8526 and a weighted F1-score of 0.8000, thereby exceeding the performance of baseline models based solely on numerical features and those utilizing Manhattan distance. Nonetheless, the improvements in performance remained limited owing to dataset-specific limitations, such as class imbalance and the inherently superficial characteristics of the textual descriptors, which restrict the semantic richness of Word2Vec embeddings. Furthermore, the IBM attrition dataset does not encompass downsizing or termination situations, highlighting conceptual and ethical constraints when utilizing similarity-based predictions for high-stakes HR decisions. Overall, the findings indicate that hybrid similarity representations, particularly the combination of Word2Vec embeddings with cosine distance, can improve the structural expressiveness of CBR, although their predictive effectiveness is still limited by data sparsity and considerations of fairness.

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