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Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Published by Universitas Pendidikan Ganesha
ISSN : 20898673     EISSN : 25484265     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Engineering
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) is a collection of scientific articles in the field of Informatics / ICT Education widely and the field of Information Technology, published and managed by Jurusan Pendidikan Teknik Informatika, Fakultas Teknik dan Kejuruan, Universitas Pendidikan Ganesha. JANAPATI first published in 2012 and will be published three times a year in March, July, and December. This journal is expected to bridge the gap between understanding the latest research Informatika. In addition, this journal can be a place to communicate and enhance cooperation among researchers and practitioners.
Arjuna Subject : -
Articles 646 Documents
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Detecting Trending Topics Using Soft Frequent Pattern Mining (SFPM) on Indonesian Language Tweets Related to Earthquake News Indra, Indra; Prakoso, M Syawaladi Kukuh; Rosul, Mekar Bunga Allamanda; Mufti, Mufti
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.85086

Abstract

Social media users frequently post updates regarding ongoing natural disasters, including specific details and locations. These posts are crucial for real-time insights into such events; however, their informal tone and use of slang can make them difficult to utilize effectively. This study employs Soft Frequent Pattern Mining to detect trending earthquake topics in Indonesia using a specific Indonesian language dataset from X. The three-week testing period revealed varied performances: the first week showed a topic recall of 0.57, the second improved to 0.72, and the third drastically decreased to 0.28, indicating a temporary lack of significant trending topics. Averaging topic recall at 0.52, keyword precision at 0.34, and keyword recall at 0.45, the results highlight substantial room for improvements. This underlines the importance of methodological optimizations in future research to enhance the system’s effectiveness in identifying and validating widely discussed issues.
Comparative Study of Deep Learning Models to Classify of Multi-Class Skin Cancer on Imbalanced Data Oktoeberza, Widhia KZ; Rahman, Muhammad Farchan Al; Vasiguhamiaz, Azvadennys; Huda, Widya Nurul; Mainil, Afdhal Kurniawan; Sari, Julia Purnama
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.85544

Abstract

Skin cancer diagnosis faces challenges in efficiency and accuracy. This research addresses the need for improved non-invasive diagnostic tools by leveraging deep learning for multi-class skin cancer classification from dermoscopic images. A key focus is overcoming the limitations of imbalanced datasets, common in medical imaging, which can hinder model performance. We propose an optimal strategy utilizing a Convolutional Neural Network (CNN) transfer learning methodology. The process involves CNN-based segmentation to isolate relevant regions, followed by feature extraction and classification. We comparatively evaluated three pre-trained transfer learning techniques: DenseNet201, ResNet50, and VGG16, using the HAM10000 dataset (10,015 images across seven skin cancer classes). To mitigate severe class imbalance, Random Oversampling was employed, chosen for its simplicity and effectiveness in balancing the dataset and enhancing model generalization. Model performance was rigorously evaluated using accuracy, precision, recall, and F1-score. DenseNet201 consistently achieved superior performance, with an accuracy of 97% post-oversampling. It also exhibited the highest precision, recall, and F1-score across all models, confirming its effectiveness in classifying both majority and minority classes. Compared to previous studies on HAM10000, our DenseNet201 model's test accuracy of 96.52% is competitive or superior to reported accuracy of 90-92%. This highlights the synergistic effect of DenseNet201's efficient feature reuse and robust data balancing. This research provides a robust framework for advanced methodologies in skin cancer classification, particularly for imbalanced medical image datasets.
Unveiling Epistemological Perspectives in Software Effort Estimation: A Comprehensive Tertiary Study Jayadi, Puguh; Patmanthara, Syaad
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.88447

Abstract

The research examined epistemology in Software Effort Estimation (SEE), focusing on the evaluation methods and metrics used. Accurate estimation of effort is essential to the success of a software project. This study also aims to understand the various methods applied in SEE and how evaluation metrics are applied in this context. The proposed method in the study is to review the Systematic Literature Review (SLR) paper to analyze articles published in reputable international journals. The experimental methodology involves the identification, selection, and in-depth analysis of relevant studies, emphasizing the quality and relevance of existing research. The results show that although many methods are available, the proper selection is still challenging for researchers and practitioners alike. In addition, diverse evaluation metrics reflect the need for a more empirical approach to assessing the effectiveness of applied methods. The conclusions of this study provide significant insights for SEE practice and open up opportunities for further research in this area.
Document Matching for Contradiction Detection in Low-Resource Legislative Texts With Self-Training and Augmentation Using Transformer Model Navastara, Dini Adni; Abdillah, Surya; Benito, Davian; Adillion, Ilham Gurat; Purwitasari, Diana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.95954

Abstract

Detecting contradictions within low-resource legislative texts presents significant challenges due to limited labeled data, complex legal language, and the vast number of verses contained within legal documents. These contradictions can lead to legal ambiguities and disputes if not addressed effectively. To tackle this problem, this study proposes a comprehensive system that combines document matching with contradiction detection. Legal documents are first clustered based on contextual similarity, enabling a more targeted analysis of potentially contradictory verses. Among several clustering approaches tested, keyword similarity-based clustering using KeyBERT produced the highest MatchingScore of 0.6111. To overcome the scarcity of labeled data, we employed a multi-step strategy involving manual annotation, generative AI-based data augmentation, and self-training techniques. The contradiction detection model was developed using the XLM-RoBERTa architecture, trained on TPU V2 with a batch size of 64. The model achieved strong performance, with 0.978 recall, 0.9356 precision, 0.982 accuracy, and a 0.9566 F1-score, completing each epoch in 82 seconds. This integrated approach significantly reduces the complexity of contradiction detection in legislative documents while ensuring high accuracy and robustness.
Multi-Label Classification of Bilingual Doctor Responses in Online Medical Consultations Using Deep Learning Juanita, Safitri; Purwitasari, Diana; Purnama, I Ketut Eddy; Raihan, Muhammad; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.96980

Abstract

Online health consultations (OHCs) have become an integral component of modern healthcare delivery. However, significant challenges remain in multilingual and low-resource contexts such as Indonesia, where language barriers and digital disparities hinder effective doctor–patient communication. Ensuring the quality of such interactions requires the identification of six key communicative functions: building relationships, gathering and providing information, decision-making, promoting disease- and treatment-related behaviour, and responding to emotions. While existing research has largely focused on English-language OHCs, studies analysing these communicative functions in Indonesian remain limited due to the lack of annotated datasets and linguistic complexity. To address this gap, we propose a deep learning framework for multi-label classification of communicative functions in bilingual (Indonesian/English) doctor response texts. The dataset used in this study was annotated by medical professionals with six predefined communicative function labels. We conducted a comprehensive comparative evaluation of three deep learning architectures namely Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Networks (CNN) equipped with cross-language word embedding to improve multilingual generalization. Model performance is evaluated through four complementary perspectives: example-based, label-based, ranking-based, and multifaceted metrics, ensuring a holistic assessment. Result show that the fine-tuned LSTM model achieved the highest precision (0.972) on Indonesian texts, while Bi-LSTM obtained the best results on English texts with 0.890 accuracy and 0.980 precision. The LSTM model also reduced false positives in Indonesian classifications, whereas Bi-LSTM improved diagnostic reliability in English, confirming the models’ cross-lingual adaptability. These findings highlight the potential of deep learning to improve communication effectiveness in bilingual and resource-constrained OHC settings.
Adaptive Maze-Based Islamic Educational Games Using MOORA Method Nugroho, Fresy; Ridho, Muhammad; Melani, Roro Inda; Pebrianti, Dwi; Hammad, Jehad AH; Lestari, Tri Mukti; Maharani, Dian; Nurrahma ‘N, Alfina
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.100064

Abstract

The degradation of students' knowledge in Islamic education is increasingly concerning, driven by the negative influence of internet exposure. This study develops an educational game, Harta Karun Pengetahuan, as an interactive gamified learning medium incorporating core Islamic content. The game applies adaptive difficulty adjustment using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method based on player performance. The success of the MOORA method in providing recommendations for players is shown by how players will be directed to the next maze according to the success of the previous game. The game was tested with 20 students (aged 18–21) with two approaches evaluation, the System Usability Scale (SUS) and the Igroup Presence Questionnaire (IPQ), achieving an average SUS score of 84 (indicating high usability) and an overall IPQ score of 4.64 (indicating strong player immersion). Results showed that General Presence and Involvement had the highest average scores, indicating that players felt emotionally engaged and present in the virtual learning world. Although the Realism dimension was generally positive, it suggests room for improvement in visual and interactive fidelity. The findings demonstrate that integrating Islamic content into digital games can provide meaningful learning experiences and support students in achieving cognitive, affective, and psychomotor competencies in a contextual IRE setting.

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