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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 666 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.
A Moodle-Based Digital Learning Approach to Enhance AI Literacy Competence in Non-STEM Programs Sigit Pambudi; Herman Dwi Surjono; Totok Sukardiyono; Akhsin Nurlayli
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 1 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i1.105958

Abstract

This study aims to develop sontoto and evaluate a Moodle-based digital learning model to improve the artificial intelligence (AI) literacy of non-STEM students in a management program. To promote enactiveness, we designed ADDIE-based learning model with integrated case-based learning, project-based assignments, interactive H5P content and Gamification features. A one-group pretest–posttest olxtogel design was used on 50 undergraduate students. Data was collected through AI Literacy Tests, Learning Analytics and Questionnaires. Before hypothesis testing using the Shapiro–Wilk test, normality of data were assessed. Results of present t-test showed that students significantly reported improvement in their competence for AI literacy after education intervention (p <0.001) with a large effect size. Learning analytics uncovered high engagement, and a strong positive relationship with time spent on the learning modules that significantly correlated with posttest scores. The qualitative analysis also indicated that students of the learning environment perceived as relevant, motivating and encouraging the development of practical skills. While the absence of control group means these results have limitations as a basis for causative inference, they indicate that such an organically structured learning model based around Moodle could work feasibly in supporting AI literacy alexistoto development across non-STEM higher education cohorts. This study provides a guide about possible integration of pedagogical techniques and interactive technologies concerning AI literacy education.
Comparison of the SMOTE Method on Naive Bayes and K-Nearest Neighbor for Sentiment Analysis of the MyPertamina Application Rian Syaputra Ainun Naim Rian; Novianti Puspitasari; Ummul Hairah
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.102755

Abstract

MyPertamina is a digital application developed by PT Pertamina as a means of non-cash payment and a verification tool in the distribution of fuel oil, as well as offering loyalty programs in the form of points, prizes, and electronic coupons to its users. The application has a rating of 3.3 on the Google Play Store and 2.1 on the App Store, with more than 10 million downloads. However, these ratings do not fully reflect user satisfaction or issues. This research aims to analyze the effectiveness of the Naïve Bayes and K-Nearest Neighbor algorithms, as well as the influence of the SMOTE technique, in classifying user sentiment toward the MyPertamina application. Review data were obtained through a scraping technique, yielding 1,500 entries, which were then cleaned and labeled as positive, negative, or neutral using TextBlob. The TF-IDF method was applied for weighting, and 10-fold cross-validation was used for data splitting. Two testing scenarios were conducted: with SMOTE to address data imbalance, and without SMOTE. The results show that Naïve Bayes with SMOTE achieved the best performance, with an accuracy of 82.07%, precision of 81.67%, recall of 81.10%, and an F1-score of 80.96%, significantly improving classification performance, particularly for the neutral class. Based on these findings, Naïve Bayes with SMOTE is proven to be the most effective method for classifying user sentiment on MyPertamina reviews, as it produces balanced and accurate performance across all classes.
Development of Academic Question-Answer System Based on Retrieval-Augmented Generation Using Local LLM and FAISS Index Lathifah Alfat; Hendi Hermawan; Rinto Priambodo
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 3 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i3.104147

Abstract

This study addresses the challenge of providing students with fast and accurate academic information by developing a document-based question-answering (Q&A) system. Many students face difficulties navigating complex campus administrative documents, from trivial inquiries to graduation policies. To solve this, we propose a novel Retrieval-Augmented Generation (RAG) system that uses a local Large Language Model (LLM) and a FAISS index for efficient information retrieval. The system's architecture ingests academic documents (PDF, DOCX), splits them into context-aware chunks, and converts them into vector representations using the all-MiniLM-L6-v2 embedding model. These vectors are then stored in topic-specific FAISS indices to enhance retrieval accuracy. Our implementation uses Qwen2.5:3B, a locally run LLM, to ensure offline functionality and minimal hardware requirements. The system is also equipped with an adaptive prompt mechanism and a domain-switching feature to handle diverse question styles. Evaluations using black-box and white-box testing confirmed the system's robustness, with an 80% success rate in automatic domain detection and an average retrieval time of 1.6 seconds. The results indicate that the primary performance bottleneck resides in the FAISS retrieval process rather than the generative model. This research demonstrates that a locally run RAG system can provide a practical, user-friendly, and effective solution for enhancing academic support services. It establishes a foundation for future developments in intelligent educational interfaces.
Understanding User Continuance Intention Toward AI-Driven Social Media Platforms: The Mediating Role of Attitude and Satisfaction Adele Mailangkay; Dedy Syamsuar; Yulia Ery Kurniawati
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.104254

Abstract

Artificial Intelligence (AI) is influencing user engagement, interactions, and perceptions on social media. The purpose of this study is to understand the user’s purpose, with the aim of determining which of the various factors linked to the user’s intention of continuing to use AI social media applications, while focusing on the role of attitude and user satisfaction as the mediators. The study used 532 responses of social media users for the purpose of this study. The study used the following seven constructs: AI Trust, Algorithmic Transparency, Information Quality, Performance Expectancy, Attitude, User Satisfaction, and Continuance Intention. A total of 532 valid responses were analyzed using PLS-SEM to demonstrate that the results indicate that Performance Expectancy significantly influences user attitude, while Information Quality indirectly affects attitude through Performance Expectancy. However, AI Trust does not have a significant effect, and Algorithmic Transparency shows a negative effect on attitude. User attitude and user satisfaction positively and significantly affect the intention to continue using, or what is termed the positive mediating effect of user attitude and user satisfaction. The results provide insights into the role of performance-driven factors in AI adoption, suggesting that improving system functionality, content relevance, and user experience is more critical than emphasizing trust and transparency in AI-driven social media environments.

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