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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 23 Documents
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Search results for , issue "Vol. 14 No. 2 (2025)" : 23 Documents clear
Enhancing Renewable Energy Utilization in Remote Areas Through an Accessible IoT Monitoring Framework: A Case Study on Tidung Island Dwiyaniti, Murie; Isdawimah, Isdawimah; Nadhiroh, Nuha; Setiana, Hatib; Muchlishah; Monika, Dezetty; Wardhani, Rika Novita; Tahazen, Tahazen
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.v14i2.93685

Abstract

This research addresses the lack of an accurate and efficient monitoring system for renewable energy utilization in Tidung Island. Despite its high solar and wind energy potential, the absence of real-time environmental data hinders optimal energy management. Existing IoT-based monitoring systems are costly and complex, making them unsuitable for remote areas. This research integrates Blynk IoT and Google Sheets database for real-time, cost-effective, and easily accessible data storage. This system enables real-time data acquisition to support efficient energy management and environmental monitoring in remote areas. Testing results show that the Telkomsel 4G modem provides a more stable connection with lower latency and minimal packet loss. Temperature remains between 25–30°C, while humidity fluctuates up to 100% at night. Wind speed is classified as low to moderate (1.0–6.1 m/s), and CO₂ levels range from 400–600 PPM, remaining within safe limits. These findings suggest the need for improved energy storage, system resilience against environmental changes, and adaptive energy management strategies for optimal hybrid renewable energy utilization.
Aspect-based Sentiment Analysis on Beauty Product Reviews using BERT and Long Short-Term Memory Al Aufar, Arya Prima; Romadhony, Ade
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.v14i2.94392

Abstract

In e-commerce, product reviews play a crucial role in influencing potential buyers by sharing user experiences and assessing product quality. This is especially important for beauty products, where poor quality can lead to physical harm. Reviews also help increase consumer interest in purchasing. Previous research has shown that product reviews differ in various aspects and content, making it challenging for consumers to quickly analyze them from multiple perspectives. This study applies aspect-based sentiment analysis to beauty product reviews on the Female Daily Network using a combination of BERT and LSTM. The goal is to provide more precise sentiment classification across different aspects, aiding consumers in selecting the best products. Several evaluation scenarios were conducted to assess different aspects of product reviews, including price, packaging, staying power, moisture, and aroma. The F-1 score revealed that the price aspect achieved the highest performance, reaching 100% in a 90%:10% test data scenario. However, the aroma aspect proved the most challenging to analyze, indicating that the model struggles to capture features related to scent effectively under the given evaluation setup.
Graph-Structured Network Traffic Modelling for Anomaly-Based Intrusion Detection Pratomo, Baskoro Adi; Haykal, Muhammad Farhan; Studiawan, Hudan; 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.v14i2.94959

Abstract

The increasing complexity of cyber threats demands more advanced network intrusion detection systems (NIDS) capable of identifying both known and emerging attack patterns. In this study, we propose a graph-based anomaly detection approach for network intrusion detection, where network traffic is modeled as graph structures capturing both attribute and topological information. Five graph anomaly detection models—DOMINANT, OCGNN, AnomalyDAE, GAE, and CONAD—are implemented and evaluated on the UNSW-NB15 dataset. The constructed graphs use info_message attributes as nodes, with edges representing sequential traffic relationships. Experimental results show that the Graph Autoencoder (GAE) and Dual Autoencoder (AnomalyDAE) models outperform other methods, achieving F1-scores of 0.8728 and 0.7939, respectively. These findings demonstrate that reconstruction-based approaches effectively capture complex network behaviors, highlighting the potential of graph-based methods to enhance the robustness and accuracy of modern NIDS. Future work will explore dynamic graph modeling, attention mechanisms, and optimization techniques to further improve detection capabilities.
A Tag-Constrained Top-k Shortest Path for Finding Diverse Routes Santoso, Bagus Jati; Tamtama Adi, Ibrahim; Ijtihadie, Royyana Muslim
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.v14i2.95815

Abstract

The top-k shortest path problem is a fundamental topic in graph theory and pathfinding applications. Traditional approaches focus solely on finding k paths with the least total cost or distance, often resulting in highly similar paths that offer limited flexibility for user selection. Moreover, real-world navigation demands often involve additional user preferences, such as specific points of interest or required amenities along the route. Motivated by this observation, this paper proposes an efficient framework for answering top-k diverse path search queries incorporating user-specified tag preferences. Specifically, given a source and destination node, a set of user-defined tags, and a similarity threshold, our method retrieves k shortest paths that not only satisfy the user's tag constraints but also maintain diversity by ensuring that the similarity among the retrieved paths remains below a given threshold. The proposed solution employs a two-phase approach: (1) preprocessing the graph structure to generate a tag-based matrix and shortest path data for efficient query processing, and (2) a hybrid search strategy that combines a modified Dijkstra’s algorithm and depth-first search with pruning based on tag satisfaction and diversity checking. Extensive experiments on synthetic road network datasets demonstrate that our method achieves significant improvements in query processing efficiency and provides a higher degree of path diversity compared to conventional approaches. Our contributions include the formal definition of the top-k diverse path search with tag preferences, the development of an efficient search framework, and comprehensive experimental validations. The results suggest that the proposed framework effectively balances path optimality, tag satisfaction, and diversity, enabling a more flexible and user-centric pathfinding system.
Attention-Driven U-Net with Ensemble Strategy for Inferior Alveolar Nerve Segmentation on 2.5D CBCT Data Arsy Bilahi Tama; Suciati, Nanik
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.v14i2.96058

Abstract

Image segmentation plays a crucial role in medical analysis, particularly in accurately identifying anatomical structures. In dental implant planning, the identification of the Inferior Alveolar Nerve (IAN) is critical to avoid complications resulting from nerve injury. However, the manual annotation process on CBCT images is time-consuming and labor-intensive. Recent studies utilizing deep learning for IAN segmentation in 3D images often face two main challenges: limited availability of annotated data and high computational requirements.To address these challenges, this study proposes a more efficient segmentation approach based on 2.5D images. We implemented a U-Net architecture enhanced with attention gates to improve the model's focus on relevant nerve structures and increase segmentation accuracy. Furthermore, to maximize performance, predictions from multiple models were combined using ensemble learning techniques, which enhance robustness and final accuracy by leveraging the predictive strengths of diverse training samples.Experimental results demonstrate that the proposed approach achieves an average Dice score of 87.7%. These findings indicate that the combination of an attention-enhanced U-Net, the use of 2.5D imaging, and ensemble learning effectively yields accurate IAN segmentation while providing a practical solution to the challenges of data scarcity and computational complexity.
Vector-Borne Disease Detection Using Random Forest and BPSO Raharja, Made Agung; Pradyto, Kadek Dwitya Adhi; Wibawa, I Gede Arta; Astawa, I Gede Santi
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.v14i2.96722

Abstract

Vector-borne diseases such as malaria, dengue fever and yellow fever still pose a serious threat to public health. To distinguish between these diseases, an accurate classification process is required. In this study, Random Forest algorithm is used as a classification method due to its ability to overcome overfitting and provide good accuracy results. However, the large number of features in the data can cause redundancy and decrease the accuracy of the model. Therefore, the Binary Particle Swarm Optimization (BPSO) method is used as a feature selection technique to optimize the performance of Random Forest. The optimization process is also complemented by finding the best parameters using Random Search and Grid Search. Evaluation was conducted on a vector-borne disease dataset with 64 features and 11 disease classes. The results showed that the accuracy of the model increased from 90.48% to 100% after feature selection by BPSO which selected 37 best features, and Random Search proved to be more efficient in computation time than Grid Search. This research shows that the combination of Random Forest and BPSO can improve classification accuracy and efficiency in detecting vector-borne diseases.
Ambidextrous IoT Governance Framework for SmartCo’s Digital Transformation Aligned with COBIT 2019 Traditional and DevOps Rahayu, Indah Sari; Mulyana, Rahmat; Fakhrurroja, Hanif
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.v14i2.98135

Abstract

SmartCo, a digital infrastructure provider, faces IoT governance challenges (security, privacy, regulation) despite leveraging IoT for digital transformation. To address this, this research proposes an ambidextrous IoT governance framework that balances exploration (innovation and adaptation) and exploitation (efficiency of existing resources). The framework integrates COBIT 2019 with agile DevOps practices to optimize IT resource value and performance. Employing the Design Science Research (DSR) methodology an approach in Information Systems that provides structured guidance for designing, evaluating, and validating technological solutions, the study assessed the current governance environment, identified design factors, and prioritized Governance and Management Objectives (GMOs). Data were collected through semi-structured interviews with key stakeholders, guided by structured questions, and validated using internal documents in iterative analysis cycles until saturation was reached. DSS05 (Managed Security Services) emerged as the most critical domain. In COBIT 2019, DSS05 includes coordination and execution of IT operational procedures, such as SOPs and monitoring. The governance capability was found at level 3. Gaps included the lack of IoT unit test documentation with a security focus and unclear responsibilities of Testing and Release Managers. Recommendations include defining clearer responsibilities to the testing and release manager roles and mandating security-based unit testing before release. These improvements are projected to raise the DSS05 maturity level from 3.71 to 3.85. This study contributes by offering a tailored IoT governance solution for SmartCo and demonstrating the practical use of ambidextrous COBIT 2019 to manage innovation in dynamic technology environments.
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.

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