cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota denpasar,
Bali
INDONESIA
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 696 Documents
 66   67   68   69   70 
Multi Coordinate Dekadal Rainfall Prediction in South Sumatera Using Long Short-Term Memory Rheza Rijaya; Derry Alamsyah; Yohannes
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.v15i1.91431

Abstract

An accurate rainfall prediction is essential to help mitigate natural disasters that may disrupt human activity. Diversity of the Indonesian climate makes it harder to predict rainfall with high accuracy, and this situation also occurs in South Sumatera. Therefore, this research aims to assess Long Short-Term Memory (LSTM) model to predict dekadal rainfall at per-coordinate level, spanning 18 dekads ahead, and across 2821 gridded coordinates in South Sumatera. The model also uses climate indices (Southern Oscillation Index (SOI), Dipole Mode Index (DMI), Niño Sea Surface Temperature (SST) anomaly) and wind streamline, together with South Sumatera dekadal rainfall to enhance the model predictive performance. The best performing model achieved by data that includes wind streamline at 925 millibars, with per-month performance of Root Mean Squared Error (RMSE) between 31.25 to 56.50 and rainfall level category accuracy between 53.26% to 78.47%. This indicates the LSTM model is able to predict dekadal rainfall on multiple coordinates throughout South Sumatera, although requiring significant improvement to be able to be incorporated to main weather prediction system.
Optimizing Stock Prediction in Supermarket: A Comparative Analysis of LightGBM and XGBoost for Enhanced Inventory Management Devi Dwi Purwanto; Philipus Suryo Subandoro; Agustinus Bimo Gumelar
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.v14i3.94433

Abstract

Stock management in supermarkets is a critical challenge due to unpredictable demand fluctuations and seasonal purchasing patterns. Inaccurate forecasting often leads to understocking or overstocking, which in turn reduces customer satisfaction and causes financial losses. To overcome this problem, machine learning approaches have gained attention for their ability to model complex patterns in sales data more effectively than traditional methods. This study compares two widely used algorithms, XGBoost and LightGBM, in forecasting daily supermarket sales. A dataset of 23,873 transactions from January 2023 to December 2024 was used, processed into daily sales per product, and enriched with seasonal and lag features. Hyperparameter tuning was conducted using GridSearchCV and RandomizedSearchCV, and model evaluation applied multiple metrics including MAE, MAPE, RMSE, and MSE. The results indicate that XGBoost outperformed LightGBM, achieving the lowest MAE of 2.413×10⁻⁵ after optimization. While LightGBM demonstrated computational efficiency, its accuracy was less optimal for this dataset. These findings highlight the superiority of XGBoost for small- to medium-scale retail time series forecasting and provide practical insights for supermarkets to enhance inventory management and supplier coordination.
Biomedical Image Processing for Enhanced Classification of Medicinal Plants Using Voting Ensemble Nova Agustina; Candra Nur Ihsan; Kelik Sussolaikah
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.v15i1.95457

Abstract

Indonesia harbors extensive biodiversity, including many medicinal plants used in health. Accurate and scalable identification remains challenging because of high intra class variation, strong visual similarity among species, and variable capture conditions in the field. This study evaluates three convolutional neural networks for Indonesian medicinal plant classification: ResNet18, VGG16, and EfficientNet_B0, and an average voting ensemble that aggregates their class probabilities. Using a held-out test set, ResNet18 achieves 87.00% accuracy, 91.16% precision, 87.00% recall, and 87.35% F1 score; VGG16 records 63.50% accuracy, 63.89% precision, 63.50% recall, and 61.10% F1 score; EfficientNet_B0 attains 94.00% accuracy, 94.49% precision, 94.00% recall, and 94.01% F1 score; the ensemble reaches 93.00% accuracy, 94.99% precision, 93.00% recall, and 93.22% F1 score. Confusion matrix analysis indicates that remaining errors concentrate on visually similar pairs, notably Aloe Vera versus Green Tea and Basil Leaf versus Green Tea. The findings show that EfficientNet_B0 is the strongest single backbone, while the ensemble improves precision and stability with accuracy close to the best individual model. These results demonstrate a practical pathway for reliable, field deployable recognition of medicinal plants in Indonesia. Future work will expand the dataset across seasons and regions, increase class balance, and explore stronger augmentation, focal or class weighted losses, and probability calibration. We also plan segmentation assisted pipelines, staged fine tuning at higher resolution, and lightweight deployment through pruning and quantization on mobile or embedded devices. To support reproducibility and adoption, the training pipeline and plotting scripts are made available in an open Kaggle notebook.
Implementation of Efficientnet and Grad-Cam for Detection and Interpretation Of Skin Cancer Melanoma in Dermoscopic Images Green Arther Sandag; Beverly Ivy Lukas
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.v14i3.96123

Abstract

This study aims to implement the EfficientNet architecture for classifying melanoma skin cancer from dermoscopic images. Melanoma, one of the deadliest forms of skin cancer, is often difficult to detect visually, making deep learning technology a promising tool for facilitating faster and more accurate diagnoses. The dataset used consists of dermoscopic images of skin cancer sourced from Kaggle, categorized into two classes: Malignant and Benign. The classification process was carried out using various EfficientNet models (B0, B3, B4, B5, and B7) with transfer learning techniques, where different optimizers, such as Adamax, RMSprop, and SGD, were tested to obtain optimal results. Experimental results showed that the EfficientNetB3 model with the Adamax optimizer achieved the highest accuracy of 95%, making it the best performer in this testing scenario. To enhance the interpretability of the model's predictions, Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to visualize the areas of the dermoscopic images that contributed most significantly to the classification decision. The resulting model was then integrated into a Flask-based web system to provide real-time predictions and visual explanations from user-uploaded dermoscopic images. This research demonstrates the potential of EfficientNet in improving the accuracy and efficiency of early melanoma detection, while also contributing to the development of technology-based diagnostic tools for medical professionals and the public.
A Systematic Literature Review on Data Mining Techniques for Smart Transportation: Trends, Challenges, and Future Prospects Ade Bastian; Andri Irfan Rifai; Jujun Badrujaman; Hendrawan Rukiyat
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.v14i3.96200

Abstract

This article presents a Systematic Literature Review (SLR) on the use of data mining techniques in intelligent transportation systems (ITS). The review follows PRISMA procedures encompassing identification, screening, feasibility evaluation, inclusion, and data synthesis. Articles were retrieved using Publish or Perish with Scopus as the primary database, and data were coded and classified using Microsoft Excel. From 4,059 publications initially identified, 36 highly relevant papers were selected and analyzed across thematic areas such as demand forecasting, accident prevention, efficiency optimization, and intelligent transport integration. The novelty of this study lies in its comprehensive synthesis and mapping of methodological patterns, data sources, and evaluation practices in ITS-related research providing an integrated perspective that has not been consolidated in prior reviews. The findings reveal that urban traffic management decisions supported by data mining particularly clustering, classification, and predictive analysis techniques can significantly enhance system responsiveness and safety. However, several challenges persist, including the limited integration between predictive models and transportation policy frameworks, inconsistent data quality, and interoperability issues. Data mining also presents opportunities to improve public transport safety and address mobility challenges in developing countries. To maximize the impact of data mining on adaptive and sustainable transport systems, future research should focus on enhancing real-time analytics, developing inclusive and policy-aware frameworks, and promoting cross-regional implementation to ensure broader applicability and long-term impact in intelligent mobility systems.
Usability Evaluation of the Undiksha Letter Management System Website Using Performance Measurement, Think Aloud, And Mouse Tracking Methods I Gusti Putu Agung Arka Putra; I Made Candiasa; I Nyoman Sukajaya
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.v14i3.100659

Abstract

Undiksha has implemented a letter management system to streamline online correspondence. However, no evaluation has been conducted regarding its performance or user feedback. Discussions with the Unit Penunjang Akademik (UPA) TIK revealed several complaints about the system. This study evaluates its usability using performance measurement, think-aloud, and mouse tracking methods to assess effectiveness, efficiency, user satisfaction, and interface issues. Fourteen respondents, categorized as skilled and novice users, participated in the evaluation. Performance measurement results show high effectiveness, with task success rates of 81% for skilled users and 82% for novices. Task completion time was classified as "very fast." The think-aloud method revealed usability issues, including navigation difficulties, system speed, and suboptimal features. Mouse tracking analysis provided insights into user interactions, highlighting frustration points through heat maps. Based on these findings, improvements are recommended, including interface redesign, feature optimization, and performance enhancements. This study aims to support the development of a more user-friendly letter management system, ultimately increasing user satisfaction at Undiksha
Optimizing TelCo Digital Transformation through Ambidextrous Cloud Governance with COBIT 2019 Traditional and DevOps Approaches Aria Riezki Fhadila; Rahmat Mulyana; Rd. Rohmat Saedudin
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.v14i3.101153

Abstract

Cloud computing is a key enabler of digital transformation in telecommunications companies, offering flexibility, scalability, and innovation. However, it also presents governance challenges in balancing agility with control, especially in regulated environments. This study examines a state owned TelCo undergoing digital transformation to design an ambidextrous cloud governance model that integrates COBIT 2019 Traditional and DevOps Focus Area frameworks. Using a Design Science Research (DSR) method, data were collected through interviews with IT and business stakeholders and analyzed alongside internal policies and national regulations, including SOE Minister Regulation No. 3/2023 and ICT Minister Regulation No. 5/2021. The research prioritizes Governance and Management Objectives (GMOs) based on COBIT 2019 design factors and DevOps practices. DSS04 (Managed Continuity) emerged as the top priority GMO due to its critical risk and business impact. A maturity gap analysis revealed key weaknesses in roles, training, scenario planning, resilience testing, IaC adoption, and incident response automation. Improvements include integrating cloud roles into BCM, regular continuity simulations, early-stage “what-if” planning, DevOps-based testing with Chaos Engineering, full IaC implementation, and enhanced automation features. These steps are expected to raise DSS04 maturity from 3.7 to 3.9, improving resilience and compliance. The proposed model uses Resource-Risk-Value (RRV) analysis for prioritization, offering a structured and scalable governance solution to support TelCo’s digital transformation while ensuring robust continuity.
Enhanced Fraud Detection Using Multi-Autoencoder Feature Extraction and Optimized SVM Ratna Salkiawati; Wowon Priatna; Hendarman Lubis; Nurfiyah Nurfiyah
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.v15i1.101728

Abstract

This study employs multiple autoencoder to learn hierarchical feature representations from transaction data. Furthermore, a Genetic Algorithm is utilized to fine-tune the hyperparameters of the SVM classifier in order to enhance its ability to detect fraudulent activities. The proposed approach was evaluated using a large-scale e-commerce transaction dataset containing 1,472,952 records, where 5.01% of the transactions were labeled as fraudulent. The model’s effectiveness was assessed using a combination of classification metrics, including accuracy, precision, and recall, along with the F1 metric and curve-based evaluations such as ROC-AUC and PR-AUC. Experimental results show that the proposed model achieved 0.9683 accuracy, 0.6679 precision, 0.7664 recall, 0.7137 F1-score, and 0.9793 AUC-ROC, outperforming baseline models and several previous studies using the same dataset. Ablation study results further demonstrate that the integration of multi-autoencoder feature extraction and evolutionary hyperparameter optimization significantly improves fraud detection performance. These findings suggest that the proposed framework is capable of effectively identifying fraudulent transactions even when dealing with highly skewed e-commerce data. In addition, the approach shows meaningful promise for being applied in practical fraud detection contexts.
Analyzing Student’s Failure in VIAT-MAP Reconstruction Process and the Relation to Learning Gain Nazario Safariesqi; Banni Satria Andoko; Erfan Rohadi; Tsukasa Hirashima
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.v14i3.101868

Abstract

This study investigates the cognitive dynamics of students' argumentative reasoning in a digital learning environment using the Toulmin Argument Model. It addresses two core issues: (1) whether the interaction between correct and incorrect argument components affects learning improvement, and (2) whether the initial selection order (Ground First vs. Warrant First) moderates this interaction. The importance of this study stems from an inadequate understanding of how students' misconceptions and sound reasoning coexist and influence learning outcomes. Using a pretest-posttest design, examines the impact of the interaction between correct responses (TT_s) and incorrect responses (TF_s) on learning outcomes. Log data capturing each student's decision making process were classified and analyzed using simple linear regression and slope analysis. The findings revealed a significant interaction between TT_s and TF_s, indicating that accurate responses have a stronger effect as students' errors increase, thus supporting the theory of compensatory cognitive processes. Although the order of argument components (FirstDomain) did not influence this effect, the model demonstrated satisfactory statistical validity (R² = 0.386, p = 0.021). These results suggest that the learning strategy should be effective not only in eliminating misconceptions but also in improving sound reasoning, especially for students with weak conceptual understanding. Strengthen the Learning Analytics domain by emphasizing the potential use of log data indicators to assess and support adaptive learning in argument based learning environments.
Optimizing Small-Scale Pumped Hydro Storage Operation for Off-Grid PV: Maximizing Stored Energy Under LPSP and Carbon Footprint Constraints Akhmad Musafa; Ardyono Priyadi; Vita Lystianingrum; Mauridhi Hery Purnomo
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.v14i3.102327

Abstract

This study explores the utilization of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Puzzle Optimization Algorithms (POA) methodologies to improve the performance of a small-scale PHS integrated in an off-grid PV system in a building. The optimization objective function is to minimize the LPSP value, with a carbon footprint of less than 10 kg/kWh. The LPSP value is related to the total energy deficit and total load, and the carbon footprint value is related to the total heat generated by the PHS pump, generator, and carbon intensity value. The optimization setup uses a multi-objective function that has been simplified into a single weighted objective function with normalized and justified weights. The case study is conducted on a 5 kW PV system in a building with a water level of 24 meters and a PHS reservoir of 5 m3. The system is tested under two conditions, namely during the rainy season (January) and the dry season (August). The PSO and GWO algorithms, based on testing results in January and August, demonstrated better performance than POA. This is based on the higher average total PHS energy compared to POA, as well as lower LPSP, LOLE, and EENS values. Meanwhile, for the average stored energy and carbon footprint values, the POA algorithm performs better than PSO and GWO, as indicated by the higher average stored energy and lower carbon footprint values.

Page 68 of 70 | Total Record : 696

 66   67   68   69   70 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol. 15 No. 2 (2026) Vol. 15 No. 1 (2026) Vol. 14 No. 3 (2025) Vol. 14 No. 2 (2025) Vol. 14 No. 1 (2025) Vol. 13 No. 3 (2024) Vol. 13 No. 2 (2024) Vol. 13 No. 1 (2024) Vol. 12 No. 3 (2023) Vol. 12 No. 2 (2023) Vol. 12 No. 1 (2023) Vol. 11 No. 3 (2022) Vol. 11 No. 2 (2022) Vol. 11 No. 1 (2022) Vol 11, No 1 (2022) Vol. 10 No. 3 (2021) Vol 10, No 2 (2021) Vol. 10 No. 2 (2021) Vol. 10 No. 1 (2021) Vol 10, No 1 (2021) Vol 9, No 3 (2020) Vol. 9 No. 3 (2020) Vol. 9 No. 2 (2020) Vol 9, No 2 (2020) Vol. 9 No. 1 (2020) Vol 9, No 1 (2020) Vol. 8 No. 3 (2019) Vol 8, No 3 (2019) Vol. 8 No. 2 (2019) Vol 8, No 2 (2019) Vol. 8 No. 1 (2019) Vol 8, No 1 (2019) Vol 8, No 1 (2019) Vol. 7 No. 3 (2018) Vol 7, No 3 (2018) Vol 7, No 3 (2018) Vol 7, No 2 (2018) Vol. 7 No. 2 (2018) Vol. 7 No. 1 (2018) Vol 7, No 1 (2018) Vol 7, No 1 (2018) Vol 6, No 3 (2017) Vol 6, No 3 (2017) Vol. 6 No. 3 (2017) Vol 6, No 2 (2017) Vol. 6 No. 2 (2017) Vol 6, No 2 (2017) Vol. 6 No. 1 (2017) Vol 6, No 1 (2017) Vol 6, No 1 (2017) Vol 5, No 3 (2016) Vol. 5 No. 3 (2016) Vol 5, No 3 (2016) Vol. 5 No. 2 (2016) Vol 5, No 2 (2016) Vol 5, No 2 (2016) Vol 5, No 1 (2016) Vol 5, No 1 (2016) Vol. 5 No. 1 (2016) Vol 4, No 3 (2015) Vol. 4 No. 3 (2015) Vol 4, No 3 (2015) Vol 4, No 2 (2015) Vol. 4 No. 2 (2015) Vol 4, No 2 (2015) Vol 4, No 1 (2015) Vol 4, No 1 (2015) Vol. 4 No. 1 (2015) Vol 3, No 3 (2014) Vol. 3 No. 3 (2014) Vol 3, No 3 (2014) Vol 3, No 2 (2014) Vol 3, No 2 (2014) Vol. 3 No. 2 (2014) Vol 3, No 1 (2014) Vol. 3 No. 1 (2014) Vol 3, No 1 (2014) Vol 2, No 3 (2013) Vol. 2 No. 3 (2013) Vol 2, No 3 (2013) Vol. 2 No. 2 (2013) Vol 2, No 2 (2013) Vol 2, No 2 (2013) Vol 2, No 1 (2013) Vol. 2 No. 1 (2013) Vol 2, No 1 (2013) Vol 1, No 3 (2012) Vol 1, No 3 (2012) Vol. 1 No. 3 (2012) Vol. 1 No. 2 (2012) Vol 1, No 2 (2012) Vol 1, No 2 (2012) Vol 1, No 1 (2012) Vol. 1 No. 1 (2012) Vol 1, No 1 (2012) More Issue