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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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The Influence of Online Visual Merchandising and Customized Cross-Selling on Traveloka Mobile Apps Intention to Reuse: With Mediation of Visual Cues and Dynamic Personalization Ariawan, Nathania Putri; Ariadi, Gede
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.91443

Abstract

This study examined the impact of Online Visual Merchandising (OVM) and Customized Cross Selling (CCS) on the Intention to Reuse (ItR) Traveloka’s mobile app, mediated by Visual Cues (VC) and Dynamic Personalization (DP). With the increasing competition in the online travel industry and the need for platforms to retain users, understanding the factors that drive repurchase intentions is crucial. A survey of 135 app users aged 25-40 was conducted, and data were analyzed using PLS-SEM. Results indicate that while OVM alone did not significantly affect ItR, CCS had a positive impact. VC and DP significantly mediated the relationships between OVM, CCS, and ItR. The findings suggest that aesthetic elements alone are insufficient for driving repetitive behavior; instead, a strategic integration of visual, personalized, and cross selling strategies is crucial. The study supports nudge theory and offers practical insights for optimizing digital commerce applications to enhance repurchase intentions.
Enhancing Diesel Backup Power Forecasting With LSTM, GRU, and Autoencoder-based Input Encoding Dewi, Ni Putu Novita Puspa; Leu, Yungho; Mustofa, Khabib; Riasetiawan, Mardhani
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.92079

Abstract

Ensuring a reliable electricity supply is crucial for Indonesia's development. This study applies deep learning to forecast diesel backup power output. One challenge in such predictions is balancing the input sequence length and the number of features to avoid overly long input sequences, which may degrade model performance. To address this, we utilized an autoencoder to compress the input sequence, improving prediction accuracy. Additionally, given the time-consuming nature of hyper-parameter optimization in deep learning, we employed Bayesian optimization to streamline the process and achieve optimal hyper-parameter settings.The study compares a General Regression Neural Network (GRNN) optimized by FOA with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models optimized by Gaussian Process (GP). Results show that LSTM and GRU with encoded inputs outperform their non-encoded counterparts. The GRU, combined with an autoencoder and Bayesian-optimized hyper-parameters, achieves the lowest prediction error, demonstrating superior forecasting capability.The dataset, obtained from evaluated feeders in Kapuas District, Central Kalimantan, covers hourly power generation and distribution from October 2017 to September 2018. Data was split into 11 months for training and 1 month for testing, with the training set further divided into 70% training and 30% validation. The best performing model achieved RMSE and MAE values of 27.5824 and 14.9804, respectively. Future research may explore further optimization, feature selection techniques, and extended dataset variations.
Analyzing User Experience and Satisfaction in the B-Block Game-Based Assessment Husniah, Lailatul; Kholimi, Ali Sofyan; Yuhana, Umi Laili; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.92784

Abstract

Game-based assessment (GBA) has developed as an innovative education method, including learning basic arithmetic operations. This study aims to analyze user experience and satisfaction using B-Block, an assessment-based game for basic arithmetic operations. The study involved 94 junior high school students with an age distribution of 12-13 years old and varying levels of gaming experience. The research used descriptive statistical analysis, validity and reliability test, Pearson correlation test, and multiple linear regression to identify factors influencing user satisfaction and continuance usage intention. The analysis showed that B-Block has good usability and educational benefits, with user satisfaction being the most dominant aspect. Validity and reliability tests confirmed that most variables were valid and reliable (Cronbach's Alpha > 0.7), except Errors, which had lower reliability (α = 0.632). Pearson correlation shows that Perceived Usefulness has a strong relationship with satisfaction (r = 0.784), while user satisfaction contributes significantly to continuance intention (r = 0.694). Multiple linear regression revealed that perceived usability and perceived usefulness were the main factors influencing user satisfaction, while confirmation and satisfaction had the most effect on continuance intention. The findings confirm that the gameplay's usability and perceived usefulness are key in increasing user satisfaction while matching the experience with initial expectations, and user satisfaction contributes to continued use.
Sentiment Analysis for Hotel Reviews Using Snowball and VADER Rustamaji, Abdullah; Huizen, Roy Rudolf; Hostiadi, Dandy Pramana
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.v14i2.82556

Abstract

In the hotel industry, the role of hotel guests is very influential in the development and sustainability of business. Therefore, hotels need to provide services that can satisfy guests. However, many hotels still do not have an analysis system for guest comments. Hotels still manually conduct analysis by discussing with operational leaders to determine whether incoming guest comments contain positive, negative, or neutral sentiments. Previous research introduced guest sentiment analysis but has yet to have optimal accuracy. This paper proposes sentiment analysis using a combination of VADER and Snowball stemmer algorithms, which are tested using real datasets. The goal is to get accurate sentiment analysis results. The experimental results show that the VADER method combined with SnowBall Stemmer has better accuracy than other sentiment analysis methods, with an accuracy of 96.21%. The sentiment analysis model can be used as a basis for decision-making for hotel business owners.
Combination of CNN and SMOTE-IPF for Early Detection of Diabetes Patients in Thermogram Images W Mega Adhi Agam Pradhana; Gede Angga Pradipta; Roy Rudolf Huizen
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.v14i2.83145

Abstract

This study aims to enhance the early detection of diabetic complications through the analysis of plantar foot thermogram images using deep learning techniques. A total of 334 thermographic images were utilized, comprising 244 images from 122 diabetic patients (DM class) and 90 images from 45 non-diabetic individuals (control group, CG class). To address the dataset’s imbalance (ratio of 2.64), the Synthetic Minority Over-sampling Technique with Iterative-Partitioning Filter (SMOTE-IPF) was applied both before and after feature extraction. Image quality was further enhanced using Adaptive Histogram Equalization (AHE) and Gamma Correction preprocessing techniques. A Convolutional Neural Network (CNN) model was trained and evaluated on an independent test set of 54 images. The model achieved outstanding results: 99.37% accuracy, 99.37% precision, 100% recall, and a 99.68% F1-score for AHE-processed images. Gamma-corrected images achieved 98.50% accuracy, while original images reached 97.20%. These findings demonstrate the combined value of data balancing and preprocessing in improving non-invasive diabetic foot ulcer detection, offering a promising diagnostic aid for clinical settings.
Enhancing Customer Satisfaction: Exploring the Earliest Due Date Method for Production Scheduling at PT. X Khusna, Arfiani Nur; Prabowo, Ferry Agung
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.v14i2.84476

Abstract

Efficient production scheduling is essential for meeting customer demands on time and enhancing a company's operational efficiency. PT. X faces challenges in optimizing production schedules and meeting order deadlines efficiently. This study aims to evaluate the effectiveness of the earliest due date (EDD) method in production scheduling at PT. X. A quantitative approach was used, involving 100 production data samples from PT. X. The EDD method was applied to set the production order based on the earliest deadline of each order. Factors considered included order specifications, machine capabilities, and set deadlines. The EDD method improved the accuracy of production schedules, efficient use of resources, and customer satisfaction with on-time delivery. The results showed a 100% success rate for the EDD method in prioritizing orders based on the earliest deadlines, thereby improving production schedules and on-time delivery to customers. The study confirms the importance of the EDD method in enhancing production scheduling practices and achieving operational efficiency at PT. X. Future research should explore additional scheduling methods to further improve production processes and customer satisfaction.
Learning Algorithms of SVR, DTR, RFR, and XGBoost (Case Study: Predictive Maintenance of Fuel Consumption) Parhusip, Hanna Arini; Lea, Lea; Trihandaru, Suryasatriya; Nugroho, Didit Budi; Santosa, Petrus Priyo; Hariadi, Adrianus Herry
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.v14i2.85657

Abstract

The most complex aspect of predictive maintenance (PdM) for heavy vehicles is accurately forecasting fuel consumption as it is both critical and challenging to achieve optimal efficiency while minimizing expenses. Overfitting and failure to capture the existing data's linear relationships seem to remain the most persistent issues with traditional methods. In order to achieve this, the following techniques were analyzed to choose the best fuel consumption forecaster: Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFT), and XGBoost. The models were implemented and their performance measured using Mean Squared Error (MSE). The analysis revealed that SVR surpassed the others with a linear kernel (C=10) achieving the lowest MSE rates of 0.26, while DTR, RFR, and XGBoost earned significantly higher 3.375, 2.857, and 3.857 (MSEs). The other models lagged behind SVR because SVR was more effective in capturing linear relations and managing overfitting, a dominating issue with decision-tree based models. This points out another important aspect of predictive maintenance (PdM) : the appropriate machine learning technique plays a very important role in accurately predicting fuel consumption of heavy trucks, which improves precision and fuel efficiency.
Multi-Source Data Fusion For Data Extraction and Integration of Scientific Publications in Academic Institution STIS Maulidya, Luthfi; Suadaa, Lya Hulliyyatus; Wijayanto, Arie Wahyu; Ridho, Farid
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.v14i2.87050

Abstract

Scientific research publication data is one of the most important data required by academic and research institution because it can be used as a reference to measure the performance of lecturers in research activities, to assess study programs and university accreditation, to identify research trends, and to plan research development policies and strategies. However, to fulfill these data needs, research data must be collected and integrated from various data sources due to the diversity of databases. One of the portals that provides scientific research publication data for universities in Indonesia is Sinta (Science and Technology Index). The integrated research databases in Sinta are Scopus, Web of Science (WoS), Garba Rujukan Digital (Garuda), and Google Scholar. However, there are limitations, namely that some scientific research publication metadata in Sinta are still not covered, such as Digital Object Identifier (DOI), abstract, author's full name, publication/journal name, publication type, and number of citations. In addition, each data source has a different data format, which requires data processing so that it can be integrated. Processing and integrating research data from different sources will be very inefficient if it is done manually and not computerized. Therefore, this study proposes a data engineering pipeline framework for the extraction and integration of scientific research publication data from various data sources using the multi-source data fusion method with the Unified Cube methodology approach, which is then implemented by building a web interface. We use Politeknik Statistika STIS, Jakarta as a case study. This framework refers to the data engineering lifecycle and multi-source data fusion method based on abstraction levels for the extraction and integration of scientific research publication data. Then, the transformed data will be classified using rule-based classification. The results show that the accuracy of the framework was more than 90% and the accuracy of the classification results was 87.5%.
Student Adaptability Level Optimization using GridsearchCV with Gaussian Naive Bayes and K-Nearest Neighbor Methods as an Effort to Improve Online Education Predictions Arifudin, Riza; Subhan, Subhan; Ifriza, Yahya Nur
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.v14i2.88972

Abstract

This increase in adaptability is sought through the application of optimization techniques using the Gaussian Naive Bayes and K-Nearest Neighbor (KNN) methods. This research utilizes GridSearchCV to find optimal parameter configurations in both methods. The Gaussian Naive Bayes method will be used to analyze and classify student adaptability patterns based on historical data. In addition, the K-Nearest Neighbor (KNN) method will be used to utilize information from students who have similar characteristics to increase prediction accuracy. The main steps of this research involve collecting student adaptability data from online education sources, processing the data to obtain relevant features, and using GridSearchCV to find the best parameters in the Gaussian Naive Bayes and KNN models. By optimizing the prediction model using the GridSearchCV technique, this research is expected to make a significant contribution to improving the quality of online education, creating a more adaptive learning environment, and helping educational institutions in designing appropriate learning models. The Receiver Operating Characteristic (ROC) curve also showed a superior Area Under the Curve (AUC) score for KNN at 0.89, compared to GNB 0.81, confirming that the optimized KNN model offers significantly better sensitivity and specificity in predicting student adaptability levels in online education.
Navigating the Generative AI Revolution in Education: A Systematic Review of Applications, Ethical Considerations, and Future Directions Tata, Tata Sutabri; Heri, Heri Suroyo; Kurniawan
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.v14i2.90367

Abstract

This study adopts a systematic review approach to explore the applications and implications of generative AI technology in education. Quantitative and qualitative methods were used to understand how generative AI has been utilized across various educational contexts, including science education, arts and design, and higher education. The systematic review emphasizes the use of generative AI to personalize learning experiences, create interactive content, and provide adaptive assessments that enhance student engagement and learning effectiveness. An analysis of 713 reviewed journals reveals that most studies focus on using AI in higher education and science education, highlighting the benefits and challenges of  integrating this technology. The findings indicate that generative AI holds significant potential to advance personalized learning, facilitate the creation of rich and dynamic content, and offer more adaptive and responsive assessment tools tailored to individual learners’ needs. Nevertheless, this review also identifies several critical concerns associated with the application of generative AI in education, particularly regarding ethical issues, data privacy, and academic integrity. These challenges necessitate the development of clear and comprehensive policies and frameworks. This study underscores the importance of evidence-based approaches to evaluate the effectiveness of generative AI in educational settings. It advocates for more research to understand the social and moral impacts of generative AI applications and to ensure that integrity and privacy principles are not compromised.

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