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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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Semantic Approach for Digital Restoration of Balinese Lontar Manuscripts Sarasvananda, Ida Bagus Gede; I Gde Eka Dharsika; Saputra, I Wayan Kelvin Widana; Welda, Welda
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84916

Abstract

Balinese lontar manuscripts represent a cultural heritage containing significant historical, religious, and scientific values. However, their centuries-old age makes them vulnerable to damage. This research proposes a semantic-based digital restoration solution to address this issue. The semantic approach comprehends the meaning and structure of the lontar text, ensuring an accurate restoration process that preserves the original meaning. The development of the semantic-based digital restoration is built using the Design Science Research Methodology (DSRM). The system is equipped with data management features that accommodate new data, ensure accurate information updates, and maintain the integrity of relationships between entities. Testing through SPARQL query approaches and black-box testing indicates that data additions, deletions, and modifications function well without conflicts or inconsistencies. Moreover, the system performs as expected and is ready for use. The implications of this research suggest that semantic-based digital restoration can be an effective solution for preserving Balinese lontar manuscripts and similar cultural heritage.
Deep Learning for Karolinska Sleepiness Scale Classification Based On Eye Aspect Ratio with SMOTE-Enhanced Data Balancing Zaini, Ahmad; Yuniarno, Eko Mulyanto; Suprapto, Yoyon K; Farodisa, Annida Miftakhul
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84962

Abstract

This paper addresses the challenge of accurately classifying sleepiness levels based on the Karolinska Sleepiness Scale (KSS) using Eye Aspect Ratio (EAR) data, especially when class imbalance leads to biased predictions. The research proposes a deep learning framework that integrates a Multi-Layer Perceptron (MLP) with the Synthetic Minority Over-sampling Technique (SMOTE) to balance the training data. EAR features, representing eye closure patterns, are extracted from video frames, and SMOTE is applied to generate synthetic data for underrepresented sleepiness classes. By training the MLP model on this balanced dataset, the system achieves a 97.6% classification accuracy in distinguishing four distinct sleepiness levels based on the KSS, demonstrating its effectiveness in reducing prediction bias and managing class imbalance, both crucial for real-time drowsiness detection systems
Real Time Automated Speech Recognition Transcription and Sign Language Character Animation on Learning Media Widiartha, Komang Kurniawan; Agustini, Ketut; Tegeh, I Made; Warpala, I Wayan Sukra
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85065

Abstract

Inclusive education for deaf students requires a technology approach to address communication and comprehension challenges. This study aims to develop innovative learning media that integrates real-time ASR (Automated Speech Recognition) transcription technology and sign language character animation to improve accessibility and comprehension of materials for deaf students. This learning media receives input from live voice, voice from learning videos, and text inputted by teachers. Using the Google Cloud API-based ASR transcription module, voice and text are converted into written text, broken down into vocabulary for sign language animation search. The search is carried out using an interpolation algorithm in the sign language animation asset database, allowing the display of animations relevant to the transcribed vocabulary. The development process follows the ADDIE instructional design model, starting with needs analysis and ending with implementation and evaluation. The analysis stage includes data collection through teacher interviews, classroom observations, and curriculum reviews. The media design is designed to meet the specific needs of deaf students, while development and implementation focus on technology integration and effective material delivery. Evaluation is carried out to assess the effectiveness of the media in improving student understanding and participation. The study's results showed that this learning media can improve deaf students' understanding of the material and increase their involvement in the learning process. ASR technology and sign language animation contribute significantly to making learning materials more accessible and understandable, supporting the goals of inclusive education.
Optimization of Sales Data Forecasting Computation Process Using Parallel Computing in Cloud Environment I Kadek Susila Satwika; I Putu Susila Handika
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85278

Abstract

The Holt-Winters Exponential Smoothing algorithm optimised using the Modified Improved Particle Swarm Optimization (MIPSO) algorithm is an algorithm that is able to provide good sales data forecasting results. However, there is a problem that when the iteration process is carried out using 1 computer, it takes a long time to finally get the test results. It is necessary to optimise the computational process to get more optimal and efficient results. This research will combine parallel computing technology and cloud computing technology to help speed up the computing process. The results of this research show that the more server used, the greater the reduction in execution time that occurs, because heavy computing tasks can be distributed more efficiently to many machines. This is evident from the comparison between single server and parallel server. Then the combination of more cores and servers produces the most optimal configuration in accelerating computation.
Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain Sulistyono, Marcelinus Yosep Teguh; Pane, Evi Septiana; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85550

Abstract

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.
Optimizing Diabetic Neuropathy Severity Classification Using Electromyography Signals Through Synthetic Oversampling Techniques Purnawan, I Ketut Adi; Wibawa, Adhi Dharma; Kurniawati, Arik; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85675

Abstract

Electromyography signals are electrical signals generated by muscle activity and are very useful for analyzing the health conditions of muscles and nerves. Data imbalance is a prevalent issue in EMG signal data, especially when addressing patients with varied health conditions and restricted data availability. A major difficulty for machine learning models is class imbalance in datasets, which frequently leads to biased predictions favoring the dominant class and neglecting the minority classes. The data augmentation method employs the Synthetic Minority Over Sampling Technique (SMOTE) and Random Over Sampling (ROS) to address data imbalances and enhance the performance of classification models for underrepresented classes. This study employs an oversampling technique to enhance the efficacy of the XG Boost model. SMOTE exhibits better efficacy relative to competing methods; the application of appropriate oversampling techniques allows models to integrate patterns from both majority and often neglected minority data.
Optimizing Healthcare Performance Through Electronic Medical Records: An Efficiency Analysis Purniari, Ni Kadek Tika; Nilna Muna
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85783

Abstract

Burnout among healthcare workers has become a significant issue in the medical field, partially due to the adaptation process to Electronic Medical Records (EMR). While EMR technology is designed to enhance efficiency and accuracy in patient care, it often poses challenges during implementation. This study aims to examine the impact of medical record digitalization on healthcare worker performance, mediated by high-quality data access and data-driven decision-making at Wangaya Regional Hospital. Using the SEM-PLS method and involving 244 healthcare workers, the research reveals that medical record digitalization significantly improves data access and data-driven decision-making. The findings indicate that EMR plays a crucial role in enhancing healthcare worker performance by facilitating quicker, more accurate, and up-to-date information access, ultimately improving service efficiency and effectiveness. These results support the implementation of digital transformation in medical record management to improve healthcare worker performance and, consequently, the overall quality of healthcare services
Application of Deep Reinforcement Learning for Stock Trading on The Indonesia Stock Exchange Saepudin, Deni; Rauf, Khalifatur
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.v14i1.83775

Abstract

In the last couple of years, stock trading has gained so much popularity because of its promising returns. However, most investors do not pay attention to the risks of trading without analysis, which can lead to a big loss. Some to reduce these risks, try their luck with automated and pre-programmed trading systems, which are called Expert Advisors. The current study examines the application of DRL for automated assistance in trading with an emphasis on decision-making enhancement, particularly the use of DRL in order to realize high asset returns with a low risk of exposure. Concretely, the two applied DRL methods within this work are A2C and PPO. By systematic testing, the A2C method produced a Sharpe Ratio of 1.6009 with a cumulative return of 1.4468, while the PPO method achieved a Sharpe Ratio of 1.7628 with a cumulative return of 1.4767. These were fine-tuned for the most optimal learning rates, cut loss, and take profit ratios, thus showing great promise with the capability to tune up trading strategies and improve trading performances. The research leverages these DRL techniques, hence arriving at better trading strategies that balance profit and risk, while underlining the promise of advanced algorithms in automated stock trading.
Prediction of Total Weight of Octopus Cyanea Using Multiple Linear Regression Method Jepriana, I Wayan; Sudarma Adnyana, I Wayan; Hanifan Sumanto, Moga Nuh
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.v14i1.83893

Abstract

Fisheries Improvement Programs (FIPs) rely on data to offer recommendations for sustainable fishing practices. The octopus cyanea FIP in East Nusa Tenggara faces difficulties in data collection, particularly the total weight of the octopus, as the heads are often removed before landing. This is because the head's contents can cause rapid spoilage and blackening due to the ink. However, these contents are also used as bait. Understanding the total weight is crucial for linking it to gonad weight data to determine the octopus's maturity level. In this study, two models were developed to estimate the total weight of an octopus using known data through Multiple Linear Regression. The most accurate model used total length and body weight without the head contents as predictors, with a Mean Absolute Error (MAE) of 27.97 grams, indicating an average error of this amount in the predictions. The model's fit was assessed with an R2-Score of 0. 983, suggesting a strong correlation with the actual data. Additionally, T-test results indicate no significant statistical difference between the predicted and actual weights. This research aims to provide an alternative method for estimating the total weight of octopuses to support the Octopus FIP in Flores, East Nusa Tenggara.
Effectiveness of Differentiated Explorative Flipbooks to Improve The Learning Independence of Junior High School Student Arimbawa, Gusti Putu Arya; Ni Putu Eva Yuliawati; Windhu, I Putu Tresna; Agustini, Ketut; Sudatha, I Gde Wawan
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.v14i1.83918

Abstract

This research was conducted to overcome the low level of learning independence of students as indicated by dependent behavior among students at the junior high school level. The ADDIE development model was chosen as a model for developing and implementing learning tools to overcome the problem of student learning independence. Data was collected through non-tests using the Learning Object Review Instrument, User Experience Questionnaire and questionnaires to measure students' learning independence. Data analysis was carried out using percentages and n-gain scores. The result of this research was the creation of a product called Mekdi with an average gain score increase of 0.39 which is in the medium category. There are various elements that support the learning process and increase students' learning independence in this media including flipbook elements, GeoGebra exploration, diagnostic assessments, collaboration spaces, and ice breaking activity.

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