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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 29 Documents
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Search results for , issue "Vol. 13 No. 3 (2024)" : 29 Documents clear
The Data-Driven Approach in Transitioning Organizational Strategies and Capabilities: Insights from Indonesia's National Narcotics Agency Komang Ari Widani; Abdullah Hasan; Benny Ranti; Muhammad Rifki Shihab; Widha Utami Putri; Syam Fikry Mardiansyah
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.v13i3.84864

Abstract

Anti-narcotics prevention measures, such as urine sampling of suspect offenders, citizen reporting of suspect narcotic activities, public education, or legal consultations used to be performed at provincial and city levels. To improve effectiveness and efficiency, Indonesia’s National Narcotics Agency (Badan Narkotika Nasional or BNN) centralized such initiatives by introducing BOSS (BNN One-Stop Service), an integrated service information system provided to the public. However, at present data generated by BOSS has not been fully exploited in the design of BNN strategy. The objective of this study is to explore the untapped potential of BOSS data to improve BNN strategy and capabilities, focusing on preventing and eradicating narcotics abuse. The methodology used is descriptive qualitative, with data collection through document analysis and interviews. This study is expected to provide a preliminary interpretation of how BOSS data can improve BNN's ability to fight narcotics abuse more effectively and efficiently. The results of the study show that the integration of BOSS data can significantly optimize the efficiency, analytical capabilities, and responsiveness of BNN in dealing with narcotics abuse, showing that the use of strategic data from BOSS is the key to BNN's digital transformation for a more effective narcotics prevention and eradication strategy.
Synthesis of Kantil Tone Using The Frequency Modulation Method Suhartana, I Ketut Gede; Dewi, Ni Kadek Yulia; Giri, Gst Ayu Vida Mastrika
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.v13i3.84874

Abstract

Music is a creative form of expression that utilizes sound arranged in specific patterns to create artistic works that are enjoyable to the listener. However, in music, excessive or continuous exposure to loud sounds can damage the hair cells in the ear, potentially leading to hearing loss or even deafness. One challenge in musical instrument craftsmanship is the variation in sound produced by different kantil artisans. These differences in sound output lead to inconsistencies in the rhythm of the angklung gamelan in Bali. This research addresses the issue by focusing on the process of synthesizing kantil sounds to achieve a more consistent output. The research begins by inputting audio files for each sound bar in format. The recorded audio data undergoes preprocessing using the Fast Fourier Transform (FFT) method, which extracts key features from the dataset, such as the fundamental frequency. Additionally, the Hilbert Transform is applied to obtain the optimal sound each blade, which will later be used in the Frequency Modulation process. Once preprocessing is completed on the dataset for each blade, the fundamental frequency and signal are acquired. To evaluate the accuracy of the synthesis, the Root Mean Square Error (RMSE) is calculated to compare the original signal with the synthesized signal. This step helps determine the degree of difference between the two signals. Ultimately, the result is a synthesized kantil sound that closely resembles the original, helping to standardize sound output among different craftsmen and ensuring consistency in musical performances
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

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