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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
 58   59   60   61   62 
The Implementation of Bayesian Optimization for Automatic Parameter Selection in Convolutional Neural Network for Lung Nodule Classification Kadek Eka Sapta Wijaya; Gede Angga Pradipta; Dadang Hermawan
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.v13i3.82467

Abstract

Lung cancer's high mortality rate makes early detection crucial. Machine learning techniques, especially convolutional neural networks (CNN), play a very important role in lung nodule detection. Traditional machine learning approaches often require manual feature extraction, while CNNs, as a specialized type of neural network, automatically learn features directly from the data. However, tuning CNN hyperparameters, such as network structure and training parameters, is computationally intensive. Bayesian Optimization offers a solution by efficiently selecting parameter values. This study develops a CNN classification model with hyperparameter tuning using Bayesian Optimization, achieving a 97.2% accuracy. Comparatively, Particle Swarm Optimization and Genetic Algorithm methods each resulted in 96.4% accuracy. The research concludes that Bayesian Optimization is an effective approach for CNN hyperparameter tuning in lung nodule classification.
Usability and Performance Comparison: Implementation of Tibero and Oracle Databases in the Context of CAMS Software Development Komang Yuli Santika; Hostiadi, Dandy Pramana; Ayu, Putu Desiana Wulaning
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.v13i3.82519

Abstract

In the world of software development, the role of database systems is very vital. Enterprise software, designed to handle the complex challenges of large organizations, requires reliable and efficient databases. Oracle, one of the top choices in the industry, stands out with its performance and flexibility. On the other hand, Tibero, a relational DBMS from TmaxSoft, offers the high performance, reliability and scalability required in business environments that require big data management. This research was conducted to analyze the technical side of the Oracle and Tibero databases in the context of the CAMS (Customer Asset Management System) application, with a focus on usability and performance aspects. This research uses the Performance Testing method to evaluate CPU, Memory, Storage resource usage and TPS (Transaction Per Second) of the two databases as well as the System Usability Scale (SUS) to measure user experience. The results provide information to software developers in selecting databases that suit business needs, while contributing to the development of the information technology industry
Facial Expression Detection System for Students in Classroom Learning Process Using YoloV7 Aglaia, Alifya Nuraisyar; Afdhaliyah, Mukhlishah; Adiba, Fhatiah; Kaswar, Andi Baso; Muhammad Fajar B; Andayani, Dyah Darma; Yahya, Muhammad
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.v13i3.83978

Abstract

The utilization of technology in education is not only about using hardware or software, but also how technology can facilitate effective learning experiences. However, in the learning process there is a problem for teachers to know the level of student attention in the classroom to the material presented, so that the teacher does not know accurately the concentration of students during the learning process until it has an impact on the teacher's learning methods that are not in accordance with the characteristics of students. The purpose of this research is to detect students' facial expressions in the classroom learning process using yolov7. The implementation of several architectural models on CNN consists of several proposed methods, namely data collection, data augmentation, data annotation, split dataset, training, and model evaluation. System testing is done by measuring accuracy and comparing with other methods, namely CNN, CNN MobileNet, CNN EfficientNet-B0 and YoloV7. The test results show the average accuracy of CNN 80%, CNN MobileNet 93%, CNN EfficientNet-B0 31% and YoloV7 96%. Based on these results, it can be concluded that the YoloV7 method can detect student concentration effectively and efficiently compared to CNN, CNN MobileNet, and CNN EfficientNet-B0.  
Optimizing The User Interface of Waste Bank Application Using UCD and UEQ Prihatini, Retno; Rianto
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.v13i3.83998

Abstract

Environmental cleanliness is an essential aspect of life to make a healthy and comfortable environment. In Indonesia, the volume of waste will reach 70 million tons by 2022, with around 24% or 16 million tons needing to be appropriately managed. Related to the significant waste growth, the Ministry of Environment has developed the Waste Bank initiative, a collaborative effort that aims to educate the public in sorting waste and raising awareness of the importance of wise waste management. The desire of the local environmental agency to connect with the community supports the researcher in developing the Waste Bank application. The application will implement an optimal User Interface (UI) and User Experience (UX) design. The User-Centered Design (UCD) method will be employed, supported by the User Experience Questionnaire (UEQ), and is used to design UI and UX for the Waste Bank mobile application. The application prototypes were tested and evaluated using UEQ. The first design achieved an average score but still required improvement. In contrast, the second design scored excellently in six aspects measured: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty, with significant improvement. These results show that the UCD and UEQ methods are effective for developing UI/UX designs to meet user needs and can be applied in mobile application developments.
Data Mining Analysis of Moodle Learning Data and Student Perceptions During and After the Covid-19 Pandemic Murwaningtyas, Chatarina Enny; De Jesus, Maria Fatima Dineri
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.v13i3.84005

Abstract

This study examines the academic performance of students from the 2020 and 2023 cohorts, highlighting differences in activity, attendance, task completion, midterm and final exam scores, and perceptions of educational metrics. A data mining approach was applied to predict students' GPA using Decision Tree, Random Forest, Multinomial Naïve Bayes, and Gaussian Naïve Bayes algorithms. The Gaussian Naïve Bayes model showed the highest accuracy of 0.93 for the 2020 cohort and 0.92 for the 2023 cohort, with the lowest error rate making it the most effective predictor. Feature importance analysis revealed that task completion and exam scores were the most influential factors, while students' perceptions had a lesser impact. The findings suggest that direct academic metrics should be the focus for improving student performance. This study emphasizes the need for further refinement of predictive models and suggests incorporating both academic metrics and student perceptions for a holistic understanding of student performance.
Implementation of a Web-Based Master-Slave Architecture for Greenhouse Monitoring Systems in Grape Cultivation Hirzen Hasfani; Uray Ristian; Wijaya, Uray Syaziman Kesuma
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.v13i3.84105

Abstract

The Internet of Things (IoT) technology enables electronic devices to connect to the Internet for real-time data collection and analysis. In greenhouses, IoT is used to monitor soil moisture and environmental conditions to support grape plant care. This study proposes a grape plant monitoring system using a master-slave architecture and the ESP-NOW protocol to reduce reliance on Wi-Fi networks, thus minimizing delay and packet loss. The system leverages direct communication between master and slave nodes. Testing results show an average delay of 1,546.65 ms, jitter of 120.56 ms, and packet loss of only 0.07% from 88,815 data transmissions in one day. Despite variations in packet loss due to power interruptions, the system consistently demonstrates reliable data transmission. Overall, this system proves to be reliable for real-time monitoring in greenhouses, offering stable performance and high data accuracy.
Smart Home for Supporting Elderly Based On Ultrawideband Positioning System Muhtadin; Nazarrudin, Ahmad Ricky; Purnama, I Ketut Eddy; Fatichah, Chastine; 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.v13i3.84186

Abstract

In 2017, the rate of dependency among the elderly was reported to be at 13.28%, which was problematic, due to the limited number of caregivers to assist them at all times. To address this issue, a robotic service and vital sign-based system were developed, but it was found to be insufficient for monitoring the activities of the elderly. Therefore, this study aimed to address the high dependency rates of elderly individuals who required constant support and care to survive by designing an ultrawideband-based positioning system. The system consisted of five sub-systems, including an indoor positioning system, a database system, a data processing system, an actuator system, and an application user interface. The system testing phase revealed several important findings, including that the position coordinates of the elderly were accurately read with differences of only 98.884 mm and 279.94 under Line of Sight and Non-Line of Sight conditions, respectively. Furthermore, the initial error rate of 164.39% was successfully reduced to only 1.096% by applying the average filter method in the data processing system. The actuator system also showed an impressive accuracy rate of 98% success, while the Android-based application user interface received a high user experience rate of 92.3%. Overall, these findings suggested that the ultrawideband-based positioning system had significant potential to support smart homes for the elderly and improve their quality of life.
Early Detection Depression Based On Action Unit and Eye Gaze Features Using a Multi-Input CNN-WoPL Framework Sugiyanto, Sugiyanto; Purnama, I Ketut Eddy; 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.v13i3.84674

Abstract

Depression is a common mental disorder with significant life impact, including a high risk of suicide. Patients with depression attempt suicide five times more often than the general population. Self-reporting, subjective judgement and clinician expertise influence conventional diagnostic methods. For timely intervention and effective treatment, early and accurate diagnosis of depression is essential. This study proposes a framework called Multi-Input CNN-WoPL, a CNN-based method without a pooling layer that combines two features - action units and gaze - to improve accuracy and robustness in automatic depression detection. Pooling layer reduces spatial dimension of feature map, resulting in loss of information related to expression data, affecting depression detection result. The performance of the proposed method results in an accuracy of 0.994 and F1 score = 0.993, the F1 score value close to 1.0 indicates that the proposed method has good precision, recall and performance.
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

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