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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 16 Documents
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Search results for , issue "Vol. 14 No. 1 (2025)" : 16 Documents clear
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.
A Security Enhancement to The Secure Mutual Authentication Protocol for Fog/Edge Farida, Yeni; Azzahra, Arsya Dyani; Lestari, Andriani Adi; Siswantyo, Sepha; Handayani, Annisa Dini; Priambodo, Dimas Febriyan
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.v14i1.84725

Abstract

A secured mutual authentication protocol (SMAP Fog/Edge) has been developed for fog computing. The protocol provides secure mutual authentication which lightweight and efficient for fog computing environments. However, based on AVISPA’s verification from Azzahra research, this protocol has been found to be vulnerable to man-in-the-middle (MITM) attacks due to the absence of an authentication scheme between authentication server and the edge user. The attacks are carried out assuming that the public key of the fog server is not distributed over a secure channel. Rhim research and Lestari research successfully enhance the security level with digital signature. In line with that, we propose modified mechanism that utilizes encryption and digital signatures to substitute the secure channel for distributing the public key of the fog server and authenticating edge users by the authentication server. All modification is using authentication server for digital signature to enhance the security of SMAP Fog/Edge and make it resistant to man-in-the-middle attacks. The proposed protocol is revalidated using the AVISPA tool to determine whether the vulnerability still exists. The result indicates prototype successfully resistant to MITM
MultiResUNet for COVID-19 Lung Infection Segmentation Based on CT Image Ferdinandus, F.X.; Setiawan, Esther Irawati; Santoso, Joan
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.v14i1.85386

Abstract

Image segmentation plays a crucial role in medical image analysis, facilitating the identification and characterization of various pathologies. During the COVID-19 pandemic, this technique has proven valuable for detecting and assessing the severity of infection. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have significantly enhanced the efficacy of image segmentation. Numerous CNN-based architectures have been proposed in the literature, with MultiResUNet emerging as a promising approach. This study investigates the application of the MultiResUNet architecture for segmenting regions of COVID-19 infection within patient lung CT images. Experimental results demonstrate the effectiveness of MultiResUNet, achieving an average Dice score of 73.10%.
CS-Bot: Smart and Interactive Digital Innovation as a Modern Solution for Early Detection of Stunting Andesti, Cyntia Lasmi; Dian, Rahmad; Ningsih, Sri Restu
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.v14i1.85660

Abstract

The high prevalence of stunting in West Sumatra Province, especially in Pariaman City, is a big challenge in efforts to improve children's health. With more than 600 children at high risk of stunting, manual approaches in examining and recording health data are less effective. This system not only slows down the detection process but also has the potential to result in data loss and redundancy. In this context, late detection hinders early prevention, which is especially important in the First 1000 Days of Life (HPK) period. Therefore, this research aims to develop a chatbot-based application that is able to detect stunting quickly and accurately. This application is named CS-Bot, namely Prevent Stunting using Chatbot. This application is designed to be easy for the public to use, while providing education about the importance of stunting prevention. Research methods include analysis of data on factors and symptoms of stunting, formation of rules using the Forward Chaining Method, and probability calculations using Certainty Factor. The data used in this study comes from health centers and some data comes from several experts who are accustomed to dealing with stunting problems in children. It is hoped that the research results will not only provide tools for parents and the community, but will also become the basis for making more effective policies in stunting prevention programs in Indonesia, especially in West Sumatra.
Multimodel Prediction Score Based on Academic Procrastination Behavior in E-Learning Sartana, Bruri Trya; Nugroho, Supeno Mardi Susiki; Yuhana, Umi Laili; 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.v14i1.85880

Abstract

This research investigates the impact of academic procrastination on student performance in online learning environments and explores a multimodel approach for grade prediction. Academic procrastination is a well-documented issue that negatively affects learning outcomes, often leading to lower academic performance and increased dropout rates in self-paced learning platforms. This study analyzes behavioral data from 377 students, extracted from Moodle activity logs, which record real-time student interactions with learning materials. To address the gap in understanding procrastination patterns through activity logs, key procrastination-related features were derived from timestamps of task access, submission, and engagement duration. Using K-Means clustering with the Elbow method, students were categorized into three procrastination clusters: low procrastination with high academic performance, high procrastination with low performance, and moderate procrastination with average performance. Seven machine learning models were evaluated for predicting student grades, with Random Forest (RF) achieving the highest accuracy (R² = 0.812, MAE = 6.248, RMSE = 8.456). These findings highlight the potential of using activity logs to analyze procrastination patterns and predict student performance, allowing educators to develop early intervention strategies that support at-risk students and improve learning outcomes.
Cost-Effective Parkinson’s Disease Diagnosis Through IoT-Based Finger Tapping and Real-Time Machine Learning Classification Arraziqi, Dwi; Sardjono, Tri Arief; 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.v14i1.86371

Abstract

Parkinson's disease (PD) is a progressive neurological condition that significantly impacts motor functions, including finger tapping (FT). This study aims to develop a cost-effective, real-time, easily implementable, IoT-enabled electronic health record (EHR)-integrated FT analysis system capable of remotely detecting PD with high accuracy. The study uses peak amplitude, the Internet of Things (IoT), and various machine learning classifiers to detect PD through FT pattern analysis on a smartphone application. K-Nearest Neighbors, Convolutional Neural Networks, Support Vector Machines, and Logistic Regression exhibited 100% accuracy, while Naïve Bayes and Decision Trees (DT) had accuracies ranging from 71% to 92%. All classifiers had an Area Under the Curve (AUC) value of 1, except DT with an AUC value of 0.75. This study introduces a novel IoT system for PD detection that demonstrates high diagnostic accuracy, cost-effectiveness, real-time monitoring capability, easy implementation, scalability for telemedicine, and accessibility to EHR during the COVID-19 pandemic. Future studies will focus on expanding the dataset.
Optimizing Brain Tumor MRI Classification with Transfer Learning: A Performance Comparison of Pre-Trained CNN Models Mardianto, M. Fariz Fadillah; Pusporani, Elly; Salsabila, Fatiha Nadia; Nitasari, Alfi Nur; Lu’lu’a, Na’imatul
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.v14i1.87377

Abstract

This study aims to classify brain MRI images into several types of brain tumors using the Convolutional Neural Network (CNN) approach with transfer learning. This method has the advantage of processing complex images in a shorter time than conventional CNN approaches. In this study, the data used was a public database from Kaggle, which consisted of four categories: glioma, meningioma, no tumor, and pituitary. Before entering the transfer learning process, data augmentation is carried out on the training data. Four pre-trained CNN models were used: VGG19, ResNet50, InceptionV3, and DenseNet121. The four models compared their ability to classify MRI images with several evaluation metrics: accuracy, precision, recall, and F1 score. The results of the performance comparison of the four pre-trained models show that the ResNet50 is the best model, with an accuracy of 98%. Meanwhile, VGG19, DenseNet121, and InceptionV3 produce 97%, 96%, and 95% accuracy, respectively. The ResNet50 architecture demonstrated superior performance in brain tumor classification, achieving 98% accuracy. It can be attributed to its residual learning structure, which efficiently manages complex MRI features.  Further research should concentrate on larger, more diverse datasets and advanced preprocessing techniques to enhance model generalizability.
Balinese Script Handwriting Recognition Using CNN and ELM Hybrid Algorithms Mas Diyasa, I Gede Susrama; Wijaya, Pandu Ali; via, Yisti Vita
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.v14i1.87524

Abstract

One of the foundational scripts used in Balinese culture is the Balinese script, known as “Aksara Bali”. In its writing, Aksara Bali follows specific rules regarding distinctive stroke shapes that must be carefully maintained to preserve authenticity and readability. This study proposes the use of a hybrid algorithm combining Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) to recognize handwritten Balinese script characters. The preprocessing stage includes dataset splitting, rescaling, data augmentation, batch size adjustment, and visualization of class distribution. The training stage utilizes the Adam Optimizer to enhance model accuracy. Using 1,691 images of various Balinese script characters, the dataset is divided into an 80:10:10 ratio for training, validation, and testing. Experimental results show that the best accuracy achieved is 91%, indicating that the CNN-ELM hybrid model effectively recognizes Balinese script characters.

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