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INDONESIA
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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Optimizing Machine Learning Performance with The Naive Bayes Classifier Process in Smart Farming Saputra, Made Yosfin; I Wayan Santiyasa
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.76926

Abstract

Indonesia is a country that relies heavily on the agricultural and plantation sectors to meet its needs for food and industrial raw materials. But farmers face challenges such as falling commodity prices and the negative impact of global warming, which has resulted in widespread drought. As a result, competition for water resources between the agricultural, industrial and household sectors is getting tighter, making it increasingly difficult for farmers to guarantee water supplies. The phenomenon of global warming has caused challenges in the current era. In Bali, although there is a method called “Subak” to manage rice field irrigation systems, it has not been fully implemented. To overcome this problem, a solution is needed that can automate water distribution based on soil moisture levels, temperature, light and air humidity. It uses machine learning techniques specifically using Naive Bayes Classifier to make real-time decisions regarding crop irrigation. The aim of this research is to increase the efficiency and effectiveness of crop irrigation in agriculture while reducing the impact of warming. The results of testing the scenario with orchid plants obtained an accuracy of around 80% and with general plants obtained around 80% which was tested every time 5 data were collected. Testing with a total of 84 training data and 26 test data. From the test results, an accuracy of 92.30769% was obtained.
Improving Sentiment Analysis and Topic Extraction in Indonesian Travel App Reviews Through BERT Fine-Tuning Irmawan, Oky Ade; Budi, Indra; Santoso, Aris Budi; Putra, Prabu Kresna
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.77028

Abstract

Abstract The increasing use of the internet in Indonesia has an influence on the presence of Online Travel Agents (OTA). Through the OTA application, users can book transportation and accommodation tickets more easily and quickly. The increasingly rigorous competition is causing companies like PT XYZ to be able to provide solutions to the needs and problems of their customers in the field of online ticket booking. Many customers submit reviews of the use of the PT XYZ application through Playstore and Appstore, and it needs a technique to group thousands of reviews and detect the topics discussed by customers automatically. In this study, we classified reviews from Android and iOS applications using BERT that had been adjusted through fine-tuning with IndoBERT, as well as modeling topics using LDA to evaluate the coherence score of each sentiment. The result of the comparison of hyperparameter models for the most optimal classification is epoch 4 with a learning rate of 5e-5. The accuracy obtained is 0.91, with an f1-score of 0.74. In addition, testing was carried out to compare BERT with other traditional machine learning. The best performing algorithm was Logistic Regression using TF-IDF word embeddings, achieving an accuracy of 0.890 and an F1-score of 0.865. Therefore, it can be inferred that the accuracy achieved by the fine-tuned classification model of IndoBert is sufficiently high for application in the PT XYZ review classification. Using a coherence score, we found 29 positive topics, 6 neutral topics, and 3 negative topics that were considered the most optimal. This finding can be used as evaluation material for PT XYZ to provide the best service to customers.
Enhancing K-Means Clustering Model to Improve Rice Harvest Productivity Areas in Aceh Utara Using Purity Sujacka Retno; Bustami; Rozzi Kesuma Dinata
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.78254

Abstract

To optimize the performance of the clustering process using K-Means, an optimalization approach employing the Purity algorithm is needed. This research was tested on a dataset of rice harvest productivity areas in Aceh Utara Regency by comprehensively analyzing the number of iterations and the DBI values produced by K-Means and Purity K-Means in clustering priority and non-priority rice production areas. This is in line with the efforts of the Regional Government to implement rice production intensification programs in Aceh Utara Regency. From the testing of Purity K-Means, an average of 5, 2, 2, 5, and 3 iterations were obtained from all tested datasets sequentially from 2019 to 2023. Meanwhile, from the testing of conventional K-Means, the average number of iterations obtained was 5.4, 4.8, 4.2, 5.6, and 3.8 iterations, sequentially. This indicates that the clustering performance conducted by Purity K-Means is better than conventional K-Means. The DBI values obtained from Purity K-Means for the entire dataset sequentially are 0.6781, 0.4175, 0.4419, 0.6182, and 0.4973. This value is lower compared to the DBI values obtained from conventional K-Means, which are 0.7178, 0.6025, 0.4971, 0.7222, and 0.5519, respectively. This also indicates that the validity level of the clustering results performed by Purity K-Means is higher than conventional K-Means.
Innovative Learning Model for Dharmagita Based on Telegram Chatbot Ni Putu Utari Dyani Laksmi; A.A. Kompiang Oka Sudana; AA.Kt.Agung Cahyawan Wiranatha
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.78535

Abstract

In the digital era, instant messaging has become a vital aspect of people's daily lives, especially among the younger generation. This presents opportunities to utilize technology that can be integrated into instant messaging as a learning medium. This research innovates to develop a learning model for Dharmagita, also known as sacred Hindu songs, using a chatbot as a platform aimed at attracting the interest of the younger generation in studying Dharmagita as a cultural heritage. This chatbot was developed using the Rasa framework, which is founded on Natural Language Understanding (NLU). Based on the results of the User Acceptance Test, the Dharmagita Chatbot received a positive response from users. The chatbot model achieved an accuracy value of 86.7%, an F1-score of 88.4%, and a precision of 91.1%. These results underscore the effectiveness and reliability of the chatbot in facilitating learning and engagement with Dharmagita content.
An Improved Utility-Based Artificial Intelligence to Capture NPC Behaviour in Fighting Games Using Genetic Algorithm Nugroho, Supeno; Affan, Lazuardi Yaqub; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.82040

Abstract

In computer fighting games , the ability of players to play with Non-Player Characters (NPC) is essential. A poorly designed NPC causes poor player engagement due to predictable behaviour, thus leads to unsatisfactory playing experience. We propose utility-based AI selected by genetic algorithm to determine the utility functions of each NPC action. We applied ELO ratings (usually used in chess game) to determine fitness function. Utility-based artificial intelligence can deliver human-like NPC with varied decision-making and can employ many forms of function to calculate the AI utility value. Tests on chromosomes in each generation were also carried out to obtain different responses. The Pearson Correlation coefficient is used to obtain an analysis of the influence of each assessment variable. The simulation results verify the validity of our analysis and show that our scheme influences the satisfaction level of game users
Sign Language Recognition Based on Geometric Features Using Deep Learning Yuniarno, Eko Mulyanto
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.82103

Abstract

Sign language plays a crucial role in facilitating communication among individuals with hearing impairments. In Indonesia, the deaf community often rely on BISINDO (Indonesian Sign Language) to communicate amongst themselves. People who are unfamiliar with sign language will face difficulties. This research aims to develop a system for recognizing sign language using geometric features extracted from hand joint coordinates using Google's MediaPipe Hands framework. The dataset contains 12 common words, each recorded 30 times with 30 frames recorded for each instance. This will facilitate communication between deaf and hearing individuals. We conducted tests on LSTM- Geometric and CNN1D- Geometric models using geometric features, and on CNN-LSTM-Spatial and CNN1D-LSTM-Spatial models using spatial features. The results indicate that the LSTM model with geometric features achieved the highest accuracy of 99%. Geometric features have been shown to be more effective than spatial features for classifying sign language gestures.
A Comparative Study on the Impact of Feature Selection and Dataset Resampling on the Performance of the K-Nearest Neighbors (KNN) Classification Algorithm Gunadi, I Gede Aris; Rachmawati, Dewi Oktofa
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.82174

Abstract

This study aims to evaluate the impact of dataset balancing and feature selection on the performance of the K-Nearest Neighbors (KNN) classification algorithm. The primary objective is to determine the effect of different training data balance ratios on classification performance. Additionally, the study analyzes the contribution of feature selection methods and data balancing to the overall performance of the classification algorithm. Three datasets (Titanic, Wine Quality, and Heart Diseases) sourced from Kaggle, were utilized in this research. Following the preprocessing stage, the datasets were subjected to three resampling scenarios with balance ratios of 0.3, 0.6, and 0.9. Feature selection was performed by combining correlation test values and information gain values, each weighted at 50%. The selected features were those with positive combined values of summation, correlation, and information gain. The KNN classification algorithm was then applied to datasets with and without feature selection. The results indicate that achieving a perfectly balanced ratio (ratio = 1) is not essential for improving classification performance. A balance ratio of 0.6 yielded results comparable to those of a perfect balance ratio. Furthermore, the findings demonstrate that feature selection has a more significant impact on classification performance than data balancing. Specifically, data with a balance ratio of 0.3 and feature selection outperformed data with a balance ratio of 0.6 but without feature selection.
QoS Analysis of Implementation Elastic WLAN Mechanism for Adaptive Bandwidth Management Systems in Smart Buildings Sukadarmika, Gede; Indra ER, Ngurah; Yoga, I Putu Sudharma; Linawati; Budiastra, IN
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.83215

Abstract

The rapid growth of the Internet of Things technology has led to various innovative creations, However, effective management of data traffic generated by numerous sensors is essential to maintain network performance. This study develops and evaluates an adaptive Bandwidth Management System using Elastic WLAN to deal with the development of IoT system traffic so that network performance is maintained. Using Raspberry Pi as an Elastic WLAN device and a Hierarchical Token Bucket (HTB) running via Python script, this system manages Bandwidth allocation based on the number of visitors in the Smart Building. Evaluation was carried out in two rooms, comparing conditions before and after Elastic WLAN implementation. The results show that the implementation of Elastic WLAN improves network performance. This is indicated by improvements in the stability of upload and download rates, as well as very significant improvements in the Jitter and Latency parameters which are used as QoS parameters.
Model GHT-SVM for Traffic Sign Detection Using Support Vector Machine Algorithm Based On Gabor Filter and Top-Black Hat Transform Noprisson, Handrie; Ayumi, Vina; Dwika Putra, Erwin; Utami, Marissa; Ani, Nur
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.v13i3.75778

Abstract

A factor that can hinder the detection and recognition of traffic signs is the variation in lighting in the image of traffic signs. This study aims to detect traffic symbols using Gabor Filter (GFT), Top Hat Transform (THT), and Black Hat Transform (BHT) methods on the Support Vector Machine (SVM) algorithm for traffic sign dataset images with data problems that tend to have dark backgrounds at night and bright backgrounds during the day. From the experimental results, GHT-SVM gets the highest accuracy compared to HSV-SVM, HSV-RF, HSV-KNN, and H2T-SVM models. Based on experimental results, H2T-SVM from HOG ⊕ ENT ⊕ BHT ⊕ SVM results get the best accuracy of 86.42%. The Gabor Filter (GFT) parameters used are the number of filters with a value of 16, ksize with a value of 30, sigma with a standard deviation value of 3.0, lambd with a sinusoidal factor value of 10.0, gamma with a spatial aspect ratio value of 0.5 and psi with a phase offset value of 0 while the Top Hat Transform (THT) and Black Hat Transform (BHT) methods use filterSize sizes with values (3, 3).
Developing a Marker-Based AR Application to Introduce Temples and Cultural Heritage to Younger Generations Sudana, Oka; Adi, Ngurah; Cahyawan, Agung
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.v13i3.76126

Abstract

Preserving Balinese cultural heritage is crucial for sustaining community identity. In Bali, temples (pura) are central to spiritual and cultural life. However, younger generations, especially temple caretakers of Pemerajan Agung Sakti Padangsambian, are increasingly losing knowledge of these sacred spaces, weakening their sense of belonging, to preserve cultural traditions. Current media efforts has failed to engage this demographic. This research addresses this challenge by developing an application-integrated images compiled into books and Android-based AR technology. The application employed a user-centered design approach involving analysis, design, development, testing, and evaluation phases. Results show AR effectively bridges the knowledge gap, with usability scores and a significant increase in user knowledge of 42.43%. This research demonstrates AR's potential for preserving and transmitting cultural heritage, including the reconstruction of damaged historical objects through 3D modeling with the marker detection technology, to ensure seamless integration between the real and virtual worlds.

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