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Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
ISSN : 20898673     EISSN : 25484265     DOI : -
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 22 Documents
Search results for , issue "Vol. 13 No. 2 (2024)" : 22 Documents clear
IoT-Based Humidity Control System for Electrospinning Hikamiah, Luluk Arifatul; Ariyanto, Dewa Pascal; Widayani, Della Astri; Nugroho, Panji Setyo; Amaratirta, Jasmine Cupid; Harjunowibowo, Dewanto; Rezeki, Yulianto Agung
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.v13i2.75896

Abstract

In recent years, electrospinning has become the most common and most widely used nanofiber manufacturing method. One parameter affecting the manufacturing process is the humidity parameter, which affects the morphology. The control tools that have been developed have not been equipped with a remote control and monitoring system. This research aims to develop a humidity control system that can be controlled and monitored remotely and is equipped with data recapitulation. This research used methods from literature studies on humidity control systems, manufacture and assembly of hardware and software, calibration, and performance tests. A humidity control device is produced using a NodeMCU microcontroller connected to the IoT-based Blynk application. This can be controlled and monitored manually with an LCD and Keypad or by using a handphone through the Blynk application, and the humidity value can be recapitulated with Google Sheets in real-time. The sensor used for reading this humidity value is a DHT 22 sensor which has an accuracy value of 99.72% and a precision of 98.81% for 45%-95% humidity. This IoT-based humidity control system device can automate the process of controlling, monitoring, and recapitulating humidity data in real-time.
Analyzing Hotel Owners Acceptance of Tiket.com Using Technology Acceptance Model Baiq Dwi Zulianti Kurrotaa'yun; Noor Alamsyah; Helmina Andriani; Santi Ika Murpratiwi
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.v13i2.76188

Abstract

Lombok, a captivating island in West Nusa Tenggara, faces tourism challenges due to the COVID-19 pandemic, causing a decline in visits and revenue. Senggigi, known for its picturesque beaches, experienced a drastic drop in hotel occupancy. Focusing on post-pandemic recovery, the aim of this research is to investigate the adoption of Tiket.com applications by hotel owners in Senggigi, utilizing the Technology Acceptance Model (TAM) with a Partial Least Squares (PLS) approach. This research method is using a quantitative methodology, the study involves 50 respondents from 20 hotels, distributing questionnaires to explore perceptions of Online Travel Agent technology adoption based on TAM variables. The result of this research is the Partial Least Squares analysis indicates that perceived ease of use significantly influences perceived usefulness, emphasizing the importance of a user-friendly platform. While perceived usefulness alone may not directly impact usage intention, positive attitudes toward the system play a crucial role. The study recommends collaborative efforts between Tiket.com service providers and Senggigi hotel owners to enhance system adjustments, aligning with user needs and expectations. This research contributes valuable insights into technology's role in post-pandemic tourism recovery, providing a nuanced understanding of factors influencing the acceptance of Tiket.com within the Senggigi hospitality sector.
Analysis of User Complaints for Telecommunication Brands on X (Twitter) using IndoBERT and Deep Learning Hakim, Valianda Farradillah; Riana, Dwiza
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.v13i2.76497

Abstract

Tweeting on different official accounts is what users of Twitter (X) do most frequently. These tweets ranging from compliments to critiques. One of the official accounts that gets a lot of tweets from its customers is Telkomsel, an Indonesian telecom company. This study aims to find the maximum accuracy that can be obtained by combining CNN and Bi-LSTM algorithms with IndoBERT embeddings. A considerable accuracy level above 90% is demonstrated by the study, with CNN obtaining the greatest accuracy of 99% at a learning rate of 6*10^-5, along with scores of 98%, 97%, and 97% for precision, recall, and F1 correspondingly.
Electronic Payment Threats and Security: A Systematic Literature Review Amelia Citra Dewi; Erik Iman Heri Ujianto; Rianto, Rianto
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.v13i2.76635

Abstract

In the emerging field of electronic payment systems, security challenges have become a major concern. This research addresses a comprehensive understanding of mitigation strategies for these threats. Through systematic literature analysis, we investigated the security vulnerabilities in electronic payment processes and discovered the latest blockchain technology as a strengthened security framework. Our findings reveal that while encryption and authentication provide the foundation of security, the integration of blockchain technology offers an unprecedented level of transaction integrity and transparency. This research not only highlights the urgent need for electronic payment security measures but also highlights the potential of blockchain and machine learning as transformative solutions. The implications of our research indicate an important shift in payment systems towards more secure and resilient electronic systems, paving the way for future research to explore the integration of cutting-edge technologies in combating ever-evolving cyber threats by leveraging blockchain technology, quantum computing and machine learning.
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.

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