cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota denpasar,
Bali
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 22 Documents
 1   2   3 
Search results for , issue "Vol. 13 No. 2 (2024)" : 22 Documents clear
Requirements Engineering Quality: a Literature Review Delima, Rosa; Azhari; Mustofa, Khabib
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.v13i2.53366

Abstract

Requirements Engineering Quality (REQ) has a large influence on the success of a software project. A systematic literature review (SLR) is conducted to get complete information about REQ. SLR reviewed 46 relevant publications from 2016 – 2022, sourced from three literature sources: Science Direct, Scopus, and IEEE. Based on the SLR, it is known that, generally, the artifacts processed for REQ are text requirements. The quality standards for REQ that are widely used are ISO/IEEE/IEC 29148 and IEEE 830, while the quality variables that are widely used are correctness, completeness, consistency, and defects/faults found in RE. A number of methods are used to perform automatic REQ. The most widely used method in publications is NLP. This is in line with most artifacts used in REQ, such as text requirements.
The Development of A Mobile-Based Area Recommendation System Using Grid-Based Area Skyline Query and Google Maps Annisa; Alyssa, T. Sandra; Agmalaro, Muhammad Asyhar
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.v13i2.66155

Abstract

Choosing a location for a business or place of residence is an essential task in our daily lives. Typically, a location is considered favorable if it is in proximity to profitable facilities that enhance its value while being distant from facilities that diminish its value. However, conducting surveys in the field to identify desirable candidate locations is not always feasible. Factors such as high costs, inclement weather, and transportation limitations can hinder survey activities. This study aims to develop a mobile-based system for location selection using the Grid-based Area Skyline (GASKY) algorithm in conjunction with Google Maps. Google Maps is widely utilized for location-based decisions and is familiar to mobile application users. GASKY is employed for its capability to recommend locations based on user-provided information regarding desired facilities near the target location and facilities to be avoided, eliminating the need to input candidate locations from survey results. The outcomes of this study include a mobile-based application that utilizes the Google Maps API to create data collection modules. Mobile-based applications utilizing GASKY offer convenience, as they can be accessed by users anytime and anywhere.
The Citizens Readiness for E-Government on The Jogja Smart Service (JSS) Application in Yogyakarta City: Kesiapan Masyarakat terhadap E-Government pada Aplikasi Jogja Smart Service (JSS) di Kota Yogyakarta Yuliantini, Lisa Sophia; Pribadi, Ulung
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.v13i2.67697

Abstract

This study aims to analyze the citizens readiness for e-Government on the Jogja smart service (JSS) application in the city of  Yogyakarta. with indicators from Citizens’ readiness for E-Government (CREG), namely ICT Infrastructure, ICT Use, Human Capital, ICT Regulation, and Trust. The research method is quantitative, with questionnaire primary data totaling 100 respondents, and using Smart PLS software version 0.3. in conducting data analysis. The results show that ICT Infrastructure and ICT Use have a significant influence on the citizens' readiness for e-Government in the Jogja Smart Service (JSS) application. Whereas Human Capital, ICT Regulation, and Trust have no significant influence on the citizens' readiness for e-Government in the Jogja Smart Service (JSS) application. The limitation of this study is the number of respondents and the limited number of respondent variables are expected to be used as recommendations for further research.
Ensembled Machine Learning Methods and Feature Extraction Approaches for Suicide-Related Social Media Merinda Lestandy; Abdurrahim Abdurrahim; Amrul Faruq; M. Irfan; Novendra Setyawan
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.v13i2.70016

Abstract

Suicide is a pressing public health concern that affects both young people and adults. The widespread use of mobile devices and social networking has facilitated the gathering of data, allowing academics to assess patterns, concepts, emotions, and opinions expressed on these platforms. This study is to detect suicidal inclinations using Reddit online dataset. It allows for the identification of people who express thoughts of suicide by analyzing their postings. The method addresses and evaluates different machine learning classification models, namely linear SVC, random forest, and ensemble learning, along with feature extraction approaches such as TF-IDF, Bag of Words, and VADER.   This study utilised a voting classifier in our ensemble model, where the projected class output is selected by the class with the highest probability. This approach, typically known as a "voting classifier," employs voting to forecast results. The results collected suggest that employing ensemble learning with the TF-IDF 2-grams approach yields the highest F1-score, specifically 0.9315. The efficacy of TF-IDF 2-grams can be determined to their capacity to capture a greater amount of contextual information and maintain the order of words.
Comparative Analysis of CNN Methods for Periapical Radiograph Classification Sumantara, I Gusti Lanang Trisna; Kesiman, Made Windu Antara; Sunarya, I Made Gede
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.v13i2.71664

Abstract

Periapical radiographs are commonly used by dentists to diagnose dental problems and overall dental health conditions. The varying abilities of dentists to diagnose may be limited by their visual acuity and individual skills. To address this issue, there is a need for an application capable of computationally recognizing and classifying periapical radiographs. The commonly used computational method for image processing, specifically image recognition, is the Convolutional Neural Network (CNN) method. This study aims to create an application that can classify periapical radiographs and analyze the capabilities of the Convolutional Neural Network (CNN) method in this classification process. In general, periapical classification is divided into five types: Primary Endo with Secondary Perio, Primary Endodontic Lesion, Primary Perio with Secondary Endo, Primary Periodontal Lesion, and True Combined Lesions. The periapical radiograph classification process was tested using four CNN models: ResNet50v2, EfficientNetB1, MobileNet, and Shalow CNN. The evaluation of the CNN method utilized a confusion matrix-based technique to generate accuracy, precision, recall, F1-score and Weighted Average F1-score values. Based on the evaluation results, the highest accuracy value was achieved by EfficientNetB1 with 82%, followed by ResNet50v2 with 76%, MobileNet with 75%, and Shallow CNN with 71%.
Pneumonia Diagnosis Through Deep Learning: ResNet50v2 Model Implementation Yufis Azhar; Zamah Sari; Wahyu Priyo Wicaksono
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.v13i2.72068

Abstract

Pneumonia is a significant global health concern, particularly affecting young children and the elderly. It is a lung infection caused by bacteria, viruses, fungi, or parasites, leading to the alveoli filling with pus or fluid. This study addresses the challenge of accurately diagnosing pneumonia using chest X-ray images, a process traditionally dependent on the expertise of radiologists. The reliance on radiologists results in lengthy diagnosis times and high costs, particularly in regions with a shortage of medical professionals. This research presents a deep-learning approach to automate the classification of pneumonia using the ResNet50v2 model, which has been pre-trained on the ImageNet dataset. The dataset used in this study, obtained from the Guangzhou Women and Children’s Medical Center, comprises 5,856 images, with 1,583 normal and 4,273 pneumonia cases. The images were preprocessed and augmented to enhance the model's robustness. The proposed model achieved an accuracy of 94%, demonstrating its potential in clinical settings to assist in the rapid and reliable diagnosis of pneumonia. This study contributes to the growing body of research in medical image analysis by employing a pre-trained ResNet50v2 model. It highlights the importance of leveraging advanced machine-learning techniques to improve diagnostic accuracy and efficiency.
Classification of Dog Emotions Using Convolutional Neural Network Method Hermawan, Slamet; Siregar, Amril Mutoi; Faisal, Sutan; Mudzakir, Tohirin Al
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.v13i2.74340

Abstract

The utilization of neural networks in dog emotion classification has great potential to improve the understanding of pet emotions. The goal is to develop a dog emotion classification system. This is important due to the lack of public ability to recognize and understand dog emotions. Neural networks able to create learning models can be used for decision-making, thus helping to reduce the risk of dangerous dog attacks. CNN itself is part of neural networks, where the CNN model has a higher accuracy rate of 74.75% compared to ResNet 65.10% and VGG 68.67%. Modeling using ROC-AUC shows the model's ability to distinguish emotion classes well. Angry has the highest AUC of 0.97, happy 0.93 and sad 0.96. While relaxed has the lowest AUC of 0.92. Classification report results show model has the highest precision and F1-Score values in angry class, while the highest recall value is in sad class.
The Effectiveness of Augmented Reality Technology in Mathematics: A Case Study of SMP Al Azhar Plus Bogor Siti, Ragil Siti Sholehah; Nuur Wachid Abdul Majid
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.v13i2.74582

Abstract

The field of mathematics education is rapidly advancing, particularly with the introduction of augmented reality (AR) technology as one of the tools used as an innovative learning medium. This research seeks to assess how the incorporation of AR influences students' grasp of mathematical concepts. This study is an experimental quantitative research using the experimental design method, with a population of 37 students from class IX of SMP Al Azhar Plus Bogor. The researcher obtained 30 students as samples based on purposive sampling technique, which were then divided into 2 groups: 15 students as the experimental group and 15 students as the control group. The experimental group underwent mathematics learning utilizing AR technology with 3D teaching materials that were prepared and accessible via smartphones through a barcode, covering topics such as congruent, similar, and spatial figures. Meanwhile, the control group followed conventional teaching methods using books as teaching materials with the same subject coverage. The study's results emphasize the substantial improvement in students' understanding of mathematical concepts through the effective utilization of AR. This improvement encompasses students' abilities to solve mathematical problems, retain conceptual memory, and actively participate in the learning process. Based on the statistical test results conducted, the experimental group obtained an average of 68.4153 or 68%, which falls into the category of moderately effective, while the control group obtained an average of 16.1508 or 16%, classified as ineffective. The Independent Sample Test yielded a Sig. (2-tailed) value of 0.000 < 0.05, indicating a significant difference in effectiveness between the experimental and control groups. Further data analysis indicates that the learning experience through AR not only provides a better understanding but also offers additional motivation to students, thereby increasing their interest in the subject of mathematics. Moreover, the study observes that a well-integrated instructional design within the curriculum, considering the context of AR usage, can contribute significantly to improved learning outcomes. The consequences of these discoveries strengthen the perspective that augmented reality (AR) is not just a successful educational instrument but can also offer a pleasurable learning encounter within the realm of mathematics education. The outcomes of this study play a substantial role in advancing more interactive approaches to teaching mathematics, with a specific emphasis on enhancing students' conceptual understanding. It is hoped that these findings can serve as a foundation for the implementation of AR in educational curricula as a strategic effort to enhance the quality of mathematics education and enrich students' learning experiences.
Analyzing Technology Acceptance Model for Lombok Traditional Food Restaurant in GoFood Application Bimantari, Joselina Rizki; Alamsyah, Noor; Murpratiwi, Santi Ika
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.v13i2.74816

Abstract

During the COVID-19 pandemic, Lombok Island, known for its stunning natural beauty, faced significant challenges in its culinary industry, resulting in a drastic decline in revenue and tourist visits. Embracing technological solutions, particularly through food delivery apps like GoFood, became pivotal in overcoming these obstacles. These apps not only sustained local restaurants during the pandemic but also preserved Lombok's distinctive cuisine, such as Sate Rembiga. Beyond pandemic resilience, GoFood played a crucial role in balancing global technological advancements and supporting daily activities. The aim of this research is to employ the Technology Acceptance Model (TAM) using Partial Least Square (PLS) approach to analyze the adoption of the GoFood application among owners of traditional Lombok cuisine restaurants, particularly focusing on the impact of the COVID-19 pandemic. This research method is designed to assess the extent of acceptance and adoption of GoFood among these restaurant owners during the pandemic, identifying key factors influencing their technological acceptance. The results of this research offer insights into the dynamics of technology acceptance within Lombok's culinary sector amid external changes such as the pandemic. In conclusion, understanding these dynamics can inform strategies to enhance the utilization of food delivery apps in traditional culinary businesses, ensuring resilience and adaptation in the face of unforeseen challenges.
Improving Image Retrieval Performance with SCS and MCS Clustering Techniques Fikri, Ulul; Prakoso, Rahmat; Azhar, Yufis
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.v13i2.75643

Abstract

This paper presents two methods, Single Cluster Search (SCS) and Multiple Cluster Search (MCS), aimed at enhancing image retrieval performance on the Corel1k, Corel5k, and Corel10k datasets, which has a wide variation of images. The Multi Texton Co-Occurrence Descriptor (MTCD) method is used for feature extraction, and the K-Medoids and DBSCAN methods are used for dataset clustering. The clusters are then ranked based on the distance of their medoids to the query image. The most relevant images are retrieved from the highest-ranking clusters. SCS selects the cluster with the highest ranking as the search area and expands the search area to the next ranking cluster if the number of images is less than 6, which is the desired number of retrieval results. MCS merges several clusters with the highest ranking and combines clusters as the search area. Both methods are evaluated using several metrics, such as AP, MRR, and retrieval time. The results are also compared with the original method, which does not use clustering (the query image and the dataset are only extracted with MTCD, and their distance is calculated). The findings indicate that both methods improve the retrieval time. In Corel1k, the SCS method reduces the time complexity by 0.001s, while the MCS method, although not surpassing the original method, still shows potential. In Corel5k, both methods reduce the time complexity by 0.052s in the SCS method and 0.015s in the MCS method. In Corel10k, both methods reduce the time complexity by 0.122s in the SCS method and 0.058s in the MCS method, compared to the original method. These results have practical implications for improving image retrieval efficiency. The paper discusses the reasons behind these results and suggests possible directions for future research.

Page 1 of 3 | Total Record : 22

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 14 No. 2 (2025) Vol. 14 No. 1 (2025) Vol. 13 No. 3 (2024) Vol. 13 No. 2 (2024) Vol. 13 No. 1 (2024) Vol. 12 No. 3 (2023) Vol. 12 No. 2 (2023) Vol. 12 No. 1 (2023) Vol. 11 No. 3 (2022) Vol. 11 No. 2 (2022) Vol. 11 No. 1 (2022) Vol 11, No 1 (2022) Vol. 10 No. 3 (2021) Vol 10, No 2 (2021) Vol. 10 No. 2 (2021) Vol 10, No 1 (2021) Vol. 10 No. 1 (2021) Vol 9, No 3 (2020) Vol. 9 No. 3 (2020) Vol. 9 No. 2 (2020) Vol 9, No 2 (2020) Vol. 9 No. 1 (2020) Vol 9, No 1 (2020) Vol 8, No 3 (2019) Vol. 8 No. 3 (2019) Vol. 8 No. 2 (2019) Vol 8, No 2 (2019) Vol 8, No 1 (2019) Vol. 8 No. 1 (2019) Vol 8, No 1 (2019) Vol 7, No 3 (2018) Vol. 7 No. 3 (2018) Vol 7, No 3 (2018) Vol. 7 No. 2 (2018) Vol 7, No 2 (2018) Vol. 7 No. 1 (2018) Vol 7, No 1 (2018) Vol 7, No 1 (2018) Vol. 6 No. 3 (2017) Vol 6, No 3 (2017) Vol 6, No 3 (2017) Vol. 6 No. 2 (2017) Vol 6, No 2 (2017) Vol 6, No 2 (2017) Vol 6, No 1 (2017) Vol 6, No 1 (2017) Vol. 6 No. 1 (2017) Vol 5, No 3 (2016) Vol. 5 No. 3 (2016) Vol 5, No 3 (2016) Vol 5, No 2 (2016) Vol 5, No 2 (2016) Vol. 5 No. 2 (2016) Vol. 5 No. 1 (2016) Vol 5, No 1 (2016) Vol 5, No 1 (2016) Vol. 4 No. 3 (2015) Vol 4, No 3 (2015) Vol 4, No 3 (2015) Vol 4, No 2 (2015) Vol. 4 No. 2 (2015) Vol 4, No 2 (2015) Vol 4, No 1 (2015) Vol. 4 No. 1 (2015) Vol 4, No 1 (2015) Vol 3, No 3 (2014) Vol 3, No 3 (2014) Vol. 3 No. 3 (2014) Vol. 3 No. 2 (2014) Vol 3, No 2 (2014) Vol 3, No 2 (2014) Vol 3, No 1 (2014) Vol. 3 No. 1 (2014) Vol 3, No 1 (2014) Vol 2, No 3 (2013) Vol 2, No 3 (2013) Vol. 2 No. 3 (2013) Vol. 2 No. 2 (2013) Vol 2, No 2 (2013) Vol 2, No 2 (2013) Vol. 2 No. 1 (2013) Vol 2, No 1 (2013) Vol 2, No 1 (2013) Vol. 1 No. 3 (2012) Vol 1, No 3 (2012) Vol 1, No 3 (2012) Vol 1, No 2 (2012) Vol. 1 No. 2 (2012) Vol 1, No 2 (2012) Vol 1, No 1 (2012) Vol 1, No 1 (2012) Vol. 1 No. 1 (2012) More Issue