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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
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Electronic Procurement Website Service Quality and Customer Loyalty Using The Pieces Method, A Case Study of The Denpasar City Government IG Wahyu Sanjaya
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.61325

Abstract

The Denpasar city government uses website facilities to display information on the procurement of goods and services. Publication of information through the website is intended to create a transparent, effective, efficient and accountable procurement system. From it’s initial use since 2013, this system has never been tested to determine whether the system is still in good condition. This study aims to evaluate the system based on user loyalty using the PIECES method. So that the website manager gets input about the quality of the website from the user's point of view. The results of the tests that have been carried out in this study indicate that the Performance Indicators scored 4.2 in the Very Satisfied category, the Information Indicators obtained a value of 4.2 in the Very Satisfied category, Economic Indicators obtained a value of 4.1 in the Satisfied category, Control and Security Indicators obtained a value of 4.3 in the Very Satisfied category, the Efficiency indicator got a score of 4.3 in the Very Satisfied category, the Service indicator got a score of 4.2 in the Very Satisfied category.
Applying Use Case 2.0 Approach to The Development of IoT-Based Rainfall Monitoring System Mohammad Fajar; Ferian Bagus Chandra; Hamdan Arfandy
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.61529

Abstract

The use of Internet of Things (IoT) technology for monitoring and controlling environmental conditions or objects is quite popular. However, most of the development of the IoT systems including rainfall monitoring systems, mainly focuses on the implementation perspective, rather than discussing the development approaches or design techniques. The use of suitable development approaches will increase maintainability aspect of the IoT system in the future. Therefore, the aim of this study is to implement and evaluate the use case 2.0 approach in modeling and designing an IoT-based monitoring rainfall system. Data collection was performed through evaluation using object-oriented metrics to measure encapsulation, polymorphism, and reusability properties of the designed system. In modeling the IoT system, collected requirements specifications are organized into user stories. The user stories are then mapped into UML use case diagrams. Each of the use case should be sliced into thinner pieces, taking into account of the basic and alternative flows of the user stories. Moreover, the use case slices are designed, implemented, and evaluated independently. The results of modeling and designing a rainfall monitoring system using the use-case 2.0 are then implemented on the NodeMCU platform and Android-based application. Evaluation results show that the implementation of use-case Reading, Viewing and Searching for Rainfall Data can be run successfully on the target platform.  The measurement uses object-oriented metrics on the designed IoT system indicating that the use case slices have an impact on the ease of system modification level.
Sentiment Analysis of Nanovest Investment Application Using Naive Bayes Algorithm Lelianto Eko Pradana; Yova Ruldeviyani
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62302

Abstract

Various applications provide simple ways for individuals interested in investing in crypto assets or stocks - both domestic and international - to do so. One of the companies in this industry, Nanovest, has launched the Nanovest investment application. Since its release in 2022, numerous positive and negative responses have been on Google Play, the App Store, and Twitter. However, Nanovest faces two main problems regarding the use of its application. First, they often receive complaints submitted to the operational team, indicating dissatisfaction or problems faced by users. Second, Nanovest has never conducted formal research regarding user experience in using their application. This indicates a lack of understanding of the perspectives, needs and challenges faced by users. This study tries to find out how the public responds to the Nanovest application through a sentiment analysis. This study used tweet and review data from January 1, 2022, to February 17, 2023. The data underwent sentiment analysis, employing the Naïve Bayes algorithm, and were classified into positive and negative sentiments. The findings revealed that 96.07% of the sentiments expressed towards Nanovest were positive, while 22.11% were negative, with these percentages calculated based on the total number of sentiments detected in the data. To evaluate the model's performance, a 10-fold cross-validation approach was utilized alongside the Naïve Bayes algorithm, resulting in an impressive accuracy rate of 94.8391%. This positive sentiment suggests that users are highly favorable towards the crypto assets and global stock investment services offered by the Nanovest application. Nevertheless, 3.93% of users still expressed dissatisfaction with the app due to some flaws that existed when Nanovest was initially launched. Based on the results that have been obtained and analyzed for the development team, it is recommended to make three improvements, namely reducing application size to minimize memory usage, increasing overall application performance, and increasing access speed across all features to allow application users to access more efficiently. It is recommended for the product team and stakeholders to consider developing the Candlestick chart feature into the application. This also increases the competitiveness of the Nanovest application against other applications.
Similar Questions Identification on Indonesian Language Subject Using Machine Learning Hasmawati; Ade Romadhony
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62582

Abstract

Question similarity is carried out to evaluate similarities between questions in a collection of questions in the question and answer forum and on other platforms. This is done to improve the performance of the question-and-answer forum so that new questions submitted by users can be identified as similar to existing questions in the database. Currently, research related to question similarity is still being carried out on foreign language datasets. The purpose of this research is to identify the similarity of questions in a collection of questions in Indonesian. The method used is Support Vector Machine and IndoBERT. For feature extraction, we evaluate the lexical features and syntax features of each question. For lexical feature extraction, we use the cosine similarity algorithm to calculate the distance between two objects which are represented as vectors. For syntax feature extraction we use the Indonesian part of speech tagger (POS Tag). The dataset used is a collection of questions on Indonesian subjects at the primary and secondary school levels. The results of this study show that the best performance of the Support Vector Machine is obtained from the use of the cosine similarity feature with an accuracy of 85%. While the use of the POS Tag feature or the combination of POS Tag and cosine similarity causes the model to be overfitted and the accuracy decreases to 77%. Meanwhile, for the IndoBERT model, an accuracy of 95% was obtained. 
Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy I Made Satria Bimantara; Anny Yuniarti
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62874

Abstract

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.
Enhancing Sales Forecasting Accuracy Through Optimized Holt-Winters Exponential Smoothing with Modified Improved Particle Swarm Optimization I Putu Susila Handika; I Kadek Susila Satwika
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.65462

Abstract

The Holt-Winters Exponential Smoothing method utilizes three smoothing parameters, namely alpha (α), beta (β), and gamma (γ), which have a significant impact on the accuracy of the forecasting process. One of the main challenges in the Holt-Winters Exponential Smoothing method is to find the best combination of the smoothing parameters, α, β, and γ, to achieve optimal forecasting accuracy. In this research, the MIPSO optimization method is used to find the optimal combination of values for α, β, and γ. The sales data used in the study covers the period from January 2021 to May 2023. The research results indicate the best accuracy achieved by combining the Holt-Winters Exponential Smoothing algorithm with the MIPSO optimization algorithm during the data period from January 2021 to May 2023, with a MAPE value of 9.1717%. Therefore, the use of the MIPSO algorithm helps discover the optimal combination of α, β, and γ parameters for forecasting.
Accuracy Analysis of WP, AHP-WP, Entropy-Topsis Methods in Determining Majors Saiful Bahri; Maria Ulfah Siregar
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.57718

Abstract

Determination of majors at SMK AR Rahma is still manual and Excel is only used to find average scores. On the other hand, the number of students is around 140 students, so the majors can cause inaccuracies. In this study, the accuracy of the WP, AHP-WP, and ENTROPY-TOPSIS methods was analyzed in determining the majors of SMK AR Rahma’s students. So that it will be known which method is more accurate in producing student majors. In the process of student majors, data are needed in the form of report cards, academic test scores, majors test scores and health scores. The result is that the majors produced by the AHP-WP method are more accurate than the majors produced by the other two methods, respectively by 70.71%, 64.29%, and 62.86%.
Web-Based Online Exhibition by Implementing Virtual and Augmented Reality to Visualize Architecture Building Design Ketut Nova Wirya Dinata Dinata; I Gede Partha Sindu Sindu; Dessy Seri Wahyuni Wahyuni
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.61609

Abstract

Web-based online exhibition using immersive technology, namely Virtual Reality & Augmented Reality, has a major effect on the efficiency of time, place, and cost in providing visitors with an understanding of building architecture. Interaction in understanding architectural modeling is also assisted by a technology called Avatar Mediated Communication (AMC) which is the scope of interaction with other visitors in a Virtual Environment created by Virtual Reality technology. Technology that is able to support all website-based reality activities is made with the Laravel & Express Js framework (Website), Spoke Mozilla Hubs (Virtual Reality Environment), and Library AR Js (Augmented Reality Marker Based).  The technology is selected, built, and combined in the stages of the Research & Development (R&D) method, the Multimedia Development Life Cycle (MDLC) model in examining the development stages and user responses to web-based Virtual & Augmented Reality development products. The results of the responses obtained are based on 6 categories in the UEQ (User Experience Questionnaire) instrument. The result showed that the three largest categories that give a positive response are stimulation, attractiveness, and novelty. Meanwhile, the three categories below are efficiency, perspicuity, and dependability. This indicates that the online exhibition in the support of immersive visualization is able to increase user curiosity in trying to enter the virtual reality room presented in web-based Virtual & Augmented Reality.
YOLOV4 Deepsort ANN for Traffic Collision Detection Arliyanti Nurdin; Bernadus Seno Aji; Yupit Sudianto; Mardhiyyah Rafrin
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.62923

Abstract

Every collision must be handled right away to prevent further harm, damage, and traffic bottlenecks. Hence, the implementation of a systematic approach for accident detection becomes imperative to expedite response mechanisms. Our proposed accident detection system operates in three stages, encompassing vehicle object detection, multiple object tracking, and vehicle interaction analysis. YOLOv4 is employed for object detection, while DeepSort is utilized to the tracking of multiple vehicle objects. Subsequently, the positional and interactional data of each object within the video frame undergo thorough analysis to identify collisions, utilizing an Artificial Neural Network (ANN). Notably, collisions involving a single vehicle and not affecting other road users are excluded from the scope of this study. The evaluation of our approach reveals that the ANN model achieves a commendable F-Measure of 0.97 for detecting objects without collisions and 0.88 for objects involved in collisions, based on the conducted tests.
Balinese Shadow Puppet Characters Detection In The Wayang Peteng Performance Using The Yolov5 Algorithm I Gusti Ngurah Bagus Putra Asmara; Made Windu Antara Kesiman; Gede Indrawan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.65906

Abstract

To generate greater public interest in Balinese shadow puppet performances, it is crucial to explore novel ways of educating viewers about the characters showcased in the plays, as many individuals may need to become more familiar with them. In Object Detection, an algorithm is called You Only Look Once (YOLO). This research utilizes the YOLOv5 algorithm to detect Balinese shadow puppet characters in the "wayang peteng" performances. The dataset consists of 5040 images, divided into training, validation, and test data, with a ratio of 7:2:1 (This ratio helps in effectively training and evaluating the YOLOv5 model on a diverse set of data). Four YOLO models are trained, each with a different number of epochs (a single iteration of training when the entire dataset has been passed forward and backward through the neural network), resulting in 12 models. All models are tested using the test data images to obtain precision, recall, and mean Average Precision (mAP) metrics. Additionally, three videos measure the average frames processed per second. The research findings reveal that the YOLOv5n model with 200 epochs achieves the best results, with a precision value of 1, recall of 1, mAP@0.5 of 0.995, mAP@0.5-0.95 of 0.985, and 128.20 frames per second.

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