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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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Classification of Rice Growth Stage on UAV Image Based on Convolutional Neural Network Method I Made Gede Sunarya; I Wayan Treman; Putu Zasya Eka Satya Nugraha
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.60959

Abstract

Currently, the majority of the agricultural sector in Indonesia is carried out by small communities. Half of the Indonesian people (approximately 10 million people) work in the agricultural sector and utilize agricultural land. Some of the tools used by farmers are still using traditional tools, but some are already using modern farming tools. In general, agricultural tools are divided into 3 categories, namely agricultural tools used before the seeds are planted, agricultural tools used when caring for seedlings that are growing and developing, and agricultural tools used when harvesting. One of the technologies used in agriculture is the use of drones or Unmanned Aerial Vehicles (UAV) in the process of sowing fertilizers and seeds and spraying pesticides. The current use of UAVs supports agriculture with manual operation and based on GPS waypoint positioning. In the process, the visual aspects that can be obtained from the UAV have not been considered, so the treatment carried out on agricultural land is the same. The problem of similarity in treatment can lead to similar treatment on heterogeneous agricultural land. Agricultural land should be treated according to the conditions of the land. Because the condition of the land will affect the growth of the planted vegetation. Another problem found in agricultural land is the different rice growth in each paddy field. Rice growth can be seen by farmers through visual aspects but farmers cannot directly see the visual condition of rice growth as a whole because of the large area of land. Utilization of UAV by taking high-resolution aerial imagery can provide visuals of the overall condition of rice from various angles of image capture. The general objective of this research is to classify rice growth on high resolution UAV images based on the Convolutional Neural Network (CNN). The data used in this study were acquired using a multirotor UAV in the same rice field area. The data consists of 500 images consisting of 5 groups. Group 1-2 is the vegetative phase, group 3 is the generative phase and group 4-5 is the ripening phase. CNN is used to conduct training with variations of epochs are 100, 250 and 500. The best accuracy results are obtained in the training epoch 500 with 96% of Accuration
North Sulawesi Single Local Fruit Detection Using Efficient Attention Module Based on Deep Learning Architecture Vecky C. Poekoel; Putro, Dwisnanto; Jane Litouw; Rivaldo Karel; Pinrolinvic D. K. Manembu; Abdul Haris Junus Ontowirjo; Feisy D. Kambey; Reynold F. Robot
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.v12i2.54754

Abstract

A Local fruit detection system is an agricultural vision field that can be implemented to increase the profit of a commodity. Besides that, North Sulawesi has a variety of local fruits which are widely used by people in their area and have a high selling value. The sorting system is an essential process of agricultural robots to sequentially separate fruit one by one. This automation process requires an accurate vision system to detect and separate fruit precisely and precisely. In addition, the implementation of a practical application demands a method to be able to work in real-time on low-cost devices. This work aims to design a local single fruit detection system for Sulawesi North by applying deep learning architecture to produce high performance. The architecture is designed to consist of an effective backbone for rapidly separating the distinctive features, an efficient attention module to improve feature extraction performance, and a classifier module employed to estimate the probabilities of each local fruit category. As a result, the designed model produces an accuracy value of 99,27% and 99,57% on the Fruits-360 and the local datasets, respectively. It outperforms other light architectures. In addition, deep learning models are designed to produce higher efficiency values than other competitors and can operate quickly at 100,488 Frames per Second.
Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia Intan Nurma Yulita; Afrida Helen; Mira Suryani
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.v12i2.58505

Abstract

In 2019, the COVID-19 pandemic appeared. There have been several efforts to curb the spread of this virus. West Java, Indonesia, employs social restrictions to prevent the spread of this disease. However, this method destroyed the economy of the people. If no instances were detected in the region, the World Health Organization (WHO) authorized the social restrictions to be relaxed. If the government lifts the social limitation, the decision must also consider the potential of future confirmed instances. By utilizing machine learning, it is possible to forecast future data. This work utilized the following algorithms: linear regression (LR), locally weighted learning (LWL), multi-layer perceptron (MLP), radial basis function regression (RBF), and support vector machine (SVM). The study investigated daily new instances of COVID-19 in West Java, Indonesia, from March 2, 2020, to October 15, 2020. The RBF algorithm was the best in this investigation. Mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) were 48.85, 89.73, 88.67, 62.99, and 60.88, respectively. The RBF prediction model may be proposed to the government of West Java for assessing data on COVID-19 instances, particularly in social restriction management. It is anticipated that West Java would have a minimum of 275 new cases every day for the following 30 days beginning on October 16, 2020. Consequently, the easing of societal limitations requires careful consideration.
The Information and Communication Technology Literacy Level of Sukoharjo’s Senior High School Students Hieronymus Purwanta; Sutiyah; Herimanto; Musa Pelu; Isawati; Dadan Adi Kurniawan
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.v12i2.58554

Abstract

The Ministry of Education and Culture inaugurated the Independent Curriculum for primary to secondary education in the new school year 2022/2023. Therefore, this study aims to obtain an overview of the readiness of information and communication technology (ICT) literacy skills mastered by public high school students. Research samples were taken from nine public high schools in Sukoharjo Regency, which implemented the Independent Curriculum. The focus of the research is directed at obtaining an overview of how their ICT literacy skills. The method used is a survey compiled based on the idea of Helsper Schneider, Deursen, and Laar about indicators of digital skills for the younger generation. They divided ICT literacy indicators into information, literacy of computers, media, communication, visuals, and technology. The survey results show that public high school students in Sukoharjo Regency have high ICT literacy skills, 59% in the good category and 5% in excellent. They are ready to take part in teaching-learning processes using the Independent Curriculum.    
Implementation of Chatbot for Merdeka Belajar Kampus Merdeka Program using Long Short-Term Memory Muhammad Rahaji Jhaerol; Sudianto Sudianto
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.v12i2.58794

Abstract

Good service can help the organization improve efficiency and effectiveness in operations. Optimal service can also improve the customer experience and provide added value to an organization that provides services. One of the services that can be optimized is the Merdeka Belajar Kampus Merdeka (MBKM) program which is a learning program organized by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek), especially MBKM services at the Institut Teknologi Telkom Purwokerto (ITTP). The problem is that the MBKM service at ITTP is not optimal due to inaccessibility to anyone and so many programs available. Thus, resulting in not optimal services provided. Therefore, this study aims to implement a Chatbot service in the MBKM program at ITTP. The method used in building a Chatbot service is the Deep Learning Long Short-Term Memory (LSTM) algorithm. LSTM is a type of artificial neural network architecture that matches text data. The results show an accuracy score of 100% and a loss of 0.121%. Meanwhile, the results of the further evaluation are in the form of average weights consisting of precision, recall, and F1-score, respectively of 100%, 100%, and 100%.
Modification of Non-local Mean Algorithm Using Parallel Calculation for Image Noise Reduction Al Farissi; Wondo Wondo
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.v12i2.58996

Abstract

Noise in digital image processing is a noise that occurs at pixel values due to random colour intensity. Several types of noise models include Gaussian noise, speckle noise, impulse noise, and Poisson noise. Before processing image data, a noise reduction process is required. One of the noise reduction algorithms used for gaussian noise models is Non-local Mean. This algorithm performs calculations sequentially on each pixel in the search block. Due to a large number of pixels and search block area in the image, the noise reduction process using the Non-local Mean algorithm is very slow. This study proposes the concept of parallel calculations for the Non-local Mean algorithm. This concept divides the search block into three parts and performs calculations on each part simultaneously. The experimental results show that the Non-local Mean algorithm with parallel calculations can reduce noise up to 30% faster if the noise standard deviation is above 30.
Knowledge Management System Evaluation Using DeLone McLean Model: A Case Study of IT Service Desk Bank XYZ Desiana Nurul Maftuhah; Lia Ellyanti; Dana Indra Sensuse; Damayanti Elisabeth; Nadya Safitri; Sofian Lusa
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.v12i2.59609

Abstract

Bank XYZ is one of the biggest banks in Indonesia. It has IT Service Desk function under Information Technology Division that specifically handles complaints or problems related to IT. Knowledge management in IT service desk can help to increase the availability of information and knowledge for the IT service desk team who must provide explanations to users. IT Service Desk of Bank XYZ has built knowledge management system using open-source platform since 2017. It is called SDKPedia which was developed inhouse by IT Service Desk team and never been evaluated since it was built. The objective of this study is to evaluate SDKPedia as knowledge management system used in IT Service Desk Bank XYZ. Evaluation was carried out based on Delone and McLean assessment criteria. Survey is distributed to IT Service Desk worker and 31 valid feedback is used in this study. To determine the indications that have a substantial impact and result in a net advantage for SDKPedia, the PLS-SEM algorithm is utilized. Service quality is the only exogen latent variable that affected intension of use. While the other two exogen latent variable are system quality and information quality, did not have significant impact for intention to use or user satisfaction. Considering the findings of this study, several improvements can be made by the IT Service Desk manager to make the quality of SDKPedia better. The points that need more attention are information quality and system quality.
Comparison Performance of K-Medoids and K-Means Algorithms In Clustering Community Education Levels Diana Dwi Aulia; Nurahman Nurahman
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.v12i2.59789

Abstract

Education is a mandatory right of all citizens and the key to the nation's superiority in global competition that must get top priority to be examined critically and comprehensively. It is known that compulsory education is at least 12 years, but not all people can do it because of minimal economic conditions. In past years, COVID-19 has also had an impact on the economy, school dropout rates, and falling academic achievement, for example in Central Kalimantan. The size of Central Kalimantan, however, makes it difficult for the government to identify the areas with the worst levels of education. To determine which regions fall into the low and high education categories, it is required to group the province's educational levels. This study also compares two algorithms by measuring their accuracy. By looking at which algorithm has the lowest Davies Bouldin Index (DBI) value, the best degree of performance can be ascertained. To process the data from as many as 1,565 sources, data mining techniques, including the clustering method, were used. K-Means and K-Medoids algorithms were employed in this work as clustering techniques. Based on the outcomes of the cluster created, both algorithms are also put to the test for performance. The results of this study obtained 6 clusters in K-Means with the lowest DBI value of -0.439, while the results in K-Medoids were in 3 clusters with the lowest DBI of -0.866. Based on accuracy testing using DBI, it is known that K-Means results are more optimal with the lowest DBI value in the grouping of education levels compared to K-Medoids. It is also known from the formation of 6 clusters of the K-Means algorithm that the low education level is in cluster_0 which is 1484 villages and the higher education level is as many as 3 villages in cluster_3.
Hole Detection in Plastic Mulch Using Template Matching and Machine Learning Algorithms Abdul Aziz; Yandra Arkeman; Wisnu Ananta Kusuma; Farohaji Kurniawan
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.v12i2.60628

Abstract

Mulch is a ground cover material to maintain soil moisture and temperature stability as a plant medium. Mulch also helps prevent weed growth for better plant growth. For planting with plastic mulch, farmers need to make holes in the mulch the day before planting. Precision agriculture is needed because it can obtain savings in input financing, labor, and better yields, so this research aims to identify holes in mulch based on Unmanned Aerial Vehicle images. The advantage of this research is that it can monitor each plant based on the mulch holes, and the number of holes identified can be used as a parameter to estimate the amount of crop production. This research combines Template Matching Algorithm and Machine Learning Algorithm to improve accuracy in predicting holes in mulch. Three machine learning algorithms are used, namely the Random Forest, Support Vector Machine, and XGBoost. The data used is an orthophoto mosaic from aerial photographs. Nine areas were taken from orthophotos to be used as research samples. The results of this study obtained the highest average recall, precision, and f-measure values using the Support Vector Machine algorithm with a recall value of 87.7%, precision of 97.5%, and f-score of 92.3%. This research focuses on reducing detected commission errors. Therefore, omission errors were still detected in the damaged or leaf-covered holes.
Application of Construct on Scaffold Concept Map in Mobile Programming Learning with Flutter Layout Topic Putra Prima Arhandi; Annisa Taufika Firdausi; Vivin Ayu Lestari; Abdurrasyid Muhasibi; Dharma Yudistira Eka Putra; Banni Satria Andoko
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.v12i2.60629

Abstract

Flutter is a framework for making mobile applications cross-platform made by Google. From 2019 to 2021 the popularity of flutter is increasing. Flutter use declarative writing style to create layouts. This makes the layout in flutter immutable, and a light blueprint. This research proposes a construct on scaffold concept map method to help students understand the concept of widget arrangement in flutter layout. Construct on scaffold will provide the learner with a framework from an incomplete expert concept map. Some of the nodes and connecting relationships in the framework have been removed, so students must fill in the missing parts with several available answer choices to complete the concept map. To prove the impact of the application of this method, the study was conducted using a pre-post-test group experimental design. Students will do a pre-test, use the EasyFlutter application, and post-test. The results of the pre-test and post-test obtained were tested for normality first, then tested to find out whether there was an average difference between the pre-test and post-test scores. The results of the normality test show that the pre-test data are not normally distributed, and the post-test data are normally distributed, so the next test will use a non-parametric test, namely the Wilcoxon test. The test results show that the post-test mean score is higher than the pre-test mean. Wilcoxon test results also show that the Asymp value. Sig. (2-tailed) of 0.01, so it can be concluded that there is a significant difference between the pre-test and post-test scores. The application of the construct on scaffold method has a significant positive impact on the post-test scores of students related to the concept of widget arrangement in flutter.

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