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 17 Documents
 1   2 
Search results for , issue "Vol. 12 No. 1 (2023)" : 17 Documents clear
Decision Tree Method for Automation of Plant Sprinklers and Monitoring Based On Soil Moisture Muhammad Muchlasin; Muhammad Hasbi; Sri Siswanti
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.v12i1.59075

Abstract

Watering the plants is an important part of plant care, but due to our busy schedules, we frequently forget to do it. It can be caused by a number of variables, including the weather, in addition to human irresponsibility. The goal of this research is to develop a device that can automatically water plants and monitor plants based on soil moisture. Automatic sprinklers that use NodeMCU ESP8266 and Telegram bots employ the decision tree approach. The C 4.5 algorithm is used by the Decision Tree approach to choose the microcontroller's course of action. The value of various case studies utilized in this study is determined using algorithm C 4.5. The Arduino IDE, Fritzing, and Visual Studio Code were the tools used to design and produce this tool. Additionally, it configures Telegram bots using the Telegram universal bot library. The website setting for plant monitoring employs the native PHP language, but the configuration of the telegram bot uses the general telegraph bot library. The end result is an automatic plant sprinkler that operates based on the soil moisture value and activates the water pump when the soil moisture level drops below a threshold of 50%. When the soil moisture reading exceeds 75%, the water pump will then turn off. The outcomes of this investigation also comprise websites and Telegram bots for tracking soil moisture levels.
Alternative Text Pre-Processing using Chat GPT Open AI Indri Tri Julianto; Dede Kurniadi; Yosep Septiana; Ade Sutedi
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.v12i1.59746

Abstract

Text Pre-Processing is the first step in Sentiment Analysis. Categorizing a sentiment in a dataset is part of the Text-Preprocessing stage to get the optimal model accuracy value. Generative Pretrained Transformer, often known as Chat GPT, is a Machine Learning model that can automatically generate realistic and meaningful text. This study aims to examine the capability of GPT Chat as an alternative in the Text-Pre-Processing stage by utilizing GPT Chat 3 from the openai.com website in the Text-Pre-Processing stage of the collected tweet data. The data used in this research is the result of crawling Twitter by inserting the keyword "Chat GPT”. This study method was carried out by measuring performance using the K-Nearest Neighbor and Naïve Bayes Algorithms to find the best performance value and compare it with the Text-Preprocessing generated by Rapidminer. It is shown that the performance accuracy produced using the K-Nearest Neighbor Algorithm is 73.57% using the Linear Sampling method. The comparison result with the Text-Preprocessing method using Rapidminer indeed shows a better accuracy of 75.33%, which means it has a narrow difference of 1.76% with the Chat GPT Text Pre-Processing method. However, both are still in the same category, which is Fair Classification. The results of this research show that Chat GPT can be an alternative in Text-Preprocessing datasets for sentiment analysis.
Systematic Literature Review of Machine Learning in Virtual Reality and Augmented Reality I Gede Partha Sindu; Rukmi Sari Hartati; Made Sudarma; Nyoman Gunantara
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.v12i1.60126

Abstract

This study aims to conduct a systematic literature review on machine learning methods used in virtual reality (VR) and augmented reality (AR). The method proposed in the SLR consists of three main phases: planning, conducting, and reporting. Researchers used data obtained through Google searches sourced from IEEE Xplore, Springer, Science Direct, Scopus, and Web of Science. The application of inclusion and exclusion criteria is used to select documents. The findings from this study consist of countries involved in machine learning research in the VR and AR fields, machine learning methods, technology used, and work sectors that use machine learning in the VR and AR fields. The results also show that when the machine learning method is used in VR and AR applications, the main advantages are high efficiency and algorithm precision. Moreover, this research observes that machine learning is the most widely applied artificial intelligence scientific technique in VR and AR applications. The results of this systematic literature review show that the combination of machine learning and VR and AR methods contributes to trends, opportunities, and applications in the fields of engineering, arts, education, industry, medicine, tourism, and technology
Trends, Technology, and Implementation of Digital Counseling in a Human Mental Health Agus Aan Jiwa Permana; Rukmi Sari Hartati; Made Sudarma; I Made Sukarsa
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.v12i1.60163

Abstract

This research discusses various trends, methods, and implementation of the art of digital-based counseling services. With increasing public awareness of mental health, since the womb, parents have thought about how to form good character and mental health for their children. Purpose of this study is to utilize digital counseling services to help early detection of depression and the resilience of children and adolescents in dealing with life's problems. This research collects articles according to the topic, then looks at cases handled, types of data, and methods used in both synchronous and asynchronous-based digital counseling. The next stage is research grouping based on research objects, data, methods, results, and deficiencies in research. The focus of the research is on cases of depression, and resilience, with machine learning methods. Digital counseling has been widely used for early detection in cases of depression, drug abuse, youth suicide, and alcohol addiction among adolescents
Plasma Cell Detection in Multiple Myeloma Cases Using Mask Region Based Convolutional Neural Network Method (Mask R-CNN) Milyun Ni'ma Shoumi; Radian Malek Rayrendra; Dwi Puspitasari; Pramana Yoga Saputra
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.v12i1.53119

Abstract

Multiple myeloma cancer is the third major of hematologic malignancy after lymphoma and leukemia, which is about 1% of 13% of hematologic malignancies. Unlike other cancers, myeloma does not form a tumor or lump, but rather causes an accumulation of abnormal plasma cells in the bone marrow which is more than 10% and paraprotein in the body. One of the first steps in diagnosing Multiple Myeloma cancer is by detecting plasma cells in a bone marrow sample taken from the patient's body. Blood samples are taken on several preparations, and the number of plasma cells will be counted from the entire sample. If the number of plasma cells is more than 30% of all cells that have nuclei, then the patient is diagnosed with Multiple Myeloma cancer. The process of detecting plasma cells and calculating the entire sample takes quite a long time and can lead to misdiagnosis due to inaccuracy in the calculation process and the fatigue factor of the medical personnel who check it. In this study, a model was developed to detect Plasma Cells in Multiple Myeloma Cases Using the Mask Region Based Convolutional Neural Network (Mask R-CNN) method, which is expected to speed up the diagnosis process. The use of the Mask Region Based Convolutional Neural Network (Mask R-CNN) method is implemented using the SegPC-2021-dataset for the model training process, and data from the Kepanjen general hospital for the testing process. Using this dataset, the mAp value is 75.94%, the mean precision is 73.93%, and the mean recall is 53.9%.
Leaf Health Identification on Melon Plants Using Convolutional Neural Network Farah Zakiyah Rahmanti; Bernadus Anggo Seno Aji; Oktavia Ayu Permata; Berliana Amelia; M. Hamim Zajuli Al Faroby
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.v12i1.58492

Abstract

Plants require complete nutrients to grow well and produce good-quality products. Some examples of symptoms in plants that lack nutrients such as wrinkled leaves and slow ripening of fruit, so plants are less productive. Plants that lack nutrients are unhealthy plants. This research aims to identify healthy and unhealthy leaves on melon plants so that immediate action can be taken to deal with them. This research will be useful for melon farmers everywhere. The dataset used is data taken directly using a digital camera with the help of melon farmers to label each data, both healthy and unhealthy leaves. This research has two main works, they are the training process and the testing process. The proposed research uses the Convolutional Neural Network (CNN) method with 10 epochs. The test results on the 20-test data achieve 100% accuracy. We used accuracy, precision, recall, and f1-score to evaluate the classification method.
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

Page 2 of 2 | Total Record : 17

 1   2 

Filter by Year

2023 2023


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