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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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Domain Analysis and Audit of IT Governance Based On COBIT 5 at Denpasar Industrial Training Center I Made Artana; Nyoman Putra Sastra; Dewa Made Wiharta
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.v12i1.55989

Abstract

Information technology has become a key element of organizations and one of institutions’ added value and competitive advantages. Therefore, IT must be properly managed and measured. Denpasar Industrial Training Center (BDI) has implemented the IT Governance Education and Training Information System, SISDIKLAT. These applications have never been evaluated from an IT governance perspective. This study aimed to determine domains and assess SISDIKLAT using methods relevant to COBIT 5. To assist the organization in focusing on its main objectives and strategies, a tailored governance system based on the specificities of SISDIKLAT is required. This research assist BDI Denpasar in establishing healthy governance and IT management by utilizing the COBIT 5 framework. Both qualitative and quantitative approaches are used to select relevant governance/management objectives. Four domains and nine subdomains were chosen based on the domain analysis. According to the assessment results, the capability value of each subdomain was 2--3, with a gap value of 0.2--0.8. To reach the target level, the nine subdomains were advised.
Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses Dian Indri Purnamasari; Vynska Amalia Permadi; Asep Saepudin; Riza Prapascatama Agusdin
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.v12i1.57144

Abstract

Small and medium-sized businesses are constantly seeking new methods to increase productivity across all service areas in response to increasing consumer demand. Research has shown that inventory management significantly affects regular operations, particularly in providing the best customer relationship management (CRM) service. Demand forecasting is a popular inventory management solution that many businesses are interested in because of its impact on day-to-day operations. However, no single forecasting approach outperforms under all scenarios, so examining the data and its properties first is necessary for modeling the most accurate forecasts. This study provides a preliminary comparative analysis of three different machine learning approaches and two classic projection methods for demand forecasting in small and medium-sized leathercraft businesses. First, using K-means clustering, we attempted to group products into three clusters based on the similarity of product characteristics, using the elbow method's hyperparameter tuning. This step was conducted to summarize the data and represent various products into several categories obtained from the clustering results. Our findings show that machine learning algorithms outperform classic statistical approaches, particularly the ensemble learner XGB, which had the least RMSE and MAPE scores, at 55.77 and 41.18, respectively. In the future, these results can be utilized and tested against real-world business activities to help managers create precise inventory management strategies that can increase productivity across all service areas.
Development of Non-Intrusive Low-Power Digital Water Meter Reading System Based On Wireless Mesh Network and Internet of Things Rifki Muhendra
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.v12i1.57180

Abstract

The application of WMN and IoT technology to the metering infrastructure is proven to provide convenience in data collection, access to information, and management of metering devices. Unfortunately, the use of this technology is still minimal in developing countries like Indonesia. In this study, the development of a digital meter reading system that focuses on low-power non-intrusive reading devices and the application of WMS in the water meter reading system is carried out. This system consists of a series of meter reader sensors that are connected mesh to a gateway where meter data can be monitored in real-time and remotely. This sensor circuit is an integrated circuit added to the existing mechanical water meter which allows recording the volume of water consumption digitally. This circuit consists of an optical sensor, real-time clock (RTC), microcontroller, Lora Transmitter, and power supply. The use of solar cells to supply energy for meter reading devices is another interesting aspect of the development of this system. A gateway is a circuit built to bridge the transmission of meter data from a radio-based sensor network to the internet. The result of this research is the establishment of a meter reading system capable of accurately reading water consumption, low power where the meter data is easily accessible using a device connected to the internet. This system has been evaluated for every part of development such as reading water consumption, power consumption, and also communication in the mesh network. The contribution of this research is that it can be used as a reference for developing a digital meter system that is more effective, has low power, and has easy access to meter data
Effect of Word2Vec Weighting with CNN-BiLSTM Model on Emotion Classification Merinda Lestandy; Abdurrahim
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.v12i1.58571

Abstract

Emotion is an element that can influence human behavior, which in turn influences a decision. Human emotion detection is useful in many areas, including the social environment and product quality. To evaluate and categorize emotions derived from text, a method is required. As a result, the CNN-BiLSTM model, a classification method, aids in the analysis of the text's emotional content. A word weighting technique employing word2vec as a word weighting will help the model. The CNN-BiLSTM model with Word2vec as a pre-trained model is being used in this study to find the findings with the highest accuracy. The information is split into two groups: training and testing, and it is categorized into six categories according to how each emotion manifests itself: surprise, sadness, rage, fear, love, and joy. The best outcome from the CNN-BiLSTM model's accuracy of emotion classification is 92.85%.
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.

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