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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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SARIMA with Sliding Window Implementation for Forecasting Seasonal Demand Data Made Rama Pradipta; Gusti Made Arya Sasmita; Anak Agung Ngurah Hary Susila
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.59971

Abstract

Demand forecasting is an essential part of business process management. A comparison of methods is needed to get the best model to provide good forecasting results. Difficulties in meeting consumer demands and predicting these requests using demand data at companies CV. ABCD is the main problem in this research. The SARIMA and decomposition methods are used for comparison and search for the best model before forecasting. SARIMA  with a windowing size of 56, indicating the smallest MAPE value of 3,91%. The value <10%, so it can be said to produce an excellent forecasting value. Forecasting results with SARIMA  show a meeting between actual and forecasting data in 2022. Therefore, it can be said the forecasting results for 2023 and 2024 can be used as a reference for the company CV. ABCD to meet customer demand and avoid stock shortages.
Development of Virtual Reality for Optical Fiber Splicing Simulation Bahaduri, Rahmat Gah; Sindu, I Gede Partha; Wahyuni, Dessy Seri
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.65358

Abstract

This study aims to determine the design and evaluation of content and media experts in the development of Virtual Reality for Optical Fiber Splicing Simulation. The research method used is Research & Development with the Multimedia Development Life Cycle (MDLC) model which consists of concept, design, material collecting, assembly, testing, and distribution stages. This Virtual Reality development uses the Unity application in making applications with the Oculus Quest 2 device.  The data collection process begins with interviews with educators about the learning that takes place, and the difficulties experienced. At the testing stage, Blackbox Test, Content Expert Test: 1.00 Validity Coefficient, and Media Expert Test: 1.00 Validity Coefficient. Virtual Reality for Fiber Optic Splicing Simulation offers simulation before implementing splicing in real.  By taking characteristics of life such as laboratories, educators, and fiber optic practicum tools. Virtual reality can provide situations and conditions for students to practice Optical Fiber Splicing by entering a virtual laboratory in cyberspace. The learning experience in Virtual Reality that provides a sense of immersion in a virtual environment using Full Hand Interaction that applies the Hand Tracking function in Oculus Quest 2. Based on the overall test, the development of Virtual Reality for Optical Fiber Splicing Simulation is feasible to use and distribute to target students.
Image Classification of Balinese Seasoning Base Genep Based on Deep Learning Prasetia, I Putu Widia; I Made Gede Sunarya
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.67967

Abstract

One of Indonesia's abundant natural wealth is spices and seasonings. Base Genep is a basic spice in making traditional Balinese culinary preparations. Base Genep uses many spices and seasonings, including turmeric, ginger, galangal, galangal, candlenuts, nutmeg, pepper, shallots, garlic, coriander, lemongrass, and cloves. From the variety of spices and seasonings that exist in Indonesia, it turns out that the knowledge of the Indonesian people is still low regarding spices and seasonings, especially among the younger generation. This is because these spices/seasonings have characteristics, shapes, and skin colors that are almost similar at first glance, making them difficult to differentiate. Based on these problems, this research was carried out with the aim of helping the public, especially the younger generation, recognize and differentiate types of spices and seasonings. Therefore, in this research, a model based on Deep Learning technology was created. The general objective of this research is to classify spices/seasonings which are often used as basic ingredients in the manufacture of Bumbu Bali Base Genep such as ginger, aromatic ginger, turmeric, and galangal using the YOLOv8 model. The data used in this study were obtained with a smartphone. The data consists of 1200 images consisting of 4 classes. The data is divided into several parts, namely training data, validation and testing data. The resulting dataset is divided into 4 dataset schemes in conducting model training. The highest score for the model in this study was obtained in dataset scheme number 4.
Development of Augmented Reality Application as An Educational Media for Visitors to Museum Pusaka Keraton Kasepuhan Cirebon Using Object Tracking Method and Fast Corner Detection Algorithm Based on Android Yuhano; Faisal Akbar
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.68925

Abstract

Augmented Reality is a technology that combines two-dimensional or three-dimensional virtual objects and projects these virtual objects in real time. One implementation of Augmented Reality in the tourism sector is to educate museum visitors. Museum Pusaka Keraton Kasepuhan Cirebon does not yet have a touch of technology to attract visitors, and the public paradigm is that visiting the museum only sees heirloom objects, nothing interesting or unique. The aim of the research carried out by the author is to apply Augmented Reality with the Object Tracking method and the FAST Corner Detection algorithm to educate museum visitors, so that it can attract visitors' attention. By utilizing these methods and algorithms, it can be easier for visitors to explore heirloom objects to obtain the desired information. So the results obtained from the research conducted by the author are that the response time for objects appearing using the Tracking Object method and the FAST Corner Detection algorithm in environments that use glass is an average of 1.52 seconds to 2.40 seconds and that does not use glass, namely 2.84 seconds to 4.71 seconds with a level of confidence at the 95% level.
Particle Swarm Optimization for Optimizing Public Service Satisfaction Level Classification Lestari, Tyastuti Sri; Ismaniah, Ismaniah; Priatna, Wowon
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.69612

Abstract

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.
Analysis Quality of Employment Information Systems Using Webqual 4.0 and Importance Performance Analysis Method I Gede, Bagus Premana Putra; Made, Sudarma; Nyoman, Gunantara
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.70713

Abstract

Information system quality analysis is an aspect that information system managers must pay attention to, especially to meet user needs, increase comfort, and increase user productivity. SISNAKER, as the main information system used by the Department of Manpower and Energy and Mineral Resources of Bali Province to support services to the community digitally, also requires a quality analysis process to ensure user comfort when interacting with SISNAKER. This research aims to measure the quality of the employment information system, often called SISNAKER belonging Department of Manpower and Energy and Mineral Resources of Bali Province based on user perceptions and provide recommendations to improve the system quality. SISNAKER quality is measured using a questionnaire based on domains of the WebQual 4.0 method especially, usability, information quality, and interaction quality parameters. Mapping recommendations to improve SISNAKER quality is made based on four priority quadrants of the Importance Performance Analysis (IPA) method. The sampling of research was based on a proportionate stratified random sampling technique, involving a total of 98 respondents. The results of the research show that the gap between performance and user expectations is 0.02, which means that system performance is in line with user expectations. Improvement is found in information quality and interaction quality parameters, with -0.02 gap, so it still needs improvement.
Random Forest-Based Assessment of Mangrove Degradation Utilizing NDVI Feature Extraction in Spatio-Temporal Analysis Santoso, Hadi; Hidayatullah, Syahrul
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.71173

Abstract

Mangrove ecosystems, vital for coastal biodiversity and protection, confront escalating degradation from human and natural influences. Addressing the imperative for precise degradation assessment, this study introduces a Random Forest-based technique, utilizing NDVI (Normalized Difference Vegetation Index) feature extraction within a spatio-temporal framework. The principal aim is to establish a robust approach for evaluating mangrove degradation and land cover shifts. This involves extracting NDVI values from satellite images to monitor vegetation health and changes chronologically. Leveraging the Random Forest algorithm, acknowledged for managing intricate relationships and classifications, further enhances the methodology.By situating the approach spatio-temporally, degradation patterns and alterations in mangrove distribution are traced over time. The temporal progression of the study area is considered, affording a thorough degradation analysis. Outcomes affirm the method's efficacy, evidenced by a Cohen's Kappa Score of 0.96 denoting substantial agreement between predictions and observations. Remarkably high scores across accuracy, precision, recall, and F1-score (all at 0.97) underscore the model's precision in classifying mangrove degradation levels. The amalgamation of the Random Forest-based approach and NDVI feature extraction emerges as a valuable instrument for precise mangrove degradation assessment. The spatio-temporal analysis augments comprehension of degradation dynamics, pivotal for proficient mangrove management and conservation strategies.
Mobile Applications for Self-Handle of Pornography Addiction Muhammad, Raditya; Ardimansyah, Mochamad Iqbal; Wahyuningsih, Yona
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.71488

Abstract

Content with pornographic nuances in the form of images, sound, and videos is widely circulating on the internet, including on social media. Teenagers have great potential to become addicted to pornographic content given the widespread use of the internet among adolescents. Pornography addiction has the potential to interfere with the physical and mental development of addicts, even a wider impact can lead to criminal cases in society, such as rape. This paper discusses the development of mobile applications that aim to help pornographic content addicts get rid of pornography addiction problems. The applications developed include a system for assessing the level of exposure to pornographic content, handling and self-care of pornographic content, and a system for detecting the user's location in solitude. The rating system was adapted from the Pornography Addiction Screening Tool (PAST). Handling and self-care pornographic content use the psychological approach of Cognitive Behavioral Therapy (CBT) which has been widely researched and used as a method for mental treatment and healing. An assessment system for the level of exposure to pornographic content and self-care is presented in the application by utilizing chatbot to increase the interactive between the user and the application. The research method uses the Design Research Methodology (DRM) while the method in developing mobile applications uses Agile models as an adaptive software development method. This application is not intended to replace the role of psychologists, but as a supporting tool that can help pornography addicts to reduce their addiction level until they recover. Through black box testing, evaluation results from a functional perspective show that this application can be used as expected.
Enhancing Rice Production Prediction: A Comparative Machine Learning Analysis of Climate Variables Yunis, Roni; Sudarto; Adiputra Pardosi, Irpan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.71527

Abstract

This study aims to enhance rice production prediction through a comparative analysis of machine learning models utilizing climate variables. Eight models were assessed on a predetermined dataset, with Support Vector Regression (SVR) emerging as the top performer. Following the identification of significant climate variables influencing rice production, the models underwent evaluation using two hyperparameter approaches: random search and manual tuning. SVR outperformed other models, achieving impressive metrics with MAE 0.180, MSE 0.186, RMSE 0.431, and an exceptionally low MAPE of 0.020. Key factors influencing rice production included productivity and area, along with humidity, rainfall, temperature, wind velocity, and sunshine duration. Favorable conditions for rice output encompassed low humidity, moderate rainfall, increased wind speed, and prolonged sunshine, while rainfall and temperature exhibited minimal impact. The success of random search emphasizes the importance of effective hyperparameter tuning. This research provides valuable insights for enhancing rice production prediction.
Incorporating Stock Prices and Social Media Sentiment for Stock Market Prediction: A Case of Indonesian Banking Company Dhenda Rizky Pradiptyo; Irfanda Husni Sahid; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.74486

Abstract

Forecasting the stock market is one of the most popular topics to be discussed in many fields. Many studies, especially in information technology have been conducted machine learning algorithms to achieve a more accurate prediction of the stock market. This research aims to find the effectiveness in predicting stock market performance by utilizing social media sentiment in combination with historical data. In addition, this research uses a machine learning algorithm to train a model to predict the stock price of each bank and training the model on a dataset that included the historical stock prices of the bank, as well as the sentiment scores of the social media posts about the bank and evaluate the performance of the model by comparing the predicted stock prices to the actual stock prices. The research shows that the R2 and RMSE score model that has been built with its historical data has slightly better performance than the model that has been built with the combination of historical data and social media sentiment. The finding indicates that the research method is closely correlated and affected to the performance of the stock market prediction.

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