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All Journal JOIV : International Journal on Informatics Visualization TIN: TERAPAN INFORMATIKA NUSANTARA Action Research Literate (ARL)
Imamudin, M.
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CSS for CVR: A Reciprocal Velocity Obstacle-Based Crowd Simulation System for Non-Playable Character Movement of Campus Virtual Reality Arif, Yunifa Miftachul; Janitra, Geovanni Azam; Imamudin, M.; Safitri A Basid, Puspa Miladin Nuraida; Setiawan, Dedy Kurnia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1997

Abstract

Along with the development of multimedia technology, an overview of the campus environment for prospective new visitors can be visualized through a 3D virtual environment based on virtual reality. A crowd simulation system is needed to provide an overview of the crowds in campus virtual reality (CVR). The simulation helps make it easier for individuals to predict crowds in certain areas virtually. In this study, we propose using the Reciprocal Velocity Obstacle (RVO) method to support the simulation of Non-Playable Character (NPC) crowds in a visualized virtual environment. RVO implements multi-agent navigation by estimating the possibility of moving without communication between agents and being able to perform collision avoidance. The use of RVO in this study aims to contribute to the collision detection development process for each NPC. The application of RVO is carried out in the development of virtual reality by using Unity3D and Blender asset support tools. The results of this study indicate that the RVO method can be applied in multi-agent navigation. These results were confirmed by the success of the NPC as a simulation agent in selecting routes and independently navigating to avoid collisions between agents without the need for communication. In every simulation, collisions will occur within a set of agents due to high density, which causes complex computations. The development of CSS can help every CVR user experience a virtual environment. In addition, each user can experience a more natural experience with the presence of 3D objects and virtual reality with RVO-based CSS. Furthermore, this research material is expected to be developed from various perspectives and themes related to crowd simulation for games and other simulation media.
Sentiment Analysis of Pedulilindungi Application Reviews Using Naive Bayes Classifier and Support Vector Machine Firdausy, Irfani; Suhartono, Suhartono; Imamudin, M.
Action Research Literate Vol. 8 No. 11 (2024): Action Research Literate
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/arl.v8i11.2340

Abstract

The spread of the Covid-19 virus since the end of 2019 in Indonesia has resulted in the Indonesian government taking several actions in various sectors. One of the government's efforts to handle and monitor the condition of the Covid-19 pandemic using information technology is by launching the PeduliLindungi application. With this application the government can monitor public data related to vaccination, tracing, telemedicine, and looking for rooms at the nearest hospital. The launch of the application and the obligation to use the PeduliLindungi application has received a response from the public, this can be monitored/seen from reviews on social media, news and also reviews on the Google Play Store regarding the application. Reviews from users on the Google Play Store can be used as parameters for input or feedback. This data is quite a lot and requires a long time to process it, even though the existing reviews could be useful as input for criticism and suggestions in future application development. From user review data, a classification process can be carried out based on sentiment type. Sentiment analysis is a branch of text classification which aims to classify sentiment (opinion) whether the text contains negative opinions, positive or neutral opinions. The aim of this research is to apply sentiment analysis to user review data of the PeduLindungi application into positive and negative classes using the Naive Bayes Classifier and Support Vector Machine classification algorithms. The dataset used after going through data pre-processing was 10,616 records. The results of testing and model evaluation carried out by randomly dividing training data and testing data obtained accuracy values, for the Naive Bayes Classifier method it was 77% and the Support Vector Machine had higher accuracy, namely 81%.
Klasifikasi Keterlambatan Pembayaran Sumbangan Pembinaan Pendidikan Menggunakan Algoritma Naïve Bayes dan Support Vector Machine Umar, Huzaifah; Kusumawati, R.; Imamudin, M.; Rohman, Moh. Ainur
TIN: Terapan Informatika Nusantara Vol 4 No 11 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i11.4969

Abstract

Payment delinquency of SPP is a commonly occurring issue in school. It affects the salary of teachers and staffs alongside school’s various development program. The study aims to classify payment delinquencies using Naïve Bayes and Support Vector Machine. Research methode is Cross-Industry Standard Process for Data Mining (CRISP-DM). Method testing was carried out with 5 trials. Based on the test results, the average performance of Naïve Bayes is accuracy (62,88%), precision (65,27%), recall (77,42%) dan f1-score (70,75%). Meanwhile, the average performance of the Support Vector Machine is accuracy 63,51%), precision (62,25%), recall (94,48%) dan f1-score (75,04%).
Co-Authors Dedy Kurnia Setiawan Firdausy, Irfani Janitra, Geovanni Azam R. Kusumawati Rohman, Moh. Ainur Safitri A Basid, Puspa Miladin Nuraida Suhartono Umar, Huzaifah Yunifa Miftachul Arif