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All Journal TEKNIK INFORMATIKA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Inovasi Pertambangan dan Lingkungan
Victor Amrizal
Unknown Affiliation
Computer Science & IT Engineering Environmental Science Materials Science & Nanotechnology

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Pengaplikasian Kalman Filter sebagai Pengendali dalam Permainan The Open Racing Car Simulator (TORCS) Rendy Andrian Yahya; A Arini; Victor Amrizal
Jurnal Processor Vol 15 No 1 (2020): Processor
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.307 KB) | DOI: 10.33998/processor.2020.15.1.740

Abstract

The Open Racing Car Simulator (TORCS) works as both a playable game and a framework to develop artificial intelligence-based controllers. As a platform for researchers, TORCS has become a platform in controller development with various approaches in artificial intelligence using sensors and actuators provided by the SCR Server. In this research, the author develops a controller using the Kalman Filter, an algorithm to predict and measure states based on previous measurements to determine future trajectory.
ARTIFICIAL NEURAL NETWORK APPLICATION FOR HEAVY EQUIPMENT GAS EMISSION CONTROL ON ROCK BREAKING ACTIVITY Mulyanto Soerjodibroto; Victor Amrizal; Wishnu Prabowo
Jurnal Inovasi Pertambangan dan Lingkungan Vol 2, No 2 (2022): Jurnal Inovasi Pertambangan dan Lingkungan
Publisher : Syarif Hidayatullah State Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jipl.v2i2.29289

Abstract

Applications of artificial intelligence (AI) software in mining activities, both for equipment automation, data analysis and processing, identification of patterns and features data, upto determining solutions have been carried out by several mining companies. This is mainly due to mining activities naturally are always facing uncertainty and natural variability conditions. One of the AI applications is to control fuel consumption aimed at increasing the efficiency of fuel use, while in the same time reducing exhaust gas emissions from internal combustion engines, which are one of the causes of rising greenhouse gases (GHG).Utilization of AI in aimed to control fuel consumption in Rock Breaking activities in limestone quarry in the Sukabumi area, resulting a deviation rate of 0.17 for  fuel consumption prediction, which is fall in “ the good category”. Increasing the volume and variety of data for “machine learning” would  improve AI performance.Keywords : Artificial intelligence application,  fuel consumption, ICE gas emission control.
Detecting Hoax News in Indonesian Language Using Web-Based Multinomial Naïve Bayes Fitri Mintarsih; Ivan Ananda Putra; Arini; Victor Amrizal; Bayu Suseno, Hendra
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.50385

Abstract

This study addresses the growing problem of hoax news in Indonesia, which has contributed to social conflicts. It aims to develop an effective detection method using the Multinomial Naive Bayes algorithm. The study integrates Indonesian specific text preprocessing and feature engineering within the CRISP-DM framework to enhance classification performance. A dataset of 5,226 news articles (2,612 non-hoax and 2,614 hoax) was collected from kompas.com and turnbackhoax.id. Preprocessing steps included case folding, tokenization, stopword removal, and stemming tailored to the Indonesian language. Feature extraction was performed using the TF-IDF weighting scheme to convert text into numerical representations. The Multinomial Naive Bayes algorithm achieved an average accuracy of 86%, precision of 86%, recall of 86%, and F1 score of 86%, indicating stable and balanced performance. Furthermore, the trained model was successfully deployed using the Flask framework and stored in (pickle/joblib) format, demonstrating its practical applicability in real world systems. The results indicate that the integration of Indonesian specific preprocessing and TF-IDF feature representation significantly supports the effectiveness of the Multinomial Naive Bayes algorithm in detecting hoax news. This study provides a scalable and implementable approach to combating the spread of false information in Indonesian digital media.
Co-Authors A Arini Arini Bayu Suseno, Hendra Fitri Mintarsih Ivan Ananda Putra Mulyanto Soerjodibroto Rendy Andrian Yahya Wishnu Prabowo