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All Journal HAYATI Journal of Biosciences ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sinergitas PkM & CSR KACANEGARA Jurnal Pengabdian pada Masyarakat Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) Jurnal Teknologi Bahan dan Barang Teknik Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal Locus Penelitian dan Pengabdian eProceedings of Engineering Jurnal Rekayasa elektrika IJoICT (International Journal on Information and Communication Technology)
Bayu Erfianto
Universitas Telkom
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences

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Journal : Indonesian Journal on Computing (Indo-JC)

 1 
Forecasting Fuel Consumption Based-On OBD II Data Satrio Nurcahya; Bayu Erfianto; Setyorini Setyorini
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.659

Abstract

Cyber Physical System consists of computing devices that communicate with each other by interacting with the physical world assisted by sensors and actuators with an iterative response. Intelligent Transportation System which aims to apply information and communication technology in every transportation area. Applying ITS to vehicles, especially in the aspect of fuel consumption, vehicles must begin to be able to analyze the use of fuel that is being used to provide users so that users can be more effective. Regarding the analysis of fuel consumption, several researchers have done this with several existing methods such as ANN, SVM and the like. The use of the Multivariate time series method is used as a solution to the forecast analysis of vehicle fuel consumption. In this study, data from vehicles obtained from OBD-II will be processed using the Multivariate time series method with output in the form of analysis and visual data from the forecast with parameters related to RPM, TPS and fuel consumption. So the expected result is the relationship between RPM, TPS and fuel consumption as well as the formation of a system model to obtain sample data related to RPM, TPS and fuel consumption.
Time Series On-Board Air Quality Index Benedictus Augusta Vianney; Bayu Erfianto
Indonesia Journal on Computing (Indo-JC) Vol. 8 No. 1 (2023): April, 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2023.8.1.695

Abstract

With the rapid development of technology, individuals forget about their health, plus during the pandemic, the indoor air quality becomes more of a concern. Maintaining air quality to be healthy and good for humans is by keeping the amount of pollutants in the air, such as Carbon Dioxide (CO2), Volatile Organic Compound (VOC), and Formaldehyde (HCHO), at a predetermined and agreed threshold. We propose an on-board air quality index detection system for indoor and forecast the AQI in the future. The system will use a Raspberry Pi 4 and a WP6003 sensor device that will capture parameters for the AQI. The parameter data is analyzed using a correlation matrix to determine the parameters that affect each other. Then classified using fuzzy logic to determine the quality index based on the value of each parameter. Then forecast using the ARIMA and LSTM methods for the next 30 minutes. The forecasting accuracy is calculated using the RMSE and MAPE metrics. The result shows that CO2, VOC, and HCHO are related. Comparison of the forecasting results of the two methods concluded that the LSTM outperformed ARIMA to forecast the AQI for the next 30 minutes based on the previous 10 hours of data.
Video Extraction Into PPG Signal To Identify Blood Pressure With XGBoost Method Adhan Mulya Rahmawan; Bedy Purnama; Bayu Erfianto
Indonesia Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.942

Abstract

Abstract
Time Series On-Board Air Quality Index Benedictus Augusta Vianney; Erfianto, Bayu
Indonesian Journal on Computing (Indo-JC) Vol. 8 No. 1 (2023): April, 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2023.8.1.695

Abstract

With the rapid development of technology, individuals forget about their health, plus during the pandemic, the indoor air quality becomes more of a concern. Maintaining air quality to be healthy and good for humans is by keeping the amount of pollutants in the air, such as Carbon Dioxide (CO2), Volatile Organic Compound (VOC), and Formaldehyde (HCHO), at a predetermined and agreed threshold. We propose an on-board air quality index detection system for indoor and forecast the AQI in the future. The system will use a Raspberry Pi 4 and a WP6003 sensor device that will capture parameters for the AQI. The parameter data is analyzed using a correlation matrix to determine the parameters that affect each other. Then classified using fuzzy logic to determine the quality index based on the value of each parameter. Then forecast using the ARIMA and LSTM methods for the next 30 minutes. The forecasting accuracy is calculated using the RMSE and MAPE metrics. The result shows that CO2, VOC, and HCHO are related. Comparison of the forecasting results of the two methods concluded that the LSTM outperformed ARIMA to forecast the AQI for the next 30 minutes based on the previous 10 hours of data.
Video Extraction Into PPG Signal To Identify Blood Pressure With XGBoost Method Rahmawan, Adhan Mulya; Purnama, Bedy; Erfianto, Bayu
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.942

Abstract

Abstract
Co-Authors Adhan Mulya Rahmawan Adysti Adrianne Andrian Rahmatsyah Andrian Rakhmatsyah Angga Juliat Saputra Arisandy, - Bedy Purnama Benedictus Augusta Vianney Benedictus Augusta Vianney Cahyani, Niken Dwi Catur Wirawan Wijiutomo Fauzi, Roki Fazmah Arif Y, Fazmah Gita Indah Hapsari Ilhamdaniah Ilhamdaniah Ilhamdaniah, Ilhamdaniah Indrarini Dyah Irawati Intan Muchtadi-Alamsyah Irma Yunita Masturi Juntriesta, Vera Khoiri, Isfa' Bil Maman Abdurohman Mas'ud Adhi Saputra Monterico Adrian Muhammad Hanif Abdillah Novian Anggis Suwastika Nugraha, Shinta Jitny Ayu Nusantara, Hadi Dharma Pahlevi , Rizka Reza Parman Sukarno Prihatni, Delita Putra, Bima Andika Rahmatsyah, Andrian Rahmawan, Adhan Mulya Ramzy Rashaun Arif Rendy Munadi Satrio Nurcahya SETIAWAN, ALDRY HERNANDA Setyorini Setyorini, - Shandikri, Rheza Sidik Prabowo Vera Juntriesta Vera Suryani Wirawan, Ilo Raditio Yuridikta Adha Muslim