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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 eProceedings of Engineering Jurnal Rekayasa elektrika
Bayu Erfianto
Universitas Telkom
Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

 1 
The Application Of Multi-Sensor Data Fusion Method with Fuzzy Time Series Model to Improve Indoor Water Prediction Accuracy Quality Khoiri, Isfa' Bil; Erfianto, Bayu
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3082

Abstract

There is a lot of indoor air pollution, especially from cigarette smoke, wall paint, air fresheners and gas. With this situation, the room uses Air Box WP6003 air quality detection device by transmitting information about air quality through visualization index. This study aims to improve prediction accuracy with fuzzy time series methods processed through 2 naïve and moving average models using forecast transformers and without transformers. The level of prediction accuracy is calculated through several metrics, namely Mean Absolute Percentage Error (MAPE), Sum of Squares Error (SSE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). These results can be calculated between the actual value and the predicted value. The data used is 204584 data from 4 parameters including Temperature, TVOC, HCHO and CO2. The test results with the difference from the forecast transformer and without transformer are comparable. Temperature value obtained using naïve with transformer from RMSE of 0.158866 and naïve without transformer of 0.782397, data using moving average with transformer obtained by 0.147546 and moving average without transformer of 0.772570. This can be explained by the error analysis that was tried, where the error rate continued to increase so that the experimental results continued to be far from the actual number. From the test results it can be concluded that the accuracy of air quality prediction using naïve forecast transformer is pretty accurate.
The Anomaly Detection in Time Series Data of VOC (Volatile Organic Compound) To Generate Indoor Air Quality Alerts Nusantara, Hadi Dharma; Erfianto, Bayu
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indoor air quality is a very important factor and needs to be considered for health. Poor indoor air quality can trigger illness, reduce productivity, and disrupt the comfort of people in the space. In residential areas, hospitals, schools, nursing homes and other specialized environments, indoor air pollution can affect groups that are more vulnerable to health problems due to their health conditions or age. This research aims to predict indoor air quality using the Long Short-Term Memory (LSTM) method and provide alerts when the prediction results exceed a predetermined limit. The accuracy level is measured using Mean Absolute Percentage Error (MAPE) by calculating the difference between the original data and the prediction results. In this study, a system was created that utilizes Internet of Things (IoT) technology that can monitor the state of indoor air quality such as temperature, TVOC, CO2 and HCHO gas levels. The system uses the WP6003 Air Box Reader tool as an indoor air quality detector that is connected to the website created. This website can display data that is being recorded, download datasets that have been recorded, visualize predictions of temperature, TVOC, CO2 and HCHO and notify if any data crosses a predetermined limit. The results obtained are quite good prediction accuracy by getting a MAPE value of 0.30452, RMSE 0.023475 and the average value of the test data is 24.035 which means that if the RMSE value is close to 0, the prediction results will be more accurate. Anomalies result in values of room temperature and HCHO that are above normal limits.
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 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