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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal Technology and Implementation Bussines Jurnal Teknologi dan Terapan Bisnis Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Jurnal Teknologi dan Terapan Bisnis eProceedings of Engineering Innovative: Journal Of Social Science Research Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika
Anifatul Faricha
Universitas Telkom
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Implementasi Sistem Klasifikasi Kualitas Udara Menggunakan Sensor Gas dan Jaringan Saraf Tiruan Anifatul Faricha; Dimas Adiputra; Rifki Dwi Putranto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3388

Abstract

Indoor air quality is an important factor for human respiration. In enclosed spaces, especially with increased activity and the number of occupants, pollutants such as carbon dioxide may increase, while oxygen levels may decrease. Therefore, a classification system is needed to determine indoor air quality. In this study, we designed an air quality measurement device using two types of gas sensors, namely carbon dioxide and oxygen sensors. The carbon dioxide gas sensor used was the MG-811, while the oxygen gas sensor used was the Gravity I2C. In addition, an artificial neural network was implemented as the air quality classification method, divided into three categories: normal, wary, and dangerous. The classification process was divided into two stages: training and testing. Based on the experimental results, an error value of 0.016 was obtained with 274 epochs during the training process. Meanwhile, in the testing process, the achieved accuracy was above 90 percent, indicating that the artificial neural network was successfully implemented for air quality classification.
Co-Authors Abdulloh Hamid Nushfi Adiputra, Dimas Aditya Nugroho, Dito Akbar, Achmad Syiham Andreas, Christopher Arif, Rangga R. DEWI RAHMAWATI Dimas Adiputra Djoko Purwanto DWI SURYANTO Gema Parasti Mindara Hasintongan, Ferdinand Ronald Hawwin Mardhiana Helmy Widyantara Ilma Amira Rahmayanti Indasyah, Enny Inna Novianty Isa Hafidz Laridho Sembiring, Lorenzo Lora Khaula Amifia M. Rivai Ma'ruf Firmansyah, Muhammad Moch. Bagus Indrastata Moch. Fauzan Rasyid Moch. Iskandar Riansyah Moh. Yani Muhammad Achirul Nanda Muhammad Falahudin Malich Salaz Muhammad Rivai Naafilaturrosyidah Ni’matut Tamimah Nurulhaq, Muhammad Iqbal Pradeka Brilyan Purwandoko Purnama Anaking Putra, Aditya Firmanda Qurrotun 'Ayun Mawadatur Rohmah Rasmana, Susjianto Tri Rifki Dwi Putranto Rika Susanti Rismen Sinambela Rizki Anhar R.P. Rizky, Khoiril Robin Addwiyansyah Alvaro Samrat Rosyid Abdillah Sholihah, Walidatush Siti Maghfirotul Ulyah Subayu, Achmad Suwito Suwito Syauqi, Fattah Rafif tsani, naufal tsani firjatulloh Ulfadilah, Anisa Ulfah Abqari