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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan International Journal of New Media Technology Compiler JOURNAL OF SCIENCE AND APPLIED ENGINEERING IJID (International Journal on Informatics for Development) Informatika : Jurnal Informatika, Manajemen dan Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Cyber Security dan Forensik Digital (CSFD) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Health Science and Technology Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer
Muhammad Habibi
Universitas Jenderal Achmad Yani Yogyakarta
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Journal : Indonesian Journal of Data and Science

 1 
Predictive Modeling of Air Quality Levels Using Decision Tree Classification: Insights from Environmental and Demographic Factors Iwan Sudipa, I Gede; Habibi, Muhammad; Jullev Atmadji, Ery Setiyawan; Arfiani, Ika
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.201

Abstract

Air pollution poses a significant global challenge, adversely impacting public health and environmental sustainability. Understanding the factors influencing air quality is essential for developing effective mitigation strategies. This study aims to analyse key environmental and demographic factors, such as PM2.5 concentration, population density, and proximity to industrial areas, to predict air quality levels using a Decision Tree model. The dataset, comprising 5000 samples, was pre-processed by encoding the target variable and applying Z-score normalization to numerical features. The model was trained on 80% of the data and evaluated on the remaining 20%, achieving an accuracy of 93%. Evaluation metrics, including a classification report and confusion matrix, demonstrated the model's effectiveness in distinguishing between four air quality categories: Good, Moderate, Poor, and Hazardous. PM2.5 emerged as the most critical predictor, followed by demographic and industrial factors. These findings underscore the potential of machine learning models in providing actionable insights for air quality management. The results contribute to public policy by highlighting the need for targeted interventions in high-risk areas and the importance of incorporating environmental data into urban planning. Future work should focus on expanding the feature set and exploring ensemble techniques to further enhance predictive accuracy and robustness.
Co-Authors A. Sinay, Stani ABIYOGA BAGUS MUSTRIYANTO Adri Priadana Alfirna Rizqi Lahitani Alfun Roehatul Jannah Alfun Roehatul Jannah Aris Wahyu Murdiyanto dayat subekti Dedy Hariyadi Elvika Fit Ari Shanti, Elvika Fit Ari Endah Puji Astuti, Endah Puji Fajar Syahruddin Fresia Nandela, Emilia I Gede Iwan Sudipa Ika Arfiani Iqbal Hadi Subekti Irmma Dwijayanti Jenny Meilila Azani Cahya Permata Jullev Atmadji, Ery Setiyawan Kharisma Kristi, Ria Kusumaningtyas, Kartikadyota Lahitani, Alfirna Rizqi M. Wahyu Indriyanto Muhammad Erwan Syah Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nasron, Ulil Amri Netania Indi Kusumaningtyas Nur Azizah Firdausa Oktaviana, Dela Pangky Putra Aziztiya Puji Winar Cahyo Purnamaningsih, Nur’Aini Purwatiningsih, Agustina Ramses Caniago Rifanny Br Girsang, Ribka Rifqi Ma’arif, Muhammad Risky Setyadi Putra Rizky Fauzi Akbar Rizky Fauzi Akbar Rizqi Lahitani, Alfirna Rukmi, Dwi Kartika Saputra, Andika Bayu Sujono Riyadi Sumiyarini, Retno Suwarno Tegar Agustian Tri Sunarsih Ulil Amri Nasron Wahyuningsiam, Astr