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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknologi dan Sistem Komputer Jurnal Nasional Komputasi dan Teknologi Informasi INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Informatika Kaputama (JIK) Infotek : Jurnal Informatika dan Teknologi Jurnal Minfo Polgan (JMP) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Aliyah Kurniasih
Universitas Ary Ginanjar
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation

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Prediksi Kualitas Udara Berbasis Citra Menggunakan Pre-Trained Inception V3 dalam Perspektif Keberlanjutan Aliyah Kurniasih; Cantika Nur Previana; Andi Purnomo
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.34839

Abstract

Advances in artificial intelligence are driving the optimization of deep learning models for image analysis. Air pollution is a significant environmental problem that impacts human health and the balance of ecosystems. Increased emissions from industrial activities, transportation, and the burning of fossil fuels are major factors contributing to deteriorating air quality in various regions. This study aims to analyze the influence of data preprocessing and training strategies on model performance, including the removal of duplicate image data, data splitting, data augmentation, the use of class weights on training data, and learning rate tuning using the Inception V3 transfer learning model. The results show that the combination of these strategies achieved an accuracy of 89.28% with an error rate of only 0.338, demonstrating stability and good model generalization capabilities. The application of appropriate and effective preprocessing and training strategies enhances model performance. Consequently, a more accurate and reliable model can support data-driven decision-making more efficiently and contribute to various sectors such as health and the environment in supporting sustainable development.
Co-Authors Ahlijati Nuarminah Ahlijati Nuraminah, Ahlijati Ahmad Nuryanto Akmal, Refido Arjunal Aldy Fadilla, Muhammad Aloysius Kurniawan Santoso Aloysius Kurniawan Santoso Santoso Andi Purnomo Andi Purnomo, Andi Astrid Noviriandini Bagus Dwi Wicaksono Bagus Dwi Wicaksono Cantika Nur Previana Cantika Previana Pardede, Hilman F Previana, Cantika Nur Pulungan, Mutiara Persada Rachman, M. Bakhara Alief Risman Nugraha Santoso, Aloysius Kurniawan Sri Hartati Sri Hartati Sundawijaya, Andika TAU, Snartek Wicaksono, Bagus Dwi