Jakarta Air Quality Classification Based On Air Pollutant Standard Index Using C4.5 And Naïve Bayes Algorithms

Duta Pramudya Ramadhan; Agung Triayudi

doi:10.58905/saga.v2i4.395

Authors

Duta Pramudya Ramadhan Informatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, Jakarta, Indonesia
Agung Triayudi Informatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, Jakarta, Indonesia

DOI:

https://doi.org/10.58905/saga.v2i4.395

Keywords:

Air Pollution, DKI Jakarta, Classification, C4.5, Naive Bayes

Abstract

Increasing air pollution in DKI Jakarta has become an increasingly pressing environmental issue, which has a direct impact on public health and environmental sustainability. Therefore, it is very important to have a system that manages data-based air pollution levels. The purpose of this research is to classify air quality in DKI Jakarta through Air Pollutant Standard Index (ISPU) data. This data consists of parameters such as dust particles (PM10, PM2.5), sulfur dioxide (SO2), carbon monoxide (CO), surface ozone (O3), and nitrogen dioxide (NO2), as well as two classification algorithms used, namely C4.5 and Naïve Bayes. This research also seeks to compare the effectiveness of the two algorithms based on ISPU data collected in 15 Jakarta areas. The approach used in this research is to divide the data using three ratio scenarios, namely 70% : 30%, 80% : 20%, and 90%: 10%. In addition, performance assessment is carried out using accuracy, precision, recall and f1 score metrics. The experimental results showed better performance of C4.5, with an average accuracy of 95%, precision of 99%, recall of 94% and f1-score of 97%. In contrast, Naïve Bayes recorded an average accuracy of 81%, precision of 93%, recall of 73% and f1-score of 82%. These findings corroborate the validity of the C4. 5 algorithm is more effective in air quality classification based on ISPU, thus making it a reliable resource for air quality monitoring and management in DKI Jakarta, as well as supporting decision-making in air pollution control policies.

References

A. A. H. Kirono, I. Asror, and Y. F. A. Wibowo, “Klasifikasi Tingkat Kualitas Udara Dki Jakarta Menggunakan Algoritma Naïve Bayes,” e-Proceeding Eng., vol. 9, no. 3, p. 1962, 2022.

A. D. Wiranata, S. Soleman, I. Irwansyah, I. K. Sudaryana, and Rizal, “Klasifikasi Data Mining Untuk Menentukan Kualitas Udara Di Provinsi Dki Jakarta Menggunakan Algoritma K-Nearest Neighbors (K-Nn),” Infotech J. Technol. Inf., vol. 9, no. 1, pp. 95–100, 2023, doi: 10.37365/jti.v9i1.164.

A. Budianita, N. Iman, F. M. Hana, and C. B. Hakim, “Komparasi Algoritma K-Nearest Neighbor dan Naive Bayes pada Klasifikasi Tingkat Kualitas Udara Kota Tangerang Selatan,” vol. 6, no. 1, pp. 320–327, 2024.

A. A. Nababan, M. Jannah, M. Aulina, and D. Andrian, “Prediksi Kualitas Udara Menggunakan Xgboost Dengan Synthetic Minority Oversampling Technique (Smote) Berdasarkan Indeks Standar Pencemaran Udara (Ispu),” JTIK (Jurnal Tek. Inform. Kaputama), vol. 7, no. 1, pp. 214–219, 2023, doi: 10.59697/jtik.v7i1.66.

R. Setiawan and A. Triayudi, “Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web,” J. Media Inform. Budidarma, vol. 6, no. 2, p. 777, 2022, doi: 10.30865/mib.v6i2.3566.

B. Valentino Jayadi, T. Handhayani, and M. Dolok Lauro, “Perbandingan Knn Dan Svm Untuk Klasifikasi Kualitas Udara Di Jakarta,” J. Ilmu Komput. dan Sist. Inf., vol. 11, no. 2, 2023, doi: 10.24912/jiksi.v11i2.26006.

I. Romli and A. T. Zy, “Penentuan Jadwal Overtime Dengan Klasifikasi Data Karyawan Menggunakan Algoritma C4.5,” J. Sains Komput. Inform. (J-SAKTI, vol. 4, no. 2, pp. 694–702, 2020.

Y. Apridiansyah, N. D. M. Veronika, and E. D. Putra, “Prediksi Kelulusan Mahasiswa Fakultas Teknik Informatika,” JSAI J. Sci. Appl. Informatics, vol. 4, no. 2, pp. 236–247, 2021.

P. S. Zakaria, R. Julianto, and R. S. Bernada, “Implementasi Naive Bayes Menggunakan Python dalam Klasifikasi Data,” BIIKMA Bul. Ilm. Ilmu Komput. dan Multimed., vol. 1, no. 2, pp. 126–131, 2023.

A. Nugroho, I. Asror, and Y. F. A. Wibowo, “Klasifikasi Tingkat Kualitas Udara DKI Jakarta Berdasarkan Open Government Data Menggunakan Algoritma Random Forest,” eProceedings Eng., vol. 10, No. 2, no. 2, pp. 1824–1834, 2023, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/20030%0Ahttps://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/20030/19395

T. H. Hasibuan and D. Mahdiana, “Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Algoritma C4.5 Pada Uin Syarif Hidayatullah Jakarta,” Skanika, vol. 6, no. 1, pp. 61–74, 2023, doi: 10.36080/skanika.v6i1.2976.

L. Y. Lumban Gaol, M. Safii, and D. Suhendro, “Prediksi Kelulusan Mahasiswa Stikom Tunas Bangsa Prodi Sistem Informasi Dengan Menggunakan Algoritma C4.5,” Brahmana J. Penerapan Kecerdasan Buatan, vol. 2, no. 2, pp. 97–106, 2021, doi: 10.30645/brahmana.v2i2.71.

M. Bagriacik and F. E. B. Otero, “Multiple fairness criteria in decision tree learning,” Appl. Soft Comput., vol. 167, no. PA, p. 112313, 2024, doi: 10.1016/j.asoc.2024.112313.

R. Blanquero, E. Carrizosa, P. Ramírez-Cobo, and M. R. Sillero-Denamiel, “Variable selection for Naïve Bayes classification,” Comput. Oper. Res., vol. 135, p. 105456, 2021, doi: 10.1016/j.cor.2021.105456.

E. Novianto, A. Hermawan, and D. Avianto, “Klasifikasi Algoritma K-Nearest Neighbor, Naive Bayes, Decision Tree Untuk Prediksi Status Kelulusan Mahasiswa S1,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 8, no. 2, pp. 146–154, 2023, doi: 10.36341/rabit.v8i2.3434.

M. M. Arif, H. Setiawan, and A. S. Fitrani, “Penggunaan Datamining Untuk Memprediksi Masa Studi Mahasiswa di Universitas Muhammadiyah Sidoarjo Dengan Algoritma Naive Bayes,” … dan Manajemen), vol. 4, no. 3, pp. 622–629, 2023, [Online]. Available: https://pkm.tunasbangsa.ac.id/index.php/kesatria/article/view/210%0Ahttps://pkm.tunasbangsa.ac.id/index.php/kesatria/article/download/210/209

M. Fahmy Amin, “Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial,” J. Eng. Res., vol. 7, no. 1, pp. 0–0, 2023, doi: 10.21608/erjeng.2023.296718.

Hozairi, Anwari, and S. Alim, “Implementasi Orange Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Dengan Model K-Nearest Neighbor, Decision Tree Serta Naive Bayes,” Netw. Eng. Res. Oper., vol. 6, no. 2, p. 133, 2021, doi: 10.21107/nero.v6i2.237.

D. Nurnaningsih, D. Alamsyah, A. Herdiansah, and A. A. J. Sinlae, “Identifikasi Citra Tanaman Obat Jenis Rimpang dengan Euclidean Distance Berdasarkan Ciri Bentuk dan Tekstur,” Build. Informatics, Technol. Sci., vol. 3, no. 3, pp. 171–178, 2021, doi: 10.47065/bits.v3i3.1019.

R. R. Adhitya, Wina Witanti, and Rezki Yuniarti, “Perbandingan Metode Cart Dan Naïve Bayes Untuk Klasifikasi Customer Churn,” INFOTECH J., vol. 9, no. 2, pp. 307–318, 2023, doi: 10.31949/infotech.v9i2.5641.