Prediksi Kenaikan Awan Di Wisata Lolai Berbasis Machine Learning
Abstract
The increase in cloud cover is an important indicator in predicting upcoming weather. However, manual observations of cloud cover are still limited and time-consuming. Therefore, this research aims to develop a cloud cover classification model based on measurement data in Lolai using the Naive Bayes machine learning method. In this study, data on cloud cover, temperature, and humidity measurements were collected directly in Lolai for 30 days and using online BMKG data. Then, the data was processed and divided into training and testing datasets. The Naive Bayes model was applied to the training data and its accuracy was tested on the testing data. The research results show that the cloud cover classification model based on Naive Bayes has varying accuracy levels depending on the data source. For direct measurement data, the model achieved an accuracy rate of 63%, while for online BMKG data, the model achieved an accuracy rate of 80%. In testing on the testing data, the model successfully classified cloud cover based on temperature and humidity data. This research contributes to identifying the relationship between temperature, humidity, and cloud conditions and evaluates the performance of the Naive Bayes model in determining the influence of air temperature and humidity on cloud conditions. It is expected that this research can serve as a basis for the development of weather prediction systems in the future.
References
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. E., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Anjasmara, R., Suhendra, T., & Yunianto, A. H. (2019). Implementasi Sistem Monitoring Kecepatan Angin, Suhu, dan Kelembaban Berbasis Web di Daerah Kepulauan. Journal of Applied Electrical Engineering, 3(2), 29–35. https://doi.org/10.30871/jaee.v3i2.1485
BPS. (2023). Tana Toraja Dalam Angka Tahun 2023. https://tatorkab.bps.go.id/publikasi.html
Guanabara, E., Ltda, K., Guanabara, E., & Ltda, K. (2023a). Kabupaten Toraja Utara Dalam Angka. Badan Pusat Statistik Kabupaten Toraja Utar. https://torutkab.bps.go.id/publication.html
Guanabara, E., Ltda, K., Guanabara, E., & Ltda, K. (2023b). Propinsi Sulawesi Selatan Dalam Angka. Badan Pusat Statistik Propinsi Sulawesi Selatan.
Hariani, S. (2020). Sistem Deteksi Cuaca Berdasarkan Analisis Histogram HCL Menggunakan Algoritma k-Nearest Neighbor (KNN). Jurnal EECCIS, 14(1), 27–30. https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/626
Jiang, S. (2018). Machine Learning Research in Big Data Environment. Iceeecs, 227–231. https://doi.org/10.25236/iceeecs.2018.048
Pasinggi, E. S., Yafet, W., Studi, P., Informatika, T., Agroteknologi, P. S., Kristen, U., & Toraja, I. (1978). Sistem penyiraman tanaman otomatis berbasis fuzzy logic 1. 1085–1091.
Pineng, M., & Tandirerung, W. Y. (2022). The Use of Simple Neural Algorithm in Classifying Single Toraja Coffee Beans. Journal of Computer Science and Technology Studies, 4(2), 172–181. https://doi.org/10.32996/jcsts.2022.4.2.21
Schultz, M., Reitmann, S., & Alam, S. (2021). Predictive classification and understanding of weather impact on airport performance through machine learning. Transportation Research Part C: Emerging Technologies, 131(August 2020), 103119. https://doi.org/10.1016/j.trc.2021.103119
Siregar, A. M. (2020). Klasifikasi Untuk Prediksi Cuaca Menggunakan Esemble Learning. Petir, 13(2), 138–147. https://doi.org/10.33322/petir.v13i2.998
Supriyadi, E. (2021). Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory (Lstm). Jurnal Meteorologi Dan Geofisika, 21(2), 55. https://doi.org/10.31172/jmg.v21i2.619
Suryaman, S. A., Magdalena, R., & Sa’idah, S. (2021). Klasifikasi Cuaca Menggunakan Metode VGG-16, Principal Component Analysis Dan K-Nearest Neighbor. Jurnal Ilmu Komputer Dan Informatika, 1(1), 1–8. https://doi.org/10.54082/jiki.1
Toraja, S. T., Oktavianus, M., Marlina, E., Aminah, S. T., Ghani, D., Salman, N., & Donny, R. (2023). Sistem Informasi Pariwisata berbasis web untuk Memperkenalkan Keunikan Tradisi. XII(1), 526–532.
Wahyu Saputro, R., Aminuddin, A., & Munarko, Y. (2020). Perbandingan Kinerja Komputasi Hadoop dan Spark untuk Memprediksi Cuaca (Studi Kasus : Storm Event Database). Jurnal Repositor, 2(4), 463–474. https://doi.org/10.22219/repositor.v2i4.93
Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K. C., & Hanzo, L. (2020). Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks. IEEE Communications Surveys and Tutorials, 22(3), 1472–1514. https://doi.org/10.1109/COMST.2020.2965856
