Air Pollution Standard Index (APSI) Detection Application Based on the Flask Model
DOI:
10.47709/brilliance.v3i2.3194Keywords:
APSI, Application, SVM, Flask, QualityDimension Badge Record
Abstract
Air pollution is a global environmental problem that threatens human health and ecosystems. The Air Pollution Standards Index (APSI) is an important metric for measuring air quality and informing the public about the pollution level in an area. In the digital era, web-based applications have become an effective tool for providing real-time APSI information to the public. This research introduces an Air Pollution Standard Index (APSI) detection application based on the Flask model using the SVM (Support Vector Machine) algorithm to predict APSI. This application collects air quality data from various sensors distributed throughout the region and uses SVM (Support Vector Machine) to process the data. APSI prediction results are then presented to users via an easy-to-use web interface. The main advantage of this application is its ability to provide real-time APSI information so that users can take appropriate action according to the level of air pollution in their area. This application can help the public and environmental authorities proactively deal with air pollution and protect human and environmental health. APSI Prediction Accuracy: Through SVM model training, this application can predict the Air Pollution Standard Index (APSI) with sufficient accuracy. While there is potential to improve accuracy through more data collection and model updates, initial results are promising
Abstract viewed = 77 times
References
Darmawan, A. C., & Iswari, L. (2022). Pengembangan Aplikasi Berbasis Web dengan Python Flask untuk Klasifikasi Data Menggunakan Metode Decision Tree C4.5. Jurnal Pendidikan Dan Konseling (JPDK), 4(5), 5351–5362. https://doi.org/10.31004/JPDK.V4I5.7492
Kurniawan, A. (2018). Pengukuran Parameter Kualitas Udara (Co, No2, So2, O3 Dan Pm10) Di Bukit Kototabang Berbasis Ispu. Jurnal Teknosains, 7(1), 1. https://doi.org/10.22146/teknosains.34658
Kurniawan Stasiun Klimatologi Mlati, A., & Meteorologi Klimatologi dan Geofisika, B. (n.d.). Pengukuran parameter kualitas udara (CO, NO2, SO2, O3 dan PM10) di Bukit Kototabang berbasis ISPU. Journal.Ugm.Ac.Id, 7(1), 22–2017. https://doi.org/10.22146/teknosains.34658
Meyer, D., e1071, F. W.-T. I. to libsvm in package, & 2015, undefined. (n.d.). Support vector machines. Vps.Fmvz.Usp.Br.
Muhammad, C., Maulana, R., Hannats, M., & Ichsan, H. (2020). Purwarupa Perahu untuk Monitoring dan Klasifikasi Kualitas Air Bendungan dengan Metode K-Nearest Neighbor (KNN). 4(2), 651–659.
Ningtyas, D. F., & Setiyawati, N. (2021). Implementasi Flask Framework pada Pembangunan Aplikasi Purchasing Approval Request. Jurnal Janitra Informatika Dan Sistem Informasi, 1(1), 19–34. https://doi.org/10.25008/janitra.v1i1.120
Octaviani, P. A., Wilandari, Y., & Ispriyanti, D. (2014). PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG. Ejournal3.Undip.Ac.Id, 3(4), 811–820.
P. A. Octaviani, Y. Wilandari, and D. Ispriyanti, “PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG,” ejournal3.undip.ac.id, vol. 3, no. 4, pp. 811–820, 2014.
Ridho, I. I., & Mahalisa, G. (2023). ANALISIS KLASIFIKASI DATASET INDEKS STANDAR PENCEMARAN UDARA (ISPU) DI MASA PANDEMI MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Technologia?: Jurnal Ilmiah, 14(1), 38. https://doi.org/10.31602/tji.v14i1.8005
Sang, A. I., Sutoyo, E., & Darmawan, I. (2021). Analisis Data Mining Untuk Klasifikasi Data Kualitas Udara DKI Jakarta Menggunakan Algoritma Decision Tree Dan Support Vector Machine. E-Proceeding of Engineering, 8(5), 8954–8963.
Sholeh, muhammad, Aji, W. L., Riady, Y., & Qasthari, B. L. (2022). Pengelolaan Pemesanan Menu Makanan Menggunakan Framework Flask Python. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(2), 916–929. https://doi.org/10.35957/JATISI.V9I2.1459
Situs, H., Jurnal, W., Toha, A., Purwono, P., Gata, W., Toha, A., Purwono, P., & Gata, W. (2022). Model Prediksi Kualitas Udara dengan Support Vector Machines dengan Optimasi Hyperparameter GridSearch CV. Buletin Ilmiah Sarjana Teknik Elektro, 4(1), 12–21. https://doi.org/10.12928/biste.v4i1.6079
Syihabuddin Azmil Umri, S. (2021). Analisis Dan Komparasi Algoritma Klasifikasi Dalam Indeks Pencemaran Udara Di Dki Jakarta. JIKO (Jurnal Informatika Dan Komputer), 4(2), 98–104. https://doi.org/10.33387/jiko.v4i2.2871
T. S. Sabrila, V. R. Sari, and A. E. Minarno, “Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor,” Fountain Informatics J., vol. 6, no. 2, p. 69, Jul. 2021, doi: 10.21111/fij.v6i2.5536.
Ulaan, G. C., Poekoel, V. C., & Ontowirjo, A. H. J. (2022). Pembuatan Aplikasi Sistem Monitoring Kualitas Udara Dalam Ruangan. Jurnal Teknik Informatika, 17(1), 93–104. https://doi.org/10.35793/JTI.17.1.2022.34695
Widi Hastomo, Nur Aini, Adhitio Satyo Bayangkari Karno, & L.M. Rasdi Rere. (2022). Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 11(2), 131–139. https://doi.org/10.22146/jnteti.v11i2.2586
Y. Nurdiansyah, “Informal?: informatics journal.,” vol. 2, no. 2, pp. 114–122, Jul. 2017.
Z. V. K. Chauhan, K. Dahiya, • Anuj Sharma, and K. A. In, “Problem formulations and solvers in linear SVM: a review,” Springer, vol. 52, no. 2, pp. 803–855, Aug. 2019, doi: 10.1007/s10462-018-9614-6.
Downloads
ARTICLE Published HISTORY
Accepted Date: 2023-11-16
Published Date: 2023-11-24
How to Cite
Issue
Section
License
Copyright (c) 2023 Galih Mahalisa, Nurarminarahmah
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
info@itscience.org, web: 
