Classification of Air Pollution Levels using Artificial Neural Network

Hamami, Faqih; Fithriyah, Inayatul

doi:10.1109/icitsi50517.2020.9264910

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…In air quality level classification, [6] proposed air quality classification using decision tree algorithm. [7] proposes to classify air quality using Naive Bayes algorithm while [8] proposes air quality using artificial neural network approach.…”

Section: Methodsmentioning

confidence: 99%

Air Quality Classification in Urban Environment using Machine Learning Approach

Hamami

Dahlan

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Air pollution comes from human activities that can threaten living things. It is affected by gasses including PM10, SO2, CO, O3, NO2 and others. Air pollution leads to dangerous diseases even death. Monitoring air quality is important task to understand pollution concentration. Air quality monitoring is better when it can classify whether air quality is habitable or not. This research proposes air quality classification using classification algorithms such as Logistic Regression, KKN, Decision Tree, and Random Forest algorithm. Dataset was taken from Jakarta’s open data for 12 months with several attributes including gas concentration and with several pre-processing steps. Based on experiment, decision tree model has the best accuracy to classify air quality level up to 100% with tuning several hyperparameters.

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Section: Methodsmentioning

confidence: 99%

Air Quality Classification in Urban Environment using Machine Learning Approach

Hamami

Dahlan

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…In this study, of the 20 data that had been trained and tested, only four data were inaccurate because the source data had unbalanced classes, with an accuracy of 80%. Research [15] Classification of air pollution levels with the method used, namely Artificial Neural Network. Data was obtained using IoT sensors.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Classification Using Extreme Gradient Boosting (XGBOOST) Algorithm

Sapari,

Hadiana,

Umbara

2023

iiroi

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Air pollution is a serious issue caused by vehicle exhaust, industrial factories, and piles of garbage. The impact is detrimental to human health and the environment. To quickly and accurately monitor classification, techniques are used. One efficient and accurate classification algorithm is XGBoost, a development of the Gradient Decision Tree (GDBT) with several advantages, such as high scalability and prevention of overfitting. The parameters used in the classification include (PM10), (PM2,5),(SO2),(CO),(O3) and (NO2). This study aims to classify air quality into three labels or categories: good, moderate, and unhealthy. In the dataset used to experience an imbalance class, to overcome the imbalance class, techniques will be carried out, namely SMOTE, Random UnderSampling, and Random OverSampling, by producing an accuracy of up to 98,61% with the SMOTE technique for class imbalance. Testing the level of accuracy is done by using the Confusion Matrix.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersianÂ // Â TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

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“…Klasifikasi adalah pendekatan supervised learning yang beberapa algoritmanya seperti Naïve Bayes, Decision Tree, Neural Network, SVM, Random Forest [1] [2]. Kasus klasifikasi dapat ditemukan diberbagai domain [3]- [5]. Salah satu pemanfaatan dalam klasifikasi adalah penentuan jenis cuaca.…”

Section: Pendahuluanunclassified

Klasifikasi Cuaca Provinsi Dki Jakarta Menggunakan Algoritma Random Forest Dengan Teknik Oversampling

Hamami

Dahlan

2022

JTI

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Nowadays Indonesia experiences extreme weather changes that cause many disasters such as floods, fires, landslides and storms. The type of weather depends on many factors such as temperature, humidity, wind direction and others. Some human activities depend on changes in weather such as in the agricultural sector, plantations, aviation, highlands and beaches. Weather prediction is important to understand extreme weather changes based on weather factors. This research adopts ensemble learning which is able to perform weather classification. The algorithm used is Random Forest combined with oversampling technique to handle the uneven amount of data from each weather class. Some of the weather categories classified are Sunny, Sunny Cloudy, Cloudy, Heavy Cloudy, Local Rain, Light Rain, Moderate Rain and Thunderstorms. The experimental results show that the Random Forest model achieves accuracy of 70%. The oversampling technique used is the Synthetic Minority Over-sampling Technique (SMOTE) method. With the combination of SMOTE the predictions of each minority class can be increased by average of 50%..

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Classification of Air Pollution Levels using Artificial Neural Network

Cited by 6 publications

References 3 publications

Air Quality Classification in Urban Environment using Machine Learning Approach

Air Quality Classification in Urban Environment using Machine Learning Approach

Air Quality Classification Using Extreme Gradient Boosting (XGBOOST) Algorithm

Klasifikasi Cuaca Provinsi Dki Jakarta Menggunakan Algoritma Random Forest Dengan Teknik Oversampling

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