Rainfall Forecasting using the Classification and Regression Tree (CART) Algorithm and Adaptive Synthetic Sampling (Study Case: Bandung Regency)

Lathifah, Siti Nur; Nhita, Fhira; Aditsania, Annisa; Saepudin, Deni

doi:10.1109/icoict.2019.8835308

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…NBC merupakan algoritma klasifikasi probabilistik sederhana berdasarkan teorema Bayes [24]- [26]. Berikutnya, CART adalah algoritma berbasis pohon keputusan [27] yang akan menghasilkan pohon klasifikasi jika target bertipe kategori dan menghasilkan pohon regresi jika target bertipe numerik atau kontinu [28], [29]. Kemudian, RF adalah algoritma klasifikasi dengan pendekatan ensamble learning berdasarkan Decission Tree [26], [30]- [32] yang menciptakan sejumlah pohon keputusan saat melakukan proses klasifikasi [26], [30], [33].…”

Section: Pendahuluanunclassified

“…Algoritma CART dapat menangani fitur bertipe kategori dan kontinu [27], [68] serta kasus klasifikasi dan regresi [68], [69]. CART akan menghasilkan pohon klasifikasi jika target bertipe kategori dan akan menghasilkan pohon regresi jika target bertipe numerik atau kontinu [28], [29]. Algoritma CART menggunakan gini index untuk membangun pohon keputusan [29], [69], [70].…”

Section: Classification and Regression Treeunclassified

“…CART akan menghasilkan pohon klasifikasi jika target bertipe kategori dan akan menghasilkan pohon regresi jika target bertipe numerik atau kontinu [28], [29]. Algoritma CART menggunakan gini index untuk membangun pohon keputusan [29], [69], [70]. Setiap node mewakili fitur, cabang mewakili nilai dari fitur, dan daun mewakili target [71].…”

Section: Classification and Regression Treeunclassified

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Seleksi Fitur pada Supervised Learning: Klasifikasi Prestasi Belajar Mahasiswa Saat dan Pasca Pandemi COVID-19

Rahmadeyan

Mustakim

2023

TEKNOSI

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Dampak pandemi COVID-19 membuat lembaga pendidikan mengubah metode belajar menjadi pembelajaran jarak jauh secara daring. Banyak perguruan tinggi menyatakan keprihatinannya pada prestasi akademik mahasiswanya selama selama periode tersebut, namun disisi lain terdapat mahasiswa yang merasa puas dan nyaman. Di masa pasca pandemi terjadi transisi bertahap untuk kembali ke pembelajaran tatap muka. Ini dilakukan karena pembelajaran tatap muka dianggap lebih efektif dibandingkan pembelajaran daring. Untuk meningkatkan dan memantau kemajuan prestasi akademik mahasiswa demi menghasilkan lulusan yang berkualitas, maka diperlukan analisis terkait perilaku dan prestasi belajar mahaiswa, salah satunya dengan menggunakan teknik data mining. Penelitian ini bertujuan untuk menemukan pola dan faktor yang mempengaruhi prestasi akademik mahasiswa saat dan pasca pandemi COVID-19. Chi-Square dan Mutual Information diterapkan sebagai seleksi fitur untuk menentukan fitur paling berpengaruh pada data. Data dengan fitur optimal akan dilakukan klasifikasi dengan algoritma NBC, CART, RF, dan SVM. Berdasarkan hasil dan analisis yang dilakukan, dapat disimpulkan seleksi fitur efektif dalam meningkatkan kemampuan model dan mempercepat waktu komputasi. Pemodelan dengan 4 algoritma dan 2 metode seleksi fitur menghasilkan CART dengan Chi-Square pada 2 fitur sebagai model terbaik dengan akurasi 89,00%, precision 87,72%, recall 93,57% dan waktu komputasi 0,01814s. Dibandingkan tanpa seleksi fitur, performa CART dengan Chi-Square mengalami peningkatan akurasi sebesar 3% dan waktu komputasi 0,00629s. Chi-Square menjadi seleksi fitur yang efektif pada penelitian ini, yang mana Chi-Square unggul pada rara-rata recall dan waktu komputasi dibandingkan Mutual Information.

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Section: Pendahuluanunclassified

Section: Classification and Regression Treeunclassified

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Seleksi Fitur pada Supervised Learning: Klasifikasi Prestasi Belajar Mahasiswa Saat dan Pasca Pandemi COVID-19

Rahmadeyan

Mustakim

2023

TEKNOSI

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“…With the development of machine learning, it is gradually being applied to precipitation observation [23][24][25][26][27]. To the best our knowledge, the earliest application of artificial neural network (ANN) to rainfall estimation can be traced back to 1992 when French et al used current data to forecast rainfall an hour later by back propagation (BP) neural network [23].…”

mentioning

confidence: 99%

“…Similar rainfall measurement method was tested in many regions by Ahuna et al and showed reliable performances [26]. In addition, Michaelides et al used the ANN to fill up missing rainfall data [24], and Lathifah et al identified different precipitation categories based on classification and regression tree (CART) [27]. In our work, we also try to propose a new rainfall inversion approach based on the machine learning because it can accurately establish the mapping relation between rainfall and satellite signal without any assumptions.…”

mentioning

confidence: 99%

Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Xian

Liu

Yin

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, the oblique earth-space links (OELs) between satellite and earth station have been used for rainfall monitoring as a supplement to existing observation methods. Most present studies achieved the rainfall measurement by OELs based on the empirical method such as power-law (PL) model. In practice, two crucial issues need to be addressed: (1) identification of rain and no-rain periods; and (2) determination of attenuation baseline. To solve these problems, this paper adopts several machine learning algorithms based on the analysis of earth-space link signal characteristics. For the first issue, we choose the support vector machine (SVM) as a classifier and the adaptive synthetic sampling algorithm (ADSYN) is deployed to eliminate the effects caused by data imbalance. For the second issue, the long short-term (LSTM) neural network is selected for the determination of attenuation baseline since it has a good ability to solve time series problem. In terms of the rainfall inversion, we establish a new model by combining the back-propagation (BP) network and genetic algorithm (GA). The PL model is also used as a comparison. To validate the proposed method, we set up an earth-space link that receives the signal from AsiaSat5 in 12.32GHz. The results demonstrate that the two issues are successfully addressed and the inversion of precipitation is also carried out. Compared to disdrometer, the correlation and mean absolute error of GA-BP model are 0.83 and 1.30 mm/h, respectively, indicating a great potential to use densely OELs for global precipitation monitoring. Index Terms-rainfall monitoring, remote sensing, machine learning, earth-space link, Ku-band I. INTRODUCTION 1 CCURATE and real-time rainfall measurement plays an important role in many aspects of human life such as agricultural issues, water resource management and natural disaster warning. Existing rainfall detection method mainly comprises rain gauge, weather radar and weather satellite [1]. Based on the exploitation of existing radio spectrum sources, the opportunistic use of microwave links has become a new approach to detecting precipitation. Messer et al. firstly suggested the application of commercial wireless communication networks (CWCNs) to environmental monitoring [2]. In recent years, the use of horizontal microwave links (HMLs) has been developed rapidly in many fields such as path-average rain intensity inversion [3, 4], radar calibration [5] and regional rainfall monitoring [6].

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Real Time Rainfall Prediction for Hyderabad Region using Machine learning Approach

Krithik¹,

Raghavan²,

K³

et al. 2021

2021 Innovations in Power and Advanced Computing Technologies (I-Pact)

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Rainfall Forecasting using the Classification and Regression Tree (CART) Algorithm and Adaptive Synthetic Sampling (Study Case: Bandung Regency)

Cited by 10 publications

References 12 publications

Seleksi Fitur pada Supervised Learning: Klasifikasi Prestasi Belajar Mahasiswa Saat dan Pasca Pandemi COVID-19

Seleksi Fitur pada Supervised Learning: Klasifikasi Prestasi Belajar Mahasiswa Saat dan Pasca Pandemi COVID-19

Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Real Time Rainfall Prediction for Hyderabad Region using Machine learning Approach

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