Rain Prediction Using Rule-Based Machine Learning Approach

Anwar, Muchamad Taufiq; Nugrohadi, Saptono; Tantriyati, Vita; Windarni, Vikky Aprelia

doi:10.26877/asset.v2i1.6019

Cited by 11 publications

(4 citation statements)

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“…C4.5 uses a single tree to make a prediction. C4.5 itself is a simple yet versatile classification method and had been used in wildfire modeling [2], rain prediction [3], [4], drug resistance prediction [5], etc. Bagging and boosting methods are a class of classification techniques that increase (boost) its performance by adding bags of models or by iteratively improve the model.…”

Section: -03mentioning

confidence: 99%

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Anwar

Pratiwi

Udhayana

2021

ASSET

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Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested.

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Section: -03mentioning

confidence: 99%

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Anwar

Pratiwi

Udhayana

2021

ASSET

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“…Terdapat tiga algoritma yang berbeda untuk besaran tersebut, yaitu Entropy Reduction, Gini, dan Chi-square. Penelitian terdahulu telah menunjukkan bahwa model decision tree memiliki performa yang lebih baik dibandingkan model data mining lain dalam berbagai keperluan termasuk untuk prakiraan cuaca [7]- [10]. Selain itu, J48 juga telah digunakan untuk memprediksi resistensi bakteri terhadap obatobatan pada penderita Tuberculosis [11].…”

Section: Pendahuluanunclassified

Model Prediksi Dropout Mahasiswa Menggunakan Teknik Data Mining

Anwar

Heriyanto²,

Fanini³

2021

JIU

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Salah satu permasalahan yang ada di Perguruan Tinggi XYZ adalah tingginya jumlah mahasiswa yang putus studi (dropout / DO), sehingga diperlukan upaya untuk minimalisasi jumlah mahasiswa yang dropout. Penelitian ini bertujuan untuk membangun sebuah model yang dapat memprediksi apakah seorang mahasiswa akan lulus ataukah dropout. Data diambil dari data akademis mahasiswa angkatan 2014-2019. Pemrosesan awal data dilakukan dengan Python dan pemodelan dilakukan dengan menggunakan algoritma C4.5 / J48 pada perangkat lunak WEKA (Waikato Environment for Knowledge Analysis). Hasil menunjukkan bahwa atribut yang paling menentukan apakah seorang mahasiswa DO atau lulus adalah Indeks Prestasi Semester 1 dan Indeks Prestasi Semester 2, dengan akurasi model mencapai sebesar 90.6%.

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“…When the parameters of the data set (minimum and maximum temperature, average temperature, average humidity, atmospheric pressure, precipitation amount, sunshine duration, maximum and average wind speed) are enlarged in precipitation forecasting systems, the forecasts' accuracy rises. While the accuracy rate of processes covering short time periods can be up to 72% with a fuzzy inference system model (Safar et al, 2019), the accuracy rate of precipitation forecasts can be up to 86% with machine learning techniques (Anwar et al, 2020). Utilizing extensive weather data six hours in advance, substantial precipitation forecasts can be made, and effective outcomes can be attained using genetic algorithms (Lee et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Precipitation forecast with logistics regression methods for harvest optimization

SAMASTI¹,

Küçükdeniz

2023

International Journal of Agriculture Environment and Food Sciences

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This paper proposes a model that forecasts the weather and then, based on that forecast, uses an income-oriented linear programming method to optimize the harvesting process. Data representing a total yearly output capacity of 472,878 tons from 214 different field locations were used to test the model for sugar beet production. Prior to optimization, long-term one-year weather rainfall forecasting was done using 10 years of actual weather data for the field locations. Weather precipitation was forecasted using logistic regression with an accuracy of 84.16%. The outcome of the weather precipitation prediction model was a parameter in the optimization model. The weather forecast for precipitation led to the 120-day harvest planning being optimized. Comparative analysis was done on the outcomes of the developed model and the current scenario. Comparing the current situation to the proposed one, revenue would have increased by 16.7%. Given that it incorporates weather forecasts into the harvest optimization process, the methodology presented in this paper is more practical than other harvest optimization models.

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Rain Prediction Using Rule-Based Machine Learning Approach

Cited by 11 publications

References 11 publications

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Model Prediksi Dropout Mahasiswa Menggunakan Teknik Data Mining

Precipitation forecast with logistics regression methods for harvest optimization

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