Metode AdaBoost dan Random Forest untuk Prediksi Peserta JKN-KIS yang Menunggak

Rahmi, Ikhlasul Amalia; Afendi, Farit Mochamad; Kurnia, Anang

doi:10.34312/jjom.v5i1.15869

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Predictions for each model are combined, usually through voting, to determine a class label. New data predictions are based on majority weights [19].…”

Section: Algorithm Adaboostmentioning

confidence: 99%

Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Elmeftahi,

Rakhman,

Rahmatulloh

2024

iiroi

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Water, a vital natural resource essential for human existence, is a fundamental human right, indispensable for a dignified life. Despite its significance, the quality of water is often compromised by a myriad of harmful substances, minerals, and contaminants stemming from various sectors like industry, agriculture, residential, and energy. Traditional methods such as WQI and STORET, relying on manual inspection, prove time-consuming. Thus, the integration of machine learning emerges as a pivotal solution to swiftly assess water quality.Numerous studies have explored this challenge using various algorithms; however, a definitive comparison is elusive due to the abundance of existing methods. In response, this research undertakes a meticulous evaluation of seven algorithms to ascertain the optimal approach for water quality classification, employing metric values as benchmarks. Notably, the Random Forest algorithm emerges as the most effective, achieving an impressive accuracy of approximately 84.8%. Following closely are the XGBoost and CatBoost algorithms, showcasing commendable performance with accuracies of 82.9% and 80.2%, respectively. Subsequent rankings include the Decision Tree algorithm at 77.3%, SVM at 72.3%, K-NN at 70.6%, and AdaBoost with the lowest accuracy at 63.33%. This comparative analysis contributes valuable insights for informed decision-making in water quality assessment.

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“…Predictions for each model are combined, usually through voting, to determine a class label. New data predictions are based on majority weights [19].…”

Section: Algorithm Adaboostmentioning

confidence: 99%

Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Elmeftahi,

Rakhman,

Rahmatulloh

2024

iiroi

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“…Pada tahap pemodelan, model dibangun dan diperoleh hasil prediksi, sedangkan tahap evaluasi model digunakan untuk mengukur kinerja model. Berikut langkah-langkah analisis data: matrix adalah tabulasi silang antara informasi kelas negative dan positive yang terdapat pada kelas prediksi dan kelas sebenarnya [12]. Kelas positif dalam penelitian ini adalah mahasiswa dengan status kelulusan tepat waktu.…”

Section: Metodeunclassified

Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa

Crismayella,

Satyahadewi,

Perdana

2023

Jambura J. Math

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Colleges provide higher education as the benchmark of education quality and evaluate higher education syllabi. Graduation rates and enrollment capacity are essential for graduation assessment and decision-making. Unfortunately, some students majoring in statistics failed to finish their studies on time, impacting the accreditation of the study program. It is necessary to examine the characteristics of students who managed and failed to complete their studies on time using the data mining classification method, namely Algorithm C5.0. In this study, Adaboost algorithm and Algorithm C5.0 was employed to classify graduation rates accurately. Graduation data of the Statistics Study Program of Universitas Tanjungpura Batch 1 of 20217/2018 to Batch II of 2022/2023 School years were regarded in this study. First, the entropy, gain, and gain ratio values were measured. After that, each data was given equal weight, and iteration was performed 100 times. The analysis using Algorithm C5.0 showed School Accreditation as the variable with the highest gain ratio, indicating that School Accreditation has the most decisive influence on graduation rates with an accuracy percentage of 70%. This percentage then increased to 82.14% after the boosting using the Adaboost algorithm. Adaboost Algorithm is regarded as good in improving the accuracy of algorithm C5.0. The results of this study can provide insight for colleges in designing policies to increase on-time graduation based on the factors that influence student graduation.

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Metode AdaBoost dan Random Forest untuk Prediksi Peserta JKN-KIS yang Menunggak

Cited by 2 publications

References 10 publications

Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water

Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa

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