Comparison of Bagging and Adaboost Methods on C4.5 Algorithm for Stroke Prediction

Saputri, Nur Diana; Khalid,; Rolliawati, Dwi

doi:10.32520/stmsi.v11i3.1684

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Sementara itu, metode AdaBoost banyak diimplementasikan dalam kasus optimasi dengan melibatkan bantuan algoritma seperti pada kasus klasifikasi penyakit diabetes dengan bantuan algoritma Naive Bayes [10], kasus penyakit stroke dengan bantuan algoritma C4.5 [11], juga pada kasus klasifikasi penyakit diabetes [12].…”

Section: Pendahuluanunclassified

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

2023

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The contribution of participants, employers, and/or the government is one of the most important things in the National Health Insurance Program-Healthy Indonesia Card (JKN-KIS) implementation. All Indonesian residents were required to participate in the JKN-KIS program which is divided into four types of participation, one of which is Non-Wage Recipient Participants (PBPU) whose contributions are paid independently. However, based on December 2021 data, 60% of PBPU participants were late in paying monthly until they were in arrears. Arrears in payment of contributions cause several problems, including payment of claims to deficits. This research utilized big data owned by the Healthcare and Social Security Agency (BPJS Kesehatan) and machine learning based on ensemble trees, namely AdaBoost and random forest to get the predictions of participants in arrears. The results showed that machine learning based on an ensemble tree was able to predict PBPU participants in arrears with high accuracy, as evidenced by the AUC values in both models above 80%. The random forest model has an F1-score and the AUC value is better than the AdaBoost, namely the F1-score of 85,43% and the AUC value of 87,20% in predicting JKN-KIS participants who are in arrears in payment of contributions.

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Section: Pendahuluanunclassified

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

2023

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“…Untuk data yang balanced dengan kernel linear penulis mendapatkan hasil akurasi tertingg 77%, sedangkan pada kernel polynomial penulis mendapatkan hasil tertinggi 76%. (Akbar et al, 2022) (Saputri et al, 2022)Berdasarkan beberapa penelitian terdahulu, Pada penelitian ini peneliti akan melakukan penelitian untuk menganalisis dan membandingkan Decision Tree C4.5 dan Support Vector Machine (SVM) Berbasis PSO untuk melihat perbandingan tingkat akurasi, presisi, dalam klasifikasi penyakit stroke. Selain itu, hasil pengujian juga harus dianalisis untuk melihat seberapa efektif kedua algoritma tersebut(Ibrahim Irawan, Gani Hilmansyah, 2022)…”

Section: Pendahuluanunclassified

Perbandingan Decision Tree C4.5 dan Support Vector Machine (SVM) Dalam Klasifikasi Penderita Stroke Berbasis PSO

Buana,

Nugroho

2023

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Stroke is a major health problem for today's modern society. At present, stroke is increasingly becoming a serious problem that is faced almost all over the world. This is because a sudden stroke can result in death, physical and mental disability in both productive and old age. To obtain stroke information data, it is necessary to carry out data mining processes such as classification. Classification is a process for determining a model that explains or distinguishes concepts or data classes, with the aim of being able to estimate the class of an object whose class is unknown, in classification a number of records are also given which are called training sets, which consist of several attributes, attributes can be be continuous or categorical, one of the attributes specifies the class for the record In the above problems regarding stroke, to be able to overcome this problem, a lot of research has been carried out in the field of computer science, including the Classification of Stroke Patients Using the C4.5 Decision Tree Algorithm and the Support Vector Machine (SVM) Algorithm to classify the most important factors for this disease. The test resulted in a fairly high accuracy of the C4.5 Decision Tree Algorithm, which was 96.11%. Referring to the results of the accuracy of the research, it can be seen that the Decision Tree produces high accuracy, but the results of this accuracy can still be improved by conducting further research to produce higher accuracy by adding Optimization Feature Weighting PSO.Keywords: Accuracy, Stroke, PSO, C4.5 Decision Tree Algorithm. Support Vector Machine (SVM) Algorithm

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“…Metode Bagging adalah salah satu metode yang digunakan untuk meningkatkan akurasi prediksi model dengan menggabungkan hasil dari beberapa model pembelajaran yang sama, biasanya dengan menggunakan teknik bootstrap resampling (Saputri et al, 2022). Dengan menerapkan optimasi metode bagging dalam model algoritma K-Nearest Neighbor, Support Vector Machine, dan Naive Bayes sehingga dapat meningkatkan akurasi yang dihasilkan dari model-model tersebut.…”

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Optimasi Model Algoritma Klasifikasi menggunakan Metode Bagging pada Stunting Balita

Masacgi,

Rohman

2023

Edumatic

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Stunting is a serious problem for the health of toddlers, who experience malnutrition during their development. Poor nutritional intake or a lack of knowledge about nutrition can be a factor in stunting. This study aims to optimize the stunting classification algorithm in toddlers using the K-Nearest Neighbor algorithm, Support Vector Machine, and Naïve Bayes by using bagging optimization. This study uses toddler data totaling 10,000 records, 7 attributes, and 2 classes taken from Krobokan Health Center, Semarang City. Based on the findings of this study, the Bagging Method shows excellent performance with 89.77% accuracy, 95.57% precision, 85.45% recall, and a 90.27% F1-score. This shows great potential for improving the model's ability to classify data with a high degree of accuracy, completeness, and balance. The SVM model has a high recall of 80.59%, while the Naïve Bayes model gets an F1-score of 71.84%, indicating that Naïve Bayes has a good balance between precision and recall. Overall, the Bagging Model is the best choice, with far superior performance compared to other models. Based on the results obtained, the Bagging Method can predict stunting data accurately, showing a very good level of truth in stunting prediction efforts.

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Comparison of Bagging and Adaboost Methods on C4.5 Algorithm for Stroke Prediction

Cited by 4 publications

References 9 publications

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

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

Perbandingan Decision Tree C4.5 dan Support Vector Machine (SVM) Dalam Klasifikasi Penderita Stroke Berbasis PSO

Optimasi Model Algoritma Klasifikasi menggunakan Metode Bagging pada Stunting Balita

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