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All Journal Jurnal Komputer Terapan Indonesian Journal of Artificial Intelligence and Data Mining Malcom: Indonesian Journal of Machine Learning and Computer Science
Firman Akbar
STMIK Amik Riau
Computer Science & IT

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Implementasi Algoritma Decision Tree C4.5 dan Support Vector Regression untuk Prediksi Penyakit Stroke: Implementation of Decision Tree Algorithm C4.5 and Support Vector Regression for Stroke Disease Prediction Firman Akbar; Hanif Wira Saputra; Adhitya Karel Maulaya; Muhammad Fikri Hidayat; Rahmaddeni Rahmaddeni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 2 No. 2 (2022): MALCOM October 2022
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (321.237 KB) | DOI: 10.57152/malcom.v2i2.426

Abstract

Data mining adalah proses pengumpulan informasi dan data penting dari sejumlah besar data yang perlu diekstraksi untuk mengubahnya menjadi informasi baru yang berguna untuk  pengambilan keputusan. Data yang digunakan dalam penulisan ini berasal dari data pengidap neurologi (saraf) tepatnya stroke, diolah menggunakan algoritma Support Vector Regression dan Decision Tree C4.5. Stroke disebabkan oleh pecahnya pembuluh darah dan tersumbatnya pembuluh darah arteri di otak, sehingga mengakibatkan kematian sel atau jaringan karena tidak mensuplai darah yang dibutuhkan untuk membawa oksigen ke bagian otak. Suatu cara untuk meninjau stroke adalah data mining, yang memakai algoritma Support Vector Regression dan Decision Tree C4.5. Hasil laporan ini mengidentifikasi pengidap penyakit stroke pada variabel yang didapati dan menganalisisnya memakai algoritma data mining Decision Tree C4.5 dan Support Vector Regression. Dapat dilihat jika error yang dihasilkan oleh algoritma Decision Tree C4.5 terhadap rasio 70 : 30 bernilai 0.235, Selanjutnya untuk algoritma Support Vector Regression terhadap rasio 70 : 30 bernilai 0.399, Dalam menggunakan algoritma  Decision Tree C4.5, maka akan menghasilkan output tambahan berupa sebuah grafik pohon keputusan dimana terdapat alur dalam memprediksi.
Komparasi Algoritma Machine Learning Untuk Memprediksi Penyakit Alzheimer Firman Akbar; Rahmaddeni
Jurnal Komputer Terapan  Vol. 8 No. 2 (2022): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.371 KB) | DOI: 10.35143/jkt.v8i2.5713

Abstract

Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. It is characterized by deterioration of memory, language, problem-solving, and other cognitive skills that affect a person's ability to perform everyday activities. This decrease occurs because nerve cells (neurons) in parts of the brain involved in cognitive function are damaged and stop working properly. One way to detect Alzheimer’s is to use models of machine learning algorithms. In this study, the authors' team aimed to compare models of machine learning algorithms to find the one that gives better results in prediction Alzheimer's disease. Machine learning models algorithms in this study were built using Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machines, and Naive Bayes. The author's team then tested his 373 Alzheimer's disease patient data from Kaggle Open Datasets and showed that the Logistic Regression algorithm model can achieve better with 85,71% accuracy rate.
Comparison of Naïve Bayes Algorithm, Support Vector Machine and Decision Tree in Analyzing Public Opinion on COVID-19 Vaccination in Indonesia Rahmaddeni Rahmaddeni; Firman Akbar
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.19966

Abstract

The spread of COVID-19 in Indonesia has caused many negative impacts. Therefore, the government is taking vaccination measures to suppress the spread of COVID-19. Public response to vaccinations on Twitter has been mixed, with some supporting it and some not. The data for this study comes from the Twitter feed of the drone portal Emprit Academy (dea). Classification is performed using SVM, decision tree and Naive Bayes algorithm. The purpose of this study is to inform the public about whether vaccination against COVID-19 is inclined toward positive, neutral, or negative opinions. Moreover, this study compares the accuracy of the three algorithms used, namely Naive Bayes (NB), Support Vector Machine (SVM) and Decision Tree, and the validation performed using the K-Fold Cross-Validation method, AdaBoost feature selection, and the TF-IDF Transformer feature extraction test. The result obtained from this study is that the accuracy of the 90:10 data keeps improving, dividing by 82.86% on the SVM algorithm, 81.43% on the Naive Bayes and 78.57% on the decision tree.
Co-Authors Adhitya Karel Maulaya Hanif Wira Saputra Muhammad Fikri Hidayat Rahmaddeni Rahmaddeni