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All Journal CESS (Journal of Computer Engineering, System and Science) Malcom: Indonesian Journal of Machine Learning and Computer Science
Hanif Wira Saputra
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.
Comparison of Machine Learning Algorithms in Analyzing Public Opinion Sentiments Against Fuel Price Increases Hanif Wira Saputra; Rahmaddeni Rahmaddeni; Fazri Fazri
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 1 (2023): January 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i1.41911

Abstract

Twitter is a social media platform that is quite widely used by the world community, especially people in Indonesia. Twitter is one of the social media that provides information, one of which is the increase in the price of crude oil which was recorded at 105 US dollars per barrel. The increase in fuel prices has a negative impact on society, causing pros and cons. Based on these problems, the authors aim to compare the performance of the artificial neural network and naïve Bayes algorithms to determine the best model for sentiment analysis of fuel price hikes. The data used amounted to 1000 datasets in the form of text documents with labeling using the lexicon and split data 90:10, 80:20, 70:30 and 60:40 as a comparison of precision values. The application of word vectorization utilizes TF-IDF in assigning a weight value to each word. Based on the results of the experiments that have been carried out, it is found that the best algorithm using an artificial neural network is capable of producing an accuracy value of 87% for 1000 data on public opinion sentiment on fuel price hikes. Based on the evaluation results, the model built can categorize public opinion sentiment into positive sentiment, negative sentiment, and neutral sentiment automatically and the polarity of public sentiment tends to be positive towards the issue of the fuel price increase that occurred.  
Co-Authors Adhitya Karel Maulaya Fazri Fazri Firman Akbar Muhammad Fikri Hidayat Rahmaddeni Rahmaddeni