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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Scientific Journal of Informatics
Amril Mutoi Siregar
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Comparison Model Optimal Machine Learning Model With Feature Extraction for Heart Attack Disease Classification Salsa Desmalia; Amril Mutoi Siregar; Kiki Ahmad Baihaqi; Tatang Rohana
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4561

Abstract

Purpose: The purpose of this study is to classify the number of people affected by heart disease and those not affected by heart disease based on various categories of heart attack causes. This study aims to urge people to take better care of their health and to serve as a reference for doctors to educate patients about the dangers of heart attacks. Methods: The model will be constructed via a machine learning methodology. The algorithms utilized in its development encompass the Support Vector Machine (SVM) algorithm, the K-Nearest Neighbor (k-NN) algorithm, and the Random Forest (RF) algorithm.  This study utilizes principal component analysis (PCA) as a means of extracting optimized features from the dataset, employing techniques for dimension reduction prior to modeling the data. Result: Cumulative explication of the concept of variance constitutes a foundational aspect of PCA (principal component analysis) within the scope of the current research, namely a dimensionality reduction technique employed in multivariate data analysis to facilitate model development, thereby enabling the creation of more optimal and comprehensive models. In this research, the dimensions of training data are incorporated during the process of model creation.   The results show KNN model exhibits the highest performance, with an accuracy of 86%, precision of 86%, recall of 91%, and F1-score of 88%. Furthermore, evaluation using the ROC metric also provides a relatively favorable value, 0.85. Novelty: Researchers used 1190 patient data sourced from Kaggle. Before modeling the algorithm, researchers conducted EDA & Preprocessing which includes missing values to find data that does not have information, then duplicate data to find duplicated data, there are 270 duplicated data, then the duplicated data is deleted so that the data becomes 737, then PCA implementation is carried out.  PCA is reducing features automatically without changing the data.
Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods Basuni, Nursela; Amril Mutoi Siregar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5401

Abstract

Drug abuse are on the rise, with many users enter the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, and they can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consist of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Networks (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1 score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% precision, 87% precision, 82% recall, 84% f1 score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and nonusers.
Co-Authors Baihaqi, Kiki Ahmad Basuni, Nursela Salsa Desmalia Tatang Rohana