Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Soft Computing Exploration
Madani, Faiq
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analysis of public opinion sentiment against COVID-19 in Indonesia on twitter using the k-nearest neighbor algorithm and decision tree Pambudi, Ryo; Madani, Faiq
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.88

Abstract

COVID-19 has become an ongoing disease pandemic across the globe. The need for information makes social media such as twitter a place to exchange information. This tweet can be used to see public sentiment towards COVID-19 in Indonesia. Sentiment analysis classifies opinions from tweets that have been processed and classified into different sentiments, namely negative, neutral, or positive. The aim of this paper is to find the algorithm that has the best accuracy. The researcher proposes to compare the K-Nearest Neighbors (KNN) and decision tree algorithms to be used in the classification of sentiment data from tweets related to COVID-19 that took place in Indonesia. The results of the evaluation of performance metrics concluded that the decision tree algorithm has a higher level of accuracy than KNN. Decision tree produces accuracy = 0.765, error = 0.235, recall = 0.76, and precision = 0.767 which is better when compared to KNN which produces accuracy = 0.69, error = 0.31, recall = 0.66, and precision = 0.702.
Heart Disease Prediction Using Optimized Weighted K-Nearest Neighbor (WKNN) Madani, Faiq; Kusworo, Kusworo; Farikhin, Farikhin
Jurnal Penelitian Pendidikan IPA Vol 10 No 11 (2024): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i11.9257

Abstract

Heart disease remains a significant challenge in the medical field, particularly in predictive diagnostics. This research aims to present a comprehensive investigation into the development and evaluation of a novel approach for heart disease detection using a Weighted k-Nearest Neighbors (WKNN) method. The method employs Euclidean distance metrics and Gaussian kernel weighting for optimal classification results. The research dataset consists of 200 data points, each with 10 key indicators such as age, sex, chest pain type, resting blood pressure, cholesterol levels, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, and ST depression relative to rest. Through rigorous experimentation, it is identified that the optimal value of K for classification is 11, with a sigma value of 1.5 for the Gaussian kernel weighting. During the training and evaluation phase, the proposed WKNN method achieved impressive performance metrics, with an accuracy of 91.8%, precision of 93%, and recall of 91%. These findings underscore the potential of the WKNN model as a reliable tool for heart disease detection, showing great promise for practical application in clinical settings. The results emphasize that the proposed method can contribute significantly to improving diagnostic accuracy for heart disease patients
Co-Authors Juwanda, Farikhin Kusworo Kusworo Ryo Pambudi, Ryo