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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA KLIK: Kajian Ilmiah Informatika dan Komputer
Muhammad Kunta Biddinika
Universitas Ahmad Dahlan, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting Syahrani Lonang; Anton Yudhana; Muhammad Kunta Biddinika
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6553

Abstract

Stunting is a serious problem caused by chronic malnutrition in children under five, causing stunted growth and having a negative impact on long-term health and productivity. Therefore, early detection of stunting is very important to reduce its negative impacts. Previous studies utilizing machine learning have proven the success of this method in various health applications, such as disease detection and the prediction of medical conditions. The results of this research are a comparative evaluation of five classifications, namely Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in classifying stunted toddlers. The dataset used contains four important attributes: age, gender, weight, and height of toddlers, as well as a binary class label that differentiates between toddlers who are stunted and those who are not. The evaluation results show that KNN at K = 3 produces the highest accuracy of 94.85%, making it the best model for classifying stunting in toddlers. Apart from accuracy, other metrics such as precision, recall, and F1-score are used to analyze the algorithm's ability to solve this problem. KNN stands out as the best model, with the highest F1-score of 89.47%. KNN also manages to maintain a balance between precision and recall, making it an excellent choice for treating stunting in toddlers. Apart from that, the use of the AUC metric from the ROC curve also shows the superiority of KNN in differentiating between stunted and non-stunting toddlers. With a combination of consistent evaluation results, both in terms of accuracy and other evaluation metrics, this research proves that KNN is the best choice for overcoming the task of classifying stunting in toddlers.
Preparing Dual Data Normalization for KNN Classfication in Prediction of Heart Failure Alya Masitha; Muhammad Kunta Biddinika; Herman
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1382

Abstract

Heart failure disease is a serious condition that is significant in affecting both a person's quality of life and health. Therefore, it is important to develop classification methods that can help detect this disease. In this research, a data preprocessing stage is performed before being used to classify heart failure diseases using machine learning models, such as K-NN. Data preprocessing is an effort to simplify data analysis and ensure accurate results, and it is a very essential step in analyzing data to improve the quality of the data used. The dataset used in this research is raw data that has not gone through the preprocessing stage. The dataset consists of 918 data with target attributes of 0 and 1, where a value of 0 indicates a normal condition and a value of 1 indicates a potential heart failure condition. Data preprocessing includes data cleaning, data transformation, and data normalization. The main objective of this research is to carry out the preprocessing stage on data derived from heart failure disease datasets. Based on the comparison between two normalization methods, namely Min-Max and Simple Feature Scale, it is found that the Simple Feature Scale normalization method has the best performance, with an accuracy rate of 85%, while the Min-Max normalization method only reaches 84%.
Co-Authors Alya Masitha Anton Yudhana Herman Syahrani Lonang