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All Journal Journal of Information Technology and Computer Science Indonesian Journal of Electrical Engineering and Computer Science
Nurul Hidayat
Brawijaya University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Prediction of hypertention drug therapy response using K-NN imputation and SVM algorithm Lailil Muflikhah; Nurul Hidayat; Dimas Joko Hariyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp460-467

Abstract

Hypertention is a degenerative disease but its healing takes a long time by consuming hypertension drugs until patient’s lifetime. The research is conducted to predict response of drug therapy using bioinformatics approach which is a blend of biological and informatics engineering methods. It is used medical record data of hypertensive patient in drug therapy which has an impact on genetic characteristics. The data is constructed as modelling for learning process. Then, it is implemented as a prediction whether the blood presure is under control or not. However, the amount data have no values, then they are required to be applied preprocessing data. Therefore, this research is proposed K-Nearest Neighbor (K-NN) Imputation algorithm for refining data. After that, it is implemented using Support Vector Machine (SVM) algorithm for prediction.The experiment result is achieved the highest accuracy rate of 90% at the best parameter value λ = 0.9, Σ = 2, C = 0.1, ε = 0.001 in ten times iterations.
Cancer Classification Based on the Features of Itemset Sequence Pattern of TP53 Protein Code Using Deep Miden - KNN Marji Marji; Imam Cholissodin; Dian Eka Ratnawati; Edy Santoso; Nurul Hidayat
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271401

Abstract

Cancer is a disease that is still difficult to identify up to today. One of the causes of cancer is genetic modification that because of mutations in p53 gene. Healthy cells have a p53 wild type protein (normal) that is able to manage DNA separation. If DNA mutates, it will be difficult to detect cancer because the composition of the protein has changed. Bioinformatics is a combination of biology and information engineering (TI) that is utilized to manage data. One of the applications of data mining in bioinformatics is the development of pharmaceutical and medical industries. Data mining classification can use variety of methods including K-Nearest Neighbor (KNN), C45, ID3, and several other methods. One of the most reliable data classification methods is KNN. In this study, the development used two algorithms. The first was with the modification of the k-fold method, which divided two data into training data and test data, in which test-1 data and test-2 data were made into slices. The second was by a method for selecting an itemset sequence pattern that had the largest Gain Information, either 2 itemsets, 3 itemsets, and so on (Deep Miden). The best accuracy result of 96.00% was obtained through the process of computation testing in the server based on variations in terms of the number of patterns of Deep Miden itemset sequences and several k values on KNN classification method.
Co-Authors Dian Eka Ratnawati Dimas Joko Hariyanto Edy Santoso Imam Cholissodin Lailil Muflikhah Marji Marji