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Journal : Journal of Advanced Computer Knowledge and Algorithms

Classification of Heart Disease Using Modified K-Nearest Neighbor (MKNN) Method Lubis, Aulia Azzahra Ma'aruf; Dinata, Rozzi Kesuma; Aidilof, Hafizh Al Kautsar
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 2 (2024): Journal of Advanced Computer Knowledge and Algorithms - April 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i2.15702

Abstract

Penyakit jantung memiliki banyak jenis dan gejala yang dialami. Penyakit jantung adalah sebuah kondisi ketika organ jantung tidak dapat bekerja sebagaimana fungsinya dengan baik. Jantung adalah organ penting dalam tubuh manusia yang dimana fungsinya adalah memompa darah ke seluruh tubuh. Karena itu dibutuhkannya diagnosa awal untuk pencegahan penyakit jantung dengan memanfaatkan system yang dapat dibuat untuk diagnosa awal pada gejala yang dialami. Yang pada penelitian ini akan menggunakan metode Modified K-Nearest Neighbor (MKNN) dalam mengklasifikasikan penyakit jantung berdasarkan kriteria atau gejala yang ada. Penelitian ini menggunakan 6 kriteria penyakit dan 3 kelas diagnosa penyakit jantung. Dengan melewati beberapa langkah pengerjaan yaitu menghitung jarak Euclidean, menghitung nilai validitas dan terakhir menghitung weight voting dengan mengandalkan nilai K yang telah ditentukan sejak awal perhitungan. Pada penelitian ini telah ditentukan nilai K=5 dan didapat hasil pengujian akurasi sebesar 85%, dengan recall 90% dan precision 85%.
Implementation of Data Mining for Vertigo Disease Classification Using the Support Vector Machine (SVM) Method Jasmin, Nadya; Dinata, Rozzi Kesuma; Sahputra, Ilham
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17807

Abstract

This research aims to implement advanced data mining techniques for the classification of vertigo disorders using the Support Vector Machine (SVM) method. Vertigo, characterized by a spinning sensation, can be triggered by various factors such as nervous system disorders and inner ear infections. With the rising prevalence of vertigo patients, there is a pressing need for more effective and efficient diagnostic tools. This study was conducted at Puskesmas Jangka in Bireuen Regency, involving the collection of vertigo patient data from the years 2023-2024. The collected data underwent a comprehensive preprocessing pipeline, including data cleaning, partitioning into training and testing datasets, and subsequent implementation of the SVM algorithm. The performance of the model was evaluated using the Mean Absolute Percentage Error (MAPE), resulting in a MAPE value of 28.47%.
K Fold Cross Validation Analysis for Electricity Meter Classification at PLN Lhoksukon Using K-NN and SVM Methods Zuboili, Zuboili; Dinata, Rozzi Kesuma; Syahputra, Irwanda
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.21340

Abstract

Electricity consumption continues to increase every year in line with the increase in national economic growth. Predicting current electricity demand is important to understand the overall electricity supplied to each region. The problem is that currently, the electricity supply in various areas of Lhoksukon has not matched the needs of the community. In addition, problems can arise if the power generated is less than the load power requirements, causing energy shortages in an area. To find out whether the electricity provided is appropriate or not, a classification using Supervised Learning method is used. After classification, we will use K-fold Cross Validation to measure how good the accuracy is between the methods. This study will use 200 electricity meter data consisting of 150 test data and 50 training data with a composition of 75%: 25%. The testing process where the data process that has been divided is then carried out in the testing process where the data process is obtained from manual calculations. So that in this study get results in the form of the K-NN method with 99.3% accuracy, 100% precision, 99.29% recall and the SVM method with 94.00% accuracy, 94.00% precision, 100% recall. And to find out how well the performance of the method is based on Supervised Learning method, it will be checked using K-Fold Cross Validation with the results of K-NN 99.53% and SVM 96.00%, with the conclusion that the K-Nearest Neighbor method has a better accuracy rate.