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SURANTO SAPUTRA
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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PENERAPAN APLIKASI SISTEM KOMPARASI METODE K-NEAREST NEIGHBOR DAN NEURAL NETWORK DALAM MENENTUKAN KEPUASAN PELAYANAN WALI MURID PADA SEKOLAH DASAR PUJI ASTUTI; SURANTO SAPUTRA
Faktor Exacta Vol 10, No 4 (2017)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (774.566 KB) | DOI: 10.30998/faktorexacta.v10i4.2241

Abstract

This study aims to create a school service system to guardians, with comparative research or comparative study that is by comparing the KNN and Neural Network will be known whether the school service to the student guardian satisfactory or not.This research activity, conducted by collecting library materials related to the taking of the title. Furthermore, researchers also directly visited the place of research to conduct surveys and do data retrieval that will be used in further research.With this research, the school will be able to make quicker decisions to provide services to guardians. This study used Comparative Method K-Nearest Neighbor And Neural Network. The process of data retrieval for this research is taken through administrative services, learning process and learning outcomes. Thus the data of this school service will be very helpful in decision making by the school.
KOMPARASI ALGORITMA BERBASIS NEURAL NETWORK DALAM MENDETEKSI PENYAKIT HEPATITIS Suranto Saputra
Faktor Exacta Vol 10, No 1 (2017)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v10i1.1304

Abstract

Hepatitisis aninfectious disease of the liverand is very dangerous in the world, several studies have been conducted to correctly diagnose patients is unknown but the most accurate method in predicting disease hepatitis in patients. In this study a comparison algorithm Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms to determine the most accurate in predicting disease hepatitis. This study uses secondary data in the form of hepatitis disease data obtained from the University of California Irvine Machine Learning repository of data. Testing the algorithms are carried out by using software that is known that rapidminer algorithm Support Vector Machine (SVM) has the highest value of accuracy is 90.64%, while the algorithm Multilayer Perceptron (MLP) has an accuracy of 84.38%. Thus the algorithm Support Vector Machine (SVM) to predic the hepatitis disease better.
Co-Authors Holyness Nurdin Singadimedja