Saragih, Kana Saputra
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Published : 2 Documents
Articles

Found 2 Documents
Search

IMPLEMENTASI METODE DECISION TREE C4.5 UNTUK MENGANALISA MAHASISWA DROPOUT

ETHOS (Jurnal Penelitian dan Pengabdian) Vol 6 No.1 (Januari, 2018) Ethos: Jurnal Penelitian dan Pengabdian (Sains & Teknologi)
Publisher : Universitas Islam Bandung

Show Abstract | Original Source | Check in Google Scholar | Full PDF (887.518 KB)

Abstract

Data Mining is the process of extracting data from large databases to find important and useful information. Classification is one of the techniques that exist in data mining. The method used is the Decision Tree and the algorithm used is the algorithm C4.5. Decision Tree is a method that alters the facts into a decision tree that represents the rules which is easy to understand. Decision Tree is useful to explore the data and find the hidden relationship between the number of input variables and targets. The Decision Tree built will obtain rules  from a case. The purpose of this study is to classify the student data in Pembnagunan Panca Budi University to determine students subjected to dropout. Attributes used consisted of the previous school, student’s age, parent’s occupation, parent’s income, and GPA. To avoid too much branching, the attributes of income, age, and GPA are grouped together. The attribute that most influence on dropout students is the previous school. The results obtained from calculation accuracy value is calculation accuracy of 59.58% and classification error of 40.42%.

Klasifikasi Belimbing Menggunakan Naïve Bayes Berdasarkan Fitur Warna RGB

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 11, No 1 (2017): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Original Source | Check in Google Scholar

Abstract

Post harvest issues on star fruit are produced on a large scale or industry is sorting. Currently, star fruit classified by rind color analysis visually human eye. This method does not effective and inefficient. The research aims to classify the starfruit sweetness level by using image processing techniques. Features extraction used is the value of Red, Green and Blue (RGB) to obtain the characteristics of the color image. Then the feature extraction results used to classify the star fruit with Naïve Bayes method. Starfruit image data used 120 consisting of 90 training data and 30 testing data. The results showed the classification accuracy using RGB feature extraction by 80%. The use of RGB as the color feature extraction can not be used entirely as a feature of the image extraction of star fruit.