Sani M. Isa

Work place: Binus Graduate Programs, Bina Nusantara University



Research Interests: Engineering


Sani M. Isa is a lecturer and researcher in the Computer Science Department, BINUS Graduate Program - Master of Computer Science. He has numerous experience in teaching and research in remote sensing and biomedical engineering areas. He got his doctoral degree in Computer Science from the University of Indonesia. He is also received his master degree in Computer Science from the University of Indonesia as well as a bachelor degree from Padjadjaran University, Bandung, Indonesia.

Author Articles
Football Match Prediction with Tree Based Model Classification

By Yoel F. Alfredo Sani M. Isa

DOI:, Pub. Date: 8 Jul. 2019

This paper presents the football match prediction using a tree-based model algorithm (C5.0, Random Forest, and Extreme Gradient Boosting). Backward wrapper model was applied as a feature selection methodology to help select the best feature that will improve the accuracy of the model. This study used 10 seasons of football data match history (2007/2008 – 2016/2017) in the English Premier League with 15 initial features to predict the match results. With the tuning process, each model showed improvement in accuracy. Random Forest algorithm generated the best accuracy with 68,55% while the C5.0 algorithm had the lowest accuracy at 64,87% and Extreme Gradient Boosting algorithm produced accuracy of 67,89%. With the output produced in this study, the Decision Tree based algorithm is concluded as not good enough in predicting a football match history.

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Data Mining to Prediction Student Achievement based on Motivation, Learning and Emotional Intelligence in MAN 1 Ketapang

By Muhammad U. Fahri Sani M. Isa

DOI:, Pub. Date: 8 Jun. 2018

The problems that exist in the school decline in student achievement ahead of class III, especially before approaching the national exam. If the learning achievement of third-grade students can be known earlier then the school can perform the actions necessary for students to achieve good learning achievement.
This research uses two methods of data mining, Neural Network Model Multilayer Perceptron, and Decision Tree. For comparison, this study also uses t-statistic test, t-test and to compare precision/recall using Roc Curve.
Neural Network Model Multilayer Perceptron Positive performance vector accuracy: 88.64% and Negative: 14.07%, precision (positive guidance class) positive 88.00% and negative 16.88%, recall (class: Ordinary guidance) positive 84.50%, and negative 21.73%. Decision Tree Positive performance vector accuracy: 84.82% and Negative: 15.24%, precision (positive guidance class) positive 86.55% and negative 18.52%, recall (class: ordinary guidance) positive 84.00% and negative 23.85%
Experiments conducted in this study aims to prove that data mining can predict student achievement by finding the best data mining method between the multilayer perceptron neural network and Decision tree to be implemented into integrated information system between student motivation data, student learning interest, and intelligence emotional students.

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