Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Vincent Elbert Budiman
Maranatha Christian University
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting Joseph Sanjaya; Erick Renata; Vincent Elbert Budiman; Francis Anderson; Mewati Ayub
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i1.2313

Abstract

Abstract — A loan is one of the most important products on the bank, which used for main revenue. All bank tries to find the most effective business strategy to persuade a customer to use the loan, but loan default has a negative effect after the application is approved. Loan default causes loss on the bank, therefore it is mandatory to calculate in order to decrease the risk of the loan default. This study uses random forest and adaptive boosting machine learning methods to get the prediction and decision. The random forest uses a voting method from many decision trees and adaptive boosting can support to increase accuracy, stability and handle an underfit or overfit problem. The experimental results show that Adaptive Boosted Random Forest outperformed normal random forest and Deep learning Neural Network (DNN) in recall rate evaluation metrics with small trade-offs in the accuracy. Keywords— Adaptive Boosting; Bank; Loan Default; Machine learning; Random Forest;
Building Acoustic and Language Model for Continuous Speech Recognition in Bahasa Indonesia Vincent Elbert Budiman; Andreas Widjaja
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2684

Abstract

Here a development of an Acoustic and Language Model is presented. Low Word Error Rate is an early good sign of a good Language and Acoustic Model. Although there are still parameters other than Words Error Rate, our work focused on building Bahasa Indonesia with approximately 2000 common words and achieved the minimum threshold of 25% Word Error Rate. There were several experiments consist of different cases, training data, and testing data with Word Error Rate and Testing Ratio as the main comparison. The language and acoustic model were built using Sphinx4 from Carnegie Mellon University using Hidden Markov Model for the acoustic model and ARPA Model for the language model. The models configurations, which are Beam Width and Force Alignment, directly correlates with Word Error Rate. The configurations were set to 1e-80 for Beam Width and 1e-60 for Force Alignment to prevent underfitting or overfitting of the acoustic model. The goals of this research are to build continuous speech recognition in Bahasa Indonesia which has low Word Error Rate and to determine the optimum numbers of training and testing data which minimize the Word Error Rate.
Co-Authors Andreas Widjaja Erick Renata Francis Anderson Joseph Sanjaya Mewati Ayub