Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Dedi Suhendro
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Engineering

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

Found 2 Documents
Search

 1 
Application of Artificial Neural Networks to Predict Exports of Four-Wheeled Vehicles by Destination Country Ema Meyliza; Eka Irawan; Dedi Suhendro
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.585 KB) | DOI: 10.55123/jomlai.v1i3.914

Abstract

Motorized vehicles are vehicles that are energized through machines and used for land transportation,, as well as movement through technical equipment in the form of electric motors and other tools that have the function of converting an energy source into power to drive the vehicle. This study aims to determine the results of the predicted number of four-wheeled vehicle exports by destination country in the years to come. The research data used is data on exports of four-wheeled motor vehicles by destination country for 2012-2020. The algorithm used in this research is an artificial neural network with Back-propagation method. The best architectural model used in this research is architect 4-4-1 with an accuracy rate of 82% epoch of 2261 iterations and MSE of 0.0081876. So it can be concluded that the model can be used to predict export data for four-wheeled vehicles by destination country.
Implementation of One-step Secant Algorithm for Forecasting Open Unemployment by Highest Educational Graduate Ismi Azhami; Eka Irawan; Dedi Suhendro
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (601.346 KB) | DOI: 10.55123/jomlai.v1i3.946

Abstract

Based on data, the open unemployment rate according to the highest education graduate in Indonesia shows the number of semester unemployment which has an unstable value, sometimes up and sometimes down. This study aims to implement the ability and performance of one of the training functions on the backpropagation algorithm, namely one-step secant, which can later be used as a reference in terms of data forecasting. The one-step secant algorithm is an algorithm that is able to train any network as long as the input, weight and transfer functions have derivative functions and this algorithm is able to make training more efficient because it does not require a very long time. The data used in this study is open unemployment data according to the highest education completed in Indonesia in 2006-2021 based on semester, which is sourced from the Indonesian Central Statistics Agency. Based on this data, a network architecture model will be formed and determined using the One-step secant method, including 14-13-2, 14-16-2, 14-19-2, 14-55-2, and 14-77- 2. From these 5 models, after training and testing, the results show that the best architectural model is 14-19-2 (14 is the input layer, 19 is the number of neurons in the hidden layer and 2 is the output layer). The accuracy level of the architectural model for semester 1 and semester 2 is 75% with MSE values of 0.00130797 and 0.00388535.
Co-Authors Eka Irawan Ema Meyliza Ismi Azhami