Article Per Year (5 Year)
p-Index From 2020 - 2025
2.266
P-Index
This Author published in this journals
All Journal CogITo Smart Journal Jurnal Nasional Komputasi dan Teknologi Informasi APTEK Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Surya Teknika Journal of Computer Networks, Architecture and High Performance Computing CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking) Jurnal Penelitian Rumpun Ilmu Teknik
Yoan Purbolingga
Universitas Andalas
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Transportation

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Sistem Informasi Rumah Moderasi STAIN Bengkalis Menggunakan Metode Research And Development (R&D) Rahmayani, Mentari Tri Indah; Purbolingga, Yoan; Yolanda, Desta
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 2 (2025): April
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i2.44319

Abstract

This study aims to develop the Rumah Moderasi (Moderation House) Information System at STAIN Bengkalis as a platform for information services and reporting, intended to strengthen religious moderation within the campus environment. The research employs the Research and Development (R&D) method using the 4D development model (Define, Design, Develop, and Disseminate). The system is designed to facilitate lecturers and students in accessing information and reporting indications of radicalism online. The test results show that the system has a positive impact in enhancing services and reinforcing values of tolerance and religious moderation in the academic setting. The implementation of this system is expected to encourage the creation of an inclusive, tolerant campus environment that is free from radical ideologies.
Comparison of MDKA Stock Price Prediction using Multi-Layer Perceptron, Long Short-Term Memory, and Gated Recurrent Unit Wajhi Akramunnas, Bastul; Hakim, Legisnal; Marta Putri, Dita; Fahrizal, Fahrizal; Rahmawati, Asde; Purbolingga, Yoan
JURNAL SURYA TEKNIKA Vol. 10 No. 1 (2023): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v10i1.5004

Abstract

Shares are rights owned by a person against a company due to the delivery of capital, either in part or in whole. Investors invest in stocks and try to get maximum results, but many investors are still unsure about the risks involved in investing. To minimize risk, investors need to predict stock prices with an accurate method. Several methods that can be implemented to predict stock data include Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The research objective to be achieved in this study is to compare the performance of each algorithm in producing a more accurate stock price forecasting model by testing neurons (10, 20, 30) and epochs (50, 75, 100). The research was conducted on the stock price data of PT. Merdeka Copper Gold Tbk (MDKA) which is a mining sector share with the largest capitalization value. Tests on some of the algorithms above got the best results using 82% training data and 18% test data, namely the MLP model with 10 neurons and 100 epochs with a MAPE training data result of 2.325 and a MAPE test data of 2.014. Based on the test results, MLP can predict MDKA stock prices for the 2018-2022 period with good performance and a relatively small error rate, while tests using the LSTM and GRU methods still produce large errors. Thus, it can be concluded that MLP can predict stock prices with more accurate results.
A Machine Learning-Based Ambiguous Alphabet Recognition for Indonesian Sign Language System (SIBI) Purbolingga, Yoan; Ridwan, Ahmad; Putri, Dila Marta
CogITo Smart Journal Vol. 11 No. 1 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i1.816.1-14

Abstract

One of the communication problems in deaf people is the inhibition of verbal communication. This is due to the limited hearing function which has an impact on the imperfection of language sound reception. To communicate with deaf people, extraordinary communication is needed so that the meaning of the conversation can be conveyed properly. Sign language is the main communication medium for deaf people. However, in the use of sign language, there are ambiguous letters, namely “D “,“E“,“M“,“N“,“R“, “S“, and “U“. This research uses the chain code method to identify and reconstruct the shape of hand gesture objects. Then, to solve the problem of ambiguity of alphabet letters, an artificial intelligence method, namely K-Nearest Neighbors (K-NN), is used. The sample used consists of 350 real-time images with variations in object recognition accuracy. Based on the research using chain code and K-NN classification method, it can be concluded that the recognition of ambiguous letters in sign language has 245 training data for K-NN which has 88.76% accuracy, and 105 test data with 90% accuracy. This test data is divided into seven letters: “D“, “E”, “M”, “R” and “U” at 100%, and “N” and “S” at 98.88%.
Animal Protection System Cats and Dogs Approaching The Substance With The Mini Computer Aulia Ramadhani, Shafira; Anandika, Arrya; Syahputra, Ronaldo; Purbolingga, Yoan
CHIPSET Vol. 6 No. 01 (2025): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/chipset.6.01.18-29.2025

Abstract

Litter bins that do not have lids, as well as the time span of garbage collection, especially when garbage is accumulating, can attract animals to enter the bin, some of the animal patterns are to carry garbage out of the bin, so that it can be consumed by dogs/cats. The purpose of this problem is to help the community in preventing and minimizing animals that litter and prevent the spread of diseases in animals due to consuming garbage, for example diseases caused by bacteria from garbage, namely rabies. This final project results in the detection of cats and dogs using YOLO with 90% accuracy, when a cat or dog is detected the system will issue an output so that the animal does not get closer and out of the trash, the output will continue to be issued until the animal is no longer detected by the camera.
Co-Authors Ahmad Ridwan Ahmad Ridwan Anandika, Arrya Arrya Anandika Asde Rahmawati Aulia Ramadhani, Shafira Bastul Wajhi Akramunnas Desi Ratna Sari Dila Marta Putri FAHRIZAL Fahrizal Fahrizal Fahrizal Fahrizal Fahrizal Fahrizal Hanisah Hanisah Hanisah, Hanisah Legisnal Hakim Lucky Prima Lukman Hakim Nasution Lukman Hakim Nasution Marta Putri, Dita Mentari Tri Indah Rahmayani Nurhayati Nurhayati Putri, Dila Marta Rahmawati, Asde Rahmayani, Mentari Tri Indah Restu Dian Saputri Ronaldo Syahputra Siti Nurjanah Syahputra, Ronaldo Wajhi Akramunnas, Bastul Yolanda, Desta