Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Journal of Computer System and Informatics (JoSYC) Journal of Social Responsibility Projects by Higher Education Forum
Heru Kurniawan
Universitas Mikroskil, Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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

Found 2 Documents
Search

 1 
Pelatihan Instalasi Jaringan Komputer Menggunakan Simulasi Cisco pada SMK Methodist Tanjung Morawa Frans Mikael Sinaga; Sio Jurnalis Pipin; Heru Kurniawan
Journal of Social Responsibility Projects by Higher Education Forum Vol 4 No 1 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v4i1.3633

Abstract

SMK Swasta Methodist Tanjung Morawa is one of the private schools under the auspices of Yayasan Methodist Kasih Imanuel Indonesia, which was established in 2008. Tanjung Morawa Methodist Private Vocational School has various majors, one of which is Network and Computer Engineering (TKJ). Network installation is one of the most interesting subjects to discuss because the students have studied it before and it is already a lesson that is in accordance with the majors of the students of Tanjung Morawa Methodist Private Vocational School, namely Computer Network Engineering (TKJ). The students have learned several computer network simulation applications such as virtual boxes but the network simulation applications studied are still limited, therefore, the Faculty of Informatics Universitas Mikroskil offers activities in the form of computer network installation training using Cisco simulation to improve the ability of students to have better competencies. This training activity lasted for 2 days and was carried out in the computer laboratory of Universitas Mikroskil. During this training activity the students were given pre-test questions, materials and case studies, post-test and final feedback.
Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation Sio Jurnalis Pipin; Ronsen Purba; Heru Kurniawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4014

Abstract

Predicting stock price movements is a complex challenge in the financial market due to unpredictable price fluctuations and high sensitivity levels. Noise in historical stock price data and temporal dependencies between previous and current prices make recognizing price movement patterns difficult. In a dynamic market environment, the model's ability to generate accurate predictions holds significant implications for more informed investment decision-making. The Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model holds great potential for stock price prediction. It captures temporal dependencies, identifies non-linear relationships, and deciphers complex trends in stock price data. This study employs deep learning techniques with the RNN-LSTM model optimized using Adaptive Moment Estimation (Adam) to enhance stock price prediction accuracy by leveraging historical stock price data and technical factors. Data preprocessing, including handling missing values and data normalization, aids the model in navigating the dataset's intricacies. Test results utilizing the Mean Squared Error (MSE) metric reveal the model's ability to produce predictions that closely resemble actual stock prices, with a low loss value of 0109012. The model also exhibits good predictive accuracy, as evidenced by a favorable Mean Percentage Error (MPE) score of 1.74% between predicted and actual values. These findings hold valuable implications for assisting investors and financial practitioners in managing complexity and uncertainty within the stock market
Co-Authors Ronsen Purba Sinaga, Frans Mikael Sio Jurnalis Pipin Sio Jurnalis Pipin