Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Armada Informatika TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Leony Hoki
STMIK TIME
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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

Found 2 Documents
Search

 1 
Pembentukan Teamwork dengan Metode DBScan untuk Meningkatkan Kinerja Karyawan Feriani Astuti Tarigan; Leony Hoki
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp268-272

Abstract

CV. Surya Jaya Security System is a private company in the city of Medan that is engaged in the security system, which sells various brands of attendance machines, CCTV and accessories and doorstops, as well as various types of other security equipment. In doing work every day, it takes several employees who work together in completing the work of the customer. In one day, CV. SJSS can accept orders from several customers at once. Therefore, it is necessary to make arrangements for the formation of a work team (teamwork) to complete the work, so that all orders are completed on time and still maintain the quality of service from these employees. To carry out the process of grouping these employees, the data grouping method can be applied or often referred to as the clustering method. Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) is one of the pioneering examples of the development of density-based clustering techniques or commonly known as density-based clustering. The result of this research is a website for the formation of teamwork by applying the DBSCAN method which is able to group employees, so as to improve employee performance.
Stock Price Prediction In Indonesia Using Deep Learning With Long Short-Term Memory Algorithm Kevin; Octara Pribadi; Leony Hoki
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.221

Abstract

Stock price prediction is one of the complex problems in the financial world because it is non-linear and influenced by many external factors. This study aims to apply the Long Short-Term Memory (LSTM) algorithm, a variant of Recurrent Neural Network (RNN) within the Deep Learning framework, to predict the closing price of banking sector stocks in the Indonesian capital market. The data used comes from five major banking issuers on the Indonesia Stock Exchange, namely BBCA, BBTN, BBRI, BBMRI, and BBNI, obtained from the Kaggle platform. The method used is a literature study by reviewing relevant literature related to the application of LSTM to financial time series data. The main parameter configurations include 50 hidden units, 100 epochs, a batch size of 32, a learning rate of 0.001, and a ReLU activation function with Adam optimizer. Model evaluation was carried out using the Root Mean Square Error (RMSE) metric of 6.61%, Mean Absolute Error (MAE) of 1.74%, Mean Square Error (MSE) of 43.69%, and a coefficient of determination R² of 0.718. The results show that the LSTM model is able to produce predictions close to actual values, reduce the risk of overfitting, and can be used as an investment decision-making tool for investors in the Indonesian stock market.
Co-Authors Feriani Astuti Tarigan Kevin Octara Pribadi