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Journal : International Journal of Engineering, Science and Information Technology

Emarketplace Performance Analysis Using PIECES Method Munirul Ula; Rizal Tjut Adek; Bustami Bustami
International Journal of Engineering, Science and Information Technology Vol 1, No 4 (2021)
Publisher : Master Program of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.5 KB) | DOI: 10.52088/ijesty.v1i4.138

Abstract

E-Marketplace is a place in cyberspace where prospective buyers meet each other to conduct transactions electronically through the internet medium. Like the market in the conventional sense, namely a meeting place for sellers and buyers, in the E-Marketplace, various companies in the world also interact without being limited by the territory of space (geography) and time. Therefore, an analysis of the performance of the website is needed to ensure the performance of the Bireuen emarketplace (meukat.com) website can run effectively in the future. The role of this emarketplace is very important, therefore in building emarketplace we must pay attention to several factors, namely: performance, information, economic, control, efficiency, and service, which is better known as the PIECES method. To analyze the performance of our self-developed emarketplace, was done by PIECES method. While the testing method in the performance analysis of the website uses the GTMetrix and Google Transparency applications. The results of the PIECES questionnaire on the dimensions of Information, Economy, Efficiency, and Service. The average score for the all dimensions is moderate, it is ranging from 42.8% to 51.45% and is in line with the expectations. The GTMetric test results of the Emarketplace website, shows that the average performance grade is 66% or grade D. This means that the quality of the Emarketplace website based on the index generated by Google is still low. It should be improved to provide good quality of service for users in future. The Emarketplace are also being analyzed by the Google transparency report, the result is “no unsafe content” was found, means this website is safe to visit. There are no applications that harm the users.
Gold Price Prediction Using Long-Short Term Memory Algorithm Based on Web Application Dalimunthe, Rodiatul Adawiyah; Adek, Rizal Tjut; Agusniar, Cut
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.724

Abstract

Gold is a significant investment asset, particularly in times of economic instability. Various factors, including decisions by financial authorities, inflation, and global economic dynamics, influence the fluctuations in gold prices. Accurately predicting gold prices is valuable for investors when making investment decisions. This study aims to utilize the Long Short-Term Memory (LSTM) algorithm for predicting gold prices and develop a web-based application connected to Yahoo Finance to acquire real-time gold price data. The LSTM algorithm was chosen because it handles time series data with long-term dependencies. LSTM has an architecture that allows the model to retain relevant information over long periods and forget irrelevant data. In this study, the developed LSTM model produced a Mean Absolute Error (MAE) of 19.81, indicating that the average prediction deviates by approximately 19.81 units from the actual value. Furthermore, an average Mean Absolute Percentage Error (MAPE) of 0.83% demonstrates the high prediction accuracy. The results of this study show that LSTM is an effective method for predicting gold prices. The resulting web application allows users to access gold price projections interactively, thereby assisting investors in making more accurate and data-driven decisions with easy access. Additionally, the web application offers customizable features such as adjusting prediction parameters and visualizing results in real time.  These features not only enhance user engagement but also improve decision-making processes. This research provides a practical tool for optimizing investment strategies in a dynamic economic environment by leveraging machine learning and seamless web integration.