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Journal : International Journal of Engineering, Science and Information Technology

Performance Analysis of SVM and Linear Regression for Predicting Tourist Visits in North Sumatera Ginting, Andriyan; Nurdin, Nurdin; 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.v5i1.667

Abstract

Indonesia, an archipelago rich in cultural diversity, historical heritage, and stunning natural scenery, offers an extraordinary travel experience to visitors who make this country their vacation destination. Tourism in Indonesia plays an essential role in the domestic economy, contributing to Gross Domestic Product. With its abundant natural and cultural resources, North Sumatra has long been recognized as an attractive destination for foreign tourists. However, the tourism sector faces significant challenges related to fluctuations in the number of visits, mainly due to the impact of the COVID-19 pandemic, which has disrupted global travel patterns and caused considerable uncertainty in tourism forecasting. Therefore, predicting the number of tourist visits becomes crucial for effectively planning and managing tourist destinations. This research aims to compare the performance of two forecasting algorithms, SVM and linear regression, in predicting foreign tourist visits in North Sumatra using historical data from 2019 to 2023. The dataset was subjected to a preprocessing phase to ensure data cleanliness and consistency, focusing on key variables such as seasonal trends, external factors, and market dynamics. Both models were evaluated based on two commonly used accuracy metrics, MAPE and RMSE, to assess how well the models could predict actual tourist arrivals. The results of the study indicate that Linear Regression outperforms SVM in terms of prediction accuracy, with a MAPE of 42.40% and an RMSE of 6735.6, compared to SVM with a MAPE of 46.65% and an RMSE of 8020.42. These findings provide valuable insights for local government authorities and tourism industry stakeholders to enhance destination planning, resource allocation, and strategies to attract more foreign tourists in the post-pandemic era.
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.
The Decision Support System for Feasibility Testing of Healthy Canteens at Universitas Malikussaleh Using the Multi-Attribute Utility Theory Method Ulfa, Nur Saufani; Abdullah, Dahlan; Agusniar, Cut
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.815

Abstract

This research aims to develop a decision support system based on the Multi-Attribute Utility Theory (MAUT) method to evaluate the feasibility of healthy canteens at Malikussaleh University. The system is designed to assess the feasibility of canteens based on six main criteria: Selection of Raw Materials, Storage of Food Ingredients, Food Processing, Food Storage, Food Transportation, and Food Serving. This study evaluated 20 canteens on campus, with feasibility values calculated based on the weights assigned to each Criterion. The results showed that the canteen with the alternative code A11 (Kopita BI) received the highest score of 0.956, followed by A6 (Umi), with a score of 0.798, and A10 (Alisha), with a score of 0.620. Out of the 20 canteens evaluated, only three canteens were categorized as "Feasible," 3 as "Sufficiently Feasible," and the remaining 14 were deemed "Not Feasible." These findings highlight the urgent need to improve the quality of most canteens. The criteria for Selection of Raw Materials and Food Processing had the highest weights, emphasizing the importance of these two aspects in maintaining food quality and health standards. Implementing this system simplifies data management and analysis and provides clear recommendations for canteen managers to improve service and health standards. Thus, this system is expected to promote healthier and higher-quality campus canteens. This research enhances canteen service quality in university environments and can serve as a reference model for other educational institutions in evaluating and improving their canteen facilities.