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Journal : International Journal of Engineering, Science and Information Technology

Implementation of the Simple Additive Weighting Algorithm for Café Recommendations in Lhokseumawe City Arkan, Raihan; Safwandi, Safwandi; Ar Razi, Ar Razi
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.885

Abstract

The selection of cafés that match customer preferences is a challenge, especially in the city of Lhokseumawe, which has 30 cafés with different characteristics. This research implements the Simple Additive Weighting (SAW) algorithm to provide recommendations for the best café based on six criteria, namely price (weight 0.25), menu (0.2), order duration (0.15), service (0.2), facilities (0.15), and discounts promotions (0.05). The recommendation system was developed using a combination of Laravel PHP and Python, where Laravel is used to build an interactive web interface. Python also plays a role in data processing and complex mathematical calculations. The results showed that the system was able to provide optimal recommendations, with Petrodollar Coffeeatery Roastery as the top choice based on the calculation of the highest preference values (3.28 for price, 2.48 for menu, 3.16 for order duration, 2.88 for service, 2.96 for facilities, and 2.8 for discounts promotions). TR Coffee and Platinum Coffee occupy the following positions. In addition, this study found that the weight of the criteria and the number of datasets (150 reviewers) significantly influence the quality of recommendations. The more representative the weights used and the larger the dataset analyzed, the more accurate the system will produce recommendations based on user preferences. Thus, weight optimization and dataset expansion are essential factors in improving the effectiveness of SAW-based recommendation systems.
Application of Data Mining with the Least Square Meth-od to Predict Web-Based Drug Inventory Halim, Abdul; Safwandi, Safwandi; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.897

Abstract

Drug supplies are an important aspect because of their large value and large quantity and are an important factor in supporting health services in community health centers. Ineffective drug management, especially in terms of needs planning, can lead to excess or shortage of stock. Both conditions have negative impacts, such as budget waste, drug expiration, or even disruption of patient services due to unavailability of drugs. At the Pante Bidari Health Center UPTD, the drug needs planning process is still carried out manually or based on rough estimates without using sophisticated technology. This study aims to design and build a web-based drug inventory prediction system using the Least Square method. The Least Square method was chosen because it is able to carry out the forecasting process quickly and with good results. In this study, the type of data obtained is drug usage data, data is grouped based on each supplier, from the health center information system during a certain period. After going through the pre-processing and calculation stages, the predicted values are calculated and displayed through a web-based system designed to be easy to use by health center officers. The web system developed in this study uses PHP as the programming language and MySQL as the database, implementing the Least Square method effectively. The results of this study are a drug usage prediction application for the future, applying the Least Square method, which displays drug usage data over a certain period. The system will present the data in the form of a table. Based on testing the drug usage data for Acyclovir Cream 5 mg from January 2023 to August 2024, the prediction result for the following month, September 2024, is estimated to be 38.415, which is rounded to 38 units of the drug.