Prototype Design of Web-Based Drug Inventory Prediction Application Using Multiple Linear Regression Methods at the X District Health Office

Abstract

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>The need for information systems is currently becoming very important, especially in terms of predicting drug needs in health care facilities, one of which is the health department. The availability of drug inventory prediction applications is expected to help health facility management optimize drug inventory levels so that they can use budgets effectively. The research aims to design an application that can meet the needs and facilitate the process of planning drug needs in health care facilities. This research is highly relevant to the aspects of pharmacy management, pharmacy data analysis, and information systems in the field of health, all of which are important topics in the development of effective and efficient health services. The research method used in this study is a qualitative method using the System Development Life Cycle (SDLC) model approach to develop information systems. The information system developed is a web-based information system that adopts a multiple linear regression system and the use of data mining as an algorithm in predicting drug needs. This is so that the allocation of drug provision budgets can be used effectively. The design of a drug data prediction information system using a linear regression method is intended to facilitate the process of planning drug needs that must be met in healthcare facilities. If this application design is implemented, it will help the management of healthcare facilities to optimize the level of drug inventory. This is because there is already a drug needs selection process that is in accordance with the drug needs condition needed by the hospital formation installation. Suggestions for further research can be developed to obtain information quickly about significant changes in drug needs or the potential risk of stockouts.</em></p><p><em><br /></em></p></td></tr></tbody></table></div><strong><em>Keywords: Data Mining, Drug Procurement, Medicine Needs</em></strong>