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Journal : Scientific Journal of Informatics

Optimizing Inventory Management: Data-Driven Insights from K-Means Clustering Analysis of Prescription Patterns Dermawan, Aulia Agung; Ansarullah Lawi; Putera, Dimas Akmarul; Kurniawan, Dwi Ely; Ummatin, Kuntum Khoiro; Jorvick Steve
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8690

Abstract

Purpose: The goal is to improve how inventory is managed in healthcare by using K-Means clustering to analyze prescription trends. This approach helps ensure better stock availability, streamlines operations, and ultimately increases sales opportunities. Methods: This research applied the K-Means clustering algorithm to analyze a comprehensive dataset of prescription behaviors from XYZ Clinic. By grouping similar prescriptions into clusters, this method highlighted patterns within the data. These insights led to the identification of unique prescription categories, enabling the creation of tailored recommendations for improving inventory management. Result: The analysis showed that Cluster 1 should be prioritized for inventory management due to its high sales potential and consistent prescription patterns. It is recommended to increase stock for the medications in Cluster 1 to improve inventory turnover and streamline clinical operations. These findings underscore the value of K-Means clustering in healthcare, especially for enhancing inventory management and operational efficiency. Novelty: This research presents a novel application of K-Means clustering in healthcare, focusing on prescription patterns and inventory management. While previous studies have primarily used K-Means clustering for areas such as risk assessment and logistics, this study provides valuable data-driven insights to improve inventory management strategies in healthcare. The results highlight how clustering methods can support better decision-making and resource allocation, ultimately leading to greater operational efficiency and improved patient care.
Co-Authors Abie Pratama Adi, Roni Adian Fatchur Rohim Afdhol Dzikri Agung Riyadi Agus Fatulloh Agus Fatulloh Ahmad Hamim Thohari Ahmad Hamim THohari Ahmad Hamim Tohari Andikha, Andikha Ari Novriansyah Arnomo, Sasa Ani Atalarik Ramli Aulia Agung Dermawan Aulia Agung Dermawan Azis Saputra Bagus Wardana Bisma Khairunnas Budi Arif Dermawan condra antoni Defriyanuar Dhining Desi Ratna Sari Dian Nurdiansyah Dimas Akmarul Putera Dodi Prima Resda Dodi Prima Resda Dwila Sempi Yusiani Eka Mutia Lubis Evita S.Tr.Kom Fadila, Aminudin Fadli Suandi Fauziah Mahmuda Finkye Priya Gunadi Firmal Firmal Hamdani Arif Hamdani Arif Handry Elsharry Adriyanto Hartadi, Nanda Rachmat Herman, Nanna Suryana Heru Wijanarko Hilda Widyastuti Iqbal Maulana Jaman, Jajam Haerul John Friadi Jorvick Steve Kerobaganet Kerobaganet Kusworo Adi Leman, Abdul Mutalib M. Yudha Putra Maidel Fani Maidel Fani Marga Raharja, Adyk Marselina, Sonia Maryani Septiana Maya Armys Roma Sitorus Mira Chandra Kirana Mirza Oktanizar Muchammad Fajri Amirul Nashrullah Muchammad Fajri Amirul Nashrullah Muhamad Naharus Surur Muhammad Agus Muljanto Muhammad Idris Muhammad Nashrullah Muhammad Zainuddin Lubis Narupi Narupi Narupi Narupi, Narupi Nelmiawati Nelmiawati Nelmiawati Nelmiawati Nur Cahyono Kushardianto Nur Cahyono Kushardianto Nur Imma Aulia Astori Permatasari, Ismi Aprilianti Prasetyawan, Purwono Pujiyono Pujiyono Rafi Dio Raynold, Raynold Resda, Dodi Prima Ridho Hafiedz Rifaldi Herikson Rina Yulius Riwinoto, Riwinoto Rizky Pratama Hudhajanto Rizky Pratama Hudhajanto Rokhayati, Yeni Satriya Bayu Aji Selly Tri Amanda Siahaan, Arta Uly Sudra Irawan Supardianto Supardianto Syafarudin Fani Thohari, Ahmad Hamim Tian Havwini Ummatin, Kuntum Khoiro Uuf Brajawidagda Vonega, Defangga Aby Wenang Anurogo Wirabuana Sakti Yan Prada Hasibuan Yosi Handayani