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Design and Construction of a Web-Based Fixed Asset Management System with a Combination of Straight Line Method, MAUT, and Telegram Bot Integration: Case Study of North Lombok District Hospital Arthana, I Made Teguh; Wirdiani, Ni Kadek Ayu; Putri, Desy Purnami Singgih
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1061

Abstract

North Lombok District Hospital is a health service institution in North Lombok District, West Nusa Tenggara Province, that provides health facilities and services to the community. Health service facilities provided to the community come from fixed assets owned by the North Lombok District Hospital. Management of fixed assets used for health service facilities at the North Lombok District Hospital is still done manually in planning, receiving, repairing, maintaining, and releasing assets. So, hospital employees have difficulty managing the assets they own. This study was conducted to help design and build a fixed asset management information system at the North Lombok Hospital using the SDLC Method with the Waterfall Model approach and system development using PHP, HTML, CSS, and JS languages with the Laravel Framework and MYSQL Database. This study uses the Straight Line Method to calculate asset depreciation, the MAUT Method to assist in decision-making for the elimination of damaged assets, and the Telegram Bot to send notifications from the website to each unit group in the hospital. The final result of this study is a web-based fixed asset management information system with developed features, namely asset planning features, asset planning change features, asset handover minutes features, asset inventory features, asset maintenance features, asset repair features, asset write-off features, asset whitening features, asset reporting features, master data features, and user access rights management features. The testing method used in this study is the Blackbox testing method, which tests the functionality of the system using 150 test scenarios on eight employees of the North Lombok Regional Hospital, with the test results showing that the system is running well and in accordance with the SOP that has been given, PSSUQ testing was carried out to evaluate user satisfaction with the system. The test results showed a SysUse subscale value of 1.93, IntQual 1.6, InfoQual 1.92, and Overall 1.93. Based on the results of the PSSUQ test, it can be concluded that the fixed asset management system has run very well and meets user expectations.
The Performance Comparison of DBSCAN and K-Means Clustering for MSMEs Grouping based on Asset Value and Turnover Sutramiani, Ni Putu; Arthana, I Made Teguh; Lampung, Pramayota Fane'a; Aurelia, Shana; Fauzi, Muhammad; Darma, I Wayan Agus Surya
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.13-24

Abstract

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country's economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability.   Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover