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All Journal Ceddi Journal of Information System and Technology (JST) Ceddi Journal of Education Bersama : Jurnal Pengabdian Masyarakat
Robbi Rahim
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Ceddi Journal of Information System and Technology (JST)

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Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm Hao Chieh Chiua; Robbi Rahim; Mahmud Mustapa; Kamaruddin; Akbar Hendra; Asnimar; Abigail, Omita
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.47

Abstract

One of the data mining processes that is often used in machine learning is the data classification process. A decision tree is a classification algorithm that has the advantage of being easy to visualize because of its simple structure. However, the decision tree algorithm is quite susceptible to incorrect classification calculations due to the presence of noise in the data or imbalance in the data, which can reduce the overall level of accuracy. Therefore, the decision tree algorithm should be combined with other methods that can increase the accuracy of classification performance. Machine Learning is used through an artificial intelligence approach to solve problems or carry out optimization. Adaptive Boosting is used to optimize classification calculations. This study aims to examine the performance of Adaptive Boosting in the process of classifying second-degree diabetes mellitus patients using the Decision Tree algorithm. Diabetes mellitus is known as a chronic condition of the human body, the cause of which is an increase in the body's blood sugar levels because the body is unable to produce insulin or is unable to utilize insulin effectively, which is usually referred to as hyperglycemia.. By using a 60:40 data split, the Decision Tree algorithm produces an accuracy value of 95.71%, while the Adaptive Boosting-based Decision Tree results reach a value of 98.99%.
Design of a Web-Based Goods Inventory Information System for an Office Stationery Store Pathak, Vijey North Sandep; Robbi Rahim; Rahmania; Kamaruddin; Erwin Gatot Amiruddin
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 2 (2024): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i2.80

Abstract

Hasco Stationery Store, located on Jalan Perintis Kemerdekaan Km 09, is a business specializing in printing and office stationery sales. Effective management of stock availability, purchasing, and sales processes is crucial to meeting customer needs. Prior to implementing an integrated information system, inventory management at the store was performed manually by employees, who relied on estimates and experience to record stock, check inventory, and place purchase orders. This manual approach proved inefficient and prone to errors, including inaccurate stock calculations, recording mistakes, and data loss. To address these challenges, this study employed the waterfall development method to design and implement a web-based inventory information system aimed at enhancing the efficiency and accuracy of inventory management processes. Data were collected through interviews with the store owner, direct observations, and analysis of relevant documents, supplemented by a literature review on stock management principles and information system development. The resulting system simplifies stock management, reduces errors, and improves overall operational efficiency, providing a robust solution for inventory management at the Hasco Stationery Store.
Co-Authors Abigail, Omita Akbar Hendra Asnimar Dewi Angraini, Dewi Ernie C. Avila Erwin Gatot Amiruddin Hao Chieh Chiua Kamaruddin M.Safii Mahmud Mustapa NAZMAH Pathak, Vijey North Sandep Rahmania Supriadi Sahibu