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Sistem Informasi Manajemen Toko Buku Bibliophile’s Spot Berbasis Online Fatihah, Amelia Alfie; Primadani, Ardi; ., Jessyca; Iman, Moh Nurul
Jurnal Sains dan Teknologi (JSIT) Vol. 4 No. 3 (2024): September - Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v4i3.2409

Abstract

With the growing market demand, the bookstore faces challenges in inventory management, transaction recording, and customer data management, which are often done manually, resulting in inefficiency and a high risk of errors. The main goal of this research is to design and implement a system that can automate these processes, making it easier for store management to manage stock, record transactions in real-time, and provide relevant information for decision-making. This research uses the Research and Development (R&D) method with an Agile approach in system development, allowing for gradual updates and responsiveness to user feedback. The use of this management information system not only reduces recording errors and accelerates the transaction process but also provides more flexible access through web and mobile-based applications. With the integrated system, transaction and inventory data can be accessed and updated in real-time, supporting management in conducting sales analysis and identifying product trends that customers are interested in.
Analisis Prediksi Serangan Jantung Menggunakan Algoritma C4.5 Berbasis Rapidminer ., Jessyca; Fatihah, Amelia Alfie; Dilla, Fara; Primadani, Ardi; Iman, Moh Nurul; Bali, Aprida Bertha; Simanjuntak, Sry Intan; Sinurat, Sefany Tiurma
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Heart disease is one of the leading causes of death in Indonesia and often develops without clear early symptoms, making early detection difficult. This study aims to predict the risk of heart attacks using the C4.5 algorithm based on RapidMiner by utilizing Indonesian public health data. The research method applies a data mining approach using the CRISP-DM framework, which includes business understanding, data understanding, data preparation, modeling, and evaluation stages. The dataset was obtained from Kaggle, consisting of 158,355 records and 28 attributes. Data preparation involved removing redundant attributes, data transformation and encoding, and dataset balancing. The evaluation results show that the C4.5 model achieved an accuracy of 90.89% with a recall value of 95.07% for the heart attack class. These results indicate that the C4.5 algorithm is effective in detecting individuals at risk of heart attacks and can be used as a basis for developing decision support systems in the healthcare sector.
Co-Authors Bali, Aprida Bertha Dilla, Fara Fatihah, Amelia Alfie Iman, Moh Nurul Primadani, Ardi Simanjuntak, Sry Intan Sinurat, Sefany Tiurma