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All Journal Jurnal Mantik Bulletin of Information Technology (BIT) Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Journal of Practical Computer Science (JPCS) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin SAINTEK
Arif Siswandi
Universitas Pelita Bangsa
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Journal : SAINTEK

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Implementasi Chatbot Whatsapp Untuk Sistem Informasi Laundry Di Laundryku Arif Siswandi; Haryo Seto Muhamad
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

This research aims to implement a WhatsApp chatbot within the Laundryku information system to enhance service quality and operational efficiency. The system is developed using the Waterfall method, which includes structured stages such as requirement analysis, system design, implementation, testing, and maintenance. This method ensures that the development process is systematic, organized, and well-documented. The chatbot is built using the TypeScript programming language along with several libraries from Baileys to enable integration with the WhatsApp platform. TypeScript is chosen for its strong support in web application and API development, as well as its ability to reduce errors through structured typing. By leveraging WhatsApp, a platform widely used and familiar to customers, the system ensures accessibility and ease of communication. The implemented chatbot is designed to facilitate customer interactions, including service booking, order status tracking, and access to additional information about laundry services. Through automation, the system minimizes manual communication, improves response time, and enhances service efficiency. Overall, the implementation of this WhatsApp chatbot is expected to improve customer experience while supporting Laundryku in optimizing its digital-based business operations.
Implementasi Data Mining untuk Prediksi Penyakit Diabetes pada Anak Menggunakan Algoritma K-Nearest Neighbor Arif Siswandi; Amir Mujahid
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Diabetes Mellitus (DM) is defined as a chronic disease or metabolic disorder with multiple etiologies characterized by high blood sugar levels accompanied by disturbances in carbohydrate, lipid, and protein metabolism as a result of insufficient insulin function. According to the International Diabetes Federation (IDF), the number of Type 1 diabetes patients was collected based on age in 2022. Globally, the estimated number of Type 1 diabetes patients reached 8.75 million people in 2022. Among them, 1.52 million people, or 17% of the total, were below 20 years of age. This age category includes children, adolescents, or young adults. The objective of this study is to implement data mining to improve the effectiveness of decision-making in the treatment of diabetes. In this research, the prediction process was conducted on a diabetes dataset using the K-Nearest Neighbor algorithm. The testing results on the dataset yielded an accuracy of 97%, precision of 100%, and recall of 95%. These results demonstrate that the K-NN algorithm provides excellent outcomes in predicting diabetes.
Co-Authors A. Yudi Permana Agung Rahmawan Amir Mujahid Asep Muhidin Aswan S Sunge Candra Naya Encep Hambali Fajar Sigit Srijati Farish Al Khairi Haryo Seto Muhamad Martina Fitriana Muhammad Iqbal Fadillah Muhammad Makmun Effendi Rani Yuliani Wulandari Salsa Fadhilah Sanudin Sanudin Wiyanto Wiyanto