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All Journal Sisforma: Journal of Information Systems JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab TEMATIKA: Jurnal Penelitian Teknik Informatika dan Sistem Informasi JIKO (Jurnal Informatika dan Komputer) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JSAI (Journal Scientific and Applied Informatics) E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal Pengabdian Kepada Masyarakat Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Sistem Informasi dan Teknologi Informasi
Phie Chyan
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Atma Jaya Makassar
Arts Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device Phie Chyan; Yudi Kasmara
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (437.471 KB) | DOI: 10.29207/resti.v5i5.3503

Abstract

The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to child safety that were studied generally focused on responses to events that could potentially harm children.This study aims to design a prototype child monitoring system consisting of a wearable device that is used on a child's wrist equipped with sensors and connected to a server via a wireless network. Monitoring software that runs on the server will collect all data parameters from wearable devices with built-in audio signal, temperature, and heart rate sensor then with machine learning algorithm implemented in software will allow the system to predict if stress conditions happen on children and then system can give warnings to child-caregiver through monitoring applications or SMS messages. Using the Decision Tree and Naive Bayes classification methods the system can effectively predict stress conditions in children with an accuracy of 82.8 percent using audio, temperature, and heart rate parameters. This shows that the system has the capability to contribute to increasing child safety in the supervision environment.
Analysis of Supermarket Product Purchase Transactions With the Association Data Mining Method Norbertus Tri Suswanto Saptadi; Phie Chyan; Jeremias Mathias Leda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4844

Abstract

The development of business world is entering the era of big data. In meeting supermarkets' sales and purchase targets, the management needs to improve themselves in managing the goods available in the store. The research aims to determine the pattern of purchases that occur in a transaction, find out related and related products in supermarkets, and improve supermarket services to customers. The method applied uses the association rules approach to data mining. Several purchasing data from customers have been able to be analyzed by displaying a diagram as a visualization of the number of specified association rules. The processing results show a relationship above 90%: sugar and coffee with a confidence of 94.4%, shirts and trousers with a confidence of 93.4%, and sugar, milk, and coffee with a confidence of 92.0%. Decisions that can be taken by supermarket management in providing places and goods need to consider and follow product relationships and proximity based on the highest confidence value to provide services to customers effectively and efficiently.
Co-Authors Adi Chandra Syarif Andani Achmad Andrew Christhoper Pratama Cakra, Kalvin Ferdinandus Sampe Fransiskus Eduardus Daromes Gormantara, Alfredo Hans Christian Marwi Hans Christian Marwi, Hans Christian Heri Heri Heri, Heri Indra Wihanry Indra Wihanry, Indra Ingrid Nurtanio Intan Sari Areni Jeremias Leda Leda, Jeremias Mathias Norbertus Tri Suswanto Saptadi Ridnaldy Yunior Carolus Sean Coonery Sumarta Sean Coonery Sumarta, Sean Coonery Sumarta, Sean Coonery Valentina Marchella Widjaja Yudi Kasmara