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This Author published in this journals
All Journal IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Journal Software Engineering and Computer Science (IJSECS)
Fegie Yoanti Wattimena
Universitas Ottow Geissler Papua
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology

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Experd System of Obesity Diagnosis Using Backward Chaining Method and Certain Factor Fegie Yoanti Wattimena; Reni Koibur; Dion R A Mamisala; Septi Andryana
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 1 No 2 (2020): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v1i2.106

Abstract

Obesity is a medical condition in the form of excess body fat that accumulates in such a way as to have a detrimental impact on health, which then decreases life expectancy and or increases health problems. Obesity is now a common health problem in this modern society with a variety of technological discoveries that make people don't need to move a lot to do something, resulting in people living a lifestyle without much Move. Researchers feel the need for an expert system application that can easily diagnose obesity with everyone just by modalizing simple applications on people's smartphones. The expert system that is built will diagnose early obesity disease by method of drawing inferences using backward chaining method and to test the level of belief conclusions using certainity factor method. The system will be able to provide output in the form of obesity diagnosis, explanation, tips and advice on obesity handling solutions. System development Methods using ESDLC (Expert System Development Life Cycle). The system is built on Android.
Investment Decision Making in Digital Business Using Tsukamoto Fuzzy Logic Muhammad Fuad; Fegie Yoanti Wattimena; Ahmad Rizani; Yuswardi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 2 (2023): AUGUST 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i2.1525

Abstract

This research investigates the application of Tsukamoto's Fuzzy Logic in investment decision making in a digital business context. By integrating human knowledge and numerical data, this method seeks to overcome the challenges of complexity and uncertainty that often arise in the fast-changing digital business environment. Through analysis of case studies and interviews with industry practitioners, this study identifies the steps for implementing effective Tsukamoto Fuzzy Logic, including the formation of fuzzy variables, determination of membership functions, application of fuzzy rules, and defuzzification processes. The results of Tsukamoto's Fuzzy Logic calculations are applied to digital investment cases, providing an overview of investment quality based on various input variables. This research shows that this method can produce a holistic and informative approach in making investment decisions. In addition, the diverse participation of practitioners in various regions provides valuable insights in dealing with the uncertainties of digital business. In this challenging digital era, this research provides guidance for decision makers in dealing with the complexities of a dynamic business environment.
E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering Legito; Fegie Yoanti Wattimena; Yulianto Umar Rofi'i; Munawir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 2 (2023): AUGUST 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i2.1527

Abstract

This research proposes and implements an e-commerce product recommendation system that combines Case-Based Reasoning (CBR) and K-Means Clustering algorithms. The main aim of this research is to provide more personalized and relevant product recommendations to e-commerce users. The CBR approach leverages users' transaction history to provide customized recommendations, whereas K-Means Clustering groups users with similar preferences increase the relevance of recommendations. This study assesses the effectiveness of the system by conducting a comprehensive evaluation by comparing system recommendations with actual user preferences. The results of this study reveal that the combined approach of CBR and K-Means Clustering can improve the performance of e-commerce product recommendations, ensure the accuracy of recommendations, and produce a more satisfying shopping experience for users. Although there are limitations in terms of the dataset used and the choice of algorithm parameters, this research makes an important contribution in developing a more adaptive and personalized recommendation system for e-commerce platforms.
Co-Authors Ahmad Rizani Dion R A Mamisala Legito Muhammad Fuad Munawir Reni Koibur Septi Andryana Yulianto Umar Rofi'i Yuswardi Yuswardi