Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Keteknikan Pertanian Jurnal CoreIT
Ligar, Bonang Waspadadi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Computer Vision for Identifying and Classifying Green Coffee Beans: A Review Ligar, Bonang Waspadadi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.141 KB) | DOI: 10.24014/coreit.v8i1.17450

Abstract

Coffee is widely consumed around the world, also considered one of the most important beverages today.  Factors contributing to the quality of coffee beans such as color, texture, size, aroma, etc. and other processes along the production chain such as plant, roasting, and grinding. Those processes will be worthless if the quality of the coffee bean is low. It is important to only use the best quality coffee beans. Therefore, the challenge is to develop a system that uses computer vision to either identify high quality beans or classify them by their species to ease the effort needed by all actors in the supply chain. Providing information for end customers is a defining factor to push forward the coffee industry. This paper aims to review literatures within the topic of using computer vision for coffee beans. After reviewing a selected number of studies which corresponds with the topic chosen in our paper, computer vision techniques were used for two main reasons, identification and classification. Researches on this topic are still limited. Hence, it can be concluded that there are still plenty of room for study on this topic. This study also aims to help provide research material for future researchers.
An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History Imantho, Harry; Seminar, Kudang Boro; Damayanthi , Evy; Suyatma , Nugraha Edhi; Priandana, Karlisa; Ligar, Bonang Waspadadi; Seminar, Annisa Utami
Jurnal Keteknikan Pertanian Vol. 12 No. 1 (2024): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.012.1.140-152

Abstract

The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system.
Co-Authors Evy Damayanthi Imantho, Harry Karlisa Priandana Kudang Boro Seminar Nugraha Edhi Suyatma Seminar, Annisa Utami