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All Journal Jurnal Keteknikan Pertanian International Journal of Supply Chain Management BIODIVERS - BIOTROP Scientific Magazine
Imantho, Harry
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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The Development of an e-Traceability System for Cattle Delivery Chains Seminar, Kudang Boro; Aditya, Edit Lesa; Imantho, Harry; Purnama, Diki Gita; Yani, Ahmad; Cyrilla, Lucia
International Journal of Supply Chain Management Vol 9, No 5 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v9i5.4318

Abstract

Transparency of livestock supply chain management is still a significant problem in Indonesia due to the unavailability of data and information accessible by the stakeholders of cattle supply chains. It is difficult to obtain information for queries, monitoring, and control purposes at any node along cattle supply chains, and thus introducing some risks of insecurity and uncertainty of cattle conditions along the supply chains. Nowadays, consumers are getting smarter and more curious about selecting healthy and high-quality beef. This requires the provision of an easily and securely accessible traceability and transparency system. The aim of this research is to develop an e-traceability system for cattle supply chains. The proposed e-traceability system was developed on the basis of a web-platform that provides wide access and easy links to all actors within a cattle supply chain and stakeholders. All actors in the cattle supply chain need to be registered and the data related to cattles need to be recorded in the traceability system database for analytic and decision-making. The potential applicability of the developed e-traceability system are examined and demonstrated to highlight the benefits of the system in improving transparency and traceability cattle deliveries from land to table for better managerial tasks.
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.
BIOTROP Taps Into Digital Learning Inovation to Strengthen Student’s Engagement on Merdeka Belajar Program Imantho, Harry; Imran, Zulhamsyah; Perdinan; Sugiarto, Slamet Widodo; Supriyanto
BIODIVERS - BIOTROP Science Magazine Vol. 2 No. 1 (2023): BIODIVERS (BIOTROP Science Magazine) : Agro-Eco-Edu-Tourism in Managing Tropica
Publisher : SEAMEO BIOTROP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56060/bdv.2023.2.1.1998

Abstract

The COVID-19 pandemic has suddenly forced the world of education to carry out a more massive digital transformation that should meet student’s requirements and strengthen their knowledge, skills and competencies. The digital media developed should facilitate student’s self-learning and freedom to learn (Merdeka Belajar) amidst physical and social interaction limitations. Vocational high school students have special requirements compared to upper secondary school students in terms of soft skills and hard skills education. It is very interesting to provide digital educational media which fits their needs. This study aims to develop an online digital platform in responding to the demand of vocational schools that have joined the SEAMEO BIOTROP SMARTS-BE program since 2015 in applied tropical biology. Since 2015, SEAMEO BIOTROP has provided face-to-face mentoring to vocational high schools spread across 10 provinces in Indonesia. The Expert System for Identifying Pest and Disease on lemon orchard is a digital transformation in the mentoring method while demonstrating a new learning experience educational materials into a web-based digital platform and Android application. This expert system demonstrates the implementation of problem base learning (ProBL) concepts to enrich online learning materials for vocational schools in the age of digital science and education.
Co-Authors Aditya, Edit Lesa ahmad yani Diki Gita Purnama, Diki Gita Evy Damayanthi Karlisa Priandana Kudang Boro Seminar Ligar, Bonang Waspadadi Lucia Cyrilla Nugraha Edhi Suyatma P. Perdinan Seminar, Annisa Utami Sugiarto, Slamet Widodo Supriyanto Zulhamsyah Imran