International Journal of Electrical and Computer Engineering (IJECE) Vol. 15, No. 2, April 2025, pp. 1968~1977 ISSN: 2088-8708, DOI: 10.11591/ijece.v15i2.pp1968-1977  1968 Journal homepage: http://ijece.iaescore.com Android-based smart digital marketplace application on agricultural commodities using a new variant recommendation system Subiyanto 1 , Sucihatiningsih Dian Wisika Prajanti 2 , Nur Azis Salim 1 , Setya Budi Arif Prabowo 1 , Deyndrawan Sutrisno 1 , Andika Anantyo 1 , Dewi Anggriani 1 1 Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Semarang, Semarang, Indonesia 2 Department of Economic Development, Faculty of Economics and Business, Universitas Negeri Semarang, Semarang, Indonesia Article Info ABSTRACT Article history: Received Jun 7, 2024 Revised Nov 14, 2024 Accepted Nov 20, 2024 In the marketing of agricultural products, addressing the challenges associated with extensive distribution chains is essential, as these directly affect sellers. Additionally, the vast array of available product options often overwhelms customers, complicating their efforts to identify and purchase items that align with their preferences. This work aims to develop a smart e-commerce application for agribusiness, specifically designed for agricultural products on the Android platform. The application integrates a recommendation system that utilizes geolocation-aware neural graph collaborative filtering (GA-NGCF), which facilitates product marketing for farmers and streamlines the product search and selection process for users based on personalized preferences. The development process encompassed various stages, from planning to rigorous testing. The application’s recommendation system, which implements GA-NGCF, operates based on three primary elements: the creation of a geolocation graph of user-item data, the integration of information between neighboring nodes, and the prediction of user preferences. The resulting smart agribusiness e-commerce application, enhanced by GA-NGCF, demonstrated marked improvements in recommendation accuracy and overall application performance during testing. Empirical results indicated substantial enhancements in recommendation metrics, with GA-NGCF achieving a recall of 0.34, a precision of 0.36, and normalized discounted cumulative gain of 0.37, thereby outperforming existing models. Keywords: Agricultural products Android application Geolocation Personalized recommendation system Smart digital market This is an open access article under the CC BY-SA license. Corresponding Author: Nur Azis Salim Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Semarang Semarang 50229, Indonesia Email: nurazissalim@mail.unnes.ac.id 1. INTRODUCTION Agribusiness holds an increasingly vital role in the global economy, encompassing not only agricultural production but also the processing and distribution sectors that supply essential goods and drive economic development [1]–[3]. With the globalization of markets, this sector has transformed into a complex network that aligns with modern marketing strategies, transitioning from traditional approaches to digital platforms to better fulfill customer demands [4]–[6]. The advent of e-commerce has notably revolutionized the agribusiness sector by expanding market access, reducing operational costs, enhancing transparency, and promoting distribution equity through direct connections between farmers and customers [7], [8].