12th Annual International Conference on Industrial Engineering and Operations Management

Indonesia’s Readiness to Implement Agriculture Data Analytic – Based Smart Village

Eneng Tita Tosida, Yeni Herdiyeni, Marimin Marimin & Suprehatin Suprehatin
Publisher: IEOM Society International
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Track: Data Analytics and Big Data
Abstract

Indonesia strengthens village development to accelerate the achievement of the Sustainable Development Goals (SDGs) targets. The Ministry of Villages, Development of Disadvantaged Regions and Transmigration (Kemendes PDTT) publishes the Village SDGs and the concept of a smart village to achieve these goals. To find out Indonesia's readiness in implementing smart villages, it is necessary to conduct a situation analysis. Because agriculture is the main activity in the village, this study focuses on smart village analysis based on Agriculture Data Analytic (ADA). This study aims to analyze Indonesia's readiness in ADA-based smart village development. We used the 2019 Village Potential Data. The methods conducted by descriptive analysis methods, multiple regression analysis and clustering. Indonesia is well prepared if it is seen from the level of participation of villagers in the management of ICT, agribusiness, transportation and renewable energy. The level of participation of villagers varies greatly depending on the character of the province. The results of multiple regression analysis show that the sources of regional budget revenue (APBD), Village Original Revenue (PAD) and self-subsistent (Swadaya) funds have a significant effect on ICT managers in the village through the poor and most residents. APBD, PAD and Swadaya also have a significant effect on agribusiness managers in the village through the poor, the private sector and the business community in the village. Other sources of funds have a significant effect on the management of renewable energy in the village. The APBD and other sources only have a significant effect on transportation managers in the village, through most of the residents. Clustering using a Self Organizing Map (SOM) on 62.847 data and 25 variables related to ICT, agribusiness, transportation and renewable energy activities, was able to map 5 levels of potential smart villages, namely: very potential, potential, quite potential, less potential and not potential.

Published in: 12th Annual International Conference on Industrial Engineering and Operations Management, Istanbul, Turkey

Publisher: IEOM Society International
Date of Conference: March 7-10, 2022

ISBN: 978-1-7923-6131-9
ISSN/E-ISSN: 2169-8767