Data Mining of Rural Digital Technology Adoption Factors Using Apriori Algorithm
DOI:
https://doi.org/10.55537/jistr.v4i3.1324Keywords:
Apriori Algorithm, Data Mining, Association Rules, Digital Technology Adoption, Rural CommunitiesAbstract
Digital technology adoption in rural communities remains a major challenge due to limited infrastructure, weak internet connectivity, and low levels of digital literacy, which contribute to persistent gaps in digital inclusion. This study aims to analyze the socio-economic factors that influence technology adoption in Kuta Baru Village by applying data mining techniques with the Apriori algorithm within the Knowledge Discovery in Database (KDD) framework. A survey was conducted on 50 respondents selected using purposive sampling, and variables such as education, income, occupation, and internet access were encoded into binary items for analysis. The Apriori algorithm was executed with a minimum support threshold of 15% and a minimum confidence threshold of 60% to extract association rules. Results show that the strongest rule was “Low Internet Access ⇒ Weak Signal” with 100% confidence and 30% support, highlighting infrastructure as the most critical barrier. Another key finding revealed that respondents with education levels above high school had an 85% confidence of using the internet, while those with monthly incomes greater than IDR 3 million demonstrated a 78% confidence of adopting digital technologies. Furthermore, formal sector occupations were associated with consistent internet usage at 72% confidence. These findings suggest that improving infrastructure must be complemented by strengthening socio-economic conditions, particularly education and income, to accelerate rural digital transformation. The study provides empirical evidence and practical implications that can inform policymakers in designing targeted programs to bridge the rural digital divide.
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