AIRAAIRA

Journal of Information Systems and Technology ResearchJournal of Information Systems and Technology Research

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.

The study reveals that weak internet signals are a primary obstacle to digital technology adoption in Kuta Baru Village.Furthermore, higher education levels and increased income are positively correlated with greater technology adoption rates.These findings underscore the need for integrated strategies that address both infrastructural limitations and socio-economic disparities to promote digital inclusion in rural communities.

Future research should expand the dataset to encompass multiple rural communities to enhance the generalizability of the findings. Investigating the effectiveness of different data mining algorithms, such as FP-Growth or machine learning models, in conjunction with Apriori could provide more robust and scalable insights into adoption patterns. Additionally, exploring the role of digital literacy training programs tailored to the specific needs of rural populations, alongside infrastructural improvements, presents a promising avenue for accelerating digital transformation and bridging the rural digital divide. These investigations should consider the interplay between technological access, skill development, and economic empowerment to formulate comprehensive strategies for fostering sustainable digital inclusion in rural areas.

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  2. Penerapan Algoritma Apriori Dalam Menganalisis Pola Minat Beli Konsumen Di Coffee Shop | JEKIN - Jurnal... doi.org/10.58794/jekin.v3i1.483Penerapan Algoritma Apriori Dalam Menganalisis Pola Minat Beli Konsumen Di Coffee Shop JEKIN Jurnal doi 10 58794 jekin v3i1 483
  3. Pengembangan Sistem Rekomendasi Produk Berdasarkan Pola Pembelian dengan Pendekatan Algoritma Apriori... ejournal.sisfokomtek.org/index.php/jikom/article/view/2718Pengembangan Sistem Rekomendasi Produk Berdasarkan Pola Pembelian dengan Pendekatan Algoritma Apriori ejournal sisfokomtek index php jikom article view 2718
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