Potential classification of Smart Village – Smart Economy with Deep Learning methods

Runanto Runanto; Muhammad Fahmi Mislahudin; Fauzan Azmi Alfiansyah; Maudy Khairunnisa Maisun Taqiyyah; Eneng Tita Tosida

doi:10.46336/ijqrm.v2i3.147

Potential classification of Smart Village – Smart Economy with Deep Learning methods

Runanto Runanto, Muhammad Fahmi Mislahudin, Fauzan Azmi Alfiansyah, Maudy Khairunnisa Maisun Taqiyyah, Eneng Tita Tosida

Abstract

Development gap in the city and village is still happening on Indonesia. It happened because of the massive urbanization factors. Poverty in the Indonesian villages are relatively higher than on the urbans. In order to reach the maximal city development, Ministry of Village, Development of Disadvantaged Regions and Transmigration of Indonesia created a sustainable village development program namely Village’s Sustainable Development Goals (SDGs) and optimized the village potential data. This study aimed to design the smart village – smart economy classification system by using deep learning methods on village potential data on Indonesia at 2020. The method used in this study is data mining processes namely KDD (Knowledge Discovery and Data mining). The result in this study showed the best models were obtained which consisting of 2 hidden layers and each layer is 128, 128 layers which using target class from the process of calculating the score is able to reach 94.93% of the accuracy from the training process and 96% on the testing process and succeeded to classify the potentials of smart village – smart economy.

Keywords

classification, village potential, smart village, smart economy, deep learning

Full Text:

PDF

References

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265-283).

Andari, R. N., & Ella, S. (2021). Model Desa Cerdas Untuk Membangun Indonesia Maju. Syiah Kuala University Press.

Arwildayanto, A., & Utoyo, S. (2019). Rintisan Desa Cerdas (RDC) melalui Program Pemberdayaan Masyarakat Desa Bakti Kecamatan Pulubala Kabupaten Gorontalo Provinsi Gorontalo. PengabdianMu: Jurnal Ilmiah Pengabdian Kepada Masyarakat, 5(1), 24-32.

European Network for Rural Development. (2018). Smart Villages: Revitalising Rural Services. European Network for Rural Development.

Fauzan, M., Adiwijaya, A., & Mubarok, M. S. (2018). Klasifikasi Multi-Label pada Topik Berita Berbahasa Indonesia menggunakan Artificial Neural Network. eProceedings of Engineering, 5(3).

Gullo, F. From patterns in data to knowledge discovery: what data mining can do. Phys. Procedia 62, 18–22 (2015). In 3rd International Conference Frontiers in Diagnostic Technologies.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.

Irham, L. G., Adiwijaya, A., & Wisesty, U. N. (2019). Klasifikasi Berita Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine. JURNAL MEDIA INFORMATIKA BUDIDARMA, 3(4), 284-292.

Kelleher, J. D. (2019). Deep learning. MIT press.

Maja, P. W., Meyer, J., & Von Solms, S. (2020). Development of Smart Rural Village Indicators in Line With Industry 4.0. IEEE Access, 8, 152017-152033.

Mardi, Y. (2017). Data Mining: Klasifikasi Menggunakan Algoritma C4. 5. Edik Informatika, 2(2), 213-219.

Ministry of Village, Development of Disadvantaged Regions, and Transmigration. (2020). Dari TPB ke SDGs desa. Retreived from https://sdgsdesa.kemendesa.go.id/dari-tpb-ke-sdgs-desa/

Ministry of Village, Development of Disadvantaged Regions, and Transmigration. (2021). Pemutahiran Data IDM Berbasis SDGs Desa 2021. Ministry of Village, Development of Disadvantaged Regions, and Transmigration.

Ministry of Village, Development of Disadvantaged Regions, and Transmigration. (2016). Peraturan Menteri Desa, Pembangunan Daerah Tertinggal dan Transmigrasi nomor 2 tahun 2016 tentang Indeks Desa Membangun. Ministry of Village, Development of Disadvantaged Regions, and Transmigration.

Puspaningrum, E. Y., & Maulana, H. (2020, November). Penerapan Metode Convolutional Neural Network untuk Klasifikasi Penyakit Daun Apel pada Imbalanced Data. In Prosiding Seminar Nasional Informatika Bela Negara (Vol. 1, pp. 169-175).

Santoso, A. D., Fathin, C. A., Effendi, K. C., Novianto, A., Sumiar, H. R., Angendari, D. A. D., & Putri, B. P. (2019). Desa Cerdas: Transformasi Kebijakan dan Pembangunan Desa Merespon Era Revolusi Industri 4.0. Monograf.(EA Purwanto & D. Permadi, Eds.). Yogyakarta: cfds Fisipol UGM.

Santoso, A., & Ariyanto, G. (2018). Implementasi deep learning berbasis keras untuk pengenalan wajah. Emitor: Jurnal Teknik Elektro, 18(1), 15-21.

Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040-53065.

Subekti, T., & Damayanti, R. (2019). Penerapan model smart village dalam pengembangan desa wisata: Studi pada desa wisata boon pring sanankerto turen kabupaten malang. Journal of Public Administration and Local Governance, 3(1), 18-28.

Tosida, E. T., Herdiyeni, Y., & Suprehatin, S. (2020, September). The Potential for Implementing a Big Data Analytic-based Smart Village in Indonesia. In 2020 International Conference on Computer Science and Its Application in Agriculture (ICOSICA) (pp. 1-10). IEEE.

Tosida, E. T., Suprehatin, S., Herdiyeni, Y., & Solihin, I. P. (2020, November). Clustering of Citizen Science Prospect to Construct Big Data-based Smart Village in Indonesia. In 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) (pp. 58-63). IEEE.

Tosida, E. T., Wahyudin, I., Andria, F., Djatna, T., Ningsih, W. K., & Lestari, D. D. (2020). Business Intelligence of Indonesian Telematics Human Resource: Optimization of Customer and Internal Balanced Scorecards. Journal of Southwest Jiaotong University, 55(2).

Village Potential Data 2020 Central Bereau of Statistics (BPS) Indonesia, Jakarta, 2020.

Wanto, A. (2017). Optimasi Prediksi Dengan Algoritma Backpropagation Dan Conjugate Gradient Beale-Powell Restarts. Jurnal Nasional Teknologi dan Sistem Informasi, 3(3), 370-380.

Zhang, Z. (2018, June). Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (pp. 1-2). IEEE.

DOI: https://doi.org/10.46336/ijqrm.v2i3.147