Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Eneng Tita Tosida, Indra Permana Solihin, Fajar Delli Wihartiko, Farhan Naufal

Judul : Application of Genetic Algorithm K-Means Clustering of Villagers Characteristics For Smart Economy

Abstrak :
One of the solutions carried out in helping rural economic growth is to implement a smart economy in smart village ecosystem. The purpose of this study is to apply the KMeans Genetic Algorithm grouping technique to analyze the readiness of villagers of Kabandungan Sub-regency Sukabumi Regency for the smart economy. In research, there is also a concept that is useful in the course of the research process, namely KDD. Tests were carried out on 350 respondents and 221 variables. Three methods are applied to the data namely GA, K-Means and GA K-Means. The optimal cluster value obtained at the elbow is 3 / K=3. The Davies Bouldin index (DBI) value is 14,511 for the GA method, next DBI value is 2,608 for the K-Means method, and the last value is 2,472 for the GA KMeans method. This makes the selection of the GA K-means method further analyzed, then the ANOVA approach is carried out to see the significance of the method results. The clustering results show that there are 23% of residents who are not ready for the smart economy, 57% of citizens who are sufficiently prepared for the smart economy, and 20% of residents who are very prepared for the smart economy. and the population of Cianaga Village who occupy the readiness is very well prepared & quite well prepared. The researchers' findings show that there are 3 levels of citizen participation, namely where cluster 1 is the crowdsourcing level, then cluster 2 distributed intelligence, and cluster 3 participatory science.

Keywords—Clustering, Genetic Algorithm, K-Means, Smart economy, Smart village.

Tahun : 2022 Media Publikasi : Prosiding

Kategori : Prosiding No/Vol/Tahun : - / 22 / 2022

ISSN/ISBN : 978-1-6654-7327-9

PTN/S : Universitas Pakuan Program Studi : ILMU KOMPUTER

Bibliography :
[1] A. D. Santoso et al., Desa Cerdas : Transformasi Kebijakan dan Pembangunan Desa Merespon EraRevolusi Industri 4.0. Bulaksumur: Universitas Gadjah Mada, 2019.

[2] R. Sutriadi, “Defining smart city, smart region, smart village, and technopolis as an innovative concept in indonesia’s urban and regional development themes to reach sustainability,” in IOP Conference Series: Earth and Environmental Science, Nov. 2018, vol. 202, no. 1, pp. 1–12. doi: 10.1088/1755-1315/202/1/012047.

[3] European Union, Smart Villages Revitalising Rural Services Smart Villages, vol. 26. Luxembourg City: ENRD Contact Point, 2018. Accessed: Jul. 26, 2021. [Online]. Available: https://enrd.ec.europa.eu

[4] L. Lin, M. Gen, and Y. Liang, “A Hybrid EA for High-dimensional Subspace Clustering Problem,” in Congress on Evolutionary Computation (CEC), Jul. 2014, pp. 2855–2860.

[5] S. Kapil, M. Chawla, and M. D. Ansari, “On K-means Data Clustering Algoritm with Genetic Algorithm,” in Fourth International Conference on Parallel 2016, Distributed and Grid Computing (PDGC), 2016, pp. 202–206.

[6] R. Kurnia, “Penerapan Metode GA-KMEANS Untuk Pengelompokan Bahan Makanan Berdasarkan Kandungan Zat Gizi,” Universitas Brawijaya, Malang, 2016.

[7] R. Nooraeni, “Cluster Method Using A Combination Of Cluster Kprototype Algorithm And Genetic Algorithm For Mixed Data,” Aplikasi Statistika & Komputasi Statistik, vol. 7, pp. 81–97, Jan. 2016, [Online]. Available: https://www.researchgate.net/publication/331873204

[8] E. T. Tosida, S. Suprehatin, Y. Herdiyeni, Marimin, and I. P. Solihin, “Clustering of Citizen Science Prospect to Construct Big Data-based Smart Village in Indonesia,” in Proceedings - 2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020, Nov. 2020, pp. 58–63. doi: 10.1109/ICIMCIS51567.2020.9354323.

[9] R. Priya and S. W. A. Rizvi, “SVM &Ga-clustering Based Feature Selection Approach for Breast Cancer Detection,” International Journal on Soft Computing, Artificial Intelligence and Applications, vol. 1, no. 10, pp. 1–10, Nov. 2020, doi: 10.5121/ijscai.2020.9401.

[10] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” American Association for Artificial Intelligence, pp. 37–54, 1996. Accessed: Jul. 26, 2021. [Online]. Available: https://www.kdnuggets.com/gpspubs/aimag-kddoverview-1996-Fayyad.pdf

[11] Y. Arkeman, K. B. Seminar, and H. Gunawan, Algoritma Genetika : Teori dan Aplikasinya untuk Bisnis dan Indonesia, 1st ed. Bogor: Penerbit IPB Press, 2012. Accessed: Jul. 26, 2021. [Online]. Available: https://repository.ipb.ac.id/handle/123456789/57902

[12] D. T. Larose and C. D. Larose, Discovering Knowledge In Data : An Introduction to Data Mining, 2nd ed. Hoboken: John Wiley & Sons, 2014.

[13] E. Tosida, Y. Herdiyeni, M. Marimin, and S. Suprehatin, “Investigating the effect of technology-based village development towards smart economy: An application of variance-based structural equation modeling,” International Journal of Data and Network Science, vol. 6, no. 3, pp. 787–804, 2022.

[14] M. Haklay, “Participatory citizen science,” in Citzen Science, S. Hecker, M. Haklay, A. Bowser, Z. Makuch, J. Vogel, and A. Bonn, Eds. UCL Press, 2013, pp. 52–62. doi: 10.14324/ 111.9781787352339.

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Detail Karya Ilmiah Dosen

Eneng Tita Tosida, Indra Permana Solihin, Fajar Delli Wihartiko, Farhan Naufal

Judul	:	Application of Genetic Algorithm K-Means Clustering of Villagers Characteristics For Smart Economy
Abstrak	:	One of the solutions carried out in helping rural economic growth is to implement a smart economy in smart village ecosystem. The purpose of this study is to apply the KMeans Genetic Algorithm grouping technique to analyze the readiness of villagers of Kabandungan Sub-regency Sukabumi Regency for the smart economy. In research, there is also a concept that is useful in the course of the research process, namely KDD. Tests were carried out on 350 respondents and 221 variables. Three methods are applied to the data namely GA, K-Means and GA K-Means. The optimal cluster value obtained at the elbow is 3 / K=3. The Davies Bouldin index (DBI) value is 14,511 for the GA method, next DBI value is 2,608 for the K-Means method, and the last value is 2,472 for the GA KMeans method. This makes the selection of the GA K-means method further analyzed, then the ANOVA approach is carried out to see the significance of the method results. The clustering results show that there are 23% of residents who are not ready for the smart economy, 57% of citizens who are sufficiently prepared for the smart economy, and 20% of residents who are very prepared for the smart economy. and the population of Cianaga Village who occupy the readiness is very well prepared & quite well prepared. The researchers' findings show that there are 3 levels of citizen participation, namely where cluster 1 is the crowdsourcing level, then cluster 2 distributed intelligence, and cluster 3 participatory science. Keywords—Clustering, Genetic Algorithm, K-Means, Smart economy, Smart village.
Tahun	:	2022	Media Publikasi	:	Prosiding
Kategori	:	Prosiding	No/Vol/Tahun	:	- / 22 / 2022
ISSN/ISBN	:	978-1-6654-7327-9
PTN/S	:	Universitas Pakuan	Program Studi	:	ILMU KOMPUTER
Bibliography	:	[1] A. D. Santoso et al., Desa Cerdas : Transformasi Kebijakan dan Pembangunan Desa Merespon EraRevolusi Industri 4.0. Bulaksumur: Universitas Gadjah Mada, 2019. [2] R. Sutriadi, “Defining smart city, smart region, smart village, and technopolis as an innovative concept in indonesia’s urban and regional development themes to reach sustainability,” in IOP Conference Series: Earth and Environmental Science, Nov. 2018, vol. 202, no. 1, pp. 1–12. doi: 10.1088/1755-1315/202/1/012047. [3] European Union, Smart Villages Revitalising Rural Services Smart Villages, vol. 26. Luxembourg City: ENRD Contact Point, 2018. Accessed: Jul. 26, 2021. [Online]. Available: https://enrd.ec.europa.eu [4] L. Lin, M. Gen, and Y. Liang, “A Hybrid EA for High-dimensional Subspace Clustering Problem,” in Congress on Evolutionary Computation (CEC), Jul. 2014, pp. 2855–2860. [5] S. Kapil, M. Chawla, and M. D. Ansari, “On K-means Data Clustering Algoritm with Genetic Algorithm,” in Fourth International Conference on Parallel 2016, Distributed and Grid Computing (PDGC), 2016, pp. 202–206. [6] R. Kurnia, “Penerapan Metode GA-KMEANS Untuk Pengelompokan Bahan Makanan Berdasarkan Kandungan Zat Gizi,” Universitas Brawijaya, Malang, 2016. [7] R. Nooraeni, “Cluster Method Using A Combination Of Cluster Kprototype Algorithm And Genetic Algorithm For Mixed Data,” Aplikasi Statistika & Komputasi Statistik, vol. 7, pp. 81–97, Jan. 2016, [Online]. Available: https://www.researchgate.net/publication/331873204 [8] E. T. Tosida, S. Suprehatin, Y. Herdiyeni, Marimin, and I. P. Solihin, “Clustering of Citizen Science Prospect to Construct Big Data-based Smart Village in Indonesia,” in Proceedings - 2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020, Nov. 2020, pp. 58–63. doi: 10.1109/ICIMCIS51567.2020.9354323. [9] R. Priya and S. W. A. Rizvi, “SVM &Ga-clustering Based Feature Selection Approach for Breast Cancer Detection,” International Journal on Soft Computing, Artificial Intelligence and Applications, vol. 1, no. 10, pp. 1–10, Nov. 2020, doi: 10.5121/ijscai.2020.9401. [10] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” American Association for Artificial Intelligence, pp. 37–54, 1996. Accessed: Jul. 26, 2021. [Online]. Available: https://www.kdnuggets.com/gpspubs/aimag-kddoverview-1996-Fayyad.pdf [11] Y. Arkeman, K. B. Seminar, and H. Gunawan, Algoritma Genetika : Teori dan Aplikasinya untuk Bisnis dan Indonesia, 1st ed. Bogor: Penerbit IPB Press, 2012. Accessed: Jul. 26, 2021. [Online]. Available: https://repository.ipb.ac.id/handle/123456789/57902 [12] D. T. Larose and C. D. Larose, Discovering Knowledge In Data : An Introduction to Data Mining, 2nd ed. Hoboken: John Wiley & Sons, 2014. [13] E. Tosida, Y. Herdiyeni, M. Marimin, and S. Suprehatin, “Investigating the effect of technology-based village development towards smart economy: An application of variance-based structural equation modeling,” International Journal of Data and Network Science, vol. 6, no. 3, pp. 787–804, 2022. [14] M. Haklay, “Participatory citizen science,” in Citzen Science, S. Hecker, M. Haklay, A. Bowser, Z. Makuch, J. Vogel, and A. Bonn, Eds. UCL Press, 2013, pp. 52–62. doi: 10.14324/ 111.9781787352339.
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