Eligibility Study of Targeted Electricity Subsidies Using DBSCAN on 450 VA and 900 VA Households at PLN UP3 Bandung

Randy Zakya Suchardy; Adi Firmansyah; Nugraha Priya Utama; Rahman Indra Kesuma

doi:10.24014/ijaidm.v7i2.26818

Eligibility Study of Targeted Electricity Subsidies Using DBSCAN on 450 VA and 900 VA Households at PLN UP3 Bandung

Randy Zakya Suchardy, Adi Firmansyah, Nugraha Priya Utama, Rahman Indra Kesuma

Abstract

PT PLN (Persero), a State-Owned Enterprise (SOE), is mandated by Law No. 30/2007 on Energy and Law No. 30/2009 on Electricity to provide subsidy funds for the poor. The objective of this study is to analyze eligibility criteria for electricity subsidy recipients for customers using 450 VA and 900 VA power groups, to target the electricity subsidy program better. The data used is postpaid customer data from UP3 Bandung in September 2023. The variables used are the amount of electricity consumption, the number of bills, late fees, installment fees, and 50 other variables. The method used in this research is DBScan Clustering which is applied to each power group. Within each group, we analyzed two normalized versions of the dataset standard version and the minmax version. Furthermore, to assess the optimal clustering results, we integrated various metrics, including the Davies-Bouldin Index and Silhouette Score with visual assessment. After that, the best factor suggestions were sought through decision trees, by performing different decision tree classifiers for each power group, using normalized versions of cluster labels. The results showed that among the 50 features available in the raw dataset, it was successful in identifying key features, such as late fees, installment fees, electricity consumption, and bill charges to be important criteria

Keywords

Classification; Clustering; DBSCAN; Decision Tree; Subsidy

Full Text:

PDF

References

R. K. Gobel, B. S. Laksmono, M. Huseini, and M. Siscawati, “Equity and Efficiency: An Examination of Indonesia’s Energy Subsidy Policy and Pathways to Inclusive Reform,” Sustainability, vol. 16, no. 1, p. 407, Jan. 2024, doi: 10.3390/su16010407.

Tim Nasional Percepatan Penanggulangan Kemiskinan (TNP2K), Policy Paper (Naskah Kebijakan) Transformasi Subsidi Listrik di Indonesia: Tujuan dan Usulan Mekanismenya, 1st ed. 2021. [Online]. Available: www.tnp2k.go.id

N. Noor Hanan and M. S. Fuady, “REVIEW OF ELECTRICITY SUBSIDIES IN INDONESIA 2015-2020,” Jurnal Pajak dan Keuangan Negara, vol. 4, no. 2, pp. 529–538, 2023.

I. Firman Ashari, E. Dwi Nugroho, R. Baraku, I. N. Yanda, and R. Liwardana, “Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta,” Journal of Applied Informatics and Computing (JAIC), vol. 7, no. 1, pp. 2548–6861, 2023, [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

W. Y. Widiawati and R. M. Atok, “Analisis Klasifikasi Pelanggan Listrik Rumah Tangga Bersubsidi Kota Surabaya Menggunakan Support Vector Machine dan Naïve Bayes Classifier,” JURNAL SAINS DAN SENI ITS Vol. 7, No. 2, vol. 7, no. 2, pp. D235–D242, 2018.

Y. M. Hutahaean and A. W. Wijayanto, “Electricity Subsidy Recipient Households Classification in Gorontalo Province in 2019 using K-Nearest Neighbor and Support Vector Machine,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 10, no. 1, p. 63, Jan. 2022, doi: 10.26418/justin.v10i1.51210.

V. G. Costa and C. E. Pedreira, “Recent advances in decision trees: an updated survey,” Artif Intell Rev, vol. 56, no. 5, pp. 4765–4800, May 2023, doi: 10.1007/s10462-022-10275-5.

A. E. Ezugwu et al., “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects,” Eng Appl Artif Intell, vol. 110, p. 104743, Apr. 2022, doi: 10.1016/j.engappai.2022.104743.

K. Pearson, “On lines and planes of closest fit to systems of points in space,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 2, no. 11, pp. 559–572, Nov. 1901, doi: 10.1080/14786440109462720.

V. Soltan, Lectures on Convex Sets. WORLD SCIENTIFIC, 2020. doi: 10.1142/11328.

S. Pitafi, T. Anwar, and Z. Sharif, “A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms,” Applied Sciences, vol. 13, no. 6, p. 3529, Mar. 2023, doi: 10.3390/app13063529.

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” kdd, 1996, [Online]. Available: www.aaai.org

D. L. Davies and D. W. Bouldin, “A Cluster Separation Measure,” IEEE Trans Pattern Anal Mach Intell, vol. PAMI-1, no. 2, pp. 224–227, Apr. 1979, doi: 10.1109/TPAMI.1979.4766909.

P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J Comput Appl Math, vol. 20, pp. 53–65, Nov. 1987, doi: 10.1016/0377-0427(87)90125-7.

M. Aupetit, M. Sedlmair, M. M. Abbas, A. Baggag, and H. Bensmail, “Toward Perception-Based Evaluation of Clustering Techniques for Visual Analytics,” in 2019 IEEE Visualization Conference (VIS), IEEE, Oct. 2019, pp. 141–145. doi: 10.1109/VISUAL.2019.8933620.

DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.26818

Refbacks

There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Journal Indexing:

IJAIDM Stats