LR-GLASSO Method for Solving Multiple Explanatory Variables of the Village Development Index
Abstract
Sustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modeling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have a large variance and overfitting in the prediction results. The modeling solution uses LASSO and GLASSO. The binary categorical response data use binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. On the basis of the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables and the correlation between them is relatively high. The Indonesian government recommendation, if it is to increase the status of VDI in Indonesia, especially in the north Maluku province, is to increase the number of electricity users, food and beverage stores, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centers, because they still need to be developed compared to other villages, and more than 50% of the villages are underdeveloped. If the Village SDGs are formulated by increasing the VDI status, it will support the achievement of the SDGs goals nationally.
Downloads
References
Kementerian PPN/Bappenas, “Pedoman Teknis Penyusunan Rencana Aksi Tujuan Pembangunan Berkelanjutan (TPB)/ Sustainable Development Goals (SDGs),” Jakarta, 2020.
E. Kurniawan, Amidi, Gunawan, N. Susilowati, L. Paranti, and D. G. Santi, Panduan UNNES GIAT Penguatan Generasi Milenial Mendukung SDGs Desa. 2022.
T. W. Rahmaddhani and N. Prasetyoningsih, “Achieving a Developing Village based on the Village Sustainable Development Goals in Tirtonirmolo Village, Bantul Regency,” Jurnal Penegakan Hukum dan Keadilan, vol. 4, no. 1, pp. 11–29, Mar. 2023, doi: 10.18196/jphk.v4i1.16043.
Permendesa Nomor 13, “Peraturan Menteri Desa, Pembangunan Daerah Tertinggal dan Transmigrasi Republik Indonesia,” Jakarta, 2020.
N. A. Utami and A. W. Wijayanto, “Classification of Village Development Index at Regency/ Municipality Level Using Bayesian Network Approach With K-Means Discretization,” Jurnal Aplikasi Statistika & Komputasi Statistik, vol. Khusus, pp. 95–106, 2022.
Muhtarom, N. Kusuma, and E. Purwanti, “Analisis Indeks Desa Membangun untuk Mengetahui Pola Perkembangan Pembangunan Desa Di Kecamatan Gadingrejo Kabupaten Pringsewu,” INOVASI PEMBANGUNAN – JURNAL KELITBANGAN, vol. 6, no. 2, pp. 179–190, 2018, [Online]. Available: http://journalbalitbangdalampung.org
H. S. B. Harmadi, U. Suchaini, and A. Adji, “Indonesia’s Village Development Indicator: In Terms of Mismatch of Village Development Measurement Indicator,” Jakarta, 2020. [Online]. Available: www.tnp2k.go.id
E. P. Yudha, B. Juanda, L. M. Kolopaking, and R. A. Kinseng, “Rural development policy and strategy in the rural autonomy era. Case study of pandeglang regency-Indonesia,” Human Geographies - Journal of Studies and Research in Human Geography, vol. 14, no. 1, pp. 125–147, May 2020, doi: 10.5719/hgeo.2020.141.8.
A. R. T. Hidayat, Y. E. Prasetya, and D. Dinanti, “Village Development Index and ICT Infrastructure in Tourism Region,” J. Ind. Tour. Dev. Std, vol. 7, no. 3, pp. 166–174, 2019, doi: 10.21776/ub.jitode.2019.007.03.05.
A. N. Astika and N. S. Subawa, “Evaluasi Pembangunan Desa Berdasarkan Indeks Desa Membangun,” Jurnal Ilmiah Muqoddimah : Jurnal Ilmu Sosial, Politik dan Humaniora, vol. 5, no. 2, 2021, [Online]. Available: http://jurnal.um-tapsel.ac.id/index.php/muqoddimah
F. Abqorunnisa, Erfiani, and A. Djuraidah, “Performance of LASSO And Elastic-Net Methods on Non-Invasive Blood Glucose Measurement Calibration Modeling,” Barekeng: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 1, pp. 0037–0042, Apr. 2023, doi: 10.30598/barekengvol17iss1pp0037-0042.
P. Rintara, S. E. Ahmed, and S. Lisawadi, “Post Improved Estimation and Prediction in the Gamma Regression Model,” Thailand Statistician, vol. 21, no. 3, pp. 580–606, 2023, [Online]. Available: http://statassoc.or.th
R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 267–288, 1996.
R. N. Rachmawati, A. C. Sari, and Yohanes, “Lasso Regression for Daily Rainfall Modeling at Citeko Station, Bogor, Indonesia,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 383–390. doi: 10.1016/j.procs.2021.01.020.
J. M. K. Aheto, H. O. Duah, P. Agbadi, and E. K. Nakua, “A predictive model, and predictors of under-five child malaria prevalence in Ghana: How do LASSO, Ridge and Elastic net regression approaches compare?,” Prev Med Rep, vol. 23, Sep. 2021, doi: 10.1016/j.pmedr.2021.101475.
O. Reangsephet, S. Lisawadi, and S. E. Ahmed, “Post Selection Estimation and Prediction in Poisson Regression Model,” Thailand Statistician, vol. 18, no. 2, pp. 176–195, 2020, [Online]. Available: http://statassoc.or.th
N. H. Pusponegoro, A. Kurnia, K. A. Notodiputro, A. M. Soleh, and E. T. Astuti, “Small Area Estimation of Sub-District’s per Capita Expenditure through Area Effects Selection using LASSO Method,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 754–761. doi: 10.1016/j.procs.2021.01.064.
A. M. Soleh and Aunuddin, “Lasso : Alternatif Seleksi Peubah Dan Penyusutan Koefisien Model Regresi Linier, Solusi,” FSK : Indonesian Journal of Statistics, vol. 18, no. 1, pp. 21–27, 2013.
S. Sardy, J. Diaz-Rodriguez, and C. Giacobino, “Thresholding tests based on affine LASSO to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension,” Comput Stat Data Anal, vol. 173, pp. 1–14, Sep. 2022, doi: 10.1016/j.csda.2022.107507.
Kusnaeni, A. M. Soleh, F. M. Afendi, and B. Sartono, “Function Group Selection of Sembung Leaves (Blumea Balsamifera) Significant to Antioxidants Using Overlapping Group Lasso,” Barekeng: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 2, pp. 721–728, Jun. 2022, doi: 10.30598/barekengvol16iss2pp721-728.
M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” J. R. Statist. Soc. B, vol. 68, no. 1, pp. 49–67, 2006.
S. Zheng and C. Ding, “A group lasso based sparse KNN classifier,” Pattern Recognit Lett, vol. 131, pp. 227–233, Mar. 2020, doi: 10.1016/j.patrec.2019.12.020.
H. F. Zhang, “Minimum Average Variance Estimation with group Lasso for the multivariate response Central Mean Subspace,” J Multivar Anal, vol. 184, Jul. 2021, doi: 10.1016/j.jmva.2021.104753.
W. Wang, H. Yuan, J. Han, and W. Liu, “PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery,” Comput Struct Biotechnol J, vol. 21, pp. 365–377, Jan. 2023, doi: 10.1016/j.csbj.2022.12.005.
M. Yunus, A. Saefudin, and A. M. Soleh, “Characteristics of group LASSO in handling high correlated data,” Applied Mathematical Sciences, vol. 11, pp. 953–961, 2017, doi: 10.12988/ams.2017.7276.
H. Chen and Y. Xiang, “The Study of Credit Scoring Model Based on Group Lasso,” in Procedia Computer Science, Elsevier B.V., 2017, pp. 677–684. doi: 10.1016/j.procs.2017.11.423.
C. Shang, H. Ji, X. Huang, F. Yang, and D. Huang, “Generalized grouped contributions for hierarchical fault diagnosis with group Lasso,” Control Eng Pract, vol. 93, Dec. 2019, doi: 10.1016/j.conengprac.2019.104193.
Q. Kang, Q. Fan, and J. M. Zurada, “Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network,” Inf Sci (N Y), vol. 553, pp. 66–82, Apr. 2021, doi: 10.1016/j.ins.2020.12.014.
Y. Sun and Q. Wang, “An adaptive group LASSO approach for domain selection in functional generalized linear models,” J Stat Plan Inference, vol. 219, pp. 13–32, Jul. 2022, doi: 10.1016/j.jspi.2021.11.003.
Alan. Agresti, Analysis of Ordinal Categorical Data, Second. Canada: John Wiley & Sons, Inc, 2010.
P. McCullagh and J. A. Nelder Frs, Generalized Linear Models, II. London: Chapman and Hall, 1989.
V. R. S. Nastiti, Y. Azhar, and R. S. Putri, “Logistic Regression Using Hyperparameter Optimization on COVID-19 Patients’ Vital Status,” JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 681–687, 2023, doi: 10.29207/resti.v7i34868.xxx.
I. Haq, M. N. Aidi, A. Kurnia, and E. Efriwati, “A Comparison of Logistic Regression and Geographically Weighted Logistic Regression (GWLR) on Covid-19 Data in West Sumatra,” Barekeng: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 3, pp. 1749–1760, Sep. 2023, doi: 10.30598/barekengvol17iss3pp1749-1760.
Kemendes PDTT RI, “Peringkat Indeks Desa Membangun Tahun 2018,” Jakarta, 2018.
Kemendes PDTT RI, “Status Indeks Desa Membangun Provinsi Kabupaten Kecamatan Tahun 2019,” Jakarta, 2019.
Kemendes PDTT RI, “Peringkat Status Indeks Desa Membangun (IDM),” Jakarta, 2020.
Kemendes PDTT RI, “Peringkat Indeks Desa Membangun Tahun 2021,” Jakarta, 2021.
Kemendes PDTT RI, “Peringkat Nilai Rata-Rata Indeks Desa Membangun Tahun 2022,” Jakarta, 2022.
BPS, “STATISTIK POTENSI DESA INDONESIA (VILLAGE POTENTIAL STATISTICS OF INDONESIA),” Jakarta, 2021.
N. R. Draper and H. Smith, Applied Regression Analysis, Third Edition. Canada: A Wiley-Interscience publication, 1998.
A. Zeinal, “Generalized two-parameter estimator in linear regression model,” Journal of Mathematical Modeling, vol. 8, no. 2, pp. 157–176, Mar. 2020, doi: 10.22124/jmm.2020.14903.1353.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second. California: Springer, 2008.
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann Stat, vol. 32, no. 2, pp. 407–451, Apr. 2004, doi: 10.1214/009053604000000067.
J. Friedman, T. Hastie, and R. Tibshirani, “A note on the group lasso and a sparse group lasso,” arXiv: Statistics Theory, Jan. 2010, [Online]. Available: http://arxiv.org/abs/1001.0736
N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, “A SPARSE-GROUP LASSO,” Journal of Computational and Graphical Statistics, vol. 22, no. 2, pp. 231–245, 2013.
Kemendes PDTT RI, “Standar Oprasional Prosedur (SOP) Update Data Indeks Desa Membangun Tahun 2020,” Jakarta, 2020.
BPS, “Kuesioner Pemuktahiran Data Perkembangan Desa 2020,” Jakarta, 2020.
P. K. Dunn and G. K. Smyth, Generalized Linear Models With Examples in R. New York: Springer, 2018. [Online]. Available: http://www.springer.com/series/417
Y. E. Prasetya, A. R. T. Hidayat, and D. Dinanti, “Village Development Index of Probolinggo Coastal Villages Case study: Bhinor Village, Paiton District,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics Publishing, Oct. 2019. doi: 10.1088/1755-1315/328/1/012056.
P. M. A. Saputra, “Understanding the Dynamics of Village Economic Activities and Development in a Developing Country: A Case Study in Java Island, Indonesia,” Sodality: Jurnal Sosiologi Pedesaan, vol. 11, no. 1, pp. 43–58, May 2023, doi: 10.22500/11202344252.
Copyright (c) 2024 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;