AbstrakPenelitian ini bertujuan untuk melakukan pemetaan kemiskinan di Kabupaten Mukomuko Provinsi Bengkulu. Metode yang digunakan dalam penelitian ini adalah small area estimation (SAE) dengan pendekatan regresi penalized spline (P-Spline). Pendugaan parameter model dasar SAE umumnya membangun suatu model linear campuran yang mengasumsikan bahwa variabel respon dan variabel prediktor mempunyai hubungan linier. Ketika asumsi tersebut tidak terpenuhi, maka dilakukan pendekatan nonparametrik sebagai alternatif pilihan. Salah satunya adalah pendekatan nonparametrik P-Spline. Pada penelitian ini, dilakukan pendugaan parameter model menggunakan P-Spline sehingga diperoleh suatu persamaan regresi efek campuran sebagai model SAE. Selanjutnya model tersebut digunakan untuk menduga tingkat kemiskinan pada area yang tersampling sehingga diperoleh penduga tingkat kemiskinan pada level desa di Kabupaten Mukomuko yang disajikan dalam bentuk peta kemiskinan. Hasil penelitian menunjukkan pendugaan menggunakan model SAE dengan P-Spline memiliki trend (kecenderungan) yang sama dengan penduga langsung. Kecamatan yang memiliki tingkat kemiskinan tinggi menyebar di bagian Timur Laut dan Tenggara dari Kabupaten Mukomuko yaitu Kecamatan Selagan Raya, Teramang Jaya, Pondok Suguh, dan Air Rami masing-masing memiliki rata-rata kemiskinan yang tinggi. Sedangkan kecamatan dengan tingkat kemiskinan rendah adalah Kecamatan Lubuk Pinang. Kata Kunci: Kemiskinan, Regresi Penalized Spline, Small Area Estimation, Kabupaten Mukomuko Abstract The main objective of this research is to map poverty level in Mukomuko district, Bengkulu Province. The method used in this study is small area estimation (SAE) using Penalized spline regression (P-Spline). In most cases, parameter estimation in SAE-

Estimation of regression parameters using the Least Squares (LS) method could not be performed when the number of explanatory variables exceeds the number of observations. An approach that can solve the problem is the LASSO (Least Absolute Shrinkage and Selection Operator) method. This method produces a stable model but with slight bias as the trade-off. Yuan and Lin [6] introduced the Group LASSO method which can be used when there are grouped structure in the variables. This current paper provided a study of the performance of the Group LASSO method through a simulation with several different scenarios. Furthermore, the Group LASSO method was applied to the Human Development Index (HDI) data of Bengkulu Province in 2019. The simulation yieled that the Group LASSO analysis was better than LASSO in term of its Mean Squared Error of Prediction (MSEP), False Negative Rate (FNR) and R-Squared. In the application of the approach to the HDI data, our result was in line with the simulation results that the analysis of Group LASSO was better than LASSO with MSEP Group LASSO of 0.25 and R-Squared of 98%.

The main objective of this research is to model poverty in Bengkulu Province using small area estimation (SAE) with semiparametric penalized spline (P-Spline). Small area estimation is a statistical method that is often used to obtain an accurate information about poverty. When the linearity assumption on the basic SAE model is violated, a nonparametric approach is used as an alternative. One is the semiparametric  penalized spline. The small area  method with semiparametric approach has a more flexible model because it accommodates the relationship between response with linear and nonlinear predictors. In this study, poverty modeling in Bengkulu Province was based on average per capita expenditure through the estimation of SAE model parameters using semiparametric P-Spline to obtain a mixed-effect model regression equation as a poverty model. Based on the analysis result, the poverty model in Bengkulu Province is P-Spline linear model with one knot. This model has a GCV value of 148928361265.95. Poverty mapping in Bengkulu Province based on sample villages indicates the estimation of poverty using SAE model with P-Spline having the same trend with the direct estimator.

Keterbukaan informasi merupakan hal yang sangat penting di era digital saat ini. Pengabdian kepada masyarakat yang dilakukan bertujuan untuk peningkatan skill perangkat desa sehingga mampu memberikan informasi yang valid, informatif dan terperinci tentang kependudukan desa. Informasi data desa yang tersaji secara menarik, informatif dan  terperinci akan menjelaskan kondisi desa kepada seluruh masyarakat desa dan umum. Lokasi pendampingan ialah Desa Pekik Nyaring Kecamatan Pondok Kelapa, Kabupaten Bengkulu Tengah. Desa ini termasuk kedalam daerah pemekaran menjadi kabupaten baru sehingga keterbukaan data penduduk sangat penting. Pelatihan pembuatan publikasi data desa dilakukan dalam tiga tahap yaitu sosialisasi, pelatihan, publikasi hasil pelatihan. Sosialisasi yang disambut baik oleh kepala desa dan perangkatnya menjadikan pelatihan dapat dilaksanakan dengan baik. Data kependudkan desa yang dianalisis secara statistik deskriptif menggunakan Ms.Excel dengan pivot table, chart, data validation, dan fungsi statistika yang telah tersedia.  Selanjutnya hasil analisis deskriptif di analisis dan di desain menjadi menarik dan dipublikasikan. Hasil publikasi dapat menjelaskan semua informasi kependudukan desa seperti jumlah penduduk, pekerjaan, agama, tingkat pendidikan, demografi desa, kepala keluarga dan jumlah penduduk. Pendampingan yang mendapat respon positif dengan ditunjukkannya adanya publikasi data desa pertama kali yang berbentuk infografis desa yang dapat diperoleh di tempat umum.Kata Kunci: data kependudukan; infografis; pendampingan. Assistance of Village Apparatus for Training on Making Village Population Data Infographics ABSTRACTInformation disclosure is very important in the digital era. Community service aims to improve the skills of village officials so that they are able to provide valid, informative and detailed information on village population. The village data information presented in an interesting, informative and detailed manner will explain the condition of the village to all village communities and the general public. The location of the assistance was Pekik Nyaring Village, Pondok Kelapa District, Bengkulu Tengah Regency. This village is a region that has become a new regency so open disclosure of population data is very important. The training on making village data publications was carried out in three stages: socialization, training, publication. The socialization was welcomed by the village head and his apparatus. Village population data were analyzed using descriptive statistics using Ms. Excel with pivot tables, charts, validation data, and statistical functions that were available. Furthermore, the results of descriptive analysis are analyzed and designed to be interesting and published. The results of the publication can explain all village population information such as population, occupation, religion, education level, village demographics, family heads and population. Assistance that received a positive response by showing the existence of the first village data publication in the form of village infographics that can be obtained in public places.Keywords: population data; infographic; accompaniment.