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Analisis Spasial Produktivitas Setengah Penganggur di Indonesia Tahun 2017: Perbandingan dengan Sektor Primer [Spatial Analysis of Underemployment Productivity in Indonesia 2017: A Comparison with Primary Sector] Kadek Aris Prasetya; Ernawati Pasaribu
Jurnal Ekonomi & Kebijakan Publik Vol 10, No 2 (2019)
Publisher : Pusat Penelitian, Badan Keahlian DPR RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22212/jekp.v10i2.1353

Abstract

There are a large number of worker in Indonesia due to its high number of population. However, goverment should pay close attention to the worker's quality since it can cause problems in the economic structures integration. The quality of labor in each region can be measured by labor productivity. Labor productivity can be seen by underemployment rate, especially in primary sector. In analyzing underemployed workers, there is a possibility of inter-provincial linkages. This study aims to identify factors that affect productivity of underemployed workers, both direct and indirect effects and comparison with primary sector. The analytical method used is descriptive analysis and inferential analysis using spatial regression method. The results showed that the productivity of underemployed workers in all sectors and primary sector was affected by different spatial effects. Labor productivity of all sectors is influenced by spillover effect of independent variables, while in primary sector is influenced by spillover effect of independent variables and spatial effect of dependent variable. The productivity of workers in all sectors is more influenced by the level of education than the level of health, while in the primary sectors is more influenced by the level of health than the level of education. Wage and investment factors have a positive effect on all sectors and primary sector. This study recommends government to revitalize primary sector in order to integrate economic structure transformation and to improve quality of health, education, and investment to increase productivity.Keywords: productivity, underemployment, SLX, SDM, spillover effectAbstrakIndonesia adalah negara dengan jumlah penduduk yang tinggi, sehingga jumlah angkatan kerjanya juga tinggi. Namun apabila kualitas dari angkatan kerja tersebut rendah, maka hanya akan menimbulkan masalah dalam integrasi struktur ekonomi. Kualitas tenaga kerja daerah dapat diukur dengan nilai produktivitas pekerja. Salah satu faktor yang digunakan untuk melihat produktivitas pekerja adalah tingkat pekerja setengah menganggur khususnya pada sektor primer. Dalam menganalisis pekerja setengah menganggur terdapat kemungkinan adanya keterkaitan antarprovinsi. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang memengaruhi produktivitas pekerja setengah menganggur, baik efek langsung maupun tidak langsung serta perbandingannya dengan sektor primer. Metode analisis yang digunakan adalah analisis deskriptif serta analisis inferensial dengan metode regresi spasial. Hasil penelitian menunjukkan bahwa produktivitas pekerja setengah menganggur di seluruh sektor dan sektor primer dipengaruhi oleh efek spasial yang berbeda. Produktivitas pekerja seluruh sektor dipengaruhi oleh spillover effect dari variabel independen, sedangkan pada sektor primer dipengaruhi oleh spillover effect variabel independen dan efek spasial variabel dependen. Produktivitas pekerja di seluruh sektor lebih dipengaruhi oleh tingkat pendidikan dibandingkan tingkat kesehatan, sedangkan di sektor primer lebih dipengaruhi oleh tingkat kesehatan dibandingkan tingkat pendidikan. Tingkat upah dan tingkat investasi berpengaruh positif baik pada seluruh sektor maupun sektor primer. Penelitian ini merekomendasikan pemerintah untuk merevitalisasi sektor primer demi pengintegrasian perubahan struktur ekonomi serta meningkatkan kualitas kesehatan, pendidikan, dan investasi demi peningkatan produktivitas.Kata kunci: produktivitas, setengah menganggur, SLX, SDM, efek tidak langsung
Permodelan Spasial pada Analisis Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka Provinsi Bangka Belitung Tahun 2018 Apriliansyah Mahmud; Ernawati Pasaribu
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 2 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i2.7034

Abstract

Unemployement is a multidimensional problem that have wide impact into progress and quality of one area. Based on that problem, it is necessary to have an analysis of factor that affected this phenomena. One economy phenomenon of one area can be influenced by neighborhood economy activity. The purpose of this study is to know factors that affected open unemployemnet rate also answer the problem of neighborhood effect by spatial model. Based on result, variables that having spatial effect are open unemployement rate, count of poor citizen, and also gross domestic product. Beside of that, it is also known that error spatial model is feasible to be a model because having smallest AIC score.
Pemodelan Kasus Kronis Filariasis di Indonesia Tahun 2019 Menggunakan Geographically Weighted Negative Binomial Regression (GWNBR) Sri Rahayu Yogyana Sinurat; Ernawati Pasaribu
Indonesian Journal of Applied Statistics Vol 5, No 1 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i1.59127

Abstract

Filariasis is a mosquito-borne disease caused by filarial worms. In Indonesia, filariasis is the third most common vector-borne and zoonotic disease in the community. Patients who in the chronic stage will fell pain due to swelling and infection in the limbs so that it can ruin the daily activities, reduce work productivity and cause economic losses for both sufferers and the country. In 2019, there were 28 filariasis endemic provinces and only 6 non-endemic provinces. This shows that the treatment of filariasis has not been fully successful. This study aims to determine the general description of chronic cases of filariasis, identify spatial heterogeneity and analyze factors that influence the number of chronic cases of filariasis using GWNBR. The modeling results five provinces groups based on significant variables. Variables that have a significant effect in all provinces are the ratio of health facilities of 100,000 population, the percentage of regions with PHBS policies and the average humidity. Meanwhile, the significant variables in several provinces are the percentage of slum households, the percentage of poor people and the average air temperature.Keywords: filariasis; overdispersion; spatial heterogeneity; negative binomial; GWNBR
Social Network Analysis untuk Identifikasi Pengguna Twitter Berpengaruh pada Topik Bencana Gempa dan Tsunami di Indonesia Ibnu Santoso; Siskarossa Ika Oktora; Siti Muchlisoh; Ernawati Pasaribu
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 1 (2023): Volume 9 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i1.62211

Abstract

Indonesia merupakan negara yang rawan terjadi bencana alam seperti gempa dan tsunami. Seiring dengan perkembangan teknologi, arus informasi mengenai kebencanaan juga mengalir di media sosial seperti Twitter. Penggunaan Twitter dalam kaitannya dengan kebencanaan telah banyak diteliti antara lain untuk penyebarluasan informasi, alat manajemen dan pengurangan resiko, pemantauan aktivitas tanggap darurat, dan lain-lain. Penelitian ini bertujuan untuk mengidentifikasi pengguna twitter berpengaruh khusus untuk topik bencana gempa dan tsunami di Indonesia dengan menggunakan Social Network Analysis (SNA) dengan dan tanpa mempertimbangkan faktor frequency dan engagement. Hasil SNA tanpa mempertimbangkan faktor frequency dan engagement menunjukkan bahwa pengguna Twitter yang dinilai paling berpengaruh pada topik bencana gempa dan tsunami adalah situs berita seperti detikcom dengan influence score sebesar 0,77. Sedangkan jika mempertimbangkan faktor frequency dan engagement menunjukkan bahwa pengguna Twitter yang dinilai paling berpengaruh pada topik bencana gempa dan tsunami adalah akun infoBMKG dengan indeks influence score sebesar 0,63. Berdasarkan hasil penelitian ini ditemukan bahwa BMKG telah berperan penting dalam pemberian informasi mengenai bencana gempa bumi dan tsunami di Indonesia dan mendapatkan kepercayaan luas dari masyarakat yang ditunjukkan dengan adanya engagement yang lebih tinggi dibandingkan akun lainnya.
Pemodelan Persentase Penduduk Miskin di Pulau Jawa dengan Pendekatan Geographically Weighted Regression (GWR) Muhammad Rafi Ikhsanudin; Ernawati Pasaribu
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27804

Abstract

Poverty is a multidimensional problem faced by all countries in the world. Poverty is the inability of individual or group to meet their basic needs in terms of expenditure. In poverty problem, there is a tendency that the poor will group in locations with certain characteristics. This spatial clustering indicates spatial diversity that making global regression analysis inappropriate for application. Therefore, the purpose of this research is to model the percentage of poor population in 119 districts on Java Island in 2021 using the Geographically Weighted Regression (GWR) method. The analysis results state that the GWR model with Kernel Fixed Bisquare provides superior results compared to the global regression model and able to overcome spatial heterogeneity problem. The model is able to provide a fairly high coefficient of determination, which is 70,73 percent. The GWR model identifies ten groups of districts based on the significance of the independent variables, with the majority of them (61 districts) having a significant RLS variable. This indicates that education is an important aspect that needs to be considered by local governments to alleviate poverty.