Unequal distribution of income is one indicator of community welfare. Improving and equalizing the standard of living people in various regions is one of the efforts to realize national economic development. The existence of an unequal distribution of income also has an impact on economic development. This study discusses panel data regression analysis to determine the factors that affect the unequal distribution of income in Java from 2014 to 2020 with the help of Eviews 10 software. The factors that are thought to affect unequal distribution of income in this study are the Human Development Index (HDI), Open Unemployment Rate (OPR), Regional Gross Domestic Product (RGDP) per capita, and total of poor people. The fixed effects model (FEM) was chosen to be the best model. The results showed that the HDI and RGDP per capita variables had negative and significant effect on unequal distribution of income in Java from 2014 to 2020.

The purpose of this research is to study the effects of GDP, population, employment of junior high school graduated, employment shares of agriculture, industry, trading and services to the number of poor people in Central Java Province.  The method used to this modeling is spatial modeling.  The results of research show that when GDP, employment shares of industry, trading and services are increase, the number of poor people is decrease.  However, when the population, employment share of agriculture and employment of junior high school graduated are increase, the number of poor people is increase.  Based on these findings there are some policies should be implemented to reduce the number of poor people, such as improvement of economic growth, improvement of education, controlling the population and  employment transformation from agricultural sector to industry, trading and services sectors.

LucyCake merupakan usaha mikro kecil menengah (UMKM) home industry yang bergerak dalam bidang pengelolaan kue dan bakery sejak tahun 2015. LucyCake dikelola oleh seorang ibu rumah tangga sekaligus karyawati swasta, LucyCake  sendiri dibuat berdasarkan pesanan atau fresh by order. Penelitian ini bertujuan untuk mengetahui keuntungan yang didapat dengan melalui proses penyusunan laporan keuangan yang berfokus pada laporan laba rugi yang akan membantu para usaha mikro kecil menengah (UMKM) dalam proses penyusunan laporan laba rugi yang benar. Penelitian ini menggunakan jenis penelitian kualitatif deskriptif, sedangkan untuk mendapatkan data yang diperlukan dalam penelitian ini adalah dengan menggunakan wawancara, observasi dan studi literatur sesuai dengan topik penelitian. Hasil Penelitian ini menunjukan bahwa usaha mikro kecil menengah (UMKM) khususnya pada UMKM Lucycake (1) Laporan keuangan yang belum tersusun (2) Pencatatan yang masih secara manual/ metode sederhana (3) Laporan laba rugi menggunakan Microsoft excel membantu para UMKM memproses pencatatan dan penyusunan laporan keuangan dengan mudah dan bisa menghasilkan laporan laba rugi yang benar. Dengan adanya sistem informasi akuntansi laba rugi berbasis Microsoft excel ini diharapkan dapat membantu UMKM LucyCake dalam mengelola laporan keuangan yang ada dalam UMKM tersebut.

This research discusses about the Ordinary Least Squares (OLS) method and robust M-estimation method; compare between the Tukey bisquare and Huber weighting from simple linier regression models that contain outliers. Data are generated through simulation with the percentages of outliers and sample sizes. Each data will be formed into a simple linier regression model, then the percentage of outliers, RSE and MAD values are calculated. The results show that RSE and MAD values produced by a simple linear regression model with the OLS method are influenced by the percentage of outliers. However, the regression model of robust M-estimation with sample size 30, 60, 90, 120, and 150 results an unstable RSE values with the change of the percentage of outlier and the MAD values that are not affected by the percentage of outliers and sample size. The robust M-estimation method with Tukey Bisquare weighting is as good as the Huber weighting. Full Article

Logistic regression (LR) is a model that associates the relationship between category-type response variables with quantitative or quantitative and qualitative predictor variables.  The prediction of the LR model is in the form of probability.  This research studied logistic regression (LR) models and Classification Trees in the case of ordinal response variable types.   The data used in this research from The Central Statistics Agency (BPS).  The research variables used are Human Development Index (HDI), gross enrollment rate for high school, percentage of poor people, open unemployment, and percentage of married age <17 years and some of the related predictor variables in Central Java Province in 2018.  The HDI data is categorized into three levels, namely very high, high, and moderate. The results of the ordinal LR model show that there are three factors that influence the HDI, they are the gross enrollment rate for high school (GER), the percentage of the poor, and the proportion of women who married at the age of less than 17 years. Comparison of the accuracy LR model and Classification Tree in classification analysis shows that if the training data used is 60%-70% the LR model is better than Classification Tree, while the training data used is more than 70% and less than 86% then the Classification Tree model is better than LR.