As a classical problem, governments in developing countries should pay more attention to poverty, its determiners, and its impact. Based on several previous studies, there is a strong relationship between poverty, education, and health status. This study aims to scrutinize the relationship between education, health, and poverty and the way they affect each other to make the priority scale in efforts to reduce poverty. Therefore, to measure the relationship between them, monetary and non-monetary models are used. By using 2SLS regression for monetary models and logistic regression for non-monetary models, it is found that education significantly affects the wealth status of household and health. In disaggregated form, the return from tertiary education have significantly higher than the return from primary and secondary levels. Other control variables such as age, gender, number of children, and residential, also have significant impacts on poverty and household health status. Based on those results, the government should enhance and intensify several programs such Program Indonesia Pintar (PIP), Bidikmisi, inclusive work environment, wage equality, PKK, and GERMAS to eradicate poverty and elevate the public’s health status through education and other supporting factors.

Poverty is still one of the main problems in economic development besides inequality, unemployment, and economic growth. This study aims to model poverty directly using a discrete choice model, namely the machine learning classification method. The data used are imbalanced data where one of the categories is small enough so that the resample of both sampling method is used. In this study, several machine learning methods were applied, including the Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), and Rotation Forest. The results show that the technique of using resample both samplings provides optimal results for the four machine learning methods. If viewed from the indicators of accuracy, specificity, sensitivity, AUC, and the highest Kappa coefficient produced, the best method is the KNN method. The KNN model has an accuracy value of 0.73 percent, sensitivity of 0.68 percent, specificity of 78 percent, and AUC of 0.73.

Economic growth is the most widely used measure of economic activity. Indonesia as one of G-20 has positive economic growth while global economy downturn. Nevertheless, income inequality rises from 0,363 in 2005 to 0,394 in 2016. High growth GDP does not guarantee that all persons will benefit equally. GDP have limitation in reflection the distribution of income, social and economic progress. Therefore, it takes more than economic growth to ensure that the growth of economic activity can be obtained by all levels of society. This study uses three main references to measure the inclusiveness of economic growth, namely techniques introduced by United Nation Development Programme (UNDP), Asian Development Bank (ADB), and World Economic Forum (WEF). The results of this study indicate that the measurement of inclusive economies in Indonesia generally shows satisfactory results. If different techniques approached is applied, there is a difference in status of inclusiveness in 33 provinces and still inequalities in some variables, mainly occurs in infrastructure, education, and income. Therefore, program priority is needed to deal with that problems.

Saat ini, Indonesia menjadi negara ketiga dengan jumlah perokok tertinggi di dunia setelah Cina dan India. Kerugian makro ekonomi akibat konsumsi rokok di Indonesia pada 2015 mencapai hampir Rp 600 triliun. Ada banyak faktor yang dapat mengakibatkan seseorang mengkonsumsi rokok di antara dari segi sosio ekonomi, demografi, lingkungan, budaya dan lainnya. Mengingat konsumsi rokok yang tinggi di Indonesia serta faktor risiko yang terjadi akibat mengkonsumsi rokok, maka penelitian ini ingin mengetahui faktor-faktor yang mempengaruhi jumlah batang rokok yang dihisap. Jumlah rokok yang dihisap setiap hari merupakan data cacah nonnegatif. Untuk pemodelan variabel respon yang berupa data cacah, model yang biasa digunakan adalah regresi Poisson, regresi Binomial, dan regresi Negative Binomial. Konsumsi rokok dalam batang per hari merupakan salah satu kasus data cacahan dengan banyak nilai 0 (excess zero). Untuk mengatasi masalah overdispersion yang terjadi, salah satu cara adalah menggunakan Zero Inflated Negative Binomial (ZINB) atau Hurdle Negative Binomial (HNB). Kedua model tersebut digunakan untuk memodelkan data count dengan banyak nilai 0 pada respon dan terjadi overdispersion. Data konsumsi rokok yang dihasilkan dari IFLS memiliki nilai zero excess dan terdapat overdispersi. Model ZINB lebih baik daripada model HNP karena memiliki nilai AIC dan BIC yang lebih kecil. Pada model log hanya variabel penghasilan yang mempengaruhi peluang mengkonsumsi merokok. pada model logit hanya variabel dummy SMP yang tidak mempengaruhi peluang untuk tidak mengkonsumsi rokok, sedangkan variabel lainnya pendidikan, kesejahteraan dan penghasilan mempengaruhi peluang tidak mengkonsumsi rokok. Semakin tinggi pendidikan dan kesejaterahan akan meningkatkan peluang orang untuk tidak merokok Kata Kunci: rokok; overdispersi; binomial negatif.

In the industry revolution 4.0 era, the agriculture sector still has an important position in human life because, without this sector, human capital development cannot be well developed. Globally, the share of agriculture, forestry, and fishing sector has declined significantly in the last two decades. However, the demand on agriculture product, especially rice, incline every year. Rice supply and demand projection with appropriate methods are very important because their result affect how agricultural policies are applied. The aim of this study is to examine the likely evolution of rice consumption in Indonesia and forecast the Indonesia rice consumption per capita based on global data. The results indicate the income elasticity of demand for rice in the Indonesia has become negative. The forecast of model show that Indonesia’s rice demand will keep incline, at least in the next five years. Due to those result, in order to maintain farmers’ wealth, modernization in agriculture is needed. Government has encouraged some programs such as Simluhtan, Katam, Si Mantap, Smart Farming, Smart Green House, Autonomous Tractor, dan Smart Irrigation to accelerate the agricultural transformation. Unfortunately, human resource quality becomes a problem. Indonesia need massive effort so that modernization in agriculture works well.