Poverty is a situation where there is an inability to fulfill basic needs such as food, clothing, shelter, education, and health. Poverty can be caused by scarcity of basic needs, or the difficulty of access to education and employment. Poverty is a global problem, some people understand this term subjectively and comparatively, while others see it from a moral and evaluative point of view, and others understand it from an established scientific perspective. The problem of poverty is a problem that arises in every country, especially in developing countries. The provincial poverty conditions in Indonesia have varying levels in cities and villages. In this study the data used was sourced from the central statistical body. The aim of the study was to determine the high and low number of cases of poverty based on the province using k-medoids. In Indonesia high poverty rates consist of 23 provinces and low poverty rates consist of 11 provinces. It is hoped that this research can provide input to the government in increasing employment, so as to improve the economy of the people in Indonesia.

Abstract

Predictions are used to determine how much the rate of increase or decrease in oil palm production at PT. Kerasaan Indonesia (KRE) in the future. This study uses Artificial Neural Networks (ANN) using the Levenberg Marquardt method. The research data is secondary data sourced from PT. Kerasaan Indonesia from 2002 to 2017. Data is divided into 2 parts, namely training data and testing data. There are 5 architectural models used in this study, 7-10-1, 7-20-1, 7-30-1, 7-40-1 and 7-50-1. Of the 5 architectural models used, the best architecture is 7-50-1 by producing an accuracy rate of 83%, MSE 1.1471332321 and a maximum iteration of 1000. So this model is good for predicting coconut production palm oil at PT. Indonesian feeling because of its accuracy between 80% and 90%.

<p><em>State Retail Sukuk is a Sharia Securities issued and its sale is regulated by the State, namely the Ministry of Finance (Depkeu). Where the government will choose the seller agent and consulting retail sukuk law. Selling agents must be obliged to have a commitment to the government in the development of the sukuk market and experience in selling Islamic financial products. The publication of this instrument is likened to a "mutualist symbiosis" between the Government and Society, both of which benefit equally. The government as the publisher benefits from the use of funds from the community, while the community benefits from investments made. This research contributes to the government and the Bank to be able to promote maximally for the next sukuk issuer. The data used is data from kemenkeu through website www.djppr.kemenkeu.go.id. The data are sukuk sales data with series 001 - 007 which are grouped into several categories namely geography, profession and age category. Algorithm used in this research is Artificial Neural Network with Backpropogation method. The input variables used are PNS (X1), Private Officer (X2), IRT (X3), Entrepreneur (X4), TNI / Polri (X5) and Others (X6) with architectural model of training and testing of 6 architectures 6-2-1, 6-5-1, 6-2-5-1 and 6-5-2-1. The output (output) generated is the best pattern of the ANN architecture. The best architectural model is 6-5-2-1 with epoch 37535, MSE 0.0009997295 and 100% accuracy rate. From this model will be conducted sensitivity analysis to see the variable that has the best performance and obtained variable Private Employees (X2) with a score of 0.3268. So obtained the results of the most investors predicted on the purchase of sukuk for the next 008 series based on the profession category is Private Employees.<br /> <br /> <strong>Keywords</strong>: Sukuk, JST, Backpropogation, Sensitivity Analysis and Prediction</em><em></em></p><p><em>Sukuk Ritel Negara</em><em> adalah </em><em>Surat berharga</em><em> </em><em>Syariah</em><em> yang diterbitkan dan penjualannya diatur oleh </em><em>Negara</em><em>, yaitu </em><em>Departemen Keuangan</em><em> (depkeu). Dimana </em><em>pemerintah</em><em> akan memilih </em><em>agen penjual</em><em> dan konsultasi hukum sukuk ritel. </em><em>Agen penjual</em><em> haruslah wajib memiliki komitmen terhadap </em><em>pemerintah</em><em> dalam pengembangan pasar </em><em>sukuk</em><em> dan berpengalaman dalam menjual </em><em>produk keuangan syariah</em><em>.</em><em> </em><em>Penerbitan instrumen ini diibaratkan sebuah “simbiosis mutualis” antara Pemerintah dan Masyarakat, dimana keduanya sama-sama memperoleh keuntungan. Pemerintah selaku penerbit memperoleh keuntungan berupa  penggunaan dana dari masyarakat, sedangkan masyarakat memperoleh keuntungan dari investasi yang dilakukan. Penelitian ini memberikan kontribusi bagi pemerintah dan Bank untuk dapat melakukan promosi secara maksimal untuk penerbitat sukuk berikutnya. Data yang digunakan adalah data dari kemenkeu melalui website </em><em>www.djppr.kemenkeu.go.id</em><em>. Data tersebut adalah data penjualan sukuk dengan seri 001 – 007 yang dikelompokkan dalam beberapa kategori yakni geografis, profesi dan kategori umur. Algoritma yang digunakan pada penelitian ini adalah Jaringan Saraf Tiruan dengan metode Backpropogation. Variabel masukan (input) yang digunakan adalah PNS (X1), Pegawai Swasta (X2), IRT (X3), Wiraswasta (X4), TNI/Polri (X5) dan Lainnya (X6) dengan model arsitektur pelatihan dan pengujian sebanyak 6 arsitektur yakni 6-2-1, 6-5-1, 6-2-5-1 dan 6-5-2-1. Keluaran (output) yang dihasilkan adalah pola terbaik dari arsitektur JST. Model arsitektur terbaik adalah </em><em>6-5-2-1 dengan epoch 37535, MSE </em><em>0,0009997295 dan tingkat akurasi 100%</em><em>. Dari model ini akan dilakukan analisis sensivitas untuk melihat variabel yang memiliki performa terbaik dan diperoleh variabel Pegawai Swasta (X2) </em><em>dengan skor 0,3268</em><em>. Sehingga didapat hasil prediksi investor terbanyak pada pembelian sukuk untuk seri 008 berikutnya berdasarkan kategori profesi adalah </em><em>Pegawai Swasta</em><em>.</em></p><p><strong><em>Kata Kunci</em></strong><em>: </em><em>Sukuk</em><em>, </em><em>JST</em><em>, </em><em>Backpropogation</em><em>,</em><em> </em><em>Analisis Sensivitas dan Prediksi</em><em></em></p>

Early Childhood Education (PAUD) is one of the government programs in guidance aimed at children from birth to the age of six years which is carried out through providing educational assistance to help growth and physical and spiritual development so that children have readiness in entering further education. MAUT application (Multi Attribute Utility Theory) is intended to select PAUD that has the right to get assistance from the government. Determination of policies taken as a basis for decision making, must use criteria that can be defined clearly and objectively. The criteria used as a requirement in the selection of receiving operational assistance (BOP) for early childhood education are parents 'work (C1), parents' income (C2), number of children (C3), number of children attending school (C4), number of college children (C5). The results of the calculation using the MAUT algorithm are alternatives that can receive the operational assistance of the organizer who has the highest value is alternative A1 with a value of 1, alternative A3 with a value of 0.7, alternative A9 with a value of 0.63, alternative A7 with a value of 0.46 , alternative A4 with a value of 0.43. Therefore, it can be concluded that the MAUT algorithm can be applied to VBNet-based applications where from the results of the calculation we get the similarity between the system and calculations using the MAUT algorithm. This research is expected to be a recommendation to the Principal in selecting the selection for receiving operational assistance for early childhood children at Kindergarten Daniel HKBP Tomuan Pematangsiantar.