Authors:Eka Susanti, Oki Dwipurwani, Evy Yuliza Pages: 119 - 123 Abstract: Penelitian ini bertujuan untuk menentukan jumlah optimal kendaraan pengangkut sampah menggunakan model goal programming (GP) dengan pendekatan fuzzy. Jumlah minimum sisa sampah yang tidak terangkut dan muatan kosong kendaraan sebagai goal. Jumlah sampah yang harus diangkut, jumlah ketersediaan kendaraan pengangkut, dan jumlah area layanan dinyatakan dalam bentuk Triangular Fuzzy Number (TFN) dan merupakan kendala pada model fuzzy goal programming (FGP). Model FGP diubah ke bentuk deterministik menggunakan teknik program fuzzy. Dipertimbangkan dua jenis kendaraaan yaitu dumb truck dan armroll. Diberikan contoh perhitungan untuk kecamatan Kalidoni kota Palembang. TFN jumlah sampah adalah (58100, 58150, 58300), TFN jumlah dump truck (190,190,193), TFN jumlah armroll (21,21,22), TFN jumlah minimal wilayah layanan (4,5,5). Diperoleh solusi optimal dengan derajat keanggotaan 0,8 untuk mengangkut sampah sebanyak 58150 kg diperlukan kendaraan jenis dump truck sebanyak 1 kendaraan dan jenis armroll sebanyak 18 kendaraan. Terdapat sisa sampah yang tidak terangkut sebanyak 140 kg. PubDate: 2018-01-25 DOI: 10.24843/JMAT.2017.v07.i02.p92 Issue No:Vol. 7, No. 2 (2018)
Authors:Zeth Arthur Leleury, Yopi Andry Lesnussa, Johan Bruiyf Bension, Yulia S. Kakisina Pages: 144 - 158 Abstract: Health is an investment to support economic development and has an important role in efforts to reduce poverty and improve the quality of human resources. One of the diseases that often become serious problem in health sector that is Dengue Hemorrhagic Fever (DHF). In Indonesia, many mosquitoes cause dangerous DHF such as Aedes aegypti, Aedes albopictus, Aedes africanus, anopheles and others. In this study, we analyzed and applied SIR (Susceptible, Infection, Recovered) mathematical models and their interpolation to determine whether a contagious disease (DHF) can become endemic or not. Therefore, in this study aimed to determine the a special form of model of SIR to analyze the spread of DHF in Maluku Province and the stability analysis of this model and also interpolating the data of DHF transmission in Maluku Province. Furthermore, it can be obtained the characteristics of equilibrium point of each sub population. Based on the research conducted it can be concluded that from the entire population of Maluku Province is 1.686.469 vulnerable people infected with DHF and endemic disease with the basic reproduction value is 3,44. PubDate: 2018-01-25 DOI: 10.24843/JMAT.2017.v07.i02.p91 Issue No:Vol. 7, No. 2 (2018)
Authors:Ferry Kondo Lembang, Patresya Yulita Lessil, Salmon Notje Aulele Pages: 76 - 84 Abstract: Regional gross domestic product is one of the important indicators to determine economic conditions in an area. Therefore it is very interesting to discuss and to determine the economic progress of a region. Cluster anlysis aims to classify objects based on the characteristics into cluster that have the properties that are relatively similar and clearly distinguish between one cluster agains another. The main objective of the research that classifies 33 provinces based on the value of regional gross domestic product at constant price in 2013 using hierarchical cluster analysis for average linkage method. The results showed that the cluster were carried out on 33 provinces in Indonesia formed 3 cluster with details of that cluster 1 consisting of Sumatera, Kalimantan, Sulawesi, Nusa Tenggara, Bali, Papua, Maluku and Jawa Tengah, DI Yogyakarta, and Banten, cluster 2 consisting of 1 provinces of DKI Jakarta and cluster 3 which consists of 2 provinces namely Jawa Barat dan Jawa Timur. PubDate: 2017-12-30 DOI: 10.24843/JMAT.2017.v07.i02.p84 Issue No:Vol. 7, No. 2 (2017)
Authors:I Nyoman Widana, Ni Made Asih Pages: 85 - 91 Abstract: Labor has a very important role for national development. One way to optimize their productivity is to guarantee a certainty to earn income after retirement. Therefore the government and the private sector must have a program that can ensure the sustainability of this financial support. One option is a pension plan. The purpose of this study is to calculate the normal cost with the interest rate assumed to follow the Vasicek model and analyze the normal contribution of the pension program participants. Vasicek model is used to match with the actual conditions. The method used in this research is the Projected Unit Credit Method and the Entry Age Normal method. The data source of this research is lecturers of FMIPA Unud. In addition, secondary data is also used in the form of the interest rate of Bank Indonesia for the period of January 2006-December 2015. The results of this study indicate that the older the age of the participants, when starting the pension program, the greater the first year normal cost and the smaller the benefit which he or she will get. Then, normal cost with constant interest rate greater than normal cost with Vasicek interest rate. This occurs because the Vasicek model predicts interest between 4.8879%, up to 6.8384%. While constant interest is only 4.25%. In addition, using normal cost that proportional to salary, it is found that the older the age of the participants the greater the proportion of the salary for normal cost. PubDate: 2017-12-30 DOI: 10.24843/JMAT.2017.v07.i02.p85 Issue No:Vol. 7, No. 2 (2017)
Authors:Fajri Zufa, Sigit Nugroho, Mudin Simanihuruk Pages: 92 - 106 Abstract: The purpose of this research is to compare the accuracy of bank classification prediction based on Capital Adequacy Ratio (CAR), Earning Asset Quality (EAQ), Non Performing Loan (NPL), Return on Assets (ROA), Net Interest Margin (NIM), Short Term Mismatch (STM) and Loan to Deposit Ratio (LDR). Discriminant analysis and ordinal logistic regression analysis are compared in classifying the prediction. The data used are secondary data, namely data classification of bank conditions in Indonesia in 2014 obtained from research institute PT Infovesta Utama. Based on Apparent Error Rate (APER) score obtained, it can be said that discriminant analysis is better in predicting the classification of bank conditions in Indonesia than that of ordinal logistic regression analysis. Discriminant analysis has the average prediction accuracy of 80%, while ordinal logistic regression analysis has the average prediction accuracy of 74,38%. PubDate: 2017-12-30 DOI: 10.24843/JMAT.2017.v07.i02.p86 Issue No:Vol. 7, No. 2 (2017)
Pages: 107 - 118 Abstract: The financial market is a place or means convergence between demand and supply of a wide range of financial instruments Long-term (over one year). Activities that occur in the financial markets in the long term will form a series of data is often called a time series that contains a set of information from time to time. Practical experience shows that many time series exhibit their periods with great volatility. The greater the volatility, the greater the chance to experience a gain or loss. Important properties are often owned by the data time series in finance, especially to return data that the probability distribution of returns are fat tails (tail fat) and volatility clustering or often referred to as a case heteroskedastisitas. Not all models are able to capture the nature of heteroscedasticity, one of the models that are able to do is Generalized Autoregressive Heteroskedasticity Condition (GARCH). So the purpose of this study was to determine the GARCH model in dealing with the volatility that occurred in the financial data. The results showed that the GARCH model is best suited to see volatility in the financial data. PubDate: 2017-12-30 DOI: 10.24843/JMAT.2017.v07.i02.p87 Issue No:Vol. 7, No. 2 (2017)
Authors:Yani Arthayanti, I Gusti Ayu Made Srinadi, G.K. Gandhiadi Pages: 124 - 131 Abstract: Linear Regression Analysis is a statistical method for modeling relation between two variable, response and explanatory variable. Geograpically Weighted Regression (GWR) is the development of linier regression analysis if the case of spatial divers case. Local multicollinearity is a condition when explanatory variables had correlated with each observation location. Geograpically Weighted Ridge Regression (GWRR) is a method used to model data containing local multicollinearity on spatial data. GWRR model was developed from ridge regression by adding weight as additional information. The study aims to model spatial data containing local multicollinearity to the Human Development Index (HDI) in the districts/municipalities of eastern Java Province in 2015. The result of this study was indicate that the indicator of the average length of school is a dominant indicator that affects HDI. PubDate: 2017-12-30 DOI: 10.24843/JMAT.2017.v07.i02.p89 Issue No:Vol. 7, No. 2 (2017)
Authors:Nurul Fitriyani, Lailia Awalushaumi, Agus Kurnia Pages: 132 - 143 Abstract: Regression model is used to analyze the relationship between dependent variable and independent variable. If the regression curve form is not known, then the regression curve estimation can be done by nonparametric regression approach. This study aimed to investigate the relationship between the value resulted by National Examination and the factors that influence it. The statistical analysis used was multivariable truncated spline, in order to analyze the relationship between variables. The research that has been done showed that the best model obtained by using three knot points. This model produced a minimum GCV value of 44.46 and the value of determination coefficient of 58.627%. The parameter test showed that all factors used were significantly influence the National Examination Score for Senior High School students in West Lombok Regency year 2017. The variables were as follows: National Examination Score of Junior High School; School or Madrasah Examination Score; the value of Student’s Report Card; Student’s House Distance to School; and Number of Student’s Siblings. PubDate: 2017-12-30 DOI: 10.24843/JMAT.2017.v07.i02.p90 Issue No:Vol. 7, No. 2 (2017)
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