Analyze of Classification Accaptence Subsidy Food Using Kernel Discriminant

Subsidy food is government program for social protection to poor households. The aims of this program are to effort households from starve and to decrease poverty. Less precisely target of this program has negative impact. So that to successful program, it’s important to know accuracy classification of admission subsidy food. The variables classification are number of household members, number of household member in work, average expenditure capita, weighted household, and floor area. Discriminant analysis is a multivariate statistical technique which can be used to classify the new observation into a specific group. Kernel discriminant analysis is a non-parametric method which is flexible because it does not have to concern about assumption from certain distribution and equal variance matrices as in parametric discriminant analysis. The classification using the kernel discriminant analysis with the normal kernel function with optimum bandwidth 0.6 gives accurate classification 75.35%

ANALISIS PENGEMBANGAN DAN PENERAPAN TEKNOLOGI TEPAT GUNA UNTUK MENINGKATKAN KUALITAS DAN KUANTITAS PRODUKSI SALE PISANG DI DESA GUNUNG SARI, KECAMATAN PULOSARI KABUPATEN PEMALANG

Sale pisang adalah salah satu jenis makanan yang terbuat dari bahan dasar pisang. Makanan ini biasa dibuat dengan cara diiris tipis terlebih dahulu kemudian dijemur lalu digoreng dengan tepung supaya lebih renyah. Sale pisang digemari oleh banyak orang mulai dari kalangan rakyat biasa sampai pejabat tinggi. Nama sale pisang saat ini juga sudah tekenal hingga mancanegara. UKM Sale Pisang Sunoto dan UKM Sale Pisang Sugiarti di Desa Gunung Sari, Kecamatan Pulosari Kabupaten Pemalang mengalam kendala dalam memenuhi permintaan pasar disebabkan oleh pengolahan yang masih bersifat konvensional. Penelitian ini dilakukan untuk meningkatkan jumlah produksi untuk memenuhi permintaan pasar. Pengembangan dan penerapan alat mesin mesin pengering mekanis otomotis yang dilengkapi dengan suhu dan timer otomatis pada Sale Pisang Sunoto diharapkan dapat mereduksi waktu pengeringan sale pisang dari 1 jam/ 4 kg menjadi 10 menit/ 4 kg, sehingga akan meningkatkan produktivitas hingga 20-30 % dibanding produksi konvensional dan produk lebih higienis. Sedangkan aplikasi teknologi tepat guna berupa mesin pengemas vakum otomatis sehingga akan mempermudah pekerja dalam mengemas sale pisang dan menghemat waktu sehingga waktu yang dibutuhkan hanya sedikit di UKM Sale Pisang Sugiarti diharapkan mampu meningkatkan produktivitas hingga 5 kali dibanding produksi awal. Kata Kunci : Pisang, Sale Pisang, Mesin Pengering Mekanis, Mesin Pengemas Vaku

THE ANALYSIS OF INDONESIA INFLATION DATA USING BOX-JENKINS MODELS

Inflation is a sustained increase in the general price level of goods and services in an over a period of time. Inflation's effects on an economy are various and can be simultaneously positive and negative. Negative effects of inflation include an increase in the opportunity cost of holding money and uncertainty over future inflation which may discourage investment and savings. The aims of this research are to analyze the Indonesia inflation data using Box-Jenkins models and to find the best model based on the smallest Mean Squared Error (MSE). Then by using this model, the inflation value of some period ahead will be predicted. Based on the historical data of Indonesia year-on-year inflation data from December 2006 until December 2013, known that the best Box-Jenkins model is subset ARIMA ([1,12],1,0). The MSE of the model reach into 0.274 and the MAPE is equal 4.36%. The prediction inflation in 2014 is 4.28%. Keywords: Inflation, Indonesia, subset ARIMA, MSE, MAPE

The Analysis Of Indonesia Inflation Data Using Box Jenkins Models

Inflation is a sustained increase in the general price level of goods and services in an over a period of time. Inflation's effects on an economy are various and can be simultaneously positive and negative. Negative effects of inflation include an increase in the opportunity cost of holding money and uncertainty over future inflation which may discourage investment and savings. The aims of this research are to analyze the Indonesia inflation data using Box-Jenkins models and to find the best model based on the smallest Mean Squared Error (MSE). Then by using this model, the inflation value of some period ahead will be predicted. Based on the historical data of Indonesia year-on-year inflation data from December 2006 until December 2013, known that the best Box-Jenkins model is subset ARIMA ([1,12],1,0). The MSE of the model reach into 0.274 and the MAPE is equal 4.36%. The prediction inflation in 2014 is 4.28%

REGRESI PROSES GAUSSIAN UNTUK PEMODELAN KALIBRASI SPEKTROSKOPI (STUDI KASUS : PENGUKURAN KONSENTRASI KURKUMIN, SEBUAH SENYAWA PENCIRI PADA TANAMAN OBAT TEMU LAWAK)

Model-model kalibrasi multivariat telah dikembangkan dengan menggunakan metode regresi melalui pendekatan teknik regresi komponen utama dan kuadrat terkecil sebagian. Penelitian ini mengusulkan penerapan regresi proses gaussian sebagai metode alternatif. Sebuah proses gaussian diturunkan dari perspektif regresi nonparametrik bayesian dimana pendugaan nilai hyperparameternya dilakukan dengan metode kemungkinan maksimum. Untuk mengatasi banyaknya peubah bebas yang terlibat, pereduksian peubah dilakukan dengan metode analisis komponen utama. Regresi proses gaussian lebih fleksibel jika dibandingkan dengan metode-metode sebelumnya, dalam arti bahwa dengan pemilihan fungsi peragam yang tepat dia mampu menangkap struktur linear maupun nonliner dari gugus-gugus data yang diteliliti

Identifikasi Variabel Yang Mempengaruhi Besar Pinjaman Dengan Metode Pohon Regresi (Studi Kasus Di Unit Pengelola Kegiatan PNPM Mandiri)

Most people need a loan to fullfil their daily needs, such as a loan of goods or money. Loan can be obtained from financial institutions or individuals. In order to the loan granted by a financial institutions is not wrong target, financial institutions usually apply precaution principle. In making decisions related to how much a decent loan granted to a customer, the financial institutions often use the help of statistical methods. One methods often used is the Classification and Regression Trees (CART). Classification and Regression Trees (CART) is a nonparametric method that can be used to identify the variable that affect the amount of the loan at a financial institutions and estimate how much worth of loans granted. Because of the loan is a continous variable so the form of the tree is a Regression Tree. In this thesis, the financial institutions is UPK PNPM Mandiri Mekar Sejati in Kecamatan Bawang Kabupaten Batang. Variables that may be affected for large loans are age, occupation, type of warranty, the number family members, and the average income per month. The analysis showed that the variables that most influence on the income of the loans. Mean Absolute Percentage Error (MAPE) value from this method is 36%

Pengelompokan Provinsi di Indonesia Berdasarkan Karakteristik Kesejahteraan Rakyat Menggunakan Metode K-means Cluster

Welfare have a relative explanation, dynamic, and quantitative. Quantitative formulation of welfare is never final because it will continue to evolve along with the development needs of human life. In 2011, the National Team for the Acceleration of Poverty Reduction (NTAPR) made priority sector that can serve as a benchmark the welfare in a region. From the priority sector will be made cluster or group which contains all 33 provinces based on the level of public welfare in the region uses data in 2012 were sourced from the Central Statistics Agency (CSA). The method that can be used to group the 33 provinces is K-Means Cluster method with number cluster as many as two, three, four, and five clusters. K-Means Cluster method is one of cluster analysis method who can partition the data into one or more clusters, so that the data with the same characteristics are grouped into the same cluster and data with different characteristics grouped into other clusters. To know the most optimal of the number of clusters we use Davies-Bouldin Index (DBI). We concluded that the optimal number of cluster is three with details the province in the first clusters have superiority in four sectors like net enrollment rate of primary school, net enrollment rate of junior high school, IMR (Infant Mortality Rate), and access to electricity. The province in the second clusters have superiority in one sector, that is open unemployment rate. The province in the third clusters have superiority in all sectors

Verifikasi Model Arima Musiman Menggunakan Peta Kendali Moving Range (Studi Kasus : Kecepatan Rata-rata Angin Di Badan Meteorologi Klimatologi Dan Geofisika Stasiun Meteorologi Maritim Semarang)

Forecasting method Box-Jenkins ARIMA (Autoregressive Integrated Moving Average) is a forecasting method that can provide a more accurate forecasting results. To verify the model obtained using the one Moving Range Chart. The control charts are used to determine the change in the pattern of file seen from the residual value (the difference between the actual file and the file forecasting). File used in this study the average wind speed in the Tanjung Emas harbor during January 2008 to December 2013. The best of Seasonal ARIMA model is ARIMA (0,0,1) (0,0,1) 12. The results of the verification using the Moving Range Control Chart on the model showed that all residual values are within control limits to the length of the shortest interval, means of verification results show that the model is a good model used for forecasting future periods. Forecasting is generated during the period of the next 15 shows the seasonal pattern. This is shown in the figure forecast 2014 average wind speeds are highest in January, as well as forecasting the 2015 figures the average speed of the highest winds also occurred in January. Forecasting results reflect past file, because the actual file used also showed a seasonal pattern with the same seasonal period is annual, where the numbers mean wind speeds are highest in January

Analisis Ekuitas Merek Sepeda Motor Honda Terhadap Keputusan Pembelian Dan Perilaku Pasca Beli Menggunakan Structural Equation Modelling (Sem)

Research on the implementation of Structural Equation Modelingto analyze the Honda brand equityon purchase decision and post-purchase behavior is based on the strength of the brand equityas a market leader Honda motorcycles in Indonesia for many years. The problem saddressed in this study is how the relationship between brand equity Honda motorcycle on purchase decision and post purchase behavior of consumers. In this study developed six variables consisting of 4 exogenous variables, namely brand awareness, brand response, the impression of quality and product loyalty, to measure brand equityas well as two endogenous variables, ie, purchase decision and post-purchase behavior. The study involved 200 students of the University of Diponegoro as respondents using purposive sampling technique.Structura lequation modeling research is Behavioral Post Buy=Purchasing Decisions + error. From the Goodness of Fittest results, structural equation modelin this study can be used with a value of 70,237 and the Chi-Square probability AGF I1000 and 0951. Brand awareness of 10.1% influence on purchasing decisions and 10% of the post-purchase behavior and is avariable that gives the effect of CR 1477-value ≤2.58. Responses highest brandin fluenceis equal to 32.7% against 32.4% purchase decision and post-purchase behavior. Thusit was concluded that brand awareness does not affect the purchase decision, while there sponse the brand, the impression of quality and product loyalty influence purchasing decisions. Purchasing decisions also provide a positive influence on post-purchase decisions