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AQUACULTURE PRODUCTION OPTIMIZATION MODEL OF BREBES REGENCY Safitri, Diah; Prajanti, Sucihatiningsih Dian Wisika
Efficient: Indonesian Journal of Development Economics Vol 2 No 3 (2019)
Publisher : Universitas Negeri Semarang

The purpose of this study was to determine the combination of aquaculture production in Brebes District and to know the intervals that might occur in variables so that the optimization model can still be used. The research variable consists of decision variables, namely the amount of aquaculture commodities that must be produced to achieve maximum profit. The commodities in question are shrimp, tilapia, catfish, milkfish, and seaweed. The constraint variable is the production factor used in aquaculture activities, including land, seeds, feed and fertilizer, and operational costs. The method of data analysis in this study uses Linear Programming analysis and sensitivity analysis. The type of data used is primary data from interviews with aquaculture households in Brebes District. The results showed the maximum profit from aquaculture production in Brebes District can be obtained when the number of vaname shrimp produced was 975,383.5 kg, catfish as much as 1,985,898 kg, milkfish as much as 885,986.6 kg, and seaweed as much as 2,532,448 kg. From the combination of aquaculture production, it can be seen that the maximum amount of profit obtained is Rp. 111,590,500,000 for one cultivation cycle. Suggestions: 1) strengthen the institutional household of aquaculture. 2) The government is expected to provide direction and incentives to cultivation FHs related to achieving production combination targets that produce maximum profits. 3) In the future, it is hoped that the related offices can have a more complete database on the fisheries sector, so that the analysis carried out has accurate results. Tujuan penelitian ini adalah mengetahui kombinasi produksi perikanan budidaya di Kecamatan Brebes serta mengetahui intervalperubahan yang mungkin terjadi pada variabel sehingga model optimasi masih dapat digubakan. Variabel penelitian terdiri dari variabel keputusan yaitu jumlah komoditas perikanan budidaya yang harus diproduksi untuk mencapai keuntungan maksimal. Adapun komoditas yang dimaksud adalah udang, nila, lele, bandeng, dan rumput laut. Variabel kendala adalah faktor produksi yang digunakan dalam kegiatan budidaya, diantaranya adalah lahan, bibit, pakan danpupuk, dan biaya operasional. Metode analisis data dalam penelitian ini menggunakan analisis program linear dan analisis sensitivitas. Jenis data yang digunakan adalah data primer dari hasil wawancara dengan rumah tangga perikanan budidaya di Kecamatan Brebes. Hasil penelitian menunjukkan euntungan maksimal dari produksi perikanan budidaya di Kecamatan Brebes dapat diperoleh ketika jumlah udang vaname yang diproduksi sebanyak 975.383,5 kg, lele sebanyak 1.985.898 kg, bandeng sebanyak 885.986,6 kg, dan rumput laut sebanyak 2.532.448 kg. Dari kombinasi produksi perikanan budidaya tersebut, dapat diketahui jumlah keuntungan maksimum yang diperoleh sebesar Rp. 111.590.500.000 untuk satu siklus budidaya. Saran: 1) memperkuat kelembagaan rumah tangga perikanan budidaya. 2) Pemerintah diharapkan memberikan arahan dan insentif kepada RTP budidaya terkait pencapaian target kombinasi produksi yang menghasilkan keuntungan maksimum. 3)Kedepannya diharapkan dinas terkait dapat memiliki database mengenai sektor perikanan yang lebih lengkap, sehingga analisis yang dilakukan memiliki hasil yang akurat.

PENGEMBANGAN ESTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING HOLT-WINTERS ADDITIVE MENGGUNAKAN METODE OPTIMASI GOLDEN SECTION Al Qarani, Muhammad Aqajahs; Santoso, Rukun; Safitri, Diah
Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v7i4.28861

Forecasting is an activity to estimate what will happen in the future, one method that can be used is Exponential Smoothing. In this study used the smoothing method of Exponential Smoothing Holt-Winters Additive with three parameters that can be used for prediction of time series data that has trend patterns and seasonal patterns. The problem that arises in this method is to determine the optimum parameter to minimize the forecast error value. This study uses the Golden Section optimization method to estimate the optimum parameters that minimize the MAPE value. The data used is data on foreign tourists who use accommodation services in Yogyakarta from the period January 2009 to December 2016 that have trend patterns and additive seasonal patterns. In simplifying the optimization calculation process, a syntax using RStudio is arranged which contains the Golden Section algorithm to determine the combination that has the optimum parameters. In this optimization there are two treshold error, namely 0.001 and 0.00001. The results showed that the parameter estimator with the Golden Section method for the treshold error of 0.001 obtained MAPE of 18,96732% and for treshold error of 0.00001 MAPE was 18,96536%. This value is in the same MAPE criteria which is 10% â”€ 20% (good) so that the selection of the best model is determined based on minimal iteration. Therefore the weighting parameter value used is the result of optimization with Îµ â‰¤ 0.001, then from the selected model it is used to predict the number of foreign tourists using accommodation services in Yogyakarta in the next 12 months.

Aquaculture Production Optimization Model of Brebes Regency Safitri, Diah; Prajanti, Sucihatiningsih Dian Wisika
Efficient: Indonesian Journal of Development Economics Vol 2 No 3 (2019)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/efficient.v2i3.35904

The purpose of this study was to determine the combination of aquaculture production in Brebes District and to know the intervals that might occur in variables so that the optimization model can still be used. The research variable consists of decision variables, namely the amount of aquaculture commodities that must be produced to achieve maximum profit. The commodities in question are shrimp, tilapia, catfish, milkfish, and seaweed. The constraint variable is the production factor used in aquaculture activities, including land, seeds, feed and fertilizer, and operational costs. The method of data analysis in this study uses Linear Programming analysis and sensitivity analysis. The type of data used is primary data from interviews with aquaculture households in Brebes District. The results showed the maximum profit from aquaculture production in Brebes District can be obtained when the number of vaname shrimp produced was 975,383.5 kg, catfish as much as 1,985,898 kg, milkfish as much as 885,986.6 kg, and seaweed as much as 2,532,448 kg. From the combination of aquaculture production, it can be seen that the maximum amount of profit obtained is Rp. 111,590,500,000 for one cultivation cycle. Suggestions: 1) strengthen the institutional household of aquaculture. 2) The government is expected to provide direction and incentives to cultivation FHs related to achieving production combination targets that produce maximum profits. 3) In the future, it is hoped that the related offices can have a more complete database on the fisheries sector, so that the analysis carried out has accurate results. Tujuan penelitian ini adalah mengetahui kombinasi produksi perikanan budidaya di Kecamatan Brebes serta mengetahui intervalperubahan yang mungkin terjadi pada variabel sehingga model optimasi masih dapat digubakan. Variabel penelitian terdiri dari variabel keputusan yaitu jumlah komoditas perikanan budidaya yang harus diproduksi untuk mencapai keuntungan maksimal. Adapun komoditas yang dimaksud adalah udang, nila, lele, bandeng, dan rumput laut. Variabel kendala adalah faktor produksi yang digunakan dalam kegiatan budidaya, diantaranya adalah lahan, bibit, pakan danpupuk, dan biaya operasional. Metode analisis data dalam penelitian ini menggunakan analisis program linear dan analisis sensitivitas. Jenis data yang digunakan adalah data primer dari hasil wawancara dengan rumah tangga perikanan budidaya di Kecamatan Brebes. Hasil penelitian menunjukkan euntungan maksimal dari produksi perikanan budidaya di Kecamatan Brebes dapat diperoleh ketika jumlah udang vaname yang diproduksi sebanyak 975.383,5 kg, lele sebanyak 1.985.898 kg, bandeng sebanyak 885.986,6 kg, dan rumput laut sebanyak 2.532.448 kg. Dari kombinasi produksi perikanan budidaya tersebut, dapat diketahui jumlah keuntungan maksimum yang diperoleh sebesar Rp. 111.590.500.000 untuk satu siklus budidaya. Saran: 1) memperkuat kelembagaan rumah tangga perikanan budidaya. 2) Pemerintah diharapkan memberikan arahan dan insentif kepada RTP budidaya terkait pencapaian target kombinasi produksi yang menghasilkan keuntungan maksimum. 3)Kedepannya diharapkan dinas terkait dapat memiliki database mengenai sektor perikanan yang lebih lengkap, sehingga analisis yang dilakukan memiliki hasil yang akurat.

KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA Puput Ramadhani; Dwi Ispriyanti; Diah Safitri
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk > Yp is 0.889823 > 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk < Yp is 0.637742 < 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords: kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability

PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017) Setya Adi Rahmawan; Diah Safitri; Tatik Widiharih
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices

PERAMALAN PRODUK DOMESTIK BRUTO (PDB) SEKTOR PERTANIAN, KEHUTANAN, DAN ‎PERIKANAN MENGGUNAKAN SINGULAR SPECTRUM ANALYSIS (SSA) Desy Tresnowati Hardi; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is the process of estimating conditions in the future by testing conditions from the past. One of the forecasting methods is Singular Spectrum Analysis (SSA) which aim of SSA is to make a decomposition of the original series into the sum of a small number of independent and interpretable components such as a slowly varying trend, oscillatory components and a structureless noise. Gross Domestic Product data in the agriculture, forestry, and fisheries sector are time series data with trend and seasonal pattern so that it can be processed using the SSA method. The forecasting process of SSA method uses the main parameter (L) of 21 obtained by the Blind Source Separation (BSS) method. From forecasting, acquired group of 3 groups. Forecasting resulted the value of Mean Absolute Percentage Error (MAPE) is 1.59% and the value of tracking signal is 2.50, which indicates that the results of forecasting is accurate. Keywords: Forecasting, Gross Domestic Product in the agriculture, forestry, and fisheries sector, Singular Spectrum Analysis (SSA)

PEMODELAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) DI PROVINSI JAWA TENGAH MENGGUNAKAN BOOTSTRAP AGGREGATING MULTIVARIATE ADAPTIVE REGRESSION SPLINES (BAGGING MARS) Maryam Jamilah An Hasibuan; Agus Rusgiyono; Diah Safitri
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Increased economic improvement is one way to improve people's welfare in certain areas. Gross Regional Domestic Product (GRDP) is one of the macroeconomic indicators used to measure economic growth in a region. Related to the economy in Central Java Province increased from year to year. Increasing economic growth is inseparable from the contribution of factors that sufficiently contribute to the GRDP. Factors that are the cause of GRDP are Regional Original Income, Foreign Investment, and Domestic Investment. The method used to model the factors that influence Gross Regional Domestic Product is the Multivariate Adaptive Regression Spline (MARS) method and combine it with Bagging. MARS method is one method that uses nonparametric regression and high dimension data. The best model used is a model with a combination of BF = 6, MI = 1, MO = 0 with GCV of 5.667,6680. Then bagging is done on the initial data set with 10, 25, 35, 40, 55, 75, 85, 90 and 100 bootstrap replications. GCV produced in bagging MARS 2.258,6192. GCV valuesobtained from MARS bagging are smaller compared to the MARS method. This shows that bagging can reduce the value of GCV and increase accuracy, making this method can be used in this study. Keywords: GRDP, GCV, MARS, Bagging

COPULA FRANK PADA VALUE at RISK (VaR) PEMBENTUKAN PORTOFOLIO BIVARIAT (Studi Kasus : Saham-Saham Perusahaan yang Meraih Predikat The IDX Top Ten Blue Tahun 2017 dengan Periode Saham 20 Oktober 2014 – 28 Februari 2018) Juria Ayu Handini; Di Asih I Maruddani; Diah Safitri
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market has an important role in society to invest in financial instruments. Investors can invest in the form of a portfolio that is by combining several shares to reduce the risk that will occur. Value at Risk (VaR) is a method for estimating the worst risk of an investment. GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is used to model high-volatile stock data that causes residual variance is not constant. Copula theory is a powerful tool for modeling joint distributions because it does not require normality assumptions that are difficult to fulfill in financial data. Copula Frank has a feature that can identify positive and negative dependencies. This study aims to measure the value of VaR using the Frank-GARCH copula method using stock returns data of PT Bank Rakyat Indonesia, Tbk (BBRI), PT Telekomunikasi Indonesia, Tbk (TLKM), and PT. Unilever Indonesia, Tbk (UNVR) for the period 20 October 2014 - 28 February. Bivariate portfolio pairs obtained namely TLKM and UNVR shares because they have the highest Rho Spearman residual correlation value of ρ = 0.3204. Based on the generation of data using Monte Carlo simulations, the results of the calculation of Value at Risk (VaR) of 1.40% at the 90% confidence level, 1.89% at the 95% confidence level, and 2.79% at the 99% confidence level. Keywords: Value at Risk, Frank copula, GARCH, Monte Carlo

ANALISIS LAPANGAN PEKERJAAN UTAMA DI JAWA TENGAH BERDASARKAN GRAFIK BIPLOT SQRT (SQUARE ROOT BIPLOT) Anik Nurul Aini; Diah Safitri; Abdul Hoyyi
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Biplot analysis is one of the methods of descriptive statistical analysis that can present data of the n objects which p variables into a two-dimensional graph. Biplot has several types according to the scale of α used. There are three scales α which is often used in the biplot analysis, that are α = 0, α = 0,5 and α = 1. Biplot with α = 1 is called the RMP biplot (Row Matric Preserving). Biplot with α = 0 is called CMP biplot (Column Matric Preserving). While biplot with α = 0,5 called SQRT biplot (Square Root Biplot). Biplot with a scale of α = 0,5 is the best biplot to describe a data, because it make a graph between variable and object spread evenly. This study aims to create a SQRT biplot amount of population aged 15 years and over who worked according to district/city and major employment opportunities in Central Java. Biplot chart shows areas that have similar characteristics with the closest Euclidean distance. The diversity of characteristics is indicated by the length of the vector, the longest vector contained in the agricultural sector. Based on the biplot analysis in this study, it was obtained that the goodness size biplot is equal to 64,19958%. Keywords: Biplot, Singular Value Decomposition, Jobs, SQRT, Square Root Biplot

PENGUKURAN RISIKO KREDIT DAN PENGUKURAN KINERJA DARI PORTOFOLIO OBLIGASI Bimbi Ardhana Rizky; Sudarno Sudarno; Diah Safitri
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Except getting coupon as a profit, there is loss probability in bond investment that is credit risks investment. One way to measure the credit risk of a bond is to use the credit metrics method. It uses the ratings of the bond issuer company and the transition rating issued by the rating company for its calculations. Mean Variance Efficient Portfolio (MVEP) can be used to make an optimal portfolio so that risk can be obtained to a minimum. An assessment of portfolio performance is needed to increase confidence to invest. Sharpe index can measure portfolio performance based on return value of bond. In this case, study has been conduct in two bonds which are Obligasi Berkelanjutan I Bank BTN Tahap II Tahun 2013 and Obligasi Berkelanjutan I PLN Tahap I Tahun 2013 Seri B. The optimum portfolio formed results 67,96% proportion for the first bond and 32,04% for the second bond. For the result, and there is Rp239,4235(billion) of portfolio risk formed. And there is 0,212496for Sharpe index performance assessment portfolio. Keywords: Bond, portfolio, credit risk, credit metrics, Mean Variance Efficient Portfolio, Sharpe index

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