Kadi Mey Ismail
Budidaya Perairan, Fakultas Perikanan dan Ilmu Kelautan, Universitas Brawijaya

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PELLET IKAN “LISA MUDAR”(LIMBAH PASAR MURAH DAN BERGIZI) OPTIMALISASI FUNGSI LIMBAH SAYURAN Pratiwi, Trini Yuni; Maidah, Erika Nur; Ismail, Kadi Mey; Trapsilo, Priyandaru Agung Eko
Program Kreativitas Mahasiswa - Kewirausahaan PKM-K 2013
Publisher : Ditlitabmas, Ditjen DIKTI, Kemdikbud RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (207.687 KB)

Abstract

Feed is one of important thing in fish culture because its contribute about 60 % for production cost. To create a good and sustain fish culture, innovation of fish feed is needed. The feed should be economized, rich nutrition, and give optimum growth. One of that feed is Lisa Mudar, an innovative and organic feed which made from vegetables waste, sausage waste, fish flour, pollard, premix vitamin, adhesive flour, probiotic, and fish oil. Lisa Mudar has good nutrition that are protein 22 %, carbohydrats 27 %, lipid 3 %, and rude fiber 18,3 %. This feed can be used for catfish culture and also tilapia culture.Keyword: Pellet, Lisa Mudar, waste, vegetable, optimum
TIME SERIES ANALYSIS USING COPULA GAUSS AND AR(1)-N.GARCH(1,1) Caraka, Rezzy Eko; Yasin, Hasbi; Sugiarto, Wawan; Ismail, Kadi Mey
MEDIA STATISTIKA Vol 9, No 1 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.146 KB) | DOI: 10.14710/medstat.9.1.1-13

Abstract

In this case, the Gaussian Copula is used to connect the data that correlates with the time and with other data sets. Most often, practitioners rely only on the linear correlation to describe the degree of dependence between two or more variables; an approach that can lead to quite misleading conclusions as this measure is only capable of capturing linear relationships. Correlation doesn?t mean causation, prediction using Copula is built on three things that the marginal distribution function, the kernel function, and the function of the Copula. Gaussian Copula involves the covariance matrix are approximated by using kernel functions. Kernel acts as the correlation between the approach of the data values that have the same characteristics. In this case, the characteristics used is the time. The advantage of the kernel function is able to calculate the correlation between random variables that have a realization using data characteristics. The advantage of using the kernel based Copula able to capture the dependencies between data and process data that have the same characteristics with time. Another benefit is that it allows a sequence of random variables have a joint distribution function so that the conditional probability of the prediction can be calculated. Keywords: Binding, Copula, GARCH, Gauss, Time Series