Syaripuddin Syaripuddin
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Model Multinomial Bayesian Network pada Data Simulasi Curah Hujan

STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 12, No 2 (2012)
Publisher : Program Studi Statistika Unisba

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Abstract

Bayesian Networks is one of simple Probabilistic Graphical Models are built from theory of bayes probability and graph theory. Probability theory Is directly related to data while graph theory directly related to the form representation to be obtained. Multinomial Bayesian Network method is one method that involves the influence of spatial linkages suggest a link between rainfall observation stations. The objective of this study was seek the result of  the model probabilistic a graph Multinomial Bayesian Network and apply it in forecasting with Oldeman classification based on one or two rainfall stations are known. This research uses simulated data for 14 stations respectively each 300 sets of data. The data generated is normal distribution of data based on parameters that have been determined and classified using the classification Oldeman. Bayesian Network structure constructed using the K2 algorithm. Markov chain transition matrix is formed based on the Bayesian of the nodes are directional. Model of Multinomial Bayesian Network was established based on Markov transition matrices. The result of probability model can predict the probability of rainfall in some stations based on one or two rainfall stations are known, which is a model graph with 14 nodes and 13 arcs.

Model Multinomial Bayesian Network pada Data Simulasi Curah Hujan

STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 12, No 2 (2012)
Publisher : Program Studi Statistika Unisba

Show Abstract | Original Source | Check in Google Scholar | Full PDF (309.321 KB)

Abstract

Bayesian Networks is one of simple Probabilistic Graphical Models are built from theory of bayesprobability and graph theory. Probability theory Is directly related to data while graph theory directlyrelated to the form representation to be obtained. Multinomial Bayesian Network method is onemethod that involves the influence of spatial linkages suggest a link between rainfall observationstations. The objective of this study was seek the result of the model probabilistic a graphMultinomial Bayesian Network and apply it in forecasting with Oldeman classification based on oneor two rainfall stations are known. This research uses simulated data for 14 stations respectively each300 sets of data. The data generated is normal distribution of data based on parameters that havebeen determined and classified using the classification Oldeman. Bayesian Network structureconstructed using the K2 algorithm. Markov chain transition matrix is formed based on the Bayesianof the nodes are directional. Model of Multinomial Bayesian Network was established based onMarkov transition matrices. The result of probability model can predict the probability of rainfall insome stations based on one or two rainfall stations are known, which is a model graph with 14 nodesand 13 arcs.

Peramalan Pendapatan Asli Daerah Provinsi Kalimantan Timur Menggunakan Model Grey-Markov (1,1)

Jambura Journal of Mathematics Vol 1, No 2 (2019): Articles in Press
Publisher : Jambura Journal of Mathematics

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Abstract

Model grey (1,1) adalah model peramalan yang digunakan ketika jumlah data yang tersedia sedikit atau terbatas. Model tersebut menggunakan persamaan differensial orde satu dengan satu variabel penelitian. Pada penelitian ini dibahas model grey-Markov (1,1) yang merupakan pengembangan dari model grey (1,1) dan diaplikasikan pada data tahunan realisasi pendapatan asli daerah Provinsi Kalimantan Timur. Tujuan penelitian ini adalah memperoleh hasil dan akurasi peramalan pendapatan asli daerah Provinsi Kalimantan Timur Tahun 2009-2018 yang terdiri dari pajak daerah, retribusi daerah, hasil pengelolaan kekayaan daerah yang dipisahkan, dan lain-lain pendapatan asli daerah yang sah menggunakan model grey-Markov (1,1). Tahap awal dalam penelitian ini yaitu bentuk barisan data aktual, tahap kedua hitung AGO, tahap ketiga hitung MGO, tahap keempat tentukan nilai parameter model grey (1,1), tahap kelima hitung nilai prediksi model grey (1,1), tahap selanjutnya hasil peramalan model grey (1,1) dimodifikasi dengan rantai Markov, sehingga diperoleh hasil peramalan model grey-Markov (1,1). Hasil penelitian menunjukkan bahwa model grey-Markov (1,1) memberikan hasil peramalan cenderung mengikuti pola data. Nilai akurasi peramalan menunjukkan bahwa tingkat akurasi model grey-Markov (1,1) untuk peramalan data hasil pengelolaan kekayaan daerah yang dipisahkan dan data lain-lain pendapatan asli daerah yang sah adalah sangat akurat, sedangkan untuk data pajak daerah dan data retribusi daerah adalah akurat.