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All Journal FORUM STATISTIKA DAN KOMPUTASI Jurnal Ilmu Dasar Media Bina Ilmiah
Sri Mumpuni Retnaningsih
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Humanities Control & Systems Engineering Mathematics Social Sciences Other

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DETEKSI DINI RISIKO KREDIT MELALUI RATING TRANSITION STOCHASTIC MATRIX DAN VALUE AT RISK (Early Detection of Credit Risk Through Rating Transition Stochastic Matrix and Value at Risk) _ Haryono; Sri Pingit Wulandari; Sri Mumpuni Retnaningsih
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (242.439 KB)

Abstract

Credit risk is the risk occurs when the debtors fail to meet their obligation in accordance with agreed term to the bank. This research is made to analyze the credit risk for industrial and trade sector in Bank X, both sectors contribute about 80% loan credit. The calculation of the VaR 95% used Markov Chain regular and ergodic and adjusted by macro economic variable which significance influence the movement of those quality rating. The result of Markov chain for industrial sector show that the ability debtor increase for repay the loan in the long run but for trade sector became worst. The VaR 95% results for industrial sector is Rp 2,17 billion or about 3,27% and for trade sector is Rp 4,46 billion or about 2,03% from outstanding credit those sectors. This results is not appropriate with the New Basel Capital Accord which recomennded to allocate capital 8% from outstanding credit to cover credit risk. The calculation of the TVaR 95% for industrial sector is Rp 4,89 billion or about 7,38% and for trade sector is Rp 16,60 billion or about 7,55% from outstanding credit both sectors. For the TVaR 95% portofolio give the results is Rp 18,99 billion or about 6,5% from outstanding credit.Keywords : Credit Risk, Markov chain, Regression, Macroeconomics, VaR, TVaR, Portofolio Risk.
Aplication of Caliberation Model using Discrete Wavelet Transformation – Partial Least Square for Giengerol Data Sony Sunaryo; Sri Mumpuni Retnaningsih
Jurnal ILMU DASAR Vol 9 No 1 (2008)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

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Abstract

The determination of concentration of gingerol compound is usually carried out through a long and expensive process using HPLC instrument. The alternative method to predict such concentration can be done using a multivariate calibration model. Since the numbers of samples (n) are less than the number of variables (p) and between the independent variables are correlated, the development of model using conventional regression is no longer valid. The combination of Discrete Wavelet Transformation (DWT) and Partial Least Square has been adopted in this research to predict concentration of gingerol and it showed a promising result. 
PEMETAAN KABUPATEN/KOTA DI PROVINSI PAPUA DAN PAPUA BARAT BERDASARKAN INDIKATOR TERJADINYA BALITA STUNTING Sri Mumpuni Retnaningsih; Nur Hidayatul Nihla; Mike Prastuti
Media Bina Ilmiah Vol. 18 No. 6: Januari 2024
Publisher : LPSDI Bina Patria

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33758/mbi.v18i6.685

Abstract

Stunting adalah gangguan perkembangan fisik dan pola pikir anak karena kurangnya asupan gizi selama kehamilan sampai anak usia dua tahun, yang disebabkan oleh banyak indikator, dan berdampak serius pada perkembangan fisik, mental, emosional anak-anak serta prestasi belajar anak usia sekolah. Angka balita stunting di Provinsi Papua pada tahun 2021 masih tergolong tinggi, yaitu sebesar 29,5%, sedangkan di Papua Barat sebesar 26,2%, bahkan jika dibanding tahun 2019 mengalami kenaikan sebesar 0,1% dan 1,6%. Penyebab balita stunting diantaranya adalah penerapan kebijakan tanpa memperhatikan karakteristik indikator terjadinya balita stunting di setiap kabupaten/kota, oleh karena itu diperlukan analisis pemetaan kabupaten/kota di Provinsi Papua dan Papua Barat berdasarkan indikator terjadinya balita stunting dengan menggunakan analisis cluster hierarki pendekatan agglomeratif. Metode terbaik yang diperoleh dari hasil penelitian ini adalah complete linkage dengan 3 kelompok. Kelompok 1 terdiri dari 25 kabupaten, dengan Indikator yang perlu diperhatikan adalah Inisiasi Menyusu Dini (IMD), sumber air minum layak, dan kehamilan pada usia dini. Kelompok 2 terdiri dari 8 kabupaten, indikator yang harus diperhatikan adalah imunisasi lengkap, penggunaan alat KB, akses layanan sanitasi layak, sumber air minum layak, dan penduduk miskin. Kelompok 3 terdiri dari 9 kabupaten, dimana hanya sumber air minum layak yang sangat baik tetapi indikator lainnya masih lebih buruk dibanding kelompok 1 dan 2, sehingga indikator terjadinya balita stunting pada kelompok 3 harus diperhatikan secara lebih khusus
Co-Authors Mike Prastuti Nur Hidayatul Nihla Sony Sunaryo Sri Pingit Wulandari _ Haryono