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All Journal Jurnal ILMU DASAR Jurnal Aplikasi Statistika & Komputasi Statistik INSIST (International Series on Interdisciplinary Research) Jurnal Siger Matematika
Khoirin Nisa
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Physics Other

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Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion Nisa, Khoirin; Setiawan, Eri; Herawati, Netti
INSIST Vol 3, No 2 (2018)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/ins.v3i2.160

Abstract

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.
Testing Normality and Bandwith Estimation Using Kernel Method For Small Sample Size Netti Herawati; Khoirin Nisa
Jurnal ILMU DASAR Vol 10 No 1 (2009)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (298.124 KB)

Abstract

This article aimed to study kernel method for testing normality and to determine the density function based on curve fitting technique (density plot) for small sample sizes. To obtain optimal bandwith we used Kullback-Leibler cross validation method. We compared the result using goodness of fit test by Kolmogorof Smirnov test statistics. The result showed that kernel method gave the same performance as Kolmogorof Smirnov for testing normality but easier and more convinient than Kolmogorof Smirnov does.
Robust Biplot Analysis of Natural Disasters in Indonesia from 2019 To 2021 Hilda Venelia; Khoirin Nisa; Rizki Agung Wibowo; Mona Arif Muda
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 13 No 2 (2021): Jurnal Aplikasi Statistika dan Komputasi Statistik
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v13i2.349

Abstract

Indonesia is one of the most natural disaster-prone countries in the world, frequently exposed to a range of hazards. Currently, Indonesia has 34 provinces and natural disasters that occur in each province are different, therefore it is necessary to analyze the mapping of natural disasters that often occur in each province to provide scientific analysis for risk management of the natural disasters. One of the quick steps in describing data that can be used is biplot analysis, as biplot analysis can describe a lot of data then summarized it into the form of a two-dimensional graph. The aim of this research is to map 34 provinces in Indonesia based on the incidence of natural disasters from 2019 to 2021 using robust biplot analysis. Based on the result, robust biplot analysis can explain 87,9% of the information on natural disasters in every province in Indonesia. Lampung, Bengkulu, Bangka Belitung, Special Region of Yogyakarta, North Sulawesi, West Sulawesi, Southeast Sulawesi, Gorontalo, East Nusa Tenggara, Bali, Maluku, West Maluku, Papua, and West Papua are provinces that have similar natural disaster characteristics. Flood, tornado and forest and land fires are natural disasters that often occur in Indonesia. The provinces that have the highest risk of flood, landslide, and tornado were West Java, Central Java, and East Java. Then, the provinces with the highest risk of forest and land fires were Aceh and South Kalimantan.
Analisis Klaster untuk Data Kategorik Menggunakan Metode K-Modes (Studi Kasus: Data Pasien COVID-19 di RSUD Dr. H. Abdul Moeloek Provinsi Lampung) Shabrina Novaindah Dwiyamti; Khoirin Nisa; Agus Sutrisno; Netti Herawati
Jurnal Siger Matematika Vol 3, No 2 (2022): Jurnal Siger Matematika
Publisher : FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jsm.v3i2.3103

Abstract

Analisis klaster merupakan salah satu analisis multivariat metode interdependensi dikarenakan tidak ada perbedaan antara variabel bebas dan variabel tak bebas. Analisis klaster terdiri dari metode hierarki dan non hierarki. K-Means merupakan salah satu metode analisis klaster non hierarki. Namun, metode K-Means terbatas pada data numerik. Sehingga dibutuhkan metode untuk mengolah data kategorik. Salah satu metode non hierarki untuk data kategorik yang sering digunakan adalah K-Modes. Penelitian ini bertujuan untuk menerapkan analisis klaster K-Modes pada data pasien COVID-19 di RSUD Dr. H. Abdul Moeloek Provinsi Lampung yang berjumlah 560 data pasien dengan variabel jenis kelamin, usia, cara masuk, dan kondisi saat keluar dari RSUD Dr. H. Abdul Moeloek Provinsi Lampung. Dengan menggunakan Davies-Bouldin Index (DBI) dan metode Silhouette, diperoleh hasil nilai  optimal untuk analisis klaster K-Modes adalah sebanyak 8 klaster, yaitu terdiri dari 145 pasien klaster 1, 227 pasien klaster 2, 16 pasien klaster 3, 30 pasien klaster 4, 30 pasien klaster 5, 74 pasien klaster 6, 4 pasien klaster 7, dan 34 pasien klaster 8. Karena anggota klaster 1 dan 2 terbanyak jika dibandingkan dengan klaster lainnya, maka diperlukan penanganan yang lebih optimal untuk klaster 1 dan 2. 
Model EGARCH dan TGARCH untuk Mengukur Volatilitas Asimetris Return Saham Sofalina Nodra Brilliantya; Khoirin Nisa; Subian Saidi; Eri Setiawan
Jurnal Siger Matematika Vol 3, No 2 (2022): Jurnal Siger Matematika
Publisher : FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jsm.v3i2.3111

Abstract

Model Generalized Autoregressive Conditional Heterocedasticity (GARCH) merupakan salah satu pemodelan data deret waktu yang digunakan untuk mengukur data yang memiliki varians residual yang tidak konstan atau bersifat heteroskedastisitas.  Heteroskedastisitas terjadi karena data deret waktu memiliki volatilitas yang tinggi.  Model Exponential GARCH (EGARCH) dan Threshold GARCH (TGARCH) adalah model-model GARCH yang dapat mengatasi efek asimetris pada volatilitas.  Data yang digunakan pada penelitian ini adalah data return saham harian PT KB Bukopin Tbk (BBKP).  Penelitan ini bertujuan untuk menerapkan model EGARCH dan TGARCH serta mendapatkan  model terbaik dalam mengukur volatilitas asimetris data return saham harian.  Pemilihan model terbaik didasarkan pada nilai Akaike Information Criterion (AIC) terkecil.  Hasil analisis menunjukan bahwa model EGARCH (2,1) adalah model terbaik untuk mengukur dan meramalkan volatilitas asimetris return saham yang digunakan.
Co-Authors Agus Sutrisno Eri Setiawan Hilda Venelia Mona Arif Muda Netti Herawati Netti Herawati Rizki Agung Wibowo Saidi, Subian Shabrina Novaindah Dwiyamti Sofalina Nodra Brilliantya