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Pika Silvianti
Department of Statistics, IPB
Decision Sciences, Operations Research & Management Engineering Mathematics

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Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index Nur Laela Fitriani; Pika Silvianti; Rahma Anisa
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v7i3.149

Abstract

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.
Analisis Pengaruh Kurs USD terhadap Jakarta Islamic Index dengan Menggunakan Model Fungsi Transfer Pika Silvianti; Nur Laela Fitriani
Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (260.722 KB) | DOI: 10.29244/xplore.v2i2.160

Abstract

The transfer function model is a time series forecasting model that combines several characteristics ofthe ARIMA model one variable with several characteristics of regression analysis. This model is used to determine the effect of an explanatory variable (input series) on the response variable (output series). This study uses a transfer function model to analyze the effect of the exchange rate on Jakarta Islamic Index. The transfer function model is structured through several stages, starting from modelidentification, estimation of the transfer function model, and model diagnostic testing. Based on the transfer function model, Jakarta Islamic Index was influenced by Jakarta Islamic Index in one and two days earlier and the exchange rate in the same period and one to two days earlier. The forecasting MAPE value of 0.6529% shows that the transfer function model obtained is good enough in forecasting.
Penggerombolan Babyshop pada Marketplace X Menggunakan Cluster Ensemble berbasis Algoritme Squeezer Gita Lestari; Cici Suhaeni; Pika Silvianti
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.202

Abstract

Marketplace is one of the most popular digital business in Indonesia. One of the category that grow in Marketplace X is shops that sell baby equipment or better known as babyshop. In order to provide the best service and keep the credibility of babyshop specialty shops, important to do qualitry monitoring on of them through clustering. Clustering based on store reputation assessment indicators consisting of variables that are categorical and numerical in scale. This study aims to classify babyshop on Marketplace X based on the characteristics of the store using cluster analysis with cluster ensemble based mixed data clustering (CEBMDC) based on the weigthed squeezer algorithm. This study use the stream data from 218 babyshop at Marketplace X which consists of service factors, reputation level, type and location of the babyshop. The optimal cluster results was into three clusters in which cluster one consists of 21% babyshop, cluster two 48% babyshop, and 31% babyshop at cluster three. The first cluster is a cluster with the tendency of the babyshop to be classified as good, the cluster two tend to have a normal (neutral) reputation, while members of cluster three has a for poor reputation.
Penggerombolan Data Panel Perusahaan Sektor Barang Konsumsi Radinda Putri Maha Dewi; Pika Silvianti; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 9 No. 1 (2020)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (240.38 KB) | DOI: 10.29244/xplore.v9i1.233

Abstract

The identification of the cluster of consumer goods sector companies is enough important study to examine the characteristics of the company based on its marketing management factors. This study seeks to cluster 23 consumer goods sector companies based on 4 marketing management factors, which are production costs, promotion costs, distribution costs, and sales value in 2012-2016. There are two parts of clustering that are carried out, the clustering of consumer goods sector companies based on the time series pattern for each marketing management factor with the ward method, and clustering of consumer goods sector companies using multivariate panel data using the k-means method. The results of the clustering for each marketing management factor using the ward method produced 2 groups in each factor, with cluster 2 having an average of each factor greater than group 1. The companies found in cluster 2 were PT Indofood CBP Sukses Makmur, PT Indofood Sukses Makmur, PT Mayora Indah, PT Unilever Indonesia Tbk, PT Handjaya Mandala Sampoerna Tbk, International Investama Tbk, PT Kalbe Farma Tbk, and PT Tempo Scan Pacific Tbk. On the other hand, clustering of multivariate panel data produced 6 groups where group 5  is the cluster with the highest average on each factor. Group 5 consists of PT Indofood Sukses Makmur and PT Handjaya Mandala Sampoerna Tbk. The company with the highest value in multivariate panel data is also found in the results of the cluster with the highest value for each marketing management factor.
Co-Authors Cici Suhaeni Gita Lestari Nur Laela Fitriani Radinda Putri Maha Dewi Rahardiantoro, Septian Rahma Anisa