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Akbar Rizki
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Xplore: Journal of Statistics
Published by Institut Pertanian Bogor
ISSN : 23025751     EISSN : 26552744     DOI : https://doi.org/10.29244/xplore
Core Subject : Science, Education, Engineering,
Decision Sciences, Operations Research & Management Engineering Mathematics
Xplore: Journal of Statistics diterbitkan berkala 3 (tiga) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika. Artikel yang dimuat berupa hasil penelitian atau kajian pustaka dalam bidang statistika dan atau penerapannya. ISSN: 2302-5751 Mulai Desember 2018, Xplore: Journal of Statistics mendapatkan ISSN baru untuk media online (eISSN:2655-2744) sesuai dengan SK no. 0005.26552744/JI.3.1/SK.ISSN/2018.12 - 13 Desember 2018. Maka sesuai ketentuan pada SK tersebut, edisi Xplore: Journal of Statistics mulai Desember 2018 akan dimulai menjadi Volume 7 dan No 3. eISSN: 2655-2744
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 106 Documents
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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.
Penanganan Overdispersi pada Regresi Poisson dengan Regresi Binomial Negatif pada Kasus Kemiskinan di Indonesia Lulu Mahdiyah Sandjadirja; Muhammad Nur Aidi; Akbar Rizki
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.v8i1.165

Abstract

Poisson regression can be used to model rare events that consist of count data. Poisson regression application is carried out to find out external factors that affect the number of poor people in Indonesia by the province in 2016. The assumptions that must be met in this analysis are equdispersion. However, in real cases there is often a problem of overdispersion, ie the value of the variance is greater than the average value. High diversity can be caused by outliers. Expenditures on outliers have not been able to deal with the problem of overdispersion in Poisson Regression. One way to overcome this problem is to replace the Poisson distribution assumption with the Negative Binomial distribution. The results of the analysis show that the Negative Binomial Regression model without outliers is better than the Poisson Regression without outliers model indicated by a smaller AIC value. Based on the Negative Binomial Regression model without this outlier the external factors that affect the number of poor people in Indonesia by the province in 2016 are the percentage of households with floor conditions of houses with soil by province, population by province, percentage of unemployment to the total workforce by province and the percentage of the workforce against the working age population.
Pemodelan Produksi Ayam Ras di Indonesia Menggunakan Regresi dengan Sisaan Deret Waktu Akhbamah Primadaniyah Febrin; Itasia Dina Sulvianti; Aji Hamim Wigena
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.v8i1.192

Abstract

The production of broiler chicken has fluctuated in recent years and many factors alleged to influence the production. The purpose of this study is modeling a structural equation of forecasting the production of broiler chicken. The study use a dependent variable (Y) that is production of broiler chickens (kilo ton) and five independent variables (X) consist of broiler chicken population (million), national chicken consumption (ton/year), retail price (Rp/kg), real price of corn (Rp), and real price of Kampung chicken (Rp). The variables are time series data with errors does not spread out randomly. Modeling method used and suitable to the conditions is regression with time series errors combined with ARIMA (Autoregressive Integrated Moving Average). The results of the regression analysis showed that only population variable and retail price variable are influencing the production of broiler chicken in Indonesia. Those two independent variables then modeled by a dependent variable using regression with time series errors. The best modeling is regression with time series errors ARIMA(1,1,0) with MAPE (Mean Average Percentage Error) value of 2.4%, RMSE (Root Mean Square Error) value of 39.800, and correlation value 0.980. The results has proved that the production of broiler chicken in Indonesia is influenced by those two variables.
Penerapan Regresi Logistik Biner Multilevel terhadap Ketepatan Waktu Lulus Mahasiswa Program Magister Sekolah Pascasarjana IPB Zana Aprillia; Farit Mochamad Afendi; Akbar Rizki
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (288.036 KB) | DOI: 10.29244/xplore.v10i2.199

Abstract

The study length of alumnus is one of the study achievement indicator of the university. Study length for Master Program can be divided into two categories which is pass on time (study length ≤24 months) and pass not on time (study length >24 months). In the classical regression analysis, each student are assumed to be independent. But in reality, each student are grouped into a different study programs so that the individuals who are in the same study program tend to have a similar characteristics. Multilevel regression is one of the analysis that accomodates the problem. The level used in this study are level 1 (individual student) and level 2 (study programs). The best multilevel regression model obtained is a model with random intercept and the variance is produced from study program is 0.6636. Factors that give an effect to the graduation’s timeliness are age, married status, and the source of the S2 education cost.
Indonesian Journal of Statistics and Its Applications Indonesian Journal of Statistics and Its Applications
Xplore: Journal of Statistics Vol. 1 No. 1 (2013)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v1i1.200

Abstract

Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802) (formerly named Forum Statistika dan Komputasi) publishes scientific papers in the area of statistical science and the applications. It is issued twice in a year. The papers should be research papers with, but not limited to, following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education.
Analisis Faktor-Faktor yang Mempengaruhi Prestasi Mahasiswa Departemen Statistika IPB menggunakan Metode SEM-PLS Zunita Sari; Muhammad Nur Aidi; La Ode Abdul Rahman
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.v8i1.201

Abstract

Education is improving human power. Education is divided into three level namely primary, secondary and high level education. High level education can be obtained from University. University as one of high level education, is a formal education place for all studying and teaching activities, research, community service and develop scientific student to become qualified workforce. Student achievement in universities is influenced by various factors that cannot be measured both direct and indirect. The method that is used to determine those factors is structural equation modeling (PPS) with partial least square (PLS) method. PPS with PLS method used when there are some assumptions on diverse PPS which is not fullfiled, like binormal distribution and the big ammount of examples. The results showed that the six exogenous latent variables (family background, motivation, environment, visual learning style, auditory learning style, and kinesthetic learning style) did not have a significant influence on endogenous variables (student achievement). The model used in this study has a R2 value 14.1%. This values ​​indicates that the model built is still weak in explaining the diversity of student achievement.
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.
Penanganan Pencilan pada Peramalan Data Deret Waktu Menggunakan Metode Pemulusan Holt dan Robust Holt Septanti Kusuma Dwi Arini; Farit Mochamad Afendi; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1018.304 KB) | DOI: 10.29244/xplore.v10i2.205

Abstract

The time series data used is time series data following the LLTM (local linear trend model) model with four different error conditions. These conditions are Clean Data (CD), Symmetric Outliers (SO), Asymmetric Outliers (AO) and Fat-tailed data (FT). The time series data contains symmetric and asymmetric outliers that can affect forecasting. The forecasting method used for the trend data pattern is the Holt smoothing method. The forecasting of the data series when it is spinning using the Holt smoothing method is not good enough so that it requires a handler with the smoothing method of Holt robustness. The Holt robustness smoothing method that is carried out on time series simulation data is better used for the condition of scattered data compared to the Holt smoothing method. This is indicated by the value of evaluating the goodness of the method, namely the value of MAD (Mean Absolute Deviation) produced. The smaller MAD value for CD condition training data is the Holt smoothing method, while the data testing method for Holt and robust Holt smoothing is almost comparable. SO's condition for training data and data testing for smaller MAD values is the smoothing method of robust Holt. The condition of AO for training data and data testing for smaller MAD values is the smoothing method of robust Holt. In addition, the MAD value in FT conditions for training data and data testing found almost comparable results between the two methods.
Penetapan Ekstrakurikuler Wajib untuk Siswa Sekolah Menengah Atas Berdasarkan Kecerdasan Majemuk Muggy David Cristian Ginzel; Asep Saefuddin; Erfiani Erfiani
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 (619.269 KB) | DOI: 10.29244/xplore.v9i1.232

Abstract

Senior high school in Indonesia is divided into two groups, namely Natural science and Social science. Those grouping of majors is allegedly not appropriate enough the potential of students yet because of the multiple intelligence of each student is different. This study aims to establish an extracurricular program for everyone grouped by multiple intelligences carried out by each student. The method used in this study are the non-hierarchical clustering k-Means and hierarchical clustering Ward method. The k-Means method used to determine the effective number of groups, while Ward method used to identify the member of each cluster and the recommendation of extracurricular in the cluster obtained. Based on the results of the clustering analysis, there are five clusters obtained, Language and Fine Arts; Communication; Leadership; Nature Lovers; and also Design and Photography.
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.

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