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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 6 Documents
 1 
Search results for , issue "Vol. 9 No. 1 (2020)" : 6 Documents clear
Pemodelan Faktor Risiko Penyakit Campak pada Balita di Provinsi DKI Jakarta: Pemodelan Faktor Risiko Penyakit Campak pada Balita di Provinsi DKI Jakarta Ayu Annisa Rahmah; Itasia Dina Sulvianti; Cici Suhaeni; Bimandra Adiputra Djaafara
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 (1015.396 KB) | DOI: 10.29244/xplore.v9i1.158

Abstract

Measles is one of the infectious caused by virus. The disease is easily transmitted and has become one of the main causes of child mortality especially toddlers. In 2016, Jakarta experienced the highest measles case in the last ten years and found the difference in the number of measles cases in each sub-district of Jakarta. This can be caused by the existence of effect of spatial location i.e. spatial heterogeneity. Geographically weighted regression (GWR) is a method that can be applied to address the presence of spatial heterogeneity in the process of developing the model. In this study, the weighting function used was the Gaussian kernel. The modelling process generated 42 local models at sub-district level. Explanatory variables that influence the incidence rate of measles in toddlers (Y) significantly are the percentage of immunization coverage measles (X1), the total annual rainfall (X4), and the percentage of the number of toddlers (X5). In this study, the GWR model is better than multiple linear regression model which were indicated by higher value of and smaller value of AIC.
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.
Perbandingan Quadratic Discriminant Analysis dan Support Vector Machine untuk Klasifikasi Tutupan Lahan di DKI Jakarta Kamaluddin Junianto Dimas; Rahma Anisa; Itasia Dina Sulvianti
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 (577.474 KB) | DOI: 10.29244/xplore.v9i1.236

Abstract

DKI Jakarta is a center of government as well as economy and business of Indonesia, thus development projects in Jakarta continue every year. Therefore, monitoring for land use has to be improved in accordance to DKI Jakarta Spatial Planning. The attempt needs to be supported by continuous data availability regarding land cover condition in Jakarta. The aforementioned data collecting process become easier due to remote sensing technology development. Remote sensing technology can be utilized for analyzing the size of land use area by using classification analysis. It has been found that the level of accuracy depends on the type of classification method and number of training data. This research evaluated the level of overall accuracy, sensitivity, and specificity of Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) along with number of data training used in classifying Jakarta land cover in 2017. The results showed that in both methods, the variance of all the aforementioned criteria were getting smaller along with the increasing number of training data. QDA and SVM had similar performance based on overall accuracy and specificity. However, SVM was better than QDA on sensitivity.
Analisis Kepuasan Terhadap Green Transportation Salvina Salvina; Akbar Rizki; Indahwati Indahwati
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 (243.654 KB) | DOI: 10.29244/xplore.v9i1.251

Abstract

ABSTRACT SALVINA. Analysis of Satisfaction against Green Transportation. Supervised by AKBAR RIZKI and INDAHWATI. One of the stages of the Green Campus 2020 program as an effort of IPB towards World Class University (WCU) is to carry out the Green Transportation (GT) movement. Buses, electric cars, bicycles and electric motorcycle taxis are the GT transportation modes in IPB. The purpose of this study was to determine the level of satisfaction of GT users and identify attributes that are important and need to be improved so that the GT service system can be improved. This study uses survey data conducted by researchers on undergraduate students who use GT transportation mode in the past week. The sampling method used is random layered sampling with layers in the form of faculties. The analytical methods used are Importance Performance Analysis (IPA), Customer Satisfaction Index (CSI), biplot analysis, and simple correspondence analysis. The CSI value obtained is 2.96 (1-4 scale) with a CSI percentage of 74% in other words the user is satisfied with the service he receives. The aspects that need to be improved are aspects of empathy and reliability on electric cars and assurance on bicycles, while other aspects have been considered good. Biplot analysis shows the diversity of satisfaction obtained from aspects (reliability, empathy, tangibles, assurance, and responsiveness) is the same. Simple correspondence analysis shows students of the Faculty of Veterinary Medicine (FKH), Faculty of Animal Husbandry (FAPET), Faculty of Forestry (FAHUTAN), and General Competency Education Program (PPKU) often use electric cars. Faculties that often use buses are Faculty of Agriculture (FAPERTA), Faculty of Agricultural Technology (FATETA), Faculty of Fisheries and Marine Sciences (FPIK) and Faculty of Mathematics and Natural Sciences (FMIPA). The mode of bicycle transportation cannot be characterized in any faculty because at least the respondents use it. Keywords: biplot, green transportation, IPA-CSI, simple correspondence
Penerapan Algoritme Genetik Untuk Seleksi Peubah Regresi Logistik Dian Ayuningtyas; Bagus Sartono; Farit Mochamad Afendi
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 (958.838 KB) | DOI: 10.29244/xplore.v9i1.363

Abstract

In a study, interaction factors are the potential to have important effects on the response variable. But research involving interaction factors often encounters two problems, namely the excessive number of variables and the difficulty of implementing the heredity principle. The alternative solution is to do variable selection using a metaheuristic optimization method, In this study, the logistic regression variable selection was done using a genetic algorithm. The genetic algorithm is modified so that every independent variable has a different probability to be included in the model. That probability is based on the absolute value of the correlation of the independent variable with the response variable. These modifications have a positive effect on the results of variable selection. To choose significant independent variables, 30 repetitions of the genetic algorithm can be performed using the objective function AIC. Of the 30 repetitions, if a variable appears in all formed models, then the variable is an independent variable that has a significant effect on the response variable. The application of this method to Myopia data can show significant variables well.

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