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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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Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel Gaussian pada Kekuatan Sinyal Seluler Logananta Puja Kusuma; . Indahwati; Kusman Sadik
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.v8i1.134

Abstract

Cellular signal strength may be affected by its location, so researches concerning signal strength need information about location and analysis method that observe spatial aspect. Spatial Regression analysis evaluates location in modeling relation between explanatory variables and response variable. One of the spatial regression analyses is Geographically Weighted Regression (GWR). This method utilizes location to create weight matrix using certain weighting function. GWR analysis with Gaussian kernel weighting function creates better model than Ordinary Least Square model. The model created using GWR is local model which parameter estimation differs in each observation point. Clustering of observation point is performed to summarize the result of GWR. The number of optimum clusters in clustering based on coefficient is five clusters while the number of optimum clusters in clustering based on p value of t test is four clusters.
Deteksi Titik Panas dan Pola Spasio-Temporal Kejadian Penyakit Demam Berdarah Dengue Di DKI Jakarta Rere Kautsar; Bagus Sartono; Cici Suhaeni; Bimandra Adiputra Djaafara
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.v8i1.138

Abstract

Dengue virus is one of the causes of infectious diseases in human with mosquitoes as vector of transmission. In Jakarta, dengue infection is still one of the most important health problems, particularly its severe clinical manifestation which is known as dengue hemorrhagic fever (DHF). Spatio-temporal approach was used in this research to analyze the spatial pattern of DHF spread in Jakarta. The data used for analysis are consisted of total number of people suffering from DHF in Jakarta at the sub-district per week, monthly total precipitation, and the total population each sub-district in DKI Jakarta per year since 2008 until 2016. Incidence rate of dengue fever in Jakarta tend to be higher in March to April period compared to the other months. The spread of DHF burden tend to be clustered during the period of 2008 until 2011 whereas in the next five years, clustered pattern was not observed. The hotspots of DHF cases were more likely to occur in the north, east, and south part of DKI Jakarta.
Penerapan Metode VAR-X untuk Pemodelan Data Deret Waktu dengan Calendar effects Ade Gusalinda; I Made Sumertajaya; Septian Rahardiantoro
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.v8i1.147

Abstract

One of the commodities that has quite varied price fluctuations is broiler and carcass chicken. The context of forecasting is quite important considering the policies that can be taken by the producer and even the strategies that can be taken by consumers. This study attempts to modeling broiler and carcass prices together with Vector Autoregressive (VAR) which is one method in time series analysis that utilizes more than one time series variable. In addition, the effect of calendar calendar events is also the topic of discussion in this study which is implemented by the VAR-X method. As a result, the calendar effects variables that affect broiler and carcass prices are February, the first week of Ramadan and Eid-ul-Fitr. Furthermore, forecasting with VAR-X produces a pretty good value than VAR with lower MAPE criteria.
Pemodelan Data Multi-Label dengan Pendekatan Multivariate Generalized Linear Mixed Model (MGLMM) Dairul Fuhron; . Indahwati; Farit Mochamad Afendi
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.v8i1.148

Abstract

Multi-label data refers to a type of categorical data where an object may has more than one corresponding label or possible values. Multi-label data are commonly found in many fields, one of them is market research of the sweetened condensed milk (SCM) and sweetened condensed creamer (SCC) products. According to product characteristic, market research for the aforementioned product is appropriately conducted on the outlet level. An outlet may use more than one product’s brand in the same time frame. That condition inflict brand choice information to be represented under multi-label data. This research used problem transformation method by tranforming a multi-label variable into several single-label variables. Multivariate Generalized Linear Mixed Modeling or MGLMM was selected under consideration of binary multiple responses and correlated responses presumption. Five responses of SCM and SCC brand choice modeling resulted correct model without overdispersion and the scaled pearson chi square statistic is 0.99. Tests of fixed effects indicate three factor significantly affect SCM and SCC brand choice at the 5% level. They are purchase total, province, and type of business. The variance of the random effect intercept is 1.53×10−18 or insignificant, hence MGLMM based model was similar compare to separated GLM based model.
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 Kepuasan Pelayanan dan Literasi TIK Pengunjung Dinas-Dinas di Kota Bogor Ryska Putri Madyasari; Anang Kurnia; Rahma Anisa; Yani Nurhadryani
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.v8i1.152

Abstract

Determining Public Satisfaction Index using analysis of Importance Performance Analysis (IPA) and Customer Satisfaction Index (CSI) can be utilized to improve service quality of Governmental Departments in X City. Analysis of IPA and CSI were used to measure the level of respondents’ satisfaction regarding the provided services. The departments were selected using purposive sampling method. Four selected departments were Population and Civil Registry Department, Transportation Department, Housing and Settlement Department, and Social Department. The result showed that customers were moderately satisfied with the services, with the following CSI index value: 70.09%, 72.95%, and 76.61% respectively for each departments. Moreover, Social Department’s customers were very satisfied with the CSI index 81.56%. In this study, aspect of Information and Communication Technology (ICT) literacy indicator were more exposing the ability to operate personal computer. There were six indicator of ICT literacy, i.e access, manage, integrate, evaluate, create, and communication. The value of evaluate indicator were quite high, it has reached score higher than 50% for each departments were. However, based on overall score, it was shown that 60% respondents still have low ICT literacy. This study also showed that ICT literacy were related to responden’s education and age. It increased along with the higher level of education that has been completed by respondents, and with the age of 17-39 years old.
Penerapan SMOTE dalam Pemodelan CHAID pada Data Keberhasilan Mahasiswa PPKU IPB Ririn Fara Afriani; Mohammad Masjkur; Utami Dyah Syafitri
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.v8i1.154

Abstract

Bogor Agricultural University (IPB) as the third rank of Indonesian non polytechnic universities in 2017 requires new students to join the General Competency Education Program (PPKU) for two semesters to improve the quality of human resources. Student achievement success can be determine from the student's academic status, where the student's academic status is divided into two, which are Drop Out (DO) and not DO. Only 1% of PPKU students who are drop out.. This means there is a data imbalance. One of the method used to handled that is Synthetic Minority Oversampling Technique (SMOTE) method. Classification analysis used is the Chi-Square Automatic Interaction Detection (CHAID) method to identify the factors that influence the success of PPKU students. The application of SMOTE to the 2016/2017 PPKU student data was able to improve the ability of classification trees with the average values ​​of accuracy, sensitivity, and specificity to 0.718, 0.575, and 0.72. The factors that influence the success of IPB's PPKU students are the entry point, gender, and regional origin.
Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID Aulia Dwi Oktavia; Aam Alamudi; Budi Susetyo
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.v8i1.156

Abstract

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.
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.
Penerapan Metode DBSCAN dalam Memperbaiki Kinerja K-Means untuk Penggerombolan Data Tweet Astri Fatimah; Anang Kurnia; Septian Rahardiantoro; Yani Nurhadryani
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.v8i1.159

Abstract

Text Mining is collecting text data mining results from a computer to get information contained therein. Text data has a form of data that is not structured and difficult to analyze. The unstructured data can be used as structured data through pre-processing stages. Text data is represented as numerical data after going through the pre-processing stages using vector space model method and weighting method of inverse frequency document frequency so that it can be used for analysis. The K-Means cluster analysis is one method that can be used for unstructured data, but the K-Means method is not robust to noise. Outliers can be detected using Density Based Spatial Clustering of Application with Noise (DBSCAN) cluster analysis. Outliers obtained from DBSCAN results can be omitted in the data. Cluster analysis was carried out again after removal of outliers using the K-Means method with the same number of k clusters. Evaluation of the cluster that is used to see the goodness of the cluster results is Silhouette Coefficient (SC). The SC value of the K-Means method after removal of outliers has a significant increase of 0.21 for a small amount of data. Adding the amount of text data to cluster analysis also affects the number of clusters. This is influenced by the number of katas in a document that is given weight. The fewer katas that are given weight, the more number of clusters will be generated

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