Xplore: Journal of Statistics
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
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106 Documents
<![CDATA[Implementasi Metode CHAID (Chi-Squared Automatic Interaction Detection) pada Segmentasi Trend Penjualan Minuman Ringan di Indonesia ]]>
<![CDATA[Via Sulviana]]>;
<![CDATA[Aji Hamim Wigena]]>;
<![CDATA[. Indahwati]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i2.91
<![CDATA[Currently some outlet sells their products by looking at sales trends over a period of time to continue developing their business and devising effective marketing strategies. CHAID (Chi-Squared Automatic Interaction Detection) method is one of the efficient non-parametric statistical methods to classify any aspects that can increase the sales of soft drinks. CHAID selects significant variables based on the Chi-Square test between categories of explanatory variables with response categories. The CHAID method is used if the response variable is nominal or ordinal. This research aims to classify characteristics that characterize diversity and determine the target market that is able to maximize profits on the sales trend of various types of soft drinks by using CHAID method. Results from CHAID are tree diagrams that divide categories of response variables by segments from explanatory variables packaged into more easily understood information. CHAID method produces 11 of 20 segments that affect the trend of soft drink sales spread across big cities of Indonesia. There are 4 independent variable from segment that form, there are city, type of outlet, source of buying and payment method which accuracy that form from segmentation are 71.4%.]]>
<![CDATA[Penanganan Data Tidak Seimbang pada Pemodelan Rotation Forest Keberhasilan Studi Mahasiswa Program Magister IPB ]]>
<![CDATA[Junjun Wijaya]]>;
<![CDATA[Agus M Soleh]]>;
<![CDATA[Akbar Rizki]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i2.99
<![CDATA[Graduate school of Bogor Agricultural University (SPs-IPB) stated that not all students of IPB master program successfully complete their studies. This becomes an evaluation for IPB to be more selective in choosing students in the future. This study aims to model the success classification of IPB master students in 2011 to 2015. The classification method used is rotation forest. The percentage of students who graduated is very large compared to those who did not pass, this can cause the evaluation value different. SMOTE (Synthetic Minority Oversampling Technique) is one of method to handle such unbalanced data by generating artificial data. The ROC (Receiver Operating Characteristic) curve is built to see the optimum cut off value. There are two classification models, they are rotation forest models before and after handled by SMOTE. The comparison results show that the rotation forest model after SMOTE with cut off value 0.6 is the best model. This model can increase the sensitivity value more than 50% although the accuracy and specificity value decreased compared to the modeling before SMOTE.]]>
<![CDATA[Analisis pada Data Harga Cabai Merah Keriting Indonesia menggunakan Model ARIMAX ]]>
<![CDATA[Muhammad Ali Umar]]>;
<![CDATA[Farit Mochamad Afendi]]>;
<![CDATA[Akbar Rizki]]>;
<![CDATA[Budi Waryanto]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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<![CDATA[The model used to analyze the time series data with one variable is Autoregresive Integrated Moving Average (ARIMA). In some cases, ARIMA model is not good enough in modeling. For instance, the time series data influenced by the outside patterns of observed variable that affect the variable. One way to capture the other patterns is with Autoregressive Integrated Moving Average Exogenous (ARIMAX). The model principle of ARIMAX is by making the other variables as the independent variables in the model used. Calender variation effects are independent variables which are often used in the modeling. In this research, ARIMAX model is applied on the weekly data of red curly chili in the period of Januari 1, 2011 to April 30, 2018. The evaluation result is there are some influential variables such as the peak of rainy season, election campaign, Eid Fitr, Eid al-Adha, and also Imlek. The best ARIMAX model gained is ARIMAX(1,1,2) model with the MAPE value of 5.054 â„….]]>
<![CDATA[Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA ]]>
<![CDATA[Dwi Yulianti]]>;
<![CDATA[I Made Sumertajaya]]>;
<![CDATA[Itasia Dina Sulvianti]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i2.105
<![CDATA[Generalized space time autoregressive integrated moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.]]>
<![CDATA[Kajian Simulasi Perbandingan Interpolasi Tetangga Terdekat dan 2-Tetangga Terdekat pada Sebaran Titik Spasial ]]>
<![CDATA[Retno Ariyanti Pratiwi]]>;
<![CDATA[Muhammad Nur Aidi]]>;
<![CDATA[Anik Djuraidah]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i2.106
<![CDATA[Spatial point distribution in an area has three types of pattern. They are random, regular, and cluster. A set of points in space is an information about the number of events in that particular space. Oftenly, the number of events in a space is difficult to obtain, thus number of events estimation is necessary in order to conduct analysis and generate the right conclusion. This research uses nearest neighbor and 2- nearest neighbors interpolation as an interpolation methods under the principle of the object location proximity. The accuracy measurements were used in both methods can be computed by the smallest MAE values. MAE is a measure to evaluate the level of accuracy by using the absolute mean of the observed and interpolation expected value difference. This research uses MAE to determine the best method. This research uses both simulated and real-life data regarding the number of Dengue Hemorrhagic Fever (DBD) patient in Central Java Province. Simulated data were generated from the Poisson, binomial, and negative binomial distribution which were set in the quadrant. The results show that the 2-nearest neighbors interpolation yield smaller MAE value than the nearest neighbor interpolation MAE either in the random, regular, or cluster spatial point distribution. The percentage of bias of the observation and estimation value of the two interpolation methods are relatively small or less than 20%. Meanwhile, in the real-life data, the 2-nearest neighbors interpolation also yield a smaller MAE value than the nearest neighbor interpolation.]]>
<![CDATA[Perbandingan Metode Dalil Limit Pusat Transformasi dan Resampling Bootstrap dalam Pembentukan Selang Kepercayaan ]]>
<![CDATA[Yuli Eka Putri]]>;
<![CDATA[Kusman Sadik]]>;
<![CDATA[Cici Suhaeni]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i2.108
<![CDATA[YULI EKA PUTRI. A Comparative Study of Central Limit Theorem, Transformation and Bootstrap Resampling in Determining Confidence Interval. Supervised by KUSMAN SADIK and CICI SUHAENI. The confidence interval is usually established under normality assumption. But, many real-life data does not belong to normal distribution. Many of them are skewed, such as chi-square distribution, generalized extreme value (GEV) or other distribution. For such data, we can use central limit theorem, transformation and bootstrap resampling method to construct confidence intervals. The performance of the methods in constructing the interval can be evaluated using confidence interval accuracy value, interval width, and standard deviation of the interval width. Thus we can determine the best method. The method is determined for having better performance if it has higher accuracy value, smaller interval width, and smaller standard deviation of interval width.This research use both simulated and real-life data. Simulated data is generated from the chi-square distribution, GEV and modified non-normal distribution. The modified non-normal distributed data is a modification of normal distributed data using quadratic and logaritm transformation. So that the data is no longer normally distributed. The results show that transformation method is well used for small sample sizes. Bootstrap resampling dan central limit theorem are better used for large sample sizes.]]>
<![CDATA[Aplikasi Structural Equation Modeling-Partial Least Squares dalam Menentukan Faktor yang Mempengaruhi Kinerja Karyawan ]]>
<![CDATA[Amanda Permata Dewi]]>;
<![CDATA[I Made Sumertajaya]]>;
<![CDATA[Aji Hamim Wigena]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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<![CDATA[Structural Equation Modeling (SEM combines factor and path analysis, so researchers can see the relationship between latent variables and their indicators and the relationship between latent variables. Partial Least Square is a soft modeling approach on SEM that has no assumption of data distribution and minimum number of observations which is often called SEM-PLS. The data used in this study is the performance of 70 constructions company employees. The number of observations is too small and couldn’t fulfill the data normality assumption so the analysis method used is SEM-PLS. This study applies SEM-PLS to identify the factors that influence the performance based on competence data from each of the existing employees. The results of this study indicate that both variables have a significant influence on the performance variables. The model tested in the research is good enough to explain the diversity of the performance variables with the evaluation value of Q2 of 75.24%.]]>
<![CDATA[Segmentasi Mahasiswa S1 IPB terhadap Sistem Peminjaman Sepeda ]]>
<![CDATA[Tania Amalia Darsono]]>;
<![CDATA[Utami Dyah Syafitri]]>;
<![CDATA[Aam Alamudi]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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<![CDATA[Green Campus is one program of IPB. One element of Green Campus is Green Transportation. There are programs in Green Transportation, one of the programs is Green Bike. There are rules in Green Bike program which were related to the system of borrowing. Based on the rules, so it was required to make segmentation of undergraduate students IPB on bicycle borrowing system. This research used data of undergraduate students IPB on bicycle borrowing system’s preferences and characteristics of respondents. Segmentation on characteristics of respondents using two step cluster method. The distance that was used in two step cluster is log-likelihood and to determinate the optimal clusters using BIC. There are 3 optimal clusters formed and quality of clustering is fair (coefficient Silhouette = 0.3). Then segmentation on bicycle borrowing system’s preferences using kmeans method. The distance that was used in k-means is euclid and there are 2 optimal clusters formed (based on the Pseudo-F value). Based on segmentation on bicycle borrowing system by combining characteristics and preferences of respondents, there are 6 cluters formed.]]>
<![CDATA[Eksplorasi Data Hasil Survei Persepsi terhadap Rektor Dengan Metode Quota Sampling ]]>
<![CDATA[Muhamad Fickri Ramadhan]]>;
<![CDATA[. Erfiani]]>;
<![CDATA[Farit Mochammad Afendi]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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<![CDATA[Rector functions as the executive leadership of Institut Pertanian Bogor, making strategic decisions and leading every element of IPB to achieveits short-term and long-term goals. Therefore, the Rector chosen by the election in 2017 has to synergize to human element of IPB to achieve the best organizational performance. This research by the means of survey is intended to gather information about the characteristics of an idealized Rector from the perspective of the academics, between the students, lecturers, and the administrative workers. Using quota sampling, descriptive statistics is used to describe the information about aspects such as character, leadership styles, and other factors that contribute to their preference. The information about the previous rector’s program is also gathered by this survey.]]>
<![CDATA[PEMODELAN SEMIPARAMETRIK STATISTICAL DOWNSCALING UNTUK MENDUGA CURAH HUJAN BULANAN DI INDRAMAYU ]]>
<![CDATA[Akbar Rizki]]>;
<![CDATA[Abdul Aziz Nurussadad]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i2.117
<![CDATA[Semiparametric statistical downscaling (SD) model is a statistical model which consists of parametric and non-parametric functional relationship between local scale and global scale variable. This study used rainfall intensity in Indramayu as local scale variable and Global Precipitation Climatology Project (GPCP) precipitation as global scale variable. GPCP precipitation data have multicollinearity, therefore they were reduced by principal component analysis. Eight principal components which have been selected then used as the prediktors and rainfall intensity in Indramayu as the response. Semiparametric SD model was used to predict the rainfall intensity in the district of Indramayu. The semiparametric model developed by mixed model approach where the nonparametric relationship is represented using spline with truncated power basis. Linier semiparametric model is the best model to estimate monthly rainfall in indramayu district. The model performance evaluated by RMSEP (root mean square error prediction) and (coefficient of determination). The result shows that the best model have values of RMSEP and are 61.64 and 71%.]]>
