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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<![CDATA[Pendugaan Produktivitas Bagan Perahu dengan Regresi Gulud, LASSO dan Elastic-net ]]>
<![CDATA[Resty Fanny]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Aam Alamudi]]>
<![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.v2i2.89
<![CDATA[Regression analysis is a statistical technique to examine and model the relationship between dependent variable and independent variable. Multiple linear regression includes more than one independent variable. Multicollinearity in multiple linear regression occurs when the independent variables has correlations. Multicolinearity causes the estimator by ordinary least square to be unstable and produce a large variety. Multicollinearity can be overcome by the addition of penalized regression coefficient. The purpose of this research is modeling ridge regression, LASSO, and elastic-net. Data which is data of fisherman catch at Carocok Beach of Tarusan Sumatera Barat as dependent variable and amount of labor, amount of fuel, volume of fishing/waring boat, number of catches, ship size, number of boat wattage, sea experience, education and age of fisher as independent variables. The best model provided by LASSO that has a RMSEP value of validated regression model is minimum than ridge regression and elastic-net. LASSO shrinked amount of labor, amount of fuel and number of wattage equal zero. There can be influence (productivity change) that is volume of fishing/waring boat and boat size that used by fisher.]]>
<![CDATA[Penerapan Teknik Prapemrosesan Smoothing Spline pada Data Hasil Pengukuran Alat Pemantau Kadar Glukosa Darah Non-Invasif ]]>
<![CDATA[Putu Gita Karlina Jayanti]]>;
<![CDATA[Rahma Anisa]]>;
<![CDATA[Muhammad Nur Aidi]]>;
<![CDATA[. Erfiani]]>
<![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.v2i2.90
<![CDATA[A non-invasive blood glucose monitoring device is performed without injuring the limbs. One method of measurement in the form of qualitative and relatively simple to use because the process is fast and requires a cheap cost, namely Fourier Transform Infrared (FTIR). Spectroscopic results allow for a shifting of the scatter, since the same object is measured several times incorrectly producing the same spectrum, requiring a preprocessing method to reduce the problem. However, in some cases it is difficult to identify the existing data pattern, so that a nonparametric approach is needed to identify the pattern of data held so that in the process of calibration model obtained accurate results. Smoothing Spline is one nonparametric method is piecewise polynomial, which is a piece of polynomial that has a segmented property on the hose k that formed at knot points, thus providing flexibility in constructing the shape of the curve that we have. The Smoothing Spline method produces an optimum value when the GCV value is minimum on the use of a linear order with sixteen knot points. The resulting varians value after Smoothing Spline method is smaller than before smoothing, this indicates that this method can minimize the effect of liquefaction in the non-invasive blood glucose value spectrum. In addition, Smoothing Spline method can also capture data patterns well.]]>
<![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[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[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%.]]>
<![CDATA[Analisis Pengaruh Kurs USD terhadap Jakarta Islamic Index dengan Menggunakan Model Fungsi Transfer ]]>
<![CDATA[Pika Silvianti]]>;
<![CDATA[Nur Laela Fitriani]]>
<![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.v2i2.160
<![CDATA[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.]]>
