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[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[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[Klasifikasi Keberhasilan Melanjutkan Pendidikan Tingkat SMA Provinsi Banten Menggunakan CART dan Random Forest ]]>
<![CDATA[Muhammad Amirullah Yusuf Albasia]]>;
<![CDATA[Budi Susetyo]]>;
<![CDATA[I. Made Sumertajaya]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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<![CDATA[Dropout rate in Indonesia has a higher percentage as education levels grow. The percentage of continuing education to senior high school in Indonesia is at 77.50%. Banten is one of the provinces that has a higher dropout percentage when the education level is also higher. Beside that, Banten is the second lowest province in Indonesia in the percentage of continuing education to senior high school that is 68.92%. The study examines importance variables and performance classification that is generated by classification tree and random forest. The results showed that importance variables that is generated by both methods were same, that is per capita expenditure (X8) and proportion of household members who are less educated than senior high school (X10). Then, based on the AUC value that obtained by 10-fold cross validation showed that random forest is better than classification tree. Experiments with values ​​of accuracy, sensitivity, and specificity at some cuts off values ​​also show that random forest can provide more optimum prediction performance than classification tree.]]>
<![CDATA[Pembentukan Selang Kepercayaan Bootstrap Kebutuhan Hidup Mahasiswa FMIPA IPB (Studi Kasus Mahasiswa FMIPA angkatan 2015 dan 2016) ]]>
<![CDATA[Dhika Firmansyah]]>;
<![CDATA[Aam Alamudi]]>;
<![CDATA[Agus M Soleh]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v7i3.119
<![CDATA[Kebutuhan hidup menjadi aspek penting dalam menunjang kebutuhan mahasiswa selama kuliah sesuai dengan keuangan yang dimiliki tiap mahasiswa. Biaya yang dikeluarkan tiap mahasiswa bergantung dengan kebutuhan dan keuangan yang dimiliki tiap mahasiswa. Biaya yang dikeluarkan oleh mahasiswa FMIPA IPB tiap bulannya memiliki sebaran yang tidak normal sehingga dilakukan analisis dengan metode yang sesuai. Statistika deskriptif dan selang kepercayaan persentil dengan metode bootstrap digunakan untuk memperkirakan besaran pengeluaran tiap bulan selama kuliah di kampus IPB. Ulangan yang digunakan dalam penelitian ini adalah 500, 1000 dan 2000 dengan masing-masing ulangan memiliki ukuran contoh yang terambil sebesar 30, 50, 100, 150, dan 200. Rata-rata pengeluaran mahasiswa FMIPA per bulan yaitu sebesar Rp1166129 dengan nilai minimum sebesar Rp250000 dan maksimum sebesar Rp3700000. Selang kepercayaan 90% dengan metode persentil dengan ulangan lima ratus dan ukuran contoh dua ratus menghasilkan lebar selang kepercayaan yang lebih presisi dan galat baku terkecil dibandingkan dengan kombinasi ulangan dan ukuran contoh lainnya. Selang kepercayaan tersebut memiliki batas bawah 1108159, batas atas 1218434 dan galat baku 32713. Selang kepercayaan kuartil untuk menduga parameter median dengan ulangan lima ratus dan ukuran contoh dua ratus memiliki selang kepercayaan yang lebih presisi dengan batas bawah sebesar 1015000, dan batas atas 1065250.]]>
<![CDATA[ANALISIS KEPUASAN DAN PREFERENSI KONSUMEN TERHADAP TEMPAT MAKAN AYAM GEPREK ]]>
<![CDATA[Rachmat Wildan]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v7i3.123
<![CDATA[The culinary business is one of businessess that believed to be an economic business has better prospects. One of culinary business in Dramaga, Bogor, West Java which has very rapid development is ayam geprek, this is evidenced by the increasing number of similar culinary businesses are increasingly penetrated in the area. Based on the condition, one of the places to eat ayam geprek in Dramaga is Ayam Geprek Pejuang (AGP) assess is very important to know the level of interest and consumer satisfaction AGP and consumer preferences of ayam geprek. The method required to explain this case was conjoint method, Importance Performance Analysis (IPA) and Customer Statisfaction Index (CSI). The data was primary data by evaluating the combination of levels and attributes by rating. Based on the level of satisfaction and interest of respondents AGP was very satisfied with the attributes associated with AGP.]]>
<![CDATA[Analisis Tingkat Kesenjangan Pendapatan antar Provinsi di Indonesia Menggunakan Regresi Data Panel Model Pengaruh Tetap ]]>
<![CDATA[Thooriq Ghaith]]>;
<![CDATA[Hari Wijayanto]]>;
<![CDATA[Anang Kurnia]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v7i3.125
<![CDATA[THOORIQ GHAITH. Analysis of Income Disparity Rates among Provinces in Indonesia Using Panel Data Regression. Supervised by HARI WIJAYANTO and ANANG KURNIA. Income disparities in Indonesia generally and in each province particularly is a serious problem from year to year. It is necessary to find out the factors that affect the income disparity rates (Gini ratio) to be taken into consideration in determining the economic policy. By using data of 33 provinces from 2007 until 2016, panel data regression with provincial fixed effect model approach was used to determine factors that affect Gini ratios in Indonesia and to capture the differences of Gini ratio characteristics of each province in form of intercept. Modeling was done for whole Indonesia and for five regions as well to find out what factors that affect the Gini ratio of provinces in Indonesia generally and what factors affect Gini ratios of provinces in each region particularly. The percentage of poor people is a significant factor to Gini ratio in the model throughout Indonesia and in the model of each region, except in Sumatera. Beside the percentage of the poor people, other explanatory variables affecting Gini ratios are GDP growth rates in Kalimantan, open unemployment rates in Sulawesi, and provincial minimum wage in Nusa Tenggara, Maluku and Papua. All of the predicted models are good enough because they produce MAPE values below 10%.]]>
<![CDATA[Perbandingan Metode Koreksi Pencaran pada Data Hasil Alat Pemantau Kadar Glukosa Darah Non-Invasif ]]>
<![CDATA[Siti Raudlah]]>;
<![CDATA[Mohammad Masjkur]]>;
<![CDATA[Kusman Sadik]]>;
<![CDATA[. Erfiani]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v7i3.127
<![CDATA[Scatter correction is one of the methods in data preprocessing that aim at eliminating the physical properties of the spectrum and reducing the variance between samples. The most commonly methods of scatter correction used are the Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) methods. The MSC method corrects the spectrum by utilizing the results of simple linear regression parameter estimation. The SNV method performs spectral correction with the median and standard deviation. Another alternative method of scatter correction is the Orthogonal Scatter Correction (OSC) applying the principle of orthogonality. The methods used in this research were MSC, SNV, and OSC methods in order to correct the result data of non-invasive blood glucose measuring instrument. The result of this research showed that the time domain spectrum data and intensity had different amount so that the summarized data was needed. Furthermore, this research found that the OSC method with the five series of statistics gained a good correction result compared to the other methods. The OSC method produced a smaller average value of the variance than the other methods.]]>
<![CDATA[Identifikasi Cepat Segmentasi Konsumen Susu Cair dalam Kemasan ]]>
<![CDATA[Fadhila Hijryani]]>;
<![CDATA[Bagus Sartono]]>;
<![CDATA[Utami Dyah Syafitri]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v7i3.128
<![CDATA[Consumer segmentation is the process of grouping customers into some segments based on some shared similar characteristics. Consumer segmentation allows companies to understand the customer's characteristics in each segment, thus make them easier to establish suitable marketing strategies for each segment's characteristics.Companies tend to use marketing strategies with demographical and consumer behavioural based scheme of consumer segmentation therefore make them easier to identify customer as the characteristics are easily measured. This research uses k-means method for segmenting 419 customers of packaged liquid milk. The life style pattern of the customers are used as the basis of the segmentation. Furthermore, this research uses decision tree algorithm to classify characteristics of the new customer. According to Hartigan index alteration (26.2433), ideal number of segments is 4. After tree pruning step, classification modelling with CART method yielded 54.61% accuracy.]]>
