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Xplore: Journal of Statistics

xplore
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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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Mengukur Indeks Kebahagiaan Mahasiswa IPB Menggunakan Analisis Faktor Aulya Permatasari; Khairil Anwar Notodiputro; Kusman Sadik
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Undergraduate students of Bogor Agricultural University are spread out in 9 Faculties and 1 School. The difference of faculties and schools illustrate the different characteristics and burdens of student lectures on each faculty and school. This distinction raises various assumptions about the level of student happiness in every faculty and school. Student happiness analysis is measured using loading factor obtained from Factor Analysis. Based on the analysis, found that Faculty of Animal Science is the happiest faculty with happiness index reaching 66.88 and the lowest index of happiness found in the Faculty of Human Ecology with happiness index of 62.39.

Analisis Lintas Sifat Morfo-Agronomis dan Fisiologis Jagung (Zea mays L.) Annisa Malik; Farit Mochamad Afendi; Akbar Rizki; . Sutoro
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Corn (Zea mays L.) is the third most important food commodity after wheat and rice based on the world's staple food, and ranks second after rice based on staple food in Indonesia. High yielding varieties of corn are highly needed to meet food, feed and industrial needs. These varieties can be obtained through plant breeding programs by utilizing the source of genes capable of producing good plant character. Gene sources can be obtained from germplasm or local varieties that exist. Character of plants that can support the productivity of plants can be used as an indicator of the selection process in corn plant breeding. This can be done through characterization of morpho-agronomic and physiological properties of each corn variety, then determine the characters that support the productivity of corn plants directly or indirectly. The direct and indirect effect of a plant's character on crop productivity is identified through path analysis. The results showed that the effective selection criteria for increasing cornâ€™s seed weight directly was the leaf area. While the effective selection criteria for increasing the weight of biomass directly is the age of female flowers out. While the effective selection criteria for increasing cornâ€™s seed weight and biomass indirectly is the plant height through the filling rate of the seeds.

Segmentasi Mahasiswa S1 IPB terhadap Sistem Peminjaman Sepeda Tania Amalia Darsono; Utami Dyah Syafitri; Aam Alamudi
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

IPB is the one campus that realize the Green Campus program. One of the elements in Green Campus is Green Transportation. In realizing this Green Transportation, IPB has several programs that include the Green Bike program. There are rules in implementation the Green Bike program related to the borrowing system. Because of the borrowing system, it is necessary to make the segmentation of S1 IPB students on bicycle borrowing system. Segmentation of respondent's characteristic used two step clustering method and the result is 3 optimal clusters. Then segmentation on respondent's preference to bicycle borrowing system used k-means method and the result is 2 optimal clusters. Segmentation of bicycle borrowing system based on respondent's characteristic and respondent's preference is 6 combinations of cluster using cross tabulation.

Penggerombolan Provinsi di Indonesia Berdasarkan Produktivitas Tanaman Pangan Tahun 2005-2015 Menggunakan Metode K-Error Emeylia Safitri; I Made Sumertajaya; Akbar Rizki
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Clustering analysis is a multivariate analysis thatâ€™s aim for gruping the observasion objects to some groups. The clusters have low similarity between the clusters and high similarity in same cluster. Classic grouping analysis have a weakness that doesnâ€™t insert measurement error information that related with data. Clustering analysis with K-Error method is expanded for solusing solving the measurement error data problem in classic grouping analysis. The research is aim for clustering the provinces in Indonesia using K-Error and K-Means method based on crops productivity. K-Error method produces better clusters than KMeans. K-Error method formed 7 clusters. Cluster 5 consist of provinces with highest productivity almost at all crops. Cluster 2 and 3 have low productivity for partial crops.

Pemodelan Support Vector Machine Data Tidak Seimbang Keberhasilan Studi Mahasiswa Magister IPB Octavia Dwi Amelia; Agus M Soleh; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Bogor Agricultural University Postgraduate School (SPs-IPB) can maintain its reputation by applying a more selective admissions system. This research predicts the success of student using Support Vector Machine (SVM) modeling by considering the characteristics and educational background of the students. But there is an imbalance of data class. SVM modeling on unbalanced data produces poor performance with a sensitivity value of 0.00%. Unbalanced data handling using Sythetic Minority Oversampling Technique (SMOTE) succeeded in improving SVM classification performance in classifying unsuccessful students. Based on accuracy, sensitivity, and specificity with the default cut off, the exact type of SVM to model student success is SVM RBF. When using the optimum cut-off value from each type of SVM, the sensitivity value can be improved again. SVM RBF still gives the best result when using cut off 0.6. The final model that will be used to predict the success of the SPs-IPB student is obtained from SVM RBF modeling with cut off 0.6 using the entire data that has been through the SMOTE stage.

Penerapan Metode Resampling dan K-Nearest Neighbor dalam Memprediksi Keberhasilan Studi Mahasiswa Program Magister IPB Devi Andrian; Agus Mohamad Soleh; Hari Wijayanto
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Graduate School IPB (SPs - IPB) has been established for a long time and is believed to produce high quality graduates and highly competitive. However, based on existing data recaps, there are a small number of students who did not graduate, either resigned or Drop Out (DO). It needs to be handled by conducting a selection process for prospective students based on the profile and educational background S1. One of them by applying the method of classification K - Nearest Neighbor (KNN). The response variable used is the success status of the study of prospective students, ie graduated and not graduated. While the explanatory variables used are the profiles and educational background of prospective students. There is an imbalance of data in the data obtained, where the class does not pass much less than the passing class. This can reduce the value of classification accuracy in minority class (sensitivity). So that the handling of data imbalance by using resampling method, either in the form of Random Over Sampling (ROS), Random Under Sampling (RUS), and Random Over-Under Sampling (ROUS). The result of comparison of evaluation result of KNN classification by using k = 1 to 6, resulted in greater sensitivity value when accompanied by the process of handling the data imbalance than without the process of handling the data imbalance, although the accuracy and specificity value becomes smaller. The greatest sensitivity value was obtained when applying the KNN classification method with k = 1, accompanied by the handling of data imbalance by the RUS method, with the mean and median sensitivity values of 0.89 and 0.90, respectively.

Pemodelan Regresi Spasial Kekar: Studi Kasus Jumlah Kunjungan WIsatawan Mancanegara Asal Eurasia di Indonesia Tahun 2015 Resti Cahyati; Anik Djuraidah; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Spatial regression model is a model used to evaluate the relationship between one variable with some other variables considering the spatial effects in each region. One of the causes of imprecise spatial regression model in predicting is the presence of outlier or extreme value. The existence of outlier or extreme value could damage spatial regression parameter estimator. However, discarding the outlier or extreme value in spatial analysis, could change the composition of the spatial effect on the data. Visitor arrivals from Eurasia to Indonesia by nationality in 2015 great diversity caused by the outlier. So in this paper, we need a spatial regression parameter estimation method which is robust where the value of the estimation is not much affected by small changes in the data. The application of the S prediction principle is carried out in the estimation of the coefficient of spatial regression parameters which is robust to the observation of silane. The result of modeling by applying the principle of the S estimator method on the estimation of the stocky spatial regression parameter is able to accommodate the existence of pencilan observation on the spatial regression model quite effectively. This is indicated by a considerable change in the coefficient coefficient estimator parameters of spatial regression is able to decrease the value of MAPE and MAD produced by spatial regression regression modeling.

Penerapan Metode Two Step Cluster pada Data Survei Angkatan Kerja Nasional (Sakernas) Maya Deanti; Farit Mochamad Afendi; Aam Alamudi
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

MAYA DEANTI. Implementation of Two Step Cluster Method on National Labor Force Survey Data (Sakernas) 2017 Bogor Regency. Supervised by FARIT MOCHAMAD AFENDI and AAM ALAMUDI. Five labor issues in Indonesia that have not been resolved by 2017 are termination of employment due to digitalization or automation, labor informalization, BPJS, high accident and occupational safety (K3), and outsourcing. In addition, the increasing number of Foreign Workers (TKA) in Indonesia can affect the decrease in local employment opportunities. Therefore, in this study will be carried out clustering to the labor force data to determine the condition of employment in Indonesia, especially Bogor regency. However, this labor force data has considerable observation with mixed data types, namely numerical and categorical. Regular cluster analysis can not be applied directly to the condition of the data, so that to be used in this research is a Two Step Cluster analysis which is a modification of existing cluster analysis. This Two Step Cluster analysis produces 3 clusters, with the characteristics of each cluster that is cluster 1 consisting of resident households or unemployed, cluster 2 consists of self-employed residents, and cluster 3 with the majority of the population working as laborers or employees. This clustering is based on work aspect only because the demography and education aspect of Bogor Regency is quite uniform. Keywords: cluster analysis, cluster, Two Step Cluster, uniform

Pendugaan Produktivitas Bagan Perahu dengan Regresi Gulud, LASSO dan Elastic-net Resty Fanny; Anik Djuraidah; Aam Alamudi
Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018
Publisher : Department of Statistics, IPB

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.

Penerapan Teknik Prapemrosesan Smoothing Spline pada Data Hasil Pengukuran Alat Pemantau Kadar Glukosa Darah Non-Invasif Putu Gita Karlina Jayanti; Rahma Anisa; Muhammad Nur Aidi; . Erfiani
Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018
Publisher : Department of Statistics, IPB

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.

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Akbar Rizki

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Akbar Rizki

Contact Email akbar.ritzki@apps.ipb.ac.id

Phone +628111144470

Journal Mail Official akbar.ritzki@apps.ipb.ac.id

Editorial Address Departemen Statistika, IPB Jl. Meranti Kampus IPB Darmaga Wing 22, Level 4 Bogor 16680

Location Kota bogor, Jawa barat INDONESIA

Abstract

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Contact Name
Akbar Rizki

Contact Email
akbar.ritzki@apps.ipb.ac.id

Phone
+628111144470

Journal Mail Official
akbar.ritzki@apps.ipb.ac.id

Editorial Address
Departemen Statistika, IPB Jl. Meranti Kampus IPB Darmaga Wing 22, Level 4 Bogor 16680

Location
Kota bogor,

Jawa barat

INDONESIA