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Xplore: Journal of Statistics

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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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Penerapan Algoritme Genetik Untuk Seleksi Peubah Regresi Logistik Dian Ayuningtyas; Bagus Sartono; Farit Mochamad Afendi
Xplore: Journal of Statistics Vol. 9 No. 1 (2020)
Publisher : Department of Statistics, IPB

In a study, interaction factors are the potential to have important effects on the response variable. But research involving interaction factors often encounters two problems, namely the excessive number of variables and the difficulty of implementing the heredity principle. The alternative solution is to do variable selection using a metaheuristic optimization method, In this study, the logistic regression variable selection was done using a genetic algorithm. The genetic algorithm is modified so that every independent variable has a different probability to be included in the model. That probability is based on the absolute value of the correlation of the independent variable with the response variable. These modifications have a positive effect on the results of variable selection. To choose significant independent variables, 30 repetitions of the genetic algorithm can be performed using the objective function AIC. Of the 30 repetitions, if a variable appears in all formed models, then the variable is an independent variable that has a significant effect on the response variable. The application of this method to Myopia data can show significant variables well.

Penggerombolan Hasil Ujian Nasional Menggunakan K-Rataan Samar Nouval Habibie; Akbar Rizki; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

National examination scores can be a basis for the government to make a mapping of education quality in order to increase it. The mapping can be done by using fuzzy cluster analysis. The objective of this experiment is to cluster districts/cities in Indonesia based on national examination score in natural and social science in 2014/2015 until 2017/2018 school year by using the fuzzy c-means method. The evaluation criteria that will be used are the standard deviation ratio, silhouette coefficient, and Xie Beni index. The best cluster size is two clusters, A and B. The clustering result shows cluster A has a higher mean from each subject than cluster B. Therefore, cluster A will be categorized as good, whereas cluster B as bad. The proportion of districts/cities that belong to cluster A decreased each year. The final cluster result can be determined by the mean of its degree of membership from those four school years. The analysis results show that the distribution of education quality is dominated in Java Island and squatter cities. East Nusa Tenggara, West Sulawesi, Central Sulawesi, and North Kalimantan donâ€™t have any districts/cities belong to cluster A.

Identifikasi Faktor-Faktor yang Memengaruhi Prestasi Mahasiswa Menggunakan Regresi Logistik Ordinal dan Random Forest Ordinal: Studi Kasus Mahasiswa FMIPA IPB Angkatan 2015-2017 Zuhdiyah Izzatun Nisa'; Agus M Soleh; Hari Wijayanto
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

Student achievement is the result of student learning processes and efforts. This research was conducted through a survey of students of the 2015-2017 FMIPA IPB with the selection of respondents using stratified random sampling. The purpose of this study is to identify the factors that influence the achievements of the 2015-2017 FMIPA IPB students using ordinal logistic regression and ordinal random forest. The response variable used is the PPKU GPA category and the last even semester GPA which is categorized based on the predicate of IPB graduation. The results of ordinal logistic regression get 7 explanatory variables that influence the PPKU GPA and 7 explanatory variables that influence the last even semester GPA. Explanatory variables that have a significant effect on ordinal logistic regression and become 10 variables with the highest level of importance in the ordinal random forest for both response variables are department, motherâ€™s education, internet access in a day for games, activity in the class, and active work on a group assignment.

Seleksi Peubah menggunakan Algoritme Genetika pada Data Rancangan Faktorial Pecahan Lewat Jenuh Dua Taraf Ani Safitri; Rahma Anisa; Bagus Sartono
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

In certain fields, experiments involve many factors and are constrained by costs. Reducing runs is one of the solutions to reduce experiment costs. But that can cause the number of runs to become less than the number of factors. This case of experimental design also is known as a supersaturated design. The important factors in this design are generally estimated by involving variable selection such as forward selection, stepwise regression, and penalized regression. Genetic algorithm is one of the methods that can be used for variable selection, especially for high dimensional data or supersaturated design. This study aims to use a genetic algorithm for variable selection in the supersaturated design and compare the genetic algorithm results with a stepwise regression which is generally used for a simple design. This study also involved fractional factorial design principles. The result showed that the main factors and interactions of the genetic algorithm and stepwise regression were quite different. But the principle was the same because the variables correlated. The genetic algorithm model had a smaller AIC and BIC and all of the main factors and interactions which had chosen were significant on the 0.1%. Therefore genetic algorithm model was chosen although computation time was much longer than stepwise regression.

Pengaruh Karakteristik Pasien 4 Diagnosis Penyakit Rawat Inap dengan Biaya Tertinggi di PT Asuransi ABC Terhadap Biaya Rawat Inap Berdasarkan Data Klaim Saskya Mary Soemartojo; Titin Siswantining; Darayani Putri; Mariam Rahmania
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

PT Asuransi ABC in collaboration with 68 companies, consists of 34960 participants, of which there are 1731 participants who filed claims. This study uses secondary data period July 1, 2013 - 30 September 2014. This study focused on inpatient claims, where there are 4 burdensome disease diagnosis PT Asuransi ABC at a high cost, those are coronary atrial diseases, chronic renal failure, typhoid fever, dengue haemorrhagic fever. Multiple correspondence analysis method is used to find the characteristics of each patient's disease diagnosis as well as the tendency of the characteristics of the patients in the cost of hospitalization . From the research, there are differences in patient characteristics between the disease and also the trend in the cost of hospitalization . Furthermore, the multiple linear regression analysis of patient characteristics influence on the cost of hospitalization . From the results of research only typhoid disease hospitalization costs are influenced by patient characteristics .

Evaluasi Faktor yang Memengaruhi Usability Aplikasi Thymun Menggunakan Structural Equation Model-Partial Least Square Rahma Dany Asyifa; Agus M Soleh; Bagus Sartono
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

Application development must be done by considering the usability factor of the application. Three aspects of usability measurement, namely usefulness, satisfaction, and ease of use, are latent variables that cannot be measured directly, so the appropriate analysis is the Structural Equation Model-Partial Least Square (SEM-PLS). PLS is a SEM analysis approach that does not require assumptions of data distribution and a minimum number of observations. The measurement of the usability of the Thymun application is described in two SEM-PLS models. This study aims to determine the best model and determine the effect of usefulness, satisfaction, and ease of use on the usability of the Thymun application. The data used is survey data to 44 Thymun application users. The sampling technique used was purposive sampling. The results showed that the best model has a good measure with an R-square value of 0.730 and Q2 0.453 with a Goodness of Fit 0.736. The variables of usefulness and ease of use have a significant effect on the 5% real level with path coefficient values â€‹â€‹of 0.255 and 0.636. While the satisfaction variable does not have a significant effect on the 5% real level with a path coefficient of 0.058. Thymun application usability score is 76.47.

Penggerombolan Daerah 3T di Indonesia Berdasarkan Rasio Tenaga Kesehatan dengan Metode Penggerombolan Berhierarki dan Cluster Ensemble Kesuma Millati; Cici Suhaeni; Budi Susetyo
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

Health is a major factor in community development. Inequality on health is most felt by people living in disadvantaged, outermost, and leading areas (3T) because of the difficulty of access to transportation and communication. Effective efforts are needed to achieve the optimal distribution of health services, one of which is by clustering 3T areas based on the ratio of health workers to see which areas are experiencing shortage of health workers and know the adequacy of the number of health workers spread in 3T areas. The object used in this research is 27 provinces 3T region in Indonesia and the applied statistical method is various hierarchical methods and Cluster Ensemble. Based on the results of this study, 3T area is divided into four clusters. The first cluster consists of 22 provinces and has good characteristics because all categories of the variables are in the medium category. The second and the third cluster consists of two provinces. The characteristics of the second cluster are good enough. The characteristics of the third cluster are not been good enough because there is one variable in the low category. The fourth cluster consists of one province and has characteristics that are not been good enough because there are several categories of the variables are in the low category.

Metode SVM untuk Klasifikasi Enam Tumbuhan Zingiberaceae Menggunakan Variabel Terpilih Hasil Algoritma Genetika Triyani Oktaria; Utami Dyah Syafitri; Mohamad Rafi; Farit M Afendi
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

Ginger, red ginger, emprit ginger, elephant ginger, red galangal and white galangal are known to have similar shapes and uses, especially those that are packaged in powder form. In this study, UV-Vis spectrum 200nm-700nm were used as a source of data from chemical compound contain in those plants for classification of the six plants. In this research, the support vector machine (SVM) classification method was used to classify the six plants. Another goal of this study was to identify the wavelengths which give more information about the chemical compound of the plants. The preprocessing procedure was implemented by construction of a genetic algorithm. There were four parameters in the genetic algorithm were set namely population size, crossover probability, mutation, and generation probability. The mutation and the population size influenced significantly the results of SVM. The best result was given by probability of mutation was 10 and population size was 30. The SVM model was better than the SVM model without preprocessing procedure.

Pemodelan dengan Geographically Weighted Negative Binomial Regression (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat) Khusnul Khotimah; Itasia Dina Sulvianti; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

Penggerombolan Sekolah pada Penerimaan Mahasiswa Baru Jalur SNMPTN di IPB Menggunakan Metode Two-Step Cluster Ni Kadek Manik Dewantari; Utami Dyah Syafitri; Aam Alamudi
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

New student admissions are opened in three pathways including SNMPTN, SBMPTN, and Seleksi Mandiri. In order to improve the SNMPTN selection system at IPB, a study was conducted on the quality of SMA/MA which registered to IPB through school clustering. In general, cluster analysis cannot handle large and mixed-type data, so this school clustering used the Two-Step Cluster method with two alternatives, namely without handling outliers and handling 5 percent outliers. Both of these alternatives produced an average Silhouette coefficient value of 0.2 and 0.3 respectively, which was still under the good category. However, clustering without handling outliers resulted in more detailed cluster criteria with 4 optimal clusters. The criteria for these four clusters include, Cluster 1 is a category of Low Commitment, Low Quality, and Low Consistency schools, Cluster 2 and 3 are categories of schools that have special criteria in certain categories, and Cluster 4 is a category of High Commitment, High Quality, and High Consistency.

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Home Page

OAI Link

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Contact Name Akbar Rizki

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

Phone +628111144470

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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