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[Klasifikasi Status Keaktifan Siswa SMA di Jawa Barat Menggunakan Random Forest dengan SMOTE ]]>
<![CDATA[M Itmamurohman]]>;
<![CDATA[Pika Silvianti]]>;
<![CDATA[La Ode Abdul Rahman]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 11 No. 2 (2022): ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v11i2.929
<![CDATA[The dropout rate in Indonesia has a higher percentage as education levels grow. The high school dropout rate in Indonesia is at 0.67%. West Java is the province with the highest high school dropout rate in the academic year 2017/2018. In the next academic year, the high school dropout rate in West Java decreased. The student who drop out of school was caused by various factors. This study examines important variables and classification performance that are generated by random forest. The number of dropout students is very small compared to the number of active students. The imbalance data is handled using SMOTE. Random forest with SMOTE is considered able to predict data classes better because it can increase sensitivity values and reduce errors in classifying dropout students as active students. Father's income, number of siblings, class, father's education level, and father's type of work are important variables that have a major influence in determining the active status of high school students in West Java.]]>
<![CDATA[Survei dan Identifikasi Faktor Awareness Mahasiswa IPB Terhadap Perilaku Pelecehan Seksual dan Kekerasan Menggunakan Regresi Logistik Biner ]]>
<![CDATA[Ibrahim Arif Muhammad]]>;
<![CDATA[Rahma Anisa]]>;
<![CDATA[Mohammad Masjkur]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 11 No. 2 (2022): ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v11i2.939
<![CDATA[The lack of public awareness of sexual harassment as well as physical and verbal abuse are still occurring frequently and becoming a concern in everyday life, especially for women. Sexual harassment is an unwanted behavior or attention from a perpetrator with sexual intentions that disturbs the victim(s). Abuse is a form of one person's action against another party that results in pain and changes both physically and psychologically. The purpose of this study is identifying the number of IPB University students that are aware on the act of sexual harassment and abuse, identifying factors that can affect awareness about it using binary logistic regression, and providing recommendations on how to increase the awareness of it. Majority of the respondents have awareness on both the act of sexual harassment and abuse, whether they have done it or not. In the logistic regression, gender and financial background of the respondents were significant factors of awareness in the act of sexual harassment, whereas in awareness of the abusive behavior, the respondents’ gender, hometown, the time amount of social media usage per day, financial background, and experience of being a victim of it factor significantly. Majority of the respondents suggest that education from various sources should be improved in order to raise awareness to the public.]]>
<![CDATA[Penggerombolan Data Panel Emiten Sektor Pertambangan selama Pandemi Covid-19 ]]>
<![CDATA[Nadhif Nursyahban]]>;
<![CDATA[Aam Alamudi]]>;
<![CDATA[Farit Mochamad Afendi]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v12i1.948
<![CDATA[The Covid-19 pandemic has made people start looking for new income, one of whichis stock investment. Mining Stock recorded the highest sectoral index increase in 2020.The high increase in the mining sector index doesn’t indicate all of the stocks have agood performance. Clustering data of mining stock can help to see which stock has thebest performance. Variables used in clustering are technical factors with details: return,trading volume, transaction frequency, bid volume, and foreign buy. Data in this researchis longitudinal data from March 2020 until January 2022 and the clustering techniqueused is k-means. Clustering on outliers data and non-outliers data is done separately.Definition of outliers is exploratively with biplot analysis. Clustering on outliers dataresults obtained are five clusters and clustering on non-outliers data results obtained aretwo clusters. Best cluster is cluster who obtained ANTM because has highest value inreturn, transaction frequency, and foreign buy.]]>
<![CDATA[Pemodelan Tingkat Kriminalitas di Indonesia Menggunakan Analisis Geographically Weighted Panel Regression ]]>
<![CDATA[Endah Febrianti]]>;
<![CDATA[Budi Susetyo]]>;
<![CDATA[Pika Silvianti]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v12i1.950
<![CDATA[Crime is one of the socio-economic problems that Indonesia has not yet resolved. Although Indonesia is categorized as a safe country to visit, in reality, there are still many Indonesian people who experience crime. The resolution of this socio-economic problem is very important because it involves the safety and comfort of the community. This study aims to identify the factors that influence the crime rate in Indonesia and determine the best model for each province by comparing the panel data regression model and the Geographically Weighted Panel Regression (GWPR) model. This research data consists of 34 provinces in Indonesia from 2016 to 2020. The analysis used is panel data regression analysis and GWPR. The result is that the adaptive kernel gaussian GWPR is the best model with of 69,89% and AIC of 167,4585. The GWPR modeling produces model equations and significant variables for each province. In general, five variables have a significant effect on the crime rate, namely percentage of poor population, open unemployment rate, Gross Regional Domestic Product at the constant price per capita, human development index, and mean years of schooling.]]>
<![CDATA[Analisis Regresi Logistik dan Cart untuk Credit Scoring dengan Penanganan Kelas Tak Seimbang ]]>
<![CDATA[Siwi Haryu Pramesti]]>;
<![CDATA[Indahwati Indahwati]]>;
<![CDATA[Utami Dyah Syafitri]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v11i3.1015
<![CDATA[The absence of collateral for a type of credit will increase the bank's credit risk (failed to pay). Banks apply the precautionary principle by managing their credit portfolios so that potential hazards that occur can be measured and controlled in a model. Credit scoring describes how likely a debtor will fail with payments. This study aimed to compare logistic regression analysis and Classification and Regression Trees (CART) in classifying debtors to evaluate credit policies. One of the problems in classification is unbalanced data. Synthetic Minority Oversampling Technique (SMOTE) is a technique to handle the unbalanced problem in classification. The results show that the logistic regression model with SMOTE has higher sensitivity than the CART model, and there was no difference in Area Under Curve (AUC). The variables that have significant effects on the classification of debtors (good, bad) are level of education, homeownership status, and income.]]>
<![CDATA[Perbandingan Kinerja Regresi Conway-Maxwell-Poisson dan Poisson-Tweedie dalam Mengatasi Overdispersi Melalui Data Simulasi ]]>
<![CDATA[Ahmad Rifai Nasution]]>;
<![CDATA[Kusman Sadik]]>;
<![CDATA[Akbar Rizki]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v11i3.1018
<![CDATA[Poisson regression is a standard method to model count data. Modeling count data frequently causes overdispersion which means that Poisson regression is less precise to model it as Poisson regression has the assumption of equidispersion. Overdispersion can be overcome by using Conway-Maxwell-Poisson (COM-Poisson) and Poisson Tweedie (Poisson-Tw) regression. The best model is determined based on the lowest value of RMSE, absolute bias, variance of parameter estimator, AIC, and BIC. This research uses simulation data. The response variable of simulation data is generated to follow Generalized Poisson distribution with combinations of and The result of simulation study shows that COM-Poisson and Compound Poisson-Tw are the alternatives to model overdispersed count data, but COM-Poisson is better to overcome overdispersion with higher dispersion parameter.]]>
<![CDATA[Klasifikasi Sekolah dalam Penerimaan Mahasiswa Baru Vokasi IPB Jalur USMI Menggunakan Metode CART ]]>
<![CDATA[Erlinda Widya Widjanarko]]>;
<![CDATA[Utami Dyah Syafitri]]>;
<![CDATA[Aam Alamudi]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v11i3.1019
<![CDATA[The selection of new student admissions for the IPB vocational school consists of several routes, one of which is the USMI route. To improve its performance, it is necessary to evaluate the USMI new student admission system. Previously, research with the same objective had been carried out using the clustering method. The study resulted in three clusters in which schools were differentiated based on commitment and quality. This study aim to create a classification model of the clusters obtained using the CART method. Classification and Regression Tree (CART) is a nonparametric classification technique that produces a single decision tree. The CART method can involve mixed-type data. The classification model generated from the 2019 data yields an accuracy of 98.52%. However, the results of the 2019 model evaluation with the 2020 data are still not good enough to predict with an accuracy of 57.22%, so the 2020 data is re-clustered and produces three clusters. Furthermore, the classification model was remade with 2020 data, resulting in an accuracy of 97.47%. However, the results of the 2020 model evaluation with the 2021 data are still not good enough to predict with an accuracy of 44.34%, so the clustering in the previous year cannot be used for predictions of the following year's data. The grouping of schools for USMI applicants needs to be done by grouping schools every year.]]>
<![CDATA[Analisis Gerombol Pautan Ward Kabupaten/Kota di Provinsi Jawa Timur Berdasarkan Indikator Kesejahteraan Rakyat ]]>
<![CDATA[annida marsa salsabila]]>;
<![CDATA[Mohammad Masjkur]]>;
<![CDATA[Indahwati]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v11i3.1024
<![CDATA[The main goal in the development of a country is to improve the welfare ofthepeople. One of the causes of the problems of people's welfare in Indonesia is that thedevelopment carried out by the government is not carried out evenly and not on target,not least in East Java Province. to group and look at the characteristics of 38regencies/cities in East Java Province based on people's welfare indicators sothat thegovernment in making policies can be evenly distributed and on target. Thisstudy useshierarchical cluster analysis. The data used is the welfare indicator data for 38districts/cities of East Java Province in 2019. The hierarchical cluster analysismethodused is the Ward method. The results of the study using dendogram cuts and the ratioof standard deviations within clusters and standard deviations betweenclusters showedthat districts/cities in East Java province could be divided into six clusters. In eachcluster, the characteristics are seen using the average value of eachvariable. Areas withvery good development are in cluster six and areas that requiremore development inmany aspects are in cluster five.]]>
<![CDATA[Pemodelan Pemodelan Angka Kematian Bayi di Jawa Barat Menggunakan Pendekatan Analisis Regresi Spline dan Kernel ]]>
<![CDATA[Riska Indah Puspita]]>;
<![CDATA[Rahma Anisa]]>;
<![CDATA[La Ode Abdul Rahman]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v11i3.1026
<![CDATA[The Infant Mortality Rate (IMR) is a very sensitive indicator of health service efforts, especially those related to newborns. IMR is also one of the problems that need to solve and the target of the SDGs number 3 (Good health and well-being). Java Province consists of 27 regencies/cities with an IMR of 3,26/1000 live births in 2019. The pattern of IMR data in West Java province had a pattern that changes at certain points so that the modeling is carried out using nonparametric regression. The selected nonparametric regression approach was spline regression which able to adapt more effectively with the characteristics of the data and kernel regression is easy to implementation. The explanatory variables used are life expectancy, the percentage of poor people, the open unemployment rate and the average length of schooling. The best model given by spline regression at 3 knot and kernel regression with bandwidth 1.2; 1.2; 1.1; and 1. Based model evaluation, the spline regression model's performance is better than the kernel regression with MSE, RMSE, and MAPE values are 0.66; 0.81, and 18.54%]]>
<![CDATA[Identifikasi Peubah yang Berpengaruh terhadap Ketidaklulusan Mahasiswa Program Sarjana BUD IPB dengan Regresi Logistik Biner ]]>
<![CDATA[Mahdiyah Riaesnianda]]>;
<![CDATA[Aam Alamudi]]>;
<![CDATA[Agus Soleh]]>;
<![CDATA[Septian Rahardiantoro]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023) ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v12i1.1055
<![CDATA[One of the entrances available at the Bogor Agricultural University (IPB) is the Regional Representatives Scholarship (BUD). Not all BUD IPB students were able to complete their studies because they dropped out (DO) or resigned. One of the efforts that IPB can do to reduce the dropout rate for BUD IPB students is to find out the variables that affect the failure of BUD IPB students. The variables that influence the failure of BUD IPB students are analyzed by binary logistic regression. There is an imbalance of data classes in the response variables so that the method that can be used to overcome this is the Synthetic Minority Over-Sampling Technique (SMOTE). The classification model with SMOTE resulted in a higher average sensitivity than the model without SMOTE from 10,66% to 61,91%. This confirms that the model with SMOTE is better at predicting the minority class (BUD IPB students who do not pass). The variables that affect the failure of BUD IPB students are gender, school status of origin, study program groups, the presence or absence of Pre-University Programs (PPU), type of sponsor, average report cards, and GPA in the Joint Preparation Stage (TPB) or General Competency Education Program (PPKU).]]>
