Bandung Conference Series: Statistics最新文献

{"title":"Regresi Logistik Dua Level Respon Biner untuk Pemodelan Data Status Kelulusan Mata Kuliah Statistika Mahasiswa Fakultas X, Y dan Z Universitas Islam Bandung Angkatan 2019","authors":"Alma Mariana Sofiah, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.8228","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8228","url":null,"abstract":"Abstract. Two-level binary logistic regression is a multilevel analysis used to analyze data that has a two-level hierarchical structure with binary response data. Hierarchical data structure is data with observation units nested in higher units. Two-level regression is the simplest multilevel model where the first level is individual data and the second level is group data. In this study, we will discuss the use of two-level binary logistic regression on data on the Passing Status of Statistics Courses of Bandung Islamic University Students Class of 2019. The data used is secondary data obtained from PSITEK Bandung Islamic University, with a total data of 1173 students nested into 3 groups (Faculty). The research variable in this study is the status of passing the statistics course as the dependent variable (Y), with two categories, namely 0 for students who do not pass and 1 for students who pass, as well as gender, regional origin and GPA as predictor variables (X) at level 1 and science clumps as predictor variables (Z) at level 2. The parameter estimator is carried out using the Maximum Likelihood via Quadrature method. Based on the results of the application of 2-level binary logistic regression, the factors that influence the response are the variables of regional origin and GPA (level 1/student) and clumps of knowledge (level 2/Faculty). \u0000Abstrak. Regresi logistik biner dua-level merupakan analisis multilevel yang digunakan untuk menganalisis data yang mempunyai struktur hirarki dua level dengan data respon biner. Struktur data hirarki adalah data dengan unit-unit observasi yang bersarang pada unit yang lebih tinggi. Regresi dua-level merupakan model multilevel yang paling sederhana dimana level kesatu data individu dan level kedua data kelompok. Dalam penelitian ini, akan membahas penggunaan regresi logistik biner dua level pada data Status Kelulusan Mata Kuliah Statistika Mahasiswa Universitas Islam Bandung Angkatan 2019. Data yang digunakan adalah data sekunder yang diperoleh dari PSITEK Universitas Islam Bandung, dengan total data sebanyak 1173 mahasiswa yang tersarang kedalam 3 kelompok (Fakultas). Variabel penelitian dalam penelitian ini adalah status lulus mata kuliah statistika sebagai variabel dependen (Y), dengan dua kategori yaitu 0 untuk mahasiswa yang tidak lulus dan 1 untuk mahasiswa yang lulus, serta jenis kelamin, asal daerah dan IPK sebagai variabel prediktor (X) pada level 1 dan rumpun ilmu sebagai variabel prediktor (Z) pada level 2. Penaksir parameter dilakukan menggunakan metode Maximum Likelihood via Quadrature. Berdasarkan hasil penerapan regresi logistik biner 2-level menyatakan bahwa fakor-faktor yang mempengaruhi respon adalah variabel asal daerah dan IPK (level 1/mahasiswa) dan rumpun ilmu (level 2/Fakultas).","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132916884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pemodelan Tingkat Pengangguran Terbuka (TPT) di Jawa Barat Menggunakan Regresi Nonparametrik Deret Fourier","authors":"Sukma Wiyasih Izumi, Teti Sofia Yanti","doi":"10.29313/bcss.v3i2.8769","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8769","url":null,"abstract":"Abstract. Regression analysis is a method used to determine the causal relationship of response variables with one or more predictor variables. It is generally divided into three regression analyses namely parametric, semiparametric, and nonparametric. Parametric regression has assumptions that can be met and the regression curve is known, but not all data can meet this. Therefore, the use of nonparametric regression can be an alternative because the use is not bound by assumptions and is used when the data does not follow a certain curve pattern. Nonparametric regression of the Fourier series is a method used to estimate the parameters of the regression curve through a data pattern approach. The best modeling uses the Generalized Cross Validation (GCV) method to derive the optimal oscillation point from the minimum GCV value. The purpose of the study is to obtain the best model of the Open Unemployment Rate (TPT) in West Java in 2022. Where the variables that are thought to affect the Open Unemployment Rate (TPT) are population density, percentage of poor people, gross participation rate (APK), Human Development Index (HDI), and Labor Force Participation Rate (TPAK). The results of the analysis show that the best fourier series nonparametric regression model for the Open Unemployment Rate (TPT) in West Java is at the 3th oscillation point with a coefficient of determination value of 84%. \u0000Abstrak. Analisis regresi adalah suatu metode yang digunakan untuk mengetahui hubungan sebab akibat dari variabel respon dengan satu atau lebih variabel prediktor. Umumnya dibagi menjadi tiga analisis regresi yaitu parametrik, semiparametrik, dan nonparametrik. Pada regresi parametrik memiliki asumsi yang dapat terpenuhi dan kurva regresi diketahui akan tetapi tidak semua data dapat memenuhi hal tersebut. Oleh karena itu penggunaan regresi nonparametrik t Rate, Generalized Cross Validation.dapat menjadi alternatif karena penggunaan tidak terikat asumsi-asumsi dan digunakan ketika data tidak mengikuti pola kurva tertentu. Regresi nonparametrik deret fourier adalah metode yang digunakan untuk mengestimasi parameter kurva regresi melalui pendekatan pola data. Pemodelan terbaik menggunakan metode Generalized Cross Validation (GCV) untuk memperoleh titik osilasi optimal dari nilai GCV minimum. Tujuan dari penelitian untuk memperoleh model terbaik Tingkat Pengangguran Terbuka (TPT) di Jawa Barat tahun 2022. Dimana variabel-variabel yang diduga mempengaruhi Tingkat Pengangguran Terbuka (TPT) yaitu kepadatan penduduk, presentase penduduk miskin, angka partisipasi kasar (APK), Indeks Pembangunan Manusia (IPM), dan Tingkat Partisipasi Angkatan Kerja (TPAK). Hasil analisis menunjukkan model regresi nonparametrik deret fourier terbaik untuk Tingkat Pengangguran Terbuka (TPT) di Jawa Barat adalah pada titik osilasi ke-3 dengan nilai koefisien determinasi sebesar 84%.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129402106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pengendalian Kualitas Limbah Cair Menggunakan Diagram Kendali Multivariate Exponentially Weighted Moving Average (MEWMA)","authors":"Tania Grahanita, Anneke Iswani Achmad","doi":"10.29313/bcss.v3i2.9261","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9261","url":null,"abstract":"Abstract. In the textile industry the resulting liquid waste has toxic characteristics and is harmful to the environment. Therefore, liquid waste needs to go through a processing and quality control process before being discharged into the waters. Control chart is one of the statistical tools used in statistical quality control. In practice we will encounter situations where it is necessary to control for two or more quality characteristics that are not normally distributed multivariate, so the control chart used is the Multivariate Exponentially Weighed Moving Average (MEWMA). This study uses data on the quality of textile industrial wastewater which has been processed using an WWTP (Wastewater Treatment Plant). There are 48 samples (months) for the period 2018 - 2021 with 6 variables of liquid waste content. The results of the quality control analysis by applying the MEWMA control chart obtained the most optimum weighting value of 0.05 with an Upper Control Limit (UCL) value of 14.6, there was 1 observation that was out of control. This shows that the quality of liquid waste in WWTP in the textile company CV. Sandang Sari is not statistically controlled. Abstrak. Pada industri tekstil limbah cair yang dihasilkan memiliki karakteristik beracun dan berbahaya bagi lingkungan hidup. Oleh karena itu limbah cair perlu melalui proses pengolahan dan pengendalian kualitas terlebih dahulu sebelum dibuang ke perairan. Diagram kendali adalah salah satu alat satatistik yang digunakan pada pengendalian kualitas statistik. Dalam praktiknya kita akan bertemu situasi dimana perlu untuk mengontrol dua atau lebih karakteristik kualitas yang tidak berdistribusi normal multivariat, maka diagram kendali yang digunakan adalah Multivariate Exponentially Weighed Moving Average (MEWMA). Penelitian ini menggunakan data kualitas limbah cair industri tekstil yang sudah melalui proses pengolahan menggunakan IPAL (Instalasi Pengolahan Air Limbah), ada sebanyak 48 sampel (bulan) periode 2018 - 2021 dengan 6 variabel kandungan limbah cair. Hasil analisis pengendalian kualitas dengan menerapkan diagram kendali MEWMA didapat nilai pembobot yang paling optimum sebesar 0,05 dengan nilai Batas Kendali Atas (BKA) sebesar 14,6 terdapat 1 pengamatan yang berada diluar kendali. Hal ini menunjukkan bahwa kualitas limbah cair pada IPAL di perusahaan tekstil CV. Sandang Sari tidak terkendali secara statistik.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124424251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Metode Regresi Komponen Utama Kernel untuk Prediksi Harga Rumah","authors":"M. Verdiansyah, Suwanda","doi":"10.29313/bcss.v3i2.9084","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9084","url":null,"abstract":"Abstract. Linear regression analysis is a statistical method used to model the relationship between the dependent variable and one or more independent variables expressed in the form of a regression equation. The Ordinary Least Square (OLS) can be used to estimate regression parameters. The regression parameter estimator obtained will be good if the assumptions that apply to MKT are met. One of the assumptions that must be met is that there is a linear relationship between the dependent variable and each independent variable. If linearity is violated, kernels can be used. In addition, another assumption that must be met is the absence of multicollinearity between independent variables. An alternative method that can be used to overcome multicollinearity is using Principal Component Analysis. If the linearity and multicollinearity assumptions are violated, the Kernel Principal Component Regression method can be applied. This study will apply the Kernel Principal Component Regression (KPCR) method, where the principal component is a nonlinear combination of independent variables through a kernel function. This KPCR method is implemented to predict house prices in South Jakarta based on building area, land area, number of bedrooms, number of bathrooms, and number of car capacity in the garage. Based on the results of the study, 3 main components of the kernel were formed with a prediction model Y ̂ = 76,3612 + 409,5373 KUK1 + 328,4967 KUK2 – 291,7757 KUK3 and a coefficient of determination (R2) of 70.4640%. \u0000Abstrak. Analisis regresi linear merupakan suatu metode statistika yang digunakan untuk memodelkan hubungan antara variabel dependen dengan satu atau lebih variabel independen yang dinyatakan dalam bentuk persamaan regresi. Dalam menaksir parameter regresi dapat menggunakan Metode Kuadrat Terkecil (MKT). Penaksir parameter regresi yang diperoleh akan baik jika asumsi-asumsi yang berlaku untuk MKT dipenuhi. Salah satu asumsi yang harus dipenuhi adalah terdapat hubungan linear antara variabel dependen dan masing-masing variabel independen. Apabila linearitas terlanggar, dapat menggunakan kernel. Selain itu, asumsi lainnya yang harus terpenuhi adalah tidak adanya multikolinearitas antar variabel independen. Metode alternatif yang dapat digunakaan untuk mengatasi multikolinearitas adalah menggunakan Analisis Komponen Utama. Apabila asumsi linearitas dan multikolinearitas terlanggar, dapat diterapkan metode Regresi Komponen Utama Kernel. Dalam penelitian ini, akan diterapkan metode Regresi Komponen Utama Kernel (RKUK), dimana komponen utama merupakan kombinasi nonlinier dari variabel independen melalui fungsi kernel. Metode RKUK ini diimplementasikan untuk memprediksi harga rumah di Jakarta Selatan berdasarkan luas bangunan, luas tanah, jumlah kamar tidur, jumlah kamar mandi, dan jumlah kapasitas mobil dalam garasi. Berdasarkan hasil penelitian, terbentuk 3 buah komponen utama kernel dengan model prediksi Y ̂ = 76,3612 + 409,5373 KUK1 + 328,4967 KUK2","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128367676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Distribusi Lindley pada Data Besar Klaim Asuransi Kendaraan Bermotor di Indonesia","authors":"Puput Aryanti, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i2.8202","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8202","url":null,"abstract":"Abstrak. Asuransi kendaraan bermotor adalah polis asuransi standar yang digunakan di Indonesia yang mencakup pertanggungan gabungan yaitu pertanggungan atas kendaraan bermotor itu sendiri dan pertanggungan tanggung jawab hukum kepada pihak ketiga. Salah satu hal yang perlu diperhatikan perusahaan asuransi sebagai lembaga penerima risiko adalah memodelkan data besar klaim. Distribusi yang cocok untuk memodelkan data besar klaim adalah distribusi kontinu yang non negatif, memiliki kemiringan yang positif, dan memiliki ekor yang tebal. Distribusi Lindley merupakan distribusi eksponensial campuran dan memiliki kriteria yang sesuai untuk pemodelan data besar klaim. Parameter yang ada ditaksir menggunakan metode kemungkinan maksimum. Pengujian kecocokan distribusi menggunakan uji Kolmogorov-Smirnov. Data yang digunakan merupakan data sekunder PT. X tahun 2019 yang terdiri dari data besar klaim pemegang polis asuransi kendaraan bermotor kategori 7 wilayah 2. Berdasarkan hasil penerapan distribusi Lindley untuk memodelkan data besar klaim asuransi kendaraan bermotor PT. X tahun 2019 dengan menggunakan uji Kolmogorov-Smirnov, dapat disimpulkan bahwa data besar klaim asuransi kendaraan bermotor PT. X tahun 2019 berasal dari populasi yang berdistribusi Lindley dengan nilai taksiran parameter distribusi Lindley sebesar θ=0,0000002995. \u0000  \u0000Abstract. Motor vehicle insurance is a standard insurance policy used in Indonesia that includes combined coverage, namely coverage of the motor vehicle itself and legal liability coverage to third parties. One of the things that insurance companies need to pay attention to as risk-receiving institution is modeling the amount of the claim. A suitable distribution for modeling the amount of the claim is a continuous distribution that is non-negative, has a positive skew, and has a heavy tail. The Lindley distribution is a mixed exponential distribution and has criteria suitable for modeling the amount of the claim. The parameters were estimated using the maximum likelihood method. Testing the suitability of the distribution using the Kolmogorov-Smirnov test. The data used is secondary data of PT. X in 2019 which consists of data on large claims of motor vehicle insurance policyholders in category 7 region 2. Based on the results of the application of the Lindley distribution to model the 2019 PT. X motor vehicle insurance claim data using the Kolmogorov-Smirnov test, it can be concluded that the 2019 PT. X motor vehicle insurance claim data comes from a Lindley-distributed population with an estimated value of the Lindley distribution parameter of θ = 0.0000002995.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116970116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pengaplikasian Model Regresi Linier Berganda Multivariat terhadap Data Cabang Olahraga Angkat Berat","authors":"Azmy Rabah, Suwanda","doi":"10.29313/bcss.v3i2.8200","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8200","url":null,"abstract":"Abstract. Regression analysis is one analysis that is widely used. Regression analysis is used to perform predictions and forecasts. Regression analysis can also be used to understand which independent variables can be related to the dependent variable, as well as to determine the form of the relationship. Multiple linear regression is a regression model that involves more than one independent variable and a dependent variable (response). For this reason, it is necessary to test the meaning of regression simultaneously. In addition, the determination of the confidence interval of response prediction must involve correlation among the dependent variables. In this thesis, the implementation of multivariate regression on weightlifting sports data will be discussed, namely squat or Squat weight modeling (Y 1), bench weight pressed or Bench (Y 2), weight lifted or Deadlift (Y3), over Age (Z1) and Body weight (Z2). From the model obtained, it can be useful to make confidence intervals from the response values of Squat (Y1), Bench (Y 2), Deadlift (Y3) based on certain age (Z1) and Body weight (Z2) values outside the analyzed sample. Because the data used are world championship results, the qualification standards above apply to world-level weightlifting events. For national level events, the standard can be lowered and for the provincial level, the standard can be lowered again lower than the national level. There is a linear relationship between the type of squat weight lift or Squat (Y 1), bench weight pressed or Bench (Y 2), weight lifted or Deadlift (Y3), over Age (Z1) and Body weight (Z2) which is significant at a real level of 5%. The effect of the Age (Z 1) variable on the type of Squat (Y1), Bench (Y 2), and Deadlift (Y2) is negative. The effect of variable Body Weight (Z 2) on the type of Squat (Y1), Bench (Y2), and Deadlift (Y3) is positive. From a 50% confidence interval, a response prediction value is obtained that can be used as a minimum standard to qualify for participation in weightlifting competitions. \u0000Abstrak. Analisis regresi adalah salah satu analisis yang luas pemakaiannya. Analisis regresi digunakan untuk melakukan prediksi dan ramalan. Analisis regresi juga dapat digunakan untuk memahami variabel – variabel bebas mana saja yang dapat berhubungan dengan variabel terikat, serta untuk mengetahui bentuk hubungan tersebut. Regresi linear berganda merupakan model regresi yang melibatkan lebih dari satu variabel independent dan sebuah variabel dependen (Respon). Untuk itu perlu dilakukan pengujian keberartian regresi secara simultan. Selain itu, penentuan selang kepercayaan prediksi respon harus melibatkan korelasi diantara variabel dependen. Dalam skripsi ini akan dibahas implementasi regresi multivariat pada data olah raga angkat berat yaitu pemodelan berat badan jongkok atau Squat (Y1), berat bangku yang ditekan atau Bench (Y2), beban yang diangkat atau Deadlift (Y3), atas Usia(Z1) dan Berat badan (Z2). Dari model yang diperoleh dapa","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114455714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Pengaruh Bullying terhadap Kepercayaan Diri Mahasiswa Fakultas Mipa Universitas Islam Bandung Angkatan 2019","authors":"Avin Rahmadian, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i2.8204","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8204","url":null,"abstract":"Abstract. Bullying is an act of using power to hurt a person or group of people verbally, physically or psychologically so that the victim feels depressed, traumatized and helpless. In addition, bullying does affect an individual's self-confidence and interferes with the happiness of the subject or victim concerned. This study aims to determine whether there is an effect of bullying on the self-confidence of statistics students of the 2019 Islamic University of Bandung. The research method used is descriptive quantitative using a Likert scale and the number of samples taken is 100 respondents from Faculty of Mathematics and Natural Sciences Unisba class of 2019. The analysis technique used is regression analysis where the research results are significant regression coefficients or it can be said that bullying has a significant effect on Confidence of students of the Faculty of Mathematics and Natural Sciences, Bandung Islamic University class of 2019. \u0000Abstrak. Bullying adalah tindakan penggunaan kekuasaan untuk menyakiti seseorang atau sekelompok orang baik secara verbal, fisik maupun psikologis sehingga korban merasa tertekan, trauma dan tak berdaya. Selain itu bullying memang mempengaruhi kepercayaan diri seorang individu dan mengganggu kebahagiaan dari subjek atau korban yang bersangkutan. Penelitian ini bertujuan untuk mengetahui apakah terdapat pengaruh bullying terhadap kepercayaan diri mahasiswa statistika Angkatan 2019 Universitas Islam Bandung. Metode penelitian yang digunakan yaitu deskriptif kuantitatif dengan menggunakan skala likert dan jumlah sampel yang diambil yaitu sebanyak 100 responden dari Mahasiswa Fakultas FMIPA Unisba angkatan 2019. Teknis analisis yang digunakan yaitu analisis regresi dimana hasil penelitiannya yaitu koefisien regresi signifikan atau dapat dikatakan Bullying berpengaruh secara signifikan terhadap Kepercayaan diri Mahasiswa Fakultas MIPA Universitas Islam Bandung angkatan 2019.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Model Regresi Beta pada Proporsi Angka Kematian Neontal Tingkat Kabupaten/Kota di Provinsi Jawa Barat Tahun 2020","authors":"Balqis Nabilah Khairunnisa, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.7893","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7893","url":null,"abstract":"Abstract. In general, if a response variable is in the form of a proportion with an open interval (0,1), a beta regression model can be used. In forming the beta regression model, it is necessary to include the average response simultaneously using the dispersion parameter, therefore it is necessary to reparameterize the density function of the beta distribution. The parameters of the beta regression model can be interpreted in the form of averages derived from the response variables, and when using the logit relationship function, these regression parameters are interpreted as odds ratios. The estimation parameter model uses the Maximum Likelihood Estimator (MLE) method, with a log-likelihood function where the estimation process can be solved numerically. This thesis will discuss the factors that cause neonatal death through parameter estimation and the application of data modeling on the proportion of neonatal mortality through a beta regression model. The data used comes from secondary data obtained from the West Java Provincial Health Office. The results of the research that was carried out using the Beta regression model showed that there was only one predictor variable that had a significant effect on the proportion of neonatal mortality, namely the number of cases of Low Birth Weight (LBW), where the highest proportion of cases was the highest out of every 27 districts/cities in West Java Province in 2020, namely Indramayu Regency and Garut Regency, while the lowest proportion of cases was Bekasi City and Tasikmalaya City. \u0000Abstrak. Pada umumnya jika suatu variabel respons yang berbentuk proporsi dengan selang terbuka (0,1) dapat menggunakan model regresi beta. Dalam membentuk model regresi beta perlu menyertakan rata-rata respons bersamaan menggunakan parameter dispersinya, oleh karena itu perlu dilakukan reparameterisasi dari fungsi densitas distribusi beta. Parameter model regresi beta dapat diinterpretasikan dalam bentuk rata-rata yang berasal dari variabel respons, serta saat menggunakan fungsi hubung logit, maka parameter regresi ini diinterpretasikan menjadi odds ratio. Penaksiran parameter model menggunakan metode Maximum Likelihood Estimator (MLE), dengan turunan fungsi log-likelihood dimana proses penaksirannya dapat diselesaikan secara numerik. Dalam skripsi ini akan membahas mengenai faktor-faktor penyebab kematian neonatal melalui penaksiran parameter dan penerapan dari pemodelan data proporsi angka kematian neonatal melalui model regresi beta. Data yang digunakan berasal dari data sekunder yang diperoleh dari Dinas Kesehatan Provinsi Jawa Barat. Dari hasil penelitian yang telah dilakukan menggunakan model regresi Beta menunjukkan bahwa hanya terdapat satu variabel prediktor yang berpengaruh secara signifikan terhadap proporsi angka kematian neonatal, yaitu banyaknya kasus Berat Badan Lahir Rendah (BBLR), dimana proporsi kasus tertinggi dari setiap 27 kabupaten/kota di Provinsi Jawa Barat tahun 2020 yaitu Kabupaten Indramayu ","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121972070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Regresi Nonparametrik B-Spline pada Model Tingkat Pengangguran Terbuka Berdasarkan Tingkat Partisipasi Angkatan Kerja dan PDRB di Provinsi Jawa Barat","authors":"Putri Ratna Wulan, Nur Azizah komara Rifai","doi":"10.29313/bcss.v3i2.8095","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8095","url":null,"abstract":"Abstract. The main problem in performing regression analysis is getting an estimate of the shape of the regression curve. There are several approaches in regression models, namely parametric, nonparametric and semiparametric regression models. The shape of the regression model depends on the curve . Non-parametric regression is a method to determine the pattern of the relationship between predictor variables and response variables whose function form is unknown. This is because there is no prior information about the shape of . B-Spline is one of the nonparametric regression methods. B-Spline is a regression model that has very specific statistical and visual interpretations such as data patterns that cannot be identified parametrically, besides that B-Spline is also able to handle smooth data characters. The best model is obtained by minimizing the Generalized Cross Validation (GCV) and the accuracy value. The data used in this study are the Labor Force Participation Rate  and GRDP , and the Open Unemployment Rate as the response variable (Y). The modeling results show that with the third order (m = 3) with  knot point is one, namely at the knot point  and the number of  knot points is three, namely at the point  with a GCV value of   and the coefficient of determination obtained is . \u0000Abstrak. Permasalahan utama dalam melakukan analisis regresi ialah mendapatkan taksiran bentuk kurva regresi. Terdapat beberapa pendekatan dalam model regresi, yaitu model regresi parametrik, nonparametrik dan semiparametrik. Bentuk model regresi tergantung pada kurva . Regresi nonparametrik merupakan suatu metode untuk mengetahui pola hubungan antara variabel prediktor dengan variabel respon yang tidak diketahui bentuk fungsinya. Hal ini karena sebelumnya tidak terdapat informasi tentang bentuk . B-Spline ialah salah satu metode regresi nonparametrik. B-Spline merupakan model regresi yang mempunyai interpretasi statistik dan visual sangat khusus seperti pola data yang tidak dapat diidentifikasi secara parametrik, disamping itu B-Spline juga mampu menangani karakter data yang mulus (smooth). Model terbaik didapat dengan meminimumkan Generalized Cross Validation (GCV) dan dari nilai akurasinya. Data yang digunakan pada penelitian ini adalah Tingkat Partisipasi Angkatan Kerja  dan PDRB, dan Tingkat Pengangguran Terbuka (TPT) sebagai variabel respon . Hasil pemodelan menunjukkan bahwa dengan orde tiga  dengan dengan titik knot  adalah satu yaitu pada titik knot  dan banyaknya titik knot  adalah tiga yaitu pada titik  dengan nilai GCV sebesar  dan koefisien determinasi yang diperoleh sebesar .","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116642849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Model Regresi Zero-Inflated Poisson pada Kasus Kematian Bayi di Kota Bandung Tahun 2020","authors":"Nanda Shofia Nur Ihsan, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.8042","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8042","url":null,"abstract":"Abstract. Poisson regression is used to analyze the relationship between predictor variables and response variables in the form of discrete data. In Poisson regression, one of the problems often faced is that the response has more than 50% zero data (zero inflation). So, the regression model to overcome the problem where the data contains too many zero values in discrete data and Poisson distribution is the Zero Inflated Poisson (ZIP) regression model. The purpose of this study is to obtain the application of the Zero-Inflated Poisson regression model to infant mortality cases in Bandung City in 2020. The data used in this study contains 80 observations based on UPT puskesmas in Bandung City with infant mortality data in Bandung city in 2020 and the factors that are thought to influence it. Parameter estimation of the ZIP regression model was carried out using the Maximum Likelihood Estimation (MLE) method and solved using the Expectation-Maximization (EM) algorithm, followed by testing the significance of the model using the likelihood ratio test and testing the significance of the parameters with the Wald test. From the results of hypothesis testing using a real level of 5%, the variable that affects infant mortality cases in the Poisson State model is unhealthy housing, while the variable that affects the chance of infant mortality cases being zero in the Zero State model is low birth weight. \u0000Abstrak. Regresi Poisson digunakan untuk menganalisis hubungan antara variabel prediktor dengan variabel respon yang berupa data diskrit. Pada regresi poisson salah satu permasalahan yang sering dihadapi yaitu responnya mempunyai data nol yang lebih dari 50% (zero inflation). Sehingga, model regresi untuk mengatasi masalah di mana data mengandung terlalu banyak nilai nol pada data diskrit dan berdistribusi Poisson adalah model regresi Zero Inflated Poisson (ZIP). Tujuan dari penelitian ini yaitu memperoleh penerapan model regresi Zero-Inflated Poisson pada kasus kematian bayi di Kota Bandung tahun 2020. Data yang digunakan dalam penelitian ini berisi 80 pengamatan berdasarkan UPT puskesmas di Kota Bandung dengan data angka kematian bayi di kota Bandung pada tahun 2020 beserta faktor-faktor yang diduga mempengaruhinya. Penaksiran parameter model regresi ZIP dilakukan dengan metode Maximum Likelihood Estimation (MLE) dan diselesaikan menggunakan algoritma Ekspektasi-Maksimalisasi (EM), dilanjutkan dengan pengujian signifikansi model dengan menggunakan likelihood ratio test dan pengujian signifikansi parameter dengan uji Wald. Dari hasil pengujian hipotesis menggunakan taraf nyata sebesar 5% variabel yang mempengaruhi kasus kematian bayi pada model Poisson State yaitu rumah tidak sehat, sedangkan variabel yang mempengaruhi peluang kasus kematian bayi bernilai nol pada model Zero State yaitu berat badan bayi lahir rendah.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123247922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}