Bandung Conference Series: Statistics最新文献

{"title":"Penerapan Metode Fuzzy Time Series Model Lee pada Peramalan Jumlah Pendaftaran Siswa SMA Negeri 1 Senayang Kepulauan Riau","authors":"Regina Maulisya, Nur Azizah komara Rifai","doi":"10.29313/bcss.v3i2.9303","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9303","url":null,"abstract":"Abstract. The Fuzzy Time Series method was first developed by Song and Chissom in 1993 which is a data forecasting method using the concept of fuzzy sets as the basis for calculation, the forecasting system by capturing patterns from past data is then used to project future data. In 1996, Chen found that the model had a poor error rate so he refined the model. Then in 2009, Lee found a model better than Chen's model with the least error rate. There are several schools that have an increasing or decreasing number of prospective students, as well as at SMA Negeri 1 Senayang. The purpose of this study was to obtain the results of forecasting the number of enrollments of SMA Negeri 1 Senayang students for the next period using Lee's Fuzzy Time Series model with sturgess rules and calculating the error rate of forecasting results. This study used data on the number of enrollments of SMA Negeri 1 Senayang students for the period 2002/2003 to 2022/2023 as many as 21 data. The forecasting results for the 2023/2024 period using Lee's Fuzzy Time Series model are 48 students with a forecasting result error rate of 13.9883 or 14% with good forecasting model criteria and can be used for forecasting the next period. \u0000Abstrak. Metode Fuzzy Time Series pertama kali dikembangkan oleh Song dan Chissom pada tahun 1993 yang merupakan metode peramalan data menggunakan konsep himpunan fuzzy sebagai dasar perhitungan, sistem peramalannya dengan menangkap pola dari data masa lampau kemudian digunakan untuk memproyeksi data yang akan datang. Pada tahun 1996, Chen menemukan bahwa model tersebut mempunyai tingkat kesalahan yang kurang baik sehingga ia menyempurnakan modelnya. Kemudian tahun 2009, Lee menemukan model yang lebih baik dari model Chen dengan tingkat kesalahan paling kecil. Terdapat beberapa sekolah yang mempunyai jumlah calon siswa yang mengalami peningkatan maupun penurunan, begitu pun di SMA Negeri 1 Senayang. Tujuan penelitian ini adalah mendapatkan hasil peramalan jumlah pendaftaran siswa SMA Negeri 1 Senayang untuk satu periode berikutnya menggunakan Fuzzy Time Series model Lee dengan aturan sturgess serta menghitung tingkat kesalahan hasil peramalan. Penelitian ini menggunakan data jumlah pendaftaran siswa SMA Negeri 1 Senayang periode 2002/2003 sampai 2022/2023 sebanyak 21 data. Hasil peramalan untuk periode 2023/2024 dengan menggunakan Fuzzy Time Series model Lee yaitu sebesar 48 siswa dengan tingkat kesalahan hasil peramalan sebesar 13.9883 atau 14% dengan kriteria model peramalan baik dan dapat digunakan untuk peramalan periode berikutnya.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"140 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":"122598602","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":"Ordinary Kriging Magnitude dan Nilai B-Value Gutenberg-Richterpada Katalog Gempa Bumi Jawa Barat","authors":"Salsabila Nur Fitriana, Sutawanir Darwis","doi":"10.29313/bcss.v3i2.8385","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8385","url":null,"abstract":"Abstract. Earthquake is a natural phenomenon that is not known when it will occur because measurements are not made in all locations. With these limitations, we need a method that can predict the level of earthquake strength at several points where measurements are not carried out, for example by the spatial interpolation method. There are several methods used to perform spatial interpolation, one of which is the ordinary kriging method. Ordinary kriging (OK) utilizes spatial values ​​at sample locations and variograms that show correlations between spatial points to predict values ​​at non-sample locations where the predicted value depends on their proximity to the sample locations. This thesis discusses the development of the ordinary kriging method on regional earthquake magnitudes and the b-value Gutenberg Richter variable using the West Java earthquake catalog. Regional magnitude variables and b-values ​​were used to model the spatial correlation via a spherical variogram. Variogram parameters were estimated using the least squares method. The results of the Ordinary Kriging assessment of an area show that the level of rock fragility (b-value) is quite high, namely 6.523 which illustrates that the area has low stress accumulation or it can be said that a high b-value has experienced a large-scale earthquake so that only energy left that is capable of producing small earthquakes. \u0000Abstrak. Gempa bumi merupakan fenomena alam yang tidak seorang pun mengetahui kapan terjadinya karena pengukuran tidak dilakukan di semua lokasi. Dengan keterbatasan tersebut, diperlukan suatu metode yang dapat memprediksi tingkat kekuatan gempa di beberapa titik yang tidak dilakukan pengukuran, misalnya dengan metode interpolasi spasial. Terdapat beberapa metode yang digunakan untuk melakukan interpolasi spasial, salah satunya metode ordinary kriging. Ordinary kriging (OK) memanfaatkan nilai spasial pada lokasi tersampel dan variogram yang menunjukkan korelasi antar titik spasial untuk memprediksi nilai pada lokasi yang tidak tersampel yang mana nilai prediksi tersebut tergantung pada kedekatannya terhadap lokasi tersampel. Skripsi ini membahas pengembangan metode ordinary kriging pada peubah teregioanal magnitude gempa dan b-value Gutenberg Richter menggunakan katalog gempa Jawa Barat.  Peubah teregional magnitude dan nilai-b digunakan untuk memodelkan korelasi spasial melalui variogram spherical.  Parameter variogram diestimasi menggunakan metode kuadrat terkecil. Hasil dari penaksiran Ordinary Kriging dari suatu wilayah menunjukkan bahwa nilai tingkat kerapuhan batuan (b-value) cukup tinggi yaitu sebesar 6.523, menggambarkan bahwa daerah tersebut memiliki akumulasi stress yang rendah atau dapat dikatakan nilai b-value yang tinggi pernah terjadi gempa bumi dengan skala besar sehingga hanya tersisa energi yang mampu menghasilkan gempa kecil.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"46 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":"133982493","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":"Pengujian pada Regresi Ridge dan Penerapannya terhadap Data Produk Domestik Regional Bruto Provinsi Jawa Barat","authors":"Weni Nuryati, Suliadi Suliadi","doi":"10.29313/bcss.v3i2.8424","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8424","url":null,"abstract":"Abstract. Ridge regression is one of the methods used to stabilize the value of the regression coefficient caused by multicollinearity. In ridge regression,  to reduce the impact of multicollinearity is carried out by adding ridge  parameter c to the hat matrix. This ridge parameter makes the regression coefficients have a smaller variance than the least squares method estimator variance. However, the ridge estimates are biased. Thus, hypothesis testing using the usual method cannot be applied to the coefficients ridge regression. Therefore Bae, et al., (2014) developed a method for testing the hypothesis of the coefficients of ridge regression. This thesis aims to apply this method to the gross regional domestic product data for West Java province in 2022. Based on the results of the research, it shows that there is a multicollinearity problem in the data, so it is modelLed using ridge regression. it was obtained The ridge regression model : . From the results of testing the hypothesis, it can be concluded that the independent variables, namely local original income (X1), general allocation funds (X2), profit sharing funds (X3), regional expenditures (X4) and labor (X5) together have a significant effect on the PDRB (Y) of West Java Province in 2022. The ridge regression model is returned to the original model . \u0000Abstract. Ridge regression is one of the methods used to stabilize the value of the regression coefficient caused by multicollinearity. In ridge regression,  to reduce the impact of multicollinearity is carried out by adding ridge  parameter c to the hat matrix. This ridge parameter makes the regression coefficients have a smaller variance than the least squares method estimator variance. However, the ridge estimates are biased. Thus, hypothesis testing using the usual method cannot be applied to the coefficients ridge regression. Therefore Bae, et al., (2014) developed a method for testing the hypothesis of the coefficients of ridge regression. This thesis aims to apply this method to the gross regional domestic product data for West Java province in 2022. Based on the results of the research, it shows that there is a multicollinearity problem in the data, so it is modelLed using ridge regression. it was obtained The ridge regression model : . From the results of testing the hypothesis, it can be concluded that the independent variables, namely local original income (X1), general allocation funds (X2), profit sharing funds (X3), regional expenditures (X4) and labor (X5) together have a significant effect on the PDRB (Y) of West Java Province in 2022. The ridge regression model is returned to the original model .","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"14 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":"130869534","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 Generalized Poisson Regression (GPR) dalam Memodelkan Kasus Campak pada Balita di Kabupaten Bandung Tahun 2020","authors":"Irna Noviana, Nur Azizah komara Rifai","doi":"10.29313/bcss.v3i2.7850","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7850","url":null,"abstract":"Abstract. Poisson regression is a regression method used to analyze count data with Poisson distributed response variables. In Poisson regression, there is an assumption that the mean value of the response variable must be equal to the variance value. If that assumption is not met, for example there is an overdispersion case where the variance value is greater than the average value and that is left unaddressed, making the standard error value of the estimated regression parameter tend to be lower than the supposed value (underestimate) resulting in a less accurate test conclusion. In this study, overdispersion cases can be solved using Generalized Poisson (GP) regression models. The GP regression model contains Generalized Poisson I regression and Generalized Poisson II regression. Generalized Poisson II regression can control a higher degree of variance in discrete data so that it can provide a more accurate parameter estimation compared to Generalized Poisson I regression in handling overdispersion cases. The purpose of this study is to model measles cases in infants in Bandung Regency in 2020. Measles cases are a form of data count. The Maximum Likhelood Estimation (MLE) method is used to estimate parameters. Calculation results show that the overdispersion case occurs in the response variable data (Y), so the study is continued using Generalized Poisson II regression method and the regression model is . From the results of the hypothesis test, the factor that has a significant effect on the number of measles cases in Bandung Regency in 2020 is the percentage of measles immunization in infants  with a Pseudo R 2 value of 0,3936. \u0000Abstrak. Regresi Poisson merupakan metode regresi yang digunakan untuk menganalisa data count (jumlah) dengan variabel respon berdistribusi Poisson. Pada regresi Poisson, terdapat asumsi yang harus dipenuhi yaitu nilai rata-rata pada variabel respon harus sama dengan nilai varians (equidispersi). Jika asumsi tersebut tidak dipenuhi, misalnya terjadi kasus overdispersi di mana nilai varians lebih besar dari nilai rata-rata dan hal tersebut dibiarkan tanpa diatasi maka akan membuat nilai standard error dari dugaan parameter regresi cenderung lebih rendah dari nilai seharusnya (underestimate) sehingga menghasilkan kesimpulan pengujian yang kurang akurat. Dalam penelitian ini, kasus overdispersi dapat diatasi dengan model regresi Generalized Poisson (GP). Model regresi GP memuat regresi Generalized Poisson I dan regresi Generalized Poisson II. Pada regresi Generalized Poisson II dapat mengendalikan tingkat varians yang lebih tinggi dalam data diskrit sehingga mampu memberikan estimasi parameter yang lebih akurat dibandingkan dengan regresi Generalized Poisson I dalam menangani kasus overdispersi. Tujuan dari penelitian ini adalah untuk memodelkan kasus campak pada balita di Kabupaten Bandung tahun 2020. Kasus campak merupakan salah satu bentuk dari data count. Metode Maximum Likelihood Estimation (MLE) digunakan untuk me","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"81 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":"116224329","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 Pengujian Rasio Dua Generalized Variance (GV) pada Data Indeks Pembangunan Manusia di Indonesia Tahun 2022","authors":"Shafira Nur Fauziah, Suliadi","doi":"10.29313/bcss.v3i2.8853","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8853","url":null,"abstract":"Abstract. One of the basic multivariate analysis is dependency analysis through the variance-covariance matrix. But sometimes it is difficult to interpret the diversity of data from the covariance matrix, so a single measure is needed, namely Generalized variance (GV). Generalized variance (GV) is a way to provide information on all the variance and covariance of the variance-covariance matrix into a single value and measure the variability of data on multivariate observations GV is defined as determinant of the sample covariance matrix. In some application one wants to know whether the variability of two groups of variables is the same or not. Jafari (2012) proposes to build confidence intervals and test hypotheses about two ratios of Generalized variance (GV) for two groups of dependent variables which can be used to see whether the Generalized variance (GV) of the two groups is the same or not. In this thesis, the ratio of two Generalized Variance (GV) is applied to Human Development Index (IPM) data to see the variability of the factors that influence the Human Development Index (IPM) which consists of 2 groups of variables, the first one is the economic variable including PDRB and real per capita expenditure, and the second one is the Education variable including Years of Schooling (HLS) and Average Length of Schooling (RLS). From the results of hypothesis testing, it was concluded that the general variance of the economic variable group and the Education variable group in the 2022 Human Development Index (IPM) data in Indonesia is different from the confidence interval, namely [6.748766, 13.38937]. \u0000Abstrak. Dengan menggunakan teknik analisis ini maka dapat menganalisis perbedaan dan hubungan beberapa variabel terhadap variabel lainnya dalam waktu yang bersamaan. Analisis multivariat secara dasar salah satunya terdapat analisis dependensi melalui matriks variance-covariance. Namun terkadang sulit untuk menginterpretasikan keragaman data dari matriks kovarians, maka dibutuhkan suatu ukuran tunggal yaitu Generalized variance (GV). Generalized variance (GV) adalah sebuah cara untuk memberikan informasi dari semua varians dan kovarians dari matriks varians-kovarians menjadi satu nilai tunggal dan untuk mengukur variabilitas data pada pengamatan yang bersifat multivariat dan merupakan determinan dari sampel matriks kovarians. Seringkali ingin diketahui apakah variabilitas dua kelompok variabel sama atau tidak. Jafari (2012) mengusulkan untuk membangun selang kepercayaan dan menguji hipotesis tentang dua rasio Generalized variance (GV) dua kelompok variabel yang tidak bebas yang dapat digunakan untuk melihat apakah Generalized variance (GV) kedua kelompok tersebut sama atau tidak. Dalam skripsi ini Rasio dua Generalized Variance (GV) ini diterapkan pada data Indeks Pembangunan Manusia (IPM) untuk melihat variabilitas dari faktor-faktor yang mempengaruhi Indeks Pembangunan Manusia (IPM) yang terdiri dari 2 variabel, variabel pertama yaitu variabel","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"164 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":"121770420","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":"Pendekatan Baru dari Transformasi Box-Cox untuk Mengatasi Masalah Ketidaknormalan pada Variabel Respon","authors":"Dwi Andini Juniawati, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.8553","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8553","url":null,"abstract":"Abstract. Regression analysis is used to model between two quantitative variables. When doing regression modeling it is often found that the data is not normally distributed, the way to overcome this is to do a transformation. One of the transformations used is the Box-Cox transformation. Box and Cox consider a single-parameter transformation class, namely the parameter λ with respect to Y so that  with λ is the parameter to be estimated. Estimation of the parameter λ uses a maximum likelihood estimator. In general, the value of λ is assumed to be known, but some values of λ (called ) the assumption of normality are not met. This has an impact on the problem of parameter estimation based on maximum likelihood to be inappropriate so that another alternative is needed to obtain the value of λ, namely using a new approach method which is carried out by means of a grid search combined with normality testing. In this study a new approach to the Box-Cox transformation method will involve the response variable, namely the amount of poverty and the predictor variables, namely the Open Unemployment Rate, Human Development Index, and Minimum Wage. In this study, we wanted to find out the factors that influence the amount of poverty in West Java Province in 2022. The data used is secondary data sourced from the Central Statistics Agency and the West Java Province Website. From the analysis results, the optimum λ estimation is 0.5 and the regression model is y^3/5 = (0.0168^5/3 + (1.2410^(5/3)X1) - (2.3160^(5/3)X2) + (0.00000066^(5/3)X3) \u0000Abstrak. Analisis regresi digunakan untuk memodelkan di antara dua variabel kuantitatif. Ketika melakukan pemodelan regresi sering kali ditemukan data tidak berdistribusi normal, cara untuk mengatasinya dapat melakukan transformasi. Salah satu transformasi yang digunakan adalah transformasi Box-Cox. Box dan Cox mempertimbangkan kelas transformasi berparameter tunggal yaitu parameter λ terhadap Y sehingga menjadi dengan λ adalah parameter yang harus diduga. Pendugaan parameter λ menggunakan penduga kemungkinan maksimum. Pada umumnya nilai λ diasumsikan sudah diketahui, namun beberapa nilai dari λ (disebut ) asumsi kenormalan tidak terpenuhi. Hal ini berdampak pada masalah pendugaan parameter yang didasarkan pada kemungkinan maksimum menjadi tidak tepat sehingga perlu alternatif lain untuk memperoleh nilai λ yaitu menggunakan metode pendekatan baru yang dilakukan dengan cara pencarian secara grid yang dikombinasikan dengan pengujian kenormalan. Dalam penelitian ini pendekatan baru metode transformasi Box-Cox akan melibatkan variabel respon yaitu Jumlah kemiskinan dan variabel prediktor yaitu Tingkat Pengangguran Terbuka, Indeks Pembangunan Manusia, dan Upah Minimum Kerja. Pada penelitian ini ingin mengetahui faktor-faktor yang mempengaruhi jumlah kemiskinan di Provinsi Jawa Barat tahun 2022. Data yang digunakan adalah data sekunder yang bersumber dari Badan Pusat Statistika dan Website Provinsi Jawa Barat. Dari hasil analisis","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"27 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":"124416018","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 Pengukuran Tingkat Efisiensi Perusahaan Menggunakan Metode Stochastic Frontier Analysis (SFA)","authors":"Salma Inayatul Afifah, Suwanda","doi":"10.29313/bcss.v3i2.8369","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8369","url":null,"abstract":"Abstract. The stochastic frontier has two error components, namely errors originating from random errors and errors originating from other factors than can be controlled, namely inefficiency errors. The Stochastic Frontier Analysis model is an extension of the deterministic frontier model developed by Aigner and Chu (1968) in Coelli et al (1998). Stochastic Frontier Analysis (SFA) is one of the best known techniques for determining efficiency. So to overcome the level of efficiency of a company researchers will use the stochastic frontier with the Cobb-Douglas production function model. The profit function (profit) is a two-way approach, and is often used to measure the level of production efficiency. Measuring the level of production efficiency, both technical and allocative efficiency, using the commonly used profit function is only relative. This study aims to rank the level of efficiency of companies in Indonesia. The results of the research based on 10 life insurance companies in Indonesia in 2017-2021 can use the application of the stochastic frontier analysis model of the Cobb-Douglas function. And for the value of the highest efficiency level obtained by PT. Sequis Financial with a value of 0.9996, and for the lowest efficiency value obtained by PT. Sequis Financial with an efficient value of 0.0845. \u0000Abstrak. Stochastic frontier memiliki dua komponen galat yaitu galat yang berasal dari kesalahan acak dan galat yang berasal dari faktor-faktor lain ang bisa dikendalikan yaitu galat inefisiensi. Model Stochastic Frontier Analysis merupakan perkembangan dari model deterministic frontier yang dikembangkan oleh Aigner dan Chu (1968) dalam Coelli et al (1998). Stochastic Frontier Analysis (SFA) adalah salah satu teknik yang paling dikenal untuk menentukan efisiensi. Maka untuk menanggulangi tingkat efisiensi suatu perusahaan peneliti akan menggunakan stochastic frontier dengan model fungsi produksi cobb-douglas. Fungsi keuntungan (profit) merupakan pendekatan dua arah, dan sering digunakan untuk mengukur tingkat efisiensi produksi. Mengukur tingkat efisiensi produksi, baik efisiensi teknis maupun alokatif, dengan menggunakan fungsi profit yang umum digunakan hanya bersifat relative. Penelitian ini bertujuan untuk mengurutkan tingkat efisiensi perusahaan di Indonesia. Hasil penelitian berdasarkan dari 10 perusahaan asuransi jiwa di Indonesia pada tahun 2017-2021 dapat menggunakan penerapan model stochastic frontier analysis fungsi cobb-douglas. Dan untuk nilai tingkat efisiensi tertinggi didapatkan oleh PT. Sequis Financial dengan nilai 0.9996, dan untuk nilai efisiensi terendah didapatkan oleh PT. Sequis Financial dengan nilai efisien 0.0845.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"5 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":"122735779","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-One Inflated Beta (BEINF) pada Proporsi Pemain Sepak Bola yang dimainkan dalam Satu Musim","authors":"Rida Faridah, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.8223","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8223","url":null,"abstract":"Abstract. Linear regression is usually used to model the relationship between a dependent variable and one or more independent variables where the response is continuous and normally distributed. However, if the assumption of normality of the response is not met, and the estimation for the linear regression model is not accurate, then zero-one inflated beta (BEINF) regression is an alternative method to model the relationship between the response variable and predictors where the response is a mixture of beta distribution and bernouli distribution. The results of this research are that there are several factors that affect the response variable, namely the BMI variable (X1), Forty Yard Run (X2), Vertical Jump (X3), Wide Jump (X4) and for predicting the chances of soccer players it can be said that, in the model 0 < Y < 1 the chance that a soccer player will be chosen to play in the match is equal to =0.4514 times more than before assuming the value of the other predictor variables is fixed. In the Y = 1 model, the chance that a soccer player will be chosen to play in the match increases by f(y=1)= 0.2086 times compared to before assuming the value of the other predictor variables remains constant. In the Y = 0 model, the chance that a soccer player will not be chosen to play in the match increases by f(y=0)=0,7576 times compared to before, assuming the values of the other predictor variables remain constant. \u0000Abstrak. Regresi linier biasanya digunakan untuk memodelkan hubungan antara variabel dependen dengan satu atau lebih variabel independen yang dimana respon bersifat kontinu dan berdistribusi normal. Namun apabila asumsi normalitas respon tidak terpenuhi, dan estimasi untuk model regresi linier tidak akurat, maka regresi zero-one inflated beta (BEINF) adalah metode alternatif untuk memodelkan hubungan antara variabel respon dan prediktor dimana respon berdistribusi campuran antara distribusi beta dan distribusi bernouli. Hasil dari penelitan ini adalah terdapat beberapa faktor yang mempengaruhi variabel respon yaitu variabel BMI (X1), Lari Empat Puluh Yard (X2), Lompat Vertikal (X3), Lompat Lebar (X4) dan untuk prediksi peluang pemain sepak bola dapat dikatakan bahwa, pada model 0 < Y < 1 peluang pemain sepak bola akan dipilih untuk bermain di pertandingan adalah sebesar =0.4514 kali lipat dibanding sebelumnya dengan asumsi nilai variabel prediktor lain tetap. Pada model Y = 1 peluang pemain sepak bola akan dipilih untuk bermain di pertandingan meningkat sebesar f(y=1)= 0.2086 kali lipat dibanding sebelumnya dengan asumsi nilai variabel prediktor lain tetap. Pada model Y = 0 peluang pemain sepak bola tidak akan dipilih untuk bermain di pertandingan meningkat sebesar f(y=0)=0,7576 kali lipat dibanding sebelumnya dengan asumsi nilai variabel prediktor lain tetap.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"4 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":"115388553","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 Diagram Kendali Multivariate Exponentially Weighted Moving Average (MEWMA) dalam Mengontrol Kualitas Air PDAM","authors":"Renaldi, Suwanda","doi":"10.29313/bcss.v3i2.9343","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9343","url":null,"abstract":"Abstract. Statistical quality control is the application of statistical techniques in controlling a process that is useful for achieving process stability and increasing ability through reducing quality variability. Tools for quality control, one of which is the control chart. In practice, control charts often used in univariate cases are Shewhart, Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA). However, when there is more than one quality characteristic (mutivariate characteristics), simultaneous control is required. The thesis discusses the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart procedure. The uniqueness of the MEWMA control chart is that it is robust to normal which means that if the data used is not normally distributed, then the MEWMA control chart can still be done. MEWMA control chart will be used to control PDAM Water Quality. The data used is the quality of customer drinking water in January-February 2022 Perumda Tugu Tirta Drinking Water Malang City which amounted to 50 observations with 5 characteristics observed including pH, nitrite, iron (Fe), manganese and residual chlorine (Cl2). Based on the results of MEWMA analysis shows that it is in a statistically uncontrolled condition because for weighting 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 there are observations that are beyond the control limit, namely in the 46th observation. As well as the optimum weighting selected, namely weighting 0.2. The results of detection of out-of-control causal variables using the EWMA control chart showed 1 variable had an out-of-control observation at the 46th observation, namely at Manganese levels. \u0000Abstrak. Pengendalian kualitas Statistik merupakan aplikasi dari teknik statistik dalam pengendalian suatu proses yang berguna untuk mencapai stabilitas proses dan meningkatkan kemampuan melalui pengurangan variabilitas mutu. Alat bantu untuk mengendalikan kualitas, salah satunya adalah diagram kendali. Dalam praktiknya, diagram kendali yang sering digunakan dalam kasus univariat adalah Shewhart, Cumulative Sum (CUSUM) dan Exponentially Weighted Moving Average (EWMA). Namun ketika terdapat lebih dari satu karakteristik mutu (karakteristik mutivariat), maka pengontrolan secara simultan diperlukan. Dalam skripsi dibahas prosedur diagram kendali Multivariate Exponentially Weighted Moving Average (MEWMA). Keunikan diagram kendali MEWMA yaitu bersifat robust terhadap normal yaitu apabila data yang digunakan tidak berdistribusi normal, maka diagram kendali MEWMA masih bisa dilakukan. Diagram kendali MEWMA akan digunakan untuk mengontrol Kualitas Air PDAM. Data yang digunakan yaitu kualitas air minum pelanggan pada bulan Januari-Februari 2022 Perumda Air Minum Tugu Tirta Kota Malang yang berjumlah 50 pengamatan dengan 5 karakteristik yang diamati diantaranya pH, nitrit (NO2), besi (Fe), Mangan dan sisa klor (Cl2). Berdasarkan hasil analisis MEWMA menunjukan berada dalam kondisi tidak terkendali secara ","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"63 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":"127496601","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 Analisis Cluster Hybrid untuk Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Indikator Kemiskinan Tahun 2022","authors":"Wardah Fauziyah, Anneke Iswani Achmad","doi":"10.29313/bcss.v3i2.8610","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8610","url":null,"abstract":"Abstract. Hybrid cluster analysis is a combination of hierarchical and non-hierarchical clusters, which has a goal as an alternative method. The advantage of hybrid cluster analysis is that it can determine k-clusters for the process of making non-hierarchical clusters through the results of making hierarchical cluster methods, which will produce the right k-clusters. With the advantages of the hybrid cluster analysis, this research will combine the single linkage method with k-means, then the ward method with k-means. The purpose of this study was to determine the grouping of districts/cities in West Java Province with the most optimal combination method based on poverty in 2022. Based on the results of hybrid cluster analysis using a combination of k-means clusters and single linkage, 4 clusters were obtained. While the results of hybrid cluster analysis using a combination of k-means clusters and the ward method obtained 3 clusters. Among the hybrid cluster analysis using a combination of k-means and single linkage clusters as well as a combination of k-means cluster and ward method, it can be said that the method with a combination of k-means and single linkage is the best or most optimal method with the smallest standard deviation ratio value of 88.38%. \u0000Abstrak. Analisis cluster hybrid merupakan kombinasi antara cluster hierarki dan non-hierarki, dimana memiliki tujuan sebagai metode alternatif. Kelebihan analisis cluster hybrid adalah dapat menentukan k-klaster untuk proses pembuatan cluster non-hierarki melalui hasil dari pembuatan cluster metode hierarki, dimana akan menghasilkan k-klaster yang tepat. Dengan kelebihan analisis cluster hybrid tersebut, maka dalam penelitian ini akan mengkombinasikan metode single linkage dengan k-means, kemudian ward method dengan k-means. Tujuan penelitian ini adalah dapat mengetahui pengelompokan Kabupaten/Kota di Provinsi Jawa Barat dengan metode kombinasi yang paling optimal berdasarkan kemiskinan tahun 2022. Berdasarkan hasil analisis cluster hybrid dengan menggunakan kombinasi cluster k-means dan single linkage diperoleh 4 cluster. Sedangkan hasil analisis cluster hybrid dengan menggunakan kombinasi cluster k-means dan ward method diperoleh 3 cluster. Diantara analisis cluster hybrid dengan menggunakan kombinasi cluster k-means dan single linkage serta kombinasi cluster k-means dan ward method, dapat dikatakan bahwa metode dengan kombinasi k-means dan single linkage merupakan metode terbaik atau yang paling optimal dengan nilai rasio simpangan baku terkecil yaitu sebesar 88,38%.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"7 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":"125351967","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}