Bandung Conference Series: Statistics最新文献

{"title":"Diagram Kendali Nonparamterik Composite Exponentially Weighted Moving Average (CEWMA) Sign dalam Proses Plating Aksesori Mobil PT. XYZ","authors":"Firdausi Ahmad, Suliadi","doi":"10.29313/bcss.v3i2.9054","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9054","url":null,"abstract":"Abstract. Control charts are one of the tools in Statistical Process Control (SPC) that are graphically used to control the production process. The first control chart to exist was the Shewhart control chart, but the control chart is not sensitive in detecting small shifts. So the alternative is the EWMA control chart which is able to detect small shifts, but there is still a problem because this diagram has the assumption that the observation process is normally distributed. Then a Nonparametric EWMA Sign control chart is proposed which can detect small shifts when the data conditions are not normally distributed. Furthermore, the combination of Shewhart and EWMA statistics has been done which aims to improve the accuracy of the diagram. Combining two EWMA statistics has also been done but assuming the distribution of quality characteristics is normal. Raza et al., 2022 proposed the CEWMA Sign Nonparametric control diagram method by combining two EWMA statistics to increase the sensitivity of the diagram. This control chart is more flexible to use because the parameter value λ in both EWMA statistics can be different (  and ). In this thesis we apply the CEWMA Sign Nonparametric control chart to the data of the car accessory plating process at PT. XYZ which is a final or finishing process that has objectives including to beautify the appearance and protect a metal from interaction with the environment. \u0000Abstrak. Diagram kendali merupakan salah satu alat dalam Statistical Process Control (SPC) yang secara grafis digunakan untuk mengontrol proses produksi. Diagram kendali yang pertama kali ada yaitu diagram kendali Shewhart, tetapi diagram kendali tersebut tidak sensitif dalam mendeteksi pergeseran yang kecil. Sehingga alternatifnya yaitu diagram kendali EWMA yang mampu mendeteksi pergeseran yang kecil, tetapi masih ada masalah karena diagram ini memiliki asumsi bahwa proses pengamatan berdistribusi normal. Kemudian diusulkanlah diagram kendali Nonparametrik EWMA Sign yang dapat mendeteksi pergeseran kecil saat kondisi data tidak berdistribusi normal. Selanjutnya penggabungan statistik Shewhart dan EWMA pernah dilakukan yang bertujuan untuk meningkatkan akurasi diagram. Penggabungan dua statistik EWMA juga pernah dilakukan tetapi dengan asumsi distribusi karakteristik kualitasnya normal. Raza et al., 2022 mengajukan metode diagram kendali Nonparametrik CEWMA Sign dengan menggabungkan dua statistik EWMA untuk meningkatkan sensitivitas diagram. Diagram kendali ini lebih fleksibel untuk digunakan karena nilai parameter  pada kedua statistik EWMA dapat berbeda (  dan ). Dalam skripsi ini kami menerapkan diagram kendali Nonparametrik CEWMA Sign pada data proses plating aksesori mobil pada PT. XYZ yang merupakan proses akhir atau finishing yang memiliki tujuan diantaranya adalah untuk memperindah tampilan dan melindungi suatu logam dari interaksi dengan lingkungan.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"2 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":"124511490","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":"Uji Kenormalan dengan Metode Jarque Bera yang Robust pada Data Durasi Proses Persidangan di Pengadilan Tata Usaha Negara Bandung Tahun 2019-2021","authors":"Rika Delia Nur'aeni, Abdul Kudus","doi":"10.29313/bcss.v3i2.7925","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7925","url":null,"abstract":"Abstract. In statistics, detecting data normality is an important assumption indicator. If one or more data points are found to be far from the general data pattern (outlier), it will cause the assumption of normality to not be met. Robust Jarque Bera (RJB) is a modification of the Jarque Bera test by using a robust estimate of the spread, using Median Absolute Deviation (MAD) and utilizing information from robust skewness and robust kurtosis. The purpose of this thesis is to apply the normality test with the Robust Jarque Bera method to data containing outliers on trial process duration data at the Bandung State Administrative Court in 2019-2021. The State Administrative Court resolves cases at the First Level Court within 5 months (150 days) at the latest, but in reality there are several cases that are resolved in more than 5 months (150 days). So that the Circular Letter of the Supreme Court of the Republic of Indonesia Number 2 of 2014 has not been implemented properly in the Bandung State Administrative Court. Based on the calculation results, it is obtained that the data on the duration of the trial process at the Bandung State Administrative Court in 2019-2021 has 34 outlier data using MAD. Then the value of robust skewness is 0.1005 and the value of robust kurtosis is 2.2301 which means that the data is not normally distributed with an RJB value of 11,5287 > chi-square. \u0000Abstrak. Dalam statistik, mendeteksi kenormalan data merupakan suatu indikator asumsi penting. Apabila ditemukannya satu atau beberapa titik data berada jauh dari pola data pada umumnya (pencilan), maka akan menyebabkan asumsi kenormalan tidak terpenuhi. Robust Jarque Bera (RJB) adalah sebuah modifikasi dari uji Jarque Bera dengan menggunakan estimasi yang robust (tahan terhadap pencilan) dari penyebaran, yaitu menggunakan Median Absolute Deviation (MAD) serta memanfaatkan informasi dari robust skewness dan robust kurtosis. Tujuan dari skripsi ini adalah untuk menerapkan uji kenormalan dengan metode Jarque Bera yang Robust terhadap data yang mengandung pencilan pada data durasi proses persidangan di Pengadilan Tata Usaha Negara Bandung (PTUN) tahun 2019-2021. PTUN menyelesaikan perkara di Pengadilan Tingkat Pertama paling lambat dalam waktu 5 bulan (150 hari), tetapi pada kenyataannya terdapat beberapa perkara yang penyelesaiannya lebih dari 5 bulan (150 hari). Sehingga Surat Edaran Mahkamah Agung RI Nomor 2 Tahun 2014 belum terlaksana dengan baik di PTUN Bandung. Berdasarkan hasil perhitungan diperoleh bahwasanya data durasi proses persidangan di Pengadilan Tata Usaha Negara Bandung (PTUN) tahun 2019-2021 memiliki 34 data pencilan dengan menggunakan MAD. Kemudian nilai dari robust skewness bernilai 0,1005 dan nilai dari robust kurtosis bernilai 2,2301 yang dimana data tersebut tidak berdistribusi normal dengan nilai RJB sebesar 11,5287 > chi-square.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"52 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":"126271219","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 Chi-Squared Automatic Interaction Detection pada Segmentasi Karakteristik yang Mempengaruhi Pengangguran di Provinsi Banten Tahun 2022","authors":"Fahry Al Hayyu Fikham, Anneke Iswani Achmad","doi":"10.29313/bcss.v3i2.8741","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8741","url":null,"abstract":"Abstract. This study will discuss the application of the Chi-Squared Auto Interaction Detection (CHAID) method for segmenting characteristics that affect the unemployed workforce in Banten Province in 2022. The CHAID method was introduced by Dr. G. V Kass in 1980. In the CHAID method, segmentation will be formed which divides a sample into two or more different groups/categories based on the chi-square test statistics, then the segmentation results will be displayed in a tree diagram. In this study, the variables of gender, age, education level, marital status, status in the household and regional classification will be used as independent variables with labor force status as the dependent variable, where all data are categorical types which are suitable for use in CHAID analysis. This study uses secondary data sourced from Badan Pusat Statistik (BPS), using data from the result of Survei Hasil Angkatan Kerja Nasional (SAKERNAS). The results of the CHAID segmentation in this study resulted in 8 different segments with 3 of them producing a classification of the workforce with unemployed status. Then, based on the CHAID tree diagram, it was found that there were 5 (five) independent variables, namely gender, age, education level, household status and regional classification which had a significant association with labor force status which resulted in a classification accuracy of 70.6%. \u0000Abstrak. Dalam penelitian ini akan membahas tentang penerapan metode Chi-Squared Auto Interaction Detection (CHAID) pada segmentasi karakteristik yang mempengaruhi angkatan kerja berstatus pengangguran di Provinsi Banten tahun 2022. Metode CHAID diperkenalkan pertama kali oleh Dr. G. V Kass pada tahun 1980. Pada metode CHAID akan membentuk segmentasi yang membagi sebuah sampel menjadi dua atau lebih kelompok/kategori yang berbeda berdasarkan dengan statistik uji chi-square kemudian hasil segmentasi akan ditampilkan dalam diagram pohon. Pada penelitian ini akan menggunakan variabel jenis kelamin, usia, tingkat pendidikan, status pernikahan, status dalam rumah tangga dan klasifikasi wilayah sebagai variabel independen dengan status angkatan kerja sebagai variabel dependen, dimana semua datanya merupakan tipe kategorik yang cocok digunakan pada analisis CHAID. Penelitian ini menggunakan data sekunder yang bersumber dari Badan Pusat Statistik (BPS), yaitu data hasil Survei Angkatan Kerja Nasional (SAKERNAS). Hasil segmentasi CHAID pada penelitian ini menghasilkan 8 segmentasi yang berbeda dengan 3 segmentasi diantaranya menghasilkan klasifikasi angkatan kerja yang berstatus pengangguran. Kemudian berdasarkan diagram pohon CHAID didapatkan bahwa terdapat 5 (lima) variabel independen yaitu jenis kelamin, usia, tingkat pendidikan, status dalam rumah tangga dan klasifikasi wilayah yang mempunyai asosiasi yang signifikan dengan status angkatan kerja yang menghasilkan ketepatan klasifikasi yaitu sebesar 70,6%.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"6 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":"125665664","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":"Metode Autoregressive Integrated Moving Average (ARIMA) untuk Meramalkan Produksi Padi di Provinsi Jawa Tengah","authors":"Luthfiani Nur Komara, Dwi Agustin Nuriani Sirodj","doi":"10.29313/bcss.v3i2.8442","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8442","url":null,"abstract":"Abstract. The ARIMA method is one method that produces forecasting based on historical data patterns. This model is a merger between the Autoregressive (AR) model, which is a model that explains the movement of a variable through the variable itself in the past, and the Moving Average (MA) is a model that looks at residual movements in the past. This method forecasts time series data based on developed statistical theory by finding patterns in the data series and then extrapolating them to the future. This method can provide accurate forecasting accuracy for short-term forecasting. Agriculture is one of the most important factors for the Indonesian economy. The Ministry of Agriculture stated that rice production in Indonesia reached 81,148,594 tons in 2018, exceeding corn and soybean production of 30,055,623 and 982,598 tons, respectively. Central Java Province is one of the most rice-producing provinces in Indonesia, however, rice production in this province had decreased from 2019 to 2020. Therefore, forecasting is one of the first steps to help the government determine the supply of rice for the community. The data used is data from January 2016 to December 2021 sourced from the Central Statistics Agency, from the results of the analysis it was found that the best model for forecasting is the ARIMA model (2,1,1) because it has a smaller AIC value than other models. The model also has a MAPE value of 0.8950% and an MSE value of 3.7698. The largest rice production occurred in February 2022 at 984,379 tons and the smallest in January at 745,081 tons. \u0000Abstrak. Metode ARIMA merupakan salah satu metode yang menghasilkan peramalan berdasarkan pola data secara historis. Model ini merupakan penggabungan antara model Autoregressive (AR) yaitu suatu model yang menjelasakan pergerakan suatu variabel melalui variabelitu sendiri di masa lalu dan Moving Average (MA) adalah model yang melihat pergerakan residual di masa lalu. Metode ini meramalkan data deret waktu yang didasarkan pada teori statistik yang telah berkembang dengan menemukan pola dalam deret data kemudian menekstrapolasikannya ke masa depan. Metode ini bisa bisa memberikan ketepatan peramalan yang cukup akurat untuk peramalan jangka pendek. Pertanian merupakan salah satu faktor yang paling penting bagi perekonomian Indonesia. Kementrian Pertanian menyatakan bahwa produksi padi di Indonesia mencapai 81.148.594 ton pada tahun 2018 melebihi produksi jagung dan kedelai yang masing-masing sebesar 30.055.623 dan 982.598 ton. Provinsi Jawa Tengah merupakan salah satu Provinsi penghasil padi terbanyak di Indonesia walaupun demikian, produksi padi di Provinsi ini sempat mengalami penurunan pada tahun 2019 sampai tahun 2020. Oleh karena itu melakukan peramalan merupakan salah satu langkah awal untuk membanu pemerintah dalam menentukan persediaan padi untuk masyarakat. Data yang digunakan adalah data dari bulan Januari 2016 sampai bulan Desember 2021 yang bersumber dari Badan Pusat Statistik, dari hasil a","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"44 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":"131560492","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":"Identifikasi Sektor Unggulan pada Sektor Ekonomi Provinsi Jawa Barat melalui Analisis Input Output","authors":"Raihani Himati Kamilah, Teti Sofia Yanti","doi":"10.29313/bcss.v3i2.8533","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8533","url":null,"abstract":"Abstract. Input output analysis can be used to analyze inter-sector linkages and relationships in the economy of a region. In this analysis, the data presented is in the form of tables which are referred to as input output tables. This table is very important to see the economic performance of a region by looking at the linkages between economic sectors in a region both backward and forward linkages. However, only knowing how the inter-sectoral linkages are is not enough to develop an economy. Therefore, it is necessary to carry out further analysis to determine which sectors are the leading sectors. West Java Province has had an average positive economic growth for the last six years and is also the third province that has the highest GRDP in Indonesia in 2021. This research will look at how the inter-sectoral relations of the West Java economy and what are the leading sectors in West Java using input output analysis. There are three analyzes used, namely backward linkage, forward linkage, and overlay analysis. The manufacturing industry is the leading sector in West Java Province, it is also the sector that has the greatest forward and backward linkages besides the electricity and gas procurement sector. In addition, the water supply, waste management, waste and recycling sectors have the potential to become leading sectors in West Java Province. \u0000Abstrak. Analisis input output dapat digunakan dalam menganalisis keterkaitan serta hubungan antarsektor dalam perekonomian suatu wilayah. Dalam analisis ini, data yang disajikan berbentuk tabel yang disebut sebagai tabel input output. Tabel ini sangat penting untuk melihat performa perekonomian suatu wilayah dengan melihat keterkaitan antarsektor perekenomian pada suatu wilayah baik keterkaitan ke belakang maupun ke depan. Namun, hanya dengan mengetahui bagaimana keterkaitan antarsektor saja belum cukup untuk mengembangkan suatu perekonomian. Maka dari itu, perlu dilakukan analisis lebih lanjut untuk menentukan sektor mana saja yang merupakan sektor unggulan. Provinsi Jawa Barat memiliki rata-rata pertumbuhan ekonomi yang positif selama enam tahun terakhir dan juga merupakan provinsi ketiga yang memiliki PDRB tertinggi di Indonesia pada tahun 2021. Dalam penelitian ini akan dilihat bagaimana keterkaitan antarsektor perekenomian Jawa Barat dan apa saja sektor unggulan di Jawa Barat menggunakan analisis input output. Terdapat tiga analisis yang digunakan yaitu keterkaitan ke belakang, keterkaitan ke depan, dan analisis overlay. Industri pengolahan merupakan sektor unggulan Provinsi Jawa Barat, juga termasuk sektor yang memiliki nilai keterkaitan ke depan dan ke belakang yang paling besar selain sektor pengadaan listrik dan gas. Selain itu sektor pengadaan air, pengelolaan sampah, limbah dan daur ulang memiliki potensi untuk menjadi sektor unggulan di Provinsi Jawa Barat.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"25 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":"131670313","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":"Selang Kepercayaan The Generalized Confidence Interval (GCI) untuk Koefisien Variasi dari Distribusi Invers Gaussian","authors":"Aneu Nurkamilah, Abdul Kudus","doi":"10.29313/bcss.v3i2.8945","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8945","url":null,"abstract":"Abstract. One of the distribution functions known in statistics is the Inverse Gaussian (IG) distribution. The IG distribution is used to deal with data that has a positive or right-skewed slope. The IG distribution was first introduced by Schrodinger in 1915 which originated from Brownian motion theory. In further statistical studies there is what is known as the coefficient of variation (CV), which is the ratio between the standard deviation and the mean value. In addition to point estimates, it is often desirable to find confidence intervals. One method to estimate the confidence interval of the CV coefficient is The Generalized Confidence Interval (GCI) method. In this thesis, the GCI method will be applied to the coefficient of variation of the IG distribution on PM 2.5 air pollution data. In the research process, the stages of analysis carried out include calculating the estimated parameters of the IG distribution using the maximum likelihood method, Kolmogorov-Smirnov test, calculating the values of Rλ, Rμ, dan Rθ for GCI to get the set of Rθ and calculating the 95% confidence interval for the parameter value , namely the CV parameter. Based on the calculation results, it is obtained that the PM 2.5 data in Malang City in 2023 comes from an IG-distributed population and the GCI confidence interval with a 95% confidence level for the CV of the IG distribution on PM 2.5 air pollution data in Malang City in 2023 is in the range [0.25; 0.42]. \u0000Abstrak. Salah satu fungsi distribusi yang dikenal dalam ilmu statistika adalah distribusi Invers Gaussian (IG). Distribusi IG digunakan untuk mengatasi data yang memiliki kemiringan positif atau miring ke kanan. Distribusi IG pertama kali diperkenalkan oleh Schrodinger tahun 1915 yang berasal dari teori gerak Brown. Dalam kajian statistika lebih lanjut ada yang dikenal dengan koefisien variasi (CV) yaitu perbandingan antara standar deviasi dengan nilai rata-rata. Selain nilai dugaan titik, seringkali juga diinginkan untuk mencari selang kepercayaan. Salah satu metode untuk menduga selang kepercayaan koefisien CV adalah metode The Generalized Confidence Interval (GCI). Dalam skripsi ini akan dilakukan penerapan metode GCI untuk koefisien variasi dari distribusi IG pada data pencemaran udara PM 2.5. Dalam proses penelitian tahapan analisis yang dilakukan meliputi menghitung taksiran parameter dari distribusi IG menggunakan metode maksimum likelihood, uji Kolmogorov-Smirnov, menghitung nilai Rλ, Rμ, dan Rθ bagi GCI hingga mendapatkan himpunan Rθ dan menghitung selang kepercayaan 95% untuk nilai parameter  yakni parameter CV. Berdasarkan hasil perhitungan diperoleh bahwa data PM 2.5 di Kota Malang tahun 2023 berasal dari populasi yang berdistribusi IG dan selang kepercayaan GCI dengan tingkat kepercayaan 95% untuk CV dari distribusi IG pada data pencemaran udara PM 2.5 di Kota Malang tahun 2023 adalah berada di dalam rentang [0.25; 0.42].","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"77 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":"115989244","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":"Aplikasi Metode Geographically Weighted Negative Binomial Regression dalam Pemodelan Tuberkulosis Paru di Provinsi Jawa Barat Tahun 2019","authors":"Hanna Anggreany Putri, Nur Azizah komara Rifai","doi":"10.29313/bcss.v3i2.8358","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8358","url":null,"abstract":"Abstract. Poisson regression analysis has a condition of equidispersion in which the variance value is equal to the mean value, the average of the Poisson is the true variance. When the variance is greater than the mean value, it is called overdispersion. An alternative that can be used is negative binomial regression. However, Poisson regression and negative binomial regression are less appropriate when applied to spatial data, or data containing geographical conditions. In this thesis, researchers suspect that the data has spatial heterogeneity and has overdispersion. The Geographically Weighted Negative Binomial Regression (GWNBR) method with Adaptive Gaussian Kernel weights is able to estimate the case. The GWNBR method will be applied to the number of pulmonary tuberculosis cases in each district/city with factors that are thought to affect it. Based on the results of the GWNBR model with Adaptive Gaussian Kernel weights, it is categorized as a very good model because it shows that the models formed are different in each district / city in West Java Province, there are 25 models formed based on significant predictor variables and 2 models formed based on insignificant predictor variables. \u0000Abstrak. Analisis regresi Poisson memiliki syarat equidispersi yang di mana nilai varians sama dengan nilai rata-ratanya, rata-rata dari Poisson itu merupakan varians yang sebenarnya. Ketika varians lebih besar dari nilai rata-rata dapat disebut dengan overdispersi. Alternatif yang dapat digunakan yaitu regresi binomial negatif. Akan tetapi regresi Poisson maupun regresi binomial negatif  kurang tepat jika diterapkan pada data spasial, atau data yang mengandung kondisi geografis. Dalam skripsi ini, peneliti menduga bahwa data  memiliki heterogenitas spasial dan memiliki overdispersi. Metode Geographically Weighted Negative Binomial Regression (GWNBR) dengan pembobot Adaptive Gaussian Kernel mampu menduga kasus tersebut. Metode GWNBR akan di aplikasikan ke dalam jumlah kasus penyakit tuberkulosis paru di setiap kabupaten/kota dengan faktor-faktor yang diduga mempengaruhinya. Berdasarkan hasil model GWNBR dengan pembobot Adaptive Gaussian Kernel di kategorikan sebagai model yang sangat baik karena menunjukan model yang terbentuk berbeda-beda di setiap kabupaten/kota di Provinsi Jawa Barat terdapat 25 model yang terbentuk berdasarkan peubah prediktor yang signifikan dan 2 model yang terbentuk berdasarkan peubah prediktor yang tidak signifikan.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"34 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":"125631716","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 Regresi Polinomial Lokal pada Nilai Ekspor Non-Migas di Indonesia","authors":"Fachrul Fauzi, Teti Sofia Yanti","doi":"10.29313/bcss.v3i2.8512","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8512","url":null,"abstract":"Abstract. Linear regression analysis is one of the statistical methods aimed at determining the influence of the independent variable (X) on the dependent variable (Y). In estimating the parameters of the linear regression model, it can be done using the method of least squares or ordinary least squares (OLS). However, if you want to model with research data with an unknown functional form, then the regression analysis used is nonparametric regression analysis. Local polynomial regression is one of the models used in nonparametric regression approaches. Estimation of parameters in local polynomial regression can be done using the Weighted Least Squares (WLS) estimation method by considering the value of the kernel function. The kernel function commonly used includes the Gaussian kernel and the Epanechnikov kernel. The best local polynomial regression model can be observed from the bandwidth, local points, weighting, kernel function, and the optimal polynomial degree. The data used in this study are non-oil and gas export values in Indonesia from January 2018 to December 2022. Based on the calculation results, the best local polynomial regression model is the local polynomial regression model with the Gaussian kernel function, with a Mean Absolute Percentage Error (MAPE) of 8.33%, and a coefficient of determination (R2) of 83.99%. The best model to predict non-oil and gas export values in Indonesia is the second-order with a local point (t0) of 22 and a bandwidth (h) of 10, i.e., yi^ = 12521.411 + 35.744 (ti-22)+8.475 (ti-22)^2 \u0000Abstrak. Analisis Regresi Linier merupakan salah satu metode statistika yang bertujuan untuk mengetahui pengaruh variabel bebas (X) terhadap variabel tak bebas (Y). Dalam melakukan estimasi parameter model regresi linier dapat dilakukan dengan menggunakan metode kuadrat terkecil atau ordinary least square (OLS). Namun apabila ingin melakukan pemodelan dengan data penelitian yang tidak diketahui bentuk fungsinya, maka analisis regresi yang digunakan adalah analisis regresi nonparametrik. Regresi polinomial lokal merupakan salah satu model yang digunakan dalam pendekatan regresi nonparametrik. Penaksiran parameter pada regresi polinomial lokal dapat dilakukan dengan metode estimasi Weighted Least Square (WLS) dengan memperhatikan nilai fungsi kernel. Fungsi kernel yang banyak digunakan adalah kernel gaussian dan kernel epanechnikov. Model regresi polinomial lokal terbaik dapat dilihat dari bandwidth, titik lokal, pembobot, fungsi kernel, serta derajat polinomial yang optimal. Data yang digunakan dalam penelitian ini adalah nilai ekspor non-migas di Indonesia pada Januari 2018 hingga Desember 2022. Berdasarkan hasil perhitungan, diperoleh model regresi polinomial lokal terbaik adalah model regresi polinomial lokal dengan fungsi kernel gaussian dengan nilai MAPE sebesar 8,33% dan nilai koefisien determinasi (R2) sebesar 83,99%. Model terbaik untuk memprediksi nilai ekspor non-migas di Indonesia adalah orde dua dengan titik lok","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"39 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":"126984219","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":"Diagram Kendali Triple Homogeneously Weighted Moving Average dan Penerapannya pada Proses Plating Aksesori Mobil PT. XYZ","authors":"Fairuza Amanda Belia, Suliadi Suliadi","doi":"10.29313/bcss.v3i2.8892","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8892","url":null,"abstract":"Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are  of 10.691 dan  of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled. \u0000Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are  of 10.691 dan  of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled.","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":"122198009","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 Structural Vector Autoregressive (SVAR) dalam Menganalisis Pengaruh Pengeluaran Konsumsi Rumah Tangga (KRT) terhadap Inflasi di Provinsi Jawa Barat Tahun 2013-2022","authors":"Tasya Apriany Hamba, Teti Sofia Yanti","doi":"10.29313/bcss.v3i2.9182","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9182","url":null,"abstract":"Abstract. The VAR model can be said to be a non-structural model because it is atheoristic or does not make use of previous information or theory. This VAR model is used if the variable is stationary and does not contain a trend. This model has the weakness that it does not have the ability to describe the contemporaneous (simultaneous) relationship of shocks between the variables analyzed. Because of the importance of knowing the contemporary effects of shocks on the economy, an alternative is used to overcome this problem, namely by using the Structural Vector Autoregressive (SVAR) model where the SVAR model can show the relationship between contemporary variables. The SVAR method uses the Impulse Response Function and Forecast Error Variance Decomposition through the application of restrictions with a matrix to identify and investigate shocks. In this study, multivariate time series data analysis was conducted to determine the impact of household consumption expenditure shocks on inflation by applying the SVAR model. Based on the research results, it was found that the SVAR model (5) is the best SVAR model. From the best model, it can be seen that the household consumption response to inflationary shocks tends to be stable from January to October 2023. Meanwhile, the inflation response to household consumption shocks in early January to March 2023 will decrease, then increase the following month and move steadily until October 2023. \u0000Abstrak. Model VAR dapat dikatakan sebagai model non-struktural karena bersifat ateoritik atau tidak memanfaatkannya informasi atau teori terdahulu. Model VAR ini digunakan jika variabel bersifat stasioner dan tidak mengandung trend. Model ini memiliki kelemahan yaitu tidak memiliki kemampuan untuk menggambarkan hubungan kontemporer (simultan) dari guncangan (shock) antara variabel yang dianalisis. Karena pentingnya dalam mengetahui efek kontemporer dari guncangan (shock) terhadap perekonomian, digunakanlah alternatif untuk mengatasi masalah tersebut yaitu dengan menggunakan model Structural Vector Autoregressive (SVAR) dimana pada model SVAR dapat memperlihatkan hubungan antara variabel kontemporer. Metode SVAR menggunakan Impuls Response Function dan Forecast Error Variance Decomposition melalui penerapan pembatasan dengan matriks untuk mengidentifikasi dan menyelidiki guncangan (shocks). Dalam penelitian ini dilakukan analisis data multivariate time series untuk mengetahui dampak shocks Pengeluaran Konsumsi Rumah Tangga terhadap inflasi dengan menerapkan model SVAR. Berdasarkan hasil penelitian diperoleh hasil bahwa model SVAR (5) merupakan model SVAR terbaik. Dari model terbaik dapat diketahui bahwa respon Konsumsi Rumah Tangga terhadap shocks Inflasi cenderung stabil dari mulai bulan Januari hingga bulan Oktober 2023. Sedangkan respon Inflasi terhadap shock Konsumsi Rumah Tangga di awal bulan Januari hingga Maret tahun 2023 mengalami penurunan, kemudian mengalami kenaikan pada bulan berikutnya dan bergerak st","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"38 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":"127129078","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}