Bandung Conference Series: Statistics最新文献

{"title":"SEM-PLS untuk Persepsi Nilai pada Aplikasi Pemesanan Tiket Pesawat","authors":"Edwiga Antirad, Marizsa Herlina","doi":"10.29313/bcss.v3i1.5734","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5734","url":null,"abstract":"Abstract. The increasing use of technology has made people switch to ordering plane tickets online. In addition, smartphone ownership makes applications the media most often used to place orders. Even so, the perception of user value is still a problem for flight ticket booking service providers. This is because users still frequently switch services erratically. Therefore, this research was conducted to determine the factors that influence the perceived value of users of flight ticket booking applications in Gen Z at the Islamic University of Bandung. This study will link 3 latent variables related to perceived value, namely search features, ease of use, and payment methods. All three latent variables are related to one another. The approach used to determine the relationship between these latent variables is the Structural Equation Modeling – Partial Least Square (SEM-PLS) method with the bootstrap parameter estimation method. This research was conducted to understand the truth of the theoretical concept regarding the factors that influence perceived value. The results showed that each of the two of the four indicators on the variable search features, ease of use and payment methods were significant and showed that all variables had a significant effect on the perceived value variable. The bootstrap estimation results for hypothesis testing also concluded that search feature variables, ease of use, and payment methods have an effect on perceived value. \u0000Abstrak. Meningkatnya penggunaan teknologi membuat masyarakat beralih untuk memesan tiket pesawat secara daring. Selain itu, kepemilikan smartphone membuat aplikasi menjadi media yang paling sering digunakan untuk melakukan pemesanan. Meskipun demikian, persepsi nilai pengguna masih menjadi permasalahan penyedia layanan pemesanan tiket pesawat. Hal ini dikarenakan pengguna masih sering berpindah-pindah layanan secara tidak menentu. Oleh karena itu, penelitian ini dilakukan untuk mengetahui faktor-faktor yang memengaruhi persepsi nilai pengguna aplikasi pemesanan tiket pesawat pada gen Z di Universitas Islam Bandung. Penelitian ini akan menghubungkan 3 variabel laten yang berkaitan dengan persepsi nilai yaitu fitur pencarian, kemudahan penggunaan, dan metode pembayaran. Ketiga variabel laten yang berbeda saling berkorelasi antara variabel satu dengan yang lainnya. Pendekatan yang digunakan untuk mengetahui hubungan variabel-variabel laten tersebut adalah Metode Structural Equation Modelling – Partial Least Square (SEM-PLS) dengan metode estimasi parameter bootstrap. Penelitian ini dilakukan untuk memahami kebenaran konsep teori mengenai faktor-faktor yang mempengaruhi nilai persepsi. Hasil penelitian menunjukkan bahwa masing-masing dua dari empat indikator pada variabel fitur pencarian, kemudahan penggunaan dan metode pembayaran signifikan dan menunjukkan bahwa semua variabel berpengaruh signifikan terhadap variabel persepsi nilai. Hasil estimasi dengan bootstrap untuk uji hipotesis juga menyimpu","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131371301","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 Ridge Parsial untuk Mengatasi Masalah Multikolinearitas untuk Memodelkan Faktor-Faktor yang Mempengaruhi Kemiskinan di Jawa Tengah pada Tahun 2020","authors":"Saquila Beninurhadi Putri, Suliadi Suliadi","doi":"10.29313/bcss.v3i1.5578","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5578","url":null,"abstract":"Abstract. In regression analysis, multicollinearity is a condition of strong collinearity between independent variables. If multicollinearity occurs, the estimated parameters will have a large and not stable variance. Ridge regression is one of the solutions to overcome multicollinearity by adding a parameter c as a bias setting to the variance-covariance matrix of the independent variables. However, in the usual ridge regression model has some drawbacks, such as the bias constant c is added to all independent variables, regardless of the high or low level of collinearity among the variables. Therefore, Chandrasekhar, et al. (2016) developed a new ridge regression method, namely partial ridge regression. This research discusses the partial ridge regression method and applies to the case of poverty in Central Java Province in 2020. The results showed multicollinearity in the data and a bias constant c which was only added to variables with low eigenvalues, namely on Eigen 4. The partial ridge regression is =0.5753 + 0.4977 -1.6661 -0.1679  and then returned to the original regression model as =85.1023+1.6967 +0.0010 -1.3272 -0.3430. \u0000Abstrak. Dalam analisis regresi, multikolinearitas adalah suatu kondisi kekolinieran yang kuat antar variabel independent. Apabila terjadi multikolinearitas maka taksiran parameter akan memiliki varians yang besar dan tidak stabil. Regresi ridge merupakan salah satu solusi untuk mengatasi multikolinearitas dengan cara menambahkan parameter c sebagai tetapan bias pada matriks varians -kovarians pada variabel independen. Namun model regresi ridge terdapat beberapa kekurangan diantaranya yaitu konstanta bias c ditambahkan ke semua variabel independent, tanpa melihat tinggi rendahnya tingkat kolinearitas diantara variabel-variabel. Oleh karena itu, Chandrasekhar, et al., (2016) mengembangkan suatu metode regresi rigde baru yaitu partial regression ridge atau regresi ridge parsial. Skripsi ini membahas tentang metode regresi ridge parsial yang diterapkan pada kasus kemiskinan di Jawa Tengah pada tahun 2020 . Hasil penelitian menunjukan adanya multikolinearitas pada data dan kontanta bias c yang hanya ditambahkan pada variabel yang berinilai eigen rendah yaitu pada eigen 4. Regresi ridge parsialnya adalah  =0.57531 + 0.4977 -1.6661 -0.1679  lalu dikembalikan ke model regresi semula menjadi  =85.1023+1.6967 +0.0010 -1.3272 -0.3430.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125483367","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 Fungsi Transfer Multivariat untuk Meramalkan Produksi Padi di Sumatera Barat","authors":"Kuntum Khairatunnisa, Anneke Iswani Achmad","doi":"10.29313/bcss.v3i1.5461","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5461","url":null,"abstract":"Abstract. Forecasting is a way to predict future events using past and present data. One of the models in forecasting is the transfer function model. The Transfer Function Model is a combination of the characteristics of multiple regression analysis with the characteristics of the time series ARIMA (Autoregressive Integrated Moving Average). In the transfer function model there is an output series (yt) which will be affected by the input series (xt) and the other inputs are combined in one group called the noise or noise series (nt). In this study the objects applied to the multivariate transfer function model are rice production (Y) as the output series, harvested area (X1) and rainfall (X2) as the input series. The data used is from January 2010 to December 2020. The purpose of this study is to find out the model and forecast results for rice production in West Sumatra from January 2021 to December 2022 with a multivariate transfer function model. In this study, a multivariate transfer function model was obtained to predict rice production in West Sumatra \u0000Yt=-0,83048Yt-1+(6,02681) X1,t-(-0,83048)(6,02681) X2,t-1+0,0079647X2,t-4-(-0,83048)(0,0079647) X2,t-5-(-0,74556)at-1+ et \u0000and the highest forecasting results in 2021, namely February of 266,909 tons of dry milled grain (GKG) and the lowest, namely in May, of 266,408 tons of dry milled grain (GKG) while for 2022 the highest production is in January of 266,560 tons of GKG and the lowest was in March with 266.539 tons of GKG. \u0000Keywords: Forecasting, ARIMA, Multivariate Transfer Function, Rice Production. \u0000Abstrak. Peramalan adalah cara untuk memprediksi kejadian masa depan dengan menggunakan data masa lalu dan sekarang. Salah satu model dalam peramalan adalah model fungsi transfer. Model Fungsi Transfer merupakan gabungan dari karakteristik analisis regrsi berganda dengan karakteristik deret berkala ARIMA (Autoregressive Integrated Moving Average). Pada model fungsi transfer terdapat deret output (yt)  yang akan dipengaruhi oleh deret input (xt) dan input-input lainnya digabungkan dalam satu kelompok yang disebut deret gangguan atau noise (nt). Pada penelitian ini objek yang diterapkan pada model fungsi transfer multivariat yaitu produksi padi (Y)  sebagai deret output , luas panen (X1) dan curah hujan (X2) sebagai deret inputnya. Dengan data yang digunakan yaitu dari Januari 2010 sampai Desember 2020. Tujuan dari penelitian ini untuk mengetahui model dan hasil ramalan produksi padi di Sumatera Barat dari Januari 2021 sampai dengan Desember 2022 dengan model fungsi transfer multivariat. Dalam penelitian ini diperoleh model fungsi transfer multivariat untuk meramalkan produksi padi di Sumatera Barat adalah \u0000Yt=-0,83048Yt-1+(6,02681) X1,t-(-0,83048)(6,02681) X2,t-1+0,0079647X2,t-4-(-0,83048)(0,0079647) X2,t-5-(-0,74556)at-1+ et \u0000dan hasil peramalan tertinggi pada tahun 2021 yaitu bulan Februari sebesar 266,909 ton Gabah Kering Giling (GKG) dan terendah yaitu pada bulan Mei yaitu sebesar 266,408 t","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124485917","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 Sintetik Coeffecient of Variation dalam Memantau Variabilitas Proses dan Penerapannya pada Data Pengukuran Core 4st","authors":"Nadya Yuliyani, Suliadi","doi":"10.29313/bcss.v3i1.5689","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5689","url":null,"abstract":"Abstract. The average control chart and variability control chart are used to monitor whether the process average and variability are stable or not. One tool to monitor process variability is the coefficient of variation (CV) control chart. Many control chart methods were used by previous studies to monitor CV, one of which was by Guo and Wang (2016) who provided a new strategy by proposing a synthetic diagram with two-sided CV sub-graphs based on equal-tailed probability limits and CRL (Conforming run length) which requires a lower control limit. Synthetic control charts have better performance compared to common control charts. In this study, we apply this method to the thickness data of the 4th core product. The 4th core product is one of the component parts on the starter motor dynamo and starter coil., It is obtained that in phase I the thickness of the 4st core was statistically controlled. Furthermore, in phase II, it is obtained that several observation points felt outside the control limits, and concluded that there were uncontrolled processes at several observation points. \u0000Abstrak. Diagram kendali rata-rata dan diagram kendali variabilitas, digunakan untuk memantau apakah rata-rata dan variabilitas proses tersebut stabil atau tidak. Salah satu alat guna memantau variabilitas proses adalah diagram kendali coeffecient of variation (CV). Banyak metode diagram kendali yang dilakukan oleh penelitian terdahulu guna memantau CV, salah satunya oleh Guo dan Wang (2016) yang memberikan strategi baru dengan mengusulkan diagram sintetik dengan sub-grafik CV dua sisi berdasarkan batas probabilitas equal-tailed dan  sub-grafik CRL (Conforming run length) yang membutuhkan batas kendali bawah. Diagram kendali sintetik memiliki kinerja yang lebih baik dibandingkan dengan diagram kendali pada umumnya. Pada penelitian ini, kami menerapkan metode tersebut terhadap data ketebalan produk core 4st . Produk core 4st merupakan salah satu bagian komponen yang berada pada dinamo stater motor serta koil stater. Diperoleh bahwa pada fase I ketebalan core 4st  terkendali secara statistik. Selanjutnya pada fase II, diperoleh beberapa titik pengamatan yang dirasa berada diluar batas kendali, dan disimpulkan bahwa terdapat proses yang tidak terkendali pada beberapa titik pengamatan.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127957696","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":"Estimasi Tabel Kematian untuk Penduduk Perempuan di Provinsi Banten dengan Metode Intersurvei Kohor Hipotesis Menggunakan Tabel Coale-Demeny","authors":"Alna Septiani Noer Ismaila, Yayat Karyana","doi":"10.29313/bcss.v3i1.5300","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5300","url":null,"abstract":"Abstract. One indicator that affects the welfare of the population is the death rate which is part of the demographic factor. If an area has a high infant mortality rate, then the welfare of the population in that area is low, conversely if infant mortality is low, the population's welfare tends to improve. This study aims to apply the Hypothesis Cohort Intersurvey method to estimate the mortality rate and then continue by modeling the table with the Western Coale-Demeny model table to get the life expectancy value. The data used comes from SP2010 and SUPAS2015. Based on the study's results, the estimated mortality table is relatively low, stating that the population's welfare in Banten Province is quite good. Meanwhile, for AHH (Life Expectancy) in this study, 69.54 years were obtained, which means that a person's life expectancy can be up to 70 years old. \u0000Keywords: Mortality, Hypothesized Cohort Intersurvey Method, Mortality Table, Western model Coale-Demeny Table. \u0000Abstrak. Salah satu indikator yang mempengaruhi kesejahteraan penduduk adalah level kematian yang menjadi bagian dari faktor demografi, dalam hal ini tingkat kematian menjadi salah satu hal yang berpengaruh. Apabila suatu wilayah memiliki tingkat kematian bayi yang terbilang tinggi, maka kesejahteraan penduduk pada wilayah tersebut terbilang rendah. Sebaliknya apabila kematian bayi rendah, maka kesejahteraan penduduk cenderung lebih baik. Tujuan penelitian ini adalah mengaplikasikan metode Intersurvei Kohor Hipotesis untuk mengestimasi tingkat kematian kemudian dilanjutkan dengan memodelkan tabel dengan tabel Coale-Demeny model Barat untuk mendapatkan nilai harapan hidup. Data yang digunakan berasal dari SP2010 dan SUPAS2015. Berdasarkan hasil penelitian diperoleh estimasi tabel kematian yang terbilang cukup rendah dan dapat dikatakan jika kesejahteraan penduduk di Provinsi Banten terbilang cukup baik. Sementara itu, untuk AHH (Angka Harapan Hidup) dalam penelitian ini didapatkan 69,54 tahun yang berarti harapan hidup seseorang bisa sampai umur 70 tahun. \u0000Kata Kunci: Mortalitas, Metode Intersurvei Kohor Hipotesis, Tabel Kematian, Tabel Coale-Demeny model Barat","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133790562","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 Campuran Lognormal-Gamma pada Data Besar Klaim Asuransi Kendaraan Bermotor","authors":"Sheli Andriani, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i1.5685","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5685","url":null,"abstract":"Abstract. Insurance is an agreement between the insurer and the insured, which requires the insured to pay a premium to provide reimbursement for the risk of loss, damage, death or loss of profits due to an unexpected event. Some of the terms in insurance, one of which is a claim, when a claim occurs when the insured experiences a risk, the insurer will compensate for the loss according to the agreement stated in the policy (written agreement). In several previous studies, there are distributions that are applied to large claims data such as the Pareto distribution and the Weillbul distribution. This study will use a mixed lognormal-gamma distribution. The mixed distribution of the lognormal gamma mixture belongs to the continuous distribution with three parameters (µ, α, and β). The data used is the big data of claims at the insurance company PT XZ in 2014 regarding the data of claims for Partial Loss motor vehicle insurance for region 1 category 5. Based on the results of applying the mixed lognormal-gamma distribution it is concluded that the big data for motor vehicle insurance claims for category 5 region 1 comes from population with mixed lognormal-gamma distribution \u0000Abstrak. Asuransi merupakan perjanjian antara penanggung dan tertanggung, yang mewajibkan tertanggung membayar sejumlah premi untuk memberikan penggantian atas risiko kerugian, kerusakan, kematian, atau kehilangan keuntungan karena suatu peristiwa yang tidak terduga. Beberapa istilah dalam asuransi salah satunya yaitu klaim, terjadinya klaim ketika tertanggung mengalami risiko maka penanggung akan mengganti kerugian sesuai dengan kesepakatan yang tertera dalam polis (perjanjian tertulis). Dalam beberapa penelitian terdahulu, terdapat distribusi yang diterapkan pada data besar klaim seperti distribusi Pareto dan distribusi Weillbul. Pada penelitian ini akan menggunakan distribusi campuran lognormal-gamma. Distribusi campuran lognormal gamma termasuk kedalam distribusi kontinu dengan tiga parameter (µ, α, dan β). Data yang digunakan yaitu data besar klaim pada perusahaan asuransi PT XZ Tahun 2014 mengenai data klaim Partial Loss asuransi kendaraan bermotor untuk wilayah 1 kategori 5. Berdasarkan hasil penerapan distribusi campuran lognormal-gamma disimpulkan bahwa data besar klaim asuransi kendaraan bermotor kategori 5 wilayah 1 berasal dari populasi yang berdistribusi campuran lognormal-gamma.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126818013","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 X Exponentially Weighted Moving Average yang Meminimalkan Median Run Length pada Data Panjang Pewarna Plastik","authors":"Shania Wilda Fitris, Suliadi","doi":"10.29313/bcss.v3i1.5513","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5513","url":null,"abstract":"Abstract. The main purpose of statistical quality control is to quickly investigate whether special causes or process shifts has occurred so that an investigation of the process and corrective action can be taken. One of the tools for statistical quality control is the control chart. The tool that can be used to measure the performance of the control chart is the average run length (ARL). The disadvantage is that the run length distribution is skewed when the process is in control or slightly out of control, thus ARL’s interpretation of the performance of the control chart is less meaningful. You et al. (2016) introduced EWMA control chart based on the median run length (MRL), that is the Xbar EWMA control chart that minimizes the median run length (MRL) and expected median run length (EMRL). This method is more informative and reliable and is not affected by the slope of the run length distribution compared to ARL. In this study we applied the method to the the plastic colorant length data of Company X. From the research conducted, it was concluded that plastic colorant length data of Company X was statistically in control both in phase-I and phase-II data. \u0000Abstrak. Tujuan utama dari pengendalian kualitas statistik yaitu menyelidiki dengan cepat apakah terjadi penyebab khusus atau pergeseran proses sedemikian sehingga penyelidikan terhadap proses tersebut dan tindakan perbaikan dapat dilakukan. Salah satu alat untuk pengendalian kualitas statistik yaitu diagram kendali. Alat yang bisa digunakan untuk melihat kinerja diagram kendali adalah average run length (ARL). Kelemahannya adalah distribusi run length miring ketika proses in control atau sedikit out of control, sehingga interpretasi ARL mengenai kinerja dari diagram kendali kurang berarti. Maka You dkk. (2016) memperkenalkan alternatif dari penggunaan diagram kendali EWMA berdasarkan median run length (MRL), yaitu mengunakan diagram kendali Xbar EWMA yang meminimalkan median run length (MRL) dan expected median run length (EMRL), karena metode ini lebih informatif dan reliabel serta tidak dipengaruhi oleh kemiringan distribusi run length dibandingkan menggunakan ARL. Dalam penelitian ini kami menerapkan metode tersebut terhadap data panjang pewarna plastik Perusahaan X. Dari penelitian yang dilakukan disimpulkan bahwa data panjang pewarna plastik Perusahaan X terkendali secara statistik baik pada data fase-I maupun data fase-II.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131670256","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 Small Area Estimation dengan Metode Empirical Bayes dalam Menduga Risiko Relatif Penyebaran TBC di Kabupaten Karawang Tahun 2021","authors":"S. Rohmah, Nusar Hajarisman","doi":"10.29313/bcss.v2i2.4765","DOIUrl":"https://doi.org/10.29313/bcss.v2i2.4765","url":null,"abstract":"Abstract. Small Area Estimation (SAE) is a statistical technique used to estimate subpopulation parameters (areas) with a small sample size. In Small Area Estimation there are methods for processing cacahan data, namely Empirical Bayes and Bayes hierarchical. This study used the Bayes empirical method with the Poisson-Gamma model in estimating the risk of TB disease in Karawang Regency. The purpose of this study is to apply the Bayes empirical method based on the Poisson-Gamma model to estimate the relative risk of TB disease in Karawang Regency and compare the results of direct estimators and bayes empirical estimators through the Mean Square Error (MSE) value. The results showed that by comparing the MSE values of the direct estimator of the standardized mortality ratio and the empirical estimator of Bayes, it was concluded that the Bayes empirical method did not provide better results than direct estimators. Although Bayes empirical estimators have a fairly good accuracy rate with an average MSE value of 0.0232, the MSE value is greater when compared to the direct estimator MSE average of 0.0059. \u0000Abstrak. Small Area Estimation (SAE) atau pendugaan area kecil merupakan teknik statistika yang digunakan untuk menduga parameter subpopulasi (area) dengan ukuran sampel kecil. Dalam Small Area Estimation terdapat metode untuk mengolah data cacahan, yaitu Empirical Bayes dan hierarchical Bayes. Penelitian ini menggunakan metode Empirical Bayes dengan model Poisson-Gamma dalam menduga risiko penyakit TBC di Kabupaten Karawang. Tujuan penelitian ini adalah menerapkan metode Empirical Bayes berbasis model Poisson-Gamma untuk menduga risiko relatif penyakit TBC di Kabupaten Karawang serta membandingkan hasil penduga langsung dan penduga Empirical Bayes melalui nilai Mean Square Error (MSE). Hasil penelitian menunjukkan dengan membandingkan nilai MSE penduga langsung standardized mortality ratio dan penduga Empirical Bayes disimpulkan bahwa metode Empirical Bayes tidak memberikan hasil yang lebih baik dibandingkan penduga langsung. Meskipun penduga Empirical Bayes memiliki tingkat keakuratan yang cukup baik dengan rata-rata nilai MSE sebesar 0.0232, tetapi nilai MSE tersebut lebih besar jika dibandingkan dengan rata-rata MSE penduga langsung sebesar 0.0059.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114204255","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":"Peramalan Produksi Mobil Menggunakan Metode Double Exponential Smoothing dengan Algoritma Golden Section","authors":"Muhammad Iqbal Wiladibrata, Nur Azizah Komara Rifai","doi":"10.29313/bcss.v2i2.4776","DOIUrl":"https://doi.org/10.29313/bcss.v2i2.4776","url":null,"abstract":"Abstract. Forecasting analysis is the process of estimating a situation in the future based on past data. The exponential smoothing method is a forecasting method that assigns a smoothing value by an exponential function to a series of previous observed values. Research data in the form of secondary data obtained from the Association of Indonesian Automotive Industries (GAIKINDO) in the form of Toyota Avanza car production data for April 2020-April 2022. In this study, the double exponential smoothing method will be used because the data pattern used has a trend tendency. In the double exponential smoothing method, two smoothing parameters are needed, namely the parameter which is used to calculate the constant value of the forecasting model and the parameter which is used to calculate the trend coefficient of the forecasting model. In this study, the golden section algorithm is used to optimize the parameters of the double exponential smoothing method. The purpose of this study is to apply the double exponential smoothing method with the golden section algorithm to predict the production of the Toyota Avanza in May 2022. The results of the study state that the combination of parameters and which produces the minimum Sum of Squares Error (SSE) value in the golden section algorithm is at the parameter value =0.618034 and parameter =0.381966 which produces a forecasting model F_(t+m)=15259.525596+1002.881415(m) with a Sum of Squares Error (SSE) value of 140294878 and an Absolute Mean Percentage Error (MAPE) is 46.67%. The results of forecasting the Toyota Avanza in May 2022 were 16262.41 or 16263 units of the Toyota Avanza. \u0000Abstrak. Analisis peramalan adalah proses memperkirakan suatu keadaan di masa mendatang berdasarkan data-data masa lampau. Metode exponential smoothing adalah suatu metode peramalan yang memberi nilai pemulusan oleh sebuah fungsi eksponensial pada serangkaian nilai pengamatan sebelumnya. Data penelitian berupa data sekunder yang diperoleh dari Gabungan Industri Kendaraan Bermotor Indonesia (GAIKINDO) berupa data produksi mobil Toyota Avanza bulan April 2020-April 2022. Pada penelitian ini akan digunakan metode double exponential smoothing karena pola data yang digunakan memiliki kecenderungan trend. Pada metode double exponential smoothing dibutuhkan dua parameter pemulusan berupa parameter α yang digunakan untuk menghitung nilai konstanta model peramalan dan parameter γ yang digunakan untuk menghitung koefisien trend model peramalan. Dalam penelitian ini digunakan algoritma golden section untuk mengoptimumkan parameter pada metode double exponential smoothing. Tujuan dari penelitian ini adalah menerapkan metode double exponential smoothing dengan algoritma golden section untuk meramalkan produksi mobil Toyota Avanza pada bulan Mei 2022. Hasil penelitian menyatakan bahwa kombinasi parameter α dan γ yang menghasilkan nilai Sum of Squares Error (SSE) minimum pada algoritma golden section berada pada nilai parameter α=0,618034 dan","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124090092","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":"Geographically Weighted Poisson Regression dengan Fungsi Pembobot Kernel Gaussian untuk Pemodelan Jumlah Kematian Bayi di Jawa Barat pada Tahun 2019","authors":"Mestika Meytiara, Anneke Iswani Achmad","doi":"10.29313/bcss.v2i2.4769","DOIUrl":"https://doi.org/10.29313/bcss.v2i2.4769","url":null,"abstract":"Abstract. Regression analysis is a statistical analysis that aims to model the relationship between independent variables with dependent variables. If the independent variable is Poisson-distributed then the regression model used is Poisson regression. Geographically Weighted Poisson Regression (GWPR) is a local form of Poisson regression where the location of data collection is considered. In this study, Geographically Weighted Poisson Regression (GWPR) will be used to model the number of infant mortality in West Java in 2019 using the Gaussian kernel weighting function. This study aims to obtain a model of the number of infant mortality in West Java Province in 2019 and find out what factors affect the number of infant mortality in West Java Province in 2019. Based on the value of Akaike's Information Criterion (AIC), it is known that the GWPR model with the Gaussian kernel weighting function is more accurate than the Poisson regression model because it has the smallest AIC value. \u0000Abstrak. Analisis regresi adalah suatu analisis statistik yang bertujuan untuk memodelkan hubungan antara variabel bebas dengan variabel terikat. Apabila variabel bebas berdistribusi Poisson maka model regresi yang digunakan adalah regresi Poisson. Geographically Weighted Poisson Regression (GWPR) merupakan bentuk lokal dari regresi Poisson dimana lokasi pengambilan data sangat diperhatikan. Dalam penelitian ini akan digunakan Geographically Weighted Poisson Regression (GWPR) untuk memodelkan jumlah kematian bayi di Jawa Barat pada tahun 2019 dengan menggunakan fungsi pembobot kernel Gaussian. Penelitian ini bertujuan untuk mendapatkan model jumlah kematian bayi di Provinsi Jawa Barat pada tahun 2019 serta mengetahui faktor-faktor apa saja yang mempengaruhi jumlah kematian bayi di Provinsi Jawa Barat pada tahun 2019. Berdasarkan nilai Akaike’s Information Criterion (AIC), diketahui bahwa model GWPR dengan fungsi pembobot kernel Gaussian lebih akurat dibandingkan model regresi Poisson karena memiliki nilai AIC terkecil.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127112266","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}