Bandung Conference Series: Statistics最新文献

{"title":"Penerapan Algoritma Fuzzy Possibilistic C-Means (FPCM) pada Pengelompokan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2022","authors":"Ghia Fauziah Aghyari, Abdul Kudus","doi":"10.29313/bcss.v3i2.7321","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7321","url":null,"abstract":"Abstract. Human resources are a crucial factor in human development and a key component in achieving prosperity in every country. The success of development is measured in various ways, one of the most popular being the calculation of the Human Development Index (HDI). The classification of districts and cities in Indonesia is necessary as a reference for government program planning and evaluation to enhance human development in those areas. Partitioning clustering is one of the clustering techniques that aims to partition data into several groups or partitions, with the number of groups usually predetermined. One of the algorithms used in partitioning clustering is Fuzzy Possibilistic C-Means (FPCM). Fuzzy Possibilistic C-Means (FPCM) is an extension of two algorithms, namely Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM). FPCM combines fuzzy and possibilistic concepts to address the weaknesses of the previous algorithms. Therefore, the Fuzzy Possibilistic C-Means (FPCM) algorithm is applied to cluster the districts and cities in Indonesia based on the indicators of the Human Development Index. Based on the results of the Modified Partition Coefficient (MPC) index, the optimal number of clusters is determined to be four clusters. Cluster 1 contains 146 districts and cities, cluster 2 contains 97 districts and cities, cluster 3 contains 141 districts and cities, and cluster 4 contains 130 districts and cities. \u0000Abstrak. Sumber daya manusia adalah faktor penting dalam pembangunan manusia yang menjadi komponen utama dalam mencapai kemakmuran di setiap negara. Keberhasilan pembangunan diukur dengan berbagai cara, salah satunya yang paling populer melalui perhitungan Indeks Pembangunan Manusia (IPM) atau Human Development Index (HDI). Pengelompokan wilayah Kabupaten/Kota di Indonesia perlu dilakukan sebagai acuan dalam perencanaan dan evaluasi program pemerintah untuk meningkatkan pembangunan manusia di daerah tersebut. Partitioning clustering adalah salah satu teknik pengelompokan yang mencoba mempartisi data ke dalam beberapa kelompok (partition) dan jumlah kelompok yang akan dibuat biasanya telah ditentukan sebelumnya dan salah satu algoritma pada partitioning clustering adalah Fuzzy Possibilistic C-Means (FPCM). Algoritma Fuzzy Possibilistic C-Means (FPCM) yang merupakan perluasan dari dua algoritma yaitu algoritma Fuzzy C-Means (FCM) dan Possibilistic C-Means (PCM). Fuzzy Possibilistic C-Means (FPCM) menggabungkan konsep fuzzy dan possibilistic untuk mengurangi kelemahan dari algoritma sebelumnya. Oleh karena itu diterapkan algoritma Fuzzy Possibilistic C-Means (FPCM) untuk mengelompokan Kabupaten/Kota di Indonesia berdasarkan indikator indeks pembangunan manusia. Berdasarkan hasil indeks Modified Partition Coefficient (MPC) jumlah klaster yang optimal adalah empat klaster. Pada klaster 1 berisi 146 Kabupaten/Kota, klaster 2 berisi 97 Kabupaten/Kota, klaster 3 berisi 141 Kabupaten/Kota, dan klaster 4 berisi 130 Kabupaten/Kota.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132381683","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 Distribusi Poisson-Amarendra pada Data Frekuensi Klaim Asuransi Kendaraan Bermotor di Indonesia","authors":"Yusuf Fahrizal, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i1.7033","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.7033","url":null,"abstract":"Abstract. Insurance is an agreement between two or more parties where the insurer promises to the insured by receiving a premium to compensate the insured against loss, damage or loss of profits. There are lots of insurance services or products, one of the insurance services that is widely used is motor vehicle insurance. Vehicle insurance itself is a type of insurance that provides benefits in the form of compensation or damage to motorized vehicles. Data on the frequency of motor vehicle insurance claims are often excessively dispersed or over-dispersive. The mixed Poisson distribution is often used as an alternative method for modeling claim frequency data when overdispersion occurs. The Poisson-Amarendra distribution (PAD) was introduced by Shanker in 2016 as one of the mixed Poisson distributions. This thesis will discuss modeling the Poisson-Amarendra distribution (PAD) on motor vehicle insurance claim frequency data in Indonesia in 2013. The Poisson-Amarendra distribution (PAD) has been matched using maximum likelihood estimation for certain data sets to test its goodness to the Poisson distribution (PD), Poisson-Lindley distribution(PLD) and Poisson-Sujatha distribution (PSD). It was found that the Poisson-Amarendra (PAD) distribution provides a better fit than the PD, PLD and PSD for the 1967 thunderstorm X event data at Cape Kennedy, Florida written by Shaker (2016). The Poisson-Amarendra distribution is rarely used to model data sets when the data is over-dispersive. The Poisson-Amarendra distribution (PAD) is suitable for modeling data on the frequency of motor vehicle insurance claims in Indonesia in 2013. It is known that based on calculations with the Chi-square test that has been carried out the null hypothesis is accepted (H_0 is accepted) and it can be concluded that the data on the frequency of motor vehicle insurance claims at PT. X category 3 34 regions in 2013 came from populations with PAD distribution. \u0000Abstrak. Asuransi adalah suatu perjanjian antara dua pihak atau lebih dimana penanggung berjanji kepada tertanggung dengan menerima premi untuk mengganti kerugian tertanggung terhadap kerugian, kerusakan, atau kehilangan keuntungan. Ada banyak sekali layanan atau produk asuransi, salah satu layanan asuransi yang banyak digunakan adalah asuransi kendaraan bermotor. Asuransi kendaraan itu sendiri adalah jenis asuransi yang memberikan manfaat berupa pemberian ganti rugi atau kerusakan pada kendaraan bermotor. Data frekuensi klaim asuransi kendaaan bermotor sering tersebar secara berlebihan atau overdispersi. Distribusi campuran Poisson sering digunakan sebagai metode alternatif untuk pemodelan data frekuensi klaim ketika terjadi overdispersi.  Distribusi Poisson-Amarendra (PAD) diperkenalkan oleh Shanker pada tahun 2016 sebagai salah satu distribusi campuran Poisson. Pada skripsi ini akan dibahas mengenai pemodelan distribusi Poisson-Amarendra (PAD) pada data frekuensi klaim asuransi kendaraan bermotor di Indonesia pada tahun ","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125003721","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 Komposit Lognormal-Pareto pada Data Klaim Asuransi Harta Benda di Indonesia","authors":"Andrea Setia Nugraha, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i1.7030","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.7030","url":null,"abstract":"Abstract. This thesis discusses modeling the lognormal-Pareto composite distribution of property insurance claims data in Indonesia. In the composite model there is a threshold value that is calculated using the square root rule heuristic method. Parameter estimation for each distribution uses the maximum likelihood estimation method through the Newton-Raphson numerical method. The initial value for each parameter is obtained from the moment estimator for each distribution. The distribution fit test was carried out using the Kolmogorov-Smirnov fit test. The data used is secondary data from the insurance company PT. XYZ in 2017. The data contains large data on property insurance policyholder claims. The results of the application show that the big data on property insurance claims of PT. XYZ in 2017 comes from a population with a lognormal-Pareto composite distribution. \u0000Abstrak. Dalam skripsi ini dibahas pemodelan distribusi komposit lognormal-Pareto pada data klaim asuransi harta benda di Indonesia. Dalam model komposit terdapat nilai ambang batas yang dihitung menggunakan metode heuristik aturan akar kuadrat. Penaksiran parameter untuk masing-masing distribusinya menggunakan metode penaksiran kemungkinan maksimum melalui metode numerik Newton-Raphson. Nilai awal untuk masing-masing parameter didapat dari penaksir moment setiap distribusinya. Pengujian kecocokan distribusi dilakukan menggunakan uji kecocokan Kolmogorov-Smirnov. Data yang digunakan adalah data sekunder dari perusahaan asuransi PT. XYZ tahun 2017. Data tersebut berisi data besar klaim pemegang polis asuransi harta benda. Hasil penerapan menunjukan bahwa data besar klaim asuransi harta benda PT. XYZ tahun 2017 berasal dari populasi yang berdistribusi komposit lognormal-Pareto.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"330 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114025796","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 Resetting EWMA Scheme (RES) dalam Mengontrol Coefficient Variation (CV) Kadar Asam Lemak Bebas","authors":"Bella Novia Syabandina, Suliadi","doi":"10.29313/bcss.v3i1.6998","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.6998","url":null,"abstract":"Abstract. One of the tools commonly used in monitoring the production process (Quality Control) is a control chart. The chart that is commonly used is the Shewhart control chart with the assumption that the average (μ) and standard deviation (","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129958174","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 Poisson Bivariat pada Data Jumlah Gol Hasil Pertandingan Sepak Bola Liga 1 Indonesia Tahun 2018-2019","authors":"Alfan Siam Nuri, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i1.5755","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.5755","url":null,"abstract":"Abstract. On every continent in the world there are several prestigious league competitions that roll on and become a place to meet the best players, for example on the continent of Europe there are some of the best leagues. For example the English Premiere League (EPL) in England, La Liga in Spain, Serie A in Italy, the Bundes League in Germany and Ligue 1 in France. In Indonesia itself there is a league competition which is currently known as the Indonesian Super League (ISL) or the Indonesian League 1 which is the highest caste football competition between clubs in Indonesia which is participated in by 18 teams. There are several univariate and bivariate distributions that can be used to model data on the number of goals scored by football teams. One such bivariate distribution is the bivariate Poisson distribution. The maximum likelihood estimator method is used to estimate the parameters of the discrete distribution. While the distribution fit test to be used is the chi-square test. The data that will be used contains information on the number of goals for the home team and the number of goals for the away team in the 2018-2019 Indonesian Super League matches. The results of the application show that the bivariate Poisson distribution is suitable for modeling bivariate data on the number of goals for the home team and the away team for the 2018-2019 Indonesian Super League. \u0000Abstrak. Di setiap benua di dunia terdapat beberapa kompetisi liga bergengsi yang bergulir dan menjadi ajang bertemu pemain-pemain terbaik, misalnya benua Eropa terdapat beberapa liga terbaik. Contohnya English Premiere League (EPL) di Inggris, La Liga di Spanyol, Serie A di Italia, Bundes Liga di Jerman dan Ligue 1 di Francis. Di Indonesia sendiri terdapat kompetisi liga yang pada saat ini dikenal dengan sebutan Indonesian Super League (ISL) atau Liga 1 Indonesia yang merupakan kompetisi sepak bola kasta tertinggi antar klub di Indonesia yang diikuti oleh 18 tim. Terdapat beberapa distribusi univariat dan bivariat yang dapat digunakan untuk memodelkan data jumlah gol tim sepakbola. Salah satu distribusi bivariat tersebut adalah distribusi Poisson bivariat. Metode penaksir kemungkinan maksimum digunakan untuk menaksir parameter distribusi diskrit tersebut. Sedangkan uji kecocokan distribusi yang akan digunakan adalah uji chi-kuadrat. Data yang akan digunakan tersebut berisi informasi jumlah gol tim kandang dan jumlah gol tim tandang pertandingan Liga 1 Indonesia tahun 2018-2019. Hasil penerapan menunjukkan bahwa distribusi Poisson bivariat cocok untuk memodelkan data bivariat jumlah gol tim kandang dan tim tandang Liga 1 Indonesia tahun 2018-2019.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"33 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":"122143082","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":"Visualisasi Prediksi Remaining Useful Life Bearing Menggunakan Regresi Bayesian","authors":"Marcelia Mutiarani, Sutawanir Darwis","doi":"10.29313/bcss.v3i1.6134","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.6134","url":null,"abstract":"Abstract. Bearing element are prone to failure, which can cause economic losses and even fatalities. Prediction of the remaining age is utilized to see conditions that may occur in order to avoid dissatisfaction. The Bayesian method is a method for estimating parameter distributions that have high accuracy. This thesis aims to apply the estimated parameters of the least squares method and Bayesian regression model to predict Remaining Useful Life (RUL) bearings. The bearing index degradation was obtained using principal components through a dimension reduction process. Time domain features are reduced from the corresponding vibration signals to construct Health Indicators (HI). Bayesian regression index degradation was used to predict RUL. The data used is secondary data on accelerated degradation related to China's XJTU-SY. RUL prediction results were acquired at tp of 60 minutes. For the horizontal direction on the standard deviation feature, RUL prediction values were obtained with KT of 54 minutes and Bayesian of 11 minutes, while for the kurtosis factor feature, RUL prediction values were earned with KT of 46 minutes and Bayesian of 40 minutes. For the vertical direction, the peak value feature with KT is 57 minutes, and Bayesian is 28 minutes. The RUL graph shows that the prediction line has an up or down trend, indicating that predictions using KT bearing degradation are slower than those utilizing Bayesian. It can be concluded that Bayesian predictions are more accurate than KT because, using Bayesian RUL value predictions, the bearing degradation is smaller, meaning that bearing degradation can be predicted more quickly. Maintenance can be carried out immediately to reduce maintenance costs. \u0000Abstrak. Elemen bearing rentan terhadap kegagalan yang dapat menyebabkan kerugian secara ekonomi bahkan korban jiwa. Prediksi sisa usia digunakan untuk melihat kondisi kelayakan bearing guna menghindari terjadinya kegagalan. Metode Bayesian merupakan metode untuk mengestimasi parameter distribusi yang memiliki akurasi yang tinggi. Skripsi ini bertujuan untuk menerapkan estimasi parameter model regresi metode kuadrat terkecil dan Bayesian pada prediksi Remaining Useful Life (RUL) bearing. Indeks degradasi bearing diperoleh melalui proses reduksi dimensi menggunakan komponen utama. Fitur domain waktu di reduksi dari sinyal vibrasi bearing untuk membangun Health Indicator (HI). Regresi Bayesian indeks degradasi digunakan untuk memprediksi RUL. Data yang digunakan merupakan data sekunder akselerasi degradasi bearing XJTU-SY China. Didapatkan hasil prediksi RUL pada tp sebesar 60 menit, untuk arah horizontal pada fitur standar deviasi didapatkan nilai prediksi RUL dengan KT sebesar 54 menit dan Bayesian sebesar 11 menit sedangkan pada fitur faktor kurtosis didapatkan nilai prediksi RUL dengan KT sebesar 46 menit dan Bayesian sebesar 40 menit. Untuk arah vertikal pada fitur nilai puncak dengan KT sebesar 57 menit dan Bayesian sebesar 28 menit. Dilihat dar","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"186 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":"123356024","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 Hierarchical Clustering Multiscale Bootstrap untuk Pengelompokan Indikator Indeks Pembangunan Manusia Tahun 2021 di Jawa Barat","authors":"Sophia Annisa Faisal, N. Rifai","doi":"10.29313/bcss.v3i1.6327","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.6327","url":null,"abstract":"Abstract. Cluster analysis is a technique for grouping objects that have the same characteristics into one group and between different groups. In general there are two methods, namely hierarchical and non-hierarchical. The average linkage method is one of the methods in the hierarchical cluster analysis method that can be used to group data, one of which is the Human Development Index (HDI) data. This study uses HDI indicator data in West Java in 2021. The average linkage method only provides solutions based on a measure of proximity, so this study uses the multiscale bootstrap method to obtain the validity of the groups formed. There are four clusters formed by the average linkage method. Of the four groups formed, there is one valid cluster, namely the fourth cluster which consists of the group with the highest average HDI score, namely Bandung City, Bekasi City, and Depok City. \u0000Abstrak. Analisis cluster adalah teknik untuk mengelompokkan objek-objek yang memiliki karakteristik sama ke dalam satu kelompok dan antar kelompok berbeda. Secara umum terdapat dua metode yaitu hierarki dan non-hierarki. Metode average linkage merupakan salah satu metode pada analisis cluster metode hierarki yang dapat digunakan untuk mengelompokkan data, salah satunya yaitu data Indeks Pembangunan Manusia (IPM). Penelitian ini menggunakan data indikator IPM di Jawa Barat Tahun 2021. Metode average linkage hanya memberikan solusi berdasarkan ukuran kedekatan jarak, sehingga pada penelitian ini menggunakan metode multiscale bootstrap untuk memperoleh validitas dari kelompok yang terbentuk. Terdapat empat cluster yang terbentuk dengan metode average linkage. Dari keempat kelompok yang terbentuk, terdapat satu cluster yang valid yaitu cluster keempat yang terdiri dari kelompok dengan nilai rata-rata IPM tertinggi yaitu Kota Bandung, Kota Bekasi, dan Kota Depok.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"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":"132933360","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 Data Besar Kerugian Asuransi Kendaraan Bermotor di Indonesia Menggunakan Distribusi Weibull-Loss","authors":"Disa Fauzana, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i1.6846","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.6846","url":null,"abstract":"Abstract. Loss data modelling is one of important stages in predicting future premiums. Modelling distribution loss data with heavy tailed is prominent research topic. Weibull distribution is a heavy tailed distribution, so that it becomes the initial choice to be used in modeling heavy tailed losses in the finance and insurance. However, the Weibull distribution fails to modelling loss data with large loses. To overcome the problems that existed in the previous distributions, it is necessary to propose a new distribution. A new family of distributions is considered to model loss data for heavy tailed. One of the distributions included in the above distribution family is the three-parameter Weibull-Loss distribution. In this article the Weibull-Loss distribution will be applied to loss data on motor vehicle insurance losses in Indonesia. The procedures for modeling loss data using the Weibull-Loss distribution are: (1) formulating the research hypothesis, (2) estimating the parameters of the Weibull-Loss distribution using the maximum likelihood estimation method, (3) testing the fit of the distribution using the Kolmogorov-Smirnov method. The materials used are secondary data obtained from the insurance company PT. XY year 2014, the data contains partial loss data of motor vehicle insurance holders at Category 8 Region 2  which consists of the DKI Jakarta, West Java and Banten areas. The calculation results show the motor vehicle insurance loss data at the insurance company PT. XY year 2014 Category 8 Region 2 in Indonesia comes from a population with a Weibull-Loss distribution. \u0000Abstrak. Pemodelan data besar kerugian merupakan salah satu tahapan penting dalam memprediksi premi di masa depan. Pemodelan distribusi besar kerugian dengan heavy tailed (ekor tebal) adalah topik penelitian yang menonjol. Distribusi Weibull termasuk distribusi heavy tailed, sehingga menjadi pilihan awal untuk digunakan dalam memodelkan besar kerugian dengan heavy tailed di bidang keuangan dan asuransi. Namun demikian distribusi Weibull gagal untuk memodelkan data besar kerugian yang nilainya besar-besar. Untuk mengatasi masalah yang ada pada distribusi-distribusi sebelumnya, perlu diusulkan distribusi baru. Keluarga distribusi baru dipertimbangkan untuk memodelkan data besar kerugian untuk heavy tailed. Salah satu distribusi yang termasuk keluarga distribusi di atas adalah distribusi Weibull-Loss tiga parameter. Dalam artikel ini distribusi Weibull-Loss akan diterapkan pada data besar kerugian asuransi kendaraan bermotor di Indonesia. Prosedur pemodelan data besar kerugian menggunakan distribusi Weibull-Loss adalah: (1) merumuskan hipotesis penelitian, (2) menaksir parameter distribusi Weibull-Loss menggunakan metode penaksiran kemungkinan maksimum, (3) uji kecocokan distribusi menggunakan uji Kolmogorov-Smirnov. Bahan yang digunakan merupakan data sekunder yang diperoleh dari perusahaan asuransi PT. XY tahun 2014, data tersebut berisi data besar kerugian Partial Loss","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"122 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":"132127344","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 Data Hasil Pertandingan Sepak Bola Menggunakan Model Bradley-Terry","authors":"M. I. Fauzi, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i1.6074","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.6074","url":null,"abstract":"Abstract. This article discusses football match result data modelling using the Bradley-Terry model. Liga 1 Indonesia is the highest caste football competition between clubs in Indonesia which is currently being participated in by 18 teams. The Indonesian League was held for the first time in 1994 which was a merger between the previous major competitions, namely association (1931-1994) and Galatama (Main Football League 1979-1994). The Bradley-Terry model is one model that can be used to model the home team's chances of winning, drawing and losing in a soccer match. The parameters in the probability of winning, drawing and losing are estimated using the maximum likelihood estimation method. Therefore will be 20 parameters that is , , dan . The materials that will be used to apply the methods discussed in this thesis are secondary data recorded from PT Liga Indonesia. The estimated values of the probability of winning, drawing and losing model parameters that can be used to calculate the estimated value of the odds of winning, drawing and losing as the home team for one season. The results of the analysis show that around 74% of match results can be estimated correctly. \u0000Abstrak. Liga 1 Indonesia adalah kompetisi sepak bola kasta tertinggi antar klub di Indonesia yang pada saat ini diikuti oleh 18 tim. Liga Indonesia diselenggarakan pertama kali pada tahun 1994 yang merupakan penggabungan antara kompetisi besar sebelumnya, yaitu Perserikatan (1931-1994) dan Galatama (Liga Sepak Bola Utama 1979-1994). Model Bradley-Terry merupakan salah satu model yang dapat digunakan untuk memodelkan peluang menang, imbang, dan kalah tim tuan rumah dalam pertandingan sepak bola. Parameter-parameter yang ada pada model peluang menang, imbang dan kalah ditaksir dengan menggunakan metode penaksiran kemungkinan maksimum. Dengan demikian akan ada sebanyak 20 parameter yaitu y, v, dan phi 1, phi 2, ..., phi 18. Bahan yang akan digunakan untuk mengaplikasikan metode yang dibahas dalam skripsi ini adalah data sekunder hasil pencatatan yang diperoleh dari PT Liga Indonesia. Nilai-nilai taksiran parameter model peluang menang, imbang, dan kalah yang dapat digunakan untuk menghitung taksiran nilai peluang menang, imbang, dan kalah sebagai tim tuan rumah selama satu musim. Hasil analisis menunjukan bahwa sekitar 74% hasil pertandingan dapat di taksir dengan tepat. Nilai-nilai taksiran parameter model peluang menang, imbang, dan kalah yang dapat digunakan untuk menghitung taksiran nilai peluang menang, imbang, dan kalah sebagai tim tuan rumah selama satu musim. Hasil analisis menunjukan bahwa sekitar 74% hasil pertandingan dapat di taksir dengan tepat.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"65 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":"115819364","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":"Multivariate Statistical Process Control untuk Mendeteksi Kerusakan Bearing","authors":"Hani Nurhapilah, Sutawanir Darwis","doi":"10.29313/bcss.v3i1.6323","DOIUrl":"https://doi.org/10.29313/bcss.v3i1.6323","url":null,"abstract":"Abstract. Multivariate Statistical Process Control is intended to see the stability of a production process so that it becomes effective. This method was developed to detect bearing damage through the T2 Hotelling control chart. Out-of-control is used as a tool to detect bearing condition. The machine is a unit of various interrelated components, resulting in a series of movements. Bearings are important machine components, supporting the shaft to rotate without experiencing excessive friction. Bearing damage detection is important to ensure optimal performance in an industry. The data used is secondary data from the results of bearing vibration experimental tests from the FEMTO-ST Institute. The vibration data used consists of seven bearings with various conditions, each of which consists of two directions, namely horizontal and vertical. Then, the variance features and RMS bearing1_2 and bearing1_4 are taken for data processing. Furthermore, the data is divided into two phases, namely phase-I which is assumed to be the bearing under normal conditions and phase-II which is assumed to be the bearing to be tested. After that, the calculation of the upper control limit (UCL) is carried out in phase-I, and is used in phase-II to detect bearing conditions. The results of tests carried out in phase-II show that there are points that are out-of-control so that it can be said that there are abnormalities in the bearings which may indicate that there are bearings that are abnormal but not necessarily damaged. \u0000Abstrak. Multivariate Statistical Process Control diperuntukkan untuk melihat stabilitas proses dari suatu produksi agar menjadi efektif. Metode ini dikembangkan untuk mendeteksi kerusakan bearing melalui diagram kendali T2 Hotelling. Out-of-control digunakan sebagai alat untuk mendeteksi kondisi bearing. Mesin merupakan kesatuan dari berbagai komponen yang saling berkaitan, sehingga menghasilkan suatu rangkaian gerakan. Bearing merupakan komponen mesin yang penting, menumpu agar poros dapat berputar tanpa mengalami gesekan yang berlebihan. Deteksi kerusakan bearing penting dilakukan untuk menjamin performa optimal sebuah industri. Data yang digunakan adalah data sekunder hasil uji eksperimen vibrasi bearing dari FEMTO- ST Institute. Data vibrasi yang digunakan terdiri dari tujuh bearing berbagai kondisi yang masing-masingnya terdiri dari dua arah yaitu horizontal dan vertikal. Kemudian, diambil fitur variansi dan RMS bearing1_2 dan bearing1_4 untuk pengolahan data. Selanjutnya, data tersebut dibagi menjadi dua fase yaitu fase-I yang diasumsikan sebagai bearing kondisi normal dan fase-II diasumsikan sebagai bearing yang akan diuji. Setelah itu, dilakukan perhitungan batas kendali atas (BKA) pada fase-I, dan digunakan pada fase-II untuk mendeteksi kondisi bearing. Hasil pengujian yang dilakukan pada fase-II menunjukkan bahwa terdapat titik-titik yang berada di luar batas kendali (out-of-control) sehingga dapat dikatakan bahwa terdapat ketidaknormala","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"37 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":"122218532","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}