Bandung Conference Series: Statistics最新文献

{"title":"Optimasi Parameter Support Vector Machine menggunakan Particle Swarm Optimization untuk Bearing Fault Diagnosis","authors":"Rizki Aulia Hawa, Sutawanir Darwis","doi":"10.29313/bcss.v3i2.8975","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8975","url":null,"abstract":"Abstract. To assist industrial activities in generating power, humans create components that are developed through machines. One of the key elements that plays an important role in the process of a shaft rotation movement on the machine is the bearing. Given their widespread use, bearing vibration components can predict machine breakdowns or forecast indicators by leveraging forecasting models to diagnose bearings before failure occurs. One of the popular methods used for forecasting machine failure is the Support Vector Machine (SVM) which was introduced by Cortes and Vapnik in 1995 to overcome the problem of dividing two conflicting groups when demonstrating superiority in nonlinear small sample pattern recognition. SVM is optimized by incorporating Particle Swarm Optimization (PSO). The advantage of the PSO method is that it is able to produce accuracy values that are more precise and accurate than other mathematical algorithms and heuristic techniques (Pambudi, Wihandika, & Putri, 2019). \u0000Untuk membantu aktivitas industri dalam menghasilkan tenaga, manusia menciptakan komponen yang dikembangkan melalui mesin. Salah satu bagian elemen kunci yang berperan penting dalam proses suatu gerakan putaran poros pada mesin adalah bearing. Mengingat penggunaannya yang luas, komponen getaran bearing dapat memprediksi kerusakan mesin atau indikator peramalan dengan memanfaatkan model peramalan untuk mendiagnosis bearing sebelum terjadinya kerusakan. Salah satu metode yang populer digunakan untuk peramalan pada kerusakan mesin adalah Support Vector Machine (SVM) yang diperkenalkan oleh Cortes dan Vapnik pada tahun 1995 untuk mengatasi masalah pembagian dua kelompok yang saling bertentangan saat menunjukkan keunggulan dalam pengenalan pola sampel kecil nonlinier.SVM dioptimalkan dengan menggabungkan Particle Swarm Optimization (PSO). Kelebihan dari metode PSO ini adalah mampu menghasilkan nilai akurasi lebih tepat dan cermat daripada algoritma matematika dan teknik heuristik yang lain (Pambudi, Wihandika, & Putri, 2019). Pada Analisis data bearing CWRU, hasil akurasi yang didapat dengan menggunakan algoritma SVM lebih unggul daripada hasil akurasi yang didapat dengan menggunakan PSO.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125762630","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":"Perbandingan Metode Seasonal ARIMA dan Metode Fuzzy Time Series-Markov Pada Prediksi Inflasi di Indonesia","authors":"Rafiq Thariq Ahsan, N. Rifai","doi":"10.29313/bcss.v3i2.9138","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9138","url":null,"abstract":"Abstract. Forecasting is the process of estimating something that will come based on existing data that will later be analyzed. Seasonal ARIMA is one of the methods in forecasting time series data that contains seasonal elements. Seasonal ARIMA is an emerging extension of the ARIMA method that makes it easier to forecast seasonally patterned data. In addition, a method that is often used for forecasting is the Fuzzy Time Series method. This study aims to forecast the future value of inflation in Indonesia using the best model from the comparison of Seasonal ARIMA and Fuzzy Time Series. The forecasting results will be compared through the error rate seen through Mean Absolute Percentage Error (MAPE). The data used is Indonesian general inflation data from January 2010 to September 2019. The results showed that the MAPE of the Seasonal ARIMA  and Fuzzy Time Series-markov methods were 24.999% and 12.273%. This shows that Fuzzy Time Series-markov is more suitable for forecasting the value of inflation in Indonesia because it provides a smaller error value. \u0000Abstrak. Peramalan (forecasting) adalah proses memperkirakan sesuatu yang akan datang berdasarkan data yang sudah ada yang nantinya akan dianalisis. Seasonal ARIMA adalah salah satu metode dalam peramalan (forecasting) data deret waktu yang mengandung unsur musiman. Seasonal ARIMA merupakan perluasan yang muncul dari metode ARIMA yang memudahkan untuk melakukan peramalan data yang berpola musiman. Selain itu, adapun metode yang sering dipakai untuk peramalan adalah metode Fuzzy Time Series. Penelitian ini bertujuan untuk meramalkan nilai inflasi di Indonesia kedepan memakai model terbaik dari hasil perbandingan Seasonal ARIMA dan Fuzzy Time Series. Hasil peramalan akan dibandingkan melalui tingkat kesalahan yang dilihat melalui Mean Absolute Percentage Error (MAPE). Data yang digunakan merupakan data inflasi umum Indonesia dari Januari 2010 sampai dengan September 2019. Hasil Penelitian menunjukkan bahwa MAPE dari metode Seasonal ARIMA  dan Fuzzy Time Series-markov adalah sebesar 24,999% dan 12,273%. Hal ini menunjukkan bahwa Fuzzy Time Series-markov lebih cocok untuk peramalan nilai inflasi di Indonesia karena memberikan nilai error yang lebih kecil.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132988595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Mediasi dalam PLS-SEM untuk Pemodelan Kepuasan Pemustaka pada UPT Perpustakaan Universitas Islam Bandung","authors":"Irham Fatin Fadhilah, Lisnur Wachidah","doi":"10.29313/bcss.v3i2.8864","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8864","url":null,"abstract":"Abstract. In the SEM method, multivariate analysis shows how a series of causal interactions are represented in a path diagram. Mediation analysis in PLS-SEM has the ability to interpret the indirect effect of each exogenous latent variable on endogenous latent variable in the model. This study aims to investigate the role of service quality intermediaries in the relationship between employee competence, library collections, library layout, and user satisfaction in the model analyzed using mediation analysis in PLS-SEM. In this study, primary data was used from active undergraduate students at the Bandung Islamic University who visited the library in 2022. The data includes user satisfaction as an endogenous latent variable ( ), employee competency as the 1st exogenous latent variable ( ), library collection as the 2nd exogenous latent variable ( ), library layout as the 3rd exogenous latent variable ( ), and service quality as a mediating variable or mediator ( ). Based on the research that has been done, it can be concluded that employee competence has a direct effect on service quality and service quality has a direct effect on user satisfaction. The results of the mediation analysis in PLS show that employee competence has a mediating effect significantly at the 5% level with the mediation model is full mediation and the magnitude of the parameter coefficient is 0,510 which means there is positive indirect effect. Where are the feasibility results of the model ( ) is 0,960 which means that the model used can explain the information contained in the research data by 96%. \u0000Abstrak. Dalam metode SEM, analisis multivariat menunjukkan bagaimana serangkaian interaksi kausal direpresentasikan dalam diagram jalur. Analisis mediasi dalam PLS-SEM memiliki kemampuan untuk menginterpretasikan pengaruh tidak langsung antara masing-masing variabel laten eksogen terhadap variabel laten endogen dalam model. Penelitian ini bertujuan untuk menyelidiki peran perantara kualitas pelayanan pada hubungan antara kompetensi pegawai, koleksi perpustakaan, tata ruang perpustakaan, dan kepuasan pemustaka dalam model yang dianalisis menggunakan analisis mediasi dalam PLS-SEM. Dalam penelitian ini menggunakan data primer dari mahasiswa aktif program sarjana Universitas Islam Bandung yang berkunjung ke perpustakaan pada tahun 2022. Data meliputi kepuasan pemustaka sebagai variabel laten endogen ( ), kompetensi pegawai sebagai variabel laten eksogen ke-1 ( ), koleksi perpustakaan sebagai variabel laten eksogen ke-2 ( ), tata ruang perpustakaan sebagai variabel laten eksogen ke-3 ( ), dan kualitas pelayanan sebagai variabel mediasi atau mediator ( ). Berdasarkan penelitian yang telah dilakukan dapat disimpulkan bahwa kompetensi pegawai berpengaruh langsung terhadap kualitas pelayanan dan kualitas pelayanan berpengaruh langsung terhadap kepuasan pemustaka. Untuk hasil analisis mediasi dalam PLS menunjukkan bahwa kompetensi pegawai terjadi efek mediasi secara signifikan pada t","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124540614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Analisis Konjoin untuk Mengukur Preferensi Wisatawan di Panti Tanjung Kerasak Kabupaten Bangka","authors":"Sabda Iman Dani, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i2.9398","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9398","url":null,"abstract":"Abstract. Tourism activities have now become a necessity for people who are important to increase productivity in their daily lives. Bangka Belitung Province is an archipelago province that has a long coastline so that it has so many and varied beach tourism destinations. But it is very unfortunate that of the many beach tourism destinations owned in the South Bangka district, it is not maximized in the processing of beach tourism so that the attractiveness of visitors is less enthusiastic. It is important to know people's preferences for what kind of tourist destination they want, so that managers can maximize tourism management strategies that can attract many tourists. According to Ghozali (2011) in Saputra S (2020), conjoin analysis is used to determine how respondents perceive an object that has certain characteristics. In marketing research, conjoin analysis is used to determine how consumer preferences for various product designs. From the results of research with conjoin analysis, it shows that the stimuli that respondents are interested in are the existence of water tourism attractions, the existence of shelters and meeting points, good road networks, the existence of travel agents, the existence of community participation, and the existence of a security division, but from this the factor that has the highest utility value is the good road network factor followed in order by the community participation factor, the existence of a security division factor. \u0000Abstrak. Kegiatan pariwisata saat ini telah menjadi kebutuhan bagi masyarakat yang penting untuk meningkatkan produktifitas dalam kesehariannya. Provinsi Bangka Belitung merupakan provinsi kepulauan yang memiliki garis pantai yang panjang sehingga memiliki destinasi wiasata pantai yang begitu banyak dan beragam. Namun sangat disayangakan dari sekian banyak destinasi wisata pantai yang dimiliki di kabupaten Bangka Selatan tidak dimaksimalkan dalam pengolahan wisata pantai sehingga daya tarik pengunjung kurang antusias. Penting untuk mengetahui preferensi masyarakat terhadap destinasi wisata seperti apa yang diinginkan, sehingga pengelola dapat memaksimalkan strategi pengelolaan pariwisata yang dapat mengundang banyak wisatawan. Menurut Ghozali (2011) dalam Saputra S (2020), analisis konjoin digunakan untuk mengetahui bagaimana persepsi responden terhadap suatu objek yang memiliki karakteristik tertentu. Dalam riset pemasaran, analisis konjoin digunakan untuk mengetahui bagaimana preferensi konsumen terhadap berbagai desain produk. Dari hasil penelitian dengan analisis konjoin menunjukan stimuli yang diminati responden adalah adanya antraksi wisata air, adanya shelter dan meeting poin, jaringan jalan baik, adanya agen perjalanan wisata, adanya partisipasi masyarakat, dan adanya divisi ke amanan, namun dari hal tersebut faktor yang memiliki nilai kegunaan tertinggi adalah faktor jaringan jalan baik di ikuti secara berurutan adanya faktor partisipasi masyarakat, adanya faktor divisi keaman","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122104267","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":"Pengaruh Infrastruktur dan Inovasi Hybrid Learning terhadap Kepuasan Mahasiswa Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Islam Bandung Angkatan 2020-2021","authors":"M Dziqri Nur Rohiim, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i2.8236","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8236","url":null,"abstract":"Abstract. The COVID-19 pandemic has an impact on all aspects of the world, including education in Indonesia. The Indonesian government together with the Ministry of Education and Culture then decides to implement a limited Face-to-Face Learning, namely Hybrid Learning. This makes University of Islam Bandung carry out Limited Face-to-Face Learnig for the even semester of the 2021/2022 academic year, the learning applied is hybrid, online, and offline. This certainly affects the satisfaction of students who are taking Hybrid Learning for the first time. Therefore, the researcher wants to examine the “Effect of Hybrid Learning Infrastructure and Innovation on Student Satisfaction of the Faculity of Mathematics and Science University of Islam Bandung Class of 2020-2021”. The population of this research is FMIPA student class 2020-2021 with a sample of 100 students. The analysis technique in this study is Multiple Linear Regression Analysis by testing several classical intruments and assumptions. Based on the results of the study, it can be concluded that infrastructure has no significant effect on satisfaction, innovation does have a significant effect on satisfaction. Obtained a value of 14,1% diversity of satisfaction can be explained by infrastructure and innovation. Meanwhile (100% - 14,1% = 86,9%) the rest is explained by other reasons outside the model. \u0000Abstrak. Pandemi COVID-19 berdampak terhadap segala aspek di dunia, termasuk pada pendidikan di Indonesia. Pemerintah Indonesia bersama dengan Kementrian Pendidikan dan Kebudayaan kemudian  memutuskan untuk menerapkan PTM terbatas, yaitu Hybrid Learning. Hal itu membuat Unversitas Islam Bandung melaksanakan Pembelajaran Tatap Muka Terbatas untuk semester genap tahun ajaran 2021/2022, pembelajaran yang  diterapkan yaitu hybrid, daring, dan luring. Hal ini tentunya berpengaruh terhadap kepuasan mahasiswa yang baru pertama kali mengikuti Hybrid Learning. Maka dari itu, peneliti ingin meneliti mengenai “Pengaruh Infrastruktur dan Inovasi Hybrid Learning terhadap Kepuasan Mahasiswa Fakultas MIPA Unisba Angkatan 2020-2021”. Populasi penelitian ini adalah mahasiswa FMIPA angkatan 2020-2021 dengan sampel sebanyak 97 mahasiswa. Teknik analisis dalam penelitian ini adalah Analisis Regresi Linear Berganda dengan menguji beberapa uji Instrumen dan Asumsi Klasik. Berdasarkan hasil penelitian, dapat disimpulkan bahwa infrastruktur tidak berpengaruh secara signifikan terhadap kepuasan, inovasi benar-benar berpengaruh secara signifikan terhadap Kepuasan. Didapat nilai 14,1% keragaman kepuasan dapat dijelaskan oleh infrastruktur dan inovasi. Sedangkan (100% - 14,1% = 86,9%) sisanya dijelaskan oleh sebab-sebab lain di luar model.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"50 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126353234","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 Zero Modified Poisson pada Data Jumlah Gol Tim Tandang Liga Sepak Bola Indonesia","authors":"Maulana Ilham Pratama, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i2.9477","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9477","url":null,"abstract":"Abstract. The Indonesian League, which is currently known as the Indonesia Super League (ISL) or Liga 1, is the highest caste football competition between clubs in Indonesia. The Indonesian League was held for the first time in 1994 (the competition system was divided into 2 regions) which was a merger between the previous major competitions. Many researchers from various countries have carried out the application of distributions to football sports data cases to find out what odds distributions are suitable for modeling the number of goals scored in home and away games in a league. Distributions that can be used to model data on the number of goals scored in soccer matches include the Poisson distribution, negative binomial, Poisson-Lindley, and Zero-Inflated Poisson. In a football match, it is generally difficult for the away team to score goals. One of the discrete distributions for the case of the Poisson distribution when there are many zeros is the Zero-Modified Poisson distribution. In this thesis, the ZMP distribution will be applied to the data on the number of goals scored by the away team in the Indonesian Football League. 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. As the application material, data on the results of the 2017-2018 Indonesian League football matches will be used. Based on the results of an analysis of the application of the Zero-Modified Poisson distribution to data on the number of goals scored by the Indonesian League 1 away team in 2017-2018, it can be concluded that the Zero-Modified Poisson distribution is suitable for modeling the frequency data of the number of goals scored by the Indonesian League 1 away team in 2017-2018. \u0000Abstrak. Liga Indonesia yang pada saat ini dikenal dengan Indonesia Super League (ISL) atau liga 1 adalah kompetisi sepak bola kasta tertinggi antar klub di Indonesia. Liga Indonesia diselenggarakan pertama kali pada tahun 1994 (sistem kompetisi dibagi 2 wilayah) yang merupakan penggabungan antara kompetisi besar sebelumnya. Penerapan distribusi pada kasus data olahraga sepak bola sudah banyak dilakukan oleh peneliti-peneliti dari berbagai negara untuk mengetahui distribusi peluang apa yang cocok untuk memodelkan jumlah gol dalam pertandingan kandang dan tandang dalam suatu Liga. Distribusi yang bisa digunakan  untuk memodelkan data jumlah gol hasil pertandingan sepak bola diantaranya adalah distribusi Poisson, binomial negatif, Poisson-Lindley, dan Zero-Inflated Poisson. Dalam suatu pertandingan sepakbola, umumnya tim tandang selalu kesulitan untuk mencetak gol. Salah satu distribusi diskrit untuk kasus distribusi Poisson ketika nilai nol-nya banyak adalah distribusi Zero-Modified Poisson. Dalam skripsi ini akan diterapkan distribusi ZMP pada data jumlah gol tim tandang Liga sepak bola Indonesia. Metode penaksir kemungkinan maksimum digunakan untuk menaksir paramete","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121736631","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 Metode Fuzzy Time Series Stevenson-Porter pada Nilai Peramalan Ekspor Non-Migas di Indonesia","authors":"Muhammad Rofiq Firdaus, Suwanda","doi":"10.29313/bcss.v3i2.9512","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9512","url":null,"abstract":"Abstract. Forecasting is the science of predicting events in the future, There are several kinds of methods used to project forecasting, since Zadeh's pioneering work in 1965, Fuzzy set theory has been applied to various fields. including Fuzzy Time Series, Fuzzy Time Series methods have been proven to improve classical forecasting methods such as handling data fluctuations, inappropriate environments, subjectivity uncertainty in data. By having the privilege of not requiring the fulfillment of special assumptions. This method was developed by Meredith Stevenson and John E. Porter. This research uses the \"Fuzzy Time Series Algorithm Using Percentage Change as the Universe of Discourse\" forecasting method proposed by Stevenson and Porter. The data component required for research using this method is trend data. In its application, research using Fuzzy Time Series Stevenson Porter forecasting results in a forecasting value of 276,193.25 million US dollars with the calculation of the error value using MAPE getting a result of 36.17% for the Stevenson Porter fuzzy time series method in modeling Indonesia's non-oil and gas export forecasting. \u0000Abstrak. Permalan (forecasting) adalah ilmu pengetahuan dalam memprediksi peristiwa pada masa yang akan dating, Terdapat beberapa macam metode yang digunakan untuk memproyeksikan peramalan, sejak karya perintis Zadeh pada tahun 1965, teori himpunan Fuzzy telah diterapkan kedalam berbagai bidang. diantaranya Fuzzy Time Series, metode Fuzzy Time Series telah terbukti dapat memperbaiki metode peramalan klasik seperti menangani fluktuasi data, lingkungan yang tidak tepat, ketidakpastian subjektivitas dalam data. Dengan memiliki keistimewaan tidak memutuhkan pemenuhan asumsi khusus. Metode ini salah satunya dikembangkan Meredith Stevenson dan John E.Porter. Penelitian ini menggunakan metode peramalan Algoritma “Fuzzy Time Series Menggunakan Perubahan Persentase Sebagai Universe of  Discourse” yang diusulkan oleh Stevenson dan Porter. Komponen data yang diperlukan untuk penelitian menggunakan metode ini berupa data yang bersifat trend. Pada penerapannya penelitian menggunakan peramalan Fuzzy Time Series Stevenson Porter ini mendapatkan hasil nilai peramalan sebesar 276.193,25 juta US$ dengan perhitungan nilai error menggunakan MAPE mendapatkan hasil sebesar 36,17% untuk metode fuzzy time series Stevenson Porter pada pemodelan peramalan ekspor nonmigas Indonesia.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116513910","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 Geographically Weighted Generalized Poisson Regression (GWGPR) pada Jumlah Kematian Ibu Hamil di Jawa Barat Tahun 2021","authors":"Panji Lokajaya Arifa, Nur Azizah Komara Rifai","doi":"10.29313/bcss.v3i2.9459","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9459","url":null,"abstract":"Abstract. One method to overcome overdispersion or underdispersion is Generalized Poisson Regression (GPR). The development of GPR that takes into account spatial factors in the form of lattitude and longitude coordinates is Geographically Weighted Generalized Poisson Regression (GWGPR) which produces parameter estimators that are local to each observation location. The number of maternal deaths is the number of women who die during their pregnancy. In this study, the GWGPR method was applied to model the number of maternal deaths in West Java Province. The most pregnant women deaths occurred in Karawang Regency with 57 deaths and the average of pregnant women deaths in West Java was 17.04 deaths with a high variance of 122.2678. Modeling with the GWGPR method has different parameter estimation values for each district/city and shows that the factors that have a significant effect on the number of maternal deaths in all districts/cities in West Java are the percentage of pregnant women who have had K4 visits (X1), the percentage of obstetric complications (X2), the number of poor people (X5) and the percentage of PHBS households (X6). The mapping performed from the GWGPR model produces 3 groups of districts/cities in West Java that have similar variables that have a significant effect on the number of maternal deaths. \u0000Abstrak. Salah satu metode untuk mengatasi overdispersi atau underdispersi adalah Generalized Poisson Regression (GPR). Pengembangan GPR yang memperhitungkan faktor spasial berupa koordinat lattitude dan longitude  adalah Geographically Weighted Generalized Poisson Regression (GWGPR) yang menghasilkan penaksir parameter yang bersifat lokal untuk setiap lokasi pengamatan. Jumlah kematian ibu hamil adalah banyaknya perempuan yang meninggal ketika dalam masa kehamilannya. Dalam penelitian diterapkan metode GWGPR untuk melakukan pemodelan terhadap jumlah kematian ibu hamil di Provinsi Jawa Barat. Kematian ibu hamil terbanyak terjadi di  Kabupaten Karawang dengan 57 kematian dan rata-rata dari kematian ibu hamil di Jawa Barat sebesar 17,04 kematian dengan variansi yang tinggi yaitu 122,2678. Pemodelan dengan metode GWGPR memiliki nilai estimasi parameter yang berbeda untuk setiap kabupaten/kota dan menunjukkan bahwa faktor-faktor yang berpengaruh signifikan terhadap jumlah kematian ibu hamil di semua kabupaten/kota di Jawa barat adalah persentase ibu hamil yang pernah melakukan kunjungan K4 (X1), persentase komplikasi kebidanan (X2), jumlah penduduk miskin (X5) dan persentase rumah tangga PHBS (X6). Pemetaan yang dilakukan dari model GWGPR menghasilkan 3 kelompok wilayah kabupaten/kota di Jawa Barat yang memiliki kesamaan variabel yang berpengaruh signifikan terhadap jumlah kematian ibu hamil.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124991006","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 Inverse Gaussian pada Data Besar Klaim Asuransi Kendaraan Bermotor di Indonesia","authors":"Fauzia Rahmayanti, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i2.9456","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9456","url":null,"abstract":"Abstract. Insurance is an agreement between the guaranteeing party and the guaranteed party where the guaranteeing party agrees with the guaranteed party to receive a premium as a replacement for losses, which will later be experienced due to events that have not yet occurred. Insurance is divided into two parts, namely general insurance and life insurance. One product of loss insurance is motor vehicle insurance. In several previous studies, there are many distributions that have been applied to insurance data (claims big data). Insurance and economic data are often positive and the distribution is usually skewed to the right. One of the most well-known parametric models with distributions that are usually skewed to the right is the inverse Gaussian which is a family of two-parameter distributions namely µ and λ. The material used in this thesis is big data on partial loss claims for motor vehicle insurance insurance company PT. ABC in 2019 Category 1 Region 2. The results of applying the inverse Gaussian distribution show that the large data for Category 1 motor vehicle insurance claims for Region 2 come from populations with an inverse Gaussian distribution. \u0000Abstrak. Asuransi merupakan persetujuan antara pihak yang menjamin dengan pihak yang dijamin di mana pihak yang menjamin bersepakat kepada pihak yang dijamin untuk menerima premi sebagai pengganti kerugian, yang nantinya akan dialami karena kejadian yang belum terjadi. Asuransi terbagi kedalam dua bagian yaitu asuransi kerugian (asuransi umum) dan asuransi jiwa. Salah satu produk dari asuransi kerugian adalah asuransi kendaraan bermotor. Pada beberapa penelitian terdahulu, terdapat banyak distribusi yang telah diterapkan pada data asuransi (data besar klaim) Data asuransi dan ekonomi seringkali positif dan distribusinya biasanya miring ke kanan. Salah satu model parametrik yang paling terkenal dengan distribusi yang biasanya miring ke kanan adalah inverse Gaussian yang merupakan keluarga distribusi dua parameter yakni  dan . Bahan yang digunakan dalam skripsi ini adalah data besar klaim partial loss asuransi kendaraan bermotor perusahaan asuransi PT. ABC tahun 2019 Kategori 1 Wilayah 2. Hasil penerapan distribusi inverse Gaussian menunjukan data besar klaim asuransi kendaraan bermotor Kategori 1 untuk Wilayah 2 berasal dari populasi yang berdistribusi inverse Gaussian.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128409777","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 Bivariat Menggunakan Copula Frank pada Jumlah Gol Hasil Pertandingan Liga 1 Indonesia 2019","authors":"Naufal Fajar, Aceng Komarudin Mutaqin","doi":"10.29313/bcss.v3i2.9463","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9463","url":null,"abstract":"Abstract. Indonesia is one of the countries that always organizes soccer competitions. Liga 1 is an Indonesian football league that brings together 18 of the best soccer teams from Indonesia. Each team played 34 times on a home-away system. The purpose of this thesis is to improve the quality of Indonesian football and mark the gradual league system of Indonesian football at a competitive level. Many studies have been conducted by researchers in various countries to process data on the number of goals scored in soccer matches for home and away teams from a soccer league. Joel Liden (2016) discusses the Copula Frank distribution followed by the marginal distribution, namely the Poisson distribution and the negative Binomial distribution, a distribution for data from discrete random variables. This thesis will discuss the application of the Copula Frank distribution to data on the number of goals for home and away teams from the highest caste Indonesian football league matches for 2018-2019 and to find out whether the Copula Frank distribution is a suitable opportunity distribution for modeling the case of the number of goals data. the. The distribution fit test used is the Chi-square test. As research material, secondary data from recording results obtained from PSSI.com will be used and presented as data on the results of the 2018-2019 Indonesian League 1 football match. 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. As an application material, secondary data will be used as a result of recording obtained from rsssf.com and flashscore.com. 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 League 1 matches. The results of the application show that the bivariate Poisson distribution using Copula Frank is not suitable for modeling bivariate data on the number of goals for the home and away team in the Indonesian League 1 in 2018-2019. \u0000Abstrak. Indonesia merupakan salah satu negara yang selalu menyelenggarakan kompetisi sepak bola. Liga 1 merupakan liga sepak bola Indonesia yang mempertemukan 18 tim sepak bola terbaik dari Indonesia. Masing-masing tim bertanding sebanyak 34 kali dengan sistem kandang-tandang. Tujuan dibuat skripsi ini untuk meningkatkan kualitas sepak bola Indonesia dan menandai sistem liga bertahap sepak bola Indonesia di tingkat kompetitif. Sudah banyak penelitian yang dilakukan oleh para peneliti di berbagai negara untuk mengolah data jumlah gol hasil pertandingan sepak bola untuk tim kandang dan tim tandang dari suatu liga sepak bola. Joel Liden (2016) membahas distribusi Copula Frank diikuti oleh distribusi marjinal yaitu distribusi Poisson dan distribusi Binomial negatif, suatu distribusi untuk data dari peubah acak diskrit. Dalam Skripsi ini akan dibahas mengenai penerapan distribusi Copula Frank","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124539141","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}