Bandung Conference Series: Statistics最新文献

{"title":"Penerapan Analisis K-Medoids Cluster untuk Mengelompokkan Wilayah di Provinsi Jawa Barat Berdasarkan Fasilitas Kesehatan Tahun 2021","authors":"Elly Nur Fitriyani, Anneke Iswani Achmad","doi":"10.29313/bcss.v3i2.8080","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8080","url":null,"abstract":"Abstract. The cluster method is a method in multivariate analysis for data that has many variables, its function is to group objects based on their characteristics. The cluster method is divided into two, namely the hierarchical method and the non-hierarchical partitioning method. The non-hierarchical partitioning method is a method where the objects in the data are grouped into k clusters with k that have been determined by the researcher. A non-hierarchical partitioning cluster method similar to K-means is K-medoids. K-medoids clustering is a modification of K-means, where K-medoids is present to overcome deficiencies in the K-means clustering algorithm, because K-means is sensitive to outliers. Therefore, this thesis will discuss the K-medoids cluster to classify 27 regions in West Java Province in 2021 based on health facilities using the K-medoids cluster, because the data used contains outliers. The data used includes data on health facilities in the form of hospitals, polyclinic maternity hospitals, health centers, auxiliary health centers, and pharmacies. By taking 3 clusters, it is found that, for cluster 1 there are 6 areas with complete health facilities, for cluster 3 there are 11 areas with moderate health facilities and for cluster 2 there are 10 areas with incomplete health facilities. \u0000Abstrak. Metode cluster merupakan salah satu metode dalam analisis multivariat untuk data yang mempunyai variabel banyak, berfungsi untuk mengelompokan objek-objek berdasarkan karakteristik yang dimilikinya. Metode cluster terbagi menjadi dua, yaitu metode hierarki dan metode non hierarki partitioning. Metode non hierarki partitioning merupakan metode dimana objek-objek dalam data dikelompokan ke dalam k cluster dengan k yang telah ditentukan oleh peneliti. Metode cluster non hierarki partitioning yang mirip dengan K-means, adalah K-medoids. K-medoids clustering merupakan modifikasi dari K-means, dimana K-medoids hadir untuk mengatasi kekurangan pada algoritma K-means clustering, karena K-means sensitif terhadap pencilan. Oleh karena itu, dalam skripsi ini akan membahas K-medoids cluster untuk mengelompokkan 27 wilayah di Provinsi Jawa Barat tahun 2021 berdasarkan fasilitas kesehatan menggunakan K-medoids cluster, karena dalam data yang dipakai terdapat pencilan. Data yang digunakan meliputi data fasilitas kesehatan berupa rumah sakit, rumah sakit bersalin poliklinik, puskesmas, puskesmas pembantu, dan apotek. Dengan mengambil 3 cluster diperoleh bahwa, untuk cluster 1 terdapat 6 wilayah dengan fasilitas kesehatan yang lengkap, cluster 3 terdapat 11 wilayah dengan fasilitas kesehatan yang sedang dan cluster 2 terdapat 10 wilayah dengan fasilitas kesehatan yang kurang lengkap.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126944978","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 Non-Hierarchical Clustering K-Means untuk Pengelompokkan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Faktor Produksi Padi Tahun 2021","authors":"Syahla Anisah, Lisnur Wachidah","doi":"10.29313/bcss.v3i2.8148","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8148","url":null,"abstract":"Abstract. Rice production in districts / cities in West Java often differences between districts / cities. The West Java regional government needs to conduct a study to determine the rice production groups in West Java Districts / Cities in order to meet the food needs of the people of West Java through policies related to increasing rice production by grouping districts / cities based on rice production. Rice production factors include planting area, harvest area, and productivity. Rice production groups are determined into 3 categories, namely low production, medium production, and high production. The grouping of districts/cities based on rice production uses non-hierarchical cluster analysis using the K-Means method. This study uses West Java rice production data in 2021. Based on the results of the study, three clusters were obtained, namely cluster 1 showing areas with low rice production with the characteristics of low planting area, low harvest area, and low productivity containing 11 districts/cities. Cluster 2 shows areas with medium rice production with the characteristics of low planting area, low harvest area, and high productivity containing 7 districts/cities. Cluster 3 shows areas with high rice production with high planting area, high harvest area, and high productivity containing 9 districts/cities. \u0000Abstrak. Produksi padi pada kabupaten/kota di Jawa Barat sering terjadi perbedaan antar kabupaten/kota. Pemerintah daerah Jawa Barat perlu melakukan sebuah kajian untuk mengetahui kelompok produksi padi di Kabupaten/Kota Jawa Barat agar dapat terpenuhinya kebutuhan pangan masyarakat Jawa Barat melalui kebijakan yang berhubungan dengan peningkatan produksi padi dengan cara mengelompokkan kabupaten/kota berdasarkan produksi padi. Faktor produksi padi diantaranya adalah luas tanam, luas panen, dan produktivitas. Kelompok produksi padi ditentukan menjadi 3 kategori yaitu produksi rendah, produksi sedang, dan produksi tinggi. Pengelompokkan kabupaten/kota berdasarkan produksi padi tersebut menggunakan analisis klaster non hierarki dengan metode K-Means. Penelitian ini menggunakan data produksi padi Jawa Barat Tahun 2021. Berdasarkan hasil penelitian diperoleh tiga klaster, yaitu klaster 1 menunjukkan daerah dengan produksi padi rendah dengan karakteristik luas tanam rendah, luas panen rendah, dan produktivitas rendah yang berisi 11 kabupaten/kota. Klaster 2 menunjukkan daerah dengan produksi padi sedang dengan karakteristik luas tanam rendah, luas panen rendah, dan produktivitas tinggi yang berisi 7 kabupetn/kota. Klaster 3 menunjukkan daerah dengan produksi padi tinggi dengan luas tanam tinggi, luas panen tinggi, dan produktivitas tinggi yang berisi 9 kabupeten/kota.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125684819","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 Indeks Harga Perdagangan Internasional Berdasarkan Data Bulan Januari 2021 – Mei 2022 Menggunakan Metode Double Brown Exponential Smoothing","authors":"Edwardo Muraema, Lisnur Wachidah","doi":"10.29313/bcss.v3i2.8423","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8423","url":null,"abstract":"Abstract. The International Trade Price Index [IHPI] measures changes in the components of the aggregate price of trade in goods and services between residents of an economy and the rest of the world. The index that measures the average change in commodity prices over time at the wholesale level is known as the International Trade Price Index [IHPI]. Based on data obtained from BPS publications in the form of International Trade Price Index [IHPI] data for January 2021 - May 2022. Based on these data the author will forecast for the next 7 months for June - December 2022 using the Double Brown Exponential Smoothing method, based on the research results obtained International Trade Price Index [IHPI] with the smallest or best Mean Square Error results at α = 0.6 of 0.089. Based on the results of forecasting for the next 7 months, for the International Trade Price Index [IHPI] the forecast results for the 18th period are obtained, namely for June 2022 of 111.061. The forecast result for the 19th period for July 2022 is 111.683. The forecast result for the 20th period for August 2022 is 112.305. The forecast result for the 21st period for September 2022 is 112.927. The forecast result for the 22nd period for October is 113.549. The forecast result for the 23rd period for November is 114.171. The forecast result for the 24th period for December 2022 is 114,793. The forecasting results are expected to assist the government in predicting the International Trade Price Index [IHPI] earlier so that the government is able to anticipate and control the prices of goods and services. \u0000Abstrak. Indeks Harga Perdagangan Internasional [IHPI] mengukur perubahan komponen harga agregat perdagangan barang dan jasa antara penduduk suatu perekonomian dan seluruh dunia. Indeks yang mengukur rata-rata perubahan harga komoditas dari waktu ke waktu di tingkat grosir yang dikenal sebagai Indeks Harga Perdagangan Internasional [IHPI]. Berdasarkan data yang diperoleh dari hasil publikasi BPS berupa data Indeks Harga Perdagangan Internasional [IHPI] pada bulan Januari 2021 – Mei 2022.  Berdasarkan data tersebut penulis akan melakukan peramalan selama 7 bulan ke depan untuk bulan Juni – Desember 2022 dengan menggunakan metode Double Brown Exponential Smoothing, berdasarkan hasil penelitian didapatkan Indeks Harga Perdagangan Internasional [IHPI] dengan hasil Mean Square Error terkecil atau yang terbaik pada α = 0,6 sebesar 0,089. Berdasarkan hasil dari peramalan selama 7 bulan ke depan, untuk Indeks Harga Perdagangan Internasional [IHPI] diperoleh hasil ramalan untuk periode ke-18 yaitu untuk bulan Juni 2022 sebesar 111,061. Hasil ramalan untuk periode ke-19 untuk bulan Juli 2022 sebesar 111,683. Hasil ramalan untuk periode ke-20 untuk bulan Agustus 2022 sebesar 112,305. Hasil ramalan untuk periode ke-21 untuk bulan September 2022 sebesar 112,927. Hasil ramalan untuk periode ke-22 untuk bulan Oktober sebesar 113,549. Hasil ramalan untuk periode ke-23 untuk bulan November sebes","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115061673","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 Diskriminan Menggunakan Stratified K-Fold Cross Validation pada Klasifikasi Gestational Diabetes Mellitus (GDM) Ibu Hamil di Rumah Sakit XYZ","authors":"S. Aisyah, Nur Azizah Komara Rifai","doi":"10.29313/bcss.v3i2.8366","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8366","url":null,"abstract":"Abstract: Discriminant analysis is used for the purpose of classifying an object based on certain criteria and predicting groups for new objects. This study will classify pregnant women affected by GDM and not who are evaluated using stratified k-fold cross validation. In the analysis, several assumptions are needed that must be met, including independent variables must be multivariate normal distributed, homogeneous covariance matrices, and there are average vector differences. The stages of analysis are data exploration, testing discriminant analysis assumptions, determining the number of k-folds, randomizing into training data and testing data gradually (stratified), conducting experiments as many as k times, estimating the discriminant model of each experiment, testing the accuracy of classification and determining the best model. The results of this study the best model was found in the first experiment with the model  with a cut off value of 0.0001739. That is, if there is a new object with a value of  ≥ 0.0001739 then the pregnant woman falls into the category of not affected by GDM, and vice versa if  < 0.0001739 then pregnant women fall into the category affected by GDM. \u0000Abstrak: Analisis diskriminan digunakan untuk tujuan mengklasifikasikan sebuah objek berdasarkan kriteria tertentu dan memprediksi kelompok untuk objek baru. Penelitian ini akan mengklasifikasikan ibu hamil yang terkena GDM dan tidak yang dievaluasi menggunakan stratified k-fold cross validation. Dalam analisisnya, diperlukan beberapa asumsi yang harus dipenuhi diantaranya variabel independen harus berdistribusi normal multivariat, matriks varians kovarian yang homogen, dan ada perbedaan vektor rata-rata. Tahapan analisisnya yaitu eksplorasi data, menguji asumsi analisis diskriminan, menentukan banyaknya k-fold, melakukan pengacakan menjadi data latih dan data uji secara bertahap (stratified), melakukan eksperimen sebanyak k kali,  mengestimasi model diskriminan dari masing-masing eksperimen, menguji ketepatan klasifikasi dan menentukan model terbaiknya. Hasil dari penelitian ini model terbaik terdapat pada eksperimen pertama dengan model  dengan nilai cut off sebesar 0.0001739. Artinya, jika ada objek baru dengan nilai    0.0001739 maka ibu hamil tersebut masuk ke dalam kategori tidak terkena GDM, dan sebaliknya apabila    0.0001739  maka ibu hamil masuk ke dalam kategori terkena GDM.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128802773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aplikasi Regresi Logistik Ordinal Multilevel untuk Pemodelan Huruf Mutu Mata Kuliah Statistika Dasar Mahasiswa Universitas Islam Bandung Tahun 2019/2020","authors":"Erfina Agustin Hidayat, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.8096","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8096","url":null,"abstract":"Abstract. Multilevel ordinal logistic regression analysis is a regression analysis for discrete-scale responses, especially with a hierarchical ordinal scale. The hierarchical structure indicates that the data analyzed comes from several levels, where lower levels are nested in higher levels. This article will discuss the application of multilevel ordinal logistic regression using the Maximum Likelihood Estimation (MLE) method in the field of education, namely regarding the quality letter of the Basic Statistics course of 2019 Bandung Islamic University students at the Faculty of Engineering, Faculty of Business Economics, and Faculty of Psychology. The results showed that the multilevel ordinal logistic regression model can be used. From the results of testing the significance of parameters partially using the Wald test statistic, it is found that the student gender variable, the student regional origin variable, and the student GPA variable before taking the Basic Statistics course have an influence on the quality letter of the Basic Statistics course. \u0000Abstrak. Analisis regresi logistik ordinal multilevel merupakan analisis regresi untuk respon berskala diskrit khususnya dengan skala ordinal yang bersifat hierarki. Struktur hierarki mengindikasikan bahwa data yang dianalisis berasal dari beberapa level, di mana level yang lebih rendah tersarang pada level yang lebih tinggi. Dalam skripsi ini akan dibahas mengenai pengaplikasian regresi logistik ordinal multilevel dengan menggunakan metode Maximum Likelihood Estimation (MLE) di bidang pendidikan yaitu mengenai huruf mutu mata kuliah Statistika Dasar mahasiswa Universitas Islam Bandung 2019 di Fakultas Teknik, Fakultas Ekonomi Bisnis, dan Fakultas Psikologi. Hasil penelitian menghasilkan bahwa model regresi logistik ordinal multilevel dapat digunakan. Dari hasil pengujian signifikansi parameter secara parsial menggunakan statistik uji Wald didapatkan bahwa variabel jenis kelamin mahasiswa, variabel asal daerah mahasiwa, dan variabel IPK mahasiswa sebelum mengambil mata kuliah Statistika Dasar memiliki pengaruh terhadap huruf mutu mata kuliah Statistika Dasar","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129173287","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":"Prediksi Curah Hujan Menggunakan Metode Regresi Bayesian di Kota Bandung pada Tahun 2022","authors":"Gilang Utama Auliarahman, Suwanda","doi":"10.29313/bcss.v3i2.7968","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7968","url":null,"abstract":"Abstract. Regression analysis is an analysis that predicts the functional relationship between the dependent variable and the independent variable and is then expressed in the form of a mathematical equation. Then regression analysis is divided into two based on the number of variables, namely linear regression analysis and multiple regression analysis. Multiple regression analysis explains the relationship between one dependent variable and two or more independent variables. To find out how much the relationship requires parameter estimation which can be explained using the Ordinary Least Squares (OLS) method or the Least Squares Method. Then in addition to using these two methods, another method that can be used is the Bayesian Regression method which is based on the Bayes Theorem. The Bayesian Regression method is a method required to estimate the parameters to be estimated by utilizing prior information from a population. This information is then combined with information from the sample used in estimating population parameters. In this method, the researcher needs to determine the prior distribution of the estimated parameters. This distribution is based on the researcher's intuition so it is objective. After the information from the data obtained from sampling is combined with the prior information of the parameter, the posterior distribution of the parameter will be obtained. Then this Bayesian method treats all unknown parameters as random variables and has a distribution. In this case it is very helpful in predicting weather, one of which is predicting rainfall. In this thesis, the method is applied to predict rainfall in Bandung City in 2022. The results show that the prediction of rainfall is greater in January, May, June, July, August, and December. But for the months of February, April, September, and November, the predicted data is close to the actual data. \u0000Abstrak. Analisis regresi merupakan analisis yang memprediksi hubungan fungsional antara variabel terikat dengan variabel bebas lalu dinyatakan dalam bentuk persamaan matematik. Lalu analisis regresi dibagi menjadi dua berdasarkan jumlah variabelnya, yaitu analisis regresi linier dan analisis regresi berganda. Analisis regresi berganda menjelaskan adanya hubungan antara satu variabel terikat dengan dua atau lebih variabel bebas. Untuk mengetahui seberapa besarnya hubungan tersebut memerlukan estimasi parameter yang dapat dijelaskan menggunakan metode Ordinary Least Squares (OLS) atau Metode Kuadat Terkecil. Lalu selain menggunakan kedua metode tersebut, metode lainnya yang dapat dipakai adalah metode Regresi Bayesian yang didasari dari Teorema Bayes. Metode Regresi Bayesian merupakan metode yang diperlukan untuk menaksir parameter yang akan diestimasi dengan memanfaatkan informasi awal (prior) dari suatu populasi. Informasi ini kemudian digabungkan dengan informasi dari sampel yang digunakan dalam mengestimasi parameter populasi. Pada metode ini, peneliti perlu menentukan distri","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115853356","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 Holt-Winters Exponential Smoothing dengan Artificial Neural Networks dalam Peramalan Produksi Cabai Besar di Kabupaten Garut Provinsi Jawa Barat","authors":"Ihram Adi Pratama, Anneke Iswani Achmad","doi":"10.29313/bcss.v3i2.7900","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7900","url":null,"abstract":"Abstract. This study discusses the comparison of Holt-Winters Exponential Smoothing method  with Artificial Neural Networks in forecasting large chili production, Garut Regency, West Java Province. The Holt-Winters Exponential Smoothing method  is a forecasting method on time series data with trend and seasonal data patterns based on three equations, one for stationary, one for trend and one for seasonal. There are two methods in Holt-Winters Exponential Smoothing, namely multiplicative and additive.Artificial Neural Networks is a method inspired by human central neural networks. One method that can be used for forecasting in ANN is Neural Network Autoregression or NNAR. NNAR can be used to forecast forecasting through pattern recognition from  inputs by utilizing lag values as inputs and then refined through the learning process using Backpropagation Algorithms. This method comparison uses RMSE (Root Mean Squared Error) to see the accuracy of forecasting accuracy. The data used is secondary data from the large chili production of Garut Regency in 2016 – 2022. Analysis of both methods resulted in the smallest RMSE value or forecasting accuracy on NNAR with RMSE Testing of 39,490.91. So to forecast m future period, you can use the Artificial Neural Networks  method with Neural Network Autoregression because it has a smaller RMSE value. \u0000Abstrak. Penelitian ini membahas mengenai perbandingan metode Holt-Winters Exponential Smoothing dengan Artificial Neural Networks dalam peramalan produksi cabai besar Kabupaten Garut Provinsi Jawa Barat. Metode Holt-Winters Exponential Smoothing merupakan metode peramalan pada data deret waktu dengan pola data trend dan musiman yang didasarkan pada tiga persamaan yaitu satu untuk stasioner, satu untuk trend dan satu untuk musiman. Terdapat dua metode dalam Holt-Winters Exponential Smoothing yaitu multiplikatif dan aditif. Artificial Neural Networks merupakan metode yang terinspirasi dari jaringan saraf pusat manusia. Salah satu metode yang dapat digunakan untuk peramalan dalam ANN adalah Neural Network Autoregression atau NNAR. NNAR dapat digunakan untuk memperkirakan peramalan melalui pengenalan pola dari input dengan memanfaatkan nilai lag sebagai input dan kemudian disempurnakan melalui proses pembelajaran menggunakan Algoritma Backpropagation. Perbandingan metode ini menggunakan RMSE (Root Mean Squared Error) untuk melihat akurasi ketepatan peramalan. Data yang digunakan adalah data sekunder dari produksi cabai besar Kabupaten Garut tahun 2016 – 2022. Analisis kedua metode tersebut menghasilkan nilai RMSE atau ketepatan peramalan terkecil pada NNAR dengan RMSE Pengujian sebesar 39.490,91. Sehingga untuk meramalkan m periode kedepan dapat menggunakan metode Artificial Neural Networks dengan Neural Network Autoregression karena memiliki nilai RMSE lebih kecil.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134544732","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 Bayesian Change Point dalam Menentukan Bahaya Gempa di Jawa Barat","authors":"Iqbal Arya Ramadhan, Sutawanir Darwis","doi":"10.29313/bcss.v3i2.8162","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8162","url":null,"abstract":"Abstract. Geologically, the area of Indonesia is prone to earthquakes, including the province of West Java, because it is located in the ring of fire and can cause severe damage, so Indonesia needs earthquake insurance. The Bayesian PSHA (Probabilistic Seismic Hazard Analysis) method can provide an assessment of the uncertainty of future earthquakes, by utilizing the prior distribution at the start of the analysis and the likelihood function is formulated into a posterior distribution to measure future observed data with uncertain parameters. The uncertainty in this research is time, taking into account the Bayesian Change Point (potential change in the rate of seismicity at that point in time). This Bayesian PSHA will produce seismic hazard values and combined with the Mean Damage Ratio (MDR) using the Yucemen method to generate insurance premiums. There were 25 earthquake events in West Java province in 2006-2021 around Kertajati Airport with coordinates 6.39'02° S, 108.10'10°E, depth of 100 km and radius of 300 km and there is a Bayesian factor of 0.0035 so that a change in the earthquake rate was identified between the 3rd and 4th incident, and the change in the earthquake rate occurred the day before the 4th incident, namely on July 17 2006 with an incident ratio rate of 0.7769. The seismic hazard value or earthquake hazard involving a Change Point for an intensity scale of 11 is 0.23%. \u0000Abstrak. Secara geologi wilayah Indonesia rawan gempa bumi, termasuk provinsi Jawa Barat karena terletak pada kawasan cincin api (ring of fire) dan dapat menimbulkan kerusakan yang sangat parah sehingga Indonesia memerlukan asuransi gempa bumi. Metode Bayesian PSHA (Probabilistic Seismic Hazard Analysis) dapat memberikan penilaian ketidakpastian gempa di masa depan, dengan memanfaatkan distribusi prior pada awal analisis dan fungsi likelihood diformulasikan menjadi distribusi posterior untuk mengukur data yang diamati di masa depan dengan parameter yang tidak pasti. Ketidakpastiaan pada peneilitian ini yaitu waktu, dengan memperhitungkan Bayesian Change Point (potensi perubahan laju kegempaan pada titik waktu). Bayesian PSHA ini akan menghasilkan nilai seismic hazard dan dikombinasikan dengan Mean Damage Ratio (MDR) menggunakan metode Yucemen untuk menghasilkan premi asuransi. Terdapat 25 kejadian gempa bumi provinsi Jawa Barat tahun 2006-2021 yang berada di sekitar Bandara Kertajati dengan titik koordinat 6.39’02° S, 108.10’10°E, kedalaman 100 km dan radius 300 km dan terdapat faktor Bayesian sebesar 0,0035 sehingga terindentifikasi perubahan laju gempa antara kejadian ke-3 dan ke-4, dan perubahan laju gempa tersebut terjadi sehari sebelum kejadian ke-4 yaitu pada tanggal 17 Juli 2006 dengan tingkat rasio kejadian sebesar 0,7769. Nilai seismic hazard atau bahaya gempa melibatkan Change Point untuk skala intensitas 11 yaitu 0,23%.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132765864","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":"Hierarchical Time Series dengan ARIMA dalam Meramalkan Nilai Ekspor Indonesia","authors":"Hilmi Azzam Robbani, Marizsa Herlina","doi":"10.29313/bcss.v3i2.8107","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8107","url":null,"abstract":"Abstract. Export is a trade activity carried out between countries by carrying or sending goods originating from domestic areas abroad with the aim of being sold or marketed. Exports as a source of state revenue are needed for the Indonesian economy, because exports can make a major contribution to Indonesia's economic stability and growth. The value of exports that have decreased or increased for the future needs to be considered. The purpose of this study is to determine the ARIMA model and get the forecast results from the Hierarchical Time Series approach for Indonesian export value data. This forecasting method is used because Indonesia's export value data is a hierarchical time series data with two levels, namely level 0 (total export value of Indonesia) and level 1 (data on Indonesia's export value to ASEAN countries). The forecasting model used is ARIMA (Autoregressive Integrated Moving Average). For the selection of the best ARIMA model using the Mean Absolute Percentage Error (MAPE) value. The results showed that the forecast results of Indonesia's export value in June 2023 for Thailand amounted to 591 million USD, Singapore country amounted to 1,090 billion USD, for Malaysia country amounted to 712 million USD, for the Philippines country 981 million USD, for Myanmar country 83 million USD and the total export value of Indonesia amounted to 3,361 billion USD. \u0000Abstrak. Ekspor merupakan kegiatan perdagangan yang dilakukan antar negara dengan cara membawa atau mengirimkan barang yang berasal dari daerah dalam negeri ke luar negeri dengan tujuan dijual atau dipasarkan. Ekspor sebagai salah satu sumber pendapatan negara sangat dibutuhkan bagi perekonomian Indonesia, karena ekspor dapat memberikan kontribusi yang besar terhadap stabilitas dan pertumbuhan ekonomi Indonesia. Nilai ekspor yang mengalami penurunan atau peningkatan untuk masa yang akan datang perlu diperhatikan. Tujuan dari penelitian ini adalah mengetahui model ARIMA dan mendapatkan hasil ramalan dari pendekatan Hierarchical Time Series untuk data nilai ekspor Indonesia. Metode peramalan ini digunakan karena data nilai ekspor Indonesia merupakan data deret waktu hierarki dengan dua tingkatan, yaitu level 0 (total nilai ekspor Indonesia) dan level 1 (data nilai ekspor Indonesia ke negara-negara ASEAN). Model peramalan yang digunakan adalah ARIMA (Autoregressive Integrated Moving Average). Untuk pemilihan model ARIMA terbaik menggunakan nilai Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa hasil ramalan nilai ekspor Indonesia pada bulan Juni 2023 untuk negara Thailand sebesar 591 Juta USD, negara Singapura sebesar 1.090 Miliar USD, untuk negara Malaysia sebesar 712 Juta USD, untuk negara Filipina 981 Juta USD, untuk negara Myanmar 83 Juta USD dan total nilai ekspor Indonesia sebesar 3.361 miliar USD.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122319315","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":"Pemodelam Regresi Conway-Maxwell-Poisson untuk Mengatasi Overdispersi pada Data Angka Kematian Ibu di Provinsi Jawa timur","authors":"Deri Dzikria Khofiyandi, Suliadi","doi":"10.29313/bcss.v3i2.7865","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7865","url":null,"abstract":"Abstract. Poisson regression is usually used to model count data.  one assumption in Poisson regression is equidispersion that meanS  the mean equals to the variance. However, in real data it is often this assumption does not meet. One way to overcome overdispersion is the Conway-Maxwell-Poisson (COM-Poisson) regression. This study  applied the COM-Poisson regression to model the effect of Pregnant women who receive a minimum of 4 antenatal care visits (X1), Active Integrated Health Post (X2), Delivery assisted by Healthcare Professional (X3), Provision of Iron Supplement Tablets to Pregnant Women (X4), Pregnant women who received Td2+ Immunization (X5) and Poverty Rate (X6) to Maternal Mortality Rate (Y) for East Java Province data of 2020. The obtained model is with dispersion parameter  0,35044. Meanwhile, the factors that influence the maternal mortality rate are pregnant women who get at least 4 checkups or check-ups at the end of their pregnancy (X1) and pregnant women who receive health services, especially at posyandu (X2). \u0000Abstrak. Regresi Poisson digunakan dalam memodelkan data cacahan. Salah satu asumsi dalam regresi Poisson adalah equdispersi yang berarti rata-rata sama dengan varians. Namun, pada data rill seringkali asumsi ini tidak terpenuhi. Salah satu cara untuk mengatasi overdispersi adalah dengan menggunakan regresi Conway-Maxwell-Poisson (COM-Poisson). Penelitian regresi COM-Poisson ini diterapkan untuk memodelkan pengaruh dari pemeriksaan akhir masa kehamilan (K4) (X1), keberadaan posyandu aktif (X2), persalinan yang ditolong tenaga kesehatan (X3), pemberian tablet penambah darah pada ibu hamil (X4), pemberian imunisasi td2+ pada ibu hamil (X5) dan persentase penduduk miskin (X6) terhadap Angka Kematian Ibu(Y) di Provinsi Jawa Timur pada data tahun 2020 Diperoleh model Conway-Maxwell-Poisson adalah dengan parameter dispersi  0,35044. Sememtara itu, faktor-faktor yang mempengaruhi angka kematian ibu adalah ibu hamil yang mendapatkan pemeriksaan minimal 4 kali (K4) atau pemeriksaan akhir masa kehamilan (X1) dan ibu hamil yang menerima pelayanan kesehatan terutama di posyandu (X2).","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114249848","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}