Bandung Conference Series: Statistics最新文献

{"title":"Perbandingan Metode Grey-Markov (1,1), Grey (1,1), dan Double Moving Average Untuk Peramalan Jumlah Balita Stunting di Jawa Barat","authors":"Silvi Oktaviani Saridewi, Suwanda","doi":"10.29313/bcss.v3i2.9154","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9154","url":null,"abstract":"Abstract. Forecasting is a process to calculate future estimates based on current values and past values. Many forecasting methods are based on time series data, including the Double Moving Average (DMA) method, which is a part of classical time series methods. However, classical time series methods require certain assumptions to be met, and a significant amount of available data is needed to achieve good accuracy. The Grey model is a forecasting method suitable for limited and fluctuating data. It is then combined with Markov Chains to predict future states based on transition probabilities over several steps. The Grey-Markov (1,1) model is a combination of the Markov Chain with the Grey (1,1) model, which involves a first-order differential and a single variable. This study aims to forecast the number of toddlers with stunting in West Java. Stunting is a growth disorder in children characterized by shorter height for their age. Three forecasting methods are used for the number of toddlers with stunting in West Java: Grey-Markov (1,1), Grey (1,1), and Double Moving Average, to determine the best forecasting model. Based on the calculation results of the three models, it is concluded that the Grey-Markov (1,1) model provides highly accurate results compared to the other two models. The estimated result for the year 2022 is 189,473 cases with model accuracy using MAPE, MAE, and posterior error ratio C are 3.29%, 9415.135, and 0.1829, respectively. The MAPE and posterior error ratio (C) values fall into the category of highly accurate, and the MAE value is smaller compared to the MAE of the Grey (1,1) model and the Double Moving Average model. \u0000Abstrak. Peramalan adalah proses untuk menghitung perkiraan masa yang akan datang berdasarkan nilai saat ini dan nilai masa lalu. Banyak metode peramalan didasarkan pada data deret waktu, diantaranya yaitu Double Moving Average (DMA) yang termasuk dalam metode deret waktu klasik. Namun, untuk menggunakan metode deret waktu klasik harus memenuhi asumsi dan data yang tersedia harus banyak untuk menghasilkan akurasi yang baik. Model Grey merupakan metode peramalan untuk data yang terbatas dan berfluktuasi, kemudian model Grey dikombinasikan dengan Rantai Markov untuk meramalkan keadaan di masa yang akan datang berdasarkan probabilitas transisi dalam beberapa langkah. Model Grey-Markov (1,1) adalah kombinasi Rantai Markov dengan model Grey (1,1) yang merupakan model dengan diferensial orde satu serta satu variabel yang digunakan. Pada penelitian ini akan dilakukan peramalan untuk jumlah balita stunting di Jawa Barat. Stunting merupakan gangguan pertumbuhan pada anak yang ditandai dengan tinggi badan anak lebih pendek dari usianya. Digunakan tiga metode untuk peramalan jumlah balita stunting di Jawa Barat yaitu Grey-Markov (1,1), Grey (1,1), dan double moving average sebagai perbandingan untuk menentukan model peramalan terbaik. Berdasarkan hasil perhitungan ketiga model, diperoleh kesimpulan bahwa model Grey-Mar","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116282696","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 Tingkat Pengangguran Terbuka Provinsi Jawa Barat Menggunakan Model Spatial Autoregressive Moving Average (SARMA)","authors":"Anisa Fikriani, Nur Azizah komara Rifai","doi":"10.29313/bcss.v3i2.8340","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8340","url":null,"abstract":"Abstract. SARMA is one of the spatial regression models that combines the SAR model and the SEM model. The SARMA model is used when there are lag and error dependencies. The SARMA parameter estimator uses the Maximum Likelihood Estimation (MLE) method. To explain the spatial relationship between observation locations, a spatial weighting matrix based on contiguity is used. West Java Province is a province that has the highest open unemployment rate in Indonesia in 2022. The purpose of this study is to model the SARMA regression and find out the factors that significantly influence the open unemployment rate in West Java Province in 2022. The results showed that the data contained lag and error spatial dependencies so that the SARMA model was feasible to use. The model shows that the average length of schooling, labor force participation rate, life expectancy, and GRDP growth rate have a significant effect on the open unemployment rate. The open unemployment rate of the ith districts/cities that are close together has a significant effect on the open unemployment rate of the surrounding districts/cities of -0.5334 and the error between ith adjacent districts/cities has an effect on the open unemployment rate of the surrounding districts/cities of 0.7292. \u0000Abstrak. SARMA merupakan salah satu model regresi spasial yang menggabungkan antara model SAR dan model SEM. Model SARMA digunakan apabila terdapat dependensi lag dan dependensi error. Penduga parameter SARMA ini menggunakan metode Maximum Likelihood Estimation (MLE). Untuk menjelaskan hubungan spasial antar lokasi pengamatan digunakan matriks pembobot spasial berdasarkan ketetanggaan (contiguity). Provinsi Jawa Barat merupakan provinsi yang memiliki tingkat pengangguran terbuka tertinggi di Indonesia pada tahun 2022. Tujuan dari penelitian ini yaitu untuk memodelkan regresi SARMA dan mengetahui faktor-faktor yang mempengaruhi tingkat pengangguran terbuka di Provinsi Jawa Barat tahun 2022 secara signifikan. Hasil penelitian menunjukan bahwa data mengandung dependensi spasial lag dan error sehingga model SARMA layak digunakan. Model menunjukan rata-rata lama sekolah, tingkat partisipasi angkatan kerja, angka harapan hidup, dan laju pertumbuhan PDRB berpengaruh secara signifikan terhadap tingkat pengangguran terbuka. Tingkat pengangguran terbuka kabupaten/kota ke-i yang berdekatan berpengaruh secara signifikan terhadap tingkat pengangguran terbuka kabupaten/kota disekitarnya sebesar -0,5334 dan  error antar kabupaten/kota ke-i yang berdekatan berpengaruh terhadap tingkat pengangguran terbuka kabupaten/kota disekitarnya sebesar 0,7292.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130987956","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 Harga Logam Mulia Antam Satu Gram Menggunakan Metode Arima Kalman Filter","authors":"Wulan Milanka Lestari, Teti Sofia Yanti","doi":"10.29313/bcss.v3i2.8496","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8496","url":null,"abstract":"Abstract. Forecasting is a process to know a picture in the future based on past data. ARIMA Kalman Filter is a forecasting method that combines analysis using the ARIMA model which is then carried out by applying the Kalman Filter algorithm in optimizing the ARIMA model. Kalman Filter is a set of mathematical equations that offer a computationally recursive and efficient way to estimate the state of a process, in such a way as to minimize the average of the squared errors. The purpose of forecasting is to get a picture of the future value that can be used for decision making, one of which is precious metal investment. In this study, forecasting will be carried out on the price of one gram of Antam's precious metal using the ARIMA Kalman Filter method. Precious metals are referred to as “safe heaven” investment instruments, which means that the value of these investment assets will be maintained. Based on the research results from forecasting data for Antam's Precious Metals price of one gram (before tax) per day at the LM Precious Metals Boutique - Bandung using the ARIMA Kalman Filter method, the best forecasting model is the ARIMA Kalman Filter (1,1,0) model with the equation With the implementation of the Kalman Filter algorithm, the results of forecasting data on the Precious Metals price of one gram (before tax) per day at the LM Precious Metals Boutique – Bandung become more accurate. This is indicated by the decrease in the MAPE value from 45.37% using the ARIMA model (1,1,0) to 38.73% after applying the Kalman Filter algorithm to the ARIMA model (1,1,0). \u0000Abstrak. Peramalan adalah suatu proses untuk mengetahui suatu gambaran di masa depan berdasarkan data masa lampau. ARIMA Kalman Filter adalah suatu metode peramalan yang menggabungkan analisis menggunakan model ARIMA yang selanjutnya dilakukan penerapan algoritma Kalman Filter dalam optimalisasi model ARIMA. Kalman Filter merupakan sekumpulan persamaan matematik yang menawarkan cara komputasi rekursif dan efisien untuk mengestimasi state dari sebuah proses, sedemikian rupa sehingga meminimumkan rata-rata dari kuadrat galat. Tujuan dari peramalan adalah mengetahui gambaran nilai masa depan yang dapat dimanfaatkan untuk pengambilan keputusan salah satunya investasi logam mulia. Pada penelitian ini akan dilakukan peramalan terhadap harga satu gram logam mulia Antam menggunakan metode ARIMA Kalman Filter. Logam mulia disebut sebagai instrumen investasi “safe heaven” yang artinya aset investasi tersebut akan tetap terjaga nilainya. Berdasarkan hasil penelitian dari peramalan data harga Logam Mulia antam satu gram (sebelum pajak) per hari di Butik Logam Mulia LM – Bandung menggunakan metode ARIMA Kalman Filter maka didapat model peramalan terbaik adalah model ARIMA Kalman Filter (1,1,0) dengan persamaan . Dengan diterapkannya algoritma Kalman Filter, hasil dari peramalan data harga Logam Mulia antam satu gram (sebelum pajak) per hari di Butik Logam Mulia LM – Bandung menjadi lebih akurat. Hal","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122765112","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 Kendali EWMA (Exponentially Weighted Moving Average) Berbasis Pearson Residual pada Data Jumlah Penundaan Keberangkatan Pesawat","authors":"Aulia Yasmin, Suwanda","doi":"10.29313/bcss.v3i2.8264","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8264","url":null,"abstract":"Abstrak. Tujuan dari pengendalian kualitas statistik adalah untuk mencapai, mempertahankan serta meningkatkan suatu alat kualitas. Alat yang biasa digunakan adalah diagram kendali shewart, namum diagram tersebut kurang efektif dalam mendeteksi pergeseran rata-rata yang relatif kecil. Salah satu alternatifnya yakni menggunakan diagram kendali EWMA (Exponentially Weighted Moving Average) yang memiliki asumsi data berdistribusi normal. Pada praktiknya terkadang variabel terikat mengikuti distribusi poisson dan karakteristik kualitas proses dicirikan sebagai fungsi linier atau non-linier yang dimodelkan menggunakan Generalized Linear Model (GLM). Sehingga diagram kendali berbasis residu diusulkan oleh beberapa peneliti karena menghasilkan kinerja yang lebih baik. Residu yang akan digunakan pada penelitian kali ini adalah pearson residual yang didapat dari model regresi poisson. Pearson residual akan digunakan sebagai nilai statistik pemantauan dalam diagram kendali EWMA. Diagram kendali EWMA berbasis pearson residual ini akan diaplikasikan pada data jumlah penundaan pesawat terbang dengan rute bandara Soekarno Hatta - I Gusti Ngurah Rai sebagai variabel terikat dan kecepatan angin serta suhu sebagai variabel bebas. Hasil pengujian pada Fase I diperoleh nilai  λ = 0.05 sebagai λ yang efektif dalam mendeteksi pergeseran proses yang kecil. Kemudian dengan nilai σ ̂_r^P = 0.7527  dan L = 2.615 dihasilkan diagram terkendali secara statistik. Serta pada Fase II dengan menggunakan nilai batas kendali pada Fase I dihasikan bahwa diagram sudah terkendali secara statistik. \u0000 \u0000Abstract. The purpose of statistical quality control is to achieve, maintain and improve a quality tool. A commonly used tool is the shewart control chart, but the diagram is less effective in detecting relatively small average shifts. One alternative is to use the EWMA (Exponentially Weighted Moving Average) control chart which assumes normally distributed data. In practice, sometimes dependent variables follow the poisson distribution and process quality characteristics are characterized as linear or non-linear functions modeled using the Generalized Linear Model (GLM). So the residue-based control diagram was proposed by some researchers because it produces better performance. The residue that will be used in this study is pearson residual obtained from the Poisson regression model. Pearson residuals will be used as monitoring statistical values in the EWMA control chart. This residual pearson-based EWMA control diagram will be applied to data on the number of aircraft delays with the Soekarno Hatta - I Gusti Ngurah Rai airport route as a bound variable and wind speed and temperature as independent variables. The test results in Phase I obtained a value of λ = 0.05 as λ which is effective in detecting small process shifts. Then with a value of σ ̂_r^P = 0.7527 and L = 2.615 a statistically controlled diagram is produced. And in Phase II using the control limit value in Phase I, it was ","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121343978","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":"Perkiraan Migrasi Neto Perkelompok Umur antar Provinsi Jawa Barat dengan Provinsi DKI Jakarta Tahun 2021","authors":"Ferdi Herdiansyah, Yayat Karyana","doi":"10.29313/bcss.v3i2.8321","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8321","url":null,"abstract":"Abstract. Population mobility or population movement exists because the needs of human life cannot always be met by the capabilities of the area where they live. Population mobility occurs partly due to differences in the potential and ability of one region to another in meeting the needs of the population. In this study, population density that occurred in West Java Province and DKI Jakarta Province occurred due to large migration flows and unequal population distribution. Due to the large economic contribution that occurred in the Province of West Java and the Province of DKI Jakarta, it encouraged residents to migrate to these areas to meet economic needs. Therefore this research was conducted to determine the value of the net migration rate per age group between West Java Province and DKI Jakarta Province in 2021. Based on the results obtained, the total net migrants per age group between West Java Province and DKI Jakarta Province in 2021 was 117,078 people, which means that the number of residents of West Java Province and DKI Jakarta Province who migrated in and out migrated was 117,078 people. West Java Province and DKI Jakarta Province in 2021 will produce a positive net migration rate which can be categorized as West Java Province and DKI Jakarta Province as migrant receiving areas for the last five years. The magnitude of the economic contribution that occurs in the province encourages residents to migrate to the area to meet economic needs. \u0000Abstrak. Mobilitas penduduk atau perpindahan penduduk ada karena kebutuhan hidup manusia tidak selalu dapat terpenuhi oleh kemampuan wilayah dimana ia bertempat tinggal. Mobilitas penduduk terjadi antara lain karena adanya perbedaan potensi dan kemampuan wilayah yang satu dengan yang lain di dalam memenuhi kebutuhan hidup penduduknya. Dalam penelitian ini kepadatan penduduk yang terjadi di Provinsi Jawa Barat dengan Provinsi DKI Jakarta terjadi karena adanya arus migrasi yang besar dan distribusi penduduk yang tidak merata. Disebabkan karena besarnya kontribusi ekonomi yang terjadi di Provinsi Jawa Barat dengan Provinsi DKI Jakarta tersebut mendorong penduduk melakukan migrasi ke daerah tersebut untuk memenuhi kebutuhan ekonomi. Oleh karena itu penelitian ini dilakukan untuk mengetahui nilai angka migrasi neto perkelompok umur antar Provinsi Jawa Barat dengan Provinsi DKI Jakarta tahun 2021. Berdasarkan hasil yang diperoleh bahwa total migran neto perkelompok umur antar Provinsi Jawa Barat dengan Provinsi DKI Jakarta tahun 2021 sebesar 117.078 jiwa yang artinya banyaknya penduduk Provinsi Jawa Barat dengan Provinsi DKI Jakarta yang melakukan migrasi masuk dan migrasi keluar sebanyak 117.078 orang. Provinsi Jawa Barat dengan Provinsi DKI Jakarta pada tahun 2021 menghasilkan angka migrasi neto bernilai positif yang dapat dikategorikan bahwa Provinsi Jawa Barat dengan Provinsi DKI Jakarta sebagai daerah penerima migran selama lima tahun terakhir. Besarnya kontribusi ekonomi yang terjadi di provinsi t","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128108499","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 Markov Switching Autoregressive untuk Peramalan Data Tingkat Inflasi di Provinsi Jawa Barat Tahun 2009-2022","authors":"Choerunnisa, Suwanda","doi":"10.29313/bcss.v3i2.9239","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9239","url":null,"abstract":"Abstract. Autoregressive model as a classic time series model which is often used in modeling time series data has not been able to explain changes in structure. In addition, other time series models that often deal with structural changes, such as TAR, SETAR, ARCH, and so on, have not been able to overcome the transition opportunities that also often occur in time series data. Thus, the Markov Switching Autoregressive method was introduced which not only addresses structural changes but also pays attention to and explains transition opportunities. This method is applied in modeling inflation rate data in West Java Province, where the inflation rate is an important right to pay attention to because it is related to economic stability and structural changes often occur. Especially in the West Java region as one of the largest provinces in Indonesia. The MS(2)AR(1) model is said to be the best model because it has the smallest BIC value with a BIC value of -91.31212. The MAPE value for the MS(2)AR(1) model is 15% which is said to be good for forecasting. Forecasting is done for the next 12 months. As well as the state duration, it was obtained that the inflation rate remained in an increasing condition for approximately 11 months while it remained in a declining condition for approximately 9 months. \u0000Abstrak. Model Autoregressive sebagai model deret waktu klasik yang seringkali digunakan dalam pemodelan data deret waktu belum mampu menjelaskan adanya perubahan struktur. Selain itu, model deret waktu lain yang seringkali mengatasi perubahan struktur seperti TAR, SETAR, ARCH, dan lain sebagainya belum mampu juga mengatasi peluang transisi yang juga seringkali terjadi pada data deret waktu. Dengan demikian, diperkenalkanlah metode Markov Switching Autoregressive yang tidak hanya mengatasi perubahan struktur tapi juga memperhatikan dan menjelaskan adanya peluang transisi. Metode ini diterapkan dalam pemodelan data tingkat Inflasi di Provinsi Jawa Barat, yang dimana tingkat inflasi menjadi suatu hak yang penting untuk diperhatikan karena berkaitan dengan stabilitas perekonomian serta seringkali terjadi perubahan struktur. Terutama di wilayah Jawa Barat sebagai salah satu Provinsi terbesar di Indonesia.Model MS(2)AR(1) dikatakan sebagai model terbaik karena memiliki nilai BIC terkecil dengan nilai BIC sebesar -91,31212. Nilai MAPE untuk model MS(2)AR(1) sebesar 15% yang dikatakan baik untuk peramalan. Peramalan dilakukan untuk 12 bulan kedepan. Serta Durasi state yang diperoleh bahwa tingkat inflasi bertahan dalam kondisi peningkatan kurang lebih 11 bulan sedangkan bertahan pada kondisi penurunan selama kurang lebih 9 bulan.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132591688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metode Random Forest untuk Klasifikasi Penyakit Diabetes","authors":"Dhea Agustina Hadi, Dwi Agustin Nuraini Sirodj","doi":"10.29313/bcss.v3i2.8354","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8354","url":null,"abstract":"Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age. \u0000Abstract. Random Forest is a supervised learning algorithm developed from decision trees with the application of boostrap aggregating (bagging). This method grows trees from decision trees to produce a forest or the best model called the random forest model. Tree growth is done with randomly selected data with returns through the bagging process. Random forest is considered to provide better performance results for diabetes data among other supervised learning methods, because random forest and has the lowest error rate compared to other methods. Random forest is also an important technique for medical data classification, especially for diagnosing diabetics. In this study, classification was carried out using Pima Indian Diabetes data, which is an American tribe that lives in Arizona and Mexico. Classification analysis was carried out using an algorithm to see the level of accuracy in random forest classification on Pima Indian diabetes data. The results show that the accuracy value of random forest classification is 74.78%, this value is in the accuracy category at the fair classification level. In this random forest classification, there are three main variables that become importance variables, namely glucose then BMI, and age.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133113879","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 Regresi Nonparametrik Smooting Spline untuk Data Tersensor dalam Memodelkan Hubungan Antara Lamanya Waktu Kesembuhan Rawat Inap Pada Pasien Diabetes Melitus Tipe-2 dengan Usia Pasien","authors":"Dea Sri Mulyani, Abdul Kudus","doi":"10.29313/bcss.v3i2.8966","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8966","url":null,"abstract":"Abstract. The aim of study is to explain the case. Regression analysis is used to model or look for patterns of relationship between one or more independent variables and one or more response variables. Since the data often does not follow a specific formulation pattern, a more flevible model is required, namely nonparametric regression model approach is an approachused when the shape of the shape of the relationship between the response variable and the independent variable is unknown or information about the shape of the regression function is not available. The nonparametric spline regression model with optimal noodes was applied to patient age and time to recovery after hospitalization in patients with type 2 diabetes mellitus containing right-censored data in a variable containing the right sensor. In this study, the generalized cross-validation (GCV) method was applied to nonparametric smoothing spline regression to determine the optimal smoothing parameter. In order to obtain the GCV value, parameter estimasi must be available to form a hat matriz formed form . Parameter estimation for the smoothing spline from the function f(.) by minimized PRSS (Penalized Residual Sum of Square). Once the optimal GCV value has been determined, it froms the best estimate of the regression function. Optimal Smoothing parameter by choosing  with a minimum GCV value. According to the research result, the minimum GCV value is 0.0378 at  and the GCV value with Kaplan Meier weights is 30.4773. \u0000Abstrak. Penelitian ini bertujuan untuk menjelaskan mengenai kasus Analisis regresi digunakan untuk memodelkan atau mencari pola hubungan antara satu atau lebih variabel bebas dengan satu atau lebih variabel respons. Seringkali data tidak mengikuti pola rumusan tertentu, sehingga diperlukan model yang lebih fleksibel, yaitu regresi nonparametrik. Pendekatan model regresi nonparametrik yaitu merupakan suatu pendekatan yang digunakan apabila bentuk hubungan antara variabel respon dan variabel bebasnya tidak diketahui atau tidak adanya informasi mengenai bentuk fungsi regresinya. Model regresi nonparametrik spline dengan titik knot optimal diaplikasikan pada usia pasien terhadap lamanya waktu kesembuhan rawat inap pada pasien yang menyandang penyakit diabetes melitus tipe-2 yang di dalamnya mengandung data tersensor kanan menjadi variabel yang berisi sensor kanan. Dalam penelitian ini diterapkannya metode GCV (Generalized Cross-Validation) pada regresi nonparametrik smoothing spline untuk menentukan parameter smoothing yang optimal. Untuk mendapatkan nilai GCV, Maka perlu adanya nilai estimasi parameter untuk membentuk hat matrix yang dibentuk dari   . Estimasi parameter untuk smoothing spline dari fungsi f(.) dengan meminimumkan PRSS (Penalized Residual Sum of Square). Ketika nilai GCV yang optimal sudah didapatkan maka akan membentuk estimasi fungsi regresi yang terbaik. Parameter penghalus optimal dengan cara memilih  yang memiliki nilai GCV minimum. Dari hasil penelitia","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121830804","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 ARIMAX dengan Efek Variasi Kalender pada Peramalan Harga Komoditas Cabai Rawit di Provinsi Jawa Barat","authors":"Rara Hera Silvia, Anneke Iswani Achmad","doi":"10.29313/bcss.v3i2.9180","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9180","url":null,"abstract":"Abstract. Forecasting is an important aid in planning and decision-making. One of the forecasting methods that is often used is the time series method. Time series are often influenced by a particular event or other variable, so it can cause the data to have a different repeating pattern each period. The ARIMAX Model is thought to be able to capture certain patterns by including certain event information as additional variables, or so-called exogenous variables. The tendency of data to show patterns at certain times based on dates in the calendar is called calendar variation, and the effect of calendar variation can appear on certain events such as Eid al-Fitr. Special treatment is needed for time series data with a calendar variation effect, where the ARIMAX model is well applied to the series data in that case, so that the model formed is ARIMAX with calendar variation. In this thesis, I conducted a study on forecasting the price of cayenne pepper in West Java province using the ARIMAX model with a variation of the calendar in which to include information on Eid al-Fitr events as an additional variable. The best ARIMAX model for forecasting is ARIMAX (0, 1, 1) with accuracy using a MAPE value of 11%, which is based on the criteria of forecasting ability. It can be concluded that the model ARIMAX (0, 1, 1) has good forecasting ability. \u0000Abstrak. Peramalan merupakan bantuan penting dalam perencanaan dan pengambilan keputusan. Salah satu metode peramalan yang sering digunakan adalah metode deret waktu (time series). Pada deret waktu seringkali dipengaruhi oleh suatu peristiwa tertentu atau variabel lain, sehingga dapat menyebabkan data memiliki pola berulang berbeda setiap periodenya. Model ARIMAX diduga mampu menangkap pola tertentu dengan memasukkan informasi peristiwa tertentu sebagai variabel tambahan atau disebut variabel eksogen. Adanya kecenderungan data untuk menampilkan pola pada waktu tertentu berdasarkan penanggalan di dalam kalender disebut variasi kalender, dimana efek variasi kalender dapat muncul pada peristiwa tertentu seperti Idul Fitri. Diperlukan perlakuan khusus untuk data deret waktu dengan efek variasi kalender, dimana model ARIMAX baik diterapkan untuk data deret dengan kasus tersebut, sehingga model yang terbentuk adalah ARIMAX dengan variasi kalender. Dalam skripsi ini melakukan penelitian pada peramalan harga cabai rawit di Provinsi Jawa Barat menggunakan model ARIMAX dengan variasi kalender dimana memasukkan informasi peristiwa Idul Fitri sebagai variabel tambahan. Didapatkan model ARIMAX terbaik untuk melakukan peramalan yaitu ARIMAX(0,1,1) dengan akurasi menggunakan nilai MAPE sebesar 11%, dimana berdasarkan kriteria kemampuan peramalan bahwa dapat disimpulkan model ARIMAX(0,1,1) memiliki kemampuan peramalan yang baik.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127010655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimasi Parameter Weibull pada Waktu Survival Pasien Kanker Serviks RSUD Kota Makassar Tahun 2017-2019","authors":"Aulia Khairunnisa, Dwi Agustin Nuriani Sirodj","doi":"10.29313/bcss.v3i2.8459","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8459","url":null,"abstract":"Abstract. The Weibull distribution is a development of the exponential distribution. The Weibull distribution consists of 3 parameters, namely scale parameters, shape parameters and location parameters. Another form of 3-parameter distribution is the Weibull 2-parameter distribution consisting of scale parameters and shape parameters. Weibull distribution parameter estimation uses Maximum Likelihood Estimation (MLE) and is solved by Newton-Raphson iterations due to nonlinear results. The data used in this study is data on cervical cancer patients at RSUD Kota Makassar in 2017-2019. From this study it can be concluded that the Weibull distribution parameters for survival time variables are 7,8383 for scale parameters and 1,5140 for shape parameters. \u0000Abstrak. Distribusi Weibull merupakan pengembangan dari distribusi eksponensial. Distribusi Weibull terdiri dari 3 parameter, yaitu parameter skala, parameter bentuk dan parameter lokasi. Bentuk lain dari dsitribusi 3 paramater yaitu distribusi Weibull 2 parameter yang terdiri dari parameter skala dan parameter bentuk. Estimasi parameter dsitribusi Weibull menggunakan Maximum Likelihood Estimation (MLE) dan diselesaikan oleh iterasi Newton-Raphson sebab hasil yang nonliear atau tidak closed form. Data yang digunakan pada penelitian ini merupakan data pasien kanker serviks RSUD Kota Makassar tahun 2017-2019. Dari penelitian ini dapat disimpulkan bahwa parameter distribusi Weibull untuk variabel waktu survival adalah 7,8383 untuk parameter skala dan 1,5140 untuk parameter bentuk.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"105 12S1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122017116","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}