Bandung Conference Series: Statistics最新文献

{"title":"Pemodelan Hurdle Poisson Regresion pada Jumlah Kasus Kematian Akibat Penyakit HIV/AIDS di Provinsi Jawa Barat","authors":"Adinda Zahrotul Rizkiah, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.9487","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9487","url":null,"abstract":"Abstract. To model discrete data related to Poisson events, one way is to use Poisson Regression. If a data contains many zero values, the data can experience overdispersion. This overdispersion problem will increase type I errors, to model the overdispersion data, Hurdle Poisson Regression modeling is needed. Transmission of HIV/AIDS is caused by receiving HIV positive blood donors, through the mother's placenta to her fetus, and sexually transmitted infections. AIDS causes the human body's ability to fight infection to disappear, which can lead to someone's death. However, AIDS-related deaths do not always occur, especially in the districts/cities of West Java Province. So it is necessary to model Hurdle Poisson Regression in cases of death from AIDS. Data obtained through the official website of Open Data Jabar. The processed data includes: Number of AIDS-related Death Cases (Y), Number of HIV Positive Blood Donor Cases (X1), and Number of Syphilis Disease Cases (X2). Based on the research results, two models were formed,  for the truncated model and  for the logit model, but in the truncated model, HIV Positive Blood Donors (X1) and Syphilis Disease Cases (X2) have an effect on Death Cases due to AIDS in West Java Province, while in the logit model is only Cases of Syphilis (X2) which affect Cases of AIDS-related Deaths in West Java Province. \u0000Abstrak. Untuk memodelkan data diskrit yang menyangkut pada kejadian Poisson, salah satunya ialah menggunakan Regresi Poisson. Apabila suatu data mengandung banyak nilai nol, data tersebut dapat mengalami overdispersi. Permasalahan overdispersi ini akan memperbesar kesalahan jenis I, untuk memodelkan data yang mengalami overdispersi tersebut perlu pemodelan Hurdle Poisson Regression. Penularan HIV/AIDS disebabkan oleh penerimaan donor darah positif HIV, melalui plasenta Ibu ke janinnya, dan Penyakit Infeksi Menular Seksual. AIDS menyebabkan kemampuan tubuh manusia untuk melawan infeksi hilang, sehingga dapat menyebabkan kematian seseorang. Akan tetapi kematian akibat AIDS tidak selalu terjadi khususnya di Kabupaten/Kota Provinsi Jawa Barat. Maka perlu pemodelan Hurdle Poisson Regression pada kasus kematian akibat AIDS ini. Data diperoleh melalui website resmi Open Data Jabar. Data yang diolah tersebut antara lain: Jumlah Kasus Kematian Akibat AIDS (Y), Jumlah Kasus Donor Darah Positif HIV ( ), dan Jumlah Kasus Penyakit Sifilis ( ). Berdasarkan hasil penelitian dimana terbentuk dua model, yakni  untuk model truncated dan  untuk model logit. Akan tetapi pada model truncated, Donor Darah Positif HIV ( ) dan Kasus Penyakit Sifilis ( ) berpengaruh terhadap Kasus Kematian akibat AIDS di Provinsi Jawa Barat, sedangkan pada model logit hanya Kasus Penyakit Sifilis ( ) yang berpengaruh terhadap Kasus Kematian akibat AIDS di Provinsi Jawa Barat).","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 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":"128229193","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":"Implementasi Diagram Kontrol Multivariate Cumulative Sum (MCUSUM) untuk Pengendalian Proses Pembuatan Turning Pin Aluminium","authors":"Iqbal fadhilah, Suwanda","doi":"10.29313/bcss.v3i2.9369","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9369","url":null,"abstract":"Abstract. In statistics, one of the tools that can be used in process control is the control diagram. A control diagram is a graph that gives an idea of the behavior of a process. On the control diagram can detect any changes in the process that may affect the quality of the product. Product quality characteristics measured numerically or numerically are called variables. If there are more than two quality characteristics, simultaneous control of the related variables is required using a multivariate control diagram. The multivariate Shewhart control diagram (T2-Hotelling) is a frequently used multivariate control diagram. This control diagram will quickly detect if there is a large shift from the average vector of the process. Multivariate control diagrams that can quickly detect small average vector shifts include the multivariate cumulative sum control diagram (MCUSUM). There are some MCUSUM statistics. In this thesis, we will discuss MCUSUM statistics made by Healy and Crosiers, namely SH and SC. Application to control the aluminum turning pin manufacturing process with five quality characteristics shows an uncontrolled process with an out of control signal given in the 4th period by the MCUSUM SH control diagram, while according to MCUSUM SC it was detected in the 5th period, and for the application of the control graph T2 Hotteling was detected in the observation period 6. \u0000Abstrak. Dalam statistika, salah satu alat yang dapat digunakan dalam pengendalian proses adalah diagram kontrol. Diagram kontrol adalah sebuah grafik yang memberi gambaran tentang perilaku sebuah proses. Pada diagram kontrol dapat mendeteksi setiap perubahan dalam proses yang dapat mempengaruhi kualitas produk. Karakteristik kualitas produk yang diukur secara numerik atau angka disebut variabel. Jika terdapat lebih dari dua buah karakteristik mutu, pengendalian secara simultan terhadap variabel-variabel terkait tersebut diperlukan dengan menggunakan diagram kontrol multivariat. Diagram kontrol Shewhart multivariat (T2-Hotelling) adalah diagram kontrol multivariat yang sering digunakan. Diagram kontrol ini akan cepat mendeteksi jika terjadi pergeseran besar dari vektor rata-rata proses. Diagram kontrol multivariat yang dapat dengan cepat mendeteksi pergeseran vektor rata-rata yang kecil salah satunya adalah diagram kontrol multivariate cumulative sum (MCUSUM). Terdapat beberapa statistik MCUSUM. Dalam skripsi ini akan dibahas statistik MCUSUM yang dibuat oleh Healy dan Crosier yaitu SH dan SC. Aplikasi pada pengontrolan proses pembuatan turning pin aluminium dengan lima karakteristik mutu menununjukkan proses tidak terkendali dengan sinyal out of kontrol diberikan pada periode ke-4 oleh diagram kontrol MCUSUM SH, sedangkan menurut MCUSUM SC terdeteksi pada periode ke-5, dan untuk penerapan grafik pengendali T2 Hotteling terdeteksi pada pengamatan periode 6.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124394337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagram Kendali Poisson Progressive Mean (PPM) dalam Pengendalian Kualitas Produksi Kemasan Minyak Goreng di PT. XY","authors":"Wildan Nur Ramadlan, Teti Sofia Yanti","doi":"10.29313/bcss.v3i2.9390","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9390","url":null,"abstract":"Abstract. In the current modern era, the need for quality products has become an important criterion for consumers when purchasing a product, as the increasing competition in a product market demands companies to produce high-quality products. Quality control is necessary in the production process. Process capability is a quality control technique aimed at estimating the capability of a production process. Statistical quality control can be achieved using control charts, which are diagrams that represent observations of a production process over a specific period of time with Upper Control Limits (UCL) and Lower Control Limits (LCL) that define the pattern of dispersion. Control charts are used to quickly identify products experiencing defects. Control charts are divided into two types: attribute and variable. Attribute control charts are used for discrete data types, such as the number of defects in a product. In real-life situations, there are many cases where monitoring statistical control processes involves attribute data derived from quality characteristics that cannot be measured numerically or quantitatively. To address this type of data, there is a method used for monitoring Poisson processes called the Progressive Mean Attribute Poisson Control Chart (PPM). This research was conducted to examine how much and how quickly the detection of defective products occurred in the production of packaged cooking oil at PT. XY in 2017 using the PPM control chart. After analyzing the PPM control chart, it was concluded that the performance of this control chart was good because it could detect a significant number of data points that were out of control or in an unstable state. \u0000Abstrak. Di era modern saat ini, kebutuhan akan produk yang berkualitas menjadi ukuran penting bagi konsumen dalam membeli sebuah produk, karena semakin banyaknya persaingan pada suatu produk membuat perusahaan diminta untuk menghasilkan suau produk yang berkualitas. Pengendalian kualitas perlu dilakukan dalam suatu proses produksi. Kapabilitas proses merupakan sebuah teknik pengendalian kualitas yang bertujuan untuk memperkirakan kemampuan suatu proses produksi. Pegendalian kulitas secara statistik yang dapat digunakan yaitu diagram kendali, diagram kendali merupakan suatu diagram yang menggambarkan pengamatan suatu prosess produksi dalam periode waktu tertentu dengan Batas Kendali Atas (BKA) dan Batas Kendali Bawah (BKB) yang mengatur pola penyebaran. Diagram kendali ini dilakukan dengan tujuan dapat mengidentifikasi secepat mungkin pada produk yang mengalami kerusakan. Diagram kendali dibagi menjadi dua jenis yaitu atribut dan variabel, diagram kendali atribut digunakan untuk tipe data diskrit seperti jumlah kerusakan pada suatu produk. Dalam kehidupan nyata, ada banyak situasi di mana pemantauan proses pengendalian statistik melibatkan data atribut yang berasal dari karakteristik kualitas yang tidak dapat diukur secara numerik atau kuantitatif. Untuk mengatasi jenis data ","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129905688","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 Transformasi Box Cox untuk Mengatasi Masalah Ketidakstasioneran dan Pola Periodik dalam Data Deret Waktu pada Ekspor Bidang Pertanian di Indonesia","authors":"Chandra Maulana, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.9371","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9371","url":null,"abstract":"Abstract. Forecasting is useful for predicting future events covering the short, medium and long term with data that is usually used is time series data which is a collection of data compiled at a certain time continuously. Forecasting techniques for time series data analysis are divided into two models, namely forecasting models based on statistical mathematical models (ARIMA, exponential smoothing, moving average, and regression) and forecasting models based on artificial intelligence (neural networks, classification, and genetic algorithms). To improve forecast accuracy, the Box Cox Transformation is used when the time series data contains the problems of data non-stationarity and cyclical problems in the data, so there must be a process of checking for these two problems which can be checked using ADF statistics and ACF calculations. If non-stationary data occurs and there is a periodic or cyclical pattern, it is overcome by doing this Box Cox transformation. This study uses secondary data, namely agricultural exports in Indonesia in 2017-2022 from the website satudata.kemenag.go.id. The best model is SARIMA (1,0,1)(1,0,0)12 is the result of Box Cox transformation with the smallest MAPE and MAE values. The results of this study Box-Cox transformation can be used on data that was previously non-stationary can become stationary, but it cannot change the previously periodic data pattern into a stationary data pattern on the data used. \u0000Abstrak. Peramalan berguna untuk memprediksi kejadian yang akan datang yang meliputi jangka pendek, menengah dan panjang dengan data yang biasanya digunakan adalah data deret waktu yang merupakan kumpulan data yang disusun pada waktu tertentu secara terus menerus. Teknik peramalan analisis data deret waktu terbagi menjadi dua model yaitu model peramalan berdasarkan model matematika statistik (ARIMA, exponential smoothing, moving average, dan regresi) dan model peramalan berdasarkan kecerdasan buatan (neural network, klasifikasi, dan algoritma genetika). Untuk meningkatkan akurasi perkiraan, Transformasi Box Cox digunakan ketika data deret waktunya mengandung masalah adanya ketidakstasioneran data dan masalah siklus dalam data, sehiggga harus ada proses pemeriksaan dari kedua masalah tadi yg dapat diperiksa menggunakan statistik ADF dan perhitungan ACF. Jika terjadi data yg tidak stasioner dan terdapat pola periodik atau siklus maka diatasi dengan melalukan transformasi Box Cox ini. Penelitian ini menggunakan data sekunder yaitu ekpor bidang pertanian di Indonesia tahun 2017-2022 dari website satudata.kemenag.go.id. Didapat model terbaik yaitu SARIMA (1,0,1)(1,0,0)12 merupakan hasil dari transformasi Box Cox dengan nilai MAPE dan MAE terkecil.Hasil dari penelitian ini transformasi Box-Cox dapat digunakan pada data yang sebelumnya tidak stasioner dapat menjadi stasioner, namun tidak dapat mengubah pola data yang sebelumnya periodik menjadi pola data yang stasioner pada data yang dipakai pada penelitian ini. Hasil pe","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132156226","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 Data Kurs Jakarta Interbank Spot Dollar Rate (JISDOR) Menggunakan Model Hybrid ARIMA-GARCH","authors":"Tasya Noor Octa Melana, Suwanda","doi":"10.29313/bcss.v3i2.9165","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9165","url":null,"abstract":"Abstract. The Autoregressive Integrated Moving Average (ARIMA) time series model is usually used to predict the value of time series data in the future. This method will be optimal if the underlying assumptions are met. One of the assumptions that must be fulfilled is homogeneity in variance. This study discusses the handling of heteroscedasticity in time series data, namely by hybridizing the ARIMA model and the GARCH model in general written ARIMA(p,d,q)-GARCH(p',q'). This model is applied to data on the rupiah exchange rate against the dollar which is based on the Jakarta Interbank Spot Dollar Rate for the period January 2022 to June 2023. The results show that from the ARIMA model, the variance of the error is not homogeneous. After analyzing the data using the hybrid model, the best model for forecasting this exchange rate data is the ARIMA(0,1,1)-GARCH(1,1) hybrid with an AIC value of -8.682784, a SIC of -8.628699 and a MAPE of 1.809280 \u0000Abstrak. Model deret waktu Autoregressive Intergrated Moving Average (ARIMA) biasanya digunakan untuk meramalkan nilai data deret waktu pada masa yang akan datang. Metode ini akan optimal apabila asumsi yang mendasarinya terpenuhi. Salah satu asumsi yang harus dipenuhi adalah kehomogenan dalam varians. Penelitian ini membahas mengenai penanganan apabila terjadi heteroskedastisitas pada data deret waktu, yaitu dengan cara hybridizing model ARIMA dan model GARCH secara umum ditulis . Model ini diaplikasikan pada data kurs rupiah terhadap dollar yang berlandaskan pada Jakarta Interbank Spot Dollar Rate pada periode Januari 2022 hingga Juni 2023. Hasilnya menunjukkan bahwa dari model ARIMA, varians dari kekeliruan tidak homogen. Setelah dilakukan analisis data dengan model hybrid, didapatkan model terbaik untuk peramalan data kurs ini adalah hybrid ARIMA(0,1,1)-GARCH(1,1) dengan nilai AIC sebesar -8.682784, SIC sebesar -8.628699 dan MAPE sebesar 1.809280.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"45 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":"125157208","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 Robust Skewness dan Kurtosis pada Data yang Mengandung Outlier","authors":"Thiflan Farhan Atqan, Abdul Kudus","doi":"10.29313/bcss.v3i2.8691","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8691","url":null,"abstract":"Abstract.  Sample data that containing outliers have a large distorting effect on the sample mean and sample variance. Some statistics are also affected by the presence of outliers. These statistics include skewness and kurtosis. Robust statistics are needed to deal with this problem. One way is to use the Decile Mean (DM) or the average decile. In this thesis, robust skewness and kurtosis statistics will be applied using the Decile Mean (DM). This method will be applied to non-special case data at the Bandung state administrative court in 2019-2022 with the variable used, namely the length of the trial process. The results obtained are, the value of skewness is 8.97 and kurtosis is 11.11. As well as the results of the calculation of robust skewness of 0.1005 and robust kurtosis of 2.23. So, it can be concluded that by using the robust skewness and robust kurtosis methods, the distribution of non-special case data at the Bandung State Administrative Court in 2019-2022 is slightly skewed to the left, and is platykurtic or has a flat peak. \u0000Abstrak. Data sampel yang mengandung outlier memiliki pengaruh distorsi yang besar pada rata-rata sampel dan varians sampel. Beberapa statistik juga terpengaruh oleh adanya outlier. Statistik tersebut diantaranya yaitu skewness dan kurtosis. Diperlukan statistik yang robust untuk menangani masalah ini. Salah satu caranya yaitu dengan memanfaatkan Decile Mean (DM) atau rata-rata desil. Dalam skripsi ini akan diterapkan statistik skewness dan kurtosis yang robust dengan  memanfaatkan Decile Mean (DM). Metode ini akan diterapkan pada data perkara non khusus di pengadilan tata usaha negara Bandung tahun 2019-2022 dengan variabel yang digunakan yaitu lamanya proses persidangan. Hasil yang diperoleh yaitu, nilai dari skewness sebesar 8,97 dan kurtosis  sebesar 11,11. Serta hasil perhitungan robust skewness sebesar 0,1005 dan robust kurtosis 2,23. Maka, dapat disimpulkan bahwa dengan menggunakan metode robust skewness dan robust kurtosis, distribusi data perkara nonkhusus di pengadilan tata usaha negara Bandung tahun 2019-2022 sedikit condong kearah kiri, dan platikurtik atau memiliki puncak datar.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"63 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":"116148990","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":"Hubungan Status Sosial Ekonomi dengan Indeks Massa Tubuh Anak","authors":"Silmi Nur Husnayaini, Suliadi","doi":"10.29313/bcss.v3i2.7695","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7695","url":null,"abstract":"Abstract. Association measure is a statistic that can be used to measure relationship between two variables. There are various methods to assess the association between paired data variables IS usually designed for one particular type of measurement scale. Two numerical variables can use Pearson correlation. Two ordinal variables can use Spearman's Rank correlation or Kendall . However, if both variables are categorical (ordinal and nominal), then contingency table analysis can be used. In this study, we applied contingency table analysis in looking at the relationship between socioeconomic status and children's Body Mass Index based on data from Kober Nuurul Falaah. Several characteristics of socioeconomic status were studied, namely parents' education and parents' occupation and family income. It was found that there was no significant relationship between socioeconomic characteristics and children's BMI. Abstrak. Ukuran asosiasi merupakan statistik yang dapat digunakan untuk mengukur keerataan hubungan di antara dua variabel. Terdapat berbagai metode untuk meninjau asosiasi antara variabel data berpasangan. Namun, suatu metode asosiasi biasanya dirancang untuk satu jenis skala pengukuran tertentu. Dua variabel numerik dapat menggunakan korelasi Pearson. Dua variabel ordinal dapat menggunakan korelasi Rank Spearman atau Kendall . Namun, jika kedua variabel berskala kategorik (ordinal dan nominal), maka dapat menggunakan analisis tabel kontingensi. Dalam penelitian ini, kami menerapkan analisis tabel kontingensi dalam melihat hubungan status sosial ekonomi dengan Indeks Massa Tubuh anak berdasarkan data dari Kober Nuurul Falaah. Beberapa karakteristik status sosial ekonomi yang diteliti, yaitu pendidikan orang tua dan pekerjaan orang tua serta pendapatan keluarga. Diperoleh hasil bahwa tidak terdapat hubungan antara karakteristik sosial ekonomi dengan Indeks Massa Tubuh (IMT) anak secara signifikan.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"73 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":"128792929","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 Rebust Menggunakan Estimasi-S dengan Pembobotan Tukey Bisquare dan Welsch dalam Mengatasi Outlier","authors":"Mutia Salsabila, N. Rifai","doi":"10.29313/bcss.v3i2.8475","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8475","url":null,"abstract":"Abstract. Multiple linear regression analysis is a method for predicting the value of the dependent variable based on more than one independent variable. If in the multiple linear regression analysis there is a violation of the classical assumption, then the Least Squares Method (MKT) is not appropriate to use. In this study, the assumption of homoscedasticity was not met because there were outliers that affected the regression model. The right solution to overcome this is using robust regression without removing outlier data. Therefore, the author will discuss the robust regression of S-estimation using Tukey Bisquare and Welsch weighting on the human development index data for Central Java Province in 2021. The data includes the human development index as the dependent variable (Y), the net enrollment rate as the 1st independent variable (X1), the number of health facilities as the 2nd independent variable (X2), and the open unemployment rate as the 3rd independent variable (X3). Based on the results of the study, it was found that Tukey Bisquare's weighted S-estimation produces the best robust regression model because the Adjusted R-Square value of Tukey Bisquare's weighting is greater than Welsch's weighting (89.83% > 89.05%) and the Residual Standard Error (RSE) value of Tukey Bisquare's weighting is smaller than Welsch's weighting (2.783 <2.860). \u0000Abstrak. Analisis regresi linear berganda adalah metode untuk memprediksi nilai variabel terikat berdasarkan lebih dari satu variabel bebas. Jika dalam analisis regresi linear berganda terdapat pelanggaran asumsi klasik maka Metode Kuadrat Terkecil (MKT) tidak tepat digunakan. Pada penelitian ini, asumsi homoskedastisitas tidak terpenuhi karena ada outlier yang mempengaruhi model regresi. Solusi yang tepat untuk mengatasinya digunakan regresi robust tanpa menghapus data pencilan. Maka dari itu, penulis akan membahas mengenai regresi robust estimasi-S menggunakan pembobotan Tukey Bisquare dan Welsch pada data indeks pembangunan manusia Provinsi Jawa Tengah tahun 2021. Data tersebut meliputi indeks pembangunan manusia sebagai variabel tak bebas (Y), angka partisipasi murni sebagai variabel bebas ke-1 (X1), jumlah sarana kesehatan sebagai variabel bebas ke-2 (X2), dan tingkat pengangguran terbuka sebagai variabel bebas ke-3 (X3). Berdasarkan hasil penelitian diperoleh bahwa estimasi-S pembobotan Tukey Bisquare menghasilkan model regresi robust terbaik karena nilai Adjusted R-Square dari pembobotan Tukey Bisquare lebih besar daripada pembobotan Welsch (89,83% > 89,05%) dan nilai Residual Standard Error (RSE) dari pembobotan Tukey Bisquare lebih kecil daripada pembobotan Welsch (2,783 < 2,860).","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"109 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":"128593745","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 Regresi Log-Binomial untuk Menduga Risiko Relatif Kemacetan Kredit Berdasarkan Karakteristik Debitur","authors":"Risma Nur Ismayani, Abdul Kudus","doi":"10.29313/bcss.v3i2.8215","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8215","url":null,"abstract":"Abstract. Relative risk is the ratio of odds between binary exposures or the ratio of odds between the exposed and unexposed groups. The concept of relative risk is often introduced in a simple way, using a 2 x 2 cross tabulation of binary exposure and binary outcome. Then go further with the use of regression to estimate relative risk. A log-binomial regression model has been recommended which can directly estimate relative risk. The data used is data on credit debtors for Bhakti to determine the ratio of the risk of credit default based on the characteristics of the debtor using a log-binomial regression model. The results of this study obtained a log-binomial regression model  is π =  (-1,1032640 -0,6335250x1 + 1,0977941x2 - 0,3544660x3 - 1,2765607x4 + 1,7367890x5 + 1,0244938x6).  The relative risk outcomes using the log-binomial regression model based on significant interval estimates were the number of dependents (RR=3.00; 95%Cl 1.68-5.36), employment status ( RR=0.28; 95%Cl 0.16-0.48), debtor type (RR =5.68; 95%Cl 3.47-9.31) and length of working (RR =2.79; 95%Cl 1.46-5.33) meaning that if the number of group members compared is the same, then, 1) in the group of dependents ≤ 2 people there are 300 debtors who experience credit missed payment, while in the group of dependents > 2 there are only 100 debtors who are in missed payment, 2) in the group of permanent employees there are 28 who are in missed payment, while in the CPNS group there are 100 who are in missed payment, 3) in the group of new debtors there are 568 who are in missed payment, while in the group of repeat debtors there are only 100 debtors who are in missed payment, and 4 ) in the group of debtors who worked ≤ 2 years there were 279 missed payment, while those who had worked > 2 years there were 100 missed payment debtors. \u0000Abstrak. Risiko relatif adalah perbandingan peluang antara paparan biner atau perbandingan peluang antara kelompok yang terpapar dan kelompok yang tidak terpapar. Konsep risiko relatif sering kali diperkenalkan dengan menggunakan cara yang sederhana yaitu menggunakan tabulasi silang  dari paparan biner dan hasil biner. Lalu lebih lanjut lagi dengan menggunakan penggunaan dari regresi untuk menduga risiko relatif. Model regresi log-binomial telah direkomendasikan yang secara langsung dapat menduga risiko relatif. Data yang digunakan adalah data debitur kredit Guna Bhakti untuk mengetahui perbandingan risiko kemacetan kredit berdasarkan karakteristik debitur menggunakan model regresi log-binomial. Adapun hasil penelitian ini diperoleh  model regresi log-binomial yaitu π =  (-1,1032640 -0,6335250x1 + 1,0977941x2 - 0,3544660x3 - 1,2765607x4 + 1,7367890x5 + 1,0244938x6). Hasil risiko relatif menggunakan model regresi log-binomial berdasarkan taksiran interval yang signifikan adalah jumlah tanggungan (RR =3,00 ; 95%Cl 1,68-5,36), status kepegawaian ( RR=0,28; 95%Cl 0,16-0,48), tipe debitur ( RR=5,68; 95%Cl 3,47-9,31) dan lama kerja  ( RR=2,79; 95%Cl 1,46-5,33","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"36 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":"133352501","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 Regresi Zero Inflated Negative Binomial pada Kasus Campak di Provinsi Jawa Barat Tahun 2020","authors":"Isma Amarita, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.9311","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.9311","url":null,"abstract":"Abstrak. Analisis regresi merupakan suatu metode yang digunakan untuk mengetahui hubungan antara variabel bebas dan variabel respon. Dalam sebuah analisis regresi dengan variabel respon yang bersifat diskrit, dapat menggunakan analisis regresi Poisson.  Pada regresi Poisson harus memenuhi asumsi equisdispersi. Namun dalam pengaplikasiannya tak jarang mengalami pelanggaran asumsi, dimana nilai varians lebih besar dari nilai rata – ratanya atau bisa disebut dengan overdispersi. Salah satu penyebab overdispersi adalah adanya nilai nol yang berlebih (excess zeros) pada data variabel respon. Metode yang tepat untuk memodelkan kasus dengan data excess zeros dan terjadi overdispersi yaitu dengan menggunakan metode Zero Inflated Negative Binomial (ZINB). Tujuan penelitian ini adalah untuk memperoleh hasil dari penerapan regresi Zero Inflated Negative Binomial dalam memodelkan kasus campak di Provinsi Jawa Barat tahun 2020 serta mengetahui faktor apa saja yang berpengaruh signifikan terhadap kasus campak di Provinsi Jawa Barat tahun 2020. Dari hasil analisis, pada model regresi Poisson data mengalami kondisi overdispersi dan data mengalami excess zeros maka harus dilanjutkan analisis menggunakan regresi Zero Inflated Negative Binomial (ZINB). Pada analisis regresi ZINB diperoleh hasil pengujian bahwa faktor – faktor yang berpengaruh signifikan terhadap besarnya nilai harapan kasus campak di Provinsi Jawa Barat tahun 2020 yaitu persentase pemberian vitamin A, persentase pemberian ASI ekslusif dan persentase balita kurang gizi. Sedangkan faktor yang berpengaruh signifikan terhadap besarnya peluang terjadi campak di Provinsi Jawa Barat tahun 2020 persentase balita kurang gizi. \u0000Abstract. Regression analysis is a method used to determine the relationship between independent variables and response variables. In a regression analysis with discrete response variables, Poisson regression analysis can be used. In regression Poisson must satisfy the assumption of equisdispersion. However, in its application, it is not uncommon to experience violations of assumptions, where the variance value is greater than the average value or can be called overdispersion. One of the causes of overdispersion is the presence of excess zeros in the response variable data. The right method to model cases with excess zeros data and overdispersion is to use the Zero Inflated Negative Binomial (ZINB) method. The purpose of this study is to obtain results from the application of Zero Inflated Negative Binomial regression in modeling measles cases in West Java Province in 2020 and find out what factors have a significant influence on measles cases.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"76 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":"124516605","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}