Bandung Conference Series: Statistics最新文献

{"title":"Perbandingan Model Regresi Zero Inflated Poisson (ZIP) dan Hurdle Poisson (HP) pada Kasus Kematian Balita di Kota Bandung Tahun 2021","authors":"Ani Ressa Nuryaningsih, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.8522","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.8522","url":null,"abstract":"Abstract. In this study, the response variable is assumed to be Poisson-distributed enumeration data.  However, in the Poisson regression model, the enumerated data often deviates from the Poisson distribution because of the proportion of excess zero values ​​in the response variable (excess zero), resulting in a larger variance than the average of the observed variables (overdispersion).  Therefore, this study aims to model the data with Zero Inflated Poisson (ZIP) and Hurdle Poisson regression.  Based on the results of the study by comparing the ZIP and Hurdle Poisson regression models using the Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) values, it is found that the Hurdle Poisson regression model is more appropriate for modeling child mortality data in the city of Bandung in 2021 or in other words the Hurdle Poisson regression model is better at dealing with overdispersion and excess zeros problems compared to the Zero Inflated Poisson (ZIP) regression model. \u0000Abstrak. Pada penelitian ini variabel respon diasumsikan merupakan data cacahan yang berdistribusi Poisson. Namun, pada model regresi Poisson data cacah seringkali menyimpang dari distribusi Poisson karena proporsi nilai nol yang berlebih pada variabel respon (excess zero), sehingga menghasilkan varian yang lebih besar dari rata-rata variabel yang diamati (overdispersi). Maka dari itu, penelitian ini bertujuan untuk memodelkan data dengan regresi Zero Inflated Poisson (ZIP) dan Hurdle Poisson. Berdasarkan hasil penelitian dengan membandingkan model regresi ZIP dan Hurdle Poisson menggunakan nilai Akaike Information Criterion (AIC) dan Bayesian Information Criteria (BIC), maka diperoleh bahwa model regresi Hurdle Poisson lebih tepat digunakan untuk memodelkan data kematian balita di Kota Bandung tahun 2021 atau dengan kata lain model regresi Hurdle Poisson lebih baik dalam menangani masalah overdispersi dan excess zeros dibandingkan dengan model regresi Zero Inflated Poisson (ZIP).","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"4 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":"125786346","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":"Kesesuaian Distribusi Magnitude Gempa dengan Distribusi Teoritis Gempa dalam Perhitungan Premi Asuransi Gempa Bumi","authors":"Nadya Chaerunisa Apriani, Sutawanir Darwis","doi":"10.29313/bcss.v3i2.7872","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7872","url":null,"abstract":"Abstract. Earthquake insurance is insurance that guarantees loss or damage to insured property and/or interests that are directly caused by an earthquake. The purpose of this study was to determine the suitability of the earthquake magnitude distribution model with the theoretical distribution using the goodness of fit method, as well as to know the calculation steps and the amount of earthquake insurance premiums for 3 regions in West Java. The method used in this research is goodness of fit (GOF) based on empirical distribution function with five types of test statistics, and the following method is used probabilistic seismic hazard analysis (PSHA). The data used in this study is secondary data in the form of historical data on earthquake events in the 3 research areas for 10 years, 2013-2022. The results of this study are that the model on the distribution of earthquake magnitudes is in accordance with the theoretical distribution, namely the exponential distribution, as well as the model for the distribution of earthquake hypocenter distances which is in accordance with the theoretical distribution that has been determined. As well as Obtained the total amount of insurance premiums for Garut Regency is Rp. 206,610, - for a house that has a total tax object of Rp. 14,681,468, - for Tasikmalaya Regency is Rp. 274,630, - for a house that has a total tax object of Rp. 15,121,366, - and for Sukabumi Regency it is Rp. 228,610, - for a house that has a total tax object of Rp. 14,969,526, - the total premium is the amount of money that must be paid by the insured to the insurance company every month. \u0000Abstrak. Asuransi gempa bumi adalah asuransi yang menjamin kerugian atau kerusakan harta benda dan/atau kepentingan yang dipertanggungkan yang secara langsung disebabkan oleh gempa bumi. Tujuan dari penelitian adalah untuk mengetahui kesesuaian model distribusi magnitude gempa dengan distribusi teoritisnya menggunakan metode goodness of fit, dan mengetahui langkah perhitungan dan besarnya premi asuransi gempa bumi untuk 3 wilayah di Jawa Barat. Metode yang digunakan yaitu goodness of fi  (GOF) berdasarkan distribusi fungsi empiris dengan lima jenis statistik uji, dan probabilistic seismic hazard analysis (PSHA). Data yang digunakan pada penelitian ini adalah data sekunder berupa data historis kejadian gempa bumi di 3 wilayah penelitian selama 10 tahun yaitu 2013-2022. Hasil dari penelitian ini adalah model pada distribusi magnitude gempa sudah sesuai dengan distribusi teoritisnya yaitu distribusi eksponensial, begitu juga untuk model distribusi jarak hiposenter gempa yang sudah sesuai dengan distribusi teoritisnya yang sudah ditentukan. Serta Diperoleh besarnya total premi asuransi untuk Kabupaten Garut adalah sebesar Rp. 206.610,- untuk rumah yang memiliki jumlah dari objek pajaknya sebesar Rp. 14.681.468,- untuk Kabupaten Tasikmalaya adalah sebesar Rp. 274.630,- untuk rumah yang memiliki jumlah dari objek pajaknya sebesar Rp. 15.121.366,- dan untuk Ka","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134103260","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":"Selang Kepercayaan Bootstrap Parametrik untuk Perbedaan Signal to Noise Ratio dari Distribusi Eksponensial Dua Parameter pada Data Waktu Kegagalan Laminasi Mylar-Poliuretana","authors":"Amirul Mukminin, Abdul Kudus","doi":"10.29313/bcss.v3i2.7627","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7627","url":null,"abstract":"Abstract. The two-parameter exponential distribution is a probability model associated with observed unit failure time data. Investigating the estimation characteristics of the Signal to Noise Ratio (SNR) difference between two populations with a two-parameter exponential distribution is intriguing. SNR represents the ratio between the mean and standard deviation. For more accurate results in estimating the SNR difference between two populations, confidence intervals can be utilized. One of the methods suitable for constructing confidence intervals is the parametric bootstrap method, particularly effective for small sample sizes with distribution assumptions. The confidence intervals built are based on bootstrap percentiles. The data used in this study consists of small-sized samples of failure times of mylar-polyurethane lamination in High Voltage Direct Current (HVDC) insulation structures, subjected to voltage loads of 100.3 kV/mm and 361.4 kV/mm. Based on the calculations, it was found that with a 95% confidence level, the parametric bootstrap confidence interval for the SNR difference of the two-parameter exponential distribution of failure times in mylar-polyurethane lamination under electric voltage loads of 100.3 kV/mm and 361.4 kV/mm falls within the range of [-0.29; 0.93]. \u0000Abstrak. Distribusi eksponensial dua parameter adalah model probabilitas yang terkait dengan data waktu kegagalan unit yang diamati. Karakteristik dari estimasi perbedaan Signal to Noise Ratio (SNR) dari dua populasi berdistribusi eksponensial dua parameter menarik untuk diteliti. SNR adalah rasio antara rata-rata dengan simpangan baku.  Untuk hasil yang lebih akurat dalam memperkirakan estimasi perbedaan SNR dari dua populasi yaitu menggunakan selang kepercayaan. Salah satu metode yang dapat digunakan dalam membangun selang kepercayaan yaitu dengan metode bootstrap parametrik yang cocok untuk ukuran data sampel yang kecil dan memiliki asumsi distribusi. Selang kepercayaan yang dibangun didasarkan pada bootstrap persentil. Data yang digunakan adalah data sampel berukuran kecil yaitu waktu kegagalan laminasi mylar-poliuretana pada struktur isolasi High Voltage Direct Current (HVDC) dengan beban tegangan 100,3 kV/mm dan 361,4 kV/mm. Berdasarkan hasil perhitungan diperoleh bahwa dengan tingkat kepercayaan 95% selang kepercayaan bootstrap parametrik untuk perbedaan SNR dari distribusi eksponensial dua parameter pada data waktu kegagalan laminasi mylar-poliuretana dengan beban tegangan listrik 100,3 kV/mm dan 361,4 kV/mm adalah berada di dalam rentang [–0,29; 0,93].","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115590608","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 Perkelompok Umur Provinsi Banten Tahun 2020","authors":"Ageng Roro Dwi Utamy, Yayat Karyana","doi":"10.29313/bcss.v3i2.7669","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7669","url":null,"abstract":"Abstract. In this research, we will calculate the estimated net migration figures for male and female population age groups in the year 2020 for the Province of Banten based on data from the 2020 Population Census. The data analysis technique used is by comparing the Population Growth Rate (PGR) of Indonesia with that of the Province of Banten. By obtaining the difference between the PGR of Indonesia and the PGR of the Province of Banten, we can determine the estimated population change due to migration, or the number of net migrants in the Province of Banten in the year 2020. According to the results of the 2020 Population Census (SP 2020), the PGR of Indonesia is 1.25% per year, while the PGR of the Province of Banten is 1.10% per year, resulting in a difference of -0.15% or -0.015. From the calculations, the estimated number of net migrants in the Province of Banten is -17,875 people. To break down the data into age groups, we use the Age Specific Net Migration Rate (ASNMR) of the Province of Banten based on data from the 2015 Inter-Census Population Survey adjusted to produce negative ASNMR values for the year 2020. This results in the Net Migration Index for males and females, which are -127 and -281 times, respectively. \u0000Abstrak. Dalam penelitian ini akan menghitung perkiraan angka migrasi neto perkelompok umur penduduk laki-laki dan penduduk perempuan tahun 2020 Provinsi Banten berdasarkan data hasil Sensus Penduduk tahun 2020. Teknik analisis data yang digunakan adalah dengan membandingkan Laju Pertumbuhan Penduduk (LPP) Indonesia dengan LPP Provinsi Banten. Dengan didapatkannya nilai selisih antara LPP Indonesia dengan LPP Provinsi Banten, dapat diketahui perkiraan perubahan penduduk karena migrasi, atau dapat diketahui jumlah migran neto penduduk Provinsi Banten tahun 2020. Hasil Sensus Penduduk (SP) 2020, LPP Indonesia adalah 1,25 % pertahun sedangkan LPP Provinsi Banten adalah 1,10% pertahun, sehingga ada selisih sebesar -0,15% atau sebesar -0,015. Dari hasil perhitungan didapat perkiraan jumlah migrasi neto penduduk Provinsi Banten adalah -17.875 orang. Untuk memecah menjadi kelompok umur digunakan Age Specific Net Migration Rate (ASNMR) Provinsi Banten berdasarkan data Survei Penduduk Antar Sensus (SUPAS) 2015 yang disesuaikan, sehingga menghasilkan perkiraan nilai ASNMR tahun 2020 bernilai negatif, dan menghasilkan Indeks Migrasi Neto untuk penduduk laki-laki dan perempuan masing-masing adalah -127 dan -281 kali.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129972767","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":"Biplot Multivariate Regression dan Penerapannya dalam Menganalisis Faktor-Faktor yang Mempengaruhi Indikator Derajat Kesehatan di Jawa Barat","authors":"Thania Indra Mutiara, Suliadi Suliadi","doi":"10.29313/bcss.v3i2.7569","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7569","url":null,"abstract":"Abstract. Multivariate regression analysis is a statistical tool that is concerned with describing and evaluating the relationship between a given set of responses and a set of predictors. This analysis involves many variables, making difficulty to analyze and interpret. Therefore, we need a method that can visualize the regression model in a comprehensive manner to facilitate interpretation. The multivariate regression biplot aims to graphically visualize the effect of the predictor variable on the response variable and to display the regression result. This multivariate regression biplot is well suited for solving problems in multivariate regression analysis. This research uses biplot multivariate regression to analyze data on factors that influence health status indicators in West Java. The results of applying multivariate regression biplot show that the Y1 variable has a positive correlation with the variables X2, X3, and X4 and has a negative correlation with the variables X1, and X5. The variable Y2 has a positive correlation with the variables X1, X2, X3, and X5 and has a negative correlation with the variable X4. The variable Y3 has a positive correlation with the variable X4 and has a negative correlation with the variable X1, X2, X3, and X5. The overall quality of the multivariate regression biplot visualization result is 98.7%. This means that the information provided by the multivariate regression biplot in the form of visualization can explain 98.7% of all the information contained in the data. \u0000Abstrak. Analisis regresi multivariat adalah alat statistik yang berkaitan dengan menggambarkan dan mengevaluasi hubungan antara satu set respon yang diberikan dan satu set prediktor. Analisis tersebut melibatkan banyak variabel, sehingga dalam menganalisis dan menginterpretasikannya menjadi sulit. Oleh karena itu diperlukan metode yang dapat memvisualisasikan model regresi secara komprehensif, sehingga memudahkan interpretasi. Biplot regresi multivariat bertujuan untuk memvisualisasikan pengaruh variabel prediktor terhadap variabel respon secara grafis dan untuk menampilkan hasil regresi. Biplot regresi multivariat ini sangat cocok digunakan untuk mengatasi permasalahan dalam analisis regresi multivariat. Penelitian ini akan membahas biplot regresi multivariat dan penerapannya pada data faktor-faktor yang mempengaruhi indikator derajat kesehatan di Jawa Barat. Hasil penerapan biplot regresi multivariat menunjukkan bahwa variabel Y1 berkorelasi positif dengan variabel X2, X3, dan X4 serta berkorelasi negatif dengan variabel X1, dan X5. Variabel Y2 berkorelasi positif dengan variabel X1, X2, X3, dan X5 serta berkorelasi negatif dengan variabel X4. Variabel Y3 berkorelasi positif dengan variabel X4 dan berkorelasi negatif dengan variabel X1, X2, X3, dan X5. Kualitas keseluruhan dari hasil visualisasi biplot regresi multivariat sebesar 98,7%. Artinya informasi yang diberikan oleh biplot regresi multivariat dalam bentuk visualisasi mampu menerang","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129211905","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":"Model Regresi Gamma pada Data Indeks Pendidikan Kabupaten/Kota di Provinsi Jawa Barat Tahun 2021","authors":"Natasya, Nusar Hajarisman","doi":"10.29313/bcss.v3i2.7834","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7834","url":null,"abstract":"Abstract. Regression analysis is a statistical method used to describe and model cause-and-effect relationships between variables. In applying regression analysis methods, there are often problems of violating normal assumptions or asymmetrical response variable data. The gamma distribution is a flexible distribution that can model continuous data with positive values. Gamma regression is a regression model that can describe cause-and-effect relationships between predictor variables and gamma-distributed response variables. The maximum likelihood  method is used in estimating the parameters of gamma regression models. Education index data can be positive or negative, it is known from the Central Bureau of Statistics of West Java Province that the education index data of West Java Province in 2021 has a positive value, so this study will discuss the application of the gamma regression model in the 2021 West Java Province education index data. Based on the results of the tests conducted, the education index data is not normally distributed and has a graph of functions that form positive skewness so that modeling can be continued using gamma regression. Factors that are thought to affect the education index of West Java Province are the poverty line, open unemployment rate, and per capita expenditure in West Java Province in 2021. From the results of hypothesis testing that has been carried out, it is concluded that per capita expenditure has a significant effect on the education index in West Java Province in 2021. The following gamma regression model is selected using AIC value criteria: \u0000Abstrak. Analisis regresi merupakan suatu metode statistik yang digunakan untuk mendeskripsikan dan memodelkan hubungan sebab-akibat antar variabel. Dalam melakukan penerapan metode analisis regresi sering terjadi masalah pelanggaran asumsi kenormalan atau data variabel respon yang tidak simetris. Distribusi gamma merupakan distribusi yang fleksibel sehingga dapat memodelkan data kontinu yang bernilai positif. Regresi gamma merupakan model regresi yang dapat menggambarkan hubungan sebab-akibat antara variabel prediktor dengan variabel respon yang berdistribusi gamma. Metode maximum likelihood digunakan dalam menaksir parameter model regresi gamma. Data indeks pendidikan dapat bernilai positif atau negatif, diketahui dari Badan Pusat Statistik Provinsi Jawa Barat bahwa data indeks pendidikan Provinsi Jawa Barat Tahun 2021 memiliki nilai yang positif, sehingga dalam penelitian ini akan dibahas terkait penerapan model regresi gamma pada data indeks pendidikan Provinsi Jawa Barat Tahun 2021. Berdasarkan hasil pengujian yang dilakukan, data indeks pendidikan tidak berdistribusi normal dan memiliki grafik fungsi yang membentuk positive skewness sehingga dapat dilanjutkan pemodelan menggunakan regresi gamma. Faktor yang diduga berpengaruh terhadap indeks pendidikan Provinsi Jawa Barat adalah garis kemiskinan, tingkat pengangguran terbuka, dan pengeluaran per kapita di P","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130406053","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 Decision On Belief (DOB) dan Diagram Kendali Progressive Mean (PM) dalam Pengendalian Kualitas Produksi Kayu Lapis di PT. XYZ","authors":"Akmal Athallah Mutakin, Nur Azizah komara Rifai","doi":"10.29313/bcss.v3i2.7791","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7791","url":null,"abstract":"Abstract. The rapid development of Science and Technology (IPTEK) in the digital age has caused competition in several sectors of the economy to increase, this makes companies required to produce quality products. In a production, the quality of a product needs to be controlled so that it always meets the targets set by the company. Statistical quality control is needed to detect as early as possible any problems in a production. Control charts are often used to control statistical quality control in a production process. Variable control diagrams are usually used when the quality characteristics can be measured using the same unit, which is different from the variable control charts, attribute control charts are usually used when the data is in the form of proportions. Several statistical quality control chart methods used to control product quality are the Decision On Belief (DOB), Cumulative SUM (CUSUM), Exponentially Weighted Moving Average (EWMA) and Progressive Mean (PM) control chart methods. In this study, we will look at the performance comparison of the Decision On Belief (DOB) control chart with the Progressive Mean (PM) control chart applied to defective product data on plywood production at PT. XYZ in September 2021. After comparing the two control charts, it is concluded that the Decision On Belief (DOB) control chart is a control chart that is better and faster in detecting data that is out of control or is in an out of control state. \u0000Abstrak. Semakin pesatnya perkembangan Ilmu dan Teknologi (IPTEK) di era digital, menyebabkan persaingan di beberapa sektor perekonomian mengalami kenaikan, hal tersebut membuat perusahaan dituntut untuk menghasilkan produk yang berkualitas. Dalam suatu produksi, kualitas dari suatu produk perlu dikendalikan agar selalu sesuai dengan target yang telah ditetapkan oleh perusahaan. Pengendalian kualitas statistik sangat diperlukan guna mendeteksi sedini mungkin adanya permasalahan dari suatu produksi. Diagram kendali sering digunakan untuk mengontrol pengendalian kualitas statistika dalam suatu proses produksi. Diagram kendali variabel biasa digunakan apabila karakteristik kualitasnya dapat diukur menggunakan satuan yang sama berbeda dengan diagram kendali variabel, diagram kendali atribut biasa digunakan apabila datanya berbentuk proporsi. Beberapa metode diagram pengendali kualitas statistik yang digunakan dalam mengontrol kualitas produk adalah metode diagram kendali Decision On Belief (DOB), Cumulative SUM (CUSUM), Exponentially Weighted Moving Average (EWMA) dan diagram kendali Progressive Mean (PM). Pada penelitian ini akan dilihat perbandingan performa dari diagram kendali Decision On Belief (DOB) dengan diagram kendali Progressive Mean (PM) yang diterapkan pada data produk cacat produksi kayu lapis di PT. XYZ bulan September 2021. Setelah membandingkan kedua diagram kendali tersebut diperoleh kesimpulan bahwa diagram kendali Decision On Belief (DOB) merupakan diagram kendali yang lebih baik dan ","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133913881","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 Metode Artificial Neural Network (ANN) Algoritma Backpropagation untuk Klasifikasi Kualitas Udara di Provinsi DKI Jakarta Tahun 2021","authors":"Lutfiah Anindya Putri, Suwanda","doi":"10.29313/bcss.v3i2.7826","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7826","url":null,"abstract":"Abstract. Classification is the systematic division of an individual into a group or category according to established rules or standards. There are several classification algorithms that can be used to classify data, including logistic regression, discriminant analysis, and Artificial Neural Network (ANN). This study uses an Artificial Neural Network (ANN) classification algorithm because the data used is not normally distributed. ANN is a classification algorithm that is widely used for character recognition problems and is a strong classifier because of its high calculation rate and accuracy. In this thesis, ANN is implemented on air quality classification data for DKI Jakarta in 2021 with particulate (PM10), sulfide (SO2), carbon monoxide (CO), Ozone (O3), and nitrogen dioxide (NO2) variables. This method will be calculated based on the accuracy, precision, and recall values obtained from the confused matrix. The most optimal classification results are obtained from the network architecture, which consists of 5 input layers, 4 hidden layers, and 2 output layers with an epoch value of 5000 and a learning rate of 0.001. The network architecture produces an accuracy value of 94%, a precision of 90%, and a recall of 100%. \u0000Abstrak. Klasifikasi adalah pembagian sistematis dari sebuah individu ke dalam suatu kelompok atau kategori menurut aturan atau standar yang ditetapkan. Terdapat beberapa algoritma klasifikasi yang dapat digunakan dalam mengklasifikasi data, diantaranya regresi logistik, analisis diskriminan, dan Artificial Neural Network (ANN). Penelitian ini menggunakan algoritma klasifikasi Artificial Neural Network (ANN) karena data yang digunakan tidak berdistribusi normal. ANN merupakan salah satu algoritma klasifikasi yang banyak digunakan untuk masalah pengenalan karakter dan merupakan classifier yang kuat karena tingkat perhitungan dan keakuratannya yang tinggi. Pada skripsi ini, ANN diimplementasikan pada data klasifikasi kualitas udara DKI Jakarta Tahun 2021 dengan variabel partikulat (PM10), sulfida (SO2), karbon monoksida (CO), Ozon (O3), dan nitrogen dioksida (NO2). Metode tersebut akan dihitung berdasarkan ukuran nilai accuracy, precision, dan recall yang didapat dari confusion matrix. Hasil pengklasifikasian yang paling optimal didapat dari arsitektur jaringan yang terdiri dari 5 input layer, 4 hidden layer, dan 2 output layer dengan nilai epoch 5000 dan learning rate 0.001. Arsitektur jaringan tersebut menghasilkan nilai accuracy sebesar 94%, precision sebesar 90%, dan recall sebesar 100%.","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121825023","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 Faktor Pengaruh Penilaian Peserta Latsar CPNS terhadap Widyaiswara Angkatan IV Pemkab Banjarnegara Tahun 2022","authors":"Sinta Asanah, Ilham Faishal Mahdy","doi":"10.29313/bcss.v3i2.7677","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7677","url":null,"abstract":"Abstract. Widyaiswara is a functional position for a Civil Servant (PNS) who is in charge of teaching, training and full education in educational programs from government agencies. In order to find out whether a widyaiswara is able to realize the competency development of a CPNS, it is necessary to know what factors are assessed by LATSAR CPNS participants regarding widyaiswara so that it can become an evaluation for a widyaiswara. Based on these problems, the problems in this study are formulated as follows: (1) Can the aspects that the LATSAR CPNS participants evaluate for Widyaiswara be factored? (2) How many factors are formed in the assessment of the Widyaiswara LATSAR CPNS participants? (3) What is the variance that can be explained by the factors formed in the LATSAR CPNS participants' assessment of the Widyaiswara?. Researchers used secondary data sourced from PPSDM Geominerba. The data consists of Widyaiswara (teachers), learning materials and 10 aspects of assessment that are filled in by each CPNS LATSAR participant. Researchers used the method of factor analysis techniques on 10 aspects of the CPNS LATSAR participant assessment. The results of this study are: All aspects are feasible to be factored and the factors formed are one factor. In addition, the factors formed have eigenvalues of 8.378 which means that these factors are able to explain 83.8% of the variability of the original ten aspects. The remaining 16.2% of the variability is influenced by other variability not examined. Abstrak. Widyaiswara merupakan jabatan fungsional bagi seorang Pegawai Negeri Sipil (PNS) yang bertugas mengajar, melatih dan mendidik secara penuh pada program pendidikan dari instansi pemerintah. Untuk mengetahui apakah seorang widyaiswara mampu mewujudkan pengembangan kompetensi seorang CPNS maka perlu diketahui faktor-faktor apa saja yang menjadi penilaian peserta LATSAR CPNS mengenai widyaiswara agar dapat menjadi evaluasi bagi seorang widyaiswara. Berdasarkan masalah tersebut, maka permasalahan dalam penelitian ini dirumuskan sebagai berikut: (1) Apakah aspek-aspek yang menjadi penilaian peserta LATSAR CPNS terhadap Widyaiswara dapat difaktorkan? (2) Berapakah faktor yang terbentuk pada penilaian peserta LATSAR CPNS terhadap Widyaiswara? (3) Berapakah variansi yang dapat dijelaskan oleh faktor yang terbentuk pada penilaian peserta LATSAR CPNS terhadap Widyaiswara?. Peneliti menggunakan data sekunder yang bersumber dari PPSDM Geominerba. Data terdiri dari Widyaiswara (pengajar), materi pembelajaran dan 10 aspek penilaian yang diisi oleh setiap peserta LATSAR CPNS. Peneliti menggunakan metode teknik analisis faktor pada 10 aspek-aspek penilaian peserta LATSAR CPNS. Hasil dari penelitian ini adalah: Semua aspek layak untuk difaktorkan dan faktor yang terbentuk sebanyak satu faktor. Selain itu, faktor yang terbentuk memiliki angka eigenvalues sebesar 8,38 yang berarti bahwa faktor tersebut mampu menjelaskan 83,78% dari variabilitas kesepuluh aspek asli","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132215818","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 Fuzzy Time Series Cheng untuk Meramalkan Nilai Ekspor Migas di Indonesia","authors":"Engki Dulfitri Eha, Suwanda","doi":"10.29313/bcss.v3i2.7604","DOIUrl":"https://doi.org/10.29313/bcss.v3i2.7604","url":null,"abstract":"Abstract. Forecasting is useful for predicting future events which include the short, medium and long term. The data commonly used is time series data which is a collection of data that is arranged at a certain time continuously. There are several methods in the analysis of time series data, namely the traditional method (ARIMA) and Fuzzy time series. The fuzzy times series method is proven to be able to improve traditional methods such as handling data fluctuations, uncertainty of data subjectivity. With the privilege of not requiring the fulfillment of the assumption test. This thesis will discuss the Fuzzy time series cheng which is the development of the fuzzy time series chen and yu which can minimize forecasting errors. The Cheng's Fuzzy time series method has been applied to the value of Indonesia's oil and gas exports based on data for 1975-2022 with the forecast model obtained with a MAPE of 19.7668%. From the results of the forecasting model obtained in 2023 it is estimated that the value of Indonesia's oil and gas exports will be 15,215.9182 (million US$), experiencing a decrease of 803,7818 (million US$) when compared to the export value in 2022 of 16,019.7 (million US$) . \u0000Abstrak. Peramalan berguna untuk memprediksi kejadian yang akan datang yang meliputi jangka pendek, menengah dan panjang. Data yang biasanya digunakan adalah data deret waktu yang merupakan kumpulan data yang disusun pada waktu tertentu secara terus menerus. Ada beberapa metode dalam analisis data deret waktu yaitu metode tradisional (ARIMA) dan Fuzzy time series. Metode fuzzy times series telah terbukti dapat memperbaiki metode tradisional seperti menangani fluktuasi data, ketidakpastian subjektivitas dalam data. Dengan keistimewaan tidak membutuhkan pemenuhan uji asumsi. Dalam skripsi ini akan di bahas Fuzzy time series cheng yang merupakan pengembangan dari Fuzzy time series chen dan yu yang dapat memperkecil kekeliruan peramalan. Metode Fuzzy time series cheng telah di terapkan pada nilai ekspor migas Indonesia berdasarkan data dari tahun 1975-2022 dengan diperoleh model ramalan dengan MAPE sebesar 19.7668%. Dari hasil model peramalan yang diperoleh pada tahun 2023 diperkirakan nilai ekspor migas Indonesia sebesar 15,215.9182(juta US$), mengalami penurunan sebesar 803.7818 (juta US$) jika dibandingkan dengan nilai ekspor tahun 2022 sebesar 16,019.7 (juta US$).","PeriodicalId":337947,"journal":{"name":"Bandung Conference Series: Statistics","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123500710","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}