Jambura Journal of Probability and Statistics最新文献_第3页

PERBANDINGAN MATRIKS PEMBOBOT ROOK DAN QUEEN CONTIGUITY DALAM ANALISIS SPATIAL AUTOREGRESSIVE MODEL (SAR) DAN SPATIAL ERROR MODEL (SEM)

Jambura Journal of Probability and Statistics Pub Date : 2022-05-29 DOI: 10.34312/jjps.v3i1.13582

Ingka Rizkyani Akolo

{"title":"PERBANDINGAN MATRIKS PEMBOBOT ROOK DAN QUEEN CONTIGUITY DALAM ANALISIS SPATIAL AUTOREGRESSIVE MODEL (SAR) DAN SPATIAL ERROR MODEL (SEM)","authors":"Ingka Rizkyani Akolo","doi":"10.34312/jjps.v3i1.13582","DOIUrl":"https://doi.org/10.34312/jjps.v3i1.13582","url":null,"abstract":"The spatial weighting matrix is very important to overview of the relationship between one location to another in the spatial regression. In this study, the authors compare the weighting matrix of queen contiguity and rook contiguity in the SAR and SEM models in stunting cases in Bone Bolango Regency, Gorontalo Province. The variables used are the number of IDL, the percentage of LBW, the amount of proper sanitation, the percentage of exclusively breastfed babies, and the number of poor people. The purpose of this study was to determine the factors that influence stunting in Bone Bolango Regency, compare the results of the analysis of the rook contiguity and queen contiguity matrices in the SAR and SEM models and determine the best model and weighting matrix in stunting modeling in Bone Bolango Regency. The results showed that the significant factor in the SAR model was the number of poor people, while the significant factors in the SEM model were the number of IDL, the number of proper sanitation, and the percentage of exclusively breastfed babies. In the SEM model, the p-value of queen contiguity is smaller than that of rook contiguity.The best model in this study is the SEM model.","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131121983","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}

引用次数: 3

ANALISIS KLASIFIKASI ARTIST MUSIC MENGGUNAKAN MODEL REGRESI LOGISTIK BINER DAN ANALISIS DISKRIMINAN 音乐艺术家分类分析使用二元物流回归模型和离散分析

Jambura Journal of Probability and Statistics Pub Date : 2022-05-29 DOI: 10.34312/jjps.v3i1.13708

A. Dani, Vita Ratnasari, Ludia Ni’matuzzahroh, Igar Calveria Aviantholib, Raditya Novidianto, Narita Yuri Adrianingsih

{"title":"ANALISIS KLASIFIKASI ARTIST MUSIC MENGGUNAKAN MODEL REGRESI LOGISTIK BINER DAN ANALISIS DISKRIMINAN","authors":"A. Dani, Vita Ratnasari, Ludia Ni’matuzzahroh, Igar Calveria Aviantholib, Raditya Novidianto, Narita Yuri Adrianingsih","doi":"10.34312/jjps.v3i1.13708","DOIUrl":"https://doi.org/10.34312/jjps.v3i1.13708","url":null,"abstract":"Characteristics of a song are an important aspect that must be kept authentic by a singer. Using the Spotify API feature, we can extract the characteristics or elements of a song sung by a singer. There are eight (8) elements that we can get from the extraction of a song, namely: Danceability, Energy, Loudness, Speechiness, Acousticness, Liveness, Valence, and Tempo. Based on the extraction results, we can label the music artist using the classification analysis method. In this study, the labels are music artists, namely Ariana Grande and Taylor Swift. This study aims to obtain the classification of music artist labels using binary logistic regression methods and discriminant analysis. The response variable used in this study is Artist Music (Y) which is categorized into two categories, namely Ariana Grande (Y=0) and Taylor Swift (Y=1). The data will be divided into training and testing data with the proportion of data 90:10 and 80:20. Based on the results of the analysis, the binary regression model that was built, with the proportion of training testing data that is 90:10 has a classification accuracy for data testing of 90.00%.","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114688835","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}

引用次数: 2

IMPLEMENTASI ALGORITMA NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE PADA KLASIFIKASI SENTIMEN REVIEW LAYANAN TELEMEDICINE HALODOC

Jambura Journal of Probability and Statistics Pub Date : 2021-11-30 DOI: 10.34312/jjps.v2i2.11364

Reynalda Nabila Cikania

{"title":"IMPLEMENTASI ALGORITMA NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE PADA KLASIFIKASI SENTIMEN REVIEW LAYANAN TELEMEDICINE HALODOC","authors":"Reynalda Nabila Cikania","doi":"10.34312/jjps.v2i2.11364","DOIUrl":"https://doi.org/10.34312/jjps.v2i2.11364","url":null,"abstract":"Halodoc is a telemedicine-based healthcare application that connects patients with health practitioners such as doctors, pharmacies, and laboratories. There are some comments from halodoc users, both positive and negative comments. This indicates the public's concern for the Halodoc application so it is necessary to analyze the sentiment or comments that appear on the Halodoc application service, especially during the COVID-19 pandemic in order for Halodoc application services to be better. The Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms are used to analyze the public sentiment of Halodoc's telemedicine service application users. The negative category sentiment classification result was 12.33%, while the positive category sentiment was 87.67% from 5,687 reviews which means that the positive review sentiment is more than the negative review sentiment. The accuracy performance of the Naive Bayes Classifier Algorithm resulted in an accuracy rate of 87.77% with an AUC value of 57.11% and a G-Mean of 40.08%, while svm algorithm with KERNEL RBF had an accuracy value of 86.1% with an AUC value of 60.149% and a G-Mean value of 49.311%. Based on the accuracy value of the model can be known SVM Kernel RBF model better than NBC on classifying the review of user sentiment of halodoc telemedicine service","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127209684","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}

引用次数: 2

PREDIKSI INDEKS STANDAR PENCEMARAN UDARA DI KOTA SURABAYA BERDASARKAN KONSENTRASI GAS KARBON MONOKSIDA 根据一氧化碳的浓度对泗水市的空气污染标准进行了预测

Jambura Journal of Probability and Statistics Pub Date : 2021-11-29 DOI: 10.34312/jjps.v2i2.11326

Mohammad MA'ARIF Syaifulloh

{"title":"PREDIKSI INDEKS STANDAR PENCEMARAN UDARA DI KOTA SURABAYA BERDASARKAN KONSENTRASI GAS KARBON MONOKSIDA","authors":"Mohammad MA'ARIF Syaifulloh","doi":"10.34312/jjps.v2i2.11326","DOIUrl":"https://doi.org/10.34312/jjps.v2i2.11326","url":null,"abstract":"Kota Surabaya merupakan pusat kegiatan dari berbagai sektor di kawasan Jawa Timur salah satunya yaitu sektor industri sehingga banyaknya lapangan pekerjaan yang tercipta. Hal ini yang mendorong masyarakat luar Surabaya untuk mencari pekerjaan di Kota Surabaya. Karena lapangan pekerjaan di Kota Surabaya menyebar, menimbulkan mobilitas masyarakat dimana transportasi sangat dibutuhkan untuk melakukan mobilitas. Jumlah kendaraan di Kota Surabaya yang berbahan bakar bensin sebanyak 2.987.437 unit dan jumlah kendaraan berbahan bakar solar sebesar 179.331 unit. Hal ini dapat mempengaruhi kondisi kualitas udara di Kota Surabaya Sehingga dilakukan penelitian tentang prediksi indeks standar pencemaran udara di Kota Surabaya berdasarkan konsentrasi CO menggunakan kombinasi metode ARIMA Box-Jenkins dan regresi linear sederhana. Hasil analisis menunjukkan bahwa model peramalan terbaik berdasarkan nilai RMSE dan MAD adalah ARIMA(1,0,0) dimana model peramalan tersebut telah memenuhi asumsi residual. Berdasarkan hasil ramalan, diperoleh prediksi indeks standar pencemaran udara dengan menggunakan regresi linier sederhana menunjukkan hasil prediksi tertinggi pada periode 1 Januari hingga 3 Januari 2021 sebesar 10,5401 dengan kategori baik.","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121443257","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}

引用次数: 2

PERAMALAN JUMLAH TITIK PANAS PROVINSI KALIMANTAN TIMUR MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK

Jambura Journal of Probability and Statistics Pub Date : 2021-11-11 DOI: 10.34312/jjps.v2i2.10292

Siti Aisyah, Sri Wahyuningsih, Fdt Amijaya

{"title":"PERAMALAN JUMLAH TITIK PANAS PROVINSI KALIMANTAN TIMUR MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK","authors":"Siti Aisyah, Sri Wahyuningsih, Fdt Amijaya","doi":"10.34312/jjps.v2i2.10292","DOIUrl":"https://doi.org/10.34312/jjps.v2i2.10292","url":null,"abstract":"Radial Basis Function Neural Network (RBFNN) is a neural that uses a radial base function in hidden layers for classification and forecasting purposes. Neural Network is developed into a radial function base with an information processing system that has characteristics similar to biological neural networks, consisting of input layers, hidden layers, and output layers. The data used in this study is data on the number of hotspots in East Kalimantan Province obtained from the official website of the National Aeronautics and Space Administration (NASA). The purpose of this research is to obtain the RBFNN model and the results of forecasting the number of hotspots for the period January 2020 to March 2020. The radial basis function used is the local Gaussian function and the linear activation function. In this study using the proportion of training data and testing data 70: 30; 80:20; and 90:10. The results showed that the input network using significant Partial Autocorrelation Function (PACF) at lag 1 and lag 2, so that the RBFNN model that was formed involved Xt-1 and Xt-2. The best Mean Absolute Percentage Error (MAPE) minimum obtained the 80:20 data proportion with 2 hidden networks. The RBFNN architecture that is formed is 2 input layers, 2 hidden layers and 1 output layer. Data from forecasting the number of hotspots in East Kalimantan Province shows that from January 2020 to February 2020 there was a decline and March 2020 an increase.","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132473457","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}

引用次数: 4

ESTIMASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED MENGGUNAKAN METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE)

Jambura Journal of Probability and Statistics Pub Date : 2021-10-20 DOI: 10.34312/jjps.v2i2.10255

N. Y. Adrianingsih, A. Dani

引用次数: 1

REGRESI GENERALIZED POISSON UNTUK MEMODELKAN JUMLAH PENDERITA GIZI BURUK PADA BALITA DI SURABAYA POISSON的通病退行性回归，以模拟泗水学童营养不良的数量

Jambura Journal of Probability and Statistics Pub Date : 2020-05-31 DOI: 10.34312/jjps.v1i1.6876

Mahfudhotin Mahfudhotin

{"title":"REGRESI GENERALIZED POISSON UNTUK MEMODELKAN JUMLAH PENDERITA GIZI BURUK PADA BALITA DI SURABAYA","authors":"Mahfudhotin Mahfudhotin","doi":"10.34312/jjps.v1i1.6876","DOIUrl":"https://doi.org/10.34312/jjps.v1i1.6876","url":null,"abstract":"The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population . The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.The expansion of Poisson regression model which is used to solve the underdispersion data or overdispersion data known as Generalized Poisson (GP) regression model. The purpose of this final project is getting the parameter estimator of generalized linear model with response for GP distribution using maximum likelihood. This GP regression model can be applied on the data of number of Marasmus Kwashiorkorpatients in 25 subdistrict in Surabaya city in 2010. The variable response is the number of Marasmus Kwashiorkor patients, where as the predictor responses are the number of people who married at early age , the number of family heads who not graduated elementary school, the number of children who participated posyandu, the number of medical , the number of visits BKIA, and the number of poor population . The result of the GP regression model with statistic test can be concluded that the number of Marasmus Kwashiorkor patientsaffected by the number of visits BKIA and education levels of parents.","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263633","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}

引用次数: 1

PENDEKATAN MODEL VECTOR AUTOREGRESSIVE (VAR) UNTUK MERAMALKAN FAKTOR-FAKTOR YANG MEMPENGARUHI INFLASI DI PROVINSI GORONTALO

Jambura Journal of Probability and Statistics Pub Date : 2020-05-30 DOI: 10.34312/jjps.v1i1.5408

Hariyati H. Usman, Ismail Djakaria, Muhammad Rezky Friesta Payu

{"title":"PENDEKATAN MODEL VECTOR AUTOREGRESSIVE (VAR) UNTUK MERAMALKAN FAKTOR-FAKTOR YANG MEMPENGARUHI INFLASI DI PROVINSI GORONTALO","authors":"Hariyati H. Usman, Ismail Djakaria, Muhammad Rezky Friesta Payu","doi":"10.34312/jjps.v1i1.5408","DOIUrl":"https://doi.org/10.34312/jjps.v1i1.5408","url":null,"abstract":"The vector autoregressive (VAR) model is a simultaneous equation modeling used to construct forecasting systems from interrelated time-series data. This study intends to predict factors that significantly influence inflation in the province of Gorontalo. Moreover, the data used in this study involved inflation data and factors that influence inflation every month in the province in the period of January 2009 - December 2018. The results of inflation forecasting in Gorontalo in 2019 show that at the beginning of 2019, the inflation was considered to be very low at around -0.48% to -0.40%. However, the inflation surged in March with -0.25% (the highest inflation rate). The percentage decreased to -0.30% and -0.33% in April and May. After the decline in April and May, in the middle of the year (June) inflation returned to -0.31% and did not experience a significant change until the end of the year, which was still in the range of -0.32%. The accuracy of the prediction results seen in the MAPE value from out sample data of variables Y1 to Y8 is on the average below 10%, indicating that VAR is a significant forecasting model.","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122394821","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}

引用次数: 2

PENGGUNAAN RESAMPLING DALAM PENGGAMBARAN QUICK COUNT

Jambura Journal of Probability and Statistics Pub Date : 2020-05-30 DOI: 10.34312/jjps.v1i1.5643

A. Setiawan

引用次数: 0