Jurnal Matematika Statistika dan Komputasi最新文献

PERAMALAN JUMLAH PENDERITA DEMAMBER DARAH DENGUE DI KABUPATEN JOMBANG JAWA TIMUR DENGAN PENDEKATAN FUNGSI TRANSFER SINGLE INPUT

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-20 DOI: 10.20956/jmsk.v15i2.5707

Sediono Sediono

{"title":"PERAMALAN JUMLAH PENDERITA DEMAMBER DARAH DENGUE DI KABUPATEN JOMBANG JAWA TIMUR DENGAN PENDEKATAN FUNGSI TRANSFER SINGLE INPUT","authors":"Sediono Sediono","doi":"10.20956/jmsk.v15i2.5707","DOIUrl":"https://doi.org/10.20956/jmsk.v15i2.5707","url":null,"abstract":"AbstractForecasting is an important things in time series analysis, because by obtaining a convenient model that is statictically appropriate. Clearly, that can be used to predict the structure of future data form. Transfer function is one of mathematical model in time series analysis, that can be used to forecasting time index data both univariate and multivariate. Transfer function describes the predictive value of the output series (Yt) based on the value of one or more input series(Xt). The single input transfer function model is a transfer function model that uses one variable as input series (Xt), where each series of both input series and output series must be a stationary time series model, both stationary in the mean and stationary in variant. One of the used transfer function is to govern a model and forecasting of the number of cases dengue fever (Yt) in Kabupaten Jombang, East Java, where the input variable based on data of rainfall (Xt). From the result of this study was obtained that model of transfer function has a equation Y𝑡 = 0,0542X𝑡+ (1 − 0,7309𝐵)(1 + 0,6568𝐵12) with parameter ωo = 0.0542, ∅1 = 0.7309 and Φ12 = -0.6568. From the model, it can be interpreted that the number of dengue sufferers for a particular month was influenced by the rainfall on those month and the months before. According to the model of the transfer function, it can be used to forecast the number of sufferers of dengue fever in Kabupaten Jombang for period next 20 months. After compared between data of forecasting and actual data, there exists equally trend, namely 15 months of 20 month that are forecasted, such that it can be explain that majority 75% of the results of forecasting in this study are valid. Keywords: forecasting , single input transfer function, stationer point, Dengue fever Abstrak Peramalan adalah sesuatu hal yang penting dalam analisis runtun waktu, karena dengan diperolehnya sebuah model yang tepat secara statistik, jelas hal tersebut dapat digunakan untuk memprediksi struktur pola data yang akan datang. Fungsi transfer merupakan salah satu model matematis dalam analisis runtun waktu yang dapat digunakan untuk peramalan data indekswaktu baik univariat maupun multivariat. Fungsi transfer menggambarkan nilai prediksi dari output series (Yt) berdasarkan nilai satu atau lebih input series (Xt). Model fungsi transfer single input adalah model fungsi transfer yang menggunakan satu variabel sebagai input series (Xt), dimana masing-masing series baik input series maupun output series keduanya harus sama-sama merupakan model runtun waktu yang stasioner, baik stasioner dalam mean maupun stasioner dalam varian. Salah satu penggunaan model fungsi transfer ini adalah untuk pembuatan model dan peramalan jumlah kasus demam berdarah dengue (Yt) di Kabupaten Jombang Jawa Timur, dengan variabel inputnya berdasarkan data curah hujan (Xt). Dari hasil penelitian diperoleh model fungsi transfer yang memiliki persamaan Y𝑡 = 0,0542X𝑡 ","PeriodicalId":150527,"journal":{"name":"Jurnal Matematika Statistika dan Komputasi","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125157791","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: 0

Penentuan Distribusi Sample Terbatas Uji-J Davidson dan Mackinnon dengan Metode Bootstrap pada Model Regresi Tak Tersarang 只确定uj Davidson和Mackinnon对unhive回归模式的引导方式的有限样本分布

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-20 DOI: 10.20956/JMSK.V15I2.5708

G. M. Tinungki

{"title":"Penentuan Distribusi Sample Terbatas Uji-J Davidson dan Mackinnon dengan Metode Bootstrap pada Model Regresi Tak Tersarang","authors":"G. M. Tinungki","doi":"10.20956/JMSK.V15I2.5708","DOIUrl":"https://doi.org/10.20956/JMSK.V15I2.5708","url":null,"abstract":"Georgina Maria Tinungki* AbstractThere are some tests proposed for un-nested hypothesis between J-Davidson Test and MacKinnon Test. J’s Test is often bad result, but it always works very well when used bootstrap. Bootstrapping for J’s Test is expected to be able to show that by using bounded sample is better, because there is no fault in counting process. Moreover, bootstrapping J-Test will omit the possibility of inconsistence of the results test previously. Simulation result of Monte Carlo will compare the proposed bounded sample test with Cox and J’s Test previously. Keywords: un-nested hypothesis, J-Davidson Test, MacKinnon Test AbstrakTerdapat beberapa pengujian yang diusulkan untuk hipotesis tak tersarang antara lain Uji-J Davidson dan MacKinnon. Uji-J sering bekerja buruk, tetapi biasanya bekerja sangat baik ketika dibootstrapkan.. Bootstrapping Uji-J diharapkan mampuh menunjukkan sampel terbatas lebih baik karena tidak mempunyai kesalahan didalam proses perhitungan. Lebih dari itu, bootstrapping J-Tests akan mengeluarkan kemungkinan dari ketidak konsistenan hasil uji yang sebelumnya. Hasil Simulasi Monte Carlo membandingkan uji sampel terbatas yang diusulkan dengan test yang sebelumnya seperti Uji Cox dan J-Test. Kata Kunci: Hipotesis tak tersarang,, Uji-J Davidson, Uji MacKinnon","PeriodicalId":150527,"journal":{"name":"Jurnal Matematika Statistika dan Komputasi","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126553141","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: 0

Some Properties of Fundamental Linear Canonical Zak Transform 基本线性正则Zak变换的一些性质

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-20 DOI: 10.20956/jmsk.v15i2.5720

Asriadi Asriadi, B. Nurwahyu, M. Bahri

引用次数: 0

APLIKASI METODE DECISION TREE DALAM MENENTUKAN PROMOSI PERGURUAN TINGGI SWASTA DI MAKASSAR DECISION TREE方法应用程序，以确定MAKASSAR的私立大学的推广

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-09 DOI: 10.20956/JMSK.V15I2.5549

Rini Angelia, Aidawayati Rangkuti, Armin Lawi

引用次数: 0

Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse Penerapan稀疏主成分分析，基于Menghasilkan矩阵加载稀疏

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-06 DOI: 10.20956/jmsk.v15i2.5713

G. M. Tinungki, Nurtiti i Sunusi

{"title":"Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse","authors":"G. M. Tinungki, Nurtiti i Sunusi","doi":"10.20956/jmsk.v15i2.5713","DOIUrl":"https://doi.org/10.20956/jmsk.v15i2.5713","url":null,"abstract":"Abstract Sparse Principal Component Analysis (Sparse PCA) is one of the development of PCA. Sparse PCA modifies new variables as a linier combination of p old variables (original variable) which is yielded by PCA method. Modifying new variables is conducted by producing a loading yang sparse matrix, such that old variable which is not effective (value of loading is zero) able be exit from PCA. In this study, Sparse PCA method was applied on data of Indonesia Poverty population in 2015, that contains 13 variables and 34 observation with variable reduction such that yields 4 (four) new variables, which can explain 80.1% of total variance data. This study show, the loading matrix that has been yielded by using Sparse PCA method to become sparse with there exist 11 elements (loading value) zero entry of matrix, such that the model that has been produced to be simpler and easy to be interpreted. Keywords: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse Abstrak Sparse Principal Component Analysis (Sparse PCA) merupakan salah satu pengembangan dari metode PCA. Sparse PCA memodifikasi variabel-variabel baru yang merupakan kombinasi linear dari variabel lama (variabel asli) yang dihasilkan oleh metode PCA. Pemodifikasian variabel baru ini dilakukan dengan dengan menghasilkan matriks loading yang sparse sehingga variabel lama yang tidak efektif (memiliki nilai loading sama dengan nol) dapat dikeluarkan dari model PCA. Pada penelitian ini, metode Sparse PCA diterapkan pada data Indikator Kemiskinan Penduduk Indonesia Tahun 2015 yang memuat 13 variabel dan 34 observasi dengan reduksi variabel menghasilkan 4 (empat) variabel baru yang telah mampu menjelaskan 80,1% dari total variansi data. Hasil penelitian menunjukkan, matriks loading yang dihasilkan menggunakan metode Sparse PCA menjadi sparse dengan terdapat 11 elemen (nilai loading) matriks bernilai nol sehingga model yang dihasilkan menjadi lebih sederhana dan mudah untuk diinterpretasikan. Kata Kunci: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse","PeriodicalId":150527,"journal":{"name":"Jurnal Matematika Statistika dan Komputasi","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129572655","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

Beberapa Sifat Fundamental Transformasi Zak Linear Kanonik

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-06 DOI: 10.20956/jmsk.v15i2.5577

Asriadi Asriadi, B. Nurwahyu, M. Bahri

引用次数: 0

Bagan Kendali Robust Multivariat untuk Pengamatan Individual 个人观察的多变量Robust控制图表

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-06 DOI: 10.20956/JMSK.V15I2.5712

E. T. Herdiani

{"title":"Bagan Kendali Robust Multivariat untuk Pengamatan Individual","authors":"E. T. Herdiani","doi":"10.20956/JMSK.V15I2.5712","DOIUrl":"https://doi.org/10.20956/JMSK.V15I2.5712","url":null,"abstract":"AbstractThe most widely used of control chart in multivariate control processing is control chart T2 Hotelling. There are 2 kinds of control chart T2 Hotelling, namely T2 Hotelling for group observation and T2 Hotelling for individual observation. In this paper, discuss the control chart T2 Hotelling for individual observation. This control chart is used for monitoring of mean vector and sample of covariance matrix. Mean vector and sample of covariance matrix are very sensitive with respect to extreme point (outliers). Therefore, it is needed an estimator of mean vector and has a stocky population covariance matrix to the outliers data. One method that can be used to detect data that contains outliers is Minimum Covariance Determinant (MCD). From the calculation results, obtained that control chart T2 Hotelling by using Fast-MCD algorithm is more sensitive to detect outliers data than T2 Hotelling classically.Keyword: T2 Hotelling, Minimum Covariance Determinant (MCD), robust, outlier AbstrakBagan kendali yang paling banyak digunakan dalam pengendalian proses secara multivariat adalah bagan kendali T2 Hotelling. Ada 2 jenis dari bagan kendali Hotelling yaitu bagan kendali Hotelling untuk pengamatan kelompok dan individual. Pada tulisan ini membahas bagan kendali Hotelling untuk pengamatan individual. Bagan kendali ini digunakan untuk memonitor vektor rata-rata dan matriks kovariansi sampel. Vektor rata-rata dan matriks kovariansi sampel sangat sensitif terhadap titik ekstrim (outliers). Oleh karena itu dibutuhkan estimator vektor rata-rata dan matriks kovariansi populasi yang kekar terhadap data outliers. Salah satu metode yang dapat digunakan untuk mendeteksi data yang mengandung outliers adalah Minimum Covariance Determinant (MCD). Dari hasil perhitungan diperoleh bahwa bagan kendali T2 Hotelling dengan algoritma Fast-MCD lebih sensitif mendeteksi data outliers daripada T2 Hotelling klasik.Kata Kunci: T2 Hotelling, Minimum Covariance Determinant (MCD), robust, outlier.","PeriodicalId":150527,"journal":{"name":"Jurnal Matematika Statistika dan Komputasi","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131075539","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: 0

Estimasi Komponen Variansi pada Rancangan Faktorial Acak Lengkap Menggunakan Metode Generalized Least Squares 使用最基本的平方方法，对随机分解成分的估计

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-06 DOI: 10.20956/JMSK.V15I2.5714

A. M. N. Angriany, G. M. Tinungki, Raupong Raupong

{"title":"Estimasi Komponen Variansi pada Rancangan Faktorial Acak Lengkap Menggunakan Metode Generalized Least Squares","authors":"A. M. N. Angriany, G. M. Tinungki, Raupong Raupong","doi":"10.20956/JMSK.V15I2.5714","DOIUrl":"https://doi.org/10.20956/JMSK.V15I2.5714","url":null,"abstract":"AbstractsExperiment design is a test or a row of test by using both statistical description and inference statistical. The aim of this test is to change an input to become an output as a respond of the experiment. In the experiment design, variance of factor A, B , AB error of variance are called as variant component. The aim of this study is to estimate variance component on complete random factorial design for fixed model and mixed model by using Generalized Least Squares (GLS)method, where GLS method as a development of Ordinary Least Square method. It used to be applied on data of complete random factorial design, namely like the influence to density pelleting food which is caused by increasing adhesive material and longtime in storage. The results show that there is no influence of increasing adhesive material to the density of pelleting food. In addition, there exist of diversity of longtime of storage and there exists a diversity interaction between adding adhesive material and long of time of storage to the density of pelleting food Keywords: Generalized Least Squares, variance component, complete random factorial design AbstrakPerancangan percobaan adalah suatu uji atau sederet uji baik itu menggunakan statistika deskripsi maupun statistika inferensi, yang bertujuan untuk mengubah peubah input menjadi suatu output yang merupakan respon dari percobaan tersebut. Dalam perancangan percobaan, variansi dari faktor A, variansi dari faktor B, variansi interaksi faktor AB, dan variansi galat disebut dengan komponen varian. Penelitian ini bertujuan untuk mengestimasi komponen variansi pada rancangan faktorial acak lengkap model tetap dan model campuran menggunakan metode Generalized Least Squares (GLS), dimana metode GLS adalah pengembangan dari metode Ordinary Least Square yang biasa digunakan untuk mengatasi asumsi homogenitas yang biasa dilanggar dalam perancangan percobaan. Metode tersebut diterapkan pada data rancangan faktorial acak lengkap yaitu pengaruh berat jenis pakan pellet dengan kombinasi perlakuan penambahan bahan perekat dan lama penyimpanan. Hasil menunjukkan bahwa tidak terdapat pengaruh penambahan bahan perekat terhadap berat jenis pakan pellet. Selain itu, terdapat keragaman faktor lama penyimpan dan terdapat keragaman interaksi antara faktor penambahan perekat dan lama penyimpanan terhadap berat jenis pakan pellet. Kata kunci: Generalized Least Squares, komponen variansi, rancangan faktorial acak lengkap ","PeriodicalId":150527,"journal":{"name":"Jurnal Matematika Statistika dan Komputasi","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134624365","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: 0

DETEKSI OUTLIER DENGAN METODE SIMPANGAN MUTLAK PADA KASUS HUBUNGAN ANTARA MOTIVASI KERJA DENGAN PRESTASI KERJA KARYAWAN 在工作动机与工作成就之间的关系中，OUTLIER侦查的方法是完全不相关的

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-06 DOI: 10.20956/jmsk.v15i2.5717

Makkulau Makkulau

{"title":"DETEKSI OUTLIER DENGAN METODE SIMPANGAN MUTLAK PADA KASUS HUBUNGAN ANTARA MOTIVASI KERJA DENGAN PRESTASI KERJA KARYAWAN","authors":"Makkulau Makkulau","doi":"10.20956/jmsk.v15i2.5717","DOIUrl":"https://doi.org/10.20956/jmsk.v15i2.5717","url":null,"abstract":"AbstractOutlier is a separated data of the other data collection. This study is to detect outlier for using absolute deviation method in case the connection between employee work motivation and employee work achievement. For detecting outlier has to be used scattered plot, leverage value (hii) and student outlier error. Outlier data is reviewed from values of X based on leverage value (hii), namely h4 = 108, h19 = 193 and h26 = 108. The three values exceed of 2 times the average of leverage values 2p/n = 0.08 such that the values of observation 4, 19 and 26 be outlier. While, based on absolute value of student outlier error, it was obtained that outlier is an observation 24 and 41. Regression model that without using outlier is Y = 38.470 + 0.952X , where R2 = 0.838. Keywords: absolute deviation method, regression model, scattered plot AbstrakOutlier (pencilan) adalah suatu data yang terpisah jauh dari kumpulan data lainnya. Penelitian ini bertujuan untuk mendeteksi outlier menggunakan metode simpangan mutlak untuk kasus data hubungan antara motivasi kerja karyawan dengan prestasi kerja karyawan. Untuk mendeteksi outlier digunakan plot pencar, nilai leverasi (hii), dan sisaan dibuang ter-student-kan. Data outlier ditinjau dari nilai-nilai X berdasarkan nilai leverasi terbesar yaitu h4 = 108, h19 = 193, dan h26 = 108. Ketiga nilai tersebut melebihi kriteria 2 kali rataan nilai leverasi, 2p/n = 0,08, sehingga nilai amatan ke-4, 19, dan 26 merupakan outlier. Sedangkan berdasarkan nilai mutlak sisaan dibuang ter-student-kan, diperoleh outlier adalah amatan ke-24 dan 41. Model regresi yang digunakan tanpa outlier adalah Y = 38,470 + 0,952X dengan nilai R2 = 0,838. Kata kunci: Metode simpangan mutlak, model regresi, outlier, dan plot pencar. ","PeriodicalId":150527,"journal":{"name":"Jurnal Matematika Statistika dan Komputasi","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121615043","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: 0

Pengembangan Modul PreprocessingTeks untuk Kasus Formalisasi dan Pengecekan Ejaan Bahasa Indonesia pada Aplikasi Web Mining Simple Solution (WMSS)

Jurnal Matematika Statistika dan Komputasi Pub Date : 2018-12-06 DOI: 10.20956/JMSK.V15I2.5718

Umi Chuzaimah Chuzaimah Zulkifli

{"title":"Pengembangan Modul PreprocessingTeks untuk Kasus Formalisasi dan Pengecekan Ejaan Bahasa Indonesia pada Aplikasi Web Mining Simple Solution (WMSS)","authors":"Umi Chuzaimah Chuzaimah Zulkifli","doi":"10.20956/JMSK.V15I2.5718","DOIUrl":"https://doi.org/10.20956/JMSK.V15I2.5718","url":null,"abstract":"Abstract Data of social media currently has been much used to analyze both sentiment analysis and another analysis. In fact, data that is obtained from the social media in generally has some mistakes which can influence the spelling in writing of words. The solution offered is word formalization and spelling check. Based on the problem, it will be built a preprocessing model to overcome two the mistakes. The method that will be used in formalization is to change the words to be formal form based on KBBI, while the method used for spelling check is spelling correction. Spelling correction method consists of distance edit, bigram and distance edit rule. In this study, in addition the application of both methods, also it will be analyzed comparing the result of spelling correction. From the result of analysis shows that distance edit rule has higher accuracy, namely 83.39% than using both edit distance and bigram method. In addition, edit distance rule method also has faster performance than another both methods. Overall, method to change word to formal word were based on KBBI and spelling correction has been able to overcome the problem of two cases, such that it can increase accuracy of the result of the analysis. Keywords: preprocessing, spelling correction, edit distance, bigram AbstrakData media sosial saat ini telah banyak digunakan untuk melakukan analisis baik analisis sentimen maupun analisis terkait lainnya. Nyatanya, data yang diperoleh dari media sosial tersebut pada umumnya memiliki kesalahan yang akan mempengaruhi hasil analisis. Kesalahan tersebut berupa penggunaan kata yang tidak baku dan adanya kesalahan ejaan dalam penulisan kata. Solusi yang ditawarkan berupa formalisasi kata dan pengecekan ejaan. Berdasarkan masalah tersebut, akan dibangun modul preprocessing untuk mengatasi dua kesalahan di atas. Metode yang digunakan pada formalisasi adalah mengubah kata ke bentuk formal berdasarkan KBBI sedangkan metode yang digunakan pada pengecekan ejaan adalah spelling correction. Metode spelling correction tersebut terdiri dari tiga yaitu edit distance, bigram dan edit distance + rule. Pada penelitian ini, selain penerapan kedua metode juga akan dilakukan analisis untuk melihat perbandingan hasil pada metode spelling correction. Dari hasil analisis tersebut, diketahui bahwa metode edit distance + rule memiliki akurasi yang lebih tinggi yaitu sebesar 83,39% dibandingkan dengan kedua metode lainnya yaitu edit distance dan bigram. Selain itu, metode edit distance + rule juga memiliki performa tercepat dibandingkan kedua metode lainnya. Secara keseluruhan, metode mengubah kata ke bentuk formal berdasarkan KBBI dan spelling correction telah mampu mengatasi masalah pada dua kasus di atas sehingga dapat meningkatkan akurasi hasil analisis. Kata Kunci:preprocessing, spelling correction, edit distance, bigram","PeriodicalId":150527,"journal":{"name":"Jurnal Matematika Statistika dan Komputasi","volume":"135 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131177663","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