Articles
<![CDATA[Algoritma Genetik: Alternatif Metode Penentuan Strata Optimum dalam Perancangan Survei ]]>
<![CDATA[Yanti, Yusma]]>;
<![CDATA[Rahardiantoro, Septian]]>
<![CDATA[ KOMPUTASI Vol 14, No 1 (2017): JURNAL KOMPUTASI ]]>
Publisher : <![CDATA[KOMPUTASI ]]>
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<![CDATA[Tujuan dari pembuatan strata ketika pengambilan contoh dalam survei adalah untuk menghasilkan penduga paremeter dengan varians kecil, sehinggapenentuan alokasi strata perlu diperoleh. Menentukanbanyaknya strata dan alokasi elemen strata dari suatu himpunan nilai respon akan menjadi fokus dari penelitian ini. Algoritma Genetik (AG) diaplikasikan untuk kasus ini dengan meminimalkan varians dalam strata pada himpunan yang tersedia, dari jumlah strata 2 sampai 6 strata. Studi empiris melalui simulasi dikembangkan dalam skema populasi yang telah diketahui banyaknya strata sebenarnya, kemudian dengan beberapa jenis banyaknya strata, AG diterapkan dalam data. Berdasarkan hasil simulasi, dapat disimpulkan bahwa AG dapat memberikan banyaknya strata yang sesuai dengan banyaknya strata sebenarnya, sehingga dapat menjadi metode alternatif yang baik untuk memilih banyaknya strata optimal dalam pengambilan contoh survei.]]>
<![CDATA[LAD-LASSO: SIMULATION STUDY OF ROBUST REGRESSION IN HIGH DIMENSIONAL DATA ]]>
<![CDATA[Septian Rahardiantoro]]>;
<![CDATA[Anang Kurnia]]>
<![CDATA[ FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015) ]]>
Publisher : <![CDATA[FORUM STATISTIKA DAN KOMPUTASI ]]>
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<![CDATA[The common issues in regression, there are a lot of cases in the condition number of predictor variables more than number of observations ( ) called high dimensional data. The classical problem always lies in this case, that is multicolinearity. It would be worse when the datasets subject to heavy-tailed errors or outliers that may appear in the responses and/or the predictors. As this reason, Wang et al in 2007 developed combined methods from Least Absolute Deviation (LAD) regression that is useful for robust regression, and also LASSO that is popular choice for shrinkage estimation and variable selection, becoming LAD-LASSO. Extensive simulation studies demonstrate satisfactory using LAD-LASSO in high dimensional datasets that lies outliers better than using LASSO.Keywords: high dimensional data, LAD-LASSO, robust regression]]>
<![CDATA[Algoritma Genetik: Alternatif Metode Penentuan Strata Optimum dalam Perancangan Survei ]]>
<![CDATA[Yusma Yanti]]>;
<![CDATA[Septian Rahardiantoro]]>
<![CDATA[ KOMPUTASI Vol 14, No 1 (2017): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ]]>
Publisher : <![CDATA[Ilmu Komputer, FMIPA, Universitas Pakuan ]]>
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DOI: 10.33751/komputasi.v14i1.275
<![CDATA[Tujuan dari pembuatan strata ketika pengambilan contoh dalam survei adalah untuk menghasilkan penduga paremeter dengan varians kecil, sehinggapenentuan alokasi strata perlu diperoleh. Menentukanbanyaknya strata dan alokasi elemen strata dari suatu himpunan nilai respon akan menjadi fokus dari penelitian ini. Algoritma Genetik (AG) diaplikasikan untuk kasus ini dengan meminimalkan varians dalam strata pada himpunan yang tersedia, dari jumlah strata 2 sampai 6 strata. Studi empiris melalui simulasi dikembangkan dalam skema populasi yang telah diketahui banyaknya strata sebenarnya, kemudian dengan beberapa jenis banyaknya strata, AG diterapkan dalam data. Berdasarkan hasil simulasi, dapat disimpulkan bahwa AG dapat memberikan banyaknya strata yang sesuai dengan banyaknya strata sebenarnya, sehingga dapat menjadi metode alternatif yang baik untuk memilih banyaknya strata optimal dalam pengambilan contoh survei.]]>
<![CDATA[PENERAPAN METODE COKRIGING DENGAN VARIOGRAM ISOTROPI DAN ANISOTROPI DALAM MEMPREDIKSI CURAH HUJAN BULANAN JAWA BARAT ]]>
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Septian Rahardiantoro]]>;
<![CDATA[Azizah Desiwari]]>
<![CDATA[ Jurnal Meteorologi dan Geofisika Vol 20, No 1 (2019) ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengembangan BMKG ]]>
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DOI: 10.31172/jmg.v20i1.594
<![CDATA[Curah hujan merupakan salah satu unsur iklim yang penting dalam pertanian. Informasi mengenai ukuran curah hujan dapat diketahui dari pos hujan pada suatu wilayah. Permasalahan yang dihadapi adalah tidak semua wilayah memiliki pos hujan, sehingga metode interpolasi spasial dapat digunakan dalam memprediksi besarnya curah hujan pada suatu wilayah. Metode cokriging merupakan salah satu metode interpolasi spasial yang bersifat Best Linear Unbiased Prediction (BLUP) dengan melibatkan minimum dua peubah. Peubah yang digunakan dalam penelitian ini dipilih berdasarkan keeratan hubungannya, yaitu peubah curah hujan dan elevasi pos hujan. Data yang digunakan dalam penelitian ini adalah curah hujan bulanan tahun 1981 hingga 2013 pada 38 pos hujan di wilayah Jawa Barat. Metode analisis diawali dengan menetukan variogram isotropi yang ditentukan berdasarkan jarak spasial dan variogram anisotropi yang ditentukan berdasarkan jarak dan arah pada kedua peubah. Selanjutnya, variogram yang terbaik digunakan untuk prediksi curah hujan. Hasil penelitian menunjukkan variogram terbaik adalah variogram isotropi dengan hasil prediksi curah hujan bulanan yang mempunyai nilai reduced means square error berkisar antara 0.54 sampai dengan 1.46 dan nilai average error hampir 0.Rainfall is one of the important climatic elements in agriculture. The information on the amount of rainfall can be known from the weather station in a region. The problem faced is not all regions have its own weather station, so that spatial interpolation can be used to predict the amount of rainfall in a region. Cokriging is one of spatial interpolation that has properties BLUP (Best Linear Unbiased Prediction) that involved at least two variables. In this study, the variables used were the amount of rainfall and elevation of the weather station because these variables have a correlation. The data used in this study were monthly rainfall from 1981 to 2013 at 38 weather stations in West Java. The first step in analysis data was determined isotropy variogram determined based on spatial distance and anisotropic variogram determined based on distance and direction in the two variables. Furthermore, the best variogram was used for the rainfall prediction. The results showed the best variogram is isotropy with the results of monthly rainfall predictions with the cokriging method having reduced means square error values ranging from 0.54 to 1.46 and the average error value of almost 0. ]]>
<![CDATA[EKSPLORASI DAN ANALISIS REGRESI LOGISTIK TERHADAP KONDISI SUNGAI TERCEMAR LIMBAH DI DESA/KELURAHAN PROVINSI DKI JAKARTA INDONESIA ]]>
<![CDATA[Septian Rahardiantoro]]>;
<![CDATA[Yusma Yanti]]>
<![CDATA[ Jurnal Matematika Sains dan Teknologi Vol. 23 No. 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Terbuka ]]>
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DOI: 10.33830/jmst.v23i1.3136.2022
<![CDATA[Provinsi DKI Jakarta memiliki kepadatan penduduk terbesar di Indonesia yang riskan dengan permasalahan lingkungan, salah satunya di sungai-sungainya. Sekitar 52.5% desa/kelurahan yang dilalui sungai memiliki kondisi sungai tercemar limbah. Penelitian ini bertujuan untuk melakukan eksplorasi kondisi sungai tercemar limbah yang disertai dengan identifikasi faktor-faktor yang diduga memengaruhinya berdasarkan data Potensi Desa (Podes) tahun 2018 (Badan Pusat Statistik, 2018). Eksplorasi data dilakukan dengan membuat plot tebaran dan ringkasan statistik, dengan hasil yang diperoleh adalah mayoritas sumber limbah berasal dari limbah rumah tangga. Selanjutnya, analisis regresi logistik beserta metode stepwise dilakukan untuk mengidentifikasi faktor-faktor dari segi kondisi lingkungan, alih fungsi, serta kondisi sosial ekonomi desa/kelurahan yang memengaruhi kondisi sungai tercemar limbah. Hasilnya, faktor yang memengaruhi sungai tercemar limbah meliputi adanya fungsi alih sungai dan banyaknya rumah tangga miskin dengan kategori tinggi. Selain itu, faktor adanya perawatan sungai dapat digunakan sebagai indikator bahwa sungai di desa/kelurahan di DKI Jakarta tercemar limbah.]]>
<![CDATA[Analysis of Regency and City Pneumonia Clusters in West Java 2020 ]]>
<![CDATA[Yusma Yanti]]>;
<![CDATA[Septian Rahardiantoro]]>
<![CDATA[ Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 1 (2023): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ]]>
Publisher : <![CDATA[Ilmu Komputer, FMIPA, Universitas Pakuan ]]>
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DOI: 10.33751/komputasi.v20i1.6412
<![CDATA[Pneumonia is an infection of the respiratory tract caused by bacteria, viruses, or fungi. The number of pneumonia cases in West Java is relatively high, therefore, it is necessary to identify some group of regencies/cities in which have a common characteristic, to make it easier to handle. The data used is data on the number of pneumonia cases in 27 regencies/cities in West Java in 2020. In this study, chi-squared test was applied to determine the characteristics of pneumonia spread in West Java. Then, a regression-based analysis by using the Irregular Graph Fused LASSO method was used to provide the cluster of regencies/cities, by considering the adjacent locations of regencies/cities as a penalty matrix. The results obtained that the cases spread unevenly. The number of cases for every 1000 people in each regency/city was relatively high in the eastern part of West Java. There were 6 clusters obtained from 27 regencies/cities, with Pangandaran regency as the location with the highest cases occurred. Depok City and Bekasi City were locations with the lowest number of cases even though they have relatively high population numbers.]]>
<![CDATA[Pemodelan Support Vector Machine Data Tidak Seimbang Keberhasilan Studi Mahasiswa Magister IPB ]]>
<![CDATA[Octavia Dwi Amelia]]>;
<![CDATA[Agus M Soleh]]>;
<![CDATA[Septian Rahardiantoro]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i1.76
<![CDATA[Bogor Agricultural University Postgraduate School (SPs-IPB) can maintain its reputation by applying a more selective admissions system. This research predicts the success of student using Support Vector Machine (SVM) modeling by considering the characteristics and educational background of the students. But there is an imbalance of data class. SVM modeling on unbalanced data produces poor performance with a sensitivity value of 0.00%. Unbalanced data handling using Sythetic Minority Oversampling Technique (SMOTE) succeeded in improving SVM classification performance in classifying unsuccessful students. Based on accuracy, sensitivity, and specificity with the default cut off, the exact type of SVM to model student success is SVM RBF. When using the optimum cut-off value from each type of SVM, the sensitivity value can be improved again. SVM RBF still gives the best result when using cut off 0.6. The final model that will be used to predict the success of the SPs-IPB student is obtained from SVM RBF modeling with cut off 0.6 using the entire data that has been through the SMOTE stage.]]>
<![CDATA[Pemodelan Regresi Spasial Kekar: Studi Kasus Jumlah Kunjungan WIsatawan Mancanegara Asal Eurasia di Indonesia Tahun 2015 ]]>
<![CDATA[Resti Cahyati]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Septian Rahardiantoro]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v2i1.85
<![CDATA[Spatial regression model is a model used to evaluate the relationship between one variable with some other variables considering the spatial effects in each region. One of the causes of imprecise spatial regression model in predicting is the presence of outlier or extreme value. The existence of outlier or extreme value could damage spatial regression parameter estimator. However, discarding the outlier or extreme value in spatial analysis, could change the composition of the spatial effect on the data. Visitor arrivals from Eurasia to Indonesia by nationality in 2015 great diversity caused by the outlier. So in this paper, we need a spatial regression parameter estimation method which is robust where the value of the estimation is not much affected by small changes in the data. The application of the S prediction principle is carried out in the estimation of the coefficient of spatial regression parameters which is robust to the observation of silane. The result of modeling by applying the principle of the S estimator method on the estimation of the stocky spatial regression parameter is able to accommodate the existence of pencilan observation on the spatial regression model quite effectively. This is indicated by a considerable change in the coefficient coefficient estimator parameters of spatial regression is able to decrease the value of MAPE and MAD produced by spatial regression regression modeling.]]>
<![CDATA[Two Step Method for Clustering Mixed Data untuk Menggerombolkan Toko Mainan Anak Digital ]]>
<![CDATA[Muhammad Shalih]]>;
<![CDATA[Cici Suhaeni]]>;
<![CDATA[Septian Rahardiantoro]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v7i3.131
<![CDATA[The development of digital trading system today, triggering the proliferation of shops that sell various needs in various marketplace. This is supported by the large number of internet users in Indonesia that facilitate the store with commercial-based digital to reach market share. One of the growing categories in a marketplace is the stores that sell toys. However, not all toy stores have a good reputation. Clustering based on store reputation indicators can be done to find out how the condition of toy stores in a marketplace. The store reputation indicators used are categorical and numerical scale variables. This study uses A Two-Step Method for Mixed Categorical and Numerical Data (TMCM), which is a clustering method that can cluster mixed numerical and categorical data that using a co-occurence concept. The result of this clustering found that the optimal number of cluster is five cluster based on the maximum value of Pseudo-F and the minimum value of ratio (R ).]]>
<![CDATA[Penerapan Metode VAR-X untuk Pemodelan Data Deret Waktu dengan Calendar effects ]]>
<![CDATA[Ade Gusalinda]]>;
<![CDATA[I Made Sumertajaya]]>;
<![CDATA[Septian Rahardiantoro]]>
<![CDATA[ Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019 ]]>
Publisher : <![CDATA[Department of Statistics, IPB ]]>
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DOI: 10.29244/xplore.v8i1.147
<![CDATA[One of the commodities that has quite varied price fluctuations is broiler and carcass chicken. The context of forecasting is quite important considering the policies that can be taken by the producer and even the strategies that can be taken by consumers. This study attempts to modeling broiler and carcass prices together with Vector Autoregressive (VAR) which is one method in time series analysis that utilizes more than one time series variable. In addition, the effect of calendar calendar events is also the topic of discussion in this study which is implemented by the VAR-X method. As a result, the calendar effects variables that affect broiler and carcass prices are February, the first week of Ramadan and Eid-ul-Fitr. Furthermore, forecasting with VAR-X produces a pretty good value than VAR with lower MAPE criteria.]]>
