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PENERAPAN METODE RANDOM FOREST DALAM DRIVER ANALYSIS Dewi, Nariswari Karina; Syafitri, Utami Dyah; Mulyadi, Soni Yadi
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 1 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Driver analysis is one approach to know which  the greatest expalanatory variables influence the response variable. This analysis is well known in marketing research. In this area, explanatatory variables (X) and response variable (Y) ussually are measured by ordinal data and the relationship between those variables is non linier. One of the approach to build model on that situation is random forest. Two important things in random forest are size of random forest and sample size of X. In this research, we worked with  simulation to know the size of random forest which give higher accuration and more stabil. The simulation showed that the best condition achieved when the size of random forest is 500 and the sample size of X is 4.      Key words : driver analysis, random forest, variable importance.
PERSEPSI MASYARAKAT MENGENAI KRITERIA CALON PRESIDEN 2004-2009 (STUDI KASUS: DAERAH PEMILIHAN KOTA BOGOR) Wijayanto, Hari; Syafitri, Utami Dyah; Wahyuningsih, Sri
FORUM STATISTIKA DAN KOMPUTASI Vol. 9 No. 2 (2004)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Pemilihan Umum (Pemilu) Presiden dan Wakil Presiden 5 Juli 2004 secara langsung  memberikan kesempatan masyarakat untuk dapat memilih calon presiden sesuai dengan harapannya. Harapan masyarakat tersebut dapat dilihat melalui pandangan masyarakat tentang kriteria apa saja yang penting dimiliki oleh seorang calon presiden dan apakah kriteria tersebut ada pada diri calon presiden itu. Hasil penelitian ini menunjukkan bahwa kriteria yang paling penting dimiliki oleh seorang calon presiden adalah mampu mengatasi krisis ekonomi. Persepsi masyarakat Bogor secara umum menilai bahwa calon presiden Amien Rais merupakan tokoh yang  tidak terlibat pelanggaran HAM, tidak terlibat Orde Baru, dan mampu menggalang hubungan internasional.   Sedangkan calon presiden Susilo Bambang Yudhoyono dinilai  mampu mengatasi krisis ekonomi, memiliki visi, misi dan program kerja yang jelas serta  memiliki intelektual yang tinggi.  Pendukung calon presiden Hamzah Haz paling tidak konsiten pada jawaban mereka mengenai kriteria-kriteria calon presiden, kemudian diikuti oleh pendukung Megawati dan Wiranto.  Sedangkan pendukung calon presiden Amien Rais merupakan pendukung yang paling konsisten, kemudian diikuti oleh pendukung Susilo Bambang Yudhoyono.
OPTIMIZATION EXTRACTION OF XYLOCARPUS GRANATUM STEM AS ANTIOXIDANT AND ANTIGLYCATION Sapitri, Eka Winarni; Batubara, Irmanida; Syafitri, Utami Dyah
HAYATI Journal of Biosciences Vol. 26 No. 2 (2019): April 2019
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.709 KB) | DOI: 10.4308/hjb.26.2.%x

Abstract

Xylocarpus granatum is an Indonesian plant that has bioactives content of phenols and high antioxidant activity on it. The aim of this research was to determine the optimum condition maceration for Xylocarpus granatum stem as antioxidant and antiglycation. The optimum conditions of maceration were effected by the extraction variables (concentration, sample/solvent ratio, extraction time) were evaluated using surface response method. The optimum condition was determined from the recovery of the respons. The optimum condition of maceration is predicted to be achieved when the solvent concentration is 52.25%, the extraction time is 15.92 hours, sample/solvent ratio is 1 g/9 ml with the response (yield, total phenol content, flavonoid, inhibition for 2,2?-diphenylpicryl hydrazyl, capacity of 2,2?-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), and inhibition antiglycation were 12.81%, 1.95 mg of gallic acid/g extract, 62.33 ?g quercetin/g extract, 41.11%, 0.71 TEAC, and 112.33%, respectively). Optimization extraction conditions shows that the extraction variables have significant effect on respons so it can reduce the extraction time, economic, and produce high bioactivite constituens.
PREFERENSI MAHASISWA IPB TERHADAP MATA KULIAH METODE STATISTIKA MENGGUNAKAN ANALISIS KONJOIN Pertiwi, Eka Dewi; Syafitri, Utami Dyah; Angraini, Yenni
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 1 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Statistical method is one of the interdepth courses in Bogor Agricultural University (BAU) therefore, it is necessary to conduct an  evaluation in order to know the student's preference towards Statistics Methods course. Conjoint analysis is an analysis that can be used to determine the preference of students on teaching methods of Statistical Methods course. The combination of teaching methods are made using fractional factorial in which the level of  factor determined  was based on preliminary survey. Sampling techniques that  has been used was multistage sampling of students who had took the Statistical Methods course in 2009/2010. Based on conjoint analysis, the module, the number of students, and the time period of lectures are the top three  choices. The students tend to prefer materials that are appropriate with their major, modules that are well structured, a communicative lecturer, students as a teacher in review session, the number of student which is less than 50 students per class, and the time period of lecture is between 7-12 am.   Keywords :  statistical methods, preferences, conjoint analysis.
METODE POHON GABUNGAN: SOLUSI PILIHAN UNTUK MENGATASI KELEMAHAN POHON REGRESI DAN KLASIFIKASI TUNGGAL Sartono, Bagus; Syafitri, Utami Dyah
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Classification and regression tree has been a widely used tool in various applied fields due to its capability to give excellent predictive analysis. Later on, ensemble tree appeared to enhance simple tree analysis and deals with some of the weakness found in simple techniques. The ensemble tree basically combines predictions values of many simple trees into a single prediction value. This paper is intended as an introductory article to give a brief overview of the available ensemble tree methods which might be found in detail in a variety of reading materials.
JARINGAN SYARAF TIRUAN DAN ALGORITMA GENETIKA DALAM PEMODELAN KALIBRASI (STUDI KASUS : TANAMAN OBAT TEMULAWAK) Sihombing, Bartho; Erfiani, .; Syafitri, Utami Dyah
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 1 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The problems in prediction of calibration model are multicolinearity and the number of variables is larger than the number of observations. Principal Component Analysis-Artificial Neural Network-Genetic Algorithm (PCA-ANN-GA) models were applied for the relationship between sample of concentration which is limited and transmittance data which is in large dimensions. A large number of variables were compressed into principal components (PC?s). From these PC?s, the ANN was employed for prediction of concentration. The principal components computed by PCA were applied as inputs to a backpropagation neural network with one hidden layer. The models was evaluated using GA for the best network structure on hidden layer. Root Mean Square Error (RMSE) for 80% training set and 20% testing set are 0.0314 and 0.5225, respectively. Distribution of data according to the percentage of training data and testing data were also very influential to obtain the best network structure with the smallest RMSE achievement. The best model for these methods is two layers Neural Network with eight neuron in the hidden layer.
L-Histidine-Modified Silica from Rice Husk and Optimization of Adsorption Condition for Extractive Concentration of Pb(II) Nurhajawarsi, Nurhajawarsi; Rafi, Mohamad; Syafitri, Utami Dyah; Rohaeti, Eti
The Journal of Pure and Applied Chemistry Research Vol 7, No 2 (2018): Edition May-August 2018
Publisher : Chemistry Department, The University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1035.086 KB) | DOI: 10.21776/ub.jpacr.2018.007.02.402

Abstract

A new chelating agent, L-histidine-modified silica from rice husk (LHSRH), was prepared to increase the adsorption capacity and selectivity for Pb(II). LHSRH was synthesized by immobilizing L-histidine on silica from rice husk (RH) modified with 3-aminopropyltrimethoxysilane (APTMS). Silica from rice husk (SRH) was synthesized via precipitation process by adding hydrochloric acid solution to rice husk ash (RHA). The RHA was subsequently destructed with sodium hydroxide and heated to obtain sodium silicate (Na2SiO3). SRH was characterized by Fourier transform infrared spectroscopy and x-ray diffraction. The LHSRH was used further to adsorp Pb(II) metal ion. The pH range, amount of adsorbent, and adsorption time were optimized by response surface methodology. The optimum condition for the adsorption of Pb(II) was pH 5, an amount of adsorbent 0.1 g; and adsorption time 15 minutes. The adsorption capacity for Pb(II) ion was found to be 62.5 mg/g. The adsorption behavior of the matrix followed the Langmuir’s model.
PENENTUAN DOMAIN DENGAN TEKNIK VARIOGRAM Wigena, Aji Hamim; Syafitri, Utami Dyah; Millafanti, Yuan
FORUM STATISTIKA DAN KOMPUTASI Vol. 12 No. 2 (2007)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Dalam banyak kesempatan, penyusunan model skoring untuk memprediksi klasifikasi calon nasabah dilakukan menggunakan model regresi logistik dan beberapa model lain.  Proses pengklasifikasian dapat juga dilakukan dengan menerapkan simple naive Bayesian classifier.  Meskipun menggunakan asumsi yang secara umum dilanggar oleh data dan proses komputasi yang jauh lebih sederhana, teknik ini mampu menghasilkan akurasi dugaan yang tidak mengecewakan.  Paper ini memberikan ilustrasi penggunaan simple naive bayesian classifier pada kasus prediksi klasifikasi status kolektibilitas calon nasabah dan membandingkannya dengan model regresi logistik dan generalized additive model.   Kata kunci: simple naive Bayesian classifier
PENGGEROMBOLAN DESA/KELURAHAN BERDASARKAN INDIKATOR KEMISKINAN DENGAN MENERAPKAN ALGORITMA TSC DAN K-PROTOTYPES Munthe, Andrew Donda; Sumertajaya, I Made; Syafitri, Utami Dyah
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.169

Abstract

Statistic Indonesia (BPS) noted that in 2014 there were 3.270 villages in Nusa Tenggara Timur Province. Most of them have a high percentage of poverty. Therefore, the village clustering based on poverty indicators is very important. The clustering algorithm that can be used on large data size and with mixed variables are Two Step Cluster (TSC) and K-Prototypes. The purpose of this research is to compare of TSC and K-Prototypes algorithm for village clustering in Nusa Tenggara Timur Province based on poverty indicators. The data were taken from 2014 village potential data (PODES 2014) collected by BPS. The best selection criteria for the cluster is the minimum ratio between variance within groups and variance between groups. The result showed that the best clustering algorithm was TSC which had the smallest ratio (2.6963). The best clustering showed that villages in Nusa Tenggara Timur Province divided into six groups with different characteristics.
KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT Rochayati, Isti; Syafitri, Utami Dyah; Sumertajaya, I Made; IJSA, Indonesian Journal of Statistics and Its Applications
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.171

Abstract

Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.