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All Journal FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Jurnal Ilmu Dasar Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Disease Prevention and Public Health Journal Forum Geografi Jurnal Penelitian Pertanian Tanaman Pangan Indonesian Journal of Statistics and Its Applications JURNAL METEOROLOGI DAN GEOFISIKA Journal of Data Science and Its Applications ESTIMASI: Journal of Statistics and Its Application SRIWIJAYA JOURNAL OF ENVIRONMENT Jurnal Meteorologi dan Geofisika

Anik Djuraidah

Departemen Statistika, FMIPA, IPB University

Author-ID : 564719

Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Physics Public Health Other

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REGRESI TERBOBOTI GEOGRAFIS DENGAN PEMBOBOT KERNEL KUADRAT GANDA UNTUK DATA KEMISKINAN DI KABUPATEN JEMBER Rita Rahmawati; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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The determination of whether rural areas are considered poor or no are usually based on the average cost per capita with a global analysis that needs independent observations and the results are applied to all villages. But it is very likely that poverty would be influenced by space and neighboring areas, so the data between observations are rarely independent. One of the statistical analysis that encounters this spatial problem is Geographically Weighted Regression (GWR), which gives different weights to each geographical observation. In this paper, the weighting used for the GWR model is kernel bi-square, with its bandwidth values respectively. Optimal bandwidth can be obtained by minimizing the value of cross validation coefficient (CV). The results showed that the GWR model is more effective than the regression to analyze the data on average expenditure per capita in Jember.

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Aplikasi model kalibrasi di bidang kimia adalah pemodelan hubungan antara kandungan senyawa aktif yang ditentukan dari High Performance Liquid Chromatography (HPLC) dengan bentuk spektrum dari spektrometer Fourier Transform Infrared (FTIR). Permasalahan utama dalam kalibrasi adalah multikolinear dan pengamatan pencilan. Regresi Kuadrat Terkecil Parsial (RKTP) merupakan sebuah teknik prediktif yang mampu mengatasi masalah multikolinearitas.. SIMPLS (Straightforward Implementation PLS) adalah algoritma pendugaan RKTP yang tidak resisten terhadap pengamatan pencilan. Hubert and Brande (2003) mengemukakan algoritma RSIMPLS yang bersifat resisten terhadap pencilan. RSIMPLS dibentuk dari matriks ragam-peragam robust dan regresi linear robust. Pada penelitian ini dilakukan modifikasi fungsi bobot pada RSIMPLS dengan penduga-M Huber dimana setiap pengamatan akan diberikan nilai bobot kecil jika jarak robust dan jarak ortogonal pengamatan ke-i melebihi nilai batas yang ditentukan, dan untuk lainnya. Dengan demikian besar tidak hanya 0 dan 1, melainkan . Hasil penelitian menunjukkan RMSEP (root mean square error) pada metode modifikasi bobot lebih kecil dibandingkan RSIMPLS

Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions, the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research ar Titin Agustin; Anikk Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
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Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions, the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research are to study the non-hierarchy cluster methods, that is C-Means and Fuzzy C-Means. Both methods have difference on membership value and weighted membership value. The result of this research showed that Fuzzy C-Means was more robust than C-Means.

PENDUGAAN REGESI SEMIPARAMETRIK DENGAN PENDEKATAN MODEL CAMPURAN LINEAR Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 2 (2009)
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Hubungan fungsional antara respon dengan peubah penjelas pada regresi linear berganda berbentuk parametrik dengan metode pendugaan parameternya adalah metode kuadrat terkecil. Pada regresi semiparametrik, hubungan fungsional antara respon dengan peubah penjelas dapat berbentuk parametrik atau nonparametrik. Metode yang banyak digunakan untuk pendugaan regresi semiparametrik adalah algoritma backfitting yang dikemukakan oleh Hastie & Tibshirani (1990). Pada penelitian ini pendugaan regresi parametrik didekati dengan model campuran linear. Keuntungan utama pendekatan dengan model campuran linear adalah menggunakan metode ML atau REML sehingga memberi kemudahan dalam seleksi model dan penarikan kesimpulan

MODEL VEKTOR AUTOREGRESSIVE UNTUK PERAMALAN CURAH HUJAN DI INDRAMAYU (Vector Autoregressive Model for Forecast Rainfall In Indramayu ) Dewi Retno Sari Saputro; Aji Hamim Wigena; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
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There are three regions of rainfall that has been formed, each rainfall regions has a variety of homogeneous and there is a correlation between rainfall stations. In each region can be determined rainfall prediction model simultaneously. The model is a model of Vector Autoregressive (VAR) which is an extension of the autoregressive model (AR). Based on this research, we can determine the VAR model by lag 1 or VAR (1) for each region. Region 1 (Anjatan and Sumurwatu), region 2 (Salamdarma and Gantar) and region 3 (Kedokan Bunder and Sudimampir), each of which has a Root Mean Square Error Prediction (RMSEP) of 3.93; 5:03; 4:48; 5.3; 2:18 and 3:53. Correlation value of observations with predictions of rainfall respectively, 0.71; 0.62; 0:57; 0:59; 0.89, and 0.91. Keywords: AR, VAR, RMSEP, correlation

OPTIMASI PENENTUAN LOKASI STASIUN PEMANTAU KUALITAS UDARA AMBIEN DI KOTA SURABAYA Anik . Djuraidah; . . Aunuddin
FORUM STATISTIKA DAN KOMPUTASI Vol. 11 No. 2 (2006)
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The ambient air quality monitoring system in Surabaya has five fixed monitoring stations. Monitoring provides important information for public, but is expensive to purchase, utilize, and maintain. Based on result from spatial prediction of spatio-temporal additive model for air pollutant PM10, it is necessary to move the existing monitoring stations at other locations. In this study, we develop a methodology for reallocation of existing monitoring network to find an optimal configuration. The result of reallocation shows that new location of monitoring network can increase the accuracy of spatial prediction, especially at area with high concentration of PM10 Key words:: spatio-temporal data, spatio-temporal additive model, spatial prediction, reallocation monitoring network

PENGGUNAAN JARINGAN SYARAF TIRUAN UNTUK PENDUGAAN MODEL LINEAR TERAMPAT DENGAN KOEFISIEN KERAGAMAN KONSTAN Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 13 No. 1 (2008)
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Secara umum model linear terampat (GLIM) dapat dipetakan secara ekivalen pada Jaringan Syaraf Tiruan (JST) dengan satu lapisan atau disebut juga dengan perceptron. Fungsi aktifasi pada JST sama dengan invers dari fungsi hubung. Pada GLIM dengan komponen acak mempunyai sebaran gamma ekivalen dengan JST tanpa lapisan tersembunyi dengan fungsi galat adalah gamma dan fungsi tujuan adalah fungsi kemungkinan maksimum atau devians. Sedangkan fungsi aktifasi untuk model gamma adalah identitas, resiprokal, atau eksponensial. Makalah ini mengkaji pendugaan model pada data yang mempunyai sebaran gamma dengan metode JST dan seberapa besar perbedaan hasil pendugaannya dibandingkan dengan GLIM. Hasil kajian menunjukkan bahwa JST menghasilkan nilai dugaan yang sama dengan GLIM. Kata kunci : jaringan syaraf tiruan, koefisien keragaman konstan, model gamma

SEJARAH PERKEMBANGAN STATISTIKA DAN APLIKASINYA Sony Sunaryo; Setiawan Setiawan; Anik Djuraidah; Asep Saefuddin
FORUM STATISTIKA DAN KOMPUTASI Vol. 8 No. 1 (2003)
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Statistika diawali sebagai ilmu untuk mengumpulkan angka (data). Pada abad 17 statistika deskriptif mulai berkembang, begitu juga ilmu peluang yang awalnya dilahirkan dari meja judi sudah mulai muncul . Ilmu peluang ini melandasi berkembangnya statistika induktif yang terjadi pada pertukaran abad 19 dan 20 dengan Karl Pearson sebagai pelopornya. Statistika induktif berkembang pesat setelah R. A. Fisher memperkenalkan metode Maximum Likelihood pada tahun 1922. Dengan adanya perkembangan teknologi komputer, metode eksplorasi data dan bootstrap mulai berkembang pada tahun 1970. Metode ini sebagai awal dari analisis data tanpa model peluang yang populer dengan data driven. Seiring dengan perkembangan statistika induktif, statistika mulai diterapkan pada berbagai bidang seperti ekonomi, industri, pertanian, sosiologi, psikologi, dan lain-lain.Di bidang ekonomi aplikasi statistika pada ekonometrika, sedangkan di bidang industri aplikasi yang sangat terkenal adalah metode Quality Control dan metode Six-Sigma.Pada abad 21 diperkirakan metode data mining akan banyak digunakan dalam bidang terapan. Perkembangan ini akan berpengaruh terhadap model pendidikan dan pengajaran statistika dewasa ini.Di Indonesia penggunaan statistika dipelopori dengan dibukanya program pendidikan statistika di bawah naungan Jurusan Statistika IPB (S1 sejak tahun 1967 dan S2 sejak tahun 1975). Peran Jurusan Statistika IPB baik lewat mata kuliah pelayanan pada jurusan lain di lingkungan IPB, maupun para lulusannya yang sudah tersebar di bergai bidang pekerjaan memberikan dampak positif bagi penggunaan statistika sebagai alat bantu analisis.Sekarang selain IPB ada PTN dan PTS yang telah membuka jurusan statistika.

PENDEKATAN KUADRAT TERKECIL PARSIAL KEKAR UNTUK PENANGANAN PENCILAN PADA DATA KALIBRASI Enny Keristiana Sinaga; Anik Djuraidah; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 18 No. 1 (2013)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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The serious problems in the calibration of multivariate estimation are multicollinearity and outliers. Partial Least Squares (PLS) is one of the statistical method used in chemometrics, to handle high or perfect multicollinearity in independent variables. Straightforward Implementation Partial Least Squares (SIMPLS) is the extension of PLS regression proposed by De Jong (1993). The SIMPLS algorithm is based on the empirical cross-variance matrix between the independent variables and the regressors. This method does not resistant toward outlier observations. Robust PLS method is used to handle the multicollinearity and outliers in the data sets. This method can be classified in two groups, there are iteratively reweighting technique and robustication of covariance matrix. Partial Regression-M (PRM) method is one of the robust PLS methods used the idea of iteratively reweighting technique that proposed by Serneels et al. (2005). Robust SIMPLS (RSIMPLS) method is one of the robust PLS methods used the idea of robustication of covariance that proposed by Huber and Branden (2003). A modified RSIMPLS used M estimator with the Huber weight function called RSIMPLS-M was proposed by Ismah (2010). These two methods (RSIMPLS-M and PRM) are applied to Fish data (Naes 1985) to know their performances. The research results indicated that the values of R2 and RMSEP of RSIMPLS-M are higher than those of PRM method. Whereas based on the confidence interval estimation of the regression coefficients by jackknife method, estimation of PRM is narrower than that RSIMPLS-M method. Therefore RSIMPLS-M method is better than PRM method for prediction, whereas PRM method is better than RSIMPLS-M method for estimation.Keywords : Partial least squares regression robust (PLSRR), partial robust M-regression (PRM), straightforward implementation partial least squares robust (RSIMPLS)

PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH RATA-RATA DATA SIRKULAR (Bootstrap Confidence Interval Estimation of Mean Direction for Circular Data) Cici Suhaeni; I Made Sumertajaya; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
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The confidence interval is an estimator based on the sampling distribution. When the sampling distribution can not be derived from population distribution, the bootstrap method can be used to estimate it. Three methods used to estimate the bootstrap confidence interval for circular data were equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). In this study, three methods were evaluated through simulation study. The most important criterion to evaluate them were true coverage and interval width. The simulation results indicated in all methods, the interval width shortened when the concentration parameter increased. True coverage approached confidence level when the concentration parameter were one or more. For small concentration parameter, all three methods appeared unstable. Based on the true coverage, SYMA was the best, while in terms the interval width, LBA was the best one. For both criterion could be summarized that ETA is the best result. ETA applicated for estimate the period of Dengue Fever outbreaks in Bengkulu. The estimation showed that Dengue Fever outbreaks in 2009 were October through January. In 2010, it were January through March, and in 2011, it were June through September.Keywords : Circular, Bootstrap confidence interval, Equal-tailed arc, Symmetric arc, Likelihood-based arc.

Co-Authors . . Aunuddin Agus M. Soleh Aji H Wigena Aji Hamim Wigena Aji Hamin Wigena Anang Kurnia Ardiansyah, Muhlis Aris Yaman Asep Andri Fauzi ASEP SAEFUDDIN Aunuddin Aunuddin Azizah Desiwari Bagus Sartono Banan Nabila Bimandra Djaafara Cici Suhaeni Cici Suheni Dewi Retno Sari Saputro, Dewi Retno Erfiani Erfiani Fitrianto, Anwar Hardinsyah . Haryanto, Sugi Herlina Hanum Herlina Hanum, Herlina I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Indahwati Intan Lukiswati Ira Yulita Ismah . Itasia Dina Sulvianti Lismayani Usman Lusi Eka Afri Mely Amelia Miranti, Ita Miranti, Ita Mohamad Arif Pramarta Muhammad Nur Aidi Novi Hidayat Pusponegoro Pika Silvianti Rahardiantoro, Septian Rahma Anisa Retsi Firda Maulina Rita Rahmawati Sarah Fadhlia Sarimah Sarimah Setiawan Setiawan Sinaga, Enny Keristiana Siti Nur Laila Sony Sunaryo Titin Agustin Utami Dyah Syafitri Winda Chairani Mastuti Yoga Primanda Zulkarnain, Rizky _ Aunuddin

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