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All Journal Xplore: Journal of Statistics FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika Jurnal Media Infotama Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan SINTECH (Science and Information Technology) Journal Indonesian Journal of Statistics and Its Applications Jambura Journal of Mathematics International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika

Anang Kurnia

Department Of Statistics, IPB University, Indonesia

Author-ID : 2479473

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

Published : 42 Documents Claim Missing Document

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PENERAPAN METODE JACKKNIFE DALAM PENDUGAAN AREA KECIL Anang Kurnia; Khairil Anwar Notodiputro
FORUM STATISTIKA DAN KOMPUTASI Vol. 11 No. 1 (2006)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Metode dasar yang sering digunakan dalam menyelesaikan model pendugaan tidak langsung pada SAE adalah BLUP/EBLUP, EB, dan HB. Namun ketidakpuasan sering muncul karena asumsi kelinieran atau sebaran tertentu tidak selalu dipenuhi dalam suatu analisis. Selain itu, penambahan komponen g2 dan g3 dari MSE( ?ˆi BP) tidak lain adalah upaya untuk mengkoreksi ketidakpastian akibat terlebih dulu melakukan pendugaan terhadap b dan s2u. Dengan teknik resampling, jackknife berkembang sebagai suatu metode untuk mengkoreksi biassuatu penduga. Penerapan jackknife pada pendugaan area kecil dilakukan untuk mengkoreksi pendugaan MSE.

CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Triscowati, Dwi Wahyu; Sartono, Bagus; Kurnia, Anang; Dirgahayu, Dede; Wijayanto, Arie Wahyu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.

Penerapan Algoritma Tree Augmented Naive Bayesian pada Penentuan Peubah Penting Pingkan Awalia; Aji Hamim Wigena; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 11, No 2 (2011)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v11i2.1053

In the era of free market competition today, improving product quality is very important. Consumerpreferences through product level of analysis is one method that many manufacturers conducted toevaluate the product. Multivariable regression is a statistical method used to determine the importantvariables. The weakness of this method is the strict assumption. This problem will be completed bythe method of bayesian networks. There are several algorithms to build the BN. This study uses TANand NB because of its simplicity. This study shows that the most accurate method at the chosen levelof classification accuracy is the TAN by 83%. The importance variable is the aspect liking of strengthof after taste.

Analyzing The Consumer’s Rice Price using Multiple Linear Regression and X-12 ARIMA Asep Saefuddin; Anang Kurnia; Dian Kusumaningrum
FORUM STATISTIKA DAN KOMPUTASI Vol. 9 No. 2 (2004)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Rice is one of the main foods in Indonesia. A change of rice price will cause a major effect in the lives of consumers. On the other hand, there are so many factors that influence the rice price. Thus finding key factors which are significant to the rice price, as well as forecasting the consumer’s rice price are needed in order to maintain the stabilization of rice price. The second objective is to find key factors which influence the rice price by using multiple linear regression models. The parameters were estimated by ordinary least square methods. There are 6 variables that are significant at α=5%, which are the consumer’s rice price at the previous period, rice production at the current and previous period, farmer’s GKP price, realization of domestic stock, and total rice import. The rice price will increase if the GKP price and realization of domestic stock increase whereas total rice import and the consumer’s rice price at the previous period have negative influences towards the rice price. The impact of imported rice is negative towards domestic rice. This condition will also drive negative effect towards the farmer’s income, in this case the price does not meet the farmers cost for production. To protect the farmers, the government applied a 430.00 Rp/Kg imported rice fee but this is not effective to decrease the amount of imported rice. In this model rice production at the current and previous period have positive signs, contradictory to the microeconomic theory where when the rice production increases, there will be an excess supply and the price will drop. That condition will occur only if the commodity is a free commodity and the rice is at the sufficiency level but in Indonesia, rice is affected by the government’s policy and the rice productivity is left behind by the demand. Forecasting the consumer’s rice price for the next five years was the last objective of this research. ARIMA Box–Jenkins Method, X-12 ARIMA, Winter’s Method, and Trend Analysis were compared to find the best statistical model to forecast the consumer’s rice price. X-12 ARIMA turns out to be the best method because it has the smallest MAPE, MAD, and MSD value. This result is a satisfactory because according to Findley et al. (1998) X-12 ARIMA has the capability to adjust seasonal and trading day factors which usually causes fluctuations in an economic time series data. Keyword : X-12 ARIMA

Perbandingan Hasil Pengelompokan menggunakan Analisis Cluster Berhirarki, K-Means Cluster, dan Cluster Ensemble (Studi Kasus Data Indikator Pelayanan Kesehatan Ibu Hamil) Cici Suhaeni; Anang Kurnia; Ristiyanti Ristiyanti
Jurnal Media Infotama Vol 14 No 1 (2018)
Publisher : UNIVED Press

Pengelompokan merupakan kegiatan di bidang riset yang banyak digunakan hingga saat ini. Terlebih di era big data seperti sekarang. Banyak metode yang berkembang untuk keperluan tersebut. Penelitian ini membandingkan hasil pengelompokan menggunakan metode cluster hierarki, k-means cluster, dan cluster ensemble pada pengelompokan provinsi di Indonesia berdasarkan indikator pelayanan kesehatan ibu hamil. Hasil analisis menunjukkan bahwa cluster ensemble merupakan metode yang paling tepat dalam mengelompokkan provinsi-provinsi tersebut. Cluster yang dihasilkan adalah 3 (tiga) cluster. Kata Kunci: analisis cluster, cluster ensemble, cluster hierarki, k-means cluster.

PENDUGAAN UMUR PERTANAMAN PADI DENGAN PEMODELAN KLASIFIKASI MULTICLASS ROTATION FOREST BERDASARKAN CITRA LANDSAT-8 Hidayat, Muhammad; Kurnia, Anang; Sartono, Bagus
Informatika Pertanian Vol 30, No 2 (2021): Desember 2021
Publisher : Sekretariat Badan Penelitian dan Pengembangan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/ip.v30n2.2021.p65-72

Pemantauan pertumbuhan tanaman padi perlu dilakukan untuk menduga keberhasilan panen. Fase pertumbuhan dan umur tanaman padi adalah parameter biofisik pertanaman yang dapat dideteksi oleh teknologi citra Landsat-8. Variabel yang diperoleh dari Landsat-8 adalah pita dan indeks vegetasi. Pemrosesan awal adalah mengolah data pertanaman padi milik PT Sang Hyang Seri di Kabupaten Subang, Jawa Barat, dari data rekapitulasi tanam dan 16 data citra Landsat-8 setiap hari selama periode 2015-2017. Penelitian ini bertujuan untuk mengetahui nilai akurasi terbaik dari teknik klasifikasi dengan terlebih dahulu menerapkan proses multiclass untuk umur pertanaman padi, rekayasa fitur, interaksi variabel dari variabel awal, dan teknik resampling. Metode klasifikasi yang digunakan adalah Multiclass Rotation Forest. Penerapan metode klasifikasi ini menghasilkan nilai akurasi untuk model tanpa varietas 75,29% dan untuk varietas Mekongga 76,33%, Ciherang 75,18%, Inpari-30 62,70%, dan PB42 62,87%.

PENDUGAAN AREA KECIL DATA PRODUKTIVITAS TANAMAN PADI DENGAN GEOADDITIVE SMALL AREA MODEL Ardiansyah, Muhlis; Djuraidah, Anik; Kurnia, Anang
Jurnal Penelitian Pertanian Tanaman Pangan Vol 2, No 2 (2018): Agustus 2018
Publisher : Pusat Penelitian dan Pengembangan Tanaman Pangan

Tanaman padi memiliki peran politik sebagai tolak ukur keberhasilan pemerintah di bidang pertanian. Pemerintah daerah membutuhkan data produktivitas tanaman padi hingga level kecamatan untuk mendukung program swasembada pangan. Permasalahannya, BPS tidak dapat menyajikan data produktivitas tanaman padi hingga level kecamatan karena ukuran contoh pada Survei Ubinan tidak representatif untuk penyajian data hingga level kecamatan. Tujuan dari penelitian ini adalah melakukan pendugaan data produktivitas tanaman padi dan produksi beras per kecamatan di Kabupaten Seruyan Provinsi Kalimantan Tengah Tahun 2016. Kabupaten ini dipilih karena memiliki lahan menganggur yang besar mencapai 479ribu hektar. Metode yang diajukan untuk menyelesaikan permasalahan di atas adalah menggunakan Geoadditive Small Area Model. Keakuratan pendugaan akan dievaluasi dengan nilai RMSE (Root Mean Squared Error) menggunakan metode jackknife dengan proses resampling. Hasil penelitian menunjukkan produktivitas tanaman padi di Kabupaten Seruyan memiliki kecenderungan bahwa semakin ke hilir Sungai Seruyan maka produktivitas tanaman padi menjadi semakin besar. Produktivitas padi tertinggi berada di Kecamatan Seruyan Hilir Timur (34.58 ku/ha) dan terendah di Seruyan Hulu (19.93 ku/ha). Hasil dugaan dengan model Geoadditive Small AreaÂ memberikan hasil yang akurat dengan nilai RMSE yang kecil. Dari seluruh kecamatan di Kabupaten Seruyan, hanya empat kecamatan mengalami surplus berasÂ yaitu Kecamatan Seruyan Hilir Timur, Danau Sembuluh, Seruyan Hulu, dan Suling Tambun sedangkan enam kecamatan lainnya mengalami defisit kebutuhan beras. Secara keceluruhan, Kabupaten Seruyan selama tahun 2016Â mengalami defisit kebutuhan beras sebesar 8 236.80 ton.Kata kunci: Produktivitas padi, Geoadditive Small Area Model, Surplus/ defisit beras.

PENERAPAN RANTAI MARKOV PADA PENGEMBANGAN UJI KETERDUGAAN KUNCI (Markov Chain Technique in Key Predictability Test Development) Sari Agustini Hafman; Anang Kurnia; Agus Buono
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (706.618 KB)

One Time Key (OTK) system with key from alphabetical sequences is one of symmetric encryption algorithm that used in Indonesia to protect secret information. Alphabetic sequences in OTK system must be cryptographically secure pseudorandom sequences. OTK system in Indonesia only tested by overlapping m-tuple test developed by Marsaglia (2005). Overlapping m-tuple test doesn’t check the unpredictability of alphabetical sequences, it just tests distribution form and indpendency of alphabetical sequences. So, an alphabetical sequence in OTK system cannot be used in cryptography application by the reason of unpredictability sequence is unknown. Because some of Pseudorandom Number Generator (PRNG) algorithm based on block cipher algorithm that has markovian properties, markov chain model used to detect predictability alphabetical sequences. Data in this study consists of two data sources i.e. simulation data that generated from four classes PRNG and OTK system keys in 2005 that used in three communication units of foreign ministry. Simulation data is used to develop key predictability test methodology by find predictability threshold value based on characteristic of match level. OTK system keys will be predictability tested by comparing characteristic of match level with threshold value that is obtained from simulation data. The first result of this study shows the alphabetical sequence generated by first, second and fourth PRNG class can't be modeled with first-order markov chain until third-order. The third PRNG class, except PRNG LCG1, LCG2, coveyou, rand and randu, also can't be modeled with first order markov chain until third-order. Sequence generated by LCG2, coveyou, rand and randu are not fit for use in cryptography because it has a high probability to be modeled by high orders of markov chain (above the order of three). The second result obtains predictability threshold value with markov chains based on the minimum and maximum match level on the second-order and third-order. The last result shows the size of training data must be greater than the size of the observation data with the best ratio between the size of training data with observational data is 100: 10. The results of testing using 10 times repeated shows that the match level average of the OTK system key match on the all of three-order less than 4.5 x 10-2, so the OTK system the is feasible to secure information in three communication units. Keywords: One Time Key (OTK), markov chain, PRNG, probability transition, match level

LAD-LASSO: SIMULATION STUDY OF ROBUST REGRESSION IN HIGH DIMENSIONAL DATA Septian Rahardiantoro; Anang Kurnia
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (733.502 KB)

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

PENDEKATAN STATISTIKA UNTUK PEMETAAN KEMISKINAN DI PROPINSI JAWA BARAT Anang . Kurnia; Utami Dyah Syafitri; Topan . Ruspayandi
FORUM STATISTIKA DAN KOMPUTASI Vol. 11 No. 2 (2006)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1003.035 KB)

Kemiskinan adalah salah satu masalah utama yang menjadi perhatian pemerintah saat ini. Pemahaman pola sebaran kemiskinan serta hubungannya dengan beberapa peubah lain seperti jarak geografis antar daerah perlu untuk diketahui sebagai langkah awal dalam memahami kemiskinan di suatu daerah, hal ini karena tingkat kemiskinan saja tidak cukup untuk dijadikan dasar dalam pengambilan kebijakan pengentasan kemiskinan. Banyak metode statistika yang dapat digunakan untuk memetakan konfigurasi kemiskinan. Dalam paper ini dikaji konfigurasi antar objek dengan menggunakan analisis penskalaan dimensi ganda (MDS), sedangkan perbandingan antar konfigurasi dilakukan dengan analisis Procrustes. Selanjutnya hubungan keeratan dan pengaruh keadaan kemiskinan antar daerah (autokorelasi spasial) dihitung dengan menggunakan Indeks Moran dan digambarkan dalam peta tematik. Kata kunci : penskalaan dimensi ganda, analisis Procrustes, autokorelasi spasial

Co-Authors . Marzuki . Sutriyati Abdullah Ilman Fahmi Agus Buono Agus M Soleh Agus Mohamad Soleh Agus Mohamad Soleh Aji Hamim Wigena Anik Djuraidah Anwar Fitrianto Ardiansyah, Muhlis Arie Anggreyani Arief Gusnanto ASEP SAEFUDDIN Azka Ubaidillah Bagus Sartono Bambang Sumantri Beny Trianjaya Budi Waryanto Christiana Anggraeni Putri Cici Suhaeni Citra Jaya Dede Dirgahayu Deiby T. Salaki Dian Handayani Dian Kusumaningrum Dian Kusumaningrum Dian Kusumaningrum, Farit Mochamad Afendi Farit Mohamad Afendi Fitri Khairani Hari Wijayanto Hari Wijayanto Hestiani Wulandari Hidayat, Muhammad Husnun Nashir I Made Sumertajaya I Wayan Mangku Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indahwati Indahwati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iwan Kurniawan Jodi jhouranda Siregar K A Notodiputro Kusman Sadik Maulida Fajrining Tyas Muhammad Nur Aidi Mulianto Raharjo Newton Newton Nurul Hidayati Pingkan Awalia Purba, Widyo Pura Rahardiantoro, Septian Rahma Anisa Retsi Firda Maulina Ristiyanti Ristiyanti Rysda Rysda Sari Agustini Hafman Setia Pramana Setyowati, Indah Rini Siti Muchlisoh Topan . Ruspayandi Triscowati, Dwi Wahyu Utami Dyah Syafitri Viarti Eminita Yenni Kurniawati Yudistira Yudistira

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