Ditemukan 79658 dokumen yang sesuai dengan query
Ivana Febriani
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ABSTRAKRanked Set Sampling merupakan salah satu metode pengambilan sampel yang dapat digunakan dalam analisis vegetasi semak. Hal ini dikarenakan metode ini cocok diterapkan pada populasi yang homogen, populasi yang tidak sengaja dibentuk, memiliki mobilitas yang rendah/menetap, serta batas wilayah dan luas wilayahnya dapat ditentukan. Melalui Ranked Set Sampling, dapat diperoleh taksiran rata-rata populasi yang tak bias. Taksiran ini menggunakan informasi awal berupa taksiran rata-rata populasi berdasarkan 1 cycle. Dalam tugas akhir ini, taksiran rata-rata populasi yang diperoleh digunakan untuk menghitung nilai parameter vegetasi. Selanjutnya, nilai parameter vegetasi digunakan untuk memperoleh suatu nilai yang dapat merepresentasikan dominansi suatu spesies tumbuhan terhadap spesies lainnya. Nilai yang diperoleh tersebut disebut dengan Indeks Nilai Penting. Penerapan metode ini dilakukan sebagai ilustrasi dalam menentukan dominansi spesies semak pada Hutan Kota Wales Barat Universitas Indonesia.
ABSTRAKRanked Set Sampling is one of the sampling methods which can be applied in analysis of shrubs vegetation. This is because the method suits the homogeneous, population unintentionally formed, has low mobility kind of population; in which the borders and area can be determined. By using Ranked Set Sampling, unbiased estimator of mean population can be obtained. These estimations use the initial information of mean population estimator based on 1 cycle. In this paper, the estimated mean population that is obtained is used to calculate vegetation parameter value. Furthermore, the vegetation parameter value is used to obtain a value that can represent the domination of one species of plant over others. The obtained value is called Importance Value Index. The application of this method is performed as an illustration of determining the domination of shrubs species in Hutan Kota Wales Barat, Universitas Indonesia."
2016
S64264
UI - Skripsi Membership Universitas Indonesia Library
Esti Ramaditia Mulatsih
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ABSTRAK Analisis cluster merupakan teknik multivariat yang digunakan untuk mengelompokkan objek berdasarkan karakteristik yang dimilikinya. Salah satu teknik dalam analisis cluster adalah metode Fuzzy K-Means lebih dikenal dengan Fuzzy C-Means , yang merupakan versi fuzzy dari metode K-Means clustering. Seperti pada metode K-Means, FCM juga sangat sensitif terhadap penentuan pusat-pusat awal cluster. Untuk mengatasi permasalahan tersebut, diusulkan modifikasi dari metode FCM dengan menggunakan metode sampling dengan probabilitas. Metode sampling digunakan untuk menaksir lokasi pusat-pusat awal cluster untuk digunakan ke dalam proses clustering. Dalam tugas akhir ini, metode sampling yang digunakan adalah simple random sampling dan ranked set sampling. Modifikasi dari metode FCM dengan menggunakan kedua metode sampling tersebut masing-masingnya disebut dengan SRS Fuzzy C-Means dan Ranked Fuzzy C-Means. Kedua metode tersebut kemudian diuji pada himpunan data pasien liver di India. Hasil eksperimen menunjukkan bahwa Ranked Fuzzy C-Means lebih efisien dibandingkan SRS Fuzzy C-Means dan FCM.
ABSTRACT Cluster analysis is a multivariate technique that is used to group objects based on characteristics. One technique in cluster analysis is a method Fuzzy C Means or better known as Fuzzy C Means , which is a fuzzy version of K Means clustering method. As the K Means method, FCM is also very sensitive to the determination of the initial cluster centers. To overcome these problems, the proposed modification of the FCM method using probability sampling methods. The sampling method is used to estimate the initial cluster centers to be used in the clustering process. In this thesis, the sampling method used was simple random sampling and ranked set sampling. Modifications of the FCM method using both the sampling method each being with SRS Fuzzy C Means and Ranked Fuzzy C Means. Both methods are then tested on a data set of liver patients in India. The experimental results showed that Ranked Fuzzy C Means is more efficient than SRS Fuzzy C Means and FCM."
Depok: Universitas Indonesia, 2017
S66638
UI - Skripsi Membership Universitas Indonesia Library
Muhammad Awais
"Recently, a varied L ranked set sampling (VLRSS) scheme has been introduced in the literature for estimating the population mean. The VLRSS scheme is a cost-efficient alternative to the classical ranked-set sampling (RSS) and median RSS (MRSS) schemes. In this paper, we construct a new cumulative sum (CUSUM) control chart for efficiently monitoring the process mean using VLRSS, named the CUSUM-VLRSS chart. The CUSUM-VLRSS chart encompasses the existing CUSUM charts based on RSS and MRSS schemes, named the CUSUM-RSS and CUSUM-MRSS charts. We use extensive Monte Carlo simulations to compute the run length characteristics of the proposed CUSUM chart. It is shown that the CUSUM-VLRSS chart is able to perform uniformly better than the CUSUM-RSS and CUSUM-MRSS charts when detecting different kinds of shift in the process mean. A similar trend is observed when the proposed CUSUM chart is constructed under imperfect rankings. A real data-set is also used to explain the implementation of the CUSUM-VLRSS chart. "
Taylor and Francis, 2018
658 JIPE 35:2 (2018)
Artikel Jurnal Universitas Indonesia Library
Gandaria Restu Siwi
"Line-Intercept Sampling merupakan salah satu metode pengambilan sampel dalam analisis vegetasi, dimana metode ini dapat digunakan untuk vegetasi berupa semak. Dengan Line-Intercept Sampling, dapat diperoleh taksiran total dan kerapatan populasi yang tak bias berdasarkan n garis transek. Taksiran ini menggunakan ide awal berupa Horvitz-Thompson Estimator berdasarkan satu garis transek. Dalam tugas akhir ini taksiran total dan kerapatan populasi yang diperoleh digunakan untuk menghitung nilai parameter vegetasi. Selanjutnya nilai parameter vegetasi digunakan untuk memperoleh suatu nilai yang dapat merepresentasikan dominansi suatu jenis tumbuhan pada area yang diamati yang disebut dengan Indeks Nilai Penting. Penerapan metode juga dilakukan sebagai ilustrasi.
Line-Intercept Sampling is a sampling method in the analysis of vegetation, which is suitable for vegetation such as bush. With Line-Intercept Sampling, the total and density estimation of population that is unbiased by n line transects can be obtained. These estimations use the initial idea of Horvitz-Thompson estimator based on a line transect. In this paper, the estimated total population and density that is obtained before is used to calculate the value of the parameter of vegetation. Furthermore, parameter of vegetation values is used to obtain a value that can represent the dominance of a species in the observed area called the Importance Value Index. The implementation method is used as an illustration as well."
Depok: Universitas Indonesia, 2015
S59599
UI - Skripsi Membership Universitas Indonesia Library
Thompson, Steven K.
"The Third Edition retains the general organization of the prior two editions, but it incorporates new material throughout the text. The book is organized into six parts: Part I covers basic sampling from simple random sampling to unequal probability sampling; Part II treats the use of auxiliary data with ratio and regression estimation and looks at the ideas of sufficient data, model, and design in practical sampling; Part III covers major useful designs such as stratified, cluster and systematic, multistage, and double and network sampling; Part IV examines detectability methods for elusive populations, and basic problems in detectability, visibility, and catchability are discussed; Part V concerns spatial sampling with the prediction methods of geostatistics, considerations of efficient spatial designs, and comparisons of different observational methods including plot shapes and detection aspects; and Part VI introduces adaptive sampling designs in which the sampling procedure depends on what is observed during the survey. For this new edition, the author has focused on thoroughly updating the book with a special emphasis on the first 14 chapters since these topics are invariably covered in basic sampling courses. The author has also implemented new approaches to explain the various techniques in the book, and as a result, new examples and explanations have been added throughout. In an effort to improve the presentation and visualization of the book, new figures as well as replacement figures for previously existing figures have been added. This book has continuously stood out from other sampling texts since the figures evoke the idea of each sampling design. The new figures will help readers to better visualize and understand the underlying concepts such as the different sampling strategies.
"
New Jersey: John Wiley & Sons, 2012
519.52 THO s
Buku Teks SO Universitas Indonesia Library
Karin Marshanda
"Instrusion Detection System (IDS) merupakan sistem untuk mendeteksi serangan dalam jaringan, baik lokal maupun internet. Dalam melakukan deteksi penyalahgunaan atau deteksi anomali, beberapa peneliti telah menggunakan data mining untuk mengidentifikasi berbagai jenis intrusi, termasuk yang jarang terjadi. Namun, data mining rentan terhadap data imbalance (data tidak seimbang) yang dapat mengurangi efektivitas algoritma klasifikasi karena asumsi mayoritas classifier terhadap distribusi yang seimbang. Berdasarkan permasalahan tersebut, maka akan dilakukan penelitian terkait penanganan data imbalance menggunakan metode Adaptive Synthetic Sampling (ADASYN) dengan cara menghasilkan data sintetis pada kelas minoritas agar algoritma klasifikasi dapat bekerja lebih baik. Metode ADASYN efektif bekerja pada variabel prediksi berjumlah 2 kelas (binary class), namun dikarenakan penelitian ini berurusan dengan masalah multiclass, makan akan digunakan pendekatan One-Vs-One (OVO) untuk menyeimbangkan kelas. Keefektifan ADASYN akan dievaluasi melalui implementasinya pada dataset Wi-Fi attacks, yaitu Aegean Wi-Fi Intrusion Dataset (AWID2). Data sebelum dan setelah rebalancing dievaluasi dengan menggunakan metode klasifikasi seperti regresi logistik dan Support Vector Machine (SVM), untuk dibandingkan nilai precision, recall, spesifisitas, serta F1-score dari kedua dataset tersebut. Meskipun ADASYN hanya meningkatkan nilai precision dalam dataset Wi-Fi attacks, dengan menggunakan metode klasifikasi SVM kernel polynomial terbukti efektif dalam mendeteksi kelas serangan, meskipun performa metrik lainnya tidak mencapai tingkat yang sama.
An Intrusion Detection System (IDS) is a system designed to detect attacks within networks, both local and internet-based. In the realm of misuse detection or anomaly detection, researchers have utilized data mining to identify various types of intrusions, including those that occur infrequently. However, data mining is susceptible to data imbalance, which can reduce the effectiveness of classification algorithms due to their assumption of balanced distribution. To address this issue, research will focus on handling data imbalance using the Adaptive Synthetic Sampling (ADASYN) method, which generates synthetic data for the minority class to enhance the performance of classification algorithms. ADASYN is effective for predictive variables with binary class scenarios, but since this study deals with multiclass problems, an One-Vs-One (OVO) approach will be employed to balance the classes. The effectiveness of ADASYN will be evaluated by implementing it on the Wi-Fi attacks dataset, specifically the Aegean Wi-Fi Intrusion Dataset (AWID2). Data before and after rebalancing will be evaluated using classification methods such as logistic regression and Support Vector Machine (SVM). Metrics including precision, recall, specificity, and F1-score will be compared between the two datasets. Although ADASYN only improves precision values in the Wi-Fi attacks dataset, using SVM with a polynomial kernel has proven effective in detecting attack classes, although other metric performances did not reach the same level."
Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2024
14-24-64198984
UI - Skripsi Membership Universitas Indonesia Library
Sucipto Budi Mulyono
Depok: Universitas Indonesia, 1998
S37039
UI - Skripsi Membership Universitas Indonesia Library
Cochran, William G.
New York: John Wiley & Sons, 1977
001.42 COC s
Buku Teks SO Universitas Indonesia Library
Williams, Bill
New York: John Wiley & Sons, 1978
519.52 WIL s
Buku Teks SO Universitas Indonesia Library
Barnes, Ralph M. (Ralph Mosser)
New York: John Wiley & Sons, 1961
658.53 BAR w
Buku Teks SO Universitas Indonesia Library
