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Ditemukan 4 dokumen yang sesuai dengan query
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Rosyda Hanavania
Abstrak :
Curse of dimensionality atau kutukan dimensi merupakan permasalahan nyata terkait dengan dimensi tinggi pada data. Fenomena ini menyebabkan model bekerja secara tidak optimal, terjadinya overfitting, dan sulitnya proses komputasi data. Kasus data dengan dimensi tinggi ini banyak ditemukan pada data IoT (Internet of Things). Kompleksitas pada ekosistem IoT tersebut membuat sistem mengalami kesulitan dalam penangkapan properti serangan dan memaksa sistem untuk memperkuat keamanannya. Salah satu upaya yang paling banyak digunakan untuk pertahanan sistem IoT adalah dengan Intrusion Detection System (IDS). Penelitian ini menggunakan dataset Aegean WIFI Intrusion Dataset (AWID2) yang berisikan lalu lintas trafik internet pada jaringan WIFI. Data AWID2 berisi 2 juta records dan dikelompokkan ke dalam empat kelas yaitu normal, impersonation, injection, dan flooding. Untuk menyelesaikan permasalahan dimensi tinggi pada data ini, dilakukan teknik reduksi dimensi yaitu seleksi fitur jenis filter. Metode filter yang digunakan yaitu, Correlation based Feature Selection (CFS), Information Gain (IG), dan ANOVA F-test. Setiap metode seleksi fitur tersebut dilanjutkan dengan metode multiclass Support Vector Machines (SVM) one vs rest dan one vs one. Hasil dari penelitian ini menunjukkan bahwa metode fitur seleksi ANOVA F-test dengan metode klasifikasi SVM kernel polynomial dengan menggunakan 7 fitur terbaik merupakan metode paling baik untuk digunakan pada klasifikasi WIFI attacks data AWID2. Hal tersebut ditunjukkan melalui nilai accuracy=0,9766, F1score=0,8385, precision=0,9854, dan recall=0,7708. ......Curse of dimensionality is a problem related to high dimensions of data. This phenomenon can cause the non-optimal performance model, overfitting, and the data will be computationally expensive. This high dimensional data is mostly found in IoT (Internet of Things) data. The complexity of the IoT ecosystem makes it difficult for the system to capture potential attacks and forces the system to strengthen its security. One of the most widely used efforts to defend IoT systems is the Intrusion Detection System (IDS). This research will use the Aegean WIFI Intrusion Dataset (AWID2) which contains internet traffic on WIFI networks. AWID2 dataset contains of 2 million records and are grouped into four classes, namely normal, impersonation, injection, and flooding. To overcome the problem of high dimensions, this study used dimensional reduction techniques, namely feature selection filter method. The filter methods used are Correlation based Feature Selection (CFS) Information Gain (IG), and ANOVA F-test. Each of these feature selection methods is then followed by building a classification model using multiclass Support Vector Machines (SVM) one vs one and one vs rest method. This study tells that combination of feature selection ANOVA F-test method and SVM with polynomial kernel is the best method to use on WIFI attacks classification. It is indicated by the score of performance metrics namely, accuracy=0,9766, F1score=0,8385, precision=0,9854, and recall=0,7708.
Depok: Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Indonesia, 2023
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UI - Skripsi Membership  Universitas Indonesia Library
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Bagaskara Ghanyvian Istiqlal
Abstrak :
Kualitas tidur yang baik sangatlah penting untuk berbagai aspek kehidupan seperti kesehatan fisik, kesehatan mental, keselamatan, konsentrasi, performa, penyembuhan, dan lain-lain. Kualitas tidur tidak hanya mencakup aspek fisiologis, tetapi juga memperhatikan aspek mental seperti: kondisi setelah tidur, kepuasan dengan tidur, dan pengaruh pada kehidupan sehari-hari. Penelitian ini mengusulkan penggabungan data objektif yang berasal dari Fitbit dan kuesioner subjektif untuk mengklasifikasi kualitas tidur menggunakan K-Nearest Neighbor. Klasifikasi ini bertujuan untuk mempelajari fitur-fitur yang paling pengaruh dalam kualitas tidur. Data objektif yang berisikan data fisiologis dan aspek tidur terukur oleh Fitbit, serta data subjektif mengenai aspek mental, keduanya dijadikan fitur deskriptif dalam model. Analisa fitur yang paling berpengaruh dilakukan dari dua sudut pandang model, yaitu fitur target kualitas tidur subjektif dan fitur target kualitas objektif. Kedua model dilatih dengan serangkaian data preprocessing yang termasuk didalamnya terdapat seleksi fitur dan ekstraksi fitur. Seleksi fitur berbasis ANOVA F Test akan dibandingkan dengan ekstraksi fitur Principal Component Analysis (PCA) dan Neighborhood Component Analysis(NCA). Seleksi fitur ANOVA F-Test lebih baik dari PCA dan NCA dengan peningkatan skor sebesar 0,06-0,08 pada model objektif, dan 0,01-0,06 pada model subjektif. Skor terbaik terbaik dari model subjektif yaitu 0,52 dengan parameter jumlah fitur = 3 dan k-neighbors = 27. Skor terbaik terbaik dari model objektif yaitu 0,72 dengan parameter jumlah fitur = 7 dan k-neighbors = 4. Pada akhirnya, ditemukan 3 Fitur yang paling berpengaruh dalam klasifikasi subjektf, dan 7 fitur yang paling berpengaruh dalam klasifikasi objektif.
Good quality sleep is very important for various aspects of life such as physical health, mental health, safety, concentration, performance, healing, and others. Sleep quality does not only include physiological aspects, but also pay attention to mental aspects such as condition after sleep, satisfaction with sleep, and influence on daily life. This study proposes combining objective data from Fitbit and subjective questionnaires to classify sleep quality using K-Nearest Neighbor. This classification aims to study the features that have the most influence in sleep quality. Objective data containing physiological data and sleep aspects measured by Fitbit, as well as subjective data on mental aspects, are both used as descriptive features in the model. The analysis of the most influential features is carried out from two viewpoints of the model, namely the subjective sleep quality target feature and the objective quality target feature. Both models are trained with a series of preprocessing data which includes feature selection and feature extraction. ANOVA F Test based on feature selection will be compared with feature extraction of Principal Component Analysis (PCA) and Neighborhood Component Analysis (NCA). ANOVA F-Test feature selection is better than PCA and NCA with an increase in scores of 0.06-0.08 in the objective model, and 0.01-0.06 in the subjective model. The best score of the subjective model is 0.52 with the parameter number of features = 3 and k-neighbors = 27. The best score of the objective model is 0.72 with the parameter number of features = 7 and k-neighbors = 4. In the end, it was found 3 the most influential features in the subjective classification, and 7 the most influential features in the objective classification.
Depok: Fakultas Teknik Universitas Indonesia, 2020
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UI - Skripsi Membership  Universitas Indonesia Library
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Abstrak :
The aim of the present study is to estimate some mortality measures such as the age specific death rates (ASDRA), infant mortality rate (IME) and me table crude death rate (CDR) for male, female and both sexes of Bangladesh in 2005. For this purpose, two abridged life tables, one for male and other for female were constructed using the corresponding secondary data on life expectancy at birth of Bangladesh in 2005 taken from Islam (2003). These were compared to the values in 199] and it was observed that these rates were showing decreasing trend during 1991-2005. Moreover, a mathematical model was fitted to the number of persons surviving at an exact age x (lx) only for male of Bangladesh in 2005. Model validation technique, cross validity prediction power (C VFP) and F-test, showed that the mathematical model was valid and hence, fit is well. Instantaneous force of mortality ( |J. X ) only for male of Bangladesh in 2005 was estimated And it was found that |.L X exhibited decreasing trend up to age 20-24 and increasing in the remaining age group but rapidly increasing after age 50 years to infinity.
Journal of Population, 11 (2) 2005 : 117-130, 2005
JOPO-11-2-2005-117
Artikel Jurnal  Universitas Indonesia Library
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Abstrak :
The aim of the present study is to build some mathematical models and then to forecast some fertility parameters in urban area of Bangladesh. For this purpose, the secondary time series data on Crude Birth Rate (CBR), Total Fertility Rate (FFR). Gross Reproduction Rate (GRR) and Net Reproduction Rate (NRR) of various issues duly published by Bangladesh Bureau of Statistics (BBS) have been used in the present study. A few mathematical time trend models have been fitted to time series data of CBR, TFR, GRR and NRR It is _found that the CBR follows quadratic H.e. parabolic) polynomial model while the TPR, GRR and NRR follow simple linear regression model. Model validation technique .such as Cross- Validity Prediction Power (C VFP), pi, , is applied to these models to verify how much these models are valid or not. It was found that all these models are more than 95%, 79%, 82%, and 72% stable respectively and their shrinkages are only 0.00739Z 0.032l33. 0. 027916, and 0.0424229, respectively. These rates have been forecasted during 1999-2005 using these time trend models.
Journal of Population, 12 ( 2) 2006 : 127-138, 2006
JOPO-12-2-2006-127
Artikel Jurnal  Universitas Indonesia Library