Wahyuni, Elok Sri (2015) Prediksi Kunjungan Halaman Website Dengan N-Gram Model. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Prediksi dan pemodelan pola kunjungan pengguna website dapat diukur kinerjanya
dengan beberapa parameter. Parameter pengukuran yang sering digunakan
yaitu kompleksitas model, kemampuan model dalam membuat prediksi (aplicability)
dan akurasi prediksi. Dalam penelitian ini kami mencoba mengeksplorasi teknik
pemodelan prediksi kunjungan halaman website yang mampu mengurangi kompleksitas
model namun tetap bisa mempertahankan aplicability model dan akurasi
prediksi. Kami menunjukkan dibandingkan dengan model n-gram, model n-gram+
yang dilengkapi dengan skema support pruning dapat mengurangi ukuran model
hingga 75% dan mampu mempertahankan aplicability model dan akurasi prediksinya.
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Prediction and modeling patterns of user visits a website can be measured
its performance with some parameters. Measurement parameters that are often used
are the complexity of the model, the ability of the model to make predictions (aplicability)
and the prediction accuracy. In this study we tried to explore the predictive
modeling techniques visit the website pages that can reduce the complexity of the
model, but retaining the aplicability models and prediction accuracy. We show compared
with n-gram models, models of n-gram + is equipped with a support scheme
pruning can reduce the size of the model up to 75 % and is able to maintain the
accuracy aplicability models and predictions.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 006.312 Wah p |
| Uncontrolled Keywords: | Web Mining, n-gram, Markov Chain, Prediksi |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105 Data Transmission Systems |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 11 Aug 2017 07:29 |
| Last Modified: | 13 May 2024 07:19 |
| URI: | http://repository.its.ac.id/id/eprint/48453 |
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