Skripsi

KLASIFIKASI JENIS KASUS DI KEJAKSAAN NEGERI CILEGON MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

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The development of text processing technology allows for automated data classification using machine learning methods. In the case classification process, manual text grouping is often time-consuming and error-prone. This study aims to build a text classification system capable of automatically grouping case descriptions into specific categories. The methods used in this study are Support Vector Machine (SVM) as the classification algorithm and Term Frequency-Inverse Document Frequency (TF-IDF) as the feature extraction method. The model evaluation process was carried out using the K-Fold Cross Validation method to determine the overall model performance. Based on the test results, the model obtained an average accuracy of 93.93%, indicating that the system has good capabilities in classifying text. The results of the study indicate that the system is capable of assisting the automatic case classification process with quite good performance.

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Detail Information

Series Title

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Call Number

T2012912026

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xv, 96 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.107

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Program Komputer--Support Vector Machine

Specific Detail Info

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Statement of Responsibility

MI

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