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All Journal Computatio : Journal of Computer Science and Information Systems Kilat
Rakhmat Arianto
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Energy

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Klasifikasi Sentiment Analysis Pada Komentar Peserta Diklat Menggunakan Metode K-Nearest Neighbor Riki Ruli A. Siregar; Zuhdiyyah Ulfah Siregar; Rakhmat Arianto
KILAT Vol 8 No 1 (2019): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (770.029 KB) | DOI: 10.33322/kilat.v8i1.421

Abstract

The process of analyzing and classifying comment data done by reading and sorting one by one negative comments and classifying them one by one using Ms. Excel not effective if the data to be processed in large quantities. Therefore, this study aims to apply sentiment analysis on comment data using K-Nearest Neighbor (KNN) method. The comment data used is the comments of the participants of the training on Udiklat Jakarta filled by each participant who followed the training. Furthermore, the comment data is processed by pre-processing, weighting the word using Term Frequency-Invers Document Frequency, calculating the similarity level between the training data and test data with cosine similarity. The process of applying sentiment analysis is done to determine whether the comment is positive or negative. Furthermore, these comments will be classified into four categories, namely: instructors, materials, facilities and infrastructure. The results of this study resulted in a system that can classify comment data automatically with an accuracy of 94.23%
Aplikasi Penentuan Dosen Penguji Skripsi Menggunakan Metode TF-IDF dan Vector Space Model Riki Ruli A. Siregar; Fera Amelia Sinaga; Rakhmat Arianto
Computatio : Journal of Computer Science and Information Systems Vol. 1 No. 2 (2017): Computatio : Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v1i2.1014

Abstract

Pada Sekolah Tinggi Teknik PLN (STT-PLN) penentuan dosen penguji tugas akhir atau skripsimerupakan tugas dari sekretaris jurusan. Penelitian ini bertujuan untuk memberikan alternativeuntuk menentukan dosen penguji skripsi. Metode yang di terapkan untuk membangun system iniadalah text mining, TF-IDF dan Vector Space Model (VSM). Text mining untuk melakukanprocessing data, dimana data yang akan diproses adalah judul dan abstrak skripsi, sedangkanVSM untuk melakukan pengklasifikasian kompetensi, penelitian ini dapat merekomendasikantiga dosen untuk menjadi dosen penguji skripsi berdasarkan kecocokan antara judul danabstrak dengan klasifikasi Pada penelitian ini, penulis menggunakan Model pengembanganperangkat lunak CRISP-DM. Adapun fase yang dimiliki oleh CRISP-DM adalah fasepemahaman bisnis, fase pemahanman data, fase pengolahan data, fase permodelan, faseevaluasi dan fase penyebaran. Hasil dari penelitian ini memiliki akurasi 93,22%.
Co-Authors Fera Amelia Sinaga Riki Ruli A. Siregar, Riki Ruli A. Zuhdiyyah Ulfah Siregar