Laga, Harris Teguh (2019) Klasifikasi Kepribadian Melalui Analisa Tanda Tangan Online Dengan Metode K Nearest Neighbor. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Tanda tangan adalah representasi dari identitas seseorang yang ditulis pada media tertentu. Tanda tangan dapat menggambarkan kepribadian seseorang yang meliputi karakter, keterampilan sosial, prestasi, cara berpikir, dan kebiasaan cara bekerja. Saat ini identifikasi kepribadian seseorang melalui tanda tangan kebanyakan masih dilakukan secara manual sehingga masih memiliki kekurangan. Penelitian terdahulu telah menganalisa tanda tangan online untuk mengetahui kepribadian seseorang, namun belum terdapat proses klasifikasi dan validasi hasil penelitian. Penelitian ini memperbaiki penelitian sebelumnya dengan meningkatkan jumlah responden, menggunakan algoritma klasifikasi, dan menggunakan tes psikologi untuk validasi bernama Big Five Inventory (BFI). Dua fitur terbaik, yaitu tekanan dan kecepatan telah dianalisis dan diklasifikasikan menggunakan algortitma k Nearest Neighbor (kNN) yang menghasilkan akurasi tertinggi sebesar 87,5%. Akurasi validasi antara hasil klasifikasi dengan hasil tes BFI 40 responden adalah sebesar 75%.
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Signature is the representation of a personal identity that written on a selected medium. Signature can describe people’s personality, which includes character, social skills, achievements, ways of thinking, and one's work habits. The identification of someone's personality through signature is mostly done manually so that it still has disadvantages. Previous research has analyzed online signatures to find out a person's personality, but there is no classification process and validation of the results of the study. This research improves previous research by increasing the number of respondents, using classification algorithms, and using a psychological test for validation called the Big Five Inventory (BFI). The two best features, namely pressure and speed were analyzed and classified using the k Nearest Neighbor (kNN) algorithm which produced the highest accuracy of 87.5%. The accuracy of validation between the results of the classification with the results of the BFI test of 40 respondents was 75%.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 006.31 Lag k-1 2019 |
| Uncontrolled Keywords: | Tanda Tangan Online, BFI, kNN, Validasi |
| Subjects: | Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. T Technology > T Technology (General) |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Harris Teguh Laga |
| Date Deposited: | 12 Jan 2024 04:03 |
| Last Modified: | 12 Jan 2024 04:03 |
| URI: | http://repository.its.ac.id/id/eprint/64164 |
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