IMPLEMENTASI NEURAL NETWORK UNTUK MENGENALI KEPRIBADIAN SESEORANG MENGGUNAKAN MODEL BIG FIVE PERSONALITY BERDASARKAN RATING GENRE VIDEO GAME YANG DIBERIKAN OLEH RESPONDEN

Reyhan Fikri Dzikriansyah, - (2020) IMPLEMENTASI NEURAL NETWORK UNTUK MENGENALI KEPRIBADIAN SESEORANG MENGGUNAKAN MODEL BIG FIVE PERSONALITY BERDASARKAN RATING GENRE VIDEO GAME YANG DIBERIKAN OLEH RESPONDEN. S1 thesis, Universitas Pendidikan Indonesia.

[img] Text
S_KOM_1602273_Title.pdf

Download (1MB)
[img] Text
S_KOM_1602273_Chapter1.pdf

Download (195kB)
[img] Text
S_KOM_1602273_Chapter2.pdf
Restricted to Staf Perpustakaan

Download (2MB)
[img] Text
S_KOM_1602273_Chapter3.pdf

Download (334kB)
[img] Text
S_KOM_1602273_Chapter4.pdf
Restricted to Staf Perpustakaan

Download (1MB)
[img] Text
S_KOM_1602273_Chapter5.pdf

Download (75kB)
[img] Text
S_KOM_1602273_Appendix.pdf
Restricted to Staf Perpustakaan

Download (1MB)
Official URL: http://repository.upi.edu

Abstract

Kepribadian seseorang merupakan hal penting yang perlu dikenali karena memiliki berbagai kegunaan, diantaranya ialah untuk melakukan crowdsourcing, memilih seseorang yang cocok menjadi pemimpin, dan meningkatkan kemampuan metakognisi guru bahasa. Salah satu machine learning yang dapat digunakan untuk mengenali kepribadian seseorang ialah Automatic Personality Recognition (APR). Pada APR, model kepribadian yang sering digunakan ialah big five personality. Model big five personality telah diteliti memiliki korelasi dengan preferensi genre video game yang berbentuk data kuesioner berskala rating. Neural network pernah digunakan sebagai algoritma APR dengan data rating desain karakter video game. Neural network juga telah diteliti memiliki kinerja yang lebih baik dari teknik statistik standar untuk data kuesioner berskala rating. Penelitian skripsi ini membahas tentang APR yang menggunakan data rating genre video game sebagai fitur, big five personality sebagai model kepribadian, dan neural network sebagai algoritma. Data rating genre video game didapat dengan kuesioner preferensi genre video game dan data big five personality didapat dengan kuesioner Big Five Inventory Socio-Economic Panel (BFI-S). Penelitian ini terdiri dari beberapa tahap, yaitu: (1) Pembuatan Kuesioner; (2) Pengumpulan Data; (3) Eksperimen; (4) Analisis Hasil. Penelitian ini mengeluarkan hasil terbaik pada dimensi kepribadian conscientiousness dengan nilai validation RMSE sebesar 0.79459. Person’s personality is an important matter that need to be recognized because it has a variety of uses, including crowdsourcing, choosing someone who is suitable to be a leader, and improving language teacher’s metacognition skill. One of machine learning that can be used to recognize person’s personality is Automatic Personality Recognition (APR). In APR, big five personality is often used as the personality model. Big five personality model has been studied to have correlation with video game genre preferences in the form of rating scale questionnaire data. Neural networks has been used as algorithm for APR with video game character design rating data. Neural networks has also been studied to have better performance than standard statistical techniques on rating scale questionnaire data. This thesis study discusses APR that uses video game genre rating data as a feature, big five personality as a personality model, and neural network as an algorithm. Video game genre rating data obtained by video game genre preferences questionnaire and big five personality data obtained by Big Five Inventory Socio-Economic Panel (BFI-S) questionnaire. This research consists of several stages, namely: (1) Questionnaire Preparation; (2) Data Collection; (3) Experiments; (4) Results Analysis. This study produced the best results on conscientiousness personality dimension with validation RMSE value of 0.79459.

Item Type: Tugas Akhir,Skripsi,Tesis,Disertasi (S1)
Additional Information: No Panggil : S KOM REY i-2020; NIM :1602273
Uncontrolled Keywords: Rating Genre Video Game, Preferensi Genre Video Game, Automatic Personality Recognition, Big Five Personality, Big Five Inventory Socio-Economic Panel, Neural Network
Subjects: L Education > L Education (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Ilmu Komputer
Depositing User: Reyhan Fikri Dzikriansyah
Date Deposited: 02 Sep 2020 04:11
Last Modified: 02 Sep 2020 04:11
URI: http://repository.upi.edu/id/eprint/51919

Actions (login required)

View Item View Item