ROSY, FATHUR (2023) IMPLEMENTASI ALGORITMA KLASIFIKASI NAÏVEBAYES UNTUK MENENTUKAN KESULITAN SOAL QUIZ MENGGUNAKAN RENPY. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Banyak game edukasi berjenis trivia dikembangkan dengan tujuan menguji kemampuan berfikir pemain. Namun, seringkali pemain menghadapi kesulitan dalam menjawab pertanyaan yang diberikan, Salah satu faktornya disebabkan oleh pertanyaan yang tidak seimbang, terlalu sulit atau terlampau mudah. Sehingga rasa monoton dan ketidaksesuaian dengan kemampuan si pemain membuat pengalaman bermain menjadi kurang memuaskan. Peluang untuk menghasilkan pertanyaan yang sesuai dengan kemampuan pemain sebenarnya bisa diprediksi berdasarkan data dari pemain dengan suatu metode. Salah satu metode yang bisa digunakan untuk mengatasi masalah ini dengan menggunakan teknik dalam data mining yaitu klasifikasi naïve bayes. Algoritma naïve bayes digunakan untuk mengklasifikasikan tingkat kesulitan soal berdasarkan parameter yang dihasilkan oleh pemain yaitu kecepatan menjawab soal,jawaban benar atau salah dan berapakali pemain salah dalam menjawab soal yang didesain dalam bentuk healtbar. Sedangkan parameter untuk keputusan tingkat kesulitan soal adalah Mudah, Sedang, dan Sulit. Hasil pengujian naïve bayes dengan sepuluh orang pemain mampu memberikan tingkat kesulitan soal yang sesuai dengan akurasi tertinggi sebesar 92.85% dan akurasi terendah sebesar 35.29% Sedangkan rata-rata akurasi yang didapat 66.5%. secara keseluruhan rata-rata akurasi Naïve Bayes menunjukkan tingkat keberhasilan yang cukup baik dalam pengujian tersebut. Kata Kunci : Naïve bayes, Trivia, Game Edukasi, Data Mining, Renpy Many educational trivia games have been developed with the aim of testing players' thinking abilities. However, players often face difficulties in answering the given questions. One of the factors is the imbalance of the questions, either being too difficult or too easy. As a result, the monotonous feeling and lack of compatibility with the player's abilities make the gaming experience less satisfying. The opportunity to generate questions that are suitable for players' abilities can actually be predicted based on data from players using a method. One of the methods that can be used to address this issue is by using the technique of naive Bayes classification in data mining. The naive Bayes algorithm is used to classify the difficulty level of questions based on parameters generated by the players, such as the speed of answering questions, correct or incorrect answers, and how many times the player answers a question incorrectly, which is designed in the form of a health bar. Meanwhile, the parameters for deciding the difficulty level of the questions are Easy, Medium, and Difficult. The results of the naive Bayes testing with ten players were able to provide the most accurate difficulty level of questions at 92.85% and the lowest accuracy at 35.29%. The average accuracy obtained was 66.5%. Overall, the average accuracy of naive Bayes demonstrates a fairly good level of success in the testing Keywords: Naïve Bayes, trivia, educational games, Data Mining, Renpy
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO 23 032 |
| NIM/NIDN Creators: | 41519210045 |
| Uncontrolled Keywords: | Naïve bayes, Trivia, Game Edukasi, Data Mining, Renpy |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 27 Sep 2023 04:27 |
| Last Modified: | 27 Sep 2023 04:27 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/81522 |
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