Classification of MathML Expressions Using Multilayer Perceptron

Nagao, Yuma; Suzuki, Nobutaka

doi:10.1145/3103010.3121026

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Penelitian Yuma Nagao,dkk [6] hasil menunjukkan bahwa metode yang digunakan dapat mengklasifikasikan ekspresi MathML dengan akurasi yang lebih tinggi dar ipada metode berbasis SVM. Penelitian oleh Musli Yanto [7] Algoritma Perceptron dalam menentukan kelulusan sidang skripsi tidak mengalami kesalahan sehingga mampu dalam pengambilan keputusan.…”

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Implementasi Jaringan Syaraf Tiruan untuk Menentukan Kepribadian Mahasiswa Menggunakan Algoritma Perceptron

Fajrizal

Lisnawita

2020

Digitalzone

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Setiap orang yang mempunyai kepribadian sesuai dengan lingkungan sekitarnya agar terjalin hubungan yang baik. Pada lingkungan Fakultas Ilmu Komputer Universitas Lancang Kuning, peneliti melakukan evaluasi akademik ketika melakukan pengajaran ada beberapa kelompok mahasiswa dengan gaya belajar yang berbeda , beberapa mahasiswa sulit aktif didalam kelas, mengenai gaya belajar ini sudah pernah kita lakukan penelitiannya, ternyata gaya belajar mempunyai korelasi dengan kepribadian mahasiswa dimana terciptanya hubungan yang baik antar dosen dan mahasiswa akan memberikan dampak yang positif karena mahasiswa tidak akan malas lagi untuk mengikuti materi yang di ambil mahasiswa tersebut dan dosen juga dapat menentukan cara mengajar yang tepat berdasarkan kepribadian mahasiswa tersebut. 4 tipe kepribadian mahasiswa yaitu guardian, artisan, idealist dan rational. Dalam penelitian ini ada 30 orang responden yang harus mengisi kuisioner. Kemudian diolah komputerisasi yang berbasis JST algoritma Perceptron.. Hasil dari penelitian adalah 5 mahasiswa dengan kepribadian Guardian, 11 Artisan, 11 Idealist dan 3 Mahasiswa Rational. Kata Kunci : Jaringan Syaraf Tiruan, Tipe Kepribadian, Perceptron Abstract Every person who has a personality following the surrounding environment to establish a good relationship. In the environment of the Faculty of Computer Science,Universitas Lancang Kuning, researchers conduct academic evaluations when conducting teaching there are several groups of students with different learning styles, some students find it difficult to be active in class, about this learning style we have already done the research, it turns out learning styles have a correlation with personality students where the creation of a good relationship between lecturers and students will have a positive impact because students will no longer be lazy to follow the material taken by the student and the lecturer can also determine the right way of teaching based on the student's personality. The 4 personality types of students are guardian, artisan, idealist and rational. In this study, 30 respondents had to fill out a questionnaire. Then computerized based on the ANN algorithm Perceptron. The results of the study were 5 students with Guardian personalities, 11 Artisan, 11 Idealist and 3 Rational Students Keywords : Artificial Neural Networks, Personality Types , Perceptron

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Implementasi Jaringan Syaraf Tiruan untuk Menentukan Kepribadian Mahasiswa Menggunakan Algoritma Perceptron

Fajrizal

Lisnawita

2020

Digitalzone

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MathAssist: A Handwritten Mathematical Expression Autocomplete Technique

Kang,

Huang,

Tong

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

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Classifying MathML Expressions by Multilayer Perceptron

Nagao

Suzuki

2018

IEICE Trans. Inf. & Syst.

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MathML is a standard markup language for describing math expressions. MathML consists of two sets of elements: Presentation Markup and Content Markup. The former is widely used to display math expressions in Web pages, while the latter is more suited to the calculation of math expressions. In this letter, we focus on the former and consider classifying Presentation MathML expressions. Identifying the classes of given Presentation MathML expressions is helpful for several applications, e.g., Presentation to Content MathML conversion, text-to-speech, and so on. We propose a method for classifying Presentation MathML expressions by using multilayer perceptron. Experimental results show that our method classifies MathML expressions with high accuracy.

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Classification of MathML Expressions Using Multilayer Perceptron

Cited by 3 publications

References 3 publications

Implementasi Jaringan Syaraf Tiruan untuk Menentukan Kepribadian Mahasiswa Menggunakan Algoritma Perceptron

Implementasi Jaringan Syaraf Tiruan untuk Menentukan Kepribadian Mahasiswa Menggunakan Algoritma Perceptron

MathAssist: A Handwritten Mathematical Expression Autocomplete Technique

Classifying MathML Expressions by Multilayer Perceptron

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