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RANCANG BANGUN APLIKASI E-LEARNING
AKSARA JEPANG DENGAN METODE
MNEMONIC DAN PATTERN RECOGNITION
(Studi Kasus: SMA Citra Kasih)
SKRIPSI
Diajukan sebagai salah satu syarat untuk memperoleh gelar
Sarjana Komputer (S. Kom.)
Astrid Tamara
14110110040
PROGRAM STUDI INFORMATIKA
FAKULTAS TEKNIK DAN INFORMATIKA
UNIVERSITAS MULTIMEDIA NUSANTARA
TANGERANG
2019
Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019
Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019
Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019
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KATA PENGANTAR
Puji syukur kepada Tuhan Yang Maha Esa, karena atas rahmat-Nya
penyusunan skripsi dengan judul “Rancang Bangun Aplikasi E-Learning Aksara
Jepang Dengan Metode Mnemonic dan Pattern Recognition (Studi Kasus: SMA
Citra Kasih)” dapat diselesaikan. Skripsi ini disusun sebagai persyaratan kelulusan
pada Program Studi Informatika Fakultas Teknik dan Informatika Universitas
Multimedia Nusantara.
Ucapan terima kasih disampaikan kepada:
1. Dr. Ninok Leksono, selaku rektor Universitas Multimedia Nusantara,
2. Seng Hansun, S.Si., M.Cs., selaku ketua program studi Informatika dan
dosen pembimbing II yang telah memberikan bimbingan selama
pengerjaan skripsi,
3. Andre Rusli, S.Kom., M.Sc., selaku dosen pembimbing I yang telah
memberikan bimbingan selama pengerjaan skripsi dengan sabar,
4. Papi, Mami, Cici Mechele Hwan, dan sepupu Gisela Felicia, yang selalu
memberi dukungan, mendorong untuk terus berprestasi, dan mendoakan
yang terbaik,
5. Keshia Tiffany Wangko, Indah Noviasari, dan Ang Rahma Febryani,
teman seperjuangan yang telah melewati manis pahit perkuliahan sejak
semester 1 di Informatika UMN, yang selalu memberi dukungan moral
yang menenangkan dan menghibur dengan cara yang unik,
6. Julio Cristian Young, S.Kom., M.Kom., yang dengan ringan hati
membantu mencari topik dan menjawab pertanyaan-pertanyaan di saat
kebingungan,
Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019
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7. Farica Perdana Putri, S.Kom., M. Sc., selaku penanggung jawab
Laboratorium Artificial Intelligence C504 yang mengijinkan pemakaian
perangkat untuk keperluan skripsi ini,
8. Sarah Widjaja, S.T., teman dekat yang selalu ada dan menemani sejak
sekolah dasar meskipun jarang bertemu,
9. Shendy Harlim, Janssen, Enrico Nathaniel, Nathania Elvina, Christofer
Derian, Kenny Wantara, Rakadetyo Alif, Ferdinand, Yudha Teguh
Hartanto, Albert Kosasi, Marisa Tri Utami, dan Willy William, teman-
teman yang sangat suportif, saling membantu dalam segala hal, dan
menghibur setiap saat,
10. Teman-teman di Laboratorium Mobile Development B507 dan
Laboratorium Artificial Intelligence C504 yang selalu bersedia
mendengarkan keluh kesah dan memberi masukkan selama pengerjaan
skripsi,
11. Teman-teman lain yang namanya tidak dapat disebutkan satu per satu
yang senantiasa menyemangati dan mendukung.
Tangerang, 28 Januari 2019
Astrid Tamara
Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019
vi
RANCANG BANGUN APLIKASI E-LEARNING
AKSARA JEPANG DENGAN METODE
MNEMONIC DAN PATTERN RECOGNITION
(Studi Kasus: SMA Citra Kasih)
ABSTRAK
Negara Jepang populer sebagai tujuan bekerja di masa depan, namun terdapat
kesulitan dalam mempelajari bahasa Jepang. Aksara Jepang berbeda dengan alfabet
yang digunakan secara internasional sehingga sulit dipelajari pelajar internasional.
Pictograph dan keyword mnemonic digunakan dalam penelitian ini sehingga aksara
Jepang dapat divisualisasikan sebagai gambar dan kalimat yang mempermudah
mengingat. Materi pembelajaran aksara Jepang dengan mnemonic dikemas dalam
aplikasi e-learning sehingga pelajar dapat belajar dimana saja dan kapan saja.
Pattern recognition menggunakan algoritma Convolutional Neural Network
diimplementasikan untuk menilai kebenaran penulisan aksara sesuai input
pengguna. Penelitian ini bertujuan untuk merancang dan membangun aplikasi e-
learning aksara Jepang dengan metode mnemonic dan pattern recognition
menggunakan algoritma Convolutional Neural Network dan mengetahui apakah
aplikasi e-learning yang dibangun menghasilkan perbedaan signifikan pada hasil
pembelajaran aksara Jepang bagi pelajar SMA Citra Kasih. Berdasarkan penelitian
yang telah dilakukan, aplikasi e-learning aksara Jepang dengan metode mnemonic
dan pattern recognition menggunakan algoritma Convolutional Neural Network
telah dirancang dan dibangun. Implementasi pattern recognition dengan algoritma
Convolutional Neural Network menghasilkan model dengan akurasi training
99,19%, validasi 100%, dan testing 88,08%. Perbedaan selisih hasil pre-test dan
post-test kelas eksperimen dan kontrol menyimpulkan bahwa tidak adanya
perbedaan signifikan, namun hasil wawancara menyatakan bahwa pelajar SMA
Citra Kasih tertarik menggunakan aplikasi untuk mempelajari bahasa Jepang.
Kata Kunci: aksara Jepang, Convolutional Neural Network, e-learning, mnemonic,
SMA Citra Kasih
Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019
vii
DEVELOPMENT OF E-LEARNING APPLICATION FOR
JAPANESE CHARACTER USING MNEMONIC
METHOD AND PATTERN RECOGNITION
(Case Study: SMA Citra Kasih)
ABSTRACT
Japan is popular for future career purpose despite the difficulty to learn Japanese.
Japanese character is different from internationally used alphabet system which
causes difficulty for international students to acquire. Pictograph and keyword
mnemonic are used in this research so Japanese characters can be visualized as
pictures and sentences to aid in memorization. Japanese character learning material
with mnemonic is packaged in an e-learning application so students can learn
anywhere and anytime. Pattern recognition using Convolutional Neural Network
algorithm is implemented to determine the correctness of user’s written input. This
research aims to design and develop Japanese character e-learning application using
mnemonic method and pattern recognition using Convolutional Neural Network
and determine whether developed e-learning application contributes to significant
difference in SMA Citra Kasih students’ Japanese character learning result. Based
on conducted research, Japanese character e-learning application using mnemonic
method and pattern recognition using Convolutional Neural Network has been
designed and developed. Implementation of pattern recognition using
Convolutional Neural Network resulted in a model with accuracy of training
99,19%, validation 100%, and testing 88,08%. While the difference between pre-
test and post-test result of experiment and control class concludes that there is no
significant difference, SMA Citra Kasih students expressed their interest to learn
Japanese with developed e-learning application during the interview.
Keywords: Convolutional Neural Network, e-learning, Japanese character,
mnemonic, SMA Citra Kasih
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DAFTAR ISI
PERNYATAAN TIDAK MELAKUKAN PLAGIAT ........................................... ii LEMBAR PENGESAHAN SKRIPSI ................................................................... iii KATA PENGANTAR ........................................................................................... iv ABSTRAK ............................................................................................................. vi ABSTRACT .......................................................................................................... vii DAFTAR ISI ........................................................................................................ viii DAFTAR TABEL .................................................................................................... x DAFTAR GAMBAR ............................................................................................. xi DAFTAR RUMUS .............................................................................................. xiii BAB I PENDAHULUAN ........................................................................................ 1
1.1 Latar Belakang .......................................................................................... 1 1.2 Rumusan Masalah ..................................................................................... 4 1.3 Batasan Masalah ........................................................................................ 4 1.4 Tujuan Penelitian ....................................................................................... 4 1.5 Manfaat Penelitian ..................................................................................... 5 1.6 Sistematika Penulisan ................................................................................ 5
BAB II LANDASAN TEORI .................................................................................. 7 2.1 Aksara Jepang ................................................................................................ 7
2.1.1 Kana「仮名」 ....................................................................................... 7
2.1.2 Kanji「漢字」....................................................................................... 9
2.1.3 Cara Penulisan ..................................................................................... 10 2.1.4 Japanese-Language Proficiency Test (JLPT) ...................................... 11
2.2. Metode Pembelajaran Mnemonic ............................................................... 12 2.2.1 Tipe-tipe Mnemonic ............................................................................ 12 2.3. E-Learning ................................................................................................... 13 2.4 Pattern Recognition ...................................................................................... 16 2.4.1 Convolutional Neural Network (CNN)................................................ 16 2.4.2 Training ................................................................................................ 19 2.5 TensorFlow .................................................................................................. 21 2.6 T-Test ........................................................................................................... 22
BAB III METODOLOGI DAN PERANCANGAN SISTEM ............................... 23 3.1 Metodologi ................................................................................................... 23
3.2 Variabel Penelitian ....................................................................................... 25 3.3 Teknik Pengumpulan Data ........................................................................... 25
3.4 Teknik Pengambilan Sampel ........................................................................ 26
3.5 Perancangan Sistem ..................................................................................... 26 3.5.1 Data Flow Diagram .................................................................................. 26 3.5.2 Flowchart .................................................................................................. 32 3.5.3 Entity Relationship Diagram .................................................................... 40
3.5.4 Struktur Tabel ........................................................................................... 40 3.5.5 Perancangan Tampilan Antarmuka .......................................................... 41
BAB IV IMPLEMENTASI DAN UJI COBA ....................................................... 52 4.1 Spesifikasi Perangkat ................................................................................... 52 4.2 Implementasi ................................................................................................ 53
4.2.1 Implementasi Perancangan Aplikasi E-learning Berbasis Mobile ........... 53 4.2.2 Implementasi Algoritma Convolutional Neural Network (CNN) ............ 62
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4.3 Skenario Pengujian ....................................................................................... 64 4.3.1 Skenario Pelatihan Algoritma Convolutional Neural Network ................ 64 4.3.2 Skenario Pengujian Aplikasi .................................................................... 64 4.4 Hasil Pengujian ............................................................................................ 66 4.4.1 Hasil Pelatihan dan Pengujian Algoritma Convolutional Neural
Network ............................................................................................................. 66 4.4.2 Hasil Pengujian Aplikasi ......................................................................... 66 4.5 Evaluasi ........................................................................................................ 69 4.5.1 Hasil Evaluasi Algoritma Convolutional Neural Network ....................... 69 4.5.2 Hasil Evaluasi Pre-Test dan Post-Test ..................................................... 69
BAB V Simpulan dan saran ................................................................................... 72 5.1 Simpulan ...................................................................................................... 72
5.2 Saran ............................................................................................................. 73 DAFTAR PUSTAKA ............................................................................................ 74 LAMPIRAN ........................................................................................................... 78
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DAFTAR TABEL
Tabel 2.1 Tabel Perbedaan Hiragana, Katakana, dan Kanji ................................... 7 Tabel 2.2 Hiragana dan Urutan Penulisannya ......................................................... 8 Tabel 2.3 Katakana dan Urutan Penulisannya ........................................................ 8 Tabel 2.4 Goresan Lurus ......................................................................................... 9 Tabel 2.5 Goresan Bersiku .................................................................................... 10 Tabel 2.6 Goresan Bersiku Banyak ....................................................................... 10
Tabel 3.1 Desain Penelitian................................................................................... 24 Tabel 3.2 Tabel Characters ................................................................................... 40 Tabel 3.3 Tabel Lists ............................................................................................. 41 Tabel 3.4 Tabel Lists_Characters .......................................................................... 41
Tabel 4.1 Jadwal Testing Aplikasi ........................................................................ 65 Tabel 4.2 Hasil Pre-Test, Post-Test, dan Selisih Kelompok Kontrol ................... 66 Tabel 4.3 Hasil Pre-Test, Post-Test, dan Selisih Kelompok Eksperimen ............. 67
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DAFTAR GAMBAR
Gambar 2.1 Contoh Kalimat dalam Bahasa Jepang, Cara Pelafalan, dan Arti dalam
Bahasa Inggris ....................................................................................................... 10 Gambar 2.2 Daftar Kanji JLPT N5 ....................................................................... 11 Gambar 2.3 Contoh Keyword Mnemonics ........................................................... 12 Gambar 2.4 Contoh Pictographs ........................................................................... 13 Gambar 2.5 Arsitektur CNN Pada Umumnya....................................................... 17 Gambar 2.6 Proses Convolution ........................................................................... 18 Gambar 2.7 Average Pooling dan Max Pooling ................................................... 18 Gambar 2.8 Fully Connected Layers .................................................................... 19 Gambar 2.9 Algoritma Adam ................................................................................ 20
Gambar 2.10 Kode Convolutional Layer Menggunakan TensorFlow .................. 21
Gambar 3.1 Context Diagram ............................................................................... 27 Gambar 3.2 Diagram Level 1 ................................................................................ 28 Gambar 3.3 Diagram Level 2 Proses Study Mode ................................................ 29 Gambar 3.4 Diagram Level 2 Proses Quiz Mode ................................................. 30 Gambar 3.5 Diagram Level 2 Proses Klasifikasi dengan Convolutional Neural
Network ................................................................................................................. 31 Gambar 3.6 Diagram Level 2 Proses Manajemen Private List ............................. 32 Gambar 3.7 Flowchart Aplikasi ............................................................................ 33 Gambar 3.8 Flowchart Study Mode ...................................................................... 35 Gambar 3.9 Flowchart Quiz Mode........................................................................ 37 Gambar 3.10 Flowchart Add To Privatelist .......................................................... 38
Gambar 3.11 Flowchart Pattern Recognition dengan Convolutional Neural
Network ................................................................................................................. 39 Gambar 3.12 Entity Relationship Diagram ........................................................... 40 Gambar 3.13 Tampilan Splash Screen .................................................................. 42 Gambar 3.14 Tampilan Study Mode Tab Levels .................................................. 42 Gambar 3.15 Tampilan Study Mode Tab Private ................................................. 43
Gambar 3.16 Tampilan Pemilihan Aksara ............................................................ 44 Gambar 3.17 Tampilan Seleksi Aksara ................................................................. 45 Gambar 3.18 Tampilan Menambahkan Aksara yang Dipilih Ke Private List ...... 45 Gambar 3.19 Tampilan Pembelajaran Aksara Kana ............................................. 46 Gambar 3.20 Tampilan Pembelajaran Aksara Kanji............................................. 47
Gambar 3.21 Tampilan Mode Quiz Tab Levels.................................................... 47 Gambar 3.22 Tampilan Mode Quiz Tab Private List ............................................ 48
Gambar 3.23 Tampilan Quiz dan Hint .................................................................. 49 Gambar 3.24 Tampilan Jawaban Benar dan Salah................................................ 49 Gambar 3.25 Tampilan Nilai Quiz ........................................................................ 50
Gambar 3.26 Tampilan About Tab Japanese dan Tab Credits .............................. 51
Gambar 4.1 Splash Screen .................................................................................... 54
Gambar 4.2 Tampilan Study Mode ....................................................................... 55 Gambar 4.3 Tampilan Pemilihan Aksara .............................................................. 56 Gambar 4.4 Tampilan Seleksi Aksara dan Penambahan ke PrivateList ............... 57 Gambar 4.5 Tampilan Study Detail ...................................................................... 58
Gambar 4.6 Tampilan Quiz Mode ........................................................................ 59 Gambar 4.7 Tampilan Pertanyaan Quiz ................................................................ 60
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Gambar 4.8 Tampilan Hint ................................................................................... 60 Gambar 4.9 Tampilan Hasil Quiz ......................................................................... 61 Gambar 4.10 Tampilan About............................................................................... 62 Gambar 4.11 Data Training dan Testing ............................................................... 62 Gambar 4.12 Implementasi CNN menggunakan Tensorflow Keras API ............. 63
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DAFTAR RUMUS
Rumus 2.1 Rumus T-Test ..................................................................................... 22
Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019
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