Ferdiawan, Fajar (2022) DETEKSI CHORD PIANO MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Universitas Stikubank.
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Abstract
Piano merupakan alat musik yang paling digemari oleh masyarakat selain gitar, piano dapat menjadi instrument yang baik untuk mengiringi penyanyi walaupun tanpa iringan alat musik yang lain. Piano terdiri dari not yang mencapai 6.5 oktaf sampai lebih dari 7 oktaf, dari not yang ada dapat dibentuk menjadi chord yang sesuai dengan tangga nadanya. Ada beberapa tangga nada yaitu pentatonic, chromatic, serta diatonic, dari ketiga tangga nada tersebut tangga nada diatonic-lah yang sering dipakai. Tangga nada diatonic juga memiliki 2 jenis yaitu diatonic major dan diatonic minor. Tangga nada diatonic major umumnya digunakan pemula untuk belajar piano. Penelitian ini akan mengklasifikasikan chord piano major scale dengan menggunakan metode Convolutional Neural Network. Convolutional Neural Network digunakan untuk mendeteksi serta mengenali object pada sebuah gambar. Penelitian ini juga menggunakan library Keras yang merupakan jaringan syaraf tiruan yang berjalan diatas TensorFlow untuk mempercepat proses pengolahan citra. Hasil uji dengan menggunakan 240 dataset chord piano menghasilkan akurasi tertinggi mencapai 98%. Piano is the most popular musical instrument by the public besides the guitar, piano can be a good instrument to accompany singers even without the accompaniment of other musical instruments. Piano consists of notes that reach 6.5 octaves to more than 7 octaves, from the existing notes can be formed into chords according to the scale. There are several scales, namely pentatonic, chromatic, and diatonic, of the three scales the diatonic scale is the one that is often used. Diatonic scales also have 2 types, namely diatonic major and diatonic minor. Diatonic major scales are generally used by beginners to learn the piano. This research will classify piano major scale chords using the Convolutional Neural Network method. Convolutional Neural Network is used to detect and recognize objects in an image. This research also uses Keras library which is an artificial neural network that runs on TensorFlow to speed up the image processing process. The test results using 240 piano chord datasets produce the highest accuracy reaching 98%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | SKR.I.05.01.2063 NIM.17.01.53.0100 |
| Uncontrolled Keywords: | Deep Learning, Chord Piano, Audio Processing, Convolutional Neural Network, Python Deep Learning, Chord Piano, Audio Processing, Convolutional Neural Network, Python |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty / Institution: | Fakultas Teknologi Informasi > Program Studi Teknik Informatika |
| Depositing User: | Teteh Hayati |
| Date Deposited: | 19 May 2022 03:25 |
| Last Modified: | 20 May 2022 01:24 |
| URI: | https://eprints.unisbank.ac.id/id/eprint/8406 |
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