Sistem Pendeteksi Pola Citra Tajwid Alquran Mad Lazim Mutsaqal Kilmi Menggunakan Metode Algoritma BAM & FAM

Authors

  • Muhammad Fikry Teknik Informatika Universitas Malikussaleh
  • Fadlisyah Fadlisyah
  • Dessayani Putri

DOI:

https://doi.org/10.29103/techsi.v10i2.885

Abstract

Alquran merupakan pedoman umat Islam dan berisikan firman Allah yang diturunkan kepada Nabi Muhammad sebagai penutup para nabi dan rasul dengan perantaraan Malaikat Jibril sebagai penyampai wahyu dan ditulis pada mushaf-mushaf yang kemudian disampaikan kepada manusia secara mutawatir, dimulai dari surat Al-Fatihah sebagai pembuka dan surat An-Nas sebagai penutup. Untuk itu penting mengetahui hukum tajwid agar bacaan Alquran menjadi benar. Pada penelitian akan dibangun sistem untuk mendeteksi tajwid dengan membandingkan keakuratan deteksi pola hukum tajwid Mad Lazim Mutsaqal Kilmi dalam surah Ali Imran dengan menggunakan metode algoritma BAM dan FAM. Dari hasil pendeteksian yang telah dilakukan didapatkan bahwa metode Bidirectionaal Associative Memory (BAM) memiliki tingkat akurasi lebih tinggi dalam melakukan pencarian pola tajwid dibandingkan dengan metode Fuzzy Associative Memory (FAM).

References

Aldape-Pérez, Mario, et al. "An Associative Memory Approach to Healthcare Monitoring and Decision Making." Sensors 18.8 (2018): 2690.

de Souza, Aline Cristina, and Marcos Eduardo Valle. "Fuzzy Kernel Associative Memories with Application in Classification." North American Fuzzy Information Processing Society Annual Conference. Springer, Cham, 2018.

Nasution, Mhd Helmi. "APLIKASI PERBAIKAN CITRA DIGITAL DENGAN MENGGUNAKAN METODE GEOMETRIC MEAN FILTER." Jurnal Ilmiah INFOTEK 1.2 (2016)

Gopalsamy, Kondalsamy, and Xue-Zhong He. "Delay-independent stability in bidirectional associative memory networks." IEEE Transactions on Neural Networks 5.6 (1994): 998-1002.

Wang, Fen, Yuanlong Chen, and Meichun Liu. "pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays." Neural Networks 98 (2018): 192-202.

Downloads

Additional Files

Published

2018-10-24

Issue

Vol. 10 No. 2 (2018)

Section

Articles

License

Authors retain copyright and grant the journal right of first publication and this work is licensed under a Creative Commons Attribution-ShareAlike 4.0 that allows others to share the work with an acknowledgement of the works authorship and initial publication in this journal.

All articles in this journal may be disseminated by listing valid sources and the title of the article should not be omitted. The content of the article is liable to the author.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

In the dissemination of articles by the author must declare the TECHSI Journal as the first party to publish the article.