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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Anisa Nur Azizah
Institut Teknologi Sepuluh Nopember
Computer Science & IT Engineering

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Tajweed-YOLO: Object Detection Method for Tajweed by Applying HSV Color Model Augmentation on Mushaf Images Anisa Nur Azizah; Chastine Fatichah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4739

Abstract

Tajweed is a basic knowledge of learning to read the Al-Qur’an correctly. Tajweed has many laws grouped into several parts so that only some people can memorize and implement Tajweed properly. Therefore, it is necessary to have an automatic detection system to facilitate the recognition of Tajweed, which can be used daily. This study presents Tajweed-YOLO, which applies the HSV color augmentation model to detect Tajweed objects in Mushaf images using YOLO. The contribution to this study was to compare the three versions of You Only Look Once (YOLO), i.e., YOLOv5, YOLOv6, and YOLOv7, and usage of the HSV color model augmentation to improve Tajweed detection performance. Comparing the three YOLO versions aims to solve problems in detecting small objects and recognizing various forms of Mushaf writing fonts in Tajweed detection. Meanwhile, the HSV color model aims to recognize Tajweed objects in various Mushaf and handle minority class problems. In this study, we collected four different Al-Qur’an mushaf with 10 Tajweed classes. The augmentation process can increase the detection performance by up to 85% compared to without augmentation 6th Class (Mad Jaiz Munfashil) using YOLOv6. The comparison of three YOLO versions concluded that YOLOv7 was better than YOLOv5 and YOLOv6, seen in data with augmentation and without augmentation. The evaluation results of mAP0.5 on 17 test data on the YOLOv7, YOLOv6, and YOLOv5 models are 80%, 69%, and 71%, respectively. These results prove that this research model’s results are suitable for the real-time detection of Tajweed.
Easy Data Augmentation untuk Data yang Imbalance pada Konsultasi Kesehatan Daring Anisa Nur Azizah; Misbachul Falach Asy'ari; Ifnu Wisma Dwi Prastya; Diana Purwitasari
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20231057082

Abstract

Pendekatan augmentasi teks sering digunakan untuk menangani imbalance data pada kasus klasifikasi teks, seperti teks Konsultasi Kesehatan Daring (KKD), yaitu alodokter.com. Teknik oversampling dapat mengatasi kondisi skewed terhadap kelas mayoritas. Namun, augmentasi teks dapat mengubah konten dan konteks teks karena kata-kata teks tambahan yang berlebihan. Penelitian kami menyelidiki algoritma Easy Data Augmentation (EDA), yang berbasis parafrase kalimat dalam teks KKD dengan menggunakan teknik Synonym Replacement (SR), Random Insertion (RI), Random Swap (RS), dan Random Deletion (RD). Kami menggunakan Tesaurus Bahasa Indonesia untuk mengubah sinonim di EDA dan melakukan percobaan pada parameter yang dibutuhkan oleh algoritma untuk mendapatkan hasil augmentasi teks yang optimal. Kemudian, percobaan menyelidiki proses augmentasi kami menggunakan pengklasifikasi Random Forest, Naïve Bayes, dan metode berbasis peningkatan seperti XGBoost dan ADABoost, yang menghasilkan peningkatan akurasi rata-rata sebesar 0,63. Hasil parameter EDA terbaik diperoleh dengan menambahkan nilai 0,1 pada semua teknik EDA mendapatkan 88,86% dan 88,44% untuk akurasi dan nilai F1-score. Kami juga memverifikasi hasil EDA dengan mengukur koherensi teks sebelum dan sesudah augmentasi menggunakan pemodelan topik Latent Dirichlet Allocation (LDA) untuk memastikan konsistensi topik. Proses EDA dengan RI memberikan koherensi yang lebih baik sebesar 0,55 dan dapat mendukung implementasi EDA untuk menangani imbalance data, yang pada akhirnya dapat meningkatkan kinerja klasifikasi.   Abstract   The text augmentation approach is often utilized for handling imbalanced data of classifying text corpus, such as online health consultation (OHC) texts, i.e., alodokter.com. The oversampling technique can overcome the skewed condition towards majority classes. However, text augmentation could change text content and context because of excessive words of additional texts. Our work investigates the Easy Data Augmentation (EDA) algorithm, which is sentence paraphrase-based in the OHC texts that often in non-formal sentences by using techniques of synonym replacement (SR), random insertion (RI), random swap (RS), and random deletion (RD). We employ the Indonesian thesaurus for changing synonyms in the EDA and do empirical experiments on parameters required by the algorithm to obtain optimal results of text augmentation. Then, the experiments investigate our augmentation process using classifiers of Random Forest, Naïve Bayes, and boosting-based methods like XGBoost and ADABoost, which resulted in an average accuracy increase of 0.63. The best EDA parameter results were acquired by adding a value of 0.1 in all EDA techniques to get 88.86% and 88.44% for accuracy and F1-score values. We also verified the EDA results by measuring coherences of texts before and after augmentation using a topic modeling of Latent Dirichlet Allocation (LDA) to ensure topic consistency. The EDA process with RI gave better coherences of 0.55, and it could support the EDA application to handle imbalanced data, eventually improving the classification performance.
Co-Authors Chastine Fatichah Diana Purwitasari Ifnu Wisma Dwi Prastya Misbachul Falach Asy'ari