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All Journal ILKOM Jurnal Ilmiah Ilmu Komputer untuk Masyarakat International Journal of Artificial Intelligence in Medical Issues
Alfiyyah, Nurul
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Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Public Health Other

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Multiclass Classification of Rupiah Banknotes Based on Image Processing Azis, Huzain; Purnawansyah, Purnawansyah; Alfiyyah, Nurul
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1784.87-99

Abstract

This research aims to classify the nominal value of Rupiah banknotes using image processing and classification methods. The research design was conducted by collecting a dataset of Rupiah banknotes consisting of 30 classes, each with 100 images. This research uses image preprocessing using Canny Segmentation to create object edges and clarify image details. The Hu Moments method, which describes pixel distribution and object shape, is used to extract special features from the image. Classification modeling is then performed using Decision Tree and Random Forest to classify banknotes based on the extracted characteristics. Model evaluation is performed by measuring accuracy, precision, recall, and f1socre performance and using cross-validation with k-fold=5. The results show that the Decision Tree method is able to classify Rupiah banknotes well. In the performance evaluation, the Decision Tree method achieved the highest accuracy of 86.83% and good precision, recall, and f1-score for several banknote classes. The Random Forest method also achieved good results, with the highest accuracy of 78.67%. The classification evaluation results show that the Decision Tree method is better than the Random forest in classifying Rupiah banknotes.
Optimizing Cardiomegaly Detection: A Random Forest Approach to Processed Chest X-ray Imagery Alfiyyah, Nurul
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v2i2.156

Abstract

This study explores the application of a Random Forest Classifier for the automated detection of Cardiomegaly from chest X-ray images, utilizing a dataset processed and derived from the NIH Chest X-ray Dataset. Given the crucial need for accurate and timely diagnosis of Cardiomegaly to inform appropriate treatment decisions, this research aims to determine the efficacy of machine learning models in augmenting diagnostic processes. Employing image pre-processing techniques such as Sobel filtering for edge detection and Hu Moments for feature extraction, the study enhances the input features for the model. The performance of the classifier was evaluated using a 5-fold cross-validation approach, yielding results with average accuracy, precision, recall, and F1-scores ranging approximately between 52% and 54%. These findings suggest a moderate level of reliability and consistency, indicating the potential utility of ensemble machine learning methods in medical imaging analysis. However, the variability in performance across different data subsets highlights the challenges and necessitates further optimization. This research contributes to the ongoing discourse on integrating machine learning into clinical settings, demonstrating the potential benefits and current limitations. Future research is recommended to expand the dataset variety, integrate advanced deep learning methodologies, and rigorously test these models in clinical environments. The findings hold significant implications for the development of automated diagnostic tools in healthcare, potentially leading to enhanced diagnostic accuracy and efficiency.
Implementasi Aplikasi Augmented Reality untuk Media Pembelajaran Flora di SD Inpres Desa MarindingToraja Umar, Fitriyani; Herdianti, Herdianti; Astuti, Wistiani; Alfiyyah, Nurul; Nurul, Sarah Fila
Ilmu Komputer untuk Masyarakat Vol 4, No 1 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v4i1.1545

Abstract

Banyak upaya yang dapat dilakukan untuk membantu proses pembelajaran. Transfer pengetahuan akan optimal jika didukung dengan media pembelajaran yang tepat. Sekolah Dasar Inpres Marinding dalam proses pembelajaran selama ini belum dapat memanfaatkan teknologi terkini yang sesuai untuk meningkatkan pemahaman siswa tentang suatu mata pelajaran. Padahal pesatnya perkembangan teknologi informasi dapat dimanfaatkan sebagai media tambahan untuk mendukung pembelajaran yang merangsang imajinasi, interaktif dan menumbuhkan minat belajar sehingga proses belajar mengajar menjadi lebih baik lagi.Demi menunjang proses pembelajaran di SDN 294 Inpres Marinding, siswa diharapkan memiliki banyak buku yang berisi satu tema tertentu sebanyak jumlah tema yang ada. Akan tetapi, pembelajaran masih berpusat pada buku tersebut dan tidak ada alat peraga khusus tentang Flora. Siswa tidak dapat melihat objek Flora secara langsung, hanya melalui gambar di buku dan kurang detailnya informasi tentang objek tersebut. Akibatnya, pembelajaran cenderung monoton, dan kurang kreatifitas.Solusi yang diusulkan adalah memberikan pelatihan untuk implementasi aplikasi Augmented Reality Pengenalan dengan tujuan untuk meningkatkan pengetahuan guru dan siswa dalam pemanfaatan teknologi informasi untuk media pembelajaran alternatif melalui Augmented Reality khususnya pembelajaran Flora.Tercapainya tujuan kegiatan telah menghasilkan luaran berupa modul dan aplikasi yang dapat menjadi alternatif media pembelajaran, publikasi pada media online dan jurnal yang diterbitkan di ILKOMAS.
Co-Authors Astuti, Wistiani Herdianti Herdianti Huzain Azis Nurul, Sarah Fila Purnawansyah Purnawansyah Umar, Fitriyani