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All Journal JAMI: Jurnal Ahli Muda Indonesia Jurnal Ilmiah Computer Science International Journal of Artificial Intelligence in Medical Issues
M Ikbal Siami
Institut Teknologi Dan Bisnis STIKOM Ambon
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Engineering Health Professions Medicine & Pharmacology Public Health Social Sciences

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Penerapan Deteksi Penggunaan Masker pada Sistem Absensi Karyawan menggunakan Metode Deep Learning M Ikbal Siami; Mawaddah Hamid
JAMI: Jurnal Ahli Muda Indonesia Vol. 3 No. 2 (2022): Desember 2022
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v3i2.118

Abstract

Objektif. Kehidupan New Normal pasca terjadinya pandemic covid-19 yang melanda seluruh Negara di dunia adalah salahsatunya dengan menjaga protocol kesehatan secara ketat. Hal ini penting dilakukan untuk menjaga agar masyarakat tidak terjangkit penyakit menular covid-19 kembali. Kedisiplinan yang tinggi dan ditopang dengan berbagai kebijakan pada lingkup instansi menjadi hal yang sangat diperlukan agar protocol kesehatan terus terlaksana. Salah satu upaya yang dilakukan adalah mengimpelementasikan sitem pendeteksi penggunaan masker pada system absensi karyawan di Institut teknologi dan Bisnis STIKOM Ambon. System pedeteksi absensi bekerja secara otomatis dan terus menerus mendeteksi penggunaan masker pada karyawan yang hendak melakukan presensi masuk dan presensi pulang. System akan menolak presensi apabila karyawan tersebut terdeteksi tidak menggunakan masker. Material and Metode. Penelitian ini menggunakan You Only Look Once (YOLO) sebuah model object detection berbasis Deep learning versi 4 atau disebut sebagai YOLOv4. Data yang digunakan pada penelitian ini adalah data gambar yang berisi objek manusia menggunakan atau tidak menggunakan masker. Hasil. Hasil pengujian model YOOv4 tingkat Precision 92 % dan Recall 88 %. Kesimpulan. Hasil penelitian menunjukan implementasi deteksi wajah pada sistem presensi dengan model YOLOv4 menunjukan kinerja yang sangat baik.
Penerapan VIKOR Method (VIšekriterijumsko KOmpromisno Rangiranje Method) Dalam Rekomendasi Pemilihan Laptop Gaming Merriam Modeong; M. Ikbal Siami
Jurnal Ilmiah Computer Science Vol. 1 No. 2 (2023): Volume 1 Number 2 January 2023
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v1i2.6

Abstract

The VIKOR method (VIšekriterijumsko KOmpromisno Rangiranje Method) is one of the techniques in the field of multi-criteria analysis used to make decisions in situations where there are several criteria that must be considered simultaneously. This method helps in decision making when there are many alternatives that must be evaluated based on several different criteria. The research conducted aims to provide recommendations to users in the selection of gaming laptops by applying a decision support system model using the VIšekriterijumsko KOmpromisno Rangiranje Method (VIKOR) method so that it becomes an input in the selection of gaming laptops. The final calculation results in the VIKOR method provide recommendations for Rank 1, namely Predator Helios 18 Laptop, Rank 2, namely ASUS ROG Zephyrus G14 Laptop, Rank 3, ASUS ROG Strix SCAR 18 Laptop, and Rank 4, Nitro 5 Laptop. The results of application testing using the black box testing method obtained a percentage result of 100% in accordance with the functions that have been created
Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Thomas Edyson Tarigan; Erma Susanti; M. Ikbal Siami; Ika Arfiani; Agus Aan Jiwa Permana; I Made Sunia Raharja
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): 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.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Co-Authors Agus Aan Jiwa Permana Erma Susanti I Made Sunia Raharja Ika Arfiani Mawaddah Hamid Merriam Modeong Thomas Edyson Tarigan