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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknologi Sistem Informasi dan Aplikasi Journal Medical Informatics Technology International Journal of Computer Technology and Science
Faruq Aziz
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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A Tripartite Machine Learning Approach for Accurate Prognosis of COVID-19 Patient Survival Faruq Aziz
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.13

Abstract

Accurate prognosis of COVID-19 patient survival is vital for healthcare decision-making. This research proposes a tripartite machine learning approach that integrates K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost for outcome prediction. Our hybrid model exploits the strengths of individual algorithms and combines their predictions using a weighted ensemble. Leveraging clinical data, KNN captures local patterns, SVM finds complex boundaries, and XGBoost enhances overall performance. Experimental results show exceptional precision (0.93), recall (0.93), and F1-score (0.93) for both classes, affirming accurate classification of "Alive" and "Died" cases. The achieved accuracy of 0.93 further demonstrates the reliability of the proposed approach. Our tripartite method holds the potential to enhance COVID-19 survival prediction, providing valuable insights for clinical practitioners and policymakers. This study contributes by seamlessly fusing KNN, SVM, and XGBoost models into a robust predictive tool, thereby aiding medical professionals in informed decision-making for patient care and resource allocation. The demonstrated success underscores the efficacy of a combined approach, highlighting its relevance in accurately predicting patient outcomes.
Efficient Skin Lesion Detection using YOLOv9 Network Faruq Aziz; Saputri, Daniati Uki Eka
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.30

Abstract

Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin lesion detection using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance in accurately identifying various skin lesion types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, and wart. We conducted comprehensive experiments using a curated dataset comprising 2721 training images, 288 validation images, and 145 test images. The model was trained and evaluated based on standard metrics such as Precision, Recall, and mean Average Precision (mAP). Our results indicate promising detection accuracy, with an overall Precision of 60.5%, Recall of 86.0%, and an mAP of 81.4%. Class-wise analysis reveals varying levels of performance across different disease classes, highlighting the model's proficiency in detecting common dermatological conditions such as acne and wart lesions. Furthermore, we provide insights into potential challenges and limitations, including dataset size and class imbalance, and discuss avenues for future research to address these issues. Our study contributes to the advancement of AI-driven solutions for dermatological diagnosis and underscores the efficacy of the YOLOv9 network in skin lesion detection
Multiclass Meat Classification Using a Hybrid Machine Learning Approach Taopik Hidayat; Daniati Uki Eka Saputri; Faruq Aziz; Nurul Khasanah
International Journal of Computer Technology and Science Vol. 2 No. 2 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i2.238

Abstract

Image classification is a key field in digital image processing with broad applications, such as object recognition and disease detection. The use of artificial neural network architectures, such as MobileNetV2, has significantly advanced pattern recognition in large datasets. However, in small datasets, challenges related to accuracy and generalization are often encountered. This study explores an RGB-based approach utilizing MobileNetV2 for image feature extraction and Support Vector Machine (SVM) as the classifier. MobileNetV2 is applied to extract features from RGB images, which are then further processed by SVM to determine image classes. The results indicate that this model achieves an accuracy of 91.67%, precision of 0.9163, recall of 0.9167, and F1-score of 0.9161. Based on the confusion matrix analysis, the model effectively distinguishes between classes, despite slight overlaps. This research contributes to the development of intelligent image classification systems that can be applied in various fields, including the food industry. With these achievements, the RGB approach integrating MobileNetV2 and SVM has proven effective in enhancing image classification accuracy, even with relatively small datasets. These findings open opportunities for applying similar methods in other image processing tasks that require high accuracy in object or disease detection and classification.
Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods Dwiza Riana; Jufriadif Na'am; Saputri, Daniati Uki Eka Saputri; Sri Hadianti; Faruq Aziz; Suryadi Putra Liawatimena; Alya Shafra Hewiz; Dika Putri Metalica; Teguh Herwanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5420

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.
Perancangan Sistem Informasi Deteksi Penyakit Daun Padi Menggunakan Metode Agile Faruq Aziz; Daniati Uki Eka Saputri; Nurul Khasanah; Taopik Hidayat
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 8 No. 3 (2025): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v8i3.46970

Abstract

The development of a rice leaf disease detection information system using the Agile method aims to provide an innovative solution for fast and accurate plant disease identification. The system can detect four major rice leaf disease classes: bacterial blight, brownspot, blast, and healthy conditions. The development process follows an iterative approach, starting from understanding user needs to system implementation and testing. Black-box testing was applied to ensure that all features, such as image upload and disease classification, function according to specifications. Evaluation results indicate that the system achieves high accuracy in disease detection based on the utilized dataset. However, dataset limitations and testing scenarios pose challenges for generalizing results to real-field conditions. Hence, intensive evaluation and dataset updates are crucial for future development. With its user-friendly interface, the system is expected to support farmers in improving productivity and efficiency in rice disease detection.
Co-Authors Alya Shafra Hewiz Daniati Uki Eka Saputri Dika Putri Metalica Dwiza Riana Jufriadif Na`am, Jufriadif Nurul Khasanah Saputri, Daniati Uki Eka Saputri Sri Hadianti Suryadi Putra Liawatimena Taopik Hidayat Taopik Hidayat Teguh Herwanto