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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Komatika: Jurnal Pengabdian Kepada Masyarakat Journal Medical Informatics Technology International Journal of Computer Technology and Science
Faruq Aziz
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Humanities Computer Science & IT Dentistry Education Engineering Medicine & Pharmacology Public Health Social Sciences

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Sosialisasi Artifical Intelligence Menuju Smart Government Untuk Kelompok Pkk Rw 06 Tegal Parang Mampang Dwiza, Riana; Agus, Subekti; Zico Pratama, Putra; Hilman Ferdinandus, Pardede; Faruq Aziz
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol 3 No 2 (2023): November 2023
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v3i2.633

Abstract

The rapid advancement of Artificial Intelligence (AI) technology has significantly impacted industries and government sectors during the fourth industrial revolution. AI offers the potential to simplify and streamline public service delivery, enabling governments to enhance service quality, build public trust, and improve efficiency. In Indonesia, the Women's Empowerment Family Welfare Movement (PKK) plays a crucial role in promoting women's participation in national development. As partners to village and sub-district governments, PKK supports population management and regional development. In line with its commitment to Community Service, Nusa Mandiri University organized the Socialization of Artificial Intelligence Towards Smart Government for PKK RW 06 Tegal Parang Mampang. The main objective of this activity was to inform the management of PKK RW 06 about the benefits and implementation of AI technology in achieving smart government and enhancing PKK's services and activities. The socialization event, attended by 12 participants, was conducted in a hybrid format, combining face-to-face meetings and digital technology. The participants exhibited great enthusiasm in grasping the material and actively engaging in interactive Q&A sessions. As a result of the socialization, participants demonstrated an improved understanding of AI's applications in smart government. To maximize the future impact of community service activities, it is recommended to develop more comprehensive materials, provide continuous training, engage additional partners, and conduct regular evaluations and improvements. By taking these steps, community service initiatives can generate greater benefits for participants and the wider community
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.
Co-Authors Agus, Subekti Alya Shafra Hewiz Daniati Uki Eka Saputri Dika Putri Metalica Dwiza Riana Dwiza, Riana Hilman Ferdinandus, Pardede Jufriadif Na`am, Jufriadif Nurul Khasanah Saputri, Daniati Uki Eka Saputri Sri Hadianti Suryadi Putra Liawatimena Taopik Hidayat Teguh Herwanto Zico Pratama, Putra