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Journal : Bulletin of Computer Science Research

Klasifikasi Gender Berbasis Citra Wajah Menggunakan Clustering Dan Deep Learning Okky Prasetia; Syaeful Machfud; Rosyani, Perani; Bobi Agustian
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.581

Abstract

Gender classification based on facial images is a significant challenge in the field of computer vision, especially when dealing with unstructured data sourced from social media platforms. This study proposes an integrated approach combining facial image preprocessing, clustering methods, and deep learning to enhance the accuracy of gender classification. The dataset used was obtained from a Big Data Competition and consists of male and female face images sourced from Instagram. Preprocessing was performed using OpenCV for face detection and cropping. Subsequently, the data were clustered using K-Means and DBSCAN algorithms to reduce noise and redundancy. Gender classification was then conducted using a sequential learning model based on Inception_v3, enhanced with Agglomerative Clustering for feature refinement. The evaluation of the system demonstrated strong performance with an accuracy of 92.97%, F1-score of 0.89556, precision of 0.97727, and recall of 0.83069. These results confirm that the integration of clustering techniques and deep learning significantly improves the effectiveness of gender classification based on facial images, especially for open-source and non-curated datasets.
Implementasi Entreprises Resource Planning Berbasis Web dan Mobile Menerapkan Metode SCRUM Syahdan, Muhammad; Nanang, Nanang; Suryaningrat, Suryaningrat; Machfud, Syaeful
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.711

Abstract

Companies operating in the property sector have highly complex business processes involving multiple divisions, such as engineering, marketing, legal, and finance. However, many property companies still manage their data manually and in a fragmented manner, leading to various risks such as data entry errors, communication failures, and other inefficiencies. This study aims to implement an integrated web- and mobile-based Enterprise Resource Planning (ERP) system to support and streamline business processes in a property company, making them more efficient and effective. The development methodology used is Agile, with data collected through interviews, observation, and documentation studies. The system was developed using web and mobile technologies to provide users with flexible access. The implementation results show that the developed ERP system is capable of supporting and improving business processes in the property sector, facilitating real-time data tracking, and increasing operational efficiency. With this system, the company no longer needs to rely on manual data recording and can improve the accuracy of decision-making. This research demonstrates that a digitally based ERP system can be a strategic and effective solution for property companies in facing the challenges of the modern era.