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All Journal Bulletin of Computer Science Research APPA : Jurnal Pengabdian Kepada Masyarakat
Okky Prasetia
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Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology

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PEMANFAATAN DIGITAL MARKETING UNTUK MENINGKATKAN BRAND AWARENESS TAMAN BACA MASYARAKAT (TBM) KOLONG CIPUTAT Gigih Amrillah Ibnurhus; Syaeful Machfud; Okky Prasetia
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 2 No 4 (2024): APPA : Jurnal Pengabdian kepada Masyarakat
Publisher : Shofanah Media Berkah

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Abstract

Penelitian ini bertujuan untuk menganalisis dan mengimplementasikan strategi digital marketing dalam meningkatkan brand awareness Taman Baca Masyarakat (TBM) kolong Ciputat. Menggunakan pendekatan kualitatif dengan metode studi pustaka, penelitian ini mengkaji berbagai sumber literatur dan data sekunder terkait implementasi digital marketing pada TBM. Hasil penelitian menunjukkan bahwa hanya 45% TBM yang telah beradaptasi dengan transformasi digital, dengan 23% memiliki presence aktif di platform digital. Implementasi strategi digital marketing yang terstruktur terbukti dapat meningkatkan visibilitas TBM hingga 156% dalam aspek pertumbuhan pengunjung. Pengembangan konten digital yang relevan, optimalisasi platform media sosial, dan penggunaan tools digital marketing yang terjangkau menjadi kunci keberhasilan peningkatan brand awareness TBM kolong Ciputat. Penelitian ini merekomendasikan pentingnya pengembangan kapasitas pengelola TBM dalam kompetensi digital dan implementasi sistem monitoring evaluasi yang terukur untuk keberlanjutan program.
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.
Co-Authors Bobi Agustian Gigih Amrillah Ibnurhus Perani Rosyani Syaeful Machfud