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All Journal Journal of Social Research COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Dewi Holilah
Universitas Pamulang
Humanities Computer Science & IT Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

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Peningkatan Kesadaran Keamanan Siber Mahasiswa Melalui Edukasi dan Simulasi Serangan Phishing di Politeknik Piksi Input Serang Dewi Holilah; Bramantyo Ardi; Mokhammad Fakhrudin Ar-Rahji; Dede Anda; Julian Baja Gunawan; Afra Afiah Ayyasy
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 6 No. 2 (2026): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v6i2.3571

Abstract

Perkembangan teknologi digital yang pesat meningkatkan risiko kejahatan siber, khususnya serangan phishing yang banyak menargetkan mahasiswa sebagai pengguna internet aktif. Rendahnya kesadaran dan literasi keamanan siber menyebabkan mahasiswa rentan terhadap pencurian data dan penyalahgunaan akun digital. Penelitian ini bertujuan meningkatkan pemahaman dan kemampuan mahasiswa dalam mengenali serta mencegah serangan phishing melalui edukasi dan simulasi keamanan siber di Politeknik Piksi Input Serang. Metode yang digunakan adalah pendekatan deskriptif dengan konsep experiential learning melalui workshop, simulasi phishing, dan praktik langsung. Pengumpulan data dilakukan menggunakan observasi, pre-test, dan post-test untuk mengukur tingkat pemahaman peserta sebelum dan sesudah kegiatan. Hasil penelitian menunjukkan adanya peningkatan pemahaman mahasiswa terkait identifikasi phishing, social engineering, penggunaan autentikasi dua faktor (2FA), dan langkah mitigasi ancaman siber. Simulasi dan praktik langsung dinilai efektif dalam meningkatkan kesadaran serta keterampilan mahasiswa dibandingkan pembelajaran teoritis. Penelitian ini berkontribusi dalam pengembangan metode edukasi keamanan siber berbasis praktik bagi mahasiswa. Kesimpulannya, edukasi dan simulasi phishing mampu meningkatkan literasi keamanan siber mahasiswa sehingga kegiatan serupa perlu dilakukan secara berkelanjutan dengan skenario yang lebih variatif dan mengikuti perkembangan ancaman digital.
Early Detection of Retinoblastoma Using K-Nearest Neighbors and HSV Color Features Dewi Holilah
Journal of Social Research Vol. 5 No. 8 (2026): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v5i8.3308

Abstract

Retinoblastoma (RB) is the most common malignant intraocular cancer in children and may lead to blindness or death if not detected early. Most existing detection methods utilize deep learning approaches, which require large datasets and high computational resources. Therefore, this study proposes a simple and efficient early detection method for retinoblastoma using HSV (Hue, Saturation, Value) color features and the K-Nearest Neighbors (KNN) algorithm. The dataset consisted of 105 eye images, including 52 normal eye images and 53 retinoblastoma-positive images. The research stages included region-of-interest extraction in the pupil area, conversion of images from the RGB color space to HSV, feature extraction based on average saturation and average brightness values, and classification using KNN. Model evaluation was performed using the Leave-One-Out Cross-Validation (LOOCV) method with k-value variations ranging from 1 to 10. The test results showed that k = 1 achieved the best performance, with an accuracy of 83.81%, sensitivity of 83.02%, specificity of 84.62%, precision of 84.62%, and an F1-score of 83.81%. These results demonstrated that the combination of HSV color features and KNN classification was able to effectively distinguish between normal eye images and retinoblastoma cases. The proposed method has the potential to serve as a simple and low-cost screening tool for early retinoblastoma detection.
Co-Authors Afra Afiah Ayyasy Bramantyo Ardi Dede Anda Julian Baja Gunawan Mokhammad Fakhrudin Ar-Rahji