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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Mantik Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Wildanil Ghozi
Universitas Dian Nuswantoro
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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Pelatihan Desain Poster Promosi untuk UMKM Binaan Dinsospermasdes Kabupaten Jepara Ghozi, Wildanil; Prabowo, Dwi Puji; Rafrastara, Fauzi Adi; Pramunendar, Ricardus Anggi; Sani, Ramadhan Rakhmat
Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 8, No 3 (2025): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v8i3.8379

Abstract

Internet sebagai salah satu dorongan utama dalam perkembangan teknologi memungkinkan setiap manusia untuk menjangakau informasi tanpa batasan ruang dan waktu. Saat ini di Indonesia, lebih dari 196,7 juta penduduk memanfaatkan internet dalam aktivitasnya sehari-hari. Pada provinsi Jawa Tengah terdapat 25,6 juta pengguna internet aktif. Tingginya pengguna internet menjadi peluang untuk memperluas target pemasaran produk UMKM. Pemerintah Kabupaten Jepara, melalui Dinas Sosial, Pemberdayaan Masyarakat dan Desa (Dinsospermasdes) Kabupaten Jepara memiliki tanggung jawab dalam program rehabilitasi Penyandang Masalah Kesejahteraan Sosial (PMKS) dimana salah satu programnya adalah pembinaan UMKM. Saat ini, UMKM binaan Dinsospermasdes belum mampu membuat desain poster promosi yang baik dan menarik pembeli. Penulis memberikan pelatihan desain poster dengan menggunakan aplikasi canva kepada para pelaku UMKM binaan. Pelatihan tersebut telah berhasil meningkatkan pemahaman konsep desain, kemampuan pengambilan foto produk, dan kemampuan membuat desain poster promosi para pelaku UMKM. Poster-poster baru yang dihasilkan pada kegiatan pelatihan menjadi indikator keberhasilan para peserta mengikuti pelatihan. Dengan demikian, diharapkan kemampuan yang telah dimiliki dapat membantu meningkatkan penjualan produk UMKM binaan.
XGBoost-Powered Ransomware Detection: A Gradient-Based Machine Learning Approach for Robust Performance Ghozi, Wildanil; Lestiawan, Heru; Sani, Ramadhan Rakhmat; Hussein, Jassim Nadheer; Rafrastara, Fauzi Adi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2405

Abstract

Ransomware remains a rapidly evolving cyber threat, causing substantial financial and operational disruptions globally. Traditional signature-based detection systems are ineffective against sophisticated, zero-day attacks due to their static nature. Consequently, machine learning-based approaches offer a more effective and adaptive alternative. This study proposes an approach utilizing XGBoost for highly effective ransomware detection. We conducted a rigorous comparative analysis of prominent ensemble learning algorithms—XGBoost, Random Forest, Gradient Boosting, and AdaBoost—on the RISS Ransomware Dataset, comprising 1,524 instances. Our experimental results unequivocally demonstrate XGBoost as the superior ensemble model, achieving an impressive 97.60% accuracy and F1-Score. This performance surpassed Gradient Boosting (97.20%), Random Forest (96.94%), and AdaBoost (96.50%). Furthermore, this study benchmarked XGBoost against established state-of-the-art (SOTA) methods, including Support Vector Machine (SVM) and the SA-CNN-IS deep learning approach. The comprehensive results confirm that XGBoost not only excels among ensemble methods but also outperforms or matches these leading SOTA techniques, solidifying its position as an exceptionally effective and adaptive solution. These findings underscore the limitations of conventional security measures and emphasize the critical need for advanced, data-driven detection methods to combat the dynamic landscape of ransomware threats.
Co-Authors Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Anargya, Muhammad Alden Nayef Bintoro, Jevan Dwi Puji Prabowo, Dwi Puji Elkaf Rahmawan Pramudya Fauzi Adi Rafrastara Gustina Alfa Trisnapradika Heribertus Himawan Heru Lestiawan Hussein, Jassim Nadheer Ilma, Zidni L. Budi Handoko Latifah, Ines Aulia Oki Setiono Osman, Waleed Mahgoub Paramita, Cinantya Putra Hartanto, Octaviano Ryan Eka Ramadhan Rakhmat Sani Ramadhani, Arsabilla Retno Astuti Setijaningsih Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Yassin, Warusia Yuminah Yuminah