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Keamanan Siber Berbasis AI untuk Mitigasi Ancaman Komputasi Kuantum Sunarti Ekowati, Maria Atik; Darsini
JITU Vol 9 No 2 (2025)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v9i2.2079

Abstract

Cybersecurity has become a major challenge in protecting data and information systems in the rapidly evolving digital era. One emerging threat is the potential impact of quantum computing on the cryptographic algorithms currently in use. Quantum computing has the potential to weaken the resilience of conventional encryption, thereby creating vulnerabilities that could be exploited by cyberattacks. Therefore, innovation in cybersecurity systems is urgently required to anticipate these threats. This study aims to develop and evaluate a cybersecurity prototype based on Artificial Intelligence (AI) designed to protect data from quantum computing threats. The research methodology includes the development of AI algorithms for anomaly detection, system resilience testing, and quantum computing threat simulation in real-world scenarios. The results indicate that the developed AI-based system is capable of identifying potential attacks and responding more quickly than traditional security systems. Moreover, the prototype demonstrates greater resilience against attacks leveraging quantum computing capabilities. The expected outcome of this research is the establishment of a cybersecurity framework that can be implemented across various sectors, along with strategic recommendations for adopting AI in addressing future cybersecurity challenges.
Predictive Modeling for Underweight Detection in Toddlers Using Support Vector Machine, K-Nearest Neighbors, and Decision Tree C4.5 Algorithms Ekowati, Maria Atik Sunarti; Hidayat, Nurul; Karim, Abdul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5439

Abstract

Gizi kurang (underweight) pada balita masih menjadi tantangan utama kesehatan masyarakat di Indonesia, dengan prevalensi mencapai 15,9% berdasarkan Survei Kesehatan Indonesia tahun 2023. Kondisi ini berdampak serius terhadap pertumbuhan fisik, perkembangan kognitif, dan kualitas hidup anak. Penelitian ini bertujuan untuk mengembangkan model prediktif guna mendeteksi dini status gizi balita dengan menggunakan metode supervised machine learning. Tiga algoritma pembelajaran terawasi diterapkan dan dievaluasi, yaitu Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Decision Tree C4.5, dengan memanfaatkan dataset berisi 9.284 catatan balita dari Kabupaten Sukoharjo yang mencakup delapan atribut dan satu label kelas status gizi. Hasil analisis menunjukkan bahwa algoritma SVM memberikan performa klasifikasi tertinggi dengan akurasi 98,56%, diikuti KNN dengan akurasi 97,99% dan Decision Tree C4.5 dengan akurasi 96,96%. Temuan ini menegaskan bahwa machine learning dapat menjadi alat yang efektif untuk identifikasi dini risiko gizi kurang pada anak, sehingga memungkinkan intervensi yang lebih cepat, tepat, dan berbasis data. Pendekatan ini berkontribusi pada peningkatan efektivitas program kesehatan anak dan mendukung pencapaian target pembangunan kesehatan nasional.
The PENDEKATAN INOVATIF UNTUK PEMBELAJARAN VISUAL: MENERAPKAN CAPCUT DAN CANVA UNTUK SISWA KANISIUS Ekowati, Maria Atik Sunarti; Dananti, Kristyana
Jurnal AbdiMas Nusa Mandiri Vol. 8 No. 2 (2026): Periode April 2026
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v8i2.6355

Abstract

Along with the development of digital technology, visual learning media has become an effective method for increasing students' understanding and involvement in the learning process. This community service aims to introduce and implement two graphic design applications, namely Capcut and Canva, as tools to increase the effectiveness of visual learning among students at Kanisius School. This activity involves training teachers and students to utilize these two applications in creating visual-based learning materials, such as educational videos, infographics and creative presentations. The expected result of this service is to increase students' skills in presenting learning material in an interesting and easy to understand manner, as well as increasing student motivation and interaction in the learning process. Apart from that, it is hoped that the use of Capcut and Canva can provide a more interactive and creative learning experience for students, as well as enrich existing teaching methods in schools. Evaluation was carried out through observation and feedback from participants to assess the extent to which the use of this tool was able to improve the quality of visual learning at Kanisius. The results of this service show an increase in students' understanding of the subject matter as well as an increase in students' digital skills that can be applied in a wider educational context.