Article Per Year (5 Year)
p-Index From 2021 - 2026
5.241
P-Index
This Author published in this journals
All Journal PROSIDING Proceeding Of The International Seminar and Conference 2015 Prosiding Seminar Nasional E-Dimas: Jurnal Pengabdian kepada Masyarakat JITU : Journal Informatic Technology And Communication Jurnal Teknik Informatika (JUTIF) International Journal of Global Operations Research Jurnal Mandiri IT International Journal of Ethno-Sciences and Education Research Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Community: Jurnal Pengabdian Pada Masyarakat JURNAL PENGABDIAN MASYARAKAT AKADEMISI JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Jurnal Akademik Pengabdian Masyarakat Journal of International Multidisciplinary Research Asian Journal of Environmental Research Journal of Scientific Insights Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat Proceeding International Conference on Mathematics and Learning Research
Maria Atik Sunarti Ekowati
Unknown Affiliation
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 42 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 42 Documents
Search

 1   2   3   4   5 
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.
Co-Authors Abdul Karim Adrian Khrisnamurti Dorestia Ahmad Fauzi Budi Al Amin Dananti , Kristyana Dananti, Kristyanan Darsini Darsini , Darsini Darsini . . Darsini Darsini Darsini Darsini Darsini Darsini Darsini Darsini Elvies Umbulolo Endang Satyawati Erix Yoga Pratama Putra Bakti Erix Yoga Pratama Putra Bakti Erlina Dyah Puspita Ningrum Gabriel Silvano Ian Nugroho Kristanto Kristyana Dananti Kristyana Dananti Kristyana Dananti Kristyana Dananti Kristyana Dananti Kristyana Dananti, Kristyana Moyo Hady Poernomo Nindyatama , Zefanya Permata Nindyatama, Zefanya Permata Nurul Hidayat Oktiyani, Rina Poernomo, Moyo Hadi Rendy Pakiding Retno Palupi Rudi Eko Sanjaya Siagian Rumekso , Ayub Widhi Rumekso, Ayub Widhi Saputra, Unggul Widya Silmano, Gabriel Silvano, Gabriel SM Santi Winarsih Sri Wening Sri Wening Sri Wening Sri Wening Sri Wening Unggul Widya Saputra Wening , Sri Widianto Widianto Zefanya Permata Nindyatama