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All Journal Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT Journal of Multiscale Materials Informatics
Ayu Pertiwi
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Pembelajaran Computational Thinking untuk Siswa SMA Institut Indonesia Semarang Erwin Yudi Hidayat; Affandy Affandy; Ayu Pertiwi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 3, No 3 (2020): September 2020
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v3i3.104

Abstract

Pembelajaran mengenai teknologi informasi telah banyak dilakukan di pendidikan menengah atas untuk mempersiapkan peserta didik menghadapi perkembangan teknologi informasi. Penerapan teknologi informasi di berbagai bidang sesungguhnya memerlukan kemampuan berpikir komputasional yang menjadi populer dalam beberapa tahun terakhir ini menjadi kemampuan yang mendasar yang seyogyanya dimiliki oleh setiap insan dalam era digital ini. Kemampuan berpikir komputasional adalah proses berpikir dalam melakukan formulasi persoalan dan solusinya sehingga solusi dapat dinyatakan dalam suatu bentuk yang dapat dikerjakan secara efektif oleh manusia atau mesin. Berpikir komputasional adalah kemampuan dasar yang diperlukan setiap orang, bukan hanya ilmuwan komputer. Selain kemampuan membaca, menulis, dan aritmatika, kita juga perlu menambahkan kemampuan berpikir komputasional kepada seorang anak untuk mengasah kemampuan analisisnya.
Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi Muhamad Akrom; Usman Sudibyo; Achmad Wahid Kurniawan; Noor Ageng Setiyanto; Ayu Pertiwi; Aprilyani Nur Safitri; Novianto Hidayat; Harun Al Azies; Wise Herawati
JoMMiT Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v7i1.721

Abstract

Baja termasuk material yang memiliki ketahanan rendah terhadap serangan korosi Ketika berada pada lingkungan korosif. Inhibitor organik mampu menghambat korosi dengan efisiensi inhibisi yang tinggi. Tinjauan komparatif penting bagi pengembangan metode evaluasi kinerja inhibitor disajikan dalam karya ini. Kami mereview perkembangan artificial intelligence berbasis mesin learning dengan model QSPR dalam kajian penghambatan korosi. Makalah ini menjelaskan bagaimana metode pembelajaran mesin berbasis data dapat menghasilkan model yang menghubungkan sifat-aktivitas molekuler dengan penghambatan korosi oleh inhibitor berbasis bahan alam (green inhibitor). Teknik ini dapat digunakan untuk memprediksi kinerja senyawa yang belum disintesis atau diuji. Keberhasilan model ini memberikan paradigma untuk penemuan senyawa baru yang cepat, penghambat korosi yang efektif untuk berbagai logam dan paduan.
Quantum Cryptography – Principles, Protocols, and Future Directions: A Review Dian Arif Rachman; Muhamad Akrom; Didik Hermanto; Moch. Anjas Aprihartha; Khafiizh Hastuti; Ayu Pertiwi; Purwanto
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15959

Abstract

The rapid advancement of quantum computing poses a significant threat to classical cryptographic systems that rely on the computational hardness of mathematical problems such as integer factorization and discrete logarithm problems. In this context, quantum cryptography has emerged as a promising paradigm for secure communication based on the fundamental principles of quantum mechanics rather than on computational assumptions. This paper presents a comprehensive review of quantum cryptography, with a particular focus on Quantum Key Distribution (QKD), the most mature application. The study discusses the theoretical foundations of quantum security, including superposition, entanglement, and the No-cloning theorem, which collectively enable eavesdropping detection and guarantee information-theoretic security. Furthermore, the review examines major QKD protocols, such as BB84 and E91, as well as their advanced variants designed to address practical vulnerabilities and enhance performance. Recent progress in real-world implementations, including fiber-optic networks, free-space communication, and satellite-based systems such as the Micius satellite, is also analyzed. In addition, the paper highlights critical challenges related to scalability, hardware limitations, and security loopholes arising from imperfect devices. Finally, emerging research directions, including hybrid cryptographic frameworks that integrate quantum and post-quantum approaches, are discussed to provide insights into the future of secure communication. This review aims to provide a structured, up-to-date understanding of quantum cryptography, bridging the gap between theoretical developments and practical implementations, and emphasizing its crucial role in shaping next-generation cybersecurity systems.
A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds Noor Ageng Setiyanto; Harun Al Azies; Usman Sudibyo; Ayu Pertiwi; Setyo Budi; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10429

Abstract

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.
Co-Authors Achmad Wahid Kurniawan Affandy Affandy Aprilyani Nur Safitri Dian Arif Rachman Didik Hermanto Erwin Yudi Hidayat Harun Al Azies Khafiizh Hastuti Moch. Anjas Aprihartha Muhamad Akrom Noor Ageng Setiyanto, Noor Ageng Novianto Hidayat Purwanto Purwanto Setyo Budi Usman Sudibyo Wise Herawati