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All Journal Dedikasi: Jurnal Pengabdian Masyarakat Journal of Multiscale Materials Informatics
Muhammad Reesa Rosyid
Universitas Dian Nuswantoro
Religion Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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Variational quantum algorithm for forecasting drugs for corrosion inhibitor Muhammad Reesa Rosyid; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 1 No. 2 (2024): October
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i2.11425

Abstract

This study explores the development and evaluation of a Variational Quantum Algorithm (VQA) for predicting a drug as a corrosion inhibitor, highlighting its advantages over traditional regression models. The VQA leverages quantum-enhanced feature mapping and optimization techniques to capture complex, non-linear relationships within the data. Comparative analysis with AutoRegressive with exogenous inputs (ARX) and Gradient Boosting (GB) models demonstrate the superior performance of VQA across key metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Deviation (MAD). The VQA achieved the lowest RMSE (4.40), MAE (3.33), and MAD (3.17) values, indicating enhanced predictive accuracy and stability. These results underscore the potential of quantum machine learning techniques in advancing predictive modeling capabilities, offering significant improvements in accuracy and consistency over classical methods. The findings suggest that VQA is a promising approach for applications requiring high precision and reliability, paving the way for broader adoption of quantum-enhanced models in material science and beyond.
Strategi Meaningful Learning Berbasis Google Teachable Machine untuk Penguatan Kompetensi Guru di SMK Negeri 9 Semarang Wahyu Aji Eko Prabowo; Achmad Wahid Kurniawan; Setyo Budi; Usman Sudibyo; Supriadi Rustad; Muhammad Reesa Rosyid; Lubna Mawaddah; Izulsyauki Imani
DEDIKASI: Jurnal Pengabdian Masyarakat Vol 8 No 2 (2026): Dedikasi: Jurnal Pengabdian Masyarakat, Juli - Desember 2026
Publisher : Pusat Pengabdian Masyarakat LPPM IAIN Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32332/dedikasi.v8i2.13213

Abstract

Perkembangan kecerdasan artifisial (AI) dalam pendidikan menuntut guru memiliki kesiapan pedagogis dalam memanfaatkan teknologi secara bermakna. Program kemitraan masyarakat ini bertujuan memperkuat literasi AI guru melalui pelatihan Google Teachable Machine (GTM) berbasis meaningful learning di SMKN 9 Semarang. Kegiatan dilaksanakan melalui enam tahap, meliputi sosialisasi dan identifikasi kebutuhan, pelatihan konsep, praktik GTM, penyusunan lesson plan, pendampingan dan evaluasi, serta keberlanjutan program. Peserta kegiatan berjumlah 40 guru. Data dikumpulkan melalui pre-test, post-test, observasi, refleksi peserta, dan dokumentasi, kemudian dianalisis secara deskriptif kuantitatif, inferensial menggunakan uji Wilcoxon signed-rank, dan deskriptif kualitatif. Analisis komparatif pengetahuan dibatasi pada butir yang memiliki substansi sebanding antara pre-test dan post-test (Q1, Q6-Q9). Hasil menunjukkan bahwa program terlaksana sesuai rancangan serta menghasilkan pengalaman praktik GTM, pengembangan lesson plan berbasis meaningful learning. Rata-rata skor pengetahuan meningkat dari 4,30 menjadi 4,73 dan menunjukkan perbedaan yang signifikan secara statistik (p=0,009). Sementara itu, skor persepsi/kesiapan meningkat dari 3,85 menjadi 4,25, tetapi belum menunjukkan perbedaan yang signifikan (p=0,091). Temuan ini menunjukkan bahwa dampak awal pelatihan terutama terlihat pada penguatan pengetahuan AI guru, sedangkan kesiapan implementatif masih memerlukan pendampingan lebih lanjut.
Co-Authors Achmad Wahid Kurniawan Izulsyauki Imani Lubna Mawaddah Muhamad Akrom Setyo Budi SUPRIADI RUSTAD Usman Sudibyo Wahyu Aji Eko Prabowo