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All Journal Dedikasi: Jurnal Pengabdian Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET)
Lubna Mawaddah
Universitas Dian Nuswantoro
Religion Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Harnessing Quantum SVR on Quantum Turing Machine for Drug Compounds Corrosion Inhibitors Analysis Akbar Priyo Santosa; Muhammad Reesa; Lubna Mawaddah; Muhamad Akrom
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.601

Abstract

Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values ​​than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.
Harnessing Quantum SVR on Quantum Turing Machine for Drug Compounds Corrosion Inhibitors Analysis Akbar Priyo Santosa; Muhammad Reesa; Lubna Mawaddah; Muhamad Akrom
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.601

Abstract

Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values ​​than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.
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 Akbar Priyo Santosa Izulsyauki Imani Muhamad Akrom Muhammad Reesa Muhammad Reesa Rosyid Setyo Budi SUPRIADI RUSTAD Usman Sudibyo Wahyu Aji Eko Prabowo