Ngā hua rapu - Muhamad Akrom

Effect of Virtual Sample Generation in Predicting Corrosion Inhibition Efficiency on Pyridazine mā Ilham Pratama Aldiansah, Muhamad Akrom

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Development of a Hybrid Classical-Quantum SVM Model for Predicting Perovskite Formation Energy mā Fauzan Arif Prayogi, Muhamad Akrom

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Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds mā Muhamad Fadil, Muhamad Akrom, Wise Herowati

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Gaussian Mixture-Based Data Augmentation Improves QSAR Prediction of Corrosion Inhibition Efficiency mā Darnell Ignasius, Muhamad Akrom, Setyo Budi

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Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors mā Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

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Enhancing the Predictive Accuracy of Corrosion Inhibition Efficiency Using Gradient Boosting with Feature Engineering and Gaussian Mixture Model mā Sahrul Amri, Muhamad Akrom, Gustina Alfa Trisnapradika

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Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds mā Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

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Prediction of Corrosion Inhibitor Efficiency Based on Quinoxaline Compounds Using Polynomial Regression mā Bastion Jader Rana, Noor Ageng Setiyanto, Muhamad Akrom

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Perbandingan Algoritma Multilinear Regression dan Decision Tree Regressor dalam Memprediksi Efisiensi Penghambatan Korosi Piridazin mā Fachriansyah Muhammad Haikal, Muhamad Akrom, Gustina Alfa Trisnapradika

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Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi mā Nicholaus Verdhy Putranto, Muhamad Akrom, Gustina Alfa Trinapradika

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds mā Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono, Ryo Maezono

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A feature restoration for machine learning on anti-corrosion materials mā Supriadi Rustad, Muhamad Akrom, Totok Sutojo, Hermawan Kresno Dipojono

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Perbandingan Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole mā Cornellius Adryan Putra Sumarjono, Muhamad Akrom, Gustina Alfa Trisnapradika

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Investigasi Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole mā Muhamad Akrom, Cornellius Adryan Putra Sumarjono, Gustina Alfa Trisnapradika

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Impact of SMOTE Oversampling on Classifying Band Gap Types in Imbalanced ABO₃ Perovskite Oxides mā Desvita Maharani, Johana Oktavia Ramadhani, Aliyah Zahratu Rizqi, Muhamad Akrom

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Variational Quantum Circuit-Based Quantum Machine Learning Approach for Predicting Corrosion Inhibition Efficiency of Expired Pharmaceuticals mā Muhamad Akrom, Muhammad Reesa Rosyid, Lubna Mawaddah, Akbar Priyo Santosa

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Implementation of the Tomek-Link Approach in Machine Learning Models for ABO3 Perovskite Bandgap Classification mā Johana Oktavia Ramadhani, Aliyah Zahratu Rizqi, Desvita Maharani, Muhamad Akrom

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Predictive Modeling of Thermal Stability in Zn-MOF Using Multilayer Perceptron mā Muhammad Diva Irnanda, Harun Al Azies, Muhamad Akrom, Ananta Surya Pratama, Taufiqul Umam

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A Robust Voting Ensemble Framework for Predicting Thermal Stability in Zn-Based Metal–Organic Frameworks mā Taufiqul Umam, Harun Al Azies, Muhamad Akrom, Ananta Surya Pratama, Muhammad Diva Irnanda

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Data enhancement to improve CNN accuracy for early detection of colorectal cancer mā Ummi Athiyah, Supriadi Rustad, Moch Arief Soeleman, Muljono Muljono, Muhamad Akrom, Rahel Amanda Konoralma

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