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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Adimas : Jurnal Pengabdian Kepada Masyarakat International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Journal of Computing Theories and Applications Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Herowati, Wise
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning Herowati, Wise; Akrom, Muhamad; Hidayat, Novianto Nur; Sutojo, Totok
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.v1i1.10448

Abstract

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.
Analyzing Preprocessing Impact on Machine Learning Classifiers for Cryotherapy and Immunotherapy Dataset Setiadi, De Rosal Ignatius Moses; Islam, Hussain Md Mehedul; Trisnapradika, Gustina Alfa; Herowati, Wise
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-2

Abstract

In the clinical treatment of skin diseases and cancer, cryotherapy and immunotherapy offer effective and minimally invasive alternatives. However, the complexity of patient response demands more sophisticated analytical strategies for accurate outcome prediction. This research focuses on analyzing the effect of preprocessing in various machine learning models on the prediction performance of cryotherapy and immunotherapy. The preprocessing techniques analyzed are advanced feature engineering and Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links as resampling techniques and their combination. Various classifiers, including support vector machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), XGBoost, and Bidirectional Gated Recurrent Unit (BiGRU), were tested. The findings of this study show that preprocessing methods can significantly improve model performance, especially in the XGBoost model. Random Forest also gets the same results as XGBoost, but it can also work better without significant preprocessing. The best results were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790, respectively, for accuracy, recall, specificity, precision, and f1 on the Immunotherapy dataset, while on the Cryotherapy dataset, respectively, they were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790. This study confirms the potential of customized preprocessing and machine learning models to provide deep insights into treatment dynamics, ultimately improving the quality of diagnosis.
A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification Akrom, Muhamad; Herowati, Wise; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11779

Abstract

This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00. The main finding is that the QML architecture successfully achieves flawless classification, contributing significantly to the field. These results underscore the potential of QML in solving complex classification problems and highlight its promise for future applications across various domains. The study concludes that QML techniques can offer transformative solutions in machine learning tasks, particularly those leveraging VQC, QNN, and QSVM.
Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds Fadil, Muhamad; Akrom, Muhamad; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8894

Abstract

Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.
Pemanfaatan Google Site dalam Pelatihan Pembuatan Website Sebagai Kegiatan Penunjang Edukasi Life Skills Pelajar SMA N 2 Mranggen Kabupaten Demak Herowati, Wise; Kurniawan, Achmad Wahid; Budi, Setyo; Muljono, Muljono; Rustad, Supriadi; Ignatius Moses Setiadi, De Rosal; Sutojo, T.; Trisnapradika, Gustina Alfa; Aprihartha, Moch Anjas
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2840

Abstract

Menghadapi persaingan kemampuan dan keterampilan terutama untuk generasi sekarang harusnya dihadapi dengan mempersiapkan pengetahuan yang mumpuni terutama kemampuan-kemampuan untuk menunjang life skills . Kemampuan tersebut perlu diperkuat sedari diri terutama pada jenjang pendidikan menengah atas atau jenjang SMA. Salah satu kemampuan yang dapat diasah pada jenjang pendidikan tersebut adalah pengetahuan dan kemampuan mengenai pembuatan sebuah website. Menciptakan sebuah website sering kali dianggap sulit dan membutuhkan kemampuan pemrograman khusus, hal ini menjadi tantangan tersendiri salah satunya bagi salah satu sekolah yakni SMA N 2 Mranggen Demak. Sebagai salah satu cara menyelesaikan tantangan tersebut, kegiatan PKM yang telah terlaksana ini memperkenalkan konsep dasar pembuatan website menggunakan Google Site. Diharapkan melalui kegiatan pelatihan tersebut para pelajar dapat memiliki keterampilan tambahan untuk menambah kemampuan guna menunjang life skills mereka
PENYULUHAN “THE PHYCOLOGY OF BOARD GAME” DI DHADHU BOARD GAME CAFÉ & SMAN 3 SEMARANG Budi, Setyo; Gamayanto, Indra; Zami, Farrikh Al; Wibowo, Sasono; Novianto, Sendi; Herowati, Wise; Haryanto, Hanny; Harisa, Ardiawan Bagus
ADIMAS Jurnal Pengabdian Kepada Masyarakat Vol 9, No 1 (2025): Maret 2025
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/adi.v9i1.8758

Abstract

Abstrak Board game memiliki banyak manfaat di dalam perkembangannya, hal ini akan dapat memberikan pengaruh positif dan negative terhadap para penggunanya. Tetapi, di sini kita akan berfokus pada dampak positif yang dimiliki oleh board game. Pengabdian masyarakat kali ini akan membahas mengenai bagaimana pengaruh board game terhadap dampak psikologi seseorang. Jika seseorang memainkan board game apakah akan mendapatkan benefit dan perubahan di dalam dirinya dan berdampak terhadap lingkungannya? Kita akan melakukan pembahasan dan penyuluhan di dalam pengabdian ini. Hal ini tentunya masih membutuhkan pembahasan yang lebih mendalam karena board game bersifat sangat luas dan dapat memberikan pengaruh yang cukup besar terhadap hal-hal lainnya. Hasil dari pengabdian masyarakat ini adalah para pengguna, pemain, staff dan lainnya yang sejatinya menyukai board game akan mendapat manfaat yang sebesar-besarnya, sehingga board game tidak lagi dijadikan sebagai permainan biasa, tetapi sudah menjadi permainan yang dapat digunakan sebagai pengembangan diri dan dapat memberikan dampak positif terhadap masyarakat dan diri sendiri.Kata kunci: pemberdayaan, Board game, permainan, implementasi, pengaruh, dampak
Machine Learning and Density Functional Theory Investigation of Corrosion Inhibition Capability of Ionic Liquid Safitri, Aprilyani Nur; Akrom, Muhamad; Al Azies, Harun; Pertiwi, Ayu; Kurniawan, Achmad Wahid; Herowati, Wise; Rustad, Supriadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1372

Abstract

This study investigated the corrosion inhibition potential of ionic liquid compounds using a QSPR-based machine learning predictive model combined with DFT calculations. The Gradient Boosting (GB) model was identified as the most effective predictor, demonstrating excellent accuracy with a high R² value of 0.98. Additionally, the model exhibited low RMSE (0.95), MAE (0.84), and MAD (0.94) values. The predicted corrosion inhibition efficiencies (CIE) for three new ionic liquid compounds (IL1, IL2, and IL3) were 88.95, 90.82, and 93.16, respectively, which aligned well with experimental data. By integrating DFT simulations into the data updating process, facilitated by machine learning, the approach proved invaluable for identifying new corrosion inhibitors. This work highlighted the continuous refinement of data related to the corrosion inhibition effects of ionic liquid compounds.
Comparative Study of Classical, Quantum, and Hybrid Stacking Models for Predicting Corrosion Inhibition Efficiency Using QSAR Descriptors Herowati, Wise; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12217

Abstract

This study investigates the performance of classical, quantum, and hybrid classical-quantum stacking models in predicting Corrosion Inhibition Efficiency (IE%) using 14 QSAR descriptors. The hybrid model combines a Gradient Boosting Regressor (GBR) and a Quantum Support Vector Regressor (QSVR) through a meta-learner (Ridge Regression). Results show a significant improvement over traditional models. The hybrid stacking model achieved an R² of 0.834, an MSE of 8.123, an MAE of 2.371, and an RMSE of 2.850, outperforming both individual classical and quantum models. These results confirm the strength of hybrid models in capturing both complex nonlinear and quantum-interaction patterns in QSAR-based molecular prediction.
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Kusuma, Muh Galuh Surya Putra; Setiadi, De Rosal Ignatius Moses; Herowati, Wise; Sutojo, T.; Adi, Prajanto Wahyu; Dutta, Pushan Kumar; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): in progress
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14873

Abstract

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
Co-Authors Achmad Wahid Kurniawan Akrom, Muhamad Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Dutta, Pushan Kumar Hanny Haryanto Harisa, Ardiawan Bagus Harun Al Azies Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Indra Gamayanto Islam, Hussain Md Mehedul Kusuma, Muh Galuh Surya Putra Minh T. Nguyen, Minh T. Moch Anjas Aprihartha Muhamad Fadil Muljono Muljono Prajanto Wahyu Adi, Prajanto Wahyu Safitri, Aprilyani Nur Sasono Wibowo Sendi Novianto Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Zami, Farrikh Al