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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Algoritma Eksergi: Chemical Engineering Journal JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Sebatik Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer METIK JURNAL Building of Informatics, Technology and Science Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) JoMMiT : Jurnal Multi Media dan IT Jurnal Algoritma Journal of Computing Theories and Applications NERO (Networking Engineering Research Operation) Journal of Multiscale Materials Informatics Jurnal Inovasi Pendidikan dan Pembelajaran
Akrom, Muhamad
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences

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Penerapan Program Pendampingan dalam Meningkatkan Kemampuan Numerasi Siswa Menggunakan Media Kartu Angka di SDN 2 Jenggala Kabupaten Lombok Utara Miyati, Hijiul; Musafir, Musafir; Habiburrahman, Lalu; Jannah, Rauhun; Akrom, Muhamad; Yani, Rindi; Hariati, Endang; Islahudin, Islahudin
Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) Vol. 6 No. 2 (2024): Desember
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmsi.v6i2.520

Abstract

Tujuan penulisan artikel ini adalah meningkatkan kemampuan numerasi siswa menggunakan media kartu angka di kelas rendah SDN 2 Jenggala. Dimana masih ditemukan sejumlah tujuh orang siswa di kelas 2, 3, dan 4 yang belum mampu mengenal simbol angka, berhitung dan menjumlahkan bilangan. Sehingga program pendampingan numerasi menggunakan media kartu angka oleh mahasiswa KKN STKIP Hamzar berfokus pada tujuh orang siswa tersebut. Teknik pendampingan numerasi ini diadakan di dua lokasi yakni, di dalam sekolah maupun di luar sekolah khususnya dusun Montong Gedeng Desa Jenggala. Metode yang digunakan yaitu kualitatif jenis deskripsi, dan cara atau teknik dalam mengumpulkan data dengan menggunakan teknik observasi atau mengamati, dokumentasi dan tes. Setelah mahasiswa KKN STKIP Hamzar menerapkan program pendampingan numerasi selama satu bulan menggunakan media kartu angka, menunjukkan hasil bahwa kemampuan numerasi tujuh siswa yang didampingi mengalami peningkatan, dimana anak sudah mampu mengenal simbol angka, mampu berhitung, dan mampu menjumlahkan bilangan. Anak juga terlihat sangat tertarik pada saat belajar menggunakan media kartu angka.
Gaussian Mixture-Based Data Augmentation Improves QSAR Prediction of Corrosion Inhibition Efficiency Ignasius, Darnell; Akrom, Muhamad; Budi, Setyo
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10895

Abstract

Predicting corrosion inhibition efficiency IE (%) is often hindered by small, heterogeneous datasets. This study proposes a Gaussian mixture–based data augmentation pipeline to strengthen QSAR generalization under data scarcity. A curated set of 70 drug-like compounds with 14 physicochemical and quantum descriptors was cleaned, split 90/10 (train/test), and transformed using a Quantile Transformer followed by a Robust Scaler. A Gaussian Mixture model (GMM) with 2–5 components selected by the variational lower bound was fitted to the transformed training features and used to generate up to 2,500 synthetic samples. Eight regressors (Gaussian Process, Decision Tree, Random Forest, Bagging, Gradient Boosting, Extra Trees, SVR, and KNN) were evaluated on the held-out test set using R2 and RMSE. Augmentation improved performance across several families: for example, Gaussian Process R2 improved from −1.54 to 0.54 (RMSE 11.71 to 5.01) and Decision Tree R2 from −0.33 to 0.63 (RMSE 8.48 to 4.44), Bagging and Random Forest showed R2 increases of 0.67 and 0.40, respectively. The optimal synthetic size varied by model.
Comparison of Linear and Non-Linear Machine Learning Algortima for Predicting the Effectiveness of Plant Extracts as Corrosion Inhibitors Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i1.3572

Abstract

This research aims to develop a Machine Learning (ML) model that can predict the corrosion inhibitor potential of plant extracts with high accuracy. Corrosion is a serious problem in industry because it can reduce the service life of materials and cause economic losses. This research focuses on the use of green inhibitors, especially plant extracts, which are considered environmentally friendly and have high anticorrosion efficiency. The dataset used includes molecular and physicochemical features of plant extracts. The ML model development process involves data normalization, selection of linear and non-linear ML algorithms, model training with k-fold crossvalidation, and model performance evaluation using regression metrics such as MSE, RMSE, MAE, and R2. Experiments compare various ML algorithms and show that the AdaBoost Regressor (ABR) model exhibits the best prediction performance with the highest R2 value of 0.993 and a low MSE of 0.002. These results provide new insights into the potential of ML models to predict effective corrosion inhibitors from plant extracts. The ABR model had a low prediction error, indicating high accuracy in predicting corrosion inhibition efficiency. In addition, the analysis of important features shows that two features, Conc and LUMO, have a significant influence on the ABR model. This research makes an important contribution to the development of effective prediction methods in the corrosion control industry. The ABR model can serve as a basis for designing more effective and environmentally friendly corrosion inhibitor materials, as well as a reference for other researchers in developing ML models that accurately predict the corrosion inhibition efficiency of plant extracts.
IMPLEMENTASI COMPUTATIONAL THINKING PADA KURIKULUM MERDEKA MENGGUNAKAN METODE UNPLUGGED PROGRAMMING ACTIVITY (UPA) Sutojo, T.; Rustad, Supriadi; Akrom, Muhamad; Herowati, Wise
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1830

Abstract

The application of Computational Thinking (CT) in the Kurikulum Merdeka is one way to strengthen fundamental competencies and holistic understanding in education. CT skills can be taught through Unplugged Programming Activities (UPA), which is an approach to teaching CT skills without using computer tools. This approach is appropriate for schools that do not have adequate technological infrastructure and for the little ones, namely students under 9 years of age. This service aims to provide UPA method training for teachers at Gaussian Kamil School (GKS) so that it can be applied to the Merdeka Curriculum at GKS. The UPA activity materials used were the games "Bee-bot" and "My Robotic Friends Activity". It is hoped that this material can provide knowledge and skills regarding CT to training participants at GKS. The results of the pre-test and post-test evaluation showed an increase in scores before and after the training process for the participants. So it can be said that the results of this service show that the UPA method is suitable for use to teach CT skills in schools that do not have adequate technological infrastructure.
Comparison of Multilinear Regression and AdaBoost Regression Algorithms in Predicting Corrosion Inhibition Efficiency Using Pyridazine Compounds Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa; Setiawan, Nabila Putri
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3809

Abstract

Abstract-Corrosion is a serious problem in various industries that leads to increased production costs, maintenance, and decreased equipment efficiency. The use of organic compounds as corrosion inhibitors has become an increasingly desirable solution due to their effectiveness and environmental friendliness. This study compares the performance of two machine learning algorithms, Multilinear Regression (MLR) and AdaBoost Regression (ABR), in predicting the corrosion inhibition efficiency (CIE) of pyridazine-derived compounds. The dataset used consists of molecular properties as independent variables and CIE values as targets. To measure the performance of the model, a k-fold cross-validation process was used, where the dataset was divided into equal subsets. Each iteration uses one subset as validation data, while the other subset as training data. Results show that the AdaBoost Regression model achieves higher accuracy (99%) than Multilinear Regression (98%) in predicting CIE. Important feature analysis showed that Total Energy (TE) and Dipole Moment (µ) were the most influential variables in the ABR model, highlighting their important role in inhibitor effectiveness. Model evaluation was performed with R2 and RMSE metrics, where nonlinear models such as ABR were shown to be superior in predicting corrosion inhibition efficiency. These findings support the use of nonlinear methods to improve the effectiveness of protecting industrial equipment from corrosion.
Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning Adiprasetya, Vicenzo Frendyatha; Akrom, Muhamad; Trisnapradika, Gustina Alfa
Eksergi Vol 21 No 2 (2024)
Publisher : Prodi Teknik Kimia, Fakultas Teknik Industri, UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/e.v21i2.10025

Abstract

Korosi memberikan kekhawatiran serius bagi sektor industri dan akademik karena mempunyai dampak negatif yang signifikan terhadap sejumlah bidang, termasuk perekonomian, lingkungan, masyarakat, industri, keamanan, dan keselamatan. Saat ini, banyak peminat topik pengendalian kerusakan bahan berbasis molekul organik. Quinoxaline mempunyai potensi sebagai inhibitor korosi karena tidak beracun, mudah diproduksi, dan efektif dalam berbagai kondisi korosif. Mengeksplorasi kemungkinan kandidat penghambat korosi melalui penelitian eksperimental adalah proses yang memakan waktu dan sumber daya yang intensif. Dengan menggunakan pendekatan machine learning (ML) berdasarkan model quantitative structure-property relationship (QSPR), kami mengevaluasi beragam algoritma linier dan non-linier sebagai model prediktif nilai corrosion inhibition efficiency (CIE) dalam penelitian ini. Kami menemukan bahwa, untuk kumpulan data senyawa quinoxaline, model non-linier Gradient Boosting Regressor (GBR) mengungguli keseluruhan model linier dan non-linier, serta hasil dari literatur dalam hal kinerja prediksi berdasarkan metrik root mean squared error (RMSE), mean squared error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) dan coefficient of determination (R2). Secara keseluruhan, penelitian kami memberikan sudut pandang baru tentang kapasitas model ML untuk memperkirakan kemampuan penghambatan korosi pada permukaan besi oleh senyawa organik quinoxaline.
Optimasi model machine learning untuk prediksi inhibitor korosi berbasis augmentasi dataset senyawa n-heterocyclic menggunakan KDE Gumelar, Rizky Syah; Akrom, Muhamad; Trisnapradika, Gustina Alfa
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v%vi%i.27945

Abstract

This study aims to optimize a machine learning model to predict the corrosion inhibitor effectiveness of N-Heterocyclic compounds.  The main challenge in this modelling is the limited dataset due to the high cost and time required to collect experimental data. To overcome this problem, this research utilizes Kernel Density Estimation (KDE) as a data augmentation technique, generating virtual samples that improve dataset diversity and model predictive performance. The developed dataset includes 11 relevant chemical features such as HOMO, LUMO, and Gap Energy. Linear (MLR, Ridge, Lasso, and ElasticNet) and non-linear (KNR, Random Forest, Gradient Boosting, Adaboost, XGBoost) machine learning models were evaluated based on Root Mean Squared Error (RMSE) and coefficient of determination (R²). The results show that data augmentation using KDE improves prediction accuracy and stability, especially in non-linear models like Random Forest and XGBoost. The application of KDE proved effective in improving the performance of predictive models. It can be recommended as an augmentation method in similar studies that require additional data to improve prediction accuracy.Keywords: Machine Learning, Kernel Density Estimator (KDE), Corrosion Inhibitor, Dataset
Quantum Neural Network in Architectures, Learning Mechanisms, and Emerging Applications Across Domains: A Review Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober (in progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.14929

Abstract

Quantum Neural Networks (QNNs) represent a novel computational paradigm that merges the principles of quantum computing with the architecture of artificial neural networks. Through the quantum phenomena of superposition, entanglement, and interference, QNNs enable parallel computation in high-dimensional Hilbert spaces, offering the potential to surpass the representational limits of classical models. This review provides a comprehensive overview of the theoretical foundations and architectures of QNNs, including Quantum Perceptrons, Variational Quantum Circuits (VQCs), Quantum Convolutional Neural Networks (QCNNs), and Quantum Recurrent Neural Networks (QRNNs). Furthermore, it discusses hybrid quantum–classical training mechanisms and key challenges such as barren plateaus, decoherence, and sampling complexity. The review also highlights recent applications of QNNs in medical diagnostics, materials science, and financial forecasting, demonstrating their potential to accelerate computation and improve predictive accuracy. Finally, future research directions are discussed in relation to computational efficiency, model interpretability, and integration with next-generation quantum hardware.
Variational Quantum Circuits Design Principles, Applications, and Challenges Toward Practical: A Review Akrom, Muhamad; Rachman, Dian Arif
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober (in progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.14935

Abstract

Variational Quantum Circuits (VQCs) have emerged as a cornerstone of hybrid quantum–classical algorithms designed to harness the computational potential of near-term quantum devices. By combining parameterized quantum gates with classical optimization, VQCs provide a flexible framework for tackling machine learning, chemistry, and optimization problems intractable for classical methods. This review comprehensively overviews VQC design principles, ansatz structures, optimization strategies, and real-world applications. Furthermore, we discuss fundamental challenges such as barren plateaus, the expressibility–trainability trade-off, and current noisy intermediate-scale quantum (NISQ) hardware limitations. Finally, we highlight emerging directions that could enable scalable, noise-resilient, and physically interpretable variational quantum models for future quantum computing applications
Co-Authors Achmad Wahid Kurniawan Adiprasetya, Vicenzo Frendyatha Aldiansah, Ilham Pratama Alfa Trisnapradika, Gustina Ardyansyah, Nibras Bahy Ariyanto, Noval Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Desrina Desrina Diah Rahayu Ningtias, Diah Rahayu Dian Arif Rachman Ditha Sabrani, Amalia Gumelar, Rizky Syah habiburrahman, Lalu Hariati, Endang Harun Al Azies Herawati, Wise Herowati, Wise Hidayat, Ari Hidayat, Novianto Hidayat, Novianto Nur Hilmy, Nur Amalina Rahmaputri HOTIMAH, NURUL Huryati, Huryati Husnawati Amini, Titik Ignasius, Darnell Imam Muslim, Rachmat Islahudin Jalaluddin Jalaluddin Jannah, Rauhun Kurniawan, Azril Lingga Dewi, Adhe Mawaddah, Lubna Miyati, Hijiul Moch Anjas Aprihartha Muhamad Fadil Muhammad Naufal, Muhammad Muhammad Sanusi Mulyana, Yudha Musafir, Musafir Noor Ageng Setiyanto, Noor Ageng Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Putra, Dzaki Asari Surya Putranto, Nicholaus Verdhy Rahman, Irfan Fauzia Rana, Bastion Jader Rizky Pratama, Muhammad Hafiz Rosyid, Muhammad Reesa Safitri, Aprilyani Nur Sahrul Kadri, Agus Santosa, Akbar Priyo setiawan, nabila putri Sudibyo, Usman Sumarjono, Cornellius Adryan Putra Supriadi Rustad T. Sutojo Trinapradika, Gustina Alfa Trisnapradika, Gustina Alfa Umam, Saripul Wahyu Astuti, Ristina Widia Wati, Via Yani, Rindi Zihab, Zihab Zulfiani, Nila