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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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Journal : NERO (Networking Engineering Research Operation)

 1 
IMPLEMENTASI QSVM DALAM KLASIFIKASI BINER PADA KASUS KANKER PROSTAT Hilmy, Nur Amalina Rahmaputri; Akrom, Muhamad
NERO (Networking Engineering Research Operation) Vol 9, No 2 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27781

Abstract

Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat
KOMPARASI SVM KLASIK DAN KUANTUM DALAM KLASIFIKASI BINER BIJI GANDUM (SEEDS) Akrom, Muhamad
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.28082

Abstract

Binary classification is one of the important tasks in machine learning, with wide applications in various fields, including agriculture and food processing. This study compares the performance of the classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) in wheat grain classification, focusing on accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The wheat grain dataset consists of physical features relevant to distinguish between two types of grains. The analysis results show that QSVM significantly outperforms classical SVM in all measured metrics, with higher accuracy and a better balance between precision and recall. The superiority of QSVM can be attributed to its ability to handle complex feature interactions and accelerate the training process through quantum algorithms. These findings demonstrate the potential of QSVM as a more effective model for binary classification applications. However, factors such as implementation complexity and availability of quantum computing resources need to be considered. This study provides valuable insights for the development of more efficient classification methods in the context of agriculture and other related fields.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Classification, Seeds
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
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
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