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All Journal Techno.Com: Jurnal Teknologi Informasi Eksergi: Chemical Engineering Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science Journal of Information Systems and Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Abdi Masya Jurnal Algoritma Community: Jurnal Pengabdian Pada Masyarakat NERO (Networking Engineering Research Operation) Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat
Trisnapradika, Gustina Alfa
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Quantum Convolutional Neural Networks: Architectures, Applications, and Future Directions: A Review Trisnapradika, Gustina Alfa; Safitri, Aprilyani Nur; Hidayat, Novianto Nur; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.15154

Abstract

Quantum Convolutional Neural Networks (QCNNs) have emerged as one of the most promising architectures in Quantum Machine Learning (QML), enabling hierarchical quantum feature extraction and offering potential advantages over classical CNNs in expressivity and scalability. This study presents a Systematic Literature Review (SLR) on QCNN development from 2019 to 2025, covering theoretical foundations, model architectures, noise resilience, benchmark performance, and applications in materials informatics, chemistry, image recognition, quantum phase classification, and cybersecurity. The SLR followed PRISMA guidelines, screening 214 publications and selecting 47 primary studies. The review finds that QCNNs consistently outperform classical baselines in small-data and high-dimensional regimes due to quantum feature maps and entanglement-driven locality. Significant limitations include noise sensitivity, limited qubit availability, and a lack of standardized datasets for benchmarking. The novelty of this work lies in providing the first comprehensive synthesis of QCNN research across theory, simulations, and real-hardware deployment, offering a roadmap for research gaps and future directions. The findings confirm that QCNNs are strong candidates for NISQ-era applications, especially in physics-informed learning.
Multi-Tier Architecture Design for Scalable and Effective Non-Formal Learning: A Redesign of Serat Kartini Women's School LMS Ardana, Primavieri Rhesa; Trisnapradika, Gustina Alfa
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1341

Abstract

Non-formal education plays a vital role in empowering women in rural areas of Central Java, Indonesia. However, the existing Learning Management System (LMS) of Woman School Serat Kartini, built on a monolithic Laravel architecture, suffers from significant performance degradation and scalability limitations under growing user loads and shared hosting constraints. This leads to high latency, frequent session interruptions, and reduced participation, ultimately undermining learning effectiveness. This study redesigns the LMS using a multi-tier application architecture through the Design Science Research (DSR) methodology. The proposed blueprint separates the system into four independent tiers: Presentation (Next.js for users, React.js for administrators), Logic (Express.js for API Layer), Cache (Redis with cache-aside strategy), and Data (MySQL). The design artifacts include detailed architecture diagrams, ERD, use case, and sequence diagrams. Conceptual evaluation demonstrates that the multi-tier approach enhances modularity, reduces latency, supports horizontal scalability, and improves resource efficiency , ensuring reliable access for women learners with limited digital literacy and unstable internet connectivity. The redesigned LMS conceptually strengthens learning accessibility, engagement, and program sustainability in resource-constrained non-formal education contexts. This research is limited to the conceptual design phase without implementation or empirical testing.
Enhancing the Predictive Accuracy of Corrosion Inhibition Efficiency Using Gradient Boosting with Feature Engineering and Gaussian Mixture Model Amri, Sahrul; Akrom, Muhamad; Trisnapradika, Gustina Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11560

Abstract

Prediction The development of Quantitative structure property relationship (QSPR) models for predicting corrosion inhibition efficiency (IE) often faces challenges due to small datasets, which heightens the risk of overfitting and results in less reliable performance assessments. This research creates an entirely leakage-free modeling framework by combining per-fold preprocessing, augmentation of training-only data, and rigorous Leave-One-Out Cross-Validation (LOOCV). A set of 20 pyridazine derivatives was evaluated using 12 quantum-chemical descriptors, including HOMO, LUMO, ΔE, dipole moment, electronegativity, hardness, softness, and the electron-transfer fraction. An initial assessment showed that all baseline models lacking augmentation Gradient Boosting, Random Forest, SVR, and XGBoost demonstrated limited predictive power (R² < 0.20), revealing the dataset's inherently low information complexity.To enhance representation in the feature space, a multi-scale Gaussian Mixture Model (GMM) was used to generate chemically valid synthetic samples, with all components trained solely on the training subset from each LOOCV fold. This strategy consistently improved model performance. The two most successful configurations, XGBoost + GMM v2 and Random Forest + GMM v3, reached R² values of 0.4457 and 0.4108, respectively, along with significant decreases in RMSE, MAE, and MAPE. These findings illustrate that GMM-based generative augmentation effectively captures multicluster structures within the descriptor space while expanding the chemical variability domain in a controlled way.While the resulting R² values remain inadequate for high-precision quantitative predictions, the proposed methodology provides a solid basis for early-stage evaluation of corrosion inhibitors in situations with limited data. Future research will aim to integrate advanced DFT-derived descriptors, molecular graph representations, and tests against larger external datasets to enhance model generalizability.
“Sailing Beyond Limit” sebagai Analogi Latihan Keterampilan Manajemen Mahasiswa dalam Upaya Implementasi Peran Agent of Change Trisnapradika, Gustina Alfa; Juhara, Kanahaya Putri; Bahri, Alfino Kautsar; Ananta, Putri Rossa; Siregar, Nadia Itona; Ningrum, Novita Kurnia
Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian kepada Masyarakat Vol. 4 No. 1 (2026): Januari: Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian kepada Masyarakat
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/bumi.v4i1.1393

Abstract

Students have a strategic role as agents of change that requires adequate leadership and managerial skills. Formal learning in the classroom has not been able to fully develop these practical skills. This community service activity aims to improve the basic management skills of students at the Faculty of Computer Science, Universitas Dian Nuswantoro through Basic Student Management Skills Training (LKMM TD) with the theme Sailing Beyond Limits. The implementation method uses a Participatory Learning Action approach that includes problem identification, strategic planning, activity implementation, and joint evaluation. The activity was attended by 193 students from student organizations and general students. The learning process was carried out through lectures, case studies, discussions, group work, and pre- and post-tests. The evaluation results showed a significant increase in participant capacity, marked by an increase in the average score from 68.21 percent in the pre-test to 91.69 percent. LKMM TD activities effectively equip students with managerial, leadership, communication, and motivational control skills as provisions for carrying out the role of student agents of change.
Co-Authors Achmad Wahid Kurniawan Adhy, Bagaskara Bayu Adiprasetya, Vicenzo Frendyatha Akrom, Muhamad Amri, Sahrul Ananta, Putri Rossa Ardana, Primavieri Rhesa Ardiansyah, Nibras Bahy Ardyansyah, Nibras Bahy Ayu Pertiwi Bahri, Alfino Kautsar Budi, Setyo Cahyana, Timothy Mulya De Rosal Ignatius Moses Setiadi Dewi, Dinar Pitania Etika Kartikadarma Gumelar, Rizky Syah Handayani, Margareta Valencia Suci Harun Al Azies Herowati, Wise Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Islam, Hussain Md Mehedul Juhara, Kanahaya Putri Kaisalana, Mustafid Kumoro, Imanuel Dimas Cahyo Liya Umaroh, Liya Moch Anjas Aprihartha Muhammad Akrom Muhammad Naufal, Muhammad Muljono Muljono Mulyana, Yudha Muttaqin, Muhammad Al Ghorizmi Nadia Itona Siregar, Nadia Itona Ningrum, Novita Kurnia Nissa, Khoirul Noor Ageng Setiyanto, Noor Ageng Nusantara, Hanif Setia Prabowo, Wahyu Aji Eko Prabowo, Wahyu Eko Aji Prakoso, Dwi Pramono, Renjiro Azhar Prawesty, Ganis Fatimah Diaz Prayogo, Sandi Yudha Putra Sumarjono, Cornellius Adryan Putra, Dzaki Asari Surya Putra, Maulana Damar Adhesyah Putra, Ricky Primayuda Putranto, Nicholaus Verdhy Rahma, Khalida Nur Rizky, Muhammad Ivan Khoirur Rohman, Adib Annur Rozada, Akfi Safitri, Aprilyani Nur Saraswati, Eka Rizky Anggi setiawan, nabila putri Shanty, Maritza Ardila Sumarjono, Cornellius Adryan Putra Supriadi Rustad Sutrisno, Hendra T. Sutojo Widyawati, Femmi Zahrah, Febrina Nabila