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All Journal Jurnal Masyarakat Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Advances in Data and Information Systems JoMMiT : Jurnal Multi Media dan IT Jurnal Pengabdian Masyarakat Nusantara Journal of Multiscale Materials Informatics
Aprilyani Nur Safitri
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi Muhamad Akrom; Usman Sudibyo; Achmad Wahid Kurniawan; Noor Ageng Setiyanto; Ayu Pertiwi; Aprilyani Nur Safitri; Novianto Hidayat; Harun Al Azies; Wise Herawati
JoMMiT Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v7i1.721

Abstract

Baja termasuk material yang memiliki ketahanan rendah terhadap serangan korosi Ketika berada pada lingkungan korosif. Inhibitor organik mampu menghambat korosi dengan efisiensi inhibisi yang tinggi. Tinjauan komparatif penting bagi pengembangan metode evaluasi kinerja inhibitor disajikan dalam karya ini. Kami mereview perkembangan artificial intelligence berbasis mesin learning dengan model QSPR dalam kajian penghambatan korosi. Makalah ini menjelaskan bagaimana metode pembelajaran mesin berbasis data dapat menghasilkan model yang menghubungkan sifat-aktivitas molekuler dengan penghambatan korosi oleh inhibitor berbasis bahan alam (green inhibitor). Teknik ini dapat digunakan untuk memprediksi kinerja senyawa yang belum disintesis atau diuji. Keberhasilan model ini memberikan paradigma untuk penemuan senyawa baru yang cepat, penghambat korosi yang efektif untuk berbagai logam dan paduan.
Predicting Methanol Space-Time Yield from CO? Hydrogenation Using Machine Learning: Statistical Evaluation of Penalized Regression Techniques Harun Al Azies; Muhamad Akrom; Setyo Budi; Gustina Alfa Trisnapradika; Aprilyani Nur Safitri
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1341

Abstract

This study investigates the effectiveness of machine learning techniques, specifically penalized regression models Ridge Regression, Lasso Regression, and Elastic Net Regression in predicting methanol space-time yield (STY) from CO? hydrogenation data. Using a dataset derived from Cu-based catalyst research, the study implemented a comprehensive preprocessing approach, including data cleaning, imputation, outlier removal, and normalization. The models were rigorously evaluated through 10-fold cross-validation and tested on unseen data. Ridge Regression outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) of 0.7706, Mean Absolute Error (MAE) of 0.5627, and Mean Squared Error (MSE) of 0.5938. In comparison, Lasso and Elastic Net Regression models exhibited higher error metrics. Feature importance analysis revealed that Gas Hourly Space Velocity (GHSV) and Molar Masses of Support significantly influence catalytic activity. These findings suggest that Ridge Regression is a promising tool for accurately predicting methanol production, providing valuable insights for optimizing catalytic processes and advancing sustainable practices in chemical engineering.
Kriptostegano Menggunakan Data Encryption Standard dan Least Significant Bit dalam Pengamanan Pesan Gambar Ifan Rizqa; Aprilyani Nur Safitri; Imanuel Harkespan
Jurnal Masyarakat Informatika Vol 13, No 2 (2022): November 2022
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.2.44547

Abstract

Aplikasi yang menerapkan metode LSB dan algoritma kriptografi DES ini berjalan dengan baik dan mampu menyisipkan dan mengekstrakan pesan dan dapat mengenkripsi dan deskripsi isi pesan. Pada penelitian Penyisipan Pesan Ke Dalama Gambar Dengan Menggunakan Metode Least Significant Bit (LSB) dan enkripsi dengan menggunakan Algoritma Data Encryption Standard (DES) yang mempunyai tujuan untuk menambah keamanan pesan agar seseorang yang tidak bertanggung jawab tidak dapat mengetahui sebuah pesan rahasia yang akan dikirim. Aplikasi ini hanya mengamankan sebuah pesan kedalam sebuah citra dan merubah isi pesan dari yang dikethaui maknanya ke yang tidak diketahui maknanya. Pada penelitian ini telah diterapkan metode LSB-DES pada gambar 281x320 pixel dengan cover berupa gambar berwarna dan pesan berupa kata. PSNR yang dihasilkan adalah 86.64 db untuk pesan kata “rahasia. Berdasarkan penelitian dapat disimpulkan hasil PSNR nilainya tinggi, maka kualitas citra bagus, maka dari itu hasil gambar steganogragi pun sangat baik.
Quantum Machine Learning Models, Limitations, and Opportunities in the NISQ Era: A Review Muhamad Akrom; Aprilyani Nur Safitri; Novianto Nur Hidayat; Wahyu Aji Eko Prabowo; Setyo Budi; Reza Pamungkas Putra Sukanli
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15955

Abstract

Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds Aprilyani Nur Safitri; Gustina Alfa Trisnapradika; Achmad Wahid Kurniawan; Wahyu AJi Eko Prabowo; Muhamad Akrom
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.v1i1.10464

Abstract

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.
Quantum Convolutional Neural Networks: Architectures, Applications, and Future Directions: A Review Gustina Alfa Trisnapradika; Aprilyani Nur Safitri; Novianto Nur Hidayat; Muhamad Akrom
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.
Three-Tier Disaster Logistics System Integrating GIS and MILP Optimization Danny Oka Ratmana; Muhammad Syaifur Rohman; Galuh Wilujeng Saraswati; Filmada Ocky Saputra; Aprilyani Nur Safitri; Imanuel Harkespan
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12727

Abstract

Effective disaster logistics management requires rapid, data-driven decision support that bridges optimization theory and operational practice. Existing systems either rely on theoretical models without implementable software, on proprietary datasets that restrict independent reconstruction, or lack validated prototypes in the Indonesian disaster context — three gaps that persist across the disaster IS literature. This study presents a three-tier web-based disaster logistics management IS integrating GIS and MILP optimization, built exclusively on public data sources (BNPB DIBI and OpenStreetMap). Using Design Science Research (DSR) across five phases, the system employs an open-source stack: Laravel 11.x presentation layer, PostgreSQL 16/PostGIS data layer, and Python FastAPI as a dedicated MILP microservice. The MILP model, a two-phase lexicographic MILP formulation with trips-aware vehicle capacity constraints is solved using the PuLP 3.3.0 + CBC solver. Three integrated modules were developed: shelter management, warehouse inventory, and logistics coordination with GIS visualization. Functional testing achieved 100% pass rate across 85 automated test cases covering all system modules, with 246ms mean response time under 50 concurrent users. The MILP solver resolved a 20-shelter problem in 0.094 seconds (99.9% below the 120-second operational planning threshold); scalability testing confirms tractability from 10 to 50 shelters (0.011–0.111 seconds), with Priority-1 shelters consistently served under both sufficient and scarce fleet conditions. Sensitivity analysis confirms lexicographic priority objectives activate correctly under resource scarcity. Comparative evaluation against heuristic and metaheuristic approaches confirms exact MILP is appropriate for the strategic planning scope of this proof-of-concept (n ≤ 50 shelters). Expert validation via ISO 25010 yielded a weighted score of 4.21/5. Usability testing with 25 participants produced a SUS score of 74.8 (Grade B, above-average per established SUS benchmarks) with 88% task completion rate. The primary contributions are a MILP-IS microservices integration pattern with explicit API specification, a comprehensively documented public-data-only implementation framework, and a proof-of-concept that closes the implementation gap between disaster logistics optimization research and operational IS deployment.
Pelatihan Bijak Gunakan AI dalam Praktik Penulisan Prompt untuk Software Development Siswa Aprilyani Nur Safitri; Novianto Nur Hidayat; Muhamad Akrom; Didik Hermanto
Jurnal Pengabdian Masyarakat Nusantara Vol 5 No 2 (2026): Juni 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/dimastara.v5i2.28674

Abstract

Perkembangan teknologi Artificial Intelligence (AI) yang semakin pesat memberikan dampak signifikan dalam berbagai bidang, termasuk dalam dunia pendidikan dan pengembangan perangkat lunak (software development). Namun, pemanfaatan AI secara bijak dan efektif masih menjadi tantangan bagi siswa, khususnya dalam memahami cara berinteraksi dengan sistem AI melalui penulisan prompt yang tepat. Oleh karena itu, kegiatan Pengabdian Kepada Masyarakat (PKM) ini dilaksanakan dengan sasaran siswa SMK Negeri 9 Semarang melalui program “Pelatihan Bijak Gunakan AI: Meningkatkan Pemahaman dan Praktik Penulisan Prompt dalam Software Development”. Kegiatan ini bertujuan untuk meningkatkan literasi AI serta kemampuan siswa dalam menyusun prompt yang efektif untuk mendukung proses pemrograman dan pengembangan perangkat lunak. Metode pelaksanaan kegiatan meliputi beberapa tahapan, yaitu penyampaian materi mengenai konsep dasar AI dan etika penggunaannya, pengenalan teknik penulisan prompt yang baik, diskusi interaktif, praktik langsung penggunaan AI dalam membantu proses software development, serta sesi pendampingan bagi peserta. Melalui kegiatan pelatihan ini diharapkan siswa dapat memahami prinsip penggunaan AI secara bijak, mampu menyusun prompt yang lebih terstruktur dan efektif, serta memanfaatkan AI sebagai alat bantu pembelajaran dan pengembangan keterampilan pemrograman. Hasil kegiatan menunjukkan bahwa pelatihan ini dapat meningkatkan pemahaman siswa terhadap penggunaan AI secara produktif dan bertanggung jawab dalam mendukung proses belajar di bidang teknologi informasi.
Co-Authors Achmad Wahid Kurniawan Ayu Pertiwi Danny Oka Ratmana Didik Hermanto Filmada Ocky Saputra Gustina Alfa Trisnapradika Harun Al Azies Ifan Rizqa Imanuel Harkespan Muhamad Akrom Muhammad Syaifur Rohman Noor Ageng Setiyanto, Noor Ageng Novianto Hidayat Novianto Nur Hidayat Reza Pamungkas Putra Sukanli Saraswati, Galuh Wilujeng Setyo Budi Usman Sudibyo Wahyu Aji Eko Prabowo Wise Herawati