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All Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK) Global Science: Journal of Information Technology and Computer Science Jurnal Komputasi
Nurul Azwanti
Universitas Nagoya Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences Other

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Perancangan E-Commerce Pada Victory Collection Berbasis Web Narti Eka Putria; Nurul Azwanti
Prosiding Vol 5 (2023): SNISTEK
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/psnistek.v5i.8091

Abstract

Along with developments in technology, companies are increasingly required to use advanced technology as a tool or media to survive and win the competition. The impact on the aspect of competition is the formation of an increasingly strong competition. The purpose of this research is to know how to design sales system at Victory Collection using web system; And to know how to optimize the sales system more effectively and efficiently at Victory Collection; As well as to find out how the solution to increase sales using a manual into a web-based system at Victory Collection. By using Waterfall Process Model method, all the system details that have been running and the system that will be proposed will be arranged and implemented to optimize the promotion and sales of existing products at Victory Collection. The Waterfall Process approach or method is a systematic and sequential systematic development model that starts at the level and progress of the system in all stages such as analysis, design, code, testing, and maintenance. This model covers activities such as engineering and modeling of information systems, needs analysis, design, coding, maintenance, and testing. The design of this information system is limited only to the food and beverage sales system. The results of this system is expected to minimize damage and loss of sales data and ordering goods at Victory Collection; And this system is expected to help facilitate the search and presentation of goods data at Victory Collection; And this system is expected to increase the sales ratio at Victory Collection.
Penerapan Algoritma K-Means Untuk Pemetaan Penerimaan Bantuan Kesejahteraan Masyarakat di Kota Batam Nurul Azwanti; Narti Eka Putria
Prosiding Vol 5 (2023): SNISTEK
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/psnistek.v5i.8092

Abstract

2020 is a very difficult year for humans in this world, with a virus outbreak that has swept across the country, this outbreak is called Coronavirus Disease 2019 (Covid-19). Including in Indonesia with cases that continue to increase every day, until now there is no sign of this outbreak ending, with this incident the government issued a policy for large-scale social restrictions or known as PSPB in various regions that fall into the category of large case zones and According to data from the Riau Islands Covid-19 cluster, in Batam City itself, the number of Covid-19 cases continued to increase so that it exceeded the number of 2000. As a result of this protracted disaster, of course, human life has been disrupted, many jobs have been neglected, so that various types of businesses have had to go out of business because their turnover has been much reduced and this has caused business voters to have to lay off their employees. This situation received rapid attention from the government through the social service agency to provide welfare assistance for people affected by COVID-19, but the problem here is from observations while researchers are still many of the assistance that is not right on target, causing polemics for the community. This research will utilize one of the datamining techniques with the K means algorithm to determine the mapping or segmentation of government assistance to communities affected by COVID-19 in order to accelerate people's purchasing power with the aim that research results can be used by community leaders such as RT, RW to record citizens who need this assistance. The results of the analysis show that the clustering segmentation with the data analyzed is 30 data stating that there are priority recipients which are divided into 5 clusters with predetermined criteria.
Pelatihan Peningkatan Usaha Pedagang Asongan Di Perumahan Buana Impian Kota Batam Arif Rahman Hakim; Narti Eka Putria; Nurul Azwanti
Prosiding Vol 5 (2023): SNISTEK
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/psnistek.v5i.8144

Abstract

This activity aims to enhance the businesses of street vendors in Buana Impian Housing Complex 1 & 2, particularly those selling various goods. Initially, Buana 1 & 2 had a single RW 014, but in 2016, Buana Impian 2 became RW 028. Along the streets of Buana Impian Housing Complex 1 & 2, there are shops and a total of 53 street vendors. As part of this Community Service Program (PKM), the street vendors will receive training and guidance through lectures and discussions addressing the challenges faced by street vendors in Buana Impian Housing Complex 1 & 2. The educational and training materials will cover business management and entrepreneurship, accounting training for income and expenses, strategies for promoting businesses, and access to funding opportunities for street vendors. By participating in this training and education program, it is expected that the businesses of the street vendors will thrive and flourish.
Quantum-Inspired Meta-Heuristic Algorithm for Large-Scale Graph Neural Network Training in Distributed Cloud-Edge Environments Eka Prasetya Adhy Sugara; Nurul Azwanti; Ivy Derla
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.171

Abstract

This paper explores the application of quantum-inspired optimization algorithms in the training of large-scale Graph Neural Networks (GNNs) within distributed cloud-edge environments. GNNs have gained significant attention due to their ability to model complex relationships in graph-structured data, yet their training presents challenges such as high computational demand, inefficient resource allocation, and slow convergence, especially for large datasets. Traditional meta-heuristic algorithms, while useful, often face scalability and performance issues when applied to such large-scale tasks. To address these challenges, we propose a quantum-inspired meta-heuristic algorithm that leverages quantum principles, such as superposition and entanglement, to enhance optimization processes. The algorithm was integrated into a hybrid cloud-edge system, where computational tasks are dynamically distributed between edge nodes and the cloud, optimizing resource utilization and reducing latency. Our experimental results demonstrate significant improvements in training speed, resource efficiency, and convergence rate when compared to traditional optimization methods such as Genetic Algorithms and Simulated Annealing. The quantum-inspired algorithm not only accelerates the training process but also reduces memory usage, making it well-suited for large-scale GNN applications. Furthermore, the system's scalability was enhanced by the hybrid cloud-edge architecture, which balances computational load and enables real-time data processing. The findings suggest that quantum-inspired optimization algorithms can significantly improve the training of GNNs in distributed systems, opening new avenues for real-time applications in areas such as social network analysis, anomaly detection, and recommendation systems. Future work will focus on refining these algorithms to handle even larger datasets and more complex GNN architectures, with potential integration into edge devices for enhanced real-time decision-making.
Perbandingan Algoritma Clustering K-Means dan DBSCAN untuk Segmentasi Outlet Ritel Berdasarkan Pola Transaksi Penjualan Nurul Azwanti; Yulia Yulia
Jurnal Komputasi Vol. 14 No. 1 (2026)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v14i1.354

Abstract

Perkembangan sektor ritel yang pesat menghasilkan volume data transaksi yang besar dan berpotensi dimanfaatkan sebagai dasar pengambilan keputusan bisnis yang lebih efektif. Namun, pemanfaatan data tersebut masih belum optimal, khususnya dalam melakukan segmentasi outlet berdasarkan pola transaksi penjualan. Penelitian ini bertujuan untuk melakukan segmentasi outlet ritel di Kabupaten Kampar menggunakan algoritma K-Means dan DBSCAN, serta membandingkan kinerja kedua metode dalam mengelompokkan data transaksi. Data yang digunakan merupakan data transaksi penjualan yang telah melalui tahap pembersihan data, agregasi dari level transaksi ke level outlet, serta normalisasi untuk menyamakan skala antar variabel. Setiap outlet direpresentasikan berdasarkan total transaksi, total quantity, total penjualan, dan rata-rata transaksi, sehingga dapat dianalisis secara komprehensif. Hasil penelitian menunjukkan bahwa algoritma K-Means mampu menghasilkan tiga cluster utama, yaitu outlet kecil, menengah, dan besar, dengan kualitas clustering yang sangat baik yang ditunjukkan oleh nilai Silhouette Score sebesar 0,960. Sementara itu, algoritma DBSCAN hanya menghasilkan satu cluster utama dan sejumlah data yang teridentifikasi sebagai noise atau outlier, sehingga kurang optimal dalam membentuk segmentasi pada dataset yang cenderung homogen. Meskipun demikian, DBSCAN memiliki keunggulan dalam mendeteksi outlet dengan karakteristik ekstrem yang memiliki kontribusi signifikan terhadap penjualan. Berdasarkan hasil perbandingan, K-Means lebih efektif digunakan sebagai metode utama dalam segmentasi outlet, sedangkan DBSCAN berperan sebagai metode pendukung dalam identifikasi outlier. Hasil penelitian ini diharapkan dapat mendukung penyusunan strategi pemasaran, distribusi, dan pengelolaan outlet yang lebih tepat sasaran berbasis analisis data.
Co-Authors Arif Rahman Hakim Eka Prasetya Adhy Sugara Ivy Derla Narti Eka Putria Yulia Yulia