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All Journal Jurnal Minfo Polgan (JMP) Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Narti Eka Putria
Universitas Nagoya Indonesia
Computer Science & IT Economics, Econometrics & Finance Library & Information Science Mathematics 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.
Analysis of the Influence of Machine Learning on Sales Prediction and Stock Management in Online Business Muhamad Mutoffar; Yudi Limbar Yasik; Ridwan Ridwan; Narti Eka Putria
Jurnal Minfo Polgan Vol. 13 No. 2 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i2.14503

Abstract

Online businesses continue to experience rapid development along with digital transformation that drives efficiency and competitiveness. However, one of the main challenges faced is the uncertainty in predicting sales, which can cause an imbalance between demand and stock. The inaccuracy of this prediction often results in overstock or understock, thus increasing operational costs and decreasing customer satisfaction levels. This study aims to analyze the effect of implementing Machine Learning (ML) algorithms on the accuracy of sales predictions and the efficiency of stock management in online businesses. Historical sales data collected from e-commerce platforms were processed using Random Forest and Long Short-Term Memory (LSTM) algorithms. The results showed that the ML algorithm was able to increase the accuracy of sales predictions by up to 20% compared to traditional methods. In addition, the implementation of ML-based predictions allows for more efficient stock management with a decrease in the level of overstock by 15% and a reduction in the risk of understock by up to 25%. These findings not only strengthen the literature related to the role of intelligent technology in digital business management but also offer practical guidance for online business actors to improve their operations through Machine Learning technology. Thus, this study makes an important contribution to digital transformation strategies in a competitive online business ecosystem.
Co-Authors Arif Rahman Hakim Muhamad Mutoffar Nurul Azwanti Ridwan Ridwan Yudi Limbar Yasik