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All Journal IJIIS: International Journal of Informatics and Information Systems
Bambang Pujiarto
Department of Information Technology, Faculty of Engineering, Universitas Muhammadiyah Magelang, Indonesia
Computer Science & IT

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Social Network Analysis for User Interaction Analysis on Social Media Regarding E-Commerce Business Nugroho Agung Prabowo; Bambang Pujiarto; Firmantya Safri Wijaya; Lutfiana Gita; Denny Alfandy
International Journal of Informatics and Information Systems Vol 4, No 2: September 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i2.106

Abstract

Abstract: E-commerce business requires promotion in introducing its products. One of the media that can be used is social media. There is a lot of information provided on social media, one of which is User Generated Content (UGC). UGC is a user's track record on social media that can be seen by other users. Social media analysis is needed to see the pattern of interaction between the company and its customers from UGC which is widely spread on social media. This can be used as an insight for companies in helping product marketing on social media. The method used in analyzing the interaction pattern of UGC in social media is Social Network Analysis (SNA). Social network modeling can help e-commerce businesses to understand the interaction patterns that occur on social media. The findings in this study show that the social network that is superior is the interaction social network regarding Lazada. Research also shows the key players for each e-commerce.
A Data Mining Practical Approach to Inventory Management and Logistics Optimization Bambang Pujiarto; Mukhtar Hanafi; Arief Setyawan; Asti Nur Imani; Eky Rizky Prasetya
International Journal of Informatics and Information Systems Vol 4, No 2: September 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i2.109

Abstract

The latent demand to optimize costs and customer service has been fostered in the current economic situations, characterized by high competitiveness and disruption in supply chains, placing inventories as a vital sector with significant potential to implement improvements in firms. Inventory management that is done correctly has a favorable impact on logistics performance indexes. Warehousing operations account for around 15% of logistics expenditures in terms of dollars. This article employs a method based on the Partitioning Around Medoids algorithm that incorporates, in a novel way, the application of a strategy for locating the optimal picking point based on cluster classification, taking into account the qualitative and quantitative factors that have the greatest impact or priority on inventory management in the company. The results obtained with this model improve the routes of distributed materials based on the identification of their characteristics such as the frequency of collection and handling of materials, allowing for the reorganization and expansion of storage capacity of the various SKUs, moving from a classification by families to a cluster classification. This article shows a suggestion for a warehouse distribution design using data mining techniques, which uses indicators and key qualities for operational success for a case study in a corporation, as well as an approach to improve inventory management decision-making.
Co-Authors Arief Setyawan Asti Nur Imani Denny Alfandy Eky Rizky Prasetya Firmantya Safri Wijaya Lutfiana Gita Mukhtar Hanafi Nugroho Agung Prabowo