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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) ILKOM Jurnal Ilmiah
Mira Nabila
Universitas Dian Nuswantoro
Computer Science & IT

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Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit Farrikh Alzami; Fikri Diva Sambasri; Mira Nabila; Rama Aria Megantara; Ahmad Akrom; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Puri Sulistiyawati
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1524.32-44

Abstract

E-commerce is selling and buying goods through an online or online system. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One thing that needs to be considered in the business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers to maintain good relationships with customers and increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature and display it in the form of a dashboard using the Streamlit framework. Several stages of research must be carried out. Firstly, taking data from the open public data site (Kaggle), then merging the data to select some data that needs to be used, understanding data by displaying it in graphic form, and conducting data selection to select features/attributes. The step follows the proposed method, performs data preprocessing, creates a model to get the cluster, and finally displays it as a dashboard using Streamlit. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
Implementation of a Supply chain Management System Blockchain-Based in Red Onion Farming Mira Nabila; Farrikh Alzami; Rama Aria Megantara; Fikri Firdaus Tananto; Hasan Aminda Syafrudin; L. Budi Handoko; Chaerul Umam
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 11 No 1 (2023): Vol. 11, No. 1, April 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2023.v11.i01.p02

Abstract

Red Onions are a horticultural commodity belonging to the spice vegetable group and an important role for economy of the Indonesian people. In red onion farming there have a problem of price fluctuations which result in an uneven and less transparent distribution of red onions yields, thus affecting both consumers and producers. To answer these problems, we designed a system to maintain and store red onion harvest data for farmers, collectors, distributors, and retailers in the form of a blockchain-based supply chain system. This system can maintain the validity of transactions in the supply chain of red onion farming with a private blockchain with Hyperledger Fabric. Then the data on the blockchain system will be displayed through the Hyperledger Explorer website. This system already passed the Black Box Testing system. From the research and testing of the system that has been made, this system can help the red onion farming to maintain the validity of transactions in the supply chain management.
Co-Authors Ahmad Akrom Alzami, Farrikh Chaerul Umam Dwi Puji Prabowo, Dwi Puji Fikri Diva Sambasri Fikri Firdaus Tananto Hasan Aminda Syafrudin L. Budi Handoko Puri Sulistiyawati Rama Aria Megantara Ricardus Anggi Pramunendar