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Ananda Aulia
Universitas Pembangunan Panca Budi Medan
Computer Science & IT

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Implementation of E-Commerce System as SME Development Strategy in the Digital Era Maulian Saputra; Susilawati; Siti Nurhaliza Sofyan; Ananda Aulia; Andi Ernawati; Ayu Oftasari; Rian Farta wijaya
Bulletin of Information Technology (BIT) Vol 5 No 3: September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i3.1545

Abstract

The implementation of e-commerce systems has become one of the main strategies in the development of Small and Medium Enterprises (SMEs) in the digital era. E-commerce allows SMEs to expand market reach, improve operational efficiency, and strengthen relationships with consumers through better data access. In addition, this digital platform offers benefits such as distribution cost savings, business process automation, and improved customer service. However, challenges in e-commerce adoption for SMEs include limited digital literacy, uneven technology infrastructure, and cybersecurity issues. To achieve the full potential of e-commerce, support from the government and private sector in the form of adequate policies, infrastructure, and training is required. This research aims to identify the benefits, challenges and solutions in implementing e-commerce for SMEs, in order to improve their competitiveness in an increasingly competitive global market.
Analisis Data Mining Dalam Pemilihan Smartphone dan Klasifikasi di Berbagai Perangkat Menggunakan Random Forest Ananda Aulia; Sri Wahyuni
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1703

Abstract

Abstract− Smartphone technology continues to develop rapidly, driving the need for effective analysis methods to assist users in selecting devices that suit their needs. This research aims to implement data mining using the Random Forest method in the process of selecting smartphones and classifying devices based on their technical specifications. The Random Forest method was chosen because of its reliable ability to handle data with a large number of attributes, produce an accurate classification model, and minimize the risk of overfitting. The dataset used includes technical specifications of various smartphones, such as camera resolution, chipset, RAM capacity, screen resolution, and support for 4K video recording. The research process involved data collection, pre-processing to handle missing values ​​and data transformation, as well as model training using the Random Forest algorithm.  The research results show that the Random Forest method is able to classify devices with high accuracy, helping users determine smartphones that meet their criteria, such as support for 4K video recording and overall performance. Additionally, this research provides insight into the importance of certain attributes in smartphone selection. Thus, implementing data mining using Random Forest can be an effective solution in supporting data-based decision making in the field of consumer technology. Keywords: Data Mining, Random Forest, Smartphone, Classification, Technical Specifications
Co-Authors Andi Ernawati Ayu Oftasari Maulian Saputra Rian Farta Wijaya Siti Nurhaliza Sofyan Sri Wahyuni Susilawati