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All Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Modem : Jurnal Informatika dan Sains Teknologi
Delyanti Putri Sitorus
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Computer Science & IT Electrical & Electronics Engineering

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Analisis Faktor Keberhasilan Aplikasi E-Commerce dalam Meningkatkan Penjualan di Era Digital Delyanti Putri Sitorus
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 3 No. 1 (2025): Januari : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v3i1.335

Abstract

This research analyzes the factors that influence the success of e-commerce applications in increasing sales in the digital era. With the development of technology and changes in consumer behavior, e-commerce has become one of the main platforms for businesses to reach customers more widely. The main factors analyzed include user experience, transaction security, product quality, customer service, digital marketing strategy and innovation. The research results show that the combination of these factors significantly influences purchasing decisions and customer loyalty. Good user experience, guaranteed data security, and effective marketing strategies are proven to increase sales and consumer trust. This study provides important insights for business people in maximizing the potential for implementing e-commerce in the competitive digital era.
Penerapan Learning Vector Quantization (LVQ) Untuk Klasifikasi Data Citra Digital Bambang Irwansyah; Delyanti Putri Sitorus; Rezki Abdillah; Rizky Febriansyah; Harry Ardian; Syahrul Syahrul; Ferry Cahyadi; Fahri Finanda Rizki
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.2072

Abstract

The rapid development of information technology has increased the utilization of digital images in various fields, creating a need for classification methods that are accurate and efficient. One method that can be applied to classify numerical data obtained from image feature extraction is Learning Vector Quantization (LVQ). This study aims to implement the LVQ method for digital image classification based on numerical features and to evaluate its performance in terms of accuracy. The data used in this study consist of grayscale digital images that have undergone a feature extraction process and are represented as numerical vectors. The dataset is divided into two classes, namely Class A and Class B. The research stages include data collection, grayscale conversion, feature extraction, LVQ training, and classification testing. The classification results are evaluated using a confusion matrix and accuracy measurement. The experimental results show that the LVQ method successfully classified all test data correctly, achieving an accuracy rate of 100%. These results indicate that Learning Vector Quantization is an effective method with good performance for classifying digital image data based on numerical features.
Co-Authors Fahri Finanda Rizki Ferry Cahyadi Harry Ardian Irwansyah, Bambang Rezki Abdillah Rizky Febriansyah Syahrul Syahrul