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Analisa dan perancangan Marketplace Q-Store studi kasus tembilahan Rizki Putra, Irwandi; Rasyid Ridha, Muh.
JURNAL PERANGKAT LUNAK Vol 2 No 1 (2020): Jurnal Perangkat Lunak
Publisher : Indragiri Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1563.095 KB) | DOI: 10.32520/jupel.v2i1.873

Abstract

Along with the times, technology and information are developing rapidly in various sectors in terms of human life. In the business world, technological development is very helpful in many ways. The phenomenon that occurs at this time is the increasingly widespread competition in the business world, especially in the field of marketplace in getting consumers to the emergence of various online marketplace sites. So far, Tembilahan online shop business is only known through social media such as Facebook, Whatsapp and Instagram or verbally to the public. Therefore, researchers are interested in taking a title, namely Marketpleace Q-Store Market Analysis and planning. Tembilahan Case Study aims to become a media promotion, and can make it easier for people to find goods that they want. In designing this Marketplace, the analysis used is PIECES and UML (Unified Modeling Language) as modeling and using the Framework code igniter to facilitate researchers in building systems. With the implementation of the Marketplace Q-Store, it provides a platform for seller to market their products.
Enhanced Classification of Brain MRI Images for Tumor Detection Using Transfer Learning and Grad-CAM-Based Explainable Convolutional Neural Network (CNN) Putra, Irwandi Rizki; Zulrahmadi; Andri Swandi; Yulia; Tasya Destria Putri
Journal of ICT Applications System Vol 4 No 2 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i2.454

Abstract

Accurate and explainable classification of brain Magnetic Resonance Imaging (MRI) is crucial for the early detection and treatment of brain tumors. This study introduces an enhanced deep learning framework that integrates transfer learning with Grad-CAM-based explainable Convolutional Neural Network (CNN) for tumor classification. The proposed approach utilizes a fine-tuned EfficientNet-B0 architecture with an optimized preprocessing pipeline consisting of Contrast Limited Adaptive Histogram Equalization (CLAHE), normalization, and multi-variant augmentation (rotation, flipping, and zoom). The model was trained on a publicly available brain MRI dataset comprising 3,000 images classified into four categories: glioma, meningioma, pituitary tumor, and non-tumor. Evaluation metrics include accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrate that the proposed model achieves an accuracy of 94.2% and an AUC of 0.965, outperforming baseline CNN models by a significant margin. The use of Grad-CAM visualization provides interpretability by localizing tumor regions within MRI scans, thereby increasing the model’s clinical transparency. This study highlights the potential of explainable deep learning models to enhance diagnostic reliability in automated brain tumor detection systems.
ANALISIS STRATEGI PEMASARAN TIKTOK SHOP DI ERA DIGITAL Juwita Astuti; Rahimah Safitri; Dewi Santika Ulandari; Sinta Wulandari; Irwandi Rizki Putra
Digital Business Insights Journal Vol 2 No 1 (2026): DIGITAL BUSINESS INSIGHTS JOURNAL
Publisher : Fakultas Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/bidi.v2i1.4967

Abstract

This study investigates the rising competition in e-commerce and the rapid growth of TikTok Shop as an interactive digital marketing platform. The primary research problem concerns how TikTok Shop’s live streaming feature, creative content, and influencer marketing affect consumers’ purchase intention for the Glad2Glow brand. The study aims to analyze the effectiveness of these strategies in building trust and improving purchasing decisions. A descriptive qualitative method was employed using literature review and content analysis based on secondary digital data. The findings reveal that live streaming is the most influential variable in increasing purchase intention, followed by creative content and influencer collaboration, which enhance brand credibility. Promotional tactics such as flash sales and real-time product demonstrations significantly increased sales conversion. The study recommends that SMEs continue optimizing live commerce features and strengthen authentic content to improve competitiveness in the digital marketplace.