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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Multidisiplin Indonesia
Taufik, Handri
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Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Engineering Physics

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The OSI and TCP/ IP Reference Models in the Era of Industry 4.0 Permana, Eka Ramdan; Wahyu, Fajar Nugraha; Taufik, Handri; T, Thoyyibah
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1324

Abstract

The research aims to evaluate the integration and relevance of the Open Systems Interconnection (OSI) and TCP/IP (Transmission Control Protocol/Internet Protocol) Reference Models in the context of Industry 4.0. This study seeks to analyze the suitability of these models with the complex and dynamic networking environment of the present era and to propose modifications or strategies to enhance their performance and security within the context of Industry 4.0. The research method employed is descriptive research with a literature study design. This descriptive research method with a literature study design will aid in depicting and analyzing the OSI and TCP/IP reference models in the Industry 4.0 era. Primary data sources are derived from scholarly journals, reference books, and other relevant publications. Data analysis is conducted using a qualitative approach, wherein information from various sources will be thoroughly analyzed to identify patterns, trends, and relationships among the studied concepts. The research findings indicate that the difference between the OSI and TCP/IP models lies in their approaches and characteristics in regulating the process of data communication within networks. OSI emphasizes reliability at each layer, while TCP/IP views reliability as an end-to-end issue. 
Implementation of Convolutional Neural Networks for Eyeglass Product Image Retrieval: A Comparative Study of ResNet-50 and MobileNetV2 Taufik, Handri; Anggai, Sajarwo; Taryo, Taswanda
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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Abstract

The increasing similarity among eyewear product designs poses significant challenges for conventional text-based search systems, highlighting the need for effective Content-Based Image Retrieval (CBIR) approaches. This study proposes a CNN-based CBIR system for eyeglass frame and sunglasses retrieval, employing a comparative analysis of ResNet50 and MobileNetV2 as feature extractors. The dataset comprises 4,500 gallery images and 300 query images, with feature similarity measured using cosine similarity and accelerated through FAISS indexing. Experimental results indicate that ResNet50 achieves higher recall (0.0622), demonstrating its ability to capture more complex visual features. In contrast, MobileNetV2 provides superior ranking performance, achieving an mAP of 0.6091 and an MRR of 0.1427, outperforming ResNet50 (mAP of 0.5019 and MRR of 0.0713), while also reducing feature extraction time (0.1348 s versus 0.2023 s). These findings suggest that ResNet50 is more suitable for accuracy-oriented retrieval tasks, whereas MobileNetV2 is better suited for real-time and resource-constrained applications.
Co-Authors Anggai, Sajarwo Permana, Eka Ramdan T, Thoyyibah Taswanda Taryo Wahyu Fajar Nugraha