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SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Published by RAM PUBLISHER
ISSN : 30901626     EISSN : 30323991     DOI : -
Core Subject : Humanities, Science, Engineering,
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan or in English the publication title Information Systems, Engineering and Applied Technology is an open access journal committed to publishing high quality research articles in the fields of Information Systems, Informatics, Digital Communication Information Technology, Tourism Technology, Transportation Technology, Agricultural Technology, Plantations, Fisheries, Marine, Environmental Technology, Artificial Intelligence, Mechanical Engineering, Electrical Engineering, Industrial Engineering and Civil Engineering. Published 4 X (Times) a year in January, April, July, and October. SITEKNIK accepts and selects quality articles and focuses on providing the best service for writers. SITEKNIK is committed to being a leading platform for researchers to share their innovative findings. We also provide a fast and transparent review process to ensure the quality and originality of each published article.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 14 Documents
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Search results for , issue "Vol. 2 No. 3 (2025): July" : 14 Documents clear
AI Web-based Computer Service Management System at PUSCOM Muhammad Irvan Shandika; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16280893

Abstract

This research aims to develop a web-based computer service management system with artificial intelligence (AI) integration at PUSCOM to address challenges in manual service management, such as customer data recording, service status tracking, and report generation. The problems faced by PUSCOM include potential data errors, loss of physical documents, and delays in performance evaluation due to manual processes. The research method used is the Agile SDLC approach, covering problem identification, data collection through interviews and documentation, functional and non-functional requirements analysis, system modeling using UML, NoSQL Firebase database design, interface design, implementation using Next.js and Javascript, and AI chatbot integration using Vercel AI SDK with the Google Gemini model. The research results demonstrate the successful development of a system capable of automating data recording, facilitating online service registration, managing products, and providing an AI chatbot to assist admins in report generation and real-time damage analysis. This system is proven to enhance operational efficiency, reduce manual errors, and support strategic decision-making at PUSCOM, contributing to improved service quality and customer satisfaction.
Comparison Of Efficientnet And Yolov8 Algorithms In Motor Vehicle Classification Ferian Fauzi Abdulloh; Favian Afrheza Fattah; Devi Wulandari; Ali Mustopa
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16561038

Abstract

The YOLOv8 accuracy curve highlights clear overfitting. As shown in the graph, the model reaches 100% training accuracy from the first epoch and remains flat, indicating it memorized the training data. However, validation accuracy lags behind, fluctuating between 90% and 92% without significant improvement. This discrepancy between training and validation performance suggests that YOLOv8 struggles to generalize to unseen data. The issue likely stems from its architecture, which is optimized for object detection tasks that prioritize object localization over feature extraction for classification. When repurposed for classification, YOLOv8 may not extract the nuanced visual patterns needed to differentiate similar classes, such as trucks and buses. Consequently, although YOLOv8 performs well on the training set, its classification accuracy in real-world scenarios is limited. Addressing this may require architectural adjustments, stronger regularization, or more diverse training data to enhance the model’s generalization for pure classification tasks.
Implementasi Teknologi Mediapipe Menggunakan Metode CNN Berbasis Website Untuk Pengamanan VVIP Dalam Mobil M. Ilham AlFatrah; Hery Sudaryanto; H. A. Danang Rimbawa
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan mengembangkan sistem deteksi gestur tangan berbasis MediaPipe dan Convolutional Neural Network (CNN) guna meningkatkan efektivitas pengamanan VVIP. Mengingat ancaman modern yang semakin kompleks, sistem ini dirancang untuk mendeteksi gestur darurat secara real-time dan memungkinkan respons cepat. Metodologi yang digunakan meliputi pengumpulan dataset gestur tangan, anotasi data menggunakan MediaPipe, dan pelatihan model CNN di Google Colab. Kinerja model dievaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Pengujian juga dilakukan dalam berbagai kondisi, seperti pencahayaan rendah dan gerakan cepat, untuk menilai ketangguhan sistem di dunia nyata. Hasilnya, sistem ini berhasil mendeteksi gestur tangan darurat dengan akurasi tinggi dan kecepatan kurang dari satu detik. Kinerja optimal, dengan akurasi mendekati 100%, tercapai pada kondisi pencahayaan yang baik. Meskipun akurasi sedikit menurun pada kondisi ekstrem, integrasi sistem pada platform website memungkinkan pengawasan dan pengambilan keputusan cepat di pusat komando. Penelitian ini membuktikan bahwa kombinasi MediaPipe dan CNN adalah solusi inovatif, namun optimasi lebih lanjut tetap dibutuhkan.
Perencanaan Strategi PT. Paragon Technology and Innovation di Era Society 5.0 Putri Puspitsari, Ika
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16555419

Abstract

Perkembangan teknologi digital yang dicakup dalam konsep Society 5.0 memiliki dampak besar ke sejumlah sektor industri. Society 5.0 yang menggunakan teknologi canggih berupa Artificial Intelligence , Internet of Things dan Big Data lebih berfokus pada peningkatan efisiensi dan keberlangsungan sistem dalam melakukan kegiatan industri. Penelitian ini menganalisis dampak Society 5.0 terhadap bisnis industri kosmetik dengan kasus PT. Paragon Technology and Innovation yang merupakan perusahaan kosmetik terbesar di kawasan ASEAN. Penelitian ini bertujuan mengetahui strategi apa yang dapat diterapkan PT. Paragon dalam menghadapi perubahan teknologi digital dan mengidentifikasi pengembangan strategi transformasi digital yang efektif mengacu pada prinsip Integrated Technology Management Planning. Data hasil analisis menunjukkan bahwa dengan penerapan teknologi digital seperti Big Data, AI dan IoT, efisiensi kerja dari PT. Paragon meningkat yang kemudian dapat meningkatkan daya saing perusahaan di pasaran global serta menjaga keberlangsungan perusahaan dan patuh terhadap bisnis hijau. PT. Paragon dapat merancang strategi transformasi berdasarkan ITMP secara integratif dan adaptif sesuai era Society 5.0

Page 2 of 2 | Total Record : 14

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