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All Journal Jurnal Ilmiah Teknologi dan Rekayasa Liaison Journal of Engineering Jurnal Komtekinfo
Triyana, Yudi
Universitas Gunadarma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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PUSAT OPERASI KENDARAAN Triyana, Yudi
Jurnal Ilmiah Teknologi dan Rekayasa Vol 22, No 2 (2017)
Publisher : Universitas Gunadarma

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Abstract

Kemacetan lalu lintas pada suatu area terjadi karena banyaknya kendaraan yang melintas dan kecilnya jalan di area tersebut. Untuk mengetahui informasi tentang kemacetan lalu lintas dapt menggunakan Global Positioning System (GPS). Fitur GPS sekarang sudah ada di handphone sehingga sangat mudah bagi orang-orang untuk menggunakan GPS.Dalam penelitian ini dibuat Sebuah system yang akan memberikan informasi kepada setiap pengguna mengenai kondisi dari traffic lalu lintas secara real time dan interaktif dari suatu jalan atau area menggunakan media HP yang memiliki fasilitas GPS dan menggunakan layanan SMS sebagai media komunikasi atau transmisi. Data hasil pemrosesan akan memperlihatkan kepadatan lalu lintas  dari suatu area yang berasal dari pengiriman data masing masing pengguna. Data tersebut akan dirubah dalam algoritma tertentu dan akan dikirim kembali kesetiap operator dan selanjtunya operator tersebut akan mengirim ke pelanggan berupa data SMS. Kata Kunci : Global Positioning System (GPS), kepadatan lalu lintas, VOC (Vehicle Operation Center)
Building a Comprehensive Content Management System with NPM, Vue.js, Node.js, Postgresql, and Strap nashri aziz alhazmy; zahran nurafi chandra; pradana atmadiputra; yudi triyana
Liaison Journal of Engineering Vol. 3 No. 1 (2023): Volume III
Publisher : Liaison Journal of Engineering

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This article presents a content management system built using NPM v6.14.17, Vue.js v3.x, Node.js v14.20.1, Postgresql v13.2.1, and Strapi v3.6.6. The system provides various functionalities such as an Ongoing request and assessment dashboard, courier request management, meeting room booking, stationery management, tools request management, and transportation request management. Users can create requests for different categories and the admin can approve or reject requests. Additionally, the system allows users to generate reports on stock flow, courier activities, and driver assessment. The Master Data section enables users to view, search, and perform CRUD operations. The system offers a comprehensive solution for efficient content management. Keywords: content management system, NPM, Vue.js, Node.js, Postgresql, Strapi, dashboard, request management, admin approval, report generation, master data, CRUD operations.
Design of Web-Based Agricultural Product Marketing System in Toapaya Village Puspabhuana, Adam; Andhika; Triyana, Yudi; Rifky Adhani, Muhamad
Jurnal KomtekInfo Vol. 12 No. 2 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i2.645

Abstract

Disaster management systems are vital in mitigating the impacts of natural and human-induced disasters. However, traditional methods often struggle with limitations in responsiveness and efficiency, particularly as disaster events become more frequent and severe. This study investigates the role of Artificial Intelligence (AI) in enhancing cloud-based disaster management systems, focusing on improving predictive, analytical, and operational capabilities. The research examines key AI technologies that can be integrated into cloud platforms, including machine learning, natural language processing, and computer vision. AI substantially improves disaster response and recovery by enhancing real-time data processing, decision-making, and resource allocation. The study also highlights AI's potential in early warning and risk assessment, providing decision-makers with more accurate and timely information. Empirical analysis suggests that AI-enhanced cloud systems significantly reduce response times and improve resource distribution during disaster events, reducing loss of life and property. The research concludes with practical recommendations for implementing AI in cloud-based disaster management and identifying areas for future exploration. The findings underscore the transformative potential of AI in creating more resilient disaster management infrastructures.
Co-Authors Andhika nashri aziz alhazmy Normalisa pradana atmadiputra Puspabhuana, Adam Rifky Adhani, Muhamad victor hugo cahyo zahran nurafi chandra