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Journal of Artificial Intelligence and Innovative Applications (JOAIIA)
Published by Universitas Pamulang
ISSN : 27161501     EISSN : 27754057     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 112 Documents
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Sistem Monitoring Helpdesk & Ticketing Dalam Penanganan Keluhan Perangkat Kerja Berbasis Web Iwan Kurniawan; Munawaroh Munawaroh
Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Vol 3, No 3 (2022): AGUSTUS
Publisher : Journal of Artificial Intelligence and Innovative Applications (JOAIIA)

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Abstract

PT Lingkar Sembilan Titian Media is one of the companies engaged in creative industry technology. This company has several services, namely Web-Based & Mobile App Development, Digital Marketing & Campaign, Manage Service Provider, and Media & Broadcasting. The work that cannot be separated from technology causes frequent problems that require the IT Helpdesk division to solve. However, currently, complaints are still being made manually, there is not even a ticket number given by the IT Helpdesk to the user. So users can only wait for confirmation via Whatsapp or the Slack application. Not only that, the recording system for disturbances/damages experienced by users is still done manually and is not integrated, which makes it difficult for the Manager to monitor IT Helpdesk activities in real-time. Thus the company needs a system that can assist, manage, record & provide ticket numbers that can be accessed easily and together at PT Lingkar Sembilan Titian Media.This research aims to build a monitoring system that can make it easier for managers to monitor the work of the IT Helpdesk and Ticketing divisions that make it easier for employees to complain about work equipment issues. This research uses the waterfall method, while the analysis is carried out by conducting observations and interviews with related parties. The process design method is focused on model development using UML (Unified Model Language) and using the PHP programming language with the Code Igniter 3.0 framework and wampp v3.2.6 with Apache as the webserver and MySQL as database storage. The results of this study are in the form of a monitoring system for the IT helpdesk division and ticketing of complaints about work equipment at PT Lingkar Sembilan Titian Media. Keyword: Monitoring System, Helpdesk, Ticketing, Waterfall Methode
Identifikasi Jenis Buah Apel Berdasarkan Ekstraksi Ciri Bentuk Dan Warna Menggunakan Metode Klasifikasi Nave Bayes Maulana Fansyuri; Devi Yunita
Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Vol 3, No 3 (2022): AGUSTUS
Publisher : Journal of Artificial Intelligence and Innovative Applications (JOAIIA)

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

Abstract

Salah satu inovasi yang berkembang begitu cepat adalah picture handling. Banyak elemen elektronik, seperti pemindai, kamera canggih, dan instrumen pembesar terkomputerisasi. Saat ini, pengolahan gambar adalah perangkat penting di banyak bagian sains, seperti rekayasa perangkat lunak, perancangan listrik dan elektronik, teknologi mekanik, ilmu fisika, sains, ilmu ekologi, sains, dan penelitian otak. Salah satu teknik pengolahan citra dalam penerapan teknologi adalah pengenalan jenis buah, yang salah satunya adalah buah apel. Apel (Malus Domestica) merupakan salah satu tipe buah yang unggul serta sangat digemari serta disantap warga. Ditambah dengan penerapan data mining dalam pengenalan citra buah apel, dapat meningkatkan kualitas akurasinya. Metode yang di terapkan didalam penelitian ini untuk masalah proses pengenalan citra buah apel adalah mengektraksi model warna HSV dan RGB dengan menambahkan fitur bentuk eccentricity, area dan metric untuk menaikan tingkat akurasi dalam pengenalan citra buah apel. Hasil ekstraksi warna dan bentuk buah tersebut diklasifikasikan menggunakan metode Naïve Bayes dengan menggunakan 600 data citra buah yang merupakan data set dari 6 jenis buah apel yang berbeda, dataset tersebut akan dibagi menjadi 480 data tes dan 120 data training. Dengan metode Confusion Matrix, hasil Analisa metode Naïve Bayes menghasilkan tingkat akurasi sebesar 77,5% dengan kategori klasifikasi yang baik.

Page 12 of 12 | Total Record : 112

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