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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sisfokom (Sistem Informasi dan Komputer) Information System Journal (INFOS)
Alfie Nur Rahmi
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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PENERAPAN METODE FIRST IN FIRST OUT (FIFO) DALAM SISTEM ANTRIAN PELAYANAN ADMINISTRASI MAHASISWA: Studi Kasus: DAAK Universitas AMIKOM Yogyakarta Moch. Farid Fauzi; Alfie Nur Rahmi
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 5 No. 2 (2021): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.824 KB) | DOI: 10.46880/jmika.Vol5No2.pp183-188

Abstract

Queuing can be interpreted as a process starting from the arrival of customers, waiting to be served, to getting service. Queuing system that is not well organized can hamper service. Therefore, along with the development of information technology, many queuing systems have been developed. This study aims to build a system with case studies at AMIKOM University Yogyakarta. Administrative services that exist on the object sometimes cause long queue problems, especially when trouble occurs when filling out the KRS which results in some students having to come directly to campus. Based on the description of the problem, researchers are interested in designing a website-based queuing system by applying the First In First Out (FIFO) method. FIFO is a method of solving the queuing problem that can be applied in a way that the first entry is assumed to be the first out.
PEMBUATAN AUGMENTED REALITY (AR) UNTUK PEMBELAJARAN ORGANEL SEL PADA TUMBUHAN DAN HEWAN: (Studi Kasus: SMA Negeri 1 Dlingo) Sevin Angga Nuransyah Pambudi; Alfie Nur Rahmi
Information System Journal Vol. 5 No. 1 (2022): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2022v5i1.840

Abstract

Augmented reality (AR) merupakan sebuah teknologi yang menggabungkan benda maya dua dimensi atau tiga dimensi yang dibuat oleh komputer kemudian memproyeksikan benda maya  tersebut secara realtime dalam waktu nyata. Hal ini dilakukan dengan cara membuat objek tiga dimensi pada marker sehingga dikenali oleh aplikasinya.Teknologi augmented reality ini dapat menyisipkan suatu informasi. Augmented reality dapat dimanfaatkan dalam berbagai bidang salah satunya bidang pendidikan terutama untuk pembelajaran di SMA Negeri 1 Dlingo. SMA Negeri 1 Dlingo sendiri merupakan salah satu sekolah menengah atas di kecamatan Dlingo. Salah satunya bidang biologi yaitu pembelajaran organel sel tumbuhan dan hewan. Sebagian besar siswa di SMA Negeri 1 Dlingo sering mengalami kejenuhan terutama untuk visual pembelajaran yang terlihat monoton dan membuat siswa malas untuk belajar. Aplikasi ini dibuat menggunakan vuforia, blender dan visual studio c# untuk mengembangkan augmented reality. Aplikasi ini dapat membaca marker pada buku pelajaran yang didalamnya terdapat gambar organel sel tumbuhan dan hewan
Object Recognition with SSD MobileNet Pre-Trained Model in the Cashier Application Nazil Ilham Burhanudin; Arif Dwi Laksito; Acihmah Sidauruk; Muhammad Resa Arif Yudianto; Alfie Nur Rahmi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1659

Abstract

Object recognition is a type of image processing technique that is frequently employed in current applications such as facial identification, vehicle detection, and automated cashiers. One issue with barcode and RFID cashier apps is that they cannot scan several products at the same time. The cashier application employing object identification using picture images is believed to be able to distinguish more than one object in order to speed up the transaction process. The usage of SSD pre-trained models with MobileNet architecture to detect items in automatic cashier applications is discussed in this paper. This study put the model to the test on three types of soft drink objects: coca-cola, floridina, and good day. A smartphone camera was used to collect the data, which totaled 203 images. The findings indicated that the product object identification method was 82.9% accurate, 97.5% precise, and 84.7% recall. The object recognition process takes between 365 and 827 milliseconds, with an average time of 695 milliseconds (0.69 seconds).
Co-Authors Acihmah Sidauruk Arif Dwi Laksito Moch. Farid Fauzi Muhammad Resa Arif Yudianto Nazil Ilham Burhanudin Sevin Angga Nuransyah Pambudi