Garuda - Garba Rujukan Digital

Sutikno, Agung

Unknown Affiliation

Author-ID : 7236048

Computer Science & IT

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PENERAPAN BUSINESS INTELLIGENCE NETWORK OPERATION & QUALITY CONTROL DASHBOARD PADA PT. BAKRIE TELECOM Sutikno, Agung
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol 1 No 1 (2022): januari : Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Information systems at PT. Bakrie Telecom has been running still focused on transactional data so only the current information system is monotonous, rigid and less interactive. This condition causes stakeholders and the level of managerial can not optimally serve in managerial field because not optimal in data analysis about the state of implementation of the program. Therefore we need a Business Intelligence Software that providing information data which will be used in decision support by using Datawarehouse. Datawarehouse support for management decision by collecting and organizing data analysis and reporting for management.

SISTEM INFORMASI PENGGAJIAN KARYAWAN PT METAGRA MENGGUNAKAN METODE WATERFALL Sutikno, Agung
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol 1 No 2 (2022): Mei : Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

PT Metagra merupakan perusahaan yang bergerak dibidang integrasi sistem yang memiliki tujuan memberikan solusi dan membangun sistem IT secara keseluruhan baik dari segi infrastruktur dan pengembangan sistem. Pada perusahaan ini memiliki kendala disisi perhitungan lembur, perjalanan dinas dan lain-lain. Oleh karena itu maka dibuatlah sebuah sistem informasi penggajian berbasis web framework codeigniter dan dibangun menggunakan metode waterfall yang nantinya diharapkan bisa untuk mempermudah perhitungan penggajian secara sistematis dan meningkatkan efisiensi waktu dalam perhitungan data gaji yang akan diterapkan pada perusahaan tersebut.

Klasifikasi Penyakit Pada Sawi Pakcoy Dengan Menggunakan Metode Convolutional Neural Network (CNN) Sutikno, Agung; Pohan, Sry Dhina; Aljabar, Andi
Jurnal Tika Vol 9 No 2 (2024): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v9i2.2665

The Brassisca Rapa L plant, commonly referred to as pakcoy, is a vegetable renowned for its economically significant leaves. Pakcoy thrives in both highland and lowland regions, characterized by its rapid harvest cycle and straightforward cultivation process. However, the marked increase in pakcoy cultivation has rendered the crop susceptible to diseases caused by fungi, viruses, pests, and other microbes, highlighting the necessity for effective management strategies to mitigate crop failure. This study explores the application of Convolutional Neural Networks (CNN) in the identification of pakcoy diseases through advanced pattern recognition and image analysis techniques. Utilizing a dataset comprising 1000 images of pakcoy leaves—500 depicting diseased specimens and 500 healthy ones—sourced from greenhouse plants, the images are processed using CNN with RGB configurations at a resolution of 512x512 pixels. The data training, conducted with the Adam optimizer, achieved an accuracy rate of 89.12% and a loss value of 0.240. The findings demonstrate that the CNN methodology is highly effective in accurately classifying diseases in pakcoy, thereby providing a robust framework for informed decision-making in disease prevention and management for pakcoy crops

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