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All Journal Jurnal TAM (Technology Acceptance Model) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JUTIM (Jurnal Teknik Informatika Musirawas) JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Jurnal Informatika Global Jurnal Abdimas Mandiri Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Digital Teknologi Informasi Jurnal Pengabdian kepada Masyarakat JIKI (Jurnal Ilmu Komputer dan Informatika) IJAIRTec Aptekmas : Jurnal Pengabdian Kepada Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Green Intelligent Systems and Applications Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi Klik - Jurnal Ilmu Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Pengabdian Publikasi Hasil Pengabdian Kepada Masyarakat. Jurnal Ilmiah Teknik Informatika dan Komunikasi MDP Student Conference Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Pengabdian Masyarakat dan Riset Pendidikan JISCOMP (Journal of Information System and Computer) INOVTEK Polbeng - Seri Informatika International Journal of Artificial Intelligence and Robotic Technology (IJAIRTec) Jurnal TAM (Technology Acceptance Model)
Hartati, Ery
Multi Data Palembang University, South Sumatra
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Journal : MDP Student Conference

 1 
Klasifikasi Spesies Kupu Kupu Menggunakan Metode Convolutional Neural Network Micheal Micheal; Ery Hartati
MDP Student Conference Vol 1 No 1 (2022): The 1st MDP Student Conference 2022
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.66 KB)

Abstract

Abstract: Butterflies are one of the insects of the ordo Lepideptora. Butterflies have many species with different wing patterns. This study aims to classify butterfly species using the Convolutional Neural Network (CNN) method with the VGG-16 architecture and LeNet with the Adam, Adagrad, and SGD optimizers. The dataset contains 5455 images and is divided into 4955 train data, 250 test data, and 250 valid data. Then the dataset is augmented on 8000 train data for each class and 800 test data for each class. In this study, the highest accuracy level for each architecture was obtained using the Adam optimizer so that the accuracy rate using VGG-16 was 93% and by using LeNet was 67%. Abstrak: Kupu kupu merupakan salah satu serangga ordo lepideptora. Kupu kupu memiliki banyak spesies dengan corak sayap yang berbeda. penelitian ini bertujuan untuk mengklasifikasi spesies kupu kupu menggunakan metode Convolutional Neural Network(CNN) dengan arsitektur VGG-16 dan LeNet dengan optimizer Adam, Adagrad, dan SGD. Dataset terdapat 5455 citra dan di bagi menjadi 4955 data train, 250 data test, dan 250 data valid. Kemudian dataset dilakukan augmentasi pada data train sebanyak 8000 untuk setiap kelas dan data test 800 untuk setiap kelas. Pada penelitian ini didapatkan tingkat akurasi tertinggi untuk setiap arsitektur dengan menggunakan optimizer Adam sehingga didapatkan tingkat akurasi menggunakan VGG-16 sebesar 93% dan dengan menggunakan LeNet sebesar 67%.
Identifikasi Ngengat menggunakan Metode Convolutional Neural Network Ricky Ricky; Ery Hartati; Eka Puji Widiyanto
MDP Student Conference Vol 2 No 1 (2023): The 2nd MDP Student Conference 2023
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (944.351 KB) | DOI: 10.35957/mdp-sc.v2i1.4292

Abstract

Ngengat merupakan salah satu jenis serangga dari Ordo Lepidoptera yang populasinya sebesar 90% dari Ordo Lepidoptera di dunia. Ngengat berfungsi sebagai indikator kondisi lingkungan yang terkait dengan fenomena degradasi lingkungan, ngengat juga dapat digunakan sebagai objek untuk penelitian konservasi keanekaragaman hayati, evolusi, etologi dan genetika karena sifat taksonominya yang mapan dan mudah dikenali. Penelitian ini bertujuan untuk mengidentifikasi spesies ngengat menggunakan metode Convolutional Neural Network dengan arsitektur VGG-16 dan optimizer Adam. Dataset yang digunakan terbagi atas 7183 data train, 250 data test, dan 250 data valid. Kemudian dilakukan augmentasi pada objek dataset yang dibagi menjadi 400.400 data train, 85.800 data test, dan 85.800 data valid. Pada penelitian ini metode CNN dengan arsitektur VGG-16 dan Optimizer Adam serta dataset yang telah diaugmentasi berhasil meingidentifikasi ngengat dan menghasilkan tingkat akurasi sebesar 95%.
Aplikasi Create History Stok Barang pada PT. Singa Perkasa Abadi Budi, Raden George Samuel; Hartati, Ery
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11184

Abstract

PT. Singa Perkasa Abadi is a company engaged in the production and sale of iron materials, such as nails, long iron, light steel and barbed wire. Currently, the process of recording the expenditure and entry of goods still uses paper notes, which have the potential to cause errors in recording and calculating stock of goods, and delays in managing stock of goods, and when shipping goods only relying on delivery letters which can cause errors during shipping of goods. This study aims to develop a website-based digitalization solution for stock management in order to reduce errors and loss of recording, increase efficiency and accuracy of recording. By implementing the Iterative development method and using the Laravel and Bootstrap frameworks. The implementation of this system can increase the company's productivity in managing stock of goods more quickly, accurately, and in an organized manner.
Penerapan Metode Design Thinking dalam Perancangan UI/UX Aplikasi Deteksi Mata Pengemudi Renaldo, Florence; Irsyad, Hafiz; Hartati, Ery
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11316

Abstract

The high rate of accidents experienced by car drivers or other vehicle operators is often caused by drowsy eyes. Therefore, this research was conducted to design a UI/UX for a driver eye detection system using the Design Thinking method, which consists of five stages: empathy, definition, ideation, prototype, and testing. These stages ensure that the application can be optimally used by users. This application is also expected to help users reduce the risk of accidents caused by drowsiness on the road.
Co-Authors ., Dewa Adrian Chandra Agustria, Kevin Akhfir, Muhammad Fadly Ukhrowi Amarullah, Rendy Ambrosius Dwi Cahyadi Andreas, Kevin Aneke Windari Ardiansyah, Aldi Ariansyah, Nova Awalludin, Nur Bertnas Valentino, Calvin Budi, Raden George Samuel Candra candra Cerwyn Asyraq Chaesa, Linus Ardel Chandra, Kelvin William Christy, Christy Daniel Udjulawa Deka Putra Pamungkas Derry Alamsyah Desy Iba Ricoida Djunaidi, Sherdian Eka Puji Widiyanto Fathimah Azzahra Fatimah Nadia FELICIA Felix Santoso Ferdian Indrahadi Fernando Fernando Feliansyah Fernando Fernando, Fernando Firnando, Januar Franko, Billy Fransiska, Julita gasim, Gasim Graciela, Michelle Hafiz Irsyad Hakiki, Muhammad Anugrah Hebert, Hocwin Hermanto , Rio Inayatullah, Inayatullah Intan Sanu Ishak , Alvin Leonardo Ivander Destian Luis Jennifer Jocelyn Jennifer Velensia Santoti Jeovanni Wong Jumhari Jumhari Kelly, Angel Keristin, Usnia Wati Kesuma, Dorie P. Kevin Antonio Khairani, Siti Kotan, Jendraja Husin Kusuma, Dorie P. Leonardo Leonardo Lim, Farrel Stefanov Lisa Amelia Fransen M Rifqi Virgiansyah M. Kurniawan, M. Maria Elana Maulana, Muhammad Ishaq Md, Ramanda Michael Michael Micheal Micheal Muhammad Maisep Muhammad Nirraca Mutia, Silvi Nataliatara Nataliatara Nicholas Edison Nicholas, Nicholas Nirraca, Muhammad Nurrahman, Wahyu Aji Oktaviani, Ayu Sri Pebrian, Hafizh Peter Reynard Susanto Prana Welas Sukma, Tangguh Prasetyo, Zavier Billy Putra Darmansius, Albertus Dwi Andhika Putra Ganda Dewata Qois Al Qorni Renaldo, Florence Reza Ardana Richie Jonathan Chaniago Ricko Andreas Kartono Ricky Ricky Rikky, Rikky Rionaldo, Daniel Sahpira, Mulia Saputra Edika, Nelson Saputra, Ade Rocky Saputra, Riganda Sasongko, Randie Se, Abd Rosyiid Selvie Selvie Sihombing, Mecha Bella Permata Sri Wahyuni Steven Hartanto Sudiadi Sudiadi Sudiadi Sudiadi Suluh Arif Wibowo Tan, Handy Christianto Tandoballa, Lucky Tanzil, Surya Pratama Teo Yulio Sihotang Umar Muhdhor Umi Karolina Vanness Bee Vasco Dee Gamma Bororing Verdy Verrino Adityya Virginia, Callista Widyakusuma, Rafael Lois Wijaya, Frisky Wilyanto, Nicholas Winardi, Eric Agustian Yogie Prakoso Yohannes, Yohannes Yulistia Yulistia