Article Per Year (5 Year)
p-Index From 2019 - 2024
3.817
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurnal Tunas Journal of Computer Networks, Architecture and High Performance Computing Dedikasi Sains dan Teknologi (DST) Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Jurnal Minfo Polgan (JMP) Prosiding Snastikom Jurnal Teknik Informatika Jurnal Pengabdian Masyarakat Gemilang (JPMG) Prioritas : Jurnal Pengabdian Kepada Masyarakat Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Deli Sains Informatika
Adidtya Perdana, Adidtya
Universitas Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
IMPLEMENTASI RCCM (RIVEST CIPHER 4 BASED ON CHAOTIC MAPPING) PADA PESAN TEKS MENGGUNAKAN GUI PYTHON Adidtya Perdana; Nurul Ain Farhana
Deli Sains Informatika Vol. 2 No. 2 (2023): Artikel Riset Juni 2023
Publisher : LPPM Universitas Deli Sumatera

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

Abstract

In securing a message, especially digital messages, a technique or algorithm is needed in the field of cryptography. Where cryptography itself is a scientific field in informatics and computer science that discusses and learns about securing a secret message. One of the algorithms or in cryptography is called a Cipher, which can be used to secure secret messages, namely RCCM. RCCM or Rivest Cipher 4 base on Chaotic Mapping is a development of RC4 which combines it with the Chaotic Map technique. RC4 itself is a cryptographic algorithm that belongs to the Stream Cipher. In the process of securing messages on RC4, key generation is carried out using KSA (Key Scheduling Algorithms) and PRGA (Pseudo-Random Generation Algorithm) which are processed based on the key entered by the user to produce a keystream. The keystream itself consists of a series of random numbers, each of which will be exclusive or (XOR) to the plaintext that produces the ciphertext. By applying the Chaotic Map, inputting the key by the user is not necessary. And for implementation, you can use Python programming and are GUI-based using the help of the Tkinter library.
Application of Multiple Linear Regression Analysis to Model Higher Education Rankings in Medan City Nurul Ain Farhana Nurul; Adidtya Perdana
Deli Sains Informatika Vol. 2 No. 2 (2023): Artikel Riset Juni 2023
Publisher : LPPM Universitas Deli Sumatera

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

Abstract

Higher Education is an Education unit that organizes Higher Education. There are 5 main components in the ranking of tertiary institutions, namely the quality of Human Resources (HR), Institutional Quality, Student Affairs Quality, Publication Quality, Research and Community Service, and Innovation Quality. These components must be realized by each tertiary institution as a condition for improving the quality of a tertiary institution. Based on the main components of tertiary institutions, multiple linear regression analysis will be applied. The purpose of this research is to look at the main components that have a dominant influence on university rankings and the major influence of each component on university rankings. From the analysis results obtained multiple linear regression model with a multiple determination coefficient value of 84.26%. This value is quite large in explaining the influence of the 5 main components of higher education in ranking. Keywords: Ranking, College, Multiple Linear Regression, Simultaneous Test, Partial Test.
Pemanfaatan Aplikasi Geogebra dalam Pembelajaran Matematika di Sekolah Menengah Kejuruan Nurul Maulida Surbakti; Sri Dewi; Dian Septiana; Nurul Ain Farhana; Adidtya Perdana
Dedikasi Sains dan Teknologi (DST) Vol. 3 No. 2 (2023): Artikel Periode Nopember 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v3i2.3008

Abstract

Berdasarkan data yang diperoleh, terlihat bahwa saat ini penggunaan komputer dalam proses pembelajaran terbatas pada penggunaan PowerPoint. Para guru mata pelajaran matematika menghadapi tantangan dalam menciptakan materi dan alat bantu pembelajaran yang efektif. Untuk mengatasi masalah ini, salah satu solusi yang diusulkan adalah dengan memberikan pelatihan penggunaan aplikasi Geogebra kepada guru-guru matematika. Pelatihan ini bertujuan untuk meningkatkan pemahaman guru terkait peran media pembelajaran dalam pembelajaran matematika dan untuk meningkatkan pengetahuan serta keterampilan mereka dalam memanfaatkan media pembelajaran virtual (mathlet) yang interaktif dan efektif. Pendekatan yang akan diterapkan dalam pelatihan ini mencakup beberapa tahap, seperti observasi langsung, wawancara, presentasi, dan sesi tanya jawab. Diharapkan bahwa hasil dari pelatihan ini akan membawa berbagai manfaat, termasuk peningkatan kualitas proses pembelajaran yang sesuai dengan pengetahuan yang diperoleh dalam pelatihan, peningkatan kemampuan guru dalam berkreasi dan berinovasi dalam merancang pembelajaran, pemahaman yang lebih mendalam tentang media pembelajaran virtual, keterampilan guru dalam menggunakan aplikasi Geogebra untuk membuat media pembelajaran virtual, serta kemampuan guru dalam mengembangkan materi visual, bahan ajar, dan instrumen penilaian yang relevan dengan materi aljabar dan geometri.
Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture Ichwanul Muslim Karo Karo; Dedy Kiswanto; Suvriadi Panggabean; Adidtya Perdana
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2023): Article Research Volume 8 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13110

Abstract

Hair diseases are common and can be caused by a variety of factors, including genetics, stress, nutritional deficiencies, as well as exposure to sunlight and air pollution. Accurate diagnosis of hair diseases is important for proper treatment, but can be challenging due to overlapping symptoms. The development of the healthcare world has widely utilized machine learning and deep learning approaches to assist in the healthcare field. This research aims to develop hair disease classification using Convolutional neural network (CNN). The CNN-based approach is expected to help health professionals diagnose hair diseases accurately and provide targeted treatment. This research involves an experimental design with three main stages: identifying the research problem, conducting a literature review, and collecting data. The research uses a dataset of hair disease images obtained from Kaggle, which are annotated and organized based on different hair disease types. After the image data is collected, the image dataset will go through the image preprocessing stage. Experiments were conducted using hair disease image data with 15 epochs on a CNN Deep Learning model with VGG-16 architecture, and resulted in an accuracy of 94.5% and a loss rate of 18.47%, with a testing epoch time of 9 hours 48 minutes. The results of this study show that CNN with VGG-16 architecture can successfully classify 10 types of hair diseases
Implementasi Text Summarization Pada Review Aplikasi Digital Library System Menggunakan Metode Maximum Marginal Relevance Ichwanul Muslim Karo Karo; Sri Dewi; Adidtya Perdana
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 1 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i1.671

Abstract

Peringkasan teks merujuk pada pembuatan rangkuman teks secara otomatis dengan pendekatan natural language processing (NLP).  Text summarization dibutuhkan saat jumlah dokumen atau review yang akan dirangkum dalam jumlah yang banyak. Sebuah rangkuman yang dihasilkan dapat menjadi pengetahuan, masukan maupun saran untuk perbaikan/pengembangan berbagai aplikasi. Aplikasi Digital Library System merupakan sebuah mobile apps untuk layanan perpustakaan Universitas Negeri Medan (Unimed). Aplikasi tersebut memiliki banyak ulasan di berbagai platform. Tentu, rangkuman ulasan tersebut merupakan pengalaman pengguna dan dapat menjadi masukan untuk pengembangan versi terbaru. Namun menjadi tantangan jika seluruh ulasan pengguna dirangkum secara manual, karena akan memakan waktu yang lama. Penelitian ini bertujuan untuk menyediakan rangkuman atas ulasan mobile Apps tersebut dengan pendekatan peringkasan teks secara otomomatis.  Algoritma yang digunakan dalam peringkasan teks di penelitian ini ialah Maximum Marginal Relevance (MMR) dan proses evaluasi menggunakan presisi, recall dan F1. Ulasan mobile apps diperoleh dari play store dan App Store. Ulasan akan melalui tahapan text pre-processing dengan bantuan library NLTK. Penelitian ini berhasil mengidentifikasi 30 review dengan nilai MMR tertinggi. Lebih lanjut, rangkuman ulasan yang disajikan merupakan rangkaian 10 ulasan dengan nilai MMR tertinggi. Rangkuman yang dihasilkan memiliki tingkat presisi sebesar 30.51%, recall sebesar 56.25%, dan skor F1 sebesar 39.56%.
Co-Authors Ade Zulkarnain Ade Zulkarnain Hasibuan Adryan Rachmadsyah Ryan Anak Agung Istri Sri Wiadnyani Andi Marwan Elhanafi Arief Budiman Arief Budiman Dedy Kiswanto Dian Septiana Dwi Syaputra Ega Zuhairi Ramadhan Ichwanul Muslim Karo Karo mardiana Mhd. Zulfansyuri Siambaton Muhammad Fauzan Akbar Muhammad Khoiruddin Harahap Muhammad Kurniawan Muhammad Rivai Nenna Irsa Syahputri Nurul Ain Farhana Nurul Ain Farhana Nurul Nurul Khairina Nurul Maulida Surbakti Putri Maulidina Fadilah Rizko Liza Sri Dewi Sri Dewi Suvriadi Panggabean Yessi Fitri Annisa Lubis Yuyun Dwi Lestari Yuyun Dwi Lestari