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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Software Engineering, Information and Communication Technology Jurnal Umum Pengabdian Masyarakat (JUPEMAS) Journal of Informatics and Data Science (J-IDS)
Dedy Kiswanto
Universitas Negeri Medan
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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A Star (A*) Algorithm Implementation to Measure Shortest Distance from Universitas Negeri Medan to Kualanamu International Airport Dedy Kiswanto
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 4, No 1: June 2023
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v4i1.59213

Abstract

Searching for the shortest path is a problem that often occurs in everyday life, to determine the best distance some information is needed such as the value / cost between points to be visited. The A* (A Star) algorithm is one of the optimal algorithms in the shortest path search category. This algorithm is very good as a solution to the pathfinding process so that it can save time and money. This research was conducted to determine the shortest distance from Medan State University to Kualanamu International Airport using the A* (A Star) algorithm. The method used in this study is by collecting data using Google Maps, building a graph model as a map representation, calculating the shortest distance and evaluating it. The research results obtained show the accuracy of the A* algorithm in determining the shortest route from Medan State University to Kualanamu Airport where this can save time and money on the way.
TRAINING PENINGKATAN KOMPETENSI INDUSTRI UNTUK SERTIFIKASI PROFESI NETWORK ENGINEER SKEMA NETWORK+ BERSAMA PT. NUSANET DAN PT. WILEARNING INDONESIA Dedy Kiswanto; Hermawan Syahputra; Suvriadi Panggabean
Jurnal Umum Pengabdian Masyarakat Vol 2 No 1 (2023): Jurnal Umum Pengabdian Masyarakat
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

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Abstract

Training peningkatan kompetensi dan sertifikasi network+ ini merupakan bentuk pengabdian masyarakat yang dilaksankaan dengan kolaborasi bersama PT Nusatnet dan PT Wilearning Indonesia. Adapun jumlah peserta terdiri dari 6 orang laki–laki karyawan PT Nusanet yang bertugas sebagai engineer network lapangan sedangkan PT Wilearning menyediakan tempat dan seluruh sarana prasarana yang dibutuhkan selama kegiatan training dan sertifikasi dalam penyampaian TIM menggunakan metode pembelajaran blended learning. 100% peserta dapat dengan baik menyelesaikan seluruh rangkaian exam dan mampu mendapatkan skor melebihi syarat minimal. Skor terkecil yang didapatkan 724 dan skor tertingi 824, jika dilakukan rata–rata pada skor peserta sebesar 783. Artinya rata–rata peserta mampu menjawab dengan benar soal yang diberikan sebesar 92% dari total 90 soal yang diberikan.
Simulasi Virtual Private Network (VPN) Menggunakan Open VPN Pada Machine Virtual Ubuntu 22.04 Dedy Kiswanto
Journal of Informatics and Data Science Vol 2, No 1 (2023): VOL 2, NO 1 (2023): VOL 2, NO 1 (2023)
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v2i1.43125

Abstract

Perkembangan teknologi serta jangkauan internet yang membuat orang-orang lebih mudah dalam mengakses internet dan melakukan pekerjaan walau terhalang jarak. Akan tetapi  jangkauan internet tersebut membuat seseorang dapat mengakses privasi orang lain. Oleh karena itu peneliti melakukan penelitian “Simulasi Virtual Private Network (VPN) Menggunakan Open VPN Pada Machine Virtual Ubuntu 22.04”, dimana OpenVPN dapat membantu pengguna dalam pertukaran data dengan aman. Server Ubuntu menyediakan layanan berupa OpenVPN yang mampu menciptakan tunnel server VPN sendiri dan dapat membatasi siapa saja yang dapat mengakses jaringan tersebut. Pada penelitian ini digunakan metode penelitian studi literatur sebagai pedoman dalam pengumpulan data untuk perancangan pembuatan VPN pada Ubuntu, serta dilakukan analisis pada isi data yang dikumpulkan. Hasil yang diperoleh ialah ketika sistem operasi/perangkat pengguna mengaktifkan VPN, maka sistem operasi/perangkat lain tidak dapat mengakses perangkat pengguna.
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
Co-Authors Adidtya Perdana, Adidtya Hermawan Syahputra Ichwanul Muslim Karo Karo Suvriadi Panggabean