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All Journal Edsence: Jurnal Pendidikan Multimedia Specta Journal of Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) LOSARI: Jurnal Pengabdian Kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Edukasi: Jurnal Pendidikan Dasar Equiva
Syamsul Mujahidin, Syamsul
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Transportation Other

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Journal : Specta Journal of Technology

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Implementation of the Elliptic Curve Cryptography Method in Digital Image Security in Medical Images Yanuar Bhakti Wira Tama; mujahidin, syamsul
SPECTA Journal of Technology Vol. 8 No. 3 (2024): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v8i3.1253

Abstract

Digital security become increasingly important particularly in medical field as impact of patient privacy and the protection of patient data. This attempt for this research will be made to use elliptic curve cryptography to hide messages in the form of digital images using multiplication matrix modified hill chipper and count entropy and time encryption and decryption. The encryption process, which utilizes matrix multiplication, ensures that the images achieve near-ideal entropy values, close to 8, indicating a high degree of randomness and security. The result is entropy for encrypted image near 8 it means that randomness of image is quite random. Meanwhile for computational time encrypted and decrypted image for one block is around 400000 nano second for encrypt image and 1500000000 nano second for decrypt image.
Analisis Sentimen Isu Vaksinasi Covid-19 pada Twitter dengan Metode Naive Bayes dan Pembobotan TF-IDF Tokenisasi 1-2 Gram Hapsari, Yashmine; Mujahidin, Syamsul; Fadhliana, Nisa
SPECTA Journal of Technology Vol. 7 No. 2 (2023): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v7i2.812

Abstract

The COVID-19 vaccination has been implemented to cut down the spread of the virus in society, but the status of the vaccine, which has been in the development stage, is one of the factors causing people to hesitate to vaccinate. Therefore, a sentiment analysis was carried out on the issue of COVID-19 vaccination with processes and parameters that could increase the model’s accuracy. In this study, sentiment classification was performed using the Naive Bayes method and a dataset of 5,000 tweets related to the vaccination of COVID-19. The weighting stage was applied using the TF-IDF method in which a comparison was made of the effect of using unigram, bigram and 1-2 gram tokenization on model accuracy. The results of one of the experiments with the Gaussian classifier and the ratio train: test is 7:3, the model accuracy is 67.4% for the unigram parameter, 65.5% for the bigram parameter, and 70% for the 1-2 gram parameter, where the model with the combined token is 1 -2 grams has a higher accuracy when compared to using only 1 type of token. Based on these results, it can be concluded that the combination of unigram and bigram tokenization types can provide added value to the model for classifying data, thereby increasing accuracy in analysis related to public sentiment.
Co-Authors Aditya, Andhika Amalia Kartika Desilya Lau Diarini, I Gusti Ayu Agung Sinta Fadhliana, Nisa Fudhail, Muhammad Thariq Hapsari, Yashmine Harli Andrika Lomo I Gede Ratnaya Insan Kamil, Muhammad Muhammad Noor Ihsan Fadhilah Paninggalih, Ramadhan Paulus Anthony Zejlstra Putu Yulia Angga Dewi Rangga Hermawa Rifqiandi, Muhammad Riska Kurniyanto Abdullah Rizki, Rifaldho Muhammad Taukhid, Mukhamad Utomo, Muchammad Chandra Cahyo Yanuar Bhakti Wira Tama