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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal EECCIS Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) ELINVO (Electronics, Informatics, and Vocational Education) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization SUSTINERE: Journal of Environment and Sustainability Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Window of Health : Jurnal Kesehatan JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Teknik Informatika (JUTIF) Aktual : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Forestry Research Dharmakayana: Journal of scientists, engineers, educators and scientific activists related to society development The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Arie Vatresia
Program Studi Teknik Infomatika, Fakultas Teknik, Universitas Bengkulu.
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health

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Prosedur Validasi Transkrip Akademik Berotentikasi Tinggi Berbasis Tanda Tangan Digital menggunakan Algoritma Rivest Shamir Adleman (RSA) dan BLAKE3 Anggriani, Kurnia; Pratama, Yogi; Utama, Ferzha Putra; Vatresia, Arie; Pratama, Enda Esyudha
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 3 (2025): Volume 11 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i3.98817

Abstract

Transkrip akademik merupakan dokumen yang sangat rahasia di lembaga pendidikan. Oleh karena itu, transkrip akademik harus memenuhi aspek keamanan informasi, yaitu autentikasi, keaslian pesan (integritas data), dan non-repudiasi. Banyak kasus telah dilaporkan terkait pemalsuan dan penyalahgunaan transkrip akademik. Hal ini disebabkan karena tidak adanya perlindungan terhadap integritas dan autentikasi transkrip akademik. Prosedur validasi diperlukan untuk menjamin integritas dan keaslian transkrip akademik. Untuk memberikan perlindungan terhadap integritas dan otentikasi, prosedur tanda tangan digital diperlukan pada transkrip akademik. Tanda tangan digital adalah skema yang terdiri dari dua komponen, yaitu penandatanganan dan verifikasi. Skema ini digunakan untuk menjamin integritas informasi dalam dokumen digital. Penelitian ini mengusulkan prosedur validasi transkrip akademik yang sangat otentik berdasarkan tanda tangan digital menggunakan algoritma Rivest Shamir Adleman (RSA) dan BLAKE3. Skema yang diusulkan memanfaatkan kombinasi algoritma RSA dan fungsi hash BLAKE3 dalam implementasi algoritma tanda tangan digital. Hasil eksperimen menunjukkan bahwa sistem dapat menjamin validitas dokumen transkrip digital dalam format PDF. Kecepatan proses penandatanganan rata-rata pada server localhost A adalah 55,0511 milidetik dan kecepatan verifikasi adalah 19,2658 milidetik. Pada server localhost B, kecepatan rata-rata proses penandatanganan adalah 41,8563 milidetik dan kecepatan verifikasi adalah 11,9715 milidetik.
Implementation of Vision Transformer (ViT) Method in Identifying Orchid Genus Based on Flower Images Vatresia, Arie; Cahyani, Seprina Dwi; Susanto, Agus; Romeida, Atra
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025 (In-Press)
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.90056

Abstract

There are about 15,000 to 20,000 orchid species around the world, spread across more than 900 genera. They come in many different shapes, sizes, and colors. This wide range of species makes it hard to tell them apart, especially for people who aren't experts. Bengkulu is one of the provinces on the island of Sumatra. It is known for its historical and cultural heritage as well as its rich biodiversity, especially its native plants like orchids. However, it is still hard to tell what they are. The identification process can be made better by using the breakthrough in artificial intelligence of the transformer. The goal of this study is to create an Android app that can use the Vision Transformer (ViT) architecture to identify five types of orchids: Bulbophyllum, Cymbidium, Dendrobium, Phalaenopsis, and Vanda. We used open-source libraries to collect data, which included 1,500 images that went through preprocessing steps. The experimental results show that the ViT-Base16 model with 25 epochs did the best job, getting an accuracy of 0.98 on the test dataset. However, it was hard to classify the genus Dendrobium in all trials because it had a lot of different shapes. The application testing gave good results, with scores of 81.13 for ease of use, 82.5 for accuracy, and 83.06 for usefulness. These results indicate that the application successfully aids in the identification of orchid genera, serving as a useful resource for both educational and practical applications
Child Stunting Risk Analysis through Machine Learning Models using XGBoost Algorithm Renaningtias, Nurul; Prihatiningrum, Atik; Hardiansyah, Hardiansyah; Setiawan, Yudi; Vatresia, Arie
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.452

Abstract

Stunting is a chronic nutritional disorder that significantly affects child growth, development, and the overall quality of future human resources. According to the 2024 Indonesian Nutritional Status Survey (SSGI), the prevalence of stunting remains high at 19.8%, equivalent to approximately 4.48 million children under five. Early detection of stunting risk is essential for timely and data-driven interventions. This study employed the CRISP-DM methodology, encompassing business understanding, data collection, preparation, modeling, and evaluation phases. The dataset was processed through cleaning, variable encoding, and stunting status classification based on WHO standards. An XGBoost-based predictive model was developed and evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved 98% accuracy in predicting stunting risk. Feature importance analysis revealed that height is the most influential variable determining stunting risk.
Co-Authors Adi Yulianto, Adi Agus Susanto Agustian, Indra Ahmad Faris, Ahmad Aprilia, Vilda Armelia, Zaskia Fasha Asahar Johar Atik Prihatiningrum, Atik Atra Romeida Aziz, Dzakwan Ammar Bismantolo, Putra Cahyani, Seprina Dwi Daratha, Novalio Darnita , Yulia Dedi Suryadi Enda Esyudha Pratama, Enda Esyudha Erlanshari, Aan Faurina, Ruvita Febrianti, Tiara E Gupta, Neeraj Hardiansyah Hardiansyah Ibrahim, Tanimu Adam Ida Ayu Putu Sri Widnyani Kennedy, John Kurnia Anggriani, Kurnia Kusuma, Teguh Nata Lestari, Etika Dwi Nata Kusuma, Teguh Nurul Renaningtias, Nurul Okta Piani, Sri Oktariani, Widhia Prasetyo, Bimo Pratama, Yogi Putri, Tiara Eka Rahma, Silvia Rais, Rendra Regen Rais, Rendra Regen Regen, Rendra Ridho, Muhammad Faiz Sadler, Jonathan P. Santosa, Hendy Simanjuntak, Yanti Sonita, Anisya Tambunan, Pieters N.P Utama, Ferzha Utama, Ferzha Putra Wijaya, Gusman Yudi Setiawan Zahra, Syakira Az Zalbuin Mase, Lindung Zarah Juaita, Aisyah Amelia