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All Journal Jurnal Buana Informatika Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Universitas Pamulang JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal E-Komtek Tamansiswa International Journal in Education and Science (TIJES) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Journal of International Conference Proceedings Riwayat: Educational Journal of History and Humanities Informasi interaktif : jurnal informatika dan teknologi informasi
Winarno, Basuki Heri
Universitas Teknologi Digital Indonesia
Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Neuroscience Social Sciences Other

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Information System for Optimizing Inventory Management with The Average Method in Modern Retail Business Kusumawati, Dara; Winarno, Basuki Heri; Noor M, Ardiansyah Restu
Riwayat: Educational Journal of History and Humanities Vol 8, No 3 (2025): July
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i3.48659

Abstract

Merchandise inventory is something important for companies, especially for retail businesses because merchandise inventory is a component of current assets that has a high level of liquidity, so recording and managing inventory is an important component for retail businesses. Amazon stores sell quite lots of types and quantities of merchandise, and the recording of merchandise inventory is still done manually, making it possible for recording errors to occur which results in inaccurate reports being produced, resulting in inaccurate decision making related to inventory. Besides that, it is also difficult to know the amount of inventory, purchases and sales of merchandise at any time. Based on the problems in the Amazon store, it is necessary to have an information system for recording and managing merchandise inventory, so that it can assist in recording purchases, sales, and making decisions regarding inventory in the Amazon store. The merchandise inventory information system using the average method created can help Amazon stores to record sales transactions, purchase transactions, supplier data, goods data and can produce expected reports such as sales reports, purchase reports, warehouse cards and inventory cards using the average method and reduce the risk of losing data related to the inventory held by Amazon stores.
AI-DRIVEN HYBRID ENCRYPTION FOR SECURE ELECTRONIC MEDICAL RECORDS Edy Prayitno; Basuki Heri Winarno; Sri Setyowati; Sutono Sutono; Riyadi Riyadi
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 2 (2026): Maret 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i2.4142

Abstract

Abstract: In the era of sensitive health data and frequent cyberattacks, securing electronic medical records (EMR) has become a critical challenge. This study proposes a hybrid encryption framework combining Affine and AES algorithms with an AI-based key management module to enhance EMR security while maintaining efficiency. A dataset of 1,000 simulated records was evaluated using five cryptographic configurations: Affine-only, AES-only, RSA-only, Affine–AES, and Affine–AES with AI. Performance was measured through encryption/decryption latency and ciphertext size, while security was assessed under brute-force, SQL injection, and phishing simulations. The AI decision tree for key generation was evaluated using accuracy, precision, recall, F1-score, and entropy metrics. Results show that the AI-enhanced hybrid method eliminates brute-force success, introduces only minor latency overhead, and generates high-entropy keys with reliability above 98%. These findings indicate that integrating AI-based dynamic key regeneration into hybrid encryption can improve EMR security while remaining practical for clinical and cloud-based healthcare systems. Future work should involve real clinical datasets and explore post-quantum cryptographic extensions. Keywords: AI key management; attack resistance; encryption performance; electronic medical records; hybrid encryption Abstrak: Di era meningkatnya sensitivitas data kesehatan dan maraknya serangan siber, perlindungan Rekam Medis Elektronik (RME) menjadi tantangan penting. Penelitian ini mengusulkan kerangka enkripsi hibrida yang menggabungkan algoritma Affine dan AES dengan modul manajemen kunci berbasis AI untuk meningkatkan keamanan RME tanpa mengorbankan efisiensi. Dataset simulasi berisi 1.000 entri diuji menggunakan lima konfigurasi kriptografi: Affine-only, AES-only, RSA-only, Affine–AES, serta Affine–AES dengan AI. Performa diukur melalui latensi enkripsi/dekripsi dan ukuran ciphertext, sedangkan keamanan dievaluasi melalui simulasi serangan brute force, SQL injection, dan phishing. Model decision tree untuk manajemen kunci dinilai menggunakan metrik akurasi, presisi, recall, F1-score, dan entropi. Hasil menunjukkan bahwa metode hibrida dengan AI menghilangkan keberhasilan brute force, menambah overhead latensi yang minimal, serta menghasilkan kunci berentropi tinggi dengan reliabilitas di atas 98%. Temuan ini menunjukkan bahwa regenerasi kunci dinamis berbasis AI dalam skema enkripsi hibrida dapat meningkatkan keamanan RME sekaligus tetap praktis untuk sistem klinis dan layanan kesehatan berbasis cloud. Penelitian selanjutnya disarankan menggunakan dataset klinis nyata dan mengeksplorasi kriptografi pascakuantum. Kata kunci: enkripsi hibrida; ketahanan serangan; kinerja enkripsi; manajemen kunci berbasis AI; rekam medis elektronik
Co-Authors Aloysius Agus Subagyo Ani Lestari Dara Kusumawati Edi Iskandar, Edi Edy Prayitno Edy Prayitno Erna Hudianti Febri Nova Lenti, Febri Nova Femi Dwi Astuti Kusumawati, Dara Noor M, Ardiansyah Restu Perdana, Ivan Jaka Ramadhani, Nur Revianti, Siska Lidya Rikie Kartadie Riyadi Riyadi Said Teguh Samudra Sri Setyowati Sutono Sutono