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IMPELEMENTASI ALGORITMA AES DAN RSA UNTUK KEAMANAN PADA APLIKASI WEB CHAT TEAM SYNC Bagas Hadista Mulyadi; Muhammad innuddin; Ondi Asroni; Muhamad Azwar; kurniadin Abdul Latif
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 9 No. 1 (2026): MISI Januari 2026
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v9i1.1807

Abstract

Perkembangan teknologi informasi meningkatkan kebutuhan akan keamanan data, khususnya pada aplikasi web chat yang digunakan untuk pertukaran informasi sensitif. Penelitian ini mengimplementasikan kriptosistem hybrid yang mengombinasikan AES-256 untuk enkripsi pesan dan RSA-2048 untuk distribusi kunci pada fitur pesan aplikasi web Team Sync. Mekanisme end-to-end encryption diterapkan di sisi klien menggunakan pustaka kriptografi berbasis JavaScript guna memastikan server tidak dapat mengakses isi pesan. Evaluasi meliputi pengujian enkripsi–dekripsi, analisis keamanan kunci, serta uji fidelity pesan. Hasil pengujian menunjukkan bahwa proses enkripsi dan dekripsi berjalan akurat tanpa kehilangan data serta mampu mendukung komunikasi real-time secara efisien. Dengan demikian, kombinasi RSA dan AES terbukti efektif dalam meningkatkan kerahasiaan, integritas, dan privasi pesan pada aplikasi Team Sync.
ANALISIS SENTIMENT KEUANGAN MENGGUNAKAN FINE-TUNED FINBERT Heroe Santoso; Raisul Azhar; Suryati, Suryati; Melati Rosanensi; I Made Yadi Dharma; Husain, Husain; Ahmat Adil; Muhamad Azwar; I Putu Hariyadi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i2.316

Abstract

Financial information is a critical type of data for analysis. However, because much of it is unstructured and widely dispersed, an appropriate analytical method is required, one of which is sentiment analysis. In the financial context, sentiment analysis is employed by the industry to assess public perceptions of companies or market conditions. This study implements a fine-tuned FinBERT model to perform sentiment analysis in the financial sector. The dataset used is a combination of FiQA (Financial Question Answering) and The Financial PhraseBank, consisting of English sentences labeled with negative, neutral, and positive sentiments. The research process involved data preprocessing, tokenization, data splitting, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that the model achieved 82% accuracy, with its best performance in the positive class (F1-score 0.88) and the neutral class (F1-score 0.85), but weaker performance in detecting the negative class (F1-score 0.49). These findings indicate that the fine-tuned FinBERT is effective for financial sentiment analysis, particularly for positive and neutral sentiments, though improvements are needed in negative sentiment detection, potentially through expanding training data diversity or applying data augmentation techniques