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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Mia Rosmiati
Universitas Bina Sarana Informatika, Pontianak
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Analisis Pengaruh Penggunaan Deepfake di Masyarakat Dengan Metode Technology Acceptance Model Lukman Satria Manggala; Mulia Rahmayu; Mia Rosmiati
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.609

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh penggunaan teknologi deepfake di masyarakat dengan menggunakan pendekatan Technology Acceptance Model (TAM). Latar belakang penelitian ini berangkat dari fenomena meningkatnya penggunaan deepfake pada media sosial, baik untuk kepentingan hiburan maupun penyalahgunaan informasi. Permasalahan utama adalah bagaimana masyarakat memandang kemudahan penggunaan (perceived ease of use), kemanfaatan (perceived usefulness), sikap terhadap penggunaan (attitude toward using), dan intensi perilaku penggunaan (behavioral intention to use) terhadap teknologi deepfake. Metodologi penelitian menggunakan pendekatan kuantitatif dengan instrumen berupa kuesioner yang disebarkan kepada 20 responden di wilayah Tangerang. Uji validitas dan reliabilitas instrumen dilakukan menggunakan analisis Pearson Product Moment dan Cronbach’s Alpha dengan bantuan perangkat lunak SPSS. Hasil pengujian menunjukkan seluruh item pernyataan valid (r-hitung > r-tabel) dan reliabel (α = 0,969 ≥ 0,6). Analisis deskriptif menunjukkan bahwa mayoritas responden menilai teknologi deepfake mudah digunakan serta bermanfaat untuk produksi konten digital. Hasil penelitian membuktikan bahwa persepsi kemudahan dan manfaat berpengaruh positif terhadap sikap, yang pada akhirnya memengaruhi niat perilaku untuk menggunakan deepfake. Penelitian ini menegaskan relevansi model TAM dalam memahami penerimaan teknologi baru di masyarakat. Kesimpulan penelitian menyatakan bahwa deepfake berpotensi diterima masyarakat apabila digunakan secara etis dan bertanggung jawab
Rancang Bangun Sistem Monitoring Sentimen Berita Media Online Menggunakan IndoBERT Berbasis Web Alexander Rikky; Muhammad Iqbal; Mia Rosmiati
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10335

Abstract

Online news media has become a primary channel shaping public opinion toward government performance, making the ability to monitor news coverage a strategic necessity for institutions such as the Department of Communication and Information (Diskominfo) of West Kalimantan Province. However, the large volume of news coverage renders manual monitoring inefficient and prone to subjectivity. This study aims to design and build a web-based online news sentiment monitoring system named SentimenIQ, which integrates automatic news collection through RSS Feed, sentiment classification using the IndoBERT model, and presentation of analysis results within a single service flow. The system was developed using the Waterfall method with a microservice architecture separating the main Laravel application from the Python FastAPI inference service. Functional testing was conducted using the black box testing method, while classification performance was measured using accuracy, precision, recall, and F1-score derived from a confusion matrix on 120 labeled news articles. The functional testing results show that all system features operated according to requirement specifications, while the classification testing produced an accuracy of 89.17% with a weighted average F1-score of 89.13%. These results prove that the IndoBERT model can be integrated into a web-based operational system and relied upon to monitor news coverage in near real-time, thus serving as a reference for developing similar systems in other government institutions.
Co-Authors Alexander Rikky Lukman Satria Manggala Muhammad Iqbal Mulia Rahmayu