DEEPFAKE-ENABLED ONLINE GENDER-BASED VIOLENCE: EVALUATING NATIONAL DIGITAL LITERACY MODULES WITH GENDER EVALUATION METHODOLOGY - Suci Fitrah Syari; Fajar Apriani; Gusti Naufal Rizky Perdana (Jurnal Harkat : Media Komunikasi Gender, 2026)
Abstract. Deepfake AI has enabled new forms of Online Gender-Based Violence (OGBV) through the manipulation of women’s images and videos into non-consensual pornographic content. This study evaluates the gender responsiveness of four 2021 National Digital Literacy Movement (GNLD) presentation modules: Digital Skills, Digital Safety, Digital Culture, and Digital Ethics, in addressing deepfake-enabled OGBV. Using directed qualitative content analysis, 279 slides were examined through four dimensions adapted from the Gender Evaluation Methodology (GEM): gender representation, response to gender-related risks, participation, and language and visuals. Findings show uneven gender responsiveness. Digital Skills provides the strongest explicit representation of women and vulnerable groups, while the other modules mainly use generalized user categories. The modules recognize several digital harms, but not as gender-specific risks; none directly addresses deepfake AI or provides gender-sensitive prevention, protection, and reporting guidance. Participation remains largely general, while neutral language and some inclusive visuals do not ensure substantive gender responsiveness. The study recommends integrating deepfake recognition, gender-sensitive safety guidance, and women’s agency. Its scholarly contribution lies in operationalizing GEM to identify gaps between general digital literacy and the gender-responsive competencies needed to address deepfake-enabled OGBV.   Abstrak. Deepfake AI telah memunculkan bentuk baru Kekerasan Berbasis Gender Online (KBGO) melalui manipulasi gambar dan video perempuan menjadi konten pornografi nonkonsensual. Penelitian ini mengevaluasi responsivitas gender empat modul presentasi Gerakan Nasional Literasi Digital (GNLD) tahun 2021: Cakap Digital, Aman Digital, Budaya Digital, dan Etika Digital, dalam merespons KBGO berbasis deepfake. Dengan analisis isi kualitatif terarah, 279 slide dianalisis menggunakan empat dimensi yang diadaptasi dari Gender Evaluation Methodology (GEM): representasi gender, respons terhadap risiko terkait gender, partisipasi, serta bahasa dan visual. Hasil menunjukkan responsivitas gender yang tidak merata. Cakap Digital memberikan representasi paling eksplisit terhadap perempuan dan kelompok rentan, sedangkan modul lainnya terutama menggunakan kategori pengguna yang bersifat umum. Modul-modul tersebut mengenali sejumlah risiko digital, tetapi belum membingkainya sebagai risiko spesifik gender; tidak satu pun membahas deepfake AI secara langsung atau menyediakan panduan pencegahan, perlindungan, dan pelaporan yang responsif gender. Partisipasi masih bersifat umum, sementara bahasa netral dan beberapa visual inklusif belum menjamin responsivitas gender substantif. Penelitian merekomendasikan integrasi pengenalan deepfake, panduan keamanan responsif gender, dan agensi perempuan. Kontribusi ilmiah terletak pada operasionalisasi GEM untuk mengidentifikasi kesenjangan antara literasi digital umum dan kompetensi responsif gender yang dibutuhkan untuk menghadapi KBGO berbasis deepfake.