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All Journal JSI (Jurnal sistem Informasi) Universitas Suryadarma Immortalis Journal of Interdisciplinary Studies
Martanto
STMIK IKMI Cirebon, Indonesia
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Peningkatan Akurasi MobileNetV2 untuk Klasifikasi Penyakit Daun Jagung Berbasis Morfologi rama maulana faz'rin; Martanto; Yudhistira Arie Wijaya; Ade Irma Purnama Sari; Nisa Dienwati Nuris
JSI (Jurnal Sistem Informasi) Universitas Suryadarma Vol. 13 No. 1 (2026): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Fakultas Ilmu Komputer dan Desain - Unsurya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v13i1.1733

Abstract

Deteksi penyakit daun jagung pada citra lapangan menghadapi tantangan besar akibat pencahayaan tidak merata, latar belakang kompleks, serta jumlah data yang terbatas dan tidak seimbang. Penelitian ini mengusulkan pipeline klasifikasi berbasis deep learning yang mengintegrasikan pra-pemrosesan morfologi erosi, dilasi, opening, dan closing—untuk memperjelas struktur lesi sebelum pelatihan model. Sebanyak 310 citra daun jagung dalam tiga kelas (Sehat, Karat, dan Hawar) dibagi secara stratifikasi menjadi data latih, validasi, dan uji. MobileNetV2 dilatih menggunakan pendekatan transfer learning dengan augmentasi dasar. Hasil evaluasi menunjukkan akurasi validasi 34,43%, akurasi uji 44,83%, dan macro-F1 sebesar 0,17, yang mengindikasikan kemampuan generalisasi rendah. Confusion matrix mengungkap terjadinya class collapse akibat ketidakseimbangan kelas dan kemiripan visual antar penyakit. Meskipun performanya terbatas, pra-pemrosesan morfologi membantu meningkatkan kejelasan fitur dan stabilitas ekstraksi pada kondisi lapangan.
Generative Artificial Intelligence in Higher Education: A Systematic Review of Educational Transformation, Assessment, and Governance Syusinka Rahmatika; Martanto; Ryan Hamonangan
Immortalis Journal of Interdisciplinary Studies Vol. 2 No. 3 (2026): July - September
Publisher : PT. Caesarindo Triloka Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67307/ijis.v2i3.132

Abstract

Generative Artificial Intelligence (GenAI) has rapidly transformed higher education practices, creating new opportunities for pedagogical innovation while introducing complex challenges related to assessment validity, academic integrity, and institutional governance. However, existing studies remain fragmented across technological adoption, learning processes, assessment practices, and ethical considerations, limiting a comprehensive understanding of how GenAI can be integrated responsibly into higher education ecosystems. This systematic literature review aims to synthesize current evidence on the educational implications of GenAI by examining its influence on teaching transformation, student learning, assessment redesign, and academic integrity governance. Following the PRISMA framework, relevant studies were systematically identified, screened, and analyzed to reveal emerging patterns, challenges, and future research directions in GenAI adoption within higher education. The synthesis revealed four interconnected themes: (1) transformation of teaching practices through AI-supported instructional design and efficiency improvement, (2) enhancement of student learning through personalization and self-regulated learning support, (3) evolution of assessment toward authentic and competency-oriented approaches, and (4) development of institutional governance frameworks addressing ethical, privacy, transparency, and integrity concerns. The findings indicate that successful GenAI integration requires a balanced approach combining technological capability, pedagogical redesign, and responsible governance. This review contributes to Artificial Intelligence in Education (AIED) research by proposing an integrated perspective for sustainable GenAI adoption and identifying priorities for future empirical investigations.
Co-Authors Ade Irma Purnama Sari Nisa Dienwati Nuris rama maulana faz'rin Ryan Hamonangan Syusinka Rahmatika Yudhistira Arie Wijaya