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All Journal Jurnal Dedikasi Jupiter Journal of Information Technology and Computer Science Jurnal Sains Dan Teknologi (SAINTEKBU) SMARTICS Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Bangun Abdimas Jurnal Pengabdian Harapan Bangsa Delta-Phi Jurnal Pendidikan Matematika Jurnal Inovasi dan Pengembangan Hasil Pengabdian Masyarakat Assyfa Journal of Islamic Studies BIMASAKTI Akiratech E-Amal: Jurnal Pengabdian Kepada Masyarakat International Journal of Technology and Education Research International Journal of Economics, Business and Innovation Research Ashlach : Journal of Islamic Education Jurnal Pengabdian Masyarakat
Sapto Hadi Riono
Universitas PGRI Wiranegara Pasuruan, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Pemanfaatan AI Dalam Pembelajaran: Upaya Peningkatan Literasi Digital dan Kreativitas Guru MAN 2 Al-Yasini Sapto Hadi Riono; Puji Utami Rakhmawati; Farosania Amanda; Adinda Sufeina Azzahra
Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2026): Juli
Publisher : LPPM Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55794/abdikat.v2i3.416

Abstract

Pengabdian kepada masyarakat merupakan wujud pelaksanaan Tridharma Perguruan Tinggi yang menuntut dosen dan mahasiswa berperan aktif menyelesaikan persoalan pendidikan di masyarakat. MAN 2 Al-Yasini Pasuruan menghadapi kendala berupa minimnya pemahaman guru terhadap platform berbasis kecerdasan buatan (AI) untuk mendukung pembelajaran yang lebih personal, adaptif, dan terukur. Kegiatan ini bertujuan memberikan pemahaman konsep AI, mengenalkan berbagai platform AI, serta melatih guru mempraktikkan langsung penggunaannya dalam menyusun bahan ajar, media pembelajaran, dan instrumen evaluasi. Metode pelaksanaan dilakukan melalui tiga tahap, yaitu ceramah dan pemaparan materi, workshop dan pendampingan praktik, serta sesi diskusi dan tanya jawab, yang dilaksanakan pada 22 Juni 2026 di MAN 2 Al-Yasini Pasuruan. Hasil kegiatan menunjukkan peningkatan pemahaman dan antusiasme guru dalam mengoperasikan platform AI untuk menghasilkan bahan ajar dan media pembelajaran yang lebih variatif, bahkan sebagian peserta berhasil menyusun soal evaluasi berbasis AI. Peningkatan ini dinilai secara kualitatif melalui observasi partisipasi dan luaran praktik peserta, mengingat instrumen pre-test/post-test formal belum diterapkan pada kegiatan ini. Kegiatan ini disimpulkan berhasil memberikan bekal awal bagi guru MAN 2 Al-Yasini Pasuruan dalam mengintegrasikan teknologi AI ke dalam pembelajaran sehari-hari, sekaligus membuka peluang kolaborasi berkelanjutan dengan Universitas PGRI Wiranegara.
Perbandingan Algoritma LSTM dan GRU dalam Prediksi Hasil Panen Padi Pulau Sumatera Hadi Riono, Sapto; Fathimah Ahmad, Risalatul; Af Fathir, Ridho
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11765

Abstract

Rice production is a strategic commodity that plays a vital role in maintaining food security in Indonesia. Rice production data, being time-series in nature, is influenced by trends, seasonal patterns, and weather and climate factors; therefore, a prediction model capable of accurately capturing temporal patterns is required. This study aims to compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting rice production on the island of Sumatra. The study utilizes secondary data obtained from Statistics Indonesia (BPS), the Meteorology, Climatology, and Geophysics Agency (BMKG), and Kaggle. Preprocessing stages include handling missing data, data normalization, and transformation into a time-series format. The data is then split into training and testing sets using an 80:20 ratio. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the GRU model outperforms the LSTM model, yielding an MAE of 0.0505, MSE of 0.0075, and RMSE of 0.0868, whereas the LSTM model produces an MAE of 0.0522, MSE of 0.0082, and RMSE of 0.0917. Thus, the GRU model is more effective at capturing the temporal patterns of rice production data and generating more accurate predictions compared to the LSTM model. These findings are expected to serve as a reference for selecting rice production prediction models to support data-driven decision-making in the agricultural sector.
Co-Authors Abdul Hamid Abdul Karim Achmad Shofiyuddin Al Farisi Adinda Sufeina Azzahra Af Fathir, Ridho Akhmad Kavin Abda Andianto, Mascun Anindita Verliana Ridho'i Azizah , Nur Devita Aurellia Prasanti Fariana, Yudha Farosania Amanda Fathimah Ahmad, Risalatul Fatikh Inayahtur Rahma Fikri, Miftahul Firmansyah, Muhammad Nafi Hadiatullah, Dimas Rega Hadiatulloh, Dimas Rega Henny May Indahsari Hussain, Nazar Latipun M. Bayu Firmansyah, M. Bayu Maesaroh Lubis Maesaroh Lubis Muhammad Ainun Naim Mujiwati, Yuniar Nina Aulia, Sabrina Ningtyas, Sherly Zakia Nundiah Zuhrohfi Immaroh Nunuk Indarti Prasanti, Devita Aurellia Prasanti Puji Utami Rakhmawati Rani Darmayanti Rizdania, Rizdania Samuel Da Silva Shinta Dwi Ranti Silva, Samuel Da