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All Journal JURNAL PENDIDIKAN TAMBUSAI Brahmana : Jurnal Penerapan Kecerdasan Buatan
Hanafiah, Mhd Ali
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Social Sciences

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Perancangan Film Animasi untuk Edukasi Pertanian Jagung “Dari Benih yang Menjadi Berkat” Hanafiah, Mhd Ali; Sakti, Sheanny Ocmi
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.27239

Abstract

Perkembangan teknologi digital telah membawa perubahan signifikan dalam cara penyampaian informasi, termasuk dalam bidang pendidikan. Namun, materi edukatif di bidang pertanian masih dominan disampaikan melalui metode konvensional yang kurang menarik bagi generasi muda. Artikel ini membahas rancangan media edukasi berbasis animasi 2D berjudul "Dari Benih Menjadi Berkat" sebagai solusi untuk meningkatkan minat dan pemahaman generasi muda terhadap pertanian, khususnya budidaya jagung. Pendekatan ini menggabungkan nilai edukatif dengan visual yang kreatif dan narasi yang inspiratif. Penggunaan animasi 2D dipilih karena fleksibilitas dalam penyampaian pesan, biaya produksi yang lebih rendah, serta efektivitasnya dalam menyederhanakan informasi kompleks. Studi ini berlandaskan pada beberapa penelitian terdahulu yang menegaskan peran media visual interaktif dalam meningkatkan efektivitas pembelajaran. Diharapkan, karya ini dapat menjadi contoh inovatif dalam penggunaan media kreatif untuk edukasi pertanian dan menginspirasi pendekatan serupa di bidang lainnya.
Analisis Perkembangan Produksi Tanaman Biofarmaka (Obat) di Indonesia Menggunakan Algoritma Resilient Satria, Indra; Manurung, Azwar Anas; Hanafiah, Mhd Ali
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.285

Abstract

Biofarmaka (medicinal plants) in Indonesia play a crucial role in the pharmaceutical industry's development, providing natural resources for drug research, and supporting the utilization of traditional herbal remedies for public health. This research aims to analyze the development of biofarmaka plant production in Indonesia through predictions. This is essential for strategic planning, resource management, and future pharmaceutical industry development, ensuring an adequate supply of raw materials and supporting sustainable growth in the bio-pharmaceutical sector. The research dataset comprises biofarmaka plant production data in Indonesia by plant type, from 2018 to 2022, obtained from the Indonesian Central Statistics Agency. The research employs the Resilient algorithm, a machine learning technique. Architectural models used include 3-5-1, 3-10-1, 3-15-1, and 3-20-1. Among the four models, the 3-5-1 model is selected as the best due to its higher accuracy of 100%, and a lower Mean Squared Error (MSE) of 0.0023021, indicating the successful application of the Resilient algorithm in predicting the development of biofarmaka plant production in Indonesia.
Kombinasi Algoritma Deteksi Tepi Prewitt dan Canny untuk Identifikasi Citra Invert Golongan Darah A+ Br Sitepu, Kristina Annatasia; Hanafiah, Mhd Ali
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.286

Abstract

Information about blood types is a crucial aspect that must be known in the medical field, especially in the process of blood transfusion and healthcare services. Identifying blood types is a vital step to ensure patient safety during blood transfusions. In this research, the primary focus is on blood type A+. Blood type A+ is one of the common and sought-after blood types because it can donate blood to individuals with blood types A or AB positive. Blood type A+ can receive blood from donors with blood types A or O positive. One method that can be utilized in the process of identifying blood type A+ is using digital image processing and identification methods with edge detection algorithms. The use of edge detection algorithms on an image will result in the edges of objects in that image. The goal is to highlight the details in the image and improve blurred points in vision that may arise due to errors or effects from the image acquisition process. This research aims to evaluate the capabilities of the combination of Prewitt and Canny edge detection algorithms in detecting inverted images. The image dataset used consists of 10 original images of blood type A+ and 10 inverted images. The research dataset was obtained from the IEEE DataPort website. Based on the analysis of 10 conducted experiments, the combination of Prewitt and Canny algorithms is excellent in edge detection, achieving a high accuracy level of 100%. Therefore, it can be concluded that for this issue, the combination of Prewitt and Canny algorithms is capable of identifying inverted images of blood type A+.
Co-Authors Indra Satria Kristina Annatasia Br Sitepu Manurung, Azwar Anas Sheanny Ocmi Sakti