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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Innovation and Applied Education Journal
Andi Fitri Novianti
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Computer Science & IT Control & Systems Engineering Education Engineering Social Sciences

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Persepsi Mahasiswa Kota Makassar Terhadap E-Learning dengan Metode Extended Technology Acceptance Model ( EXT-TAM ) Jannah, Devi Miftahul; Andi Fitri Novianti; Annajmi Rauf; Wahyu Hidayat M
Innovation and Applied Education Journal Volume 1, Issue 3, Oktober 2024
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/iaej.v1i3.245

Abstract

Pendidikan di era digital semakin menggantungkan diri pada teknologi, khususnya E-Learning, sebagai metode inovatif dalam proses belajar-mengajar. Penelitian ini menggunakan Extended Technology Acceptance Model (EXT-TAM) untuk menganalisis persepsi mahasiswa di Kota Makassar terhadap penerapan E-Learning. Temuan utama menunjukkan bahwa mahasiswa memiliki persepsi positif terhadap aspek Awareness, Usage, Evaluation, dan Etika dalam penggunaan E-Learning. Meskipun pemahaman terhadap perangkat pintar dan keterampilan menggunakan aplikasi AI mendapatkan penilaian tinggi, pemahaman terhadap teknologi AI dan kesadaran privasi masih memerlukan perhatian lebih. Hasil penelitian ini memberikan gambaran bahwa mahasiswa di Kota Makassar mendukung implementasi E-Learning dengan EXT-TAM, namun perlu upaya untuk meningkatkan pemahaman tertentu terkait teknologi. Penelitian ini memberikan kontribusi pada pemahaman ekstensif tentang penerimaan E-Learning di konteks lokal, dengan implikasi bagi pengembangan strategi pendidikan berbasis teknologi di masa depan.
KLASIFIKASI TINGKAT KESEGARAN DAUN BAWANG MENGGUNAKAN JARINGAN SYARAF TIRUAN BERBASIS PENGOLAHAN CITRA DIGITAL Andi Fitri Novianti; Muhammad Atthariq; Juliano Nufiansyach Dini; Andi Baso Kaswar; Jessica Crisfin Lapendy
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 7 No. 2 (2024): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v7i2.3378

Abstract

Green onions, commonly used in Indonesian cuisine, have significant agricultural potential. Despite high production, their quality, particularly freshness, is traditionally evaluated visually, leading to inconsistent and subjective results. This study aims to develop an objective and accurate method for classifying the freshness of green onions using an Artificial Neural Network (ANN). Previous studies have employed ANN but have not specifically targeted the freshness classification of leeks. The proposed method utilizes the color and texture features of green onions.The research methodology includes image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification using ANN. A total of 300 images were acquired and categorized into three freshness levels: not fresh, less fresh, and fresh. During the training phase, 240 images were used, and 80 images were reserved for testing. The optimal feature combination identified includes HSV and LAB color features along with texture features (Contrast + Energy). The results demonstrated that the freshness classification of green onions achieved 100% accuracy in both training and testing phases. The training process, with 240 images, had a computation time of 142.684 seconds, while the testing process, with 80 images, took 35.648 seconds. These findings indicate that using ANN based on color and texture features is highly effective in determining the freshness level of green onions.
Co-Authors Andi Baso Kaswar Andi Baso Kaswar Annajmi Rauf Jannah, Devi Miftahul Jessica Crisfin Lapendy Juliano Nufiansyach Dini Muhammad Atthariq Wahyu Hidayat M