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All Journal Jurnal Pendidikan Terapan Journal of Deep Learning, Computer Vision and Digital Image Processing
Nur Azizah Ayu Safanah
Universitas Negeri Makassar
Humanities Computer Science & IT Education Social Sciences Other

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EVALUASI SISTEM PEMBELAJARAN BLENDED LEARNING BERBASIS MODEL UTAUT DI JURUSAN TEKNIK INFORMATIKA DAN KOMPUTER, UNIVERSITAS NEGERI MAKASSAR Fadhlirrahman Baso; Nur Azizah Ayu Safanah; Ahmad Faris Al Faruq; Ardi Ansya; Andi Muh Achyar AM
Jurnal Pendidikan Terapan Vol 1, No 1 January (2023)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (242.639 KB) | DOI: 10.61255/jupiter.v1i1.4

Abstract

Blended learning is a response to government policy that seeks to establish a new normalcy of life. Blended learning is one of the innovative learning methods where students are introduced to the concept of combining face-to-face learning in class and online learning. Therefore, the purpose of this study is to assess the blended learning system that was examined using the UTAUT model. This research uses a quantitative approach with descriptive research methods. This study included 61 majoring in informatics and computer engineering students from Makassar State University. Data was obtained by data collection techniques in the form of questionnaires distributed through the google form. The instrument in the form of a research questionnaire with data collection techniques and then analyzing questionnaires distributed through Google forms was carried out using a Likert scale to obtain research results. It is known that the blended learning model has a good influence on the learning process.
Persepsi E-learning Dengan Kepuasan Belajar Mahasiswa Pada Masa Pandemi Israwati Hamsar; Niswa Nurpratiwi; M Ismul Azzam; Maulidyah Juniarti Yunus; Nur Azizah Ayu Safanah; Sri Erfiana Nur
Jurnal Pendidikan Terapan Vol 1, No 2, May (2023)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.6 KB) | DOI: 10.61255/jupiter.v1i2.71

Abstract

The purpose of this study was conducted to determine students' perceptions of e-learning towards learning satisfaction during a pandemic. The research design was carried out with quantitative descriptive research with an online questionnaire type data collection model (google form) for active students during the pandemic and the data analysis technique used was descriptive analysis. The results showed that 67% of students knew and were able to use online learning, 51% of students supported the implementation of online learning, and 37% of students expressed satisfaction with online learning. The results of the data in general, show an analysis that the perception of e-learning with student learning satisfaction during the pandemic states that it is useful, and can increase motivation, facilitate and take learning materials, and can help readiness in online learning. Based on the analysis conducted by researchers, a conclusion can be drawn that students have a high commitment in terms of distance or online learning during the pandemic. Assessment of the perception of student satisfaction with online learning services by providing a large role in its implementation can be done with google classroom services and zoom meetings as learning platforms.
SULSEL TYPICAL BATIK MOTIF CLASSIFICATION USING NEURAL NETWORK METHOD WITH GLCM FEATURE EXTRACTION: KLASIFIKASI MOTIF BATIK KHAS SULSEL MENGGUNAKAN METODE JST DENGAN EKSTRAKSI FITUR GLCM Trisakti Akbar; Muhammad Fajar B; Muhammad Akbar Amir; Andi Akram Nur Risal; Nur Azizah Ayu Safanah; M. Miftach Fakhri
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 1 Issue 1 Maret 2023
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v1i1.49

Abstract

Batik adalah salah satu warisan budaya Indonesia yang terbuat dari corak/gambar di atas sebuah kain. Di Sulawesi Selatan, terdapat begitu banyak motif batik. Motif-motif itu pun terus mengalami perkembangan seiring berjalannya waktu. Karena jumlahnya yang banyak, tentunya akan membuat masyarakat kesulitan untuk mengidentifikasi motif batik yang ada saat ini. Untuk mengatasi permasalahan tersebut, penulis melakukan penelitian untuk mengklasifikasikan motif batik menggunakan Jaringan Syaraf Tiruan (JST) dengan ekstraksi fitur Gray Level Co-Occurrence Matrix (GLCM). Sampel yang digunakan adalah batik khas Sulawesi Selatan, yaitu motif Tongkonan, motif Kapal Pinisi, motif Lontara, dan motif Toraja gabungan. GLCM digunakan untuk mengekstraksi fitur, terdiri dari Angular Second Moment (ASM), kontras, Inverse Difference Moment (IDM), entropi, dan korelasi, yang kemudian diklasifikasikan dengan metode JST. Berdasarkan hasil uji coba menggunakan 120 data latih dan 40 data uji dari masing-masing jenis batik, didapatkan tingkat akurasi yang sangat tinggi yaitu 100%.
Co-Authors Ahmad Faris Al Faruq Andi Akram Nur Risal Andi Muh Achyar AM Andi Muh. Achyar AM Ardi Ansya Fadhlirrahman Baso Hartini Ramli Israwati Hamsar M Ismul Azzam M. Miftach Fakhri Maulidyah Juniarti Yunus Muhammad Akbar Amir Muhammad Fajar B Niswa Nurpratiwi Sri Erfiana Nur Trisakti Akbar