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All Journal Tadris: Jurnal keguruan dan Ilmu Tarbiyah INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JSFK (Jurnal Sains Farmasi & Klinis) Jurnal Bimas Islam IQ (Ilmu Al-qur'an): Jurnal Pendidikan Islam Jurnal Mekanik Terapan Jurnal Ilmiah Wahana Pendidikan Media Jurnal Informatika Simpatik: Jurnal sistem Informasi dan Informatika Jurnal Ekonomi dan Manajemen (JEKMA) JOMPA ABDI: Jurnal Pengabdian Masyarakat Takuana: Jurnal Pendidikan, Sains, dan Humaniora The Journal of City: Branding and Authenticity Jurnal Teknologi Informasi Indonesia Tamaddun: Jurnal Ilmu Sosial, Seni, dan Humaniora Jurnal Abdimas Untag Samarinda Madania: Jurnal Kajian Keislaman Journal of Modern Social and Humanities
Fikri Maulana, Fikri
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A Multidimensional Framework for Improving Qur’anic Teacher Performance in Formal Islamic Educational Institutions Marlius, Farizal; Shunhaji, Akhmad; Siskandar, Siskandar; Bachrie Tanrere, Syamsul; Anwar, Chairul; Maulana, Fikri
Tadris: Jurnal Keguruan dan Ilmu Tarbiyah Vol 10 No 2 (2025): Tadris: Jurnal Keguruan dan Ilmu Tarbiyah
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/tadris.v10i1.26725

Abstract

This study investigates the effectiveness of Qur’anic teachers in delivering instruction to learners across various age groups within formal Islamic educational institutions. The research is motivated by the absence of standardized professional criteria for Qur’anic educators, which has contributed to inconsistent teaching practices, instructional challenges, and varying interpretive outcomes among students. The study aims to (1) identify the key factors that influence Qur’anic teachers’ performance and (2) explore the strategies implemented by school leaders to enhance instructional quality. Using a qualitative design, data were gathered through interviews, classroom observations, and document analysis involving Qur’anic teachers, students, and institutional administrators. The credibility of the data was strengthened through prolonged engagement, persistent observation, and methodological triangulation. The findings indicate that effective Qur’anic teaching performance is demonstrated through strong content mastery, the application of appropriate pedagogical strategies, and the ability to address students’ inquiries using reliable scholarly sources. The study highlights the importance of clearly defined performance standards and targeted professional development initiatives in enhancing the overall quality of Qur’anic education.
Classification of Banana Ripeness Using a VGG16-Based Convolutional Neural Network (CNN) Maulana, Fikri
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5930

Abstract

The ripeness level of bananas is a crucial factor that affects the quality, taste, and selling value of the commodity, but the manual sorting process that is commonly carried out is still subjective, inconsistent, and time-consuming. This study aims to implement and evaluate the performance of a VGG16-based Convolutional Neural Network (CNN) architecture in automatically classifying the ripeness level of bananas. The research dataset consists of 5,616 digital images obtained from the Roboflow Universe platform and grouped into six specific classes: freshripe, freshunripe, overripe, ripe, rotten, and unripe. The system development methodology includes data division using stratified splitting techniques, image pre-processing with data augmentation strategies to prevent overfitting, and the application of transfer learning. The model was trained using the Stochastic Gradient Descent (SGD) optimization algorithm with a learning rate of 0.001 for 25 epochs on GPU-based hardware. Performance evaluation was conducted in depth using a confusion matrix, F1-Score metrics, and Precision-Recall curve analysis. The experimental results showed that the VGG16 model achieved an overall accuracy of 97.13%. Class-by-class analysis shows perfect performance in the freshunripe category, although there is a slight decrease in precision in the ripe class due to the similarity of visual characteristics with the overripe class. The stability of the training and validation accuracy curves also indicates that the model has good generalization capabilities. This study concludes that the VGG16 architecture is a reliable and accurate solution to support the efficiency of smart farming systems.
Co-Authors Abdul Rahman, Mohd Safian Bin Abdussomad, Abdussomad Abidin Riam , Zaenal ACHMAD FAUZI Agung Wibowo Alam, Mufassirul Anggeraini, Juliya Anwar, Muhammad Syaiful Bachrie Tanrere, Syamsul Bakrie, Ismail Banuaji, Tristan BENNI ISKANDAR Chairul Anwar Desri, Syuryatman Destiani, Risma Dwiny Meidelfi Erwin Budi Setiawan Fajriani, Fenny Fatahillah, Irfan Fauziah, Erna Firda, Muti'ah Graha, Fager Van Hamid, Ihsan HUSNI, MUHAMMAD FAHD DIYAR Ihsan Ikhtiarudin Ilham Kurniawan Irawati Irawati Jumani, Jumani juraidi, ahmad Kaimudin, Kaimudin Marlius, Farizal Muflihin, Iin Yunus Muhit, Abdul Mulyawati, Husni Muslimin Muslimin Neni Frimayanti Rahma Dona Ramadhan, Abdillah Riam, Zaenal Abidin Rinata, Radisa Rohman, Baeti Saputra, Sahrul Aji Shunhaji, Akhmad Silalahi, Nova Tantri Siskandar Siskandar, Siskandar Sitorus, Yalizar Rahayu Sugeng Mulyono Sunaryo, Silfiyanti Syafitri, Anisa Nur'aini Tagor Rambey Winika, Tiara Resti Zaenal Abidin