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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Aptekmas : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Education and Social Studies
Andrian Wijaya
Universitas Bandar Lampung
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Teacher Challenges in Implementing the Independent Curriculum: Strengthening HOTS in Religious Learning Solihin, Mohammad; Wijaya, Andrian
Indonesian Journal of Education and Social Studies Vol 3, No 1 (2024)
Publisher : Nurul Jadid University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/ijess.v3i1.7085

Abstract

This research aims to identify the challenges teachers face in implementing the Independent Curriculum, especially in strengthening higher-order thinking Skills (HOTS) in religious learning at school. The research uses a qualitative research method with the type of case study research. Data is collected through direct observation, in-depth interviews with Islamic boarding school caregivers, principals, and teachers, and analysis of related documents. The study results show that the readiness of teachers and school staff, the change in mindset from teacher-centric to student-centric, and the application of varied learning methods are the main challenges in implementing the Independent Curriculum. However, there has been an increase in students' critical thinking skills since the implementation of this curriculum. This research emphasizes the importance of ongoing support and intensive training for teachers to ensure the successful implementation of a more interactive and student-centered curriculum. The implications of this research are the need to develop educational policies that are more adaptive and responsive to the needs in the field, as well as the importance of continuous assistance to improve the quality of religious education in Indonesia.
KEGIATAN SOSIALISASI PENGENALAN MARKETING SECARA DIGITAL BERSAMA TOKO KERUPUK DAN KEMPLANG 770 Wijaya, Andrian; Hartanti, Ery; Feriyanto; Wijaya, Laurentius Ricardo; Vincent
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): APTEKMAS Volume 7 Nomor 2 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/apts.v7i2.8761

Abstract

This community service activity aimed to introduce digital marketing to the employees of Toko Kerupuk dan Kemplang 770. The approach involved delivering educational materials on digital marketing and training on creating poster designs for marketing purposes. Conducted on April 21, 2024, the activity successfully helped employees understand and apply the concepts of digital marketing. The main advantage was the enhancement of employees' knowledge and skills in digital marketing, although the attendance was not optimal, affecting the overall effectiveness of the training. This initiative is expected to assist the store in attracting more customers through improved marketing strategies.
Klasifikasi Penyakit Daun Mangga Menggunakan YOLOv11 Berbasis Deep Learning dan Computer Vision Wijaya, Andrian; Rachmat, Nur
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9168

Abstract

Indonesia’s mango agriculture sector continues to face significant challenges due to leaf diseases that reduce crop productivity. Conventional disease identification methods remain inefficient because they rely on subjective visual observation. This study aims to develop a mango leaf disease classification model using the YOLOv11 deep learning algorithm. YOLOv11 is chosen for its capability in real-time object classification with an optimal balance between accuracy and processing speed. The research will utilize the Mango Leaf Disease dataset from Kaggle, consisting of eight classes (seven disease types and one healthy class). The planned methodology includes preprocessing, image augmentation, data splitting using K-Fold Cross Validation, and hyperparameter tuning on optimizer, learning rate, epoch, and batch size. Model performance will be evaluated using the Confusion Matrix. This research is expected to produce an accurate and efficient classification model that enables objective and rapid early detection of mango leaf diseases. The research utilizes a dataset from Kaggle consisting of 4,000 images across eight classes—comprising seven disease types and one healthy leaf class. The methodology involves preprocessing (resizing to 640x640 pixels and normalization), image augmentation, and data splitting using 10-Fold Cross Validation. Performance was optimized through hyperparameter tuning of the Adam optimizer, a learning rate of 0.001, a batch size of 16, and various epoch settings. The experimental results demonstrate that the YOLOv11s model achieves exceptional and stable performance. Evaluation using a Confusion Matrix shows that the model reached a 100% accuracy, precision, recall, and F1-score on the dataset used in this study. The model recorded an average training loss of 0.0979 and a validation loss of 0.0027. These findings confirm that YOLOv11s is not only highly accurate but also computationally efficient, making it a viable candidate for real-time detection systems on mobile or edge computing devices to support early disease detection in mango orchards. As the main contribution, this study provides a comprehensive evaluation of YOLOv11s for mango leaf disease classification using a 10-Fold Cross Validation scheme, stability analysis based on validation loss, and an assessment of its potential for real-time deployment on mobile and edge computing devices.
Co-Authors Feriyanto Hartanti, Ery Nur Rachmat Solihin, Mohammad Vincent Wijaya, Laurentius Ricardo