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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Society: Jurnal Pengabdian Masyarakat
Putri, Khansa Aqila
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Educating on the Application of Tensorflow in Artificial Intelligence, Machine Learning and Deep Learning Santoso, Ilham Budi; Aji, Irfan Pandu; Franskusuma, Sutio; Putri, Khansa Aqila; Ardharani, Yana; Mujiastuti, Rully; Nurbaya Ambo, Sitti; Meilina, Popy; Rosanti, Nurvelly; Amri, Nurul
Society : Jurnal Pengabdian Masyarakat Vol 4, No 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.547

Abstract

In addition to bringing positive impacts, technological developments also provide new challenges in improving people's technological literacy, especially related to Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). One of the main challenges is the low public understanding of these technologies, which are increasingly relevant in the era of digital transformation. On the other hand, Google developed a library with the name TensorFlow which is widely used for data processing in Artificial Intelligence, Machine Learning, and Deep Learning. Based on this, educational activities were carried out in the form of introducing and training the use of TensorFlow to the general public in the form of webinars and workshops with the theme ‘Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning’. The activity was carried out in two stages, namely webinars for delivering basic material and workshops for hands-on practice. Based on evaluation through a Likert scale questionnaire, the majority of participants stated that they were very satisfied with the quality of the material, presenters, and implementation of activities. The post-test results also showed an increase in participants' understanding of the material, as evidenced by correct answers on topics such as TensorFlow functions, supervised learning, and neural networks. The participation of 52 participants from various institutions shows the success of this activity in achieving its goals.  
Educating on the Application of Tensorflow in Artificial Intelligence, Machine Learning and Deep Learning Santoso, Ilham Budi; Aji, Irfan Pandu; Franskusuma, Sutio; Putri, Khansa Aqila; Ardharani, Yana; Mujiastuti, Rully; Nurbaya Ambo, Sitti; Meilina, Popy; Rosanti, Nurvelly; Amri, Nurul
Society : Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.547

Abstract

In addition to bringing positive impacts, technological developments also provide new challenges in improving people's technological literacy, especially related to Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). One of the main challenges is the low public understanding of these technologies, which are increasingly relevant in the era of digital transformation. On the other hand, Google developed a library with the name TensorFlow which is widely used for data processing in Artificial Intelligence, Machine Learning, and Deep Learning. Based on this, educational activities were carried out in the form of introducing and training the use of TensorFlow to the general public in the form of webinars and workshops with the theme ‘Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning’. The activity was carried out in two stages, namely webinars for delivering basic material and workshops for hands-on practice. Based on evaluation through a Likert scale questionnaire, the majority of participants stated that they were very satisfied with the quality of the material, presenters, and implementation of activities. The post-test results also showed an increase in participants' understanding of the material, as evidenced by correct answers on topics such as TensorFlow functions, supervised learning, and neural networks. The participation of 52 participants from various institutions shows the success of this activity in achieving its goals.  
DETEKSI JARAK KOSONG PADA PERKEBUNAN TEBU MENGGUNAKAN METODE YOLO V5 Putri, Khansa Aqila; Meilina, Popy
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Vol 16 No 1 (2025): September
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

ASugarcane plantations play an important role in food security and the national economy. However, problems such as empty areas or suboptimal planting distances still frequently occur and affect land productivity. This study aims to develop an automatic detection model for empty spaces between sugarcane plants using the YOLOv5 method. The data used consists of 1,000 digital images of sugarcane fields obtained via drone from PT Perkebunan Nusantara III (Persero). The research method follows the AI project cycle, which includes problem scoping, data acquisition, data exploration, modeling, evaluation, and deployment. The labeling of empty areas was performed using Roboflow, and model training was conducted on Google Colab. The model was evaluated using metrics such as IoU, precision, recall, F1-score, and accuracy. The best results showed that the model achieved an accuracy of 35.37%, precision of 0.4774, recall of 0.5771, and F1-score of 0.5225. Additionally, the model results were applied to an interactive dashboard based on Streamlit to facilitate visualization and decision-making in the field. This study demonstrates that YOLOv5 has potential in assisting with the detection of empty areas in sugarcane fields; however, the model's performance can still be improved through data optimization and model parameter tuning.
Co-Authors Aji, Irfan Pandu Amri, Nurul Ardharani, Yana Franskusuma, Sutio Nurvelly Rosanti, Nurvelly Popy Meilina Rully Mujiastuti Santoso, Ilham Budi Sitti Nurbaya Ambo