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All Journal International Journal of Electrical and Computer Engineering GEMA TEKNOLOGI Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) Register: Jurnal Ilmiah Teknologi Sistem Informasi JIEET (Journal of Information Engineering and Educational Technology) Jurnal Kesehatan Barometer Jurnal Profesi Insinyur Universitas Lampung International Journal of Electronics and Communications Systems Journal of Applied Science, Engineering and Technology (J. ASET) Electrician : Jurnal Rekayasa dan Teknologi Elektro JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Teknologi Riset Terapan
Muhammad, Meizano Ardhi
Universitas Lampung
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Other

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Journal : Jurnal Teknologi Riset Terapan

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Technology-Based Classification of Clerodendrum Paniculatum Using CNN and Confusion Matrix Wijaya, Pandu; Makarim, Alvin Reihansyah; Muhammad, Meizano Ardhi; Febriyani, Cela; Hidayatullah, Vezan; Annisa, Resty
Jurnal Teknologi Riset Terapan Vol. 2 No. 1 (2024): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v2i1.4598

Abstract

Purpose: This study aims to develop a classification system for the Clerodendrum paniculatum plant (Bunga Pagoda), focusing on its key parts—stems, flowers, leaves, and trees—using the Convolutional Neural Network (CNN) algorithm. The objective is to support conservation efforts and facilitate digital data grouping through technology-based classification. Methodology: The research involved collecting a dataset of images representing different parts of the Clerodendrum paniculatum plant. These images were then used to train a CNN model. The training process included 200 epochs to optimize performance. The model's accuracy and performance were evaluated using a confusion matrix to measure classification success across the plant's various parts. Results: The CNN model achieved its highest accuracy of 97.78% when trained for 200 epochs. The results indicated a significant improvement in evaluation metrics compared to models trained with fewer epochs. The mo   del successfully classified the plant parts with high precision, demonstrating its robustness and reliability for rare plant classification. Conclusions: This study confirms that the CNN algorithm is effective in classifying the parts of the Clerodendrum paniculatum plant. Increasing the number of training epochs substantially enhances the model's performance, making it a practical tool for digital plant conservation initiatives. Limitations: The study is limited by its reliance on a specific dataset, which may not encompass all possible variations of the Clerodendrum paniculatum plant under different environmental conditions. Contributions: This research contributes to digital plant conservation by developing a CNN-based classification system for rare plants. It highlights the importance of deep learning in biodiversity preservation and provides a foundation for future AI-driven botanical studies.
Classification of Rare Mussaenda Species in Indonesia's Tropical Forests Using the CNN Algorithm Raja, H. F. Muchammad; Muhammad, Meizano Ardhi; Martinus, Martinus; Pandu, W.; Muhkito, A.; Muhammad, A.
Jurnal Teknologi Riset Terapan Vol. 2 No. 2 (2024): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v2i2.5011

Abstract

Purpose: Mussaenda frondosa is a rare plant species native to Indonesia’s tropical forests, with limited research focused on its classification and identification, particularly using machine learning. This study aims to develop a classification model for Mussaenda species using a Convolutional Neural Network (CNN) approach to support the advancement of automated plant identification systems. Methodology/approach: The dataset used consists of 650 labeled images, categorized into six primary parts of the plant: leaves, stems, twigs, fruits, flowers, and trees. A CNN model was developed and trained over 200 epochs to classify the images according to these categories. Preprocessing techniques such as resizing, normalization, and data augmentation were applied to enhance model performance. Results/findings: The trained CNN model achieved an accuracy of 80%, demonstrating its ability to classify Mussaenda frondosa components despite the relatively small dataset. Visual inspection of prediction outputs showed consistent identification across several categories, particularly leaves and flowers. Conclusion: The results suggest that CNN can be effectively used to classify rare plant species like Mussaenda frondosa. The model's performance also indicates that even a limited dataset, when properly processed, can yield promising classification results. Limitations: The main limitation of this research is the small dataset size, which may restrict the model's generalizability to broader plant species or more diverse environmental conditions.. Contribution: This study contributes to the field of plant classification by providing a foundation dataset and a validated CNN model for rare tropical species. It opens pathways for further research in biodiversity monitoring and conservation using AI.
Co-Authors -, Ubaidah Achmad Yahya Teguh Panuju Adhi Nurhartanto, Adhi Ahmad Abdullah Ahmad Saudi Samosir Akbar, Gustian Ilham Akmal Junaidi Andrian, Rico Andrianto, Rifqi Rhama Annisa, Resty Arbain, Ahmad Ariesta, Rio Bagaskara, Pratama Yuda Budiyanto, Deny Cahyani, Putri Anggia Deni Achmad Dikpride Despa Dillah, Arya Djausal, Gita Paramita Dwi Indria Anggraini Febriyani, Cela Fitarina, Fitarina Fitriyani Gigih Forda Nama Hadi Prayitno, Hadi Hakim, Lukmanul Hery Dian Septama Hidayatullah, Vezan Isnanto, Fajri Krismaningrum, Hasti Primadilla Lania, Siska M Komarudin, M Maharani, Devi Mahendra Pratama Makarim, Alvin Reihansyah MARDIANA Mardiana - Mardiana Mardiana Mareli Telaumbanua Martinus, Martinus Muda, Mona Arif Muhammad Komarudin Muhammad, A. Muhkito, A. Mulyani, Yessi Pandu, W. Pradipta, Rio Ariestia Pujiarti, Daning Puput Budi Wintoro, Puput Budi Puspita, Maylan Anggi Raden Arum Setia Priadi Raden Arum Setia Priadi, Raden Arum Rahmayani, Fidha Raja, H. F. Muchammad Rhomadhona, Nazmah Wulan Robert Bestak Saipul Anwar Septiangraini, Dzihan Setia, Harlika Nobra Sony Ferbangkara Sumadi - Surya Saputra, Surya Saputra Titin Yulianti Trisya Septiana ubaidah ubaidah Wahyu Eko Sulistiono Wahyudi, Fajar Desma Wicaksono, Alun Paranggi Wijaya, Pandu