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Journal : Journal of Applied Data Sciences

Performance Evaluation of Fuzzy Logic System for Dendrobium Identification Based on Leaf Morphology Putra, Arie Setya; Syarif, Admi; Mahfut, Mahfut; Sulistiyanti, Sri Ratna; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.224

Abstract

Dendrobium is the second-largest family of flowering plants in the world. There are several classes of Dendrobium, which usually identify by its, including leaves and flowers. Due to the similarity of its characteristics, identifying orchid types is complicated and usually can only be done by an expert. Moreover, those characteristics are typically non-deterministic; examining the orchid species is very challenging. This research aims to develop a novel fuzzy-based system to identify the species of orchid based on unprecise existing leaf characteristics. We used the main characteristics of Dendrobium leaves, including shape, length, width, and tips of the leaves. Based on the information from the expert, we develop the membership for each class of Dendrobium. By adopting this knowledge, we develop the system by using compatible programming with this case, and Borland Delphi as complex application development. The experiment is done by using 200 real datasets from the Liwa Botanical Gardens, West Lampung Regency, Lampung Province, Indonesia. The results are compared with those given by a Dendrobium expert. A confusion matrix is a valuable evaluation tool for measuring the performance of classification models. From the above results, we can determine the confusion matrix and calculate the TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative). The confusion matrix given from the experiments is shown in Table 6. This indicates that the system can provide the same results as experts recommended. It is shown that the system can identify orchid types with an accuracy value of 94,6 %.  Thus, this system will be beneficial for automatically determining the orchid genus.
Co-Authors Agus Rahardi, Agus Agus Wantoro Agus Wantoro AGUSTINA RAHAYU Akmal Junaidi Ami Zuraida Andrian, Rico Anggi Puspitasari Anggun N. Azizah Ani Kurniawati Aqshal Dwi Setiawan Arafia Isnayu Akaf Ari Ardianto Arie Setya Putra Aristoteles, Aristoteles Asmanto, Budi Ayu Nadila Ayu Sangging, Putu Ristyaning Azi Mediantara Bambang Hermanto Dedy Miswar Deswita Sari Dimas A. Dhafa Dwi Sakethi Erika Fadia Salsabila Faiqa Marina Fatimah Fahurian Fazri, Yudistira Febi Eka Febriansyah Fitriyana, Silfia Ghraito Arip Greacella Risky Amanda Hari Soetanto Heni Sulistiani Heningtyas, Yunda Iva Mutiara Indah Kendari, Putri Khairun Nisa Krisna Rendi Awalludin Kurnia Muludi Kurnia Muludi Lumbanraja, Favorisen R M Said Hasibuan M. Juandhika Rizky Machudor Yusman Mahfut Maya Asterita Michelle Jovelyna Mohammad Surya Akbar Muhammad Irfan Ardiansyah Muhammad Jamaludin Muhammad Reza Faisal, Muhammad Reza Muhammad Rizki Muhammad Tegar Sabilillah Nabila Z. Muhammad Ni K. Aprilliani Nisa Berawi, Khairun Noverina Rahmaniyanti Novita Dwilestari Nur Indriani Prabowo, Rizky Prabowo, Rizky Putri Ayu Penita Qory Aprilarita Raden Mohamad Herdian Bhakti Rahmat Safe'i Raras Silviana Redy Susanto, Erliyan Salsabila, Diana Shofi, Imam Marzuki Shofiana, Dewi Asiah Sintiya Paramitha Sri Ratna Sulistiyanti Sri Wahyuningsih Sugaluh Yulianti Sukamto, Ika Sumiyarsi Sumaryo Gitosaputro Susiyanti, Endah Sutyarso Sutyarso Syachrul Priyo Wibowo TANJUNG, AKBAR RISMAWAN Timotius Pascha Tristiyanto Tundjung Tripeni Handayani Wahyu Caesarendra Warsito Warsito Wildhan Wahyudi Wulansari, Ossy Endah Dwi Yarmaidi Yarmaidi Yoannisa Egeustin Yodhi Yuniarthe Yokie Rahman Yulia K. Wardani Yulia Kusuma Wardani Yuliyanto, Kurniawan Dwi Zahra, Rizka Aulia