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All Journal International Journal of Electrical and Computer Engineering Jurnal Media Infotama Jurnal Pseudocode Jurnal Informatika Upgris JSAI (Journal Scientific and Applied Informatics) Journal of Technopreneurship and Information System (JTIS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Serawai Jurnal Komputer, Informasi dan Teknologi Jurnal Multidisiplin Dehasen (MUDE) Jurnal Sistem Informasi Dan E-Bisnis Jurnal Ilmu Komputer Dan Informatika JIMAKUKERTA Prosiding University Research Colloquium Borobudur Informatics Review Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Triwikrama: Jurnal Ilmu Sosial JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Multidisciplinary Journals Jurnal Pengabdian Masyarakat Ilmu Komputer International Journal of Information Technology and Business Indonesian Journal of Guidance and Counseling: Theory and Application JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Sonita, Anisya
Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Bengkulu
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Herbal Plant Classification Using EfficientNetV2B0 Model and CRISP-DM Approach Sonita, Anisya; Anggriani, Kurnia; Vatresia, Arie; Putri, Tiara Eka; Darnita , Yulia; Zahra, Syakira Az; Aprilia, Vilda; Aziz, Dzakwan Ammar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5141

Abstract

Herbal remedies have long been utilized by Indonesian communities as part of traditional medicine. However, identification of these natural resources is often challenging due to the morphological similarities among various species, which demand expert knowledge to differentiate. This study aims to implement the EfficientNetV2B0 model architecture for classifying medicinal leaves through an Android-based application designed to support recognition tasks. The dataset was composed of augmented images of plant foliage. The model was trained using the TensorFlow framework and evaluated to measure classification performance. Results demonstrate that EfficientNetV2B0 achieves excellent accuracy, with validation scores exceeding 97%, outperforming several other deep learning models. The resulting application allows the general public to identify local medicinal species more easily. This study contributes to the field of computer vision by providing an accurate and efficient classification framework, particularly beneficial for health-related informatics in biodiversity-rich regions.
Automated Classification of Mungkus Fish Freshness Based on Eye and Gill Images Using the Naive Bayes Algorithm Darnita, Yulia; Toyib, Rozali; Sonita, Anisya; Putra, Andika
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5146

Abstract

The problem of assessing the freshness of fish, especially Mungkus fish, is usually directed at several physical indicators, such as eye appearance, gill condition, meat quality, and odor. This traditional method is often considered inaccurate and requires certain expertise, therefore a more effective and objective method is needed to assess the freshness level of Mungkus fish, which in turn can provide benefits for both fishermen and the public in general. The solution to this problem by using the Naïve Bayes method in classifying the freshness level of Mungkus fish based on eye and gill images has proven to be a fairly efficient approach. The Naïve Bayes method itself is a simple but very effective algorithm in the field of machine learning, and operates based on Bayes' Theorem with the assumption that features are independent of each other. This method can be applied in the initial stage of classification by utilizing basic features taken from images of fish eyes and gills. Based on testing 30 new data sets, the clustering system demonstrated an accuracy rate of 66.67%, indicating that 20 data sets were correctly classified according to their actual conditions. On the other hand, 10 data sets, or 33.33%, could not be categorized correctly. Of the 30 old data sets tested, the system was able to correctly classify 19 (63.33%), while 11 (36.67%) still had errors in their classification predictions. Overall, the system successfully performed data clustering with 65% accuracy, with the remaining 35% still showing errors in the classification process.  
Co-Authors Abdullah, Dedy Achmad, Fariz Ade Ihza dwi Putra Adriansyah, M. Ari Affandi Mussa, Anitya Putri Alan Andeka Amaliah, Asma Amandha, Lufti Andika Putra Anggraini, Laura ANJAYA RIDUANSYAH Ansyori, Adzan Anugrah Ilahi, Puja Apriance, Cici Apriansyah, Eko Saputra Apridiansyah, Yovi Aprilia, Vilda Ardi wijaya Arie Vatresia Arif Susanto Ayu Lestari Aziz, Dzakwan Ammar Beta Yuniarti Charles Roenal Krisubiyantoro Checario, Devano Chindy Erliani Cici Apriance Dandi Sunardi Darnita , Yulia Darnita, Yulia Dedy Abdullah Dedy Abdullah Dedy Agung Prabowo Deslianti, Dwita Deslianti, Dwita Diana Diana Diki Zulfahmi Dwita Deslianti Dwita Deslianti Eka Sahputra Eko Saputra Apriansyah Elni Mutmainnah F Fraternesi Fabriandi, Gilang Pramudia Fadila, Aldevia Febrian Nurtaneo Fikri Ikbal P Fitri Lestari, Fitri Fraternesi, F Handrawijaya, Khairus Syah Heni Sulusyawati hidayah, agung kharisma Ika Yurika Sari Imanullah, Muhammad Jefri Zulkarnain Jestika Safitri Juhardi, Ujang Karniawan, Roni Khairullah, Khairullah Khairunnisyah Khairunnisyah Khairunnisyh Khairunnisyh Khairunnisyh, Khairunnisyh Kirman Kirman, Kirman Kurnia Anggriani, Kurnia Laura Anggraini Lukman, Musfirah Putri M Faishal M Khairunnas M Rafli Yudhatama Mahfuzhi, A.R Walad Mahfuzi, A.R Walad Marcelina Novi Zarti Marissa Utami MAYANG SARI Meilisa Tri Ulansari Miswanti Yuli Muhammad Fajri Muhammad Husni Rifqo Muhira Dzar Faraby, Muhira Dzar Mukhlizar, Mukhlizar Muntahanah Muntahanah Muntahanah, Muntahanah Mustika Mustika Nofriansyah Praja Nurhayati Nurhayati Nurul Jannah Pahrizal Pahrizal Pahrizal Pahrizal, Pahrizal Pariza, Rahmat Pedro Ginal Victori Pedro Putra, Erwin Dwik Putra, Erwin Dwika Putri Dwi L Putri, Tiara Eka Raffles, Richard Rahmalia, Rahmalia Rahman Fadli Tanjung Rahmat Pariza Ramadhan Saputra Alpani Rendika Efando Reza Anisa Ria Elda Fitri RIDUANSYAH, ANJAYA Rifqo, Muhammad Husni Rinni Rio Eka Prayuda Riozi, M Fakhrur Rizki Fitrah Fardianitama Robian Kundari Ronaldo, Ronaldo Rossa Ayuni Rozali Toyib Sahputra, Eka Sandi, Zainove Saputra, Surya Ade Sirad, Mochammad Apriyadi Hadi sofyan sofyan Sri Ekowati Sri Handayani Sri Handayani sumardi Surya Ade Saputera Susi Hardianti Susilo Dwi Prabowo Syaputra, Weki Syaputri, Yopita Tanjung, Rahman Fadli Thaha, Sarma Tiara Ayu Lestari Toyib, Rozali Tri Putra, Bagus Weki Syaputra widya kartika sari Wijaya, Ardi Witriyono, Harry Yoan Hadi Kusuma Admaja Yuli Asmi Rahman Yulia Darmi Yulia Darnita Yuza Reswan Zahra, Syakira Az Zarti, Marcelina Novi