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All Journal Transformasi: Jurnal Pengabdian Masyarakat JURNAL PENGABDI ILKOM Jurnal Ilmiah Journal of Information System, Applied, Management, Accounting and Research Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Multimedia Journal of Information Systems and Informatics TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian UNDIKMA ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Riset, Ekonomi, Akuntansi dan Perpajakan (REKAN) International Journal of Electronics and Communications Systems Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Seminar Nasional Aplikasi Iptek (SINAPTEK) Explore ORAHUA : Jurnal Pengabdian Kepada Masyarakat Edu Elektrika Journal
Kartarina, Kartarina
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Image Classification of Medicinal Plants Using Inception V3 and CNN: A Novel Implementation Kartarina, Kartarina; Islamiah, Nuratun; Supatmiwati, Diah; Zulfiqri, Muhammad; Triwijoyo, Bambang Krismono; Amrullah, Rahayun
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042//ijecs.v5i2.27930

Abstract

Indonesia is recognized as one of the world's biodiversity hotspots, with around 30,000 of the 40,000 global medicinal plant species found in its territory. This biological wealth is a strategic asset for health innovation and digital preservation. In areas with limited access to healthcare services, medicinal plants are the primary source of treatment, but their use is still hampered by the lack of a technology-based identification and documentation system. This study aims to develop and test a classification model for medicinal plants using a Convolutional Neural Network with Inception V3 architecture. The study uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, which ensures systematic stages of business understanding, data preparation, modeling, and evaluation. The dataset used consists of 2,750 leaf images in 25 classes, compiled from previous research and independent collections. The data was divided into 1,921 images for training and 823 images for testing using a 70:30 ratio. The model was evaluated using accuracy, precision, recall, and F1 score. The results showed that the Inception V3-based CNN achieved a training accuracy of 96%, which increased to 97% with optimized weights, while maintaining strong precision, recall, and F1 scores. This proves that the Inception V3-based approach is capable of providing high and stable classification performance for the identification of Indonesian medicinal plants. These findings highlight the effectiveness of the model in identifying Indonesian medicinal plants from leaf images, providing a promising foundation for the development of knowledge and potential real-world applications
Klasifikasi Gizi Lansia Menggunakan Metode Naïve Bayes Classifier Kartarina Kartarina; Adelia Azzahrah Hatina; Ria Rismayati; Baiq Fitria Rahmiati; Fatimatuzzahra Fatimatuzzahra; Rahayun Amrullah Husaini
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2024): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i2.502

Abstract

Elderly people are a group that is vulnerable to experiencing various problems in terms of nutrition and health caused by changes in eating patterns. Nutritional status affects the independence of an elderly person, where good nutritional status means less dependence on other people and vice versa. It is necessary to treat malnutrition or malnutrition as early as possible, one of which is by having an elderly posyandu. Posyandu for the elderly as a community service provides services and assistance in special health for the elderly, by regularly recording, controlling and reviewing the medical records of the elderly in a document. The data processing method in this research uses the Naïve Bayes method, where the data used comes from the medical records of the elderly and then used as a reference as to whether the elderly have good nutrition or are malnourished and require further action. Medical record documents play an important role in posyandu services for the elderly, so that medical record documents should be digitally based and systematic in recommending the nutritional status of the elderly. The Naïve Bayes algorithm is an algorithm that can help in classifying data in diagnosis using criteria for the condition of elderly patients. Naïve Bayes also has precise accuracy when implemented in applications that have databases with large data and makes it easier for users to interpret the results. This is proven by this research which produces an accuracy value of 91% with the data used as a sample of 110 elderly patients. The system design aims to help users as posyandu cadres in knowing whether the condition of the elderly is good, whether the elderly are at risk of malnutrition and provide treatment that is appropriate to the condition of elderly patients as well as assisting the Posbindu PTM in transforming documents into computerized ones.
SISTEM INFORMASI PEMASARAN PRODUK DESA BERBASIS WEB Kartarina, Kartarina; Irfan, Pahrul; Satria, Cristofer
ILKOM Jurnal Ilmiah Vol 11, No 3 (2019)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v11i3.471.214-221

Abstract

Batu Mekar Village is one of the villages in West Lombok Regency in Lingsar District. Batu Mekar village has many products in the form of handicrafts such as bags, lamp decorations made from ketak or agricultural and plantation products such as coffee, palm sugar and others. The problem that exists in Batu Mekar Village is the low sales price of handicraft products because the product is only left at a souvenir shop. The decrease in the number of sales was not due to a decrease in the number of tourists who ended up on the island of Lombok due to the earthquake. In addition, the limited promotion media. This research was developed using the Rapid Application Development method. This research resulted in an online sales information system as a medium for handicraft sales, so that the public could directly sell their products without going through intermediaries as well as promotional media so that handicraft products can be sold directly and can be recognized by the wider community. The developed application can be used as a promotional medium as well as a means of selling Batu Mekar village handicraft products.
Co-Authors Adelia Azzahrah Hatina Amrullah, Rahayun Andi Sofyan Anas Anwar, Riky Adbul Gani Apriani Apriani Apriliansyah Apriliansyah Asgap Putra Zulian, Asgap Ayu Dasriani, Ni Gusti Cahyablindar, Ayu Cristofer Satria Diah Supatmawati Diah Supatmiwati Dian Syafitri Chani Saputri Dyah Supatmiwati Dyah Susilowati Fadli, M. Najmul Fatimatuzzahra Fatimatuzzahra Gayatri Ramadhani Hairul Imam Hammad, Rifqi Hilda Hastuti I Made Yadi Dharma Ibjan Syarif Hidayatullah Indah Puji Lestari Islamiah, Nuratun Ismarmiaty Ismarmiaty, Ismarmiaty Kurniadin Abd Latif Lalu Erwin Lalu Ganda Rady Putra Lalu Naufal Azmi M. Hidayatullah Made Nugraha Dwitama Mardedi, Lalu Zazuli Azhar Melati Rosanensi Miftahul Jihad Miftahul Madani Muhammad Haris Nasri Muhammad Jayadi Ni Ketut Sri Winarti, Ni Ketut Sri Ni Ketut Sriwinarti Ni luh Putu Juniarti Okta Travelian Pahrul Irfan Rahayun Amrullah Husaini Rahmiati, Baiq Fitria Regina Aprilia Riberu Regina Aprilia Riberu Ria Rismayati Rini Anggriani Satria, Cristofer Setiawan, Pandu Dwi Syahrir, Moch. Triwijoyo, Bambang Krismono Wahyu Kamil Syarifaturrahman Wahyu Kamil Syarifaturrahman, Wahyu Kamil Widia Febriana Wisnu Yunanda Wiya Suktiningsih Zainuddin Zulfiqri, Muhammad