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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Dewi Marini Umi Atmaja
Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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IMPLEMENTATION OF CNN FOR CLASSIFYING PATCHOULI LEAF IMAGES BASED ON ACCURACY AND EVALUATION Arif Rahman Hakim; Dewi Marini Umi Atmaja
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6207

Abstract

Patchouli (Nilam leaves) holds significant potential as a high-value natural material, especially in the perfume and essential oil industries. However, the classification and quality analysis of patchouli leaves remain a challenge that requires an automated solution based on technology. This study aims to develop a Convolutional Neural Network (CNN) model capable of automatically classifying the condition of patchouli leaves. The image data of patchouli leaves were processed through several preprocessing stages and divided into training and testing data. The designed CNN model utilizes several convolutional layers, pooling, dropout, and dense layers for the training process. The evaluation results using the confusion matrix showed that the model had a very low error rate, with only 1 misprediction in the training data. For the testing data, the model achieved an accuracy of 85% with a loss value of 0.6191496. The model also demonstrated an accuracy of 98.75% with a loss of 0.443462 on the training data. However, improvements in model generalization are still needed to achieve more consistent performance on new data
Implementasi Sistem Pakar untuk Diagnosis Penyakit Lambung Menggunakan Pendekatan Fuzzy Mamdani Berbasis Website Ilham Roni Yansyah; Dewi Marini Umi Atmaja; Arif Rahman Hakim; Niko Suwaryo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3534

Abstract

This study aims to develop a more specific diagnostic approach for various gastric diseases in humans, such as gastritis, peptic ulcers, gastric cancer, gastric tumors or polyps, dyspepsia, gastroesophageal reflux disease (GERD), gastroparesis, and gastroenteritis. This approach seeks to enhance the accuracy of disease identification based on more detailed symptoms. An expert system utilizing the Fuzzy Mamdani method is designed to reduce reliance on internal medicine specialists, enabling patients to gain preliminary insights into the type of gastric disease they may have. This expert system is implemented on a web-based platform, leveraging information technology to integrate large-scale databases, supporting efficiency, accuracy, and relevance to the latest developments in medical science. By analyzing digestive disorder symptoms, the system can provide detailed diagnoses, offer insights into identified symptoms, and recommend appropriate treatment solutions.
Co-Authors Arif Rahman Hakim Ilham Roni Yansyah Suwaryo, Niko