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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Pendidikan Islam Iqra' IKRA-ITH EKONOMIKA Jurnal Kreativitas PKM Jurnal Teknik Informatika (JUTIF) Journal La Multiapp Indonesian Journal of Innovation Studies Jurnal Minfo Polgan (JMP) Jurnal Sains Informatika Terapan (JSIT) Innovative: Journal Of Social Science Research
Hasibuan, Alfiansyah
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Journal : Journal La Multiapp

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A Digital Image Processing–Based Moler Disease Detection System for Shallot Leaves Wahyuni, Reski; Hasibuan, Alfiansyah; Santa, Kristofel
Journal La Multiapp Vol. 7 No. 1 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i1.2738

Abstract

This study aims to design and develop a leaf moler disease detection system on shallots (Allium cepa L.) based on digital image processing in Enrekang Regency, South Sulawesi. Moler disease caused by the fungus Fusarium oxysporum f. sp. cepae is one of the main factors that reduce the quality and productivity of shallots. So far, disease identification is still done manually through direct observation by farmers, which is subjective and time-consuming. To overcome this problem, this study applies the Convolutional Neural Network (CNN) algorithm to automatically classify shallot leaf images into two categories, namely healthy and infected with moler disease. The number of datasets used is 502 images, consisting of 251 healthy images and 251 infected images, with data division of 70% for training, 15% for validation, and 15% for testing. The CNN architecture used consists of convolution, pooling, flatten, and fully connected layers with ReLU and sigmoid activation functions in the output layer. The training process used the Adam optimizer with a learning rate of 0.001 and a binary cross-entropy loss function. Test results showed a training accuracy of 97.14%, a validation accuracy of 94.73%, and a testing accuracy of 97.37%, indicating the model has a good level of precision and generalization ability without overfitting. This system is implemented as a Flask-based web application that allows users to upload leaf images and obtain detection results instantly. This system is expected to help farmers detect diseases more quickly and increase shallot productivity in Enrekang Regency.
Leaf Type Recognition System Using Image Processing Method Using Convolutional Neural Network Algorithm Kolauw, Evan; Hasibuan, Alfiansyah; Kumajas, Sondy C
Journal La Multiapp Vol. 7 No. 2 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i2.3057

Abstract

A digital image-based leaf recognition system is one of the modern solutions in the fields of botany and agriculture to identify plants automatically. This study developed a leaf recognition system using image processing methods and Convolutional Neural Network (CNN) algorithms. CNN was chosen because of its ability to independently extract features through convolution layers, thus capturing important visual patterns such as shape, edges, textures, and leaf veins without requiring manual feature engineering processes. The research dataset consists of a collection of leaf images from several types of plants obtained through direct photo-taking and public dataset sources. Each image goes through a pre-processing stage, including cropping, resizing, image quality enhancement, and pixel normalization to ensure data consistency before entering the training stage. The CNN model is designed with several convolutional layers, pooling, activation functions, and fully connected layers to produce optimal classification performance. Model training is carried out by dividing training and testing data, as well as augmentation techniques to increase image variation. System performance is evaluated using accuracy, precision, recall, and confusion matrix. The test results show that the CNN model is able to recognize leaf types with a high level of accuracy and is stable under various test conditions, including variations in lighting and shooting angles. Overall, this study proves that CNN is an effective and reliable approach in building an automatic leaf recognition system. This system has the potential to be applied in the fields of precision agriculture, mobile application-based plant identification, and botanical research that require speed and accuracy in plant classification.
Co-Authors Derek, Sesilia Djami Olii Gladly Caren Rorimpandey Keith Francis Ratumbuisang Kolauw, Evan Kristofel Santa Kumajas, Sondy C Kumajas, Sondy Campvid Kumowal, Jaklin Agnes Liow, Merlin Aprilia Mantik, Felitia Theona Geofani Maramis, Glenn D. P. Marpaung, Effenril Agung Merriam Listiany Modeong Mokoginta, Fadlia Mustafa Musung, Matthew Albert Alexander Putra, Purwa Hasan Quido Conferti Kainde Raharusun, Oliver Simon Hardianto Runtu, Rafael Revi Situmorang, Boby Putra Sondy C. Kumajas Wahyuni, Reski Walujan, Bryan Chelvan Winarto, Lewi Yusrel