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Muhammad Abdul Muin
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INDONESIA
Transformasi
Published by STMIK Bina Patria
ISSN : 19785569     EISSN : 28278550     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics
Jurnal transformasi sebagai wadah untuk mengembangkan Dan mensosialosasikan IPTEk berbasis penelitian dan kajian ilmiah (artikel review) dalam lingkup Informatika, elektronika, manajemen, pendidikan & pembelajaran.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 292 Documents
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KLASIFIKASI PENYAKIT PADA DAUN TOMAT MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Parhusip, Jadiaman; Maulana, Ferdy Afriza; Mahendra, Rizqullah Falah; Dwi Putri, Athay Setya
TRANSFORMASI Vol 21, No 2 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i2.479

Abstract

Tomato leaf diseases significantly affect crop productivity, and manual inspection often leads to misclassification due to the visual similarity of symptoms. Recent studies have shown that Convolutional Neural Networks (CNN) provide high accuracy in leaf–based plant disease classification across various plant species, highlighting their potential for early disease detection. This study aims to develop an accurate tomato leaf disease classification system using a CNN model trained on the Kaggle tomato leaf dataset consisting of four classes: Leaf Blight, Bacterial Spot, Leaf Scab, and Healthy. The methodology includes literature review, dataset acquisition, preprocessing, augmentation, CNN architecture design, model training, and performance evaluation. Preprocessing techniques such as resizing and normalization were applied, followed by augmentation using random flipping and rotation to increase dataset variability. The proposed model was trained for 40 epochs with a batch size of 16. Results show consistent accuracy improvement, reaching 0.98 training accuracy with a loss of 0.07, while validation accuracy peaked at 0.94. Testing on both single and multiple images demonstrates strong prediction confidence, with minor misclassifications in visually similar cases. Overall, the system effectively identifies tomato leaf diseases and reinforces the suitability of CNN for supporting early plant disease detection in smart agriculture applications.
INOVASI DIGITALISASI UMKM KOPI MELALUI SISTEM E-COMMERCE BERBASIS WEB PADA WARUNG KOPI GARASI NDESO Fatkhurrochman, Fatkhurrochman; Wibowo, Teguh Hadi; Arifah, Fatimah Nur; Yusnanto, Tri; Kapti, Kapti; Muin, Muhammad Abdul
TRANSFORMASI Vol 21, No 2 (2025): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v21i2.457

Abstract

The rapid development of digital technology has significantly impacted various sectors, including micro, small, and medium enterprises (UMKM). One of the most influential technological applications is the e-commerce information system, which facilitates online buying and selling activities without spatial and temporal limitations. This study aims to design and develop a web-based e-commerce system as a digitalization innovation for UMKM coffee businesses at Warung Kopi Garasi Ndeso, with the goal of expanding market reach and improving business management efficiency. The system development process adopts the Waterfall model, consisting of several stages: communication, planning, modeling, construction, and deployment. The system design employs Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD), while implementation utilizes the PHP programming language and MySQL as the database management system. System testing was carried out using the Blackbox Testing method to verify functionality, and user evaluation was conducted using the Likert scale involving 18 respondents, including the owner, admin, and customers. The testing results show that all system functions operate properly and meet user requirements. The user evaluation obtained a score of 647 out of 810 or 79.88%, indicating that the developed web-based e-commerce system has a good level of usability. Therefore, the implementation of digital innovation through this e-commerce system effectively supports Warung Kopi Garasi Ndeso in enhancing operational efficiency, expanding market reach, and strengthening the competitiveness of local coffee UMKM in the digital era.

Page 30 of 30 | Total Record : 292

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