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All Journal TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Transformasi
Maulana, Ferdy Afriza
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics

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Pemanfaatan Algoritma A Star dalam Menentukan Rute Wisata di Kota Palangka Raya Sriyanto, Naufal Ihsan; Maulana, Ferdy Afriza; Aprilian, Rivan; Christian, Efrans; Pranatawijaya, Viktor Handrianus
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol. 12 No. 1 (2024): TELEKONTRAN vol 12 no 1 April 2024
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v12i1.12645

Abstract

The Utilization of the A* Algorithm in Determining Tour Routes in Palangka Raya City is a study aimed at providing a solution for determining optimal tourist routes for visitors in Palangka Raya City. By employing the A* algorithm, this research offers an efficient tool to find the shortest route between selected tourist destinations, considering both distance and travel cost. The method involves initializing a graph that represents the relationships between tourist destinations, followed by the implementation of the A* algorithm for finding the best route. The research findings demonstrate that this algorithm can provide optimal routes with accurate estimations of distance and cost. A brief discussion on the research results highlights the strengths and limitations of this algorithm in the context of tourism applications. In conclusion, this research makes a significant contribution to facilitating visitors in planning their tourist trips in Palangka Raya City, with the potential positive impact on the tourism industry and visitor experiences.
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.
Co-Authors Aprilian, Rivan Dwi Putri, Athay Setya Efrans Christian Jadiaman Parhusip, Jadiaman Mahendra, Rizqullah Falah Sriyanto, Naufal Ihsan Viktor Handrianus Pranatawijaya