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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT) Journal Innovation in Information and Computer Technology
Dhony Manggala Putra
Politeknik Negeri Jember, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Implementation of the Template Matching Algorithm for Smart Light Control through Speech Recognition for People with Disabilities Sholihah Ayu Wulandari; Adisty Pramudita Putri Rudi; Adi Sucipto; Bekti Maryuni Susanto; Dhony Manggala Putra
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.449

Abstract

Voice control systems in smart homes provide significant convenience for people with disabilities, especially in operating household devices such as lights without physical interaction. This study develops a voice-based light control system that runs locally on IoT devices using the template matching method. This system utilizes Mel-Frequency Cepstral Coefficients (MFCC) for voice feature extraction and Dynamic Time Warping (DTW) to match test voices with pre-recorded templates. Out of 66 voice samples tested, the system successfully recognized 13 out of 22 voices belonging to the primary user and rejected 43 out of 44 voices from other users, with an accuracy rate of 84.85%. Thus, this system shows potential as an inclusive, efficient, and disability-friendly voice control solution for smart home environments
Deep Learning-Based Tomato Leaf Disease Classification Using CNN, EfficientNetB0, and InceptionResNetV2 Rifqi Aji Widarso; Adi Sucipto; Dhony Manggala Putra; Tamara Maharani
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.494

Abstract

Tomato leaf diseases threaten agricultural productivity because symptoms such as early blight, late blight, leaf mold, septoria leaf spot, and yellow curl virus often produce visually similar color changes, necrotic lesions, and leaf deformation. Manual visual diagnosis is subjective and depends heavily on expert experience; therefore, image-based deep learning is a relevant approach for supporting preliminary disease identification. This study compares five deep learning architectures, namely a custom convolutional neural network, EfficientNetB0, MobileNetV2, DenseNet121, and InceptionResNetV2, for classifying six tomato leaf categories using 7,192 images from a Kaggle dataset. The research workflow includes dataset preparation, image resizing and normalization, model training using the Adam optimizer, and evaluation through accuracy, loss, precision, recall, F1-score, and confusion matrix analysis. Based on the notebook results, EfficientNetB0 achieved the best validation accuracy of 89.44% after 20 epochs, followed by MobileNetV2 at 85.12%, DenseNet121 at 82.77%, the custom CNN at 70.69% test accuracy, and InceptionResNetV2 at 45.76% test accuracy. The results indicate that lightweight transfer learning models are more suitable for medium-sized agricultural image datasets than large architectures trained for only a few epochs. Future work should validate the model using real field images, harmonize all models on the same test set, and report class-wise metrics to ensure reliability before deployment as a farmer-oriented diagnostic support system.
Quality Analysis of JavaFX-Based Parking System Desktop Application Using ISO/IEC 25010 Model Yunik Yuroidah; Lingga Wardana; Naufal Budiasan; Dhony Manggala Putra
Journal Innovation in Information and Computer Technology Vol. 3 No. 1 (2026): (January) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v3i1.90

Abstract

An organized and efficient parking system is essential for managing high-density parking areas. This study developed a JavaFX-based desktop parking system application with MongoDB database support, thermal printer and webcam integration. The application is designed to automatically record vehicle entry and exit transactions, print manual tickets, and display CCTV camera monitoring. Furthermore, the system also provides tariff management and transaction reporting features accessible through a dashboard. Software quality evaluation was conducted using the ISO/IEC 25010 model, with six main characteristics: Functionality, Reliability, Usability, Efficiency, Maintainability, and Portability. The results showed that the application met basic functional aspects, but still had weaknesses in performance efficiency and maintainability. Load testing using Apache JMeter indicated an exponential increase in response time under high load and an error rate of up to 8.82%. This study provides technical recommendations to improve system efficiency through indexing optimization, architectural refactoring to the MVC pattern, and integration of automated I/O devices such as parking barriers and ANPR.
Co-Authors Adi Sucipto Adisty Pramudita Putri Rudi Bekti Maryuni Susanto Lingga Wardana Naufal Budiasan Rifqi Aji Widarso Sholihah Ayu Wulandari Tamara Maharani Yunik Yuroidah