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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal INKOM Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Journal of Information Technology and Computer Science INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Informatika dan Komputer
Esa Prakasa, Esa
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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 1 
Texture Feature Extraction by Using Local Binary Pattern Prakasa, Esa
INKOM Journal Vol 9, No 2 (2015)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (898.893 KB) | DOI: 10.14203/j.inkom.420

Abstract

Local Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces. By applying LBP, texture pattern probability can be summarised into a histogram. LBP values need to be determined for all of the image pixels. Texture regularity might be determined based on the distribution shape of the LBP histogram. The implementation results of LBP on two texture types - synthetic and natural textures - shows that extracted texture feature can be used as input for pattern classification. Euclidean distance method is applied to classify the texture pattern obtained from LBPcomputation.
Corn Seeds Identification Based on Shape and Colour Features Yafie, Haddad Alwi; Rachmawati, Ema; Prakasa, Esa; Nur, Amin
Khazanah Informatika Vol. 6 No. 2 October 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.10840

Abstract

Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and colour features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%.
KLASIFIKASI KUALITAS PERMUKAAN JALAN RAYA MENGGUNAKAN METODE CNN BERBASIS ARSITEKTUR XCEPTION Gaho, Ridol Liusman; Ali, Irsan Taufik; Prakasa, Esa
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4213

Abstract

Jalan raya merupakan prasarana utama untuk transportasi darat, semakin baik kondisi suatu jalan raya semakin baik pula kecepatan dan keselamatan pengendara yang melintas. Oleh karena hal tersebut pengawasan dan perawatan kondisi permukaan jalan sangat penting dilakukan. Pengecekan kualitas jalan raya umumnya dilakukan secara manual, cara ini memakan waktu dan tenaga yang cukup besar. Oleh karena itu, dikembangkan sistem "Klasifikasi Kualitas Perukaan Jalan Raya Menggunakan Metode CNN Berbasi Aristektur Xception” sebagai salah satu alternatif untuk melakukan pengecekan kualitias permukaan jalan raya. Metode ini menggunakan deep learning CNN dengan arsitektur transfer learning Xception, pemilihan Xception dipilih karena mempunyai arsitektur yang kompleks namun efisien dalam penggunaan waktunya dan memiliki akurasi yang tinggi untuk melakukan klasifikasi gambar, menghasilkan model akurat dengan waktu pelatihan singkat. Model dibuat menggunakan dataset dengan pembagian 4 kelas berdasarkan pada tingkat kerusakan yang rilis oleh Kementrian PUPR. Hasil pengujian tertinggi menunjukkan akurasi model 90,11% dan 90% untuk pengujian.
Enhanced you only look once approach for automatic phytoplankton identification Wisnu Ardhi, Ovide Decroly; Retnaningsih Soeprobowati, Tri; Adi, Kusworo; Prakasa, Esa; Rachman, Arief
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3426-3436

Abstract

Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YOLO compared to the YOLOv8m model, further demonstrated by the decreased giga floating-point operations (GFLOP), decreased parameter count, and compact dimensions, significantly improves the automation of phytoplankton species identification. This suggests that the ACVIT-YOLO model could produce a better prediction system for identifying phytoplankton with similar accuracy to the original YOLOv8m model but with lower computational power and resource usage.
Wood Species Identification using Convolutional Neural Network (CNN) Architectures on Macroscopic Images Oktaria, Anindita Safna; Prakasa, Esa; Suhartono, Efri
Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1569.891 KB) | DOI: 10.25126/jitecs.201943155

Abstract

Indonesia is a country that is very rich in tree species that grow in forests. Wood growth in Indonesia consists of around 4000 species that have different names and characteristics. These differences can determine the quality and exact use of each type of wood. The procedure of standard identification is currently still carried out through visual observation by the wood anatomist. The wood identification process is very in need of the availability of wood anatomists, with a limited amount of wood anatomist will affect the result and the length of time to make an identification. This thesis uses an identification system that can classify wood based on species names with a macroscopic image of wood and the implementation of the Convolutional Neural Network (CNN) method as a classification algorithm. Supporting architecture used is AlexNet, ResNet, and GoogLeNet. Architecture is then compared to a simple CNN architecture that is made namely Kayu30Net. Kayu30Net architecture has a precision performance value reaching 84.6%, recall 83.9%, F1 score 83.1% and an accuracy of 71.6%. In the wood species classification system using CNN, it is obtained that AlexNet as the best architecture that refers to a precision value of 98.4%, recall 98.4%, F1 score 98.3% and an accuracy of 96.7%.
Klasifikasi Kualitas Varietas Benih Jagung Bima 20 Menggunakan Metode Random Forest Putra, Muhamad Zahara Anugrah; Candra, Feri; Prakasa, Esa
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i2.2177

Abstract

Varietas benih jagung Bima-20 merupakan salah satu varietas yang populer dan banyak digunakan oleh petani. Namun, untuk memastikan kualitas benih yang dihasilkan, diperlukan metode yang dapat membedakan kualitas benih Bima-20 dengan akurasi tinggi. Salah satu cara untuk meningkatkan akurasi dalam proses grading benih jagung adalah dengan menggunakan teknologi pengolahan citra digital. Beberapa fitur yang dapat diekstraksi dari citra digital antara lain bentuk, tekstur, dan warna. Karakteristik bentuk benih jagung dapat diekstraksi dengan menggunakan metode segmentasi citra dan ekstraksi fitur bentuk seperti area dan perimeter atau keliling. Sedangkan karakteristik tekstur benih jagung dapat diekstraksi dengan menggunakan fitur gray-level co- occurrence matrix (GLCM) serta dapat diklasifikasi menggunakan metode Random Forest. Metode Random Forest adalah salah satu metode yang populer dalam klasifikasi citra. Metode ini menggunakan kombinasi dari beberapa pohon keputusan (decision tree) untuk mengklasifikasikan data. Kelebihan dari metode Random Forest adalah kemampuannya dalam mengatasi overfitting dan mampu menghasilkan prediksi yang akurat. Dengan menerapkan ekstraksi fitur dan metode tersebut menghasilkan bahwa ekstraksi fitur tekstur menggunakan Gray Level Co- occurrence Matrix (GCLM) dan ekstraksi fitur bentuk memperoleh nilai yang dapat diklasifikasikan menggunakan metode random forest. Hasil klasifikasi yang diperoleh tersebut memiliki tingkat akurasi 100% akurat sesuai dengan pernyataan melalui survei yang dilakukan kepada seorang kepala SMK di Pesantren Teknologi Riau dan juga seorang guru dalam bidang pertanian di Pesantren Teknologi Riau yaitu ibu Azrida Syamsi M.Si.
Enhanced U-Net models with encoder and augmentation for phytoplankton segmentation Ardhi, Ovide Decroly Wisnu; Soeprobowati, Tri Retnaningsih; Adi, Kusworo; Prakasa, Esa; Rachman, Arief
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp1009-1018

Abstract

This study comprehensively analyzes U-Net models for semantic segmentation in phytoplankton image recognition, leveraging encoders such as EfficientNet-B5, MobileNetV2, ResNet50, and ResNeXt50 and employing the Adam optimizer. The research highlights the U-Net MobileNetV2 model with optical distortion, which achieves notable test scores with 93.69% Dice, 88.14% intersection over union (IoU), 99.89% Precision, and 100% Recall, underscoring the efficacy of the applied augmentation strategies, including geometric and distortion transforms, and color and blur techniques. The U-Net ResNet50 model with mix transform consistently demonstrates high accuracy in critical metrics, outperforming others, while EfficientNet-B5 with blur suggests increased model sensitivity with improved recall. These results underscore the crucial role of encoder-augmentation synergy in model performance. Training and testing times across models have remained under 250 seconds, reflecting methodological efficiency. Overall, these results demonstrate the model's excellent performance for the semantic segmentation task.
Co-Authors AMIN NUR Ardhi, Ovide Decroly Wisnu Arief Rachman Efri Suhartono Feri Candra Gaho, Ridol Liusman Haddad Alwi Yafie IRSAN TAUFIK ALI Kusworo Adi Oktaria, Anindita Safna Putra, Muhamad Zahara Anugrah Retnaningsih Soeprobowati, Tri Syamsul Arifin Tri Retnaningsih Soeprobowati Wisnu Ardhi, Ovide Decroly