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All Journal Majalah Ilmiah Teknologi Elektro Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System Indonesian Journal of Electrical Engineering and Computer Science Jurnal Lembaga Pengabdian Kepada Masyarakat Undana RENATA Jurnal Pengabdian Masyarakat Kita Semua Majalah Ilmiah Teknologi Elektro Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Journal of Applied Research In Computer Science and Information Systems
Silvester Tena
Teknik Elektro, Universitas Nusa Cendana, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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FAST IMAGE RETRIEVAL BERBASIS LOCALITY SENSITIVE HASHING DAN CONVOLUTIONAL NEURAL NETWORK Tena, Silvester; Dwiandiyanta, Bernadectus Yudi; Ina, Wenefrida Tulit
Jurnal Media Elektro Vol 13 No 1 (2024): April 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jme.v13i1.15137

Abstract

Image retrieval systems with a fast search process are still challenging for researchers. Fast search methods are one of the most important parts of image retrieval. One of the techniques used is reducing feature dimensions using the Locality Sensitivity Hashing (LSH) method. Apart from that, feature types and image extraction methods are selected. Image feature extraction uses the Convolutional Neural Network (CNN) method in this research. Measuring similarity using the Hamming Distance (HD) and Euclidean Distance (ED) methods. The datasets used are TenunIkatNet and Batik300. The LSH method forms a hash table as a bucket to group similar images based on probability and in the form of binary code. The research results show that the LSH+HD+ED method provides faster search results than ED. The image retrieval time for the LSH+HD+ED and ED methods is 0.252 seconds and 4.5 seconds, respectively, for the TenunIkatNet dataset. Meanwhile, the Batik300 dataset is 0.03 seconds and 0.9 seconds. Using the LSH method is very effective for large datasets. Retrieval accuracy using the LSH+HD+ED method was 99.705% and 84% for the TenunIkatNet and Batik300 datasets, respectively. Meanwhile, the ED method produces 94.17% and 82% retrieval accuracy, respectively.
KLASIFIKASI BENIH JAGUNG UNGGUL MENGGUNAKAN METODE MACHINE LEARNING K-NEAREST NEIGHBORS Seyk, Madeleine Nizara; Ina, Wenefrida Tulit; Djahi, Hendrik J.; Tena, Silvester
Jurnal Media Elektro Vol 13 No 2 (2024): Oktober 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jme.v13i2.19054

Abstract

Classifying the quality of corn seeds by manual visual observation takes a long time. It also produces products with uneven quality due to visual limitations, fatigue, and differences in observer perception. This research aims to classify superior corn seeds using the machine learning method, namely K-Nearest Neighbors (K-NN). The research data uses 500 images of corn seeds consisting of 400 training images and 100 test images. Extraction of corn image features uses the Gray Level Co-occurrence Matrix (GLCM) method to obtain texture characteristics. The texture characteristic values ​​of metric natural corn images concist of contrast, energy, homogeneity and correlation. Based on the image texture characteristic values, classification is carried out using the K-Nearest Neighbor (K-NN) method. The classification results consist of classes of viable and non-viable corn seeds. The performance evaluation metric method calculates accuracy, sensitivity and specificity using a confusion matrix. This research shows that the value of k=5 is the most optimal, and the accuracy, sensitivity and sensitivity values, respectively, are 75%, 77% and 72% found in the ninth fold
Co-Authors Beby H. A. Manafe Beby H.A. Manafe Brian J. Mooy Dominikus, Wara Sabon Dwiandiyanta, Bernadectus Yudi Galla, Wellem Fridz Gregorius Klau Halla, Maria Arnoldia Hendrik J. Djahi Hendro FJ Lami Herry Zadrak Kotta I Wayan Sukarjita Igi Ardiyanto Johanis F.M Bowakh Kalvein Rantelobo Lejap, Marianus Yakobus Lili Manafe, Beby H. A. Melzando L. F Tari Muhammad Khoiruddin Mulki Indana Zulfa, Mulki Indana Ndoloe, Welmy M Nursalim Nursalim Nursalim Nursalim Pratama, Bima Gerry Rizkiono, Sampurna Dadi Rudy Hartanto Sampeallo, Agusthinus S Samy Y. Doo Sarlince O. Manu Seyk, Madeleine Nizara Stephanie I. Pella Sudirman Syam Syahrul Syahrul Tari, Melzando L. F Tulit Ina, Wenefrida Varly M Ndolu Welmy M. Ndoloe Wenefrida T Ina Yoga Arya Setyadi