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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer JURNAL MEDIA INFORMATIKA BUDIDARMA KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JPM: JURNAL PENGABDIAN MASYARAKAT Bulletin of Computer Science Research Jurnal Janitra Informatika dan Sistem Informasi BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Data Science JOMLAI: Journal of Machine Learning and Artificial Intelligence Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Winanjaya, Riki
STIKOM Tunas Bangsa Pematangsiantar, Indonesia
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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Kombinasi Algoritma Sobel dan Canny (SoCan) dalam Identifikasi Citra Inversi Albatros Laysan Winanjaya, Riki; GS, Acmad Daengs; Anggraini, Fitri
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1415.232 KB) | DOI: 10.47065/bits.v4i1.1660

Abstract

Utilizing an edge detection algorithm in an image will produce the edges of the image object. The aim is to mark the part that becomes the image's detail and correct the point of blurring of vision that occurs due to errors or the effects of the image acquisition process. This study aims to see the ability of the combination of Sobel and Canny edge detection algorithms (SoCan) to detect the inverted image. The image dataset used is the image of the Laysan Albatross, which consists of 10 original images and ten images that have been inverted based on the standard image dataset. The Laysan albatross is a large species of seabird found in the North Pacific. 99.7% of the total population is found in the Northwest Hawaiian Islands. The research dataset was obtained from the Caltech Vision Lab website http://www.vision.caltech.edu/datasets/cub_200_2011/ with dimensions of 500 x 271 pixels. Based on the analysis of 10 experiments carried out, the combination of the Sobel and Canny algorithm (SoCan) is not good at performing edge detection because it only has an average accuracy of 47.79% with an average accuracy error rate of 52.21%. Thus, in this case, the combination of the Sobel and Canny algorithms (SoCan) is not able to identify the Inversion Image
Optimisasi Fungsi Aktivasi pada Arsitektur LeNet untuk Meningkatkan Akurasi Klasifikasi Citra Tumor Otak Harliana, Harliana; Rahadjeng, Indra Riyana; Winanjaya, Riki
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7108

Abstract

Brain hemorrhage is a critical medical condition that requires early and accurate detection to improve patient recovery outcomes. However, conventional image classification methods for brain hemorrhage still face limitations in terms of accuracy and efficiency. To address this issue, this study proposes optimizing the LeNet model using various activation functions—ReLU, Sigmoid, Tanh, and Swish—to enhance classification performance. Several optimization strategies were applied, including data augmentation techniques (flipping, rotation, shearing, rescaling) and fine-tuning of hyperparameters, to improve model generalization. Experimental results indicate that the model utilizing the Swish activation function achieves the most stable overall performance, with an accuracy of 55%, recall of 54%, precision of 54%, F1-score of 54%, and a ROC AUC value of 0.45. Although this performance is still below clinical application standards, the findings serve as an initial step toward exploring activation function optimization in CNN architectures. Further research is needed to significantly enhance classification accuracy and enable clinical viability.
Co-Authors Agus Perdana Windarto Agus Tiranda Sipayung Diana Pratiwi Elischa Febrivani Fadillah Alwi Pambudi Fitri Anggraini, Fitri Fitri Rizki GS , Achmad Daengs GS, Acmad Daengs Harliana Harliana Hartama, Dedy Hendry Qurniawan Hendry Qurniawan Indra Riyana Rahadjeng Irawan Irawan Irfan Sudahri Damanik Jaya Tata Hardinata Joko Pamungkas Josua Fernando Simanjuntak M Safii Mhd Wendico Herdian Muhammad Fauzan Muhammad Safii Nugroho, Muhammad Rizky Tri Nurhayati Nurhayati Okprana, Harly Putri Mai Sarah Tarigan Putri Mai Sarah Tarigan Qurniawan, Hendry Rajes Wasimson Sinaga Reza Fauzy Rizki, Fitri Rizky Khairunnisa Sormin S Saifullah S Susianti Safii, M Saifullah Saifullah Saifullah Sanri Sunervia Yunika Damanik Sari, Hanifah Urbach Simanjorang, Fernandi Susiani Susiani Susiani Susiani, Susiani Syahputra, Muhammad Syahputra, Muhammad Riza Tarigan, Putri Mai Sarah Wanto, Anjar Wendi Robiansyah Yuni Radana Sembiring Yusuf Maulana Zulia Almaida Siregar