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All Journal Jurnal Masyarakat Informatika Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Dinamika Rekayasa (DINAREK) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Jurnal Karya untuk Masyarakat (JKuM)
Ibnu Utomo Wahyu Mulyono
Universitas Dian Nuswantoro
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Journal : Jurnal Masyarakat Informatika

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Regionprops Segmentation in Convolutional Neural Network for Identification of Lung Cancer Disease and Position Zahra Ghina Syafira; Christy Atika Sari; Ibnu Utomo Wahyu Mulyono; Feri Agustina; Suprayogi Suprayogi; Mohamed Doheir
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): November 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.73967

Abstract

Lung cancer is one of the leading causes of death in the world, so early detection is very important to increase the chances of patient recovery. This study aims to develop a method for identifying lung cancer types using Convolutional Neural Network (CNN) combined with Regionprops segmentation technique to determine the position of cancer in CT scan images. The dataset used consists of 1,294 CT scan images classified into three classes, namely Benign, Malignant, and Normal, with variations in the ratio of training and testing data: 80:20, 70:30, 60:40, 50:50, and 40:60. The CNN method is used to perform classification, while the Regionprops segmentation technique is applied to determine the position of the cancer. The results showed that the model with a data ratio of 80:20 achieved the highest accuracy of 99.54%, indicating a very good generalization ability of the model. The Regionprops segmentation technique successfully separated the nodule area in the CT scan image clearly, thus providing more detailed information regarding the position of the cancer. The conclusion of this study shows that the combination of CNN and Regionprops segmentation methods is effective in detecting and analyzing lung cancer and has the potential to be used as a diagnostic tool in the medical field. This study recommends further testing with a larger dataset and optimization of model parameters to improve classification and segmentation performance.
Analisa Visual Citra Hasil Kombinasi Steganografi dan Kriptografi Berbasis Least Significant Bit Dalam Cipher Ibnu Utomo Wahyu Mulyono; Yupie Kusumawati; Novita Kurnia Ningrum
Jurnal Masyarakat Informatika Vol 14, No 1 (2023): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.14.1.51484

Abstract

Kriptografi dan steganografi adalah teknik yang digunakan untuk mengamankan data untuk meminimalkan pencurian data dan akses oleh orang yang tidak berwenang. Kombinasi Rivest Cipher 4 - Least Significant Bit diusulkan dalam penelitian ini untuk memberikan perlindungan bagi pesan dan berbagai format file yang tertanam dalam gambar digital. Pesan rahasia dienkripsi dengan metode RC4 sebelum dimasukkan kedalam gambar menggunakan LSB. Studi ini juga menganalisis kinerja kombinasi algoritma LSB – RC4 pada berbagai file dan ukuran gambar sampul. Gambar sampul menggunakan gambar dengan saluran RGB. Untuk pengukuran kinerja imperceptibilitas digunakan Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), dan analisis histogram.Nilai rata – rata PSNR yang didapatkan pada penelitian ini lebih dari 30 dB, ini membuktikan bahwa kualitas gambar stego sangat baik dan kualitas gambar stego yang baik memiliki nilai PSNR minimal 30 dB. Nilai PSNR yang didapatkan secara keseluruhan lebih dari 30 dB dengan nilai terendahnya 45,15 dB dengan ukuran citra 128x128 pixel.
Co-Authors Aji Kusumah Ramdhani Ajib Susanto Anggia Rizkika Hanin Christy Atika Sari De Rosal Ignatius Moses Setiadi Dian Indriana Hapsari Dimas Irawan Ihya’ Ulumuddin Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Feri Agustina Ihya Ulumuddin, Dimas Irawan Imang Dapit Pamungkas Imang Dapit Pamungkas Izza Ulumuddin Ahmad Asshofi Joseph Aldo Irawan Mohamed Doheir Novita Kurnia Ningrum Nurjanah Nurjanah Nurjanah Rahayu, Emik Sudaryanto Sudaryanto Suprayogi Suprayogi Tiara Anggraeny Tiara Widya Kusuma Widyatmoko Karis Yupie Kusumawati Zahra Ghina Syafira Zahrotul Umami