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All Journal Jurnal Masyarakat Informatika Wikrama Parahita : Jurnal Pengabdian Masyarakat
Suprayogi Suprayogi
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Social Sciences

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IBM Pengelolaan Data Kependudukan Kelurahan Krobokan Semarang Suprayogi Suprayogi; Khafiizh Hastuti; Ajib Susanto
Wikrama Parahita : Jurnal Pengabdian Masyarakat Vol. 1 No. 1 (2017)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jpmwp.v1i1.266

Abstract

Pengelolaan data kependudukan Kelurahan Krobokan Semarang masih dilakukan secara manual. Pengelolaan secara manual rentan dengan masalah. Data yang disimpan dalam bentuk dokumen memiliki potensi kehilangan data, dan kerusakan. Selain rentan dengan masalah, staf kelurahan sering merasa direpotkan dengan pembuatan laporan bulanan data kependudukan antara lain kelahiran, kematian, perpindahan penduduk dari luar kelurahan atau sebaliknya. Sistem pengelolaan data kependudukan telah diimplementasikan, dan mampu mengakomodasi  kebutuhan user. Staf kelurahan menyatakan bahwa sistem yang dibuat mampu mengatasi masalah dan mudah digunakan. Semua tahapan kegiatan berjalan dengan keberhasilan mencapai 100%.
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.
Co-Authors Ajib Susanto Christy Atika Sari Feri Agustina Ibnu Utomo Wahyu Mulyono Khafiizh Hastuti Mohamed Doheir Zahra Ghina Syafira