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All Journal ComEngApp : Computer Engineering and Applications Journal Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING SEINASI-KESI Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informasi dan Teknologi Jurnal Digital Teknologi Informasi Jurnal Generic Informanika Jurnal Pengabdian Kepada Masyarakat Inovasi Teknologi The Indonesian Journal of Computer Science
Kesuma, Lucky Indra
Universitas Muhammadiyah
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Identification of Floods in Palembang Area Using Fuzzy Logic Method of Mamdani and Sugeno Ade Sukmawati; Lemi Iryana; Pandi Adriansyah; Lucky Indra Kesuma
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8146

Abstract

Floods are one of the natural events that often occur, especially in areas that have a large population. Palembang is one of the cities in Sumatra Province which often experiences flooding due to its dense population and lack of infiltration areas. One way to improve flood preparedness is to identify floods by using fuzzy logic. This study aims to identify floods in the Palembang area using fuzzy logic mamdani and Sugeno methods. Flood identification is carried out in 6 stages, namely variable determination, fuzzification, inference, application of fuzzy rules, defuzzification, and evaluation. The variables used in this study are rainfall, temperature, area elevation, and the results of flood identification that have a value of flood or not flood. The results of flood identification using the Mamdani method achieved an accuracy of 82% while the Sugeno method achieved an accuracy of 91%. The results of this study indicate that the mamdani and Sugeno fuzzy logic methods are quite good in identifying floods so that they can improve flood preparedness attitudes.
Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease Umilizah, Nia; Octavia, Pipin; Kesuma, Lucky Indra; Rayani, Ira; Suedarmin, Muhammad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10221

Abstract

Liver disease is a disease caused by various factors such as the spread of viruses. Liver damage causes the ability to break down red blood cells to be disrupted. Detection of liver disease can be done using the segmentation. Segmentation is useful for separating an area of the liver in an image from other areas. Segmentation carried out manually requires experts and a long time, so automatic segmentation is needed. CNN can be used to perform automatic segmentation. One of the CNN architectures is the U-Net architecture. Segmentation requires quality images to improve recognition of image patterns, so image improvement is needed in the form of contrast enhancement. Contrast improvement was carried out by taking Green Channel images. Contrast enhancement was carried out using the Contrast Stretching and CLAHE methods. The image improvement results show MSE and SSIM values 66.1844 and 0.7088. Evaluation of the image improvements obtained provides significant changes. The improved image is used at the segmentation stage. Segmentation is carried out using the U-Net architecture. The segmentation results obtained performance evaluation values in the form of accuracy 99.6%, sensitivity 98.9%, and specificity 99.7%. This shows that the proposed method can detect liver disease in liver images well
Combination of Image Enhancement and U-Net Architecture for Cervical Cell Semantic Segmentation Rudiansyah, Rudiansyah; Iryani, Lemi; Kesuma, Lucky Indra; Sari, Puspa; Alamsyah, Agung
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10399

Abstract

Cervical cancer is the second leading cause of death in women and ranks fourth as a disease that occurs in women worldwide. Cervical cancer is a disease that is difficult to detect and can be detected when it is in an advanced stage. This requires early prevention by carrying out a pap-smear examination. Pap-smear examination manually requires a relatively long time, so a tool is needed by segmentation. Segmentation is image processing by performing perfection between the intended object and the background. One of the CNN methods commonly used in medical image segmentation is the U-Net architecture. Segmentation in this study was carried out on the nucleus and cytoplasm of the Herlev dataset using the U-Net architecture combined with data augmentation and image enhancement. In the learning process, this research resulted in a fairly high IoU value of 78% and an RMSE close to 20%. The results of this study also yielded an accuracy value of 89%, with an average precision, recall and F1 score of 89%, 89% and 88.67%, respectively. This shows that the combination of the CNN U-Net architecture with image quality improvement and data augmentation is quite good at segmenting cervical cells for the nucleus and cytoplasm
Co-Authors Ade Sukmawati Ade Sukmawati Ahmad Fali Oklilas Ahmad Rifai Airlambang, Dwiki Alamsyah, Agung Anggara, M Ikhsan Azhar Andika Putra BAKHTIAR K Batubara, Gracia Mianda Caroline Bayu Wijaya Putra Buchari, M. Ali Darmo, Cahyo Pambudi Dite Geovani Dwi Fitri Brianna Erly Krisnanik Fitri Brianna, Dwi Frans Sudirjo Geovani, Dite Intan Hesti Indriana Iryani, Lemi Karina Karina Lemi Iryana M. Ali Buchari Marissa Cristianti Mira Afrina Muhammad Ikhsan Anggara Naraloka, Therezia Nia Umilizah Octavia , Pipin Octavia, Pipin PAISAL PAISAL Pandi Adriansyah Purwita Sari Purwita Sari Purwita Sari, Purwita Puspa Sari, Puspa Putri Maharani Ratna Tungga Dewa, Dominica Maria Rayani, Ira Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah, Rudiansyah Sari , Purwita Suedarmin, Muhammad Suryaningsih, Lilik Therezia Naraloka Tuti Ayu Lestari Umilizah, Nia Wiwik Handayani Yuniarti Utami, Eva