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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Polinema (JIP)
Anan Nugroho
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Library & Information Science

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SIMPLIFIKASI MODEL CV BERPADU OPERASI MORFOLOGI UNTUK DETEKSI OBJEK KANKER PADA CITRA USG Anan Nugroho; Anas Fauzi; Budi Sunarko; Hari Wibawanto; Nur Iksan
Jurnal Informatika Polinema Vol. 8 No. 2 (2022): Vol 8 No 2 (2022)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v8i2.923

Abstract

Saat ini, Computer Aided Diagnosis (CAD) tengah dikembangkan secara masif sebagai second opinion reader di berbagai modalitas pencitraan medis, salah satunya ultrasonografi (USG). Untuk skrining otomatis citra USG yang banyak, berulang-ulang dan terus-menerus, teknik deteksi objek memainkan peran krusial pada sistem CAD. Deteksi objek kanker pada citra USG tidak mudah karena objek-objek tersebut berkontras rendah dan bertepi kabur akibat gangguan derau speckle dan artifak. Studi ini mengatasi tantangan ini dengan mengusulkan metode deteksi berbasis model active-contour Chan-Vese (CV) tersimplifikasi diikuti operasi morfologi. Adapun performa kuantitatif diperoleh menggunakan skor Intersection of Union (IoU) antara objek-objek terdeteksi dengan ground truth-nya. Usulan metode divalidasi menggunakan 20 citra USG tiroid dan payudara dengan hasil rerata skor IoU mencapai 92,36%. Performa yang menjanjikan ini menunjukkan bahwa usulan metode layak diimplementasikan pada sistem CAD.
Analisis Area Wajah Berdasarkan Tekstur Wajah untuk Mengidentifikasi Risiko Penyakit Jantung Koroner Budi Sunarko; Agung Adi Firdaus; Yudha Andriano Rismawan; Anan Nugroho
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i1.13658

Abstract

Early screening for coronary heart disease (CHD) remains insufficiently addressed, underscoring the need for a more effective screening tool. Previous studies have reported a classification accuracy of only 72.73%, which is inadequate. This study aimed to develop and evaluate a machine learning model or diagnose CHD using facial texture features and to compare the performance across different facial regions to provide recommendations for improvement. The research involved constructing a machine learning model that extracted texture features from six facial regions of interest (ROIs) using the gray level co-occurrence matrix (GLCM) and employed an artificial neural network (ANN) algorithm. The datasets were full-face images of CHD patients (positive) and healthy people (negative). The face parts identified were the right crow’s feet, right canthus, nose bridge, forehead, left canthus, and left crow’s feet. A total of 132 (72 positive and 60 negative CHD) datasets were divided into 80% (n = 106) training data and 20% (n = 26) testing data. The developed model achieved a notable accuracy of 76.9%. The findings revealed that two facial regions—canthus and forehead—demonstrated excellent accuracy of 80.97% and 90%, respectively. Meanwhile, the crow’s feet and nose bridge regions showed good accuracies at 73.50% and 65%, respectively. Based on the results, this research has proven to be able to become a model for early CHD screening with good accuracy and faster execution.
Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES Alrahma, Hanan Iqbal; Anan Nugroho; Hastawan, Ahmad Fashiha; Arief, Ulfah Mediaty
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i2.1236

Abstract

The Digital Center (DC) building at UNNES is a new building on the campus that currently lacks evacuation routes. Therefore, it is necessary to create an evacuation route plan in accordance with the Minister of Health Regulation Number 48 of 2016. Creating a manual evacuation route plan can be inefficient and prone to errors, especially for large buildings with complex interiors. To address this issue, learning techniques such as reinforcement learning (RL) are being used. In this study, Q-learning will be utilized to find the shortest path to the exit doors from 11 rooms on the first floor of the DC building. The study makes use of the floor plan data of the DC building, information about the location of the exit doors, and the distance required to reach them. The results of the study demonstrate that Qlearning successfully identifies the shortest evacuation routes for the first-floor DC building. The routes generated by Q-learning are identical to the manually created shortest paths. Even when additional obstacles are introduced into the environment, Q-learning is still able to find the shortest routes. On average, the number of episodes required for convergence in both environments is less than 1000 episodes, and the average computation time needed for both environments is 0.54 seconds and 0.76 seconds, respectively.
Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method Anan Nugroho; Sunarko, Budi; Wibawanto, Hari; Mulwinda, Anggraini; Fauzi, Anas; Oktaviyanti, Dwi; Savitri, Dina Wulung
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i2.1298

Abstract

Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.
Co-Authors Agung Adi Firdaus Ahmad Fashiha Hastawan Alrahma, Hanan Iqbal Anggraini Mulwinda Budi Sunarko Dwi Oktaviyanti, Dwi FAUZI, ANAS Hari Wibawanto Nur Iksan Savitri, Dina Wulung Ulfah Mediaty Arief Wiwik Handayani Yudha Andriano Rismawan