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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Manajemen Informatika dan Sistem Informasi Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Yudha, Ery Permana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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SISTEM PREDIKSI PRODUKSI PADI DI SUMATERA MENGGUNAKAN REGRESI LINEAR Yudha, Ery Permana; Arif Rohmadi; Agung Teguh Setyadi
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 8 No. 1 (2025): MISI Januari 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v8i1.1411

Abstract

Pulau Sumatera merupakan salah satu pulau yang menjadi lumbung padi nasional karena sebagai salah satu daerah penghasil padi terbesar di Indonesia. Namun, produktivitas yang tinggi di pulau Sumatera juga terdapat beberapa tantangan seperti perubahan iklim yang tidak menentu, luas lahan, curah hujan, kelembapan, dan suhu rata-rata. Untuk mengatasi permasalahan tersebut perlu strategi yang inovatif dan berbasis data. Salah satu strategi tersebut dengan menerapkan pengolahan data untuk menghasilkan model prediksi produktivitas padi. Teknik ini melibatkan algoritma dan pembelajaran mesin untuk menganalisis pola dan tren dalam pertanian. Model ini mempermudah stakeholder terkait untuk mempersiapkan kebutuhan pangan nasional agar selalu terpenuhi. Pada penelitian ini, diusulkan sebuah metode prediksi produktivitas padi di Sumatera menggunakan metode regresi linear. Penelitian ini menghasilkan model prediksi masing-masing di setiap provinsi di Sumatera. Secara umum, tahapan yang dilakukan yaitu preprocessing, seleksi fitur, training dan testing, dan evaluasi. Uji coba yang dilakukan dengan menghitung nilai Mean Squarred Error (MSE). Beberapa algoritma yaitu Regresi Linear, Support Vector Regression (SVR), Random Forest Regression (RFR) menghasilkan nilai rata-rata MSE sebesar 0,022; 0,075; 0,026. Regresi linear mampu menghasilkan model yang lebih baik dibandingkan metode SVR dan RFR.
Improving Accuracy and Efficiency of Medical Image Segmentation Using One-Point-Five U-Net Architecture with Integrated Attention and Multi-Scale Mechanisms Fathur Rohman, Muhammad Anang; Prasetyo, Heri; Yudha, Ery Permana; Hsia, Chih-Hsien
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.949

Abstract

Medical image segmentation is essential for supporting computer-aided diagnosis (CAD) systems by enabling accurate identification of anatomical and pathological structures across various imaging modalities. However, automated medical image segmentation remains challenging due to low image contrast, significant anatomical variability, and the need for computational efficiency in clinical applications. Furthermore, the scarcity of annotated medical images due to high labelling costs and the requirement of expert knowledge further complicates the development of robust segmentation models. This study aims to address these challenges by proposing One-Point-Five U-Net, a novel deep learning architecture designed to improve segmentation accuracy while maintaining computational efficiency. The main contribution of this work lies in the integration of multiple advanced mechanisms into a compact architecture: ghost modules, Multi-scale Residual Attention (MRA), Enhanced Parallel Attention (EPA) in skip connections, the Convolutional Block Attention Module (CBAM), and Multi-scale Depthwise Convolution (MSDC) in the decoder. The proposed method was trained and evaluated on four public datasets: CVC-ClinicDB, Kvasir-SEG, BUSI, and ISIC2018. One-Point-Five U-Net achieved sensitivity, specificity, accuracy, DSC, and IoU of of 94.89%, 99.63%, 99.23%, 95.41%, and 91.27% on CVC-ClinicDB; 91.11%, 98.60%, 97.33%, 90.93%, and 83.84% on Kvasir-SEG; 85.35%, 98.65%, 96.81%, 87.02%, and 78.18% on BUSI; and 87.67%, 98.11%, 93.68%, 89.27%, and 83.06% on ISIC2018. These results outperform several state-of-the-art segmentation models. In conclusion, One-Point-Five U-Net demonstrates superior segmentation accuracy with only 626,755 parameters and 28.23 GFLOPs, making it a highly efficient and effective model for clinical implementation in medical image analysis.
Hybrid features to classify lung tumor using machine learning Rahmawan, Rizki Dwi; Salamah, Umi; Yudha, Ery Permana
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.101

Abstract

A lung tumor is an abnormal mass of cells inside a body. As a benign tumor is unproblematic, but a malignant tumor is cancerous because it can travel across the body and interfere with its surrounding tissue. Detecting these cancerous cells in the lung is important because delayed detection may hamper effective treatment options, leading to a lower survival rate. However, classifying tumor malignancy is highly dependent on the knowledge and experience of the radiologist. This study combines texture-based features extracted from lung Computed Tomography Scan (CT Scan) images such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GLRLM), Gray Level Size-zone Matrix (GLSZM), and Haralick Features aims to create a lung tumor classification system. This research contributes by creating an efficient and reliable system through Relief-F feature selection that uses features with the highest weight in rank that are able to differentiate classes of tumor malignancy and help medical professionals diagnose tumors more early in the treatment.  As a comparison, several conventional machine learning classifiers, including SVM RBF, KNN, RF, DT, and XGBoost, were utilized to evaluate classifier performance. The result showed that the accuracy of the proposed hybrid features with a random forest classifier was the most performing approach with an evaluation score of accuracy of 99.55%, precision of 99.55%, recall of 99.55%, and F1-Score of 99.54%. Furthermore, accuracy among other classifiers was also higher than 90%. Proofing the selected features retain essential class information, demonstrating the study’s applicability in developing automated lung tumor classification systems from CT scans.
Co-Authors Agung Teguh Setyadi Arif Rohmadi Fathur Rohman, Muhammad Anang Hsia, Chih-Hsien Prasetyo, Heri Rahmawan, Rizki Dwi Umi Salamah