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All Journal Journal Automation Computer Information System (JACIS) International Journal of Engineering Continuity
Sidqi, Akbar
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Analisis Pengukuran Kompleksitas Website SOC Menggunakan Function Point Sidqi, Akbar; Yuswanto, Andrie; Yaqin, Muhammad Ainul
Journal Automation Computer Information System Vol. 4 No. 2 (2024): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v4i2.82

Abstract

Pengukuran kompleksitas perangkat lunak telah menjadi fokus utama dalam pengembangan perangkat lunak, merupakan cara untuk menggambarkan seberapa rumitnya suatu program, bertujuan untuk menilai dan memprediksi berbagai karakteristik, seperti tingkat kompleksitas komputasi dan keandalan model, serta kemampuan evaluasi model tersebut, dengan memanfaatkan beragam metrik dan alat analisis. pada penelitian ini bertujuan mengukur kompleksitas dengan metode pengukuran Function Point Analysis dengan pendekatan berorientasi pada fungsi yang banyak digunakan untuk mengukur fungsionalitas perangkat lunak. pada setiap dashboard website diukur menggunakan kriteria FPA yang menyatakan tingkat kompleksitas dengan level simple, average, dan complex. pada penelitian ini bertujuan mengukur nilai kuantitatif dari fungsionalitas perangkat lunak website soc untuk menghasilkan tingkat kompleksitas website. Hasil dari pengukuran terdapat 3 faktor yaitu: Crude Function Point, Relative Complexity Adjustment Factor, dan Function Point. yang menunjukkan nilai CFP 310, RCAF 70, dan FP 418,5 yang menunjukkan kompleksitas tinggi pada website soc
Ensemble Combination of CNN for MRI-Based Brain Tumor Classification Sidqi, Akbar; Budi Santoso, Irwan; Harini, Sri
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.357

Abstract

Classifying 17 types of brain tumors remains a major challenge in the medical field, especially in improving diagnostic accuracy and accelerating patient care. This study proposes a CNN-based model with an ensemble combination approach to improve accuracy by integrating multiple architectures through Majority Voting and Weighted Average for more reliable predictions. The models are evaluated using accuracy, precision, recall, and F1-score metrics. The results show that CNN3 with Nadam achieves the best performance (accuracy: 0.90–0.91), outperforming CNN1 (0.87–0.89) and CNN2 (0.82–0.87). The ensemble combination improves accuracy across all models, with CNN3 achieving the highest accuracy (0.96), followed by CNN1 (0.94–0.95) and CNN2 (0.91–0.92). This study demonstrates that the ensemble combination approach can improve the performance of brain tumor classification using deep learning, contributing to faster and more accurate medical diagnosis. Furthermore, these findings open up opportunities for further research in advancing brain tumor detection systems.
Co-Authors Budi Santoso, Irwan Ida Ayu Putu Sri Widnyani Sri Harini Yuswanto, Andrie