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All Journal Jurnal Ilmu Komputer JAIS (Journal of Applied Intelligent System) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Manajemen Informatika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Infotekmesin Jurnal Manajemen Informatika dan Sistem Informasi Journal of Robotics and Control (JRC) Abdimasku : Jurnal Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Inovasi Sosial: Jurnal Pengabdian Masyarakat
Winarsih, Nurul Anisa Sri
Universitas Dian Nuswantoro
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences

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Journal : Infotekmesin

 1 
Perbandingan Arsitektur VGG16, MobileNetV2, InceptionV3, ResNet50, dan CNN Kustom untuk Klasifikasi Gambar Furnitur Epiphany Shavna Gracia; Nurul Anisa Sri Winarsih
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2500

Abstract

The rapid development of technology in the current digital era is driving increased demand across various sectors, including the furniture industry. Classifying furniture images is one of the critical challenges in image processing and computer vision, mainly due to the diversity of types. This research aims to understand how pre-trained models can affect image classification accuracy using furniture dataset results. This study uses five CNN architectures and focuses on comparing the performance of a custom architecture with four pre-trained architectures, namely VGG-16, MobileNetV2, InceptionV3, and ResNet-50, using furniture images that have five classes such as chairs, tables, cabinets, sofas, and mattresses. The research results show that the models produced by the pre-trained architectures provide higher accuracy and performance, with VGG-16 reaching 97%, MobileNetV2 at 96%, and InceptionV3 and ResNet-50 at 98%. Meanwhile, the custom model only achieved an accuracy of 85%. This research shows that using pre-trained model algorithms significantly improves performance in image classification.
Perbandingan Kinerja Djaka, Thesa Permatasari Djaka; Nurul Anisa Sri Winarsih
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2504

Abstract

Polycystic ovary syndrome (PCOS) is a hormonal disorder that is the most common cause of anovulation and infertility in women of reproductive age, affecting approximately 5-10% of the population, with up to 70% of cases undiagnosed. This highlights the need for early detection methods with high accuracy for timely treatment. Previous research utilized a classification method based on the K-Nearest Neighbor (KNN) algorithm, which demonstrated good performance with an accuracy of 93%, precision of 100%, recall of 82%, and F1-Score of 90%. This study proposes using an ensemble learning method with a voting classifier technique that combines several classification models: Random Forest Classifier, Logistic Regression, and XGBoost Classifier. The results show that the proposed method performs better with an accuracy of 95%, precision of 100%, recall of 85%, F1-Score of 92%, and an AUC (Area Under Curve) value of 94.34%
Perbandingan Arsitektur VGG16, MobileNetV2, InceptionV3, ResNet50, dan CNN Kustom untuk Klasifikasi Gambar Furnitur Epiphany Shavna Gracia; Nurul Anisa Sri Winarsih
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2500

Abstract

The rapid development of technology in the current digital era is driving increased demand across various sectors, including the furniture industry. Classifying furniture images is one of the critical challenges in image processing and computer vision, mainly due to the diversity of types. This research aims to understand how pre-trained models can affect image classification accuracy using furniture dataset results. This study uses five CNN architectures and focuses on comparing the performance of a custom architecture with four pre-trained architectures, namely VGG-16, MobileNetV2, InceptionV3, and ResNet-50, using furniture images that have five classes such as chairs, tables, cabinets, sofas, and mattresses. The research results show that the models produced by the pre-trained architectures provide higher accuracy and performance, with VGG-16 reaching 97%, MobileNetV2 at 96%, and InceptionV3 and ResNet-50 at 98%. Meanwhile, the custom model only achieved an accuracy of 85%. This research shows that using pre-trained model algorithms significantly improves performance in image classification.
Analisis Kinerja Ensemble Learning dan Algoritma Tunggal dalam Klasifikasi Sindrom Ovarium Polikistik Menggunakan Random Forest, Logistic Regression, dan XGBoost Djaka, Thesa Permatasari Djaka; Nurul Anisa Sri Winarsih
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2504

Abstract

Polycystic ovary syndrome (PCOS) is a hormonal disorder that is the most common cause of anovulation and infertility in women of reproductive age, affecting approximately 5-10% of the population, with up to 70% of cases undiagnosed. This highlights the need for early detection methods with high accuracy for timely treatment. Previous research utilized a classification method based on the K-Nearest Neighbor (KNN) algorithm, which demonstrated good performance with an accuracy of 93%, precision of 100%, recall of 82%, and F1-Score of 90%. This study proposes using an ensemble learning method with a voting classifier technique that combines several classification models: Random Forest Classifier, Logistic Regression, and XGBoost Classifier. The results show that the proposed method performs better with an accuracy of 95%, precision of 100%, recall of 85%, F1-Score of 92%, and an AUC (Area Under Curve) value of 94.34%
Co-Authors Adhitya Nugraha Ahmad Zainul Fanani Akmaludin Akbar Alvin, Fris Astuti, Yani Parti Azzahra, Tarissa Aura Danny Oka Ratmana Dhani, Iqbal Djaka, Thesa Permatasari Djaka Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Epiphany Shavna Gracia Erlin Dolphina Etika Kartikadarma Fafaza, Safira Alya Fakhrurrozi Fakhrurrozi, Fakhrurrozi Filmada Ocky Saputra Filmada Ocky Saputra Guruh Fajar Shidik Hamid, Maulana As’an Ilmi, Nur Aqliah Imam Prayogo Pujiono Kartika, Gita Kusumawati, Yupie Mardiantara, Naya Alifiah az Azar Putri Miranti Alysha Zulia Larasati Muhammad Syaifur Rohman Muhammad Syaifur Rohman Mulyanto, Edy Naufal Fawwazi Purwanto Purwanto Raihatuzzahra, Farah Ramadhani, Irfan Wahyu Ratmana, Danny Oka Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Romadhoni, Mellynda Noor Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Satria Andromeda, Rayhan Sekar Wangi, Sari Asih Septian Enggar Sukmana Sutrisno, Hendra Wibowo, Alrico Rizki Yani Parti Astuti Yani Parti Astuti Zainal Arifin Hasibuan