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All Journal Jurnal Pendidikan Teknologi dan Kejuruan BERKALA SAINSTEK Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal TAM (Technology Acceptance Model) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Indonesian Journal of Data and Science JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Riset Rumpun Seni, Desain dan Media (JURRSENDEM) Jurnal Sistem Informasi Journal of Social Work and Empowerment Jurnal TAM (Technology Acceptance Model) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Aristamy, I Gusti Ayu Agung Mas
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Mathematics Physics Social Sciences Other

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A Comparative Study of MobileNetV2 and ResNet50 for Multi-Class AI- Generated and Real Image Classification Pramudita, I Gusti Ngurah Agus Ega Patria; Sudipa, I Gede Iwan; Fittryani, Yuri Prima; Iswara, Ida Bagus Ary Indra; Aristamy, I Gusti Ayu Agung Mas
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15682

Abstract

This study aims to classify AI-generated and real images using Convolutional Neural Network (CNN) architecture by comparing the performance of MobileNetV2 and ResNet50. Previous studies on AI-generated image detection have primarily focused on binary classification without explicitly analyzing object-level context in multi-class scenarios, leaving a gap in understanding model performance across diverse visual categories. The dataset consists of 23,941 images divided into two main classes of real and fake and five subclasses of human, animal, art, view, and vehicle. The training process employs data augmentation and a K-Fold Cross Validation strategy on the training and validation set to maintain balanced class proportions, while a separate unseen test set is used exclusively for final performance evaluation. Model evaluation is performed based on accuracy, precision, recall, and F1-score metrics on test data. The results showed that MobileNetV2 achieved the best accuracy of 89% at the 10th epoch, but experienced a decline in performance at the 30th and 50th epochs, indicating overfitting. In contrast, ResNet50 showed the most stable performance with the highest accuracy of 93% at the 30th epoch and consistently high precision, recall, and F1-score values. Thus, ResNet50 was found to be the most effective architecture for classification of AI-generated and real images on multi-class datasets, while MobileNetV2 remains relevant for implementation on devices with computational limitations.
VISUALISASI KEBAYA BALI BERBASIS AUGMENTED REALITY UNTUK PENINGKATAN PROMOSI UMKM Aristamy, I Gusti Ayu Agung Mas; Iswardani, Putu Risanti; Meinarni, Ni Putu Suci
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 8 No 1 (2026): Maret 2026
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v8i1.572

Abstract

Penjualan produk kebaya Bali pada UMKM HitaFelicite masih menggunakan media promosi konvensional berupa foto katalog dan media sosial dua dimensi sehingga konsumen belum dapat melihat bentuk produk secara detail dan interaktif. Keterbatasan tersebut menyebabkan pengalaman pengguna dalam mengeksplorasi produk menjadi kurang optimal. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan aplikasi katalog produk kebaya Bali berbasis Augmented Reality (AR) sebagai media visualisasi produk yang lebih interaktif. Metode pengembangan aplikasi menggunakan tahapan pemodelan sistem, pembuatan aset 3D, serta implementasi teknologi AR berbasis perangkat mobile. Pengujian sistem dilakukan menggunakan Black Box Testing, pengujian spesifikasi perangkat (device compatibility), dan User Experience Questionnaire (UEQ) untuk mengetahui tingkat keberhasilan fungsi aplikasi dan pengalaman pengguna. Evaluasi pengalaman pengguna menggunakan User Experience Questionnaire (UEQ) terhadap 20 responden menghasilkan penilaian positif, dengan aspek Efisiensi berada pada kategori Excellent, sementara Daya Tarik, Kejelasan, dan Ketepatan pada kategori Good. Dengan demikian, aplikasi AR yang dikembangkan dapat menjadi media promosi digital yang lebih interaktif bagi UMKM HitaFelicite Kebaya.
Comparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTubeComparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTube Ni Wayan Indah Juliandewi; Aniek Suryanti Kusuma; Kompiang Martina Dinata Putri; I Gusti Agung Indrawan; I Gusti Ayu Agung Mas Aristamy
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.366

Abstract

The advancement of digital technology has increased public engagement in expressing opinions and responding to issues on social media platforms such as X and YouTube. A prominent topic of recent public debate concerns Danantara's management of state-owned banks. This study analyzes public sentiment regarding this issue by comparing the performance of the Naïve Bayes and Random Forest classification methods. A dataset comprising 25,565 entries was collected from both platforms between January 2025 and May 2025. The data underwent text pre-processing, labeling with the InSet Lexicon, and feature weighting using term frequency-inverse document frequency (TF-IDF). The dataset was split at 80:20, and class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) prior to classification. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that Random Forest performed stably, achieving 84% accuracy both before and after sampling. In contrast, Naïve Bayes achieved 74% accuracy before sampling, which increased to 79% after sampling. These findings suggest that Random Forest is more robust to data imbalance than Naïve Bayes, which is more susceptible to bias toward the majority class.
Co-Authors Afifah Nurul Izzati Al Lukman, Moh Alif Alaik, Zain Anggarawati, Putu Ratna Aniek Suryanti Kusuma, Aniek Suryanti Ardinata, I Putu Adi Ayu Gede Willdahlia Azmi Pratama Bhawanaputra, I Gusti Made Ngurah Ari Christina Purnama Yanti Devi Valentino Waas Dewi, Ni Luh Ervina Sunari Dharsika, Eka Hendrawati, Theresia I Gede Iwan Sudipa I Gusti Agung Indrawan I Kadek Dwi Mahendra Putra I Komang Arya Ganda Wiguna I Made Adi Swardana I Nyoman Agus Suarya I Nyoman Anom Fajaraditya I Wayan Yoga Widya Putra Ida Bagus Ary Indra Iswara Ida Bagus Nyoman Pascima Indra Pratistha Iswardani, Putu Risanti Izzati, Afifah Nurul Komang Redy Winatha Kompiang Martina Dinata Putri Libraeni, Luh Gede Bevi Mahardika, Putu Arya Aditya Meinarni, Ni Putu Suci Najwa, Nina Fadilah Naratama, I Made Edo Netty Amelia Ni Kadek Nita Noviani Pande Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Wayan Indah Juliandewi Nina Fadilah Najwa Nur Aini Rakhmawati Permana, I Kadek Adi Pramudita, I Gusti Ngurah Agus Ega Patria Pratama, Azmi Putra, I Putu Satria Udyana Qinantha, I Kadek Mahesa Chandra Rahayu, Ni Made Astini Rizkita Ayu Mutiarani Rizkita Ayu Mutiarani Sanjaya, Wayan Putra Adi Santi Ika Murpratiwi Sephiarini, Ni Putu Satya Thalib, Emmy Febriani Wayan Gede Suka Parwita Willdahlia, Ayu Gede Winatha, Komang Redy Yuri Prima Fittryani, Yuri Prima Zain Alaik