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All Journal Dinamik Semantik Techno.Com: Jurnal Teknologi Informasi Telematika : Jurnal Informatika dan Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Techne : Jurnal Ilmiah Elektroteknika Jurnal Sistem Cerdas EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Computer Science and Technology (JACOST) Journal La Multiapp Community: Jurnal Pengabdian Pada Masyarakat
Ardytha Luthfiarta
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Single-Image Face Recognition For Student Identification Using Facenet512 And Yolov8 In Academic Environtment With Limited Dataset Imam Muttaqin, Almas Najiib; Luthfiarta, Ardytha; Nugraha, Adhitya; Salsabila, Pramesya Mutia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.3908

Abstract

Face recognition has become one of the most significant research areas in image processing and computer vision, mainly due to its wide applications in security, identity verification, and human and machine interaction. In this study, FaceNet512 and YOLOv8 models are used to overcome the challenges in face recognition with a limited dataset, which is only one formal photo per individual. The application of image augmentation to the model achieved 90% accuracy and ROC curve of 0.82, while the model without augmentation achieved 89% accuracy and ROC curve of 0.79. FaceNet512 showed superiority in producing more accurate and detailed facial representations compared to other models, such as ArcFace and FaceNet, especially in handling minimal facial variations. Meanwhile, YOLOv8 provides efficient face detection across various lighting conditions and viewing angles. The main challenge in this research is the low quality of the original image, which can reduce the accuracy of face recognition. These results show the great potential of using deep learning-based face recognition systems in the real world, especially for automatic attendance applications in academic environments.
Comparison of IndoNanoT5 and IndoGPT for Advancing Indonesian Text Formalization in Low-Resource Settings Firdausillah, Fahri; Luthfiarta, Ardytha; Nugraha, Adhitya; Dewi, Ika Novita; Hafiizhudin, Lutfi Azis; Mumtaz, Najma Amira; Syarifah, Ulima Muna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4935

Abstract

The rapid growth of digital communication in Indonesia has led to a distinct informal linguistic style that poses significant challenges for Natural Language Processing (NLP) systems trained on formal text. This discrepancy often degrades the performance of downstream tasks like machine translation and sentiment analysis. This study aims to provide the first systematic comparison of IndoNanoT5 (encoder-decoder) and IndoGPT (decoder-only) architectures for Indonesian informal-to-formal text style transfer. We conduct comprehensive experiments using the STIF-INDONESIA dataset through rigorous hyperparameter optimization, multiple evaluation metrics, and statistical significance testing. The results demonstrate clear superiority of the encoder-decoder architecture, with IndoNanoT5-base achieving a peak BLEU score of 55.99, significantly outperforming IndoGPT's highest score of 51.13 by 4.86 points—a statistically significant improvement (p<0.001) with large effect size (Cohen's d = 0.847). This establishes new performance benchmarks with 28.49 BLEU points improvement over previous methods, representing a 103.6% relative gain. Architectural analysis reveals that bidirectional context processing, explicit input-output separation, and cross-attention mechanisms provide critical advantages for handling Indonesian morphological complexity. Computational efficiency analysis shows important trade-offs between inference speed and output quality. This research advances Indonesian text normalization capabilities and provides empirical evidence for architectural selection in sequence-to-sequence tasks for morphologically rich, low-resource languages.
Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model Cahya, Leno Dwi; Luthfiarta, Ardytha; Krisna, Julius Immanuel Theo; Winarno, Sri; Nugraha, Adhitya
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 3 (2023): Desember 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.290-298

Abstract

Sentiment and emotion analysis is a common classification task aimed at enhancing the benefit and comfort of consumers of a product. However, the data obtained often lacks balance between each class or aspect to be analyzed, commonly known as an imbalanced dataset. Imbalanced datasets are frequently challenging in machine learning tasks, particularly text datasets. Our research tackles imbalanced datasets using two techniques, namely SMOTE and Augmentation. In the SMOTE technique, text datasets need to undergo numerical representation using TF-IDF. The classification model employed is the IndoBERT model. Both oversampling techniques can address data imbalance by generating synthetic and new data. The newly created dataset enhances the classification model's performance. With the Augmentation technique, the classification model's performance improves by up to 20%, with accuracy reaching 78%, precision at 85%, recall at 82%, and an F1-score of 83%. On the other hand, using the SMOTE technique, the evaluation results achieve the best values between the two techniques, enhancing the model's accuracy to a high 82% with precision at 87%, recall at 85%, and an F1-score of 86%.
Sistem Rekomendasi Pembelian Smartphone berbasis Algoritma K-Means dan Singular Value Decomposition Zuhdiansyah, Ivan; Luthfiarta, Ardytha
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 1 (2024): April 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i1.2024.45-53

Abstract

Perkembangan teknologi informasi yang pesat, memberi dampak pada ketesedian informasi yang berlimpah. Hal ini menjadikan suatu masalah yang disebut kelebihan informasi, menyebabkan pengguna internet sulit memahami dan membuat keptusan. E-commerce merupakan salah satu yang terdampak dari kelebihan informasi, dengan banyaknya produk dan pengguna baik dari penjual maupun pembeli yang ada. Sistem rekomendasi adalah bagian penting dari e-commerce yang menjadi salah satu cara menangani kelebihan informasi, dengan memberikan rekomendasi produk kepada pembeli agar membantu menentukan pilihan. Dalam sistem rekomendasi memiliki permasalahan scalability, dimana banyaknya produk yang tersedia membuatnya menjadi tidak efektif dan efisien dalam memberikan rekomendasi kepada pembeli. Maka, penelitian ini mengusulkan metode sistem rekomendasi yang dikombinasikan teknik clustering. Menggunakan algoritma K-Means untuk mengelompokkan produk, kemudian algoritma Singular Value Decomposition (SVD) untuk membuat rekomendasi di dalam cluster yang terbentuk. Hasil keluaran model yaitu, rekomendasi produk dan prediksi rating yang diberikan pembeli dari produk yang direkomendasikan. Evaluasi model mendapatkan nilai dbi sebesar 0,703 untuk clustering, nilai rata-rata terbaik MAE 0.8150 dan RMSE 1.1781 untuk rekomendasi yang dihasilkan. Kesimpulan yang didapat bahwa metode ini dapat menangani masalah scalability dan memberikan rekomendasi yang akurat dengan nilai evaluasi yang lebih baik dibandingkan penelitian sebelumnya.
Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abu Salam Abu Salam Adhitya Nugraha Adhitya Nugraha Adhitya Nugraha Affandy Affandy Althoff, Mohammad Noval Aris Febriyanto Aryanti, Firda Ayu Dwi Astuti, Yani Parti Bagus Dwi Satya, Mohammad Wahyu Basiron, Halizah Cahya, Leno Dwi Catur Supriyanto Catur Supriyanto Defri Kurniawan Dhita Aulia Octaviani Dzaki, Azmi Abiyyu Edi Faisal Edi Sugiarto Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Fahreza, Muhammad Daffa Al Fahrezi, Sahrul Fahrezi Fahrezi, Sahrul Yudha Fahri Firdausillah Fairuz Dyah Esabella Farandi, Muhammad Naufal Erza Farsya, Nabila Zibriza Fauzyah, Zahrah Asri Nur Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Hafiizhudin, Lutfi Azis Haresta, Alif Agsakli Harun Al Azies Hasan Shobri Heru Lestiawan Huda, Alam Muhammad Ika Novita Dewi Imam Muttaqin, Almas Najiib Indrawan, Michael Irham Ferdiansyah Katili Ivan Zuhdiansyah Julius Immanuel Theo Krisna Junta Zeniarja Krisna, Julius Immanuel Theo L. Budi Handoko Leno Dwi Cahya Maharani, Zahra Nabila Mahardika, Pramesthi Qisthia Hanum Md. Mahadi Hasan, Md. Mahadi Michael Indrawan Muhammad Daffa Al Fahreza Muhammad Jamhari Muhammad Rafid Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nauval Dwi Primadya Nisa, Laila Rahmatin Octaviani, Dhita Aulia Primadya, Nauval Dwi Rafid, Muhammad Ramadhan Rakhmat Sani Rismiyati Rismiyati Sahrul Yudha Fahrezi Salsabila, Pramesya Mutia Satya, Mohammad Wahyu Bagus Dwi Setiawan, Dicky Soeroso, Dennis Adiwinata Irwan Sri Winarno Sri Winarno Suprayogi Suprayogi Suryaningtyas Rahayu Syarifah, Ulima Muna Utomo, Danang Wahyu Wibowo Wicaksono Wibowo Wicaksono Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine Zuhdiansyah, Ivan