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All Journal Jurnal Buana Informatika JIKO (Jurnal Informatika dan Komputer) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JUTIM (Jurnal Teknik Informatika Musirawas) Jiko (Jurnal Informatika dan komputer) Jurnal Ilmiah Media Sisfo JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) MANDAR: Management Development and Applied Research Journal Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Information System Journal (INFOS) INFOSYS (INFORMATION SYSTEM) JOURNAL Batara Wisnu : Indonesian Journal of Community Services Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Ilmiah Pengabdian dan Inovasi Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science FIMERKOM: Journal of Information Systems and Technology
Nuri Cahyono
Universitas Amikom Yogyakarta
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 Library & Information Science Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Optimization of IndoBERT for Sentiment Analysis of FOMO on Social Media Through Fine-Tuning and Hybrid Labeling Adhim, Nadhif Fauzil; Cahyono, Nuri
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11686

Abstract

The rapid growth of social media in Indonesia has given rise to social phenomena such as Fear of Missing Out (FOMO). Expressions of FOMO on platforms like X (previously Twitter) often written informally, filled with abbreviations, slang, and emotional nuances, posing challenges for traditional Natural Language Processing (NLP) methods. This research aims to develop an optimized sentiment classification model for FOMO-related posts by fine-tuning the IndoBERT architecture and applying comprehensive data enhancement strategies. The study introduces three key innovations: (1) systematic text normalization to handle informal expressions, (2) a hybrid labeling framework combining automated model prediction, lexicon-based validation, and manual annotation to construct high-quality ground-truth data, and (3) hyperparameter tuning using both GridSearchCV for traditional machine learning models and Bayesian Optimization (Optuna) for deep learning models to maximize performance. The experimental results demonstrate that the optimized IndoBERT achieved superior performance with an Accuracy of 94.50%, F1-Score of 94.52%, and Macro AUC of 0.987. These results significantly surpass comparative models, including BiLSTM (Accuracy 86.60%), Support Vector Machine (88.06%), and Naive Bayes (80.73%). These results confirm that integrating hybrid labeling and fine-tuned IndoBERT significantly enhances sentiment classification performance. The findings contribute to developing reliable sentiment analysis systems for detecting social anxiety dynamics and computational social science research in Indonesian contexts.
Evaluation of YOLOv8 and Faster R-CNN for Image-Based Food Detection Hananta, Julian Kiyosaki; Cahyono, Nuri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11684

Abstract

Difficulties in manually tracking nutrition lead to the need for automatic food detection systems. However, Indonesian food presents tough challenges to recognize because similar-looking foods and different serving styles make it hard. This study looks at two deep learning models that follow different approaches: YOLOv8, which is known for being fast and efficient, and Faster R-CNN, which is known for being very accurate. The goal is to find the best model for use on mobile devices. This research uses a strict and standardized way to test the models to make sure the comparison is fair. A public dataset with 1,325 images from Roboflow was used. To deal with uneven class distribution, the images were split using Stratified Random Sampling. Before training, the images were resized using letterbox method to keep their original shape and normalized for pixel values. Both models were trained for the same number of epochs (100) and used the same optimizer (SGD) to ensure fair comparisons. The results show that YOLOv8 performs better in all areas. It achieved 88.6% mAP@50 accuracy and 62.0% mAP@50-95 precision. Faster R-CNN got 85.5% and 55.6% respectively. YOLOv8 also excels in sensitivity or Recall, reaching 87.7% compared to 61.7% for Faster R-CNN. The F1-Score, which balances accuracy and sensitivity, is 84.0% for YOLOv8 and 72% for Faster R-CNN. In terms of speed and size, YOLOv8 is much better. It runs in 13.5 ms and is 21.5 MB in size. That makes it 7.7 times faster and 7.3 times smaller than Faster R-CNN. Based on these results, YOLOv8 is the best choice for developing mobile-based nutrition tracking systems.
Co-Authors Adhim, Nadhif Fauzil Adi Saputro, Johan Afnan Wijaya, Hafid Ahlun, Mohamad Alfataah, Sevilla Andika Joni, Gusti Angga Reni Dwi Astuti Angga Reni Dwi Astuti Anggista Oktavia Praneswara Anggita, Sharazita Dyah Anggoro Saputro, Uyock Anna Baita Arif Rahmat Hidayatullah Arifiyanto Hadinegoro, Arifiyanto Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Bagus Candrahutomo, Rio Baita, Anna Bayu Prayoga, Mahendra Bayu Wicaksono Dewi Setiyawati Dewi Setiyawati Dyah Anggita, Sharazita Eli Pujastuti, Eli Ema Utami Ermawan, Bagas Restya Fahlevie, Reychandra Tristan Faisal Salim, Muhammad Fauzan Baehaqi Gustafito, Naufal Nuha Hananta, Julian Kiyosaki Hendra Kurniawan Inggar Adi Prasetyo Kamarudin Kamarudin Kusuma Wardana, Raka Kusuma, Ikhsan Habib M Iqbal Novananda Sugiharto Marsel Pratama, Aldino Mas’ud Mahfudz Darmawan, Muhammad Nugroho, Bagas Julio NUR AINI Nurcahyo, Narwanto Nurhayanto, Nurhayanto Oktafria, Fikri Pamungkas, Adji Surya Pramudya Hidayat, Muhammad Rifqi Rakarahayu Putri, Raniya Ramadhan Bima Saputra Restu Agung, Akmal Fauzan Rizal Gibran Aldrin Pratama Rizki Gunawan, Rizki Salamun Sukriandi Santoso, Riki Adi Siswahyudianto Subektiningsih Subektiningsih Sugiharto, M Iqbal Novananda Susanti, Rahmadhani Tofa Nurcholis, Muhammad Uray Nur Khadijah Uyock Anggoro Saputro Wahyu Hidayat Wardhana, Raditya Wulandari, Irma Rofni Yoga Pristyanto Yuliana , Dwi Eva Zurni Laila