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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika Creative Information Technology Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JUTEI (Jurnal Terapan Teknologi Informasi) JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Telematika JIEMS (Journal of Industrial Engineering and Management Systems) JOISIE (Journal Of Information Systems And Informatics Engineering) Zonasi: Jurnal Sistem Informasi JISA (Jurnal Informatika dan Sains) Jurnal Algoritma, Logika dan Komputasi
Thenata, Angelina P.
Universitas Bunda Mulia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Analysis of Public Sentiment Towards President Prabowo's Work Program Using The CNN Thenata, Angelina Pramana; Saputra, Dimas Sakti Reka
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9394

Abstract

Digital media has now become the primary means for Indonesians to receive and respond to information, including the work programs presented by Prabowo Subianto. One of the programs that is widely discussed by the public is related to efforts to improve the national economy. Public responses to this issue are widespread on social media, reflecting diverse sentiments. Therefore, this study aims to analyze the sentiment of comments from social media users X regarding President Prabowo's work programs in the economic sector, using a deep learning approach based on the Convolutional Neural Network (CNN) architecture. The methods employed include data collection, text preprocessing, and training a CNN model. The dataset used consisted of 2,467 data points, with 1,086 labeled as positive and 1,381 labeled as negative. The test results showed that the model achieved an accuracy of 87.45% and an Area Under the Curve (AUC) score of 0.9373, indicating excellent classification performance in distinguishing between positive and negative sentiments. This study proves that the combination of CNN and FastText is a practical approach to understanding text-based public opinion from social media.
Classification of Facial Acne Types Based on Self-Supervised Learning using DINOv2 Chardaputeri, Gantari; Thenata, Angelina Pramana
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.v10i1.11856

Abstract

Acne is a common inflammatory skin condition that can affect an individual’s psychological well-being and overall quality of life. The inability to independently recognize specific types of acne often leads to the use of inappropriate skincare products. This situation highlights the need for an image-based classification system that can provide accurate visual identification. The self-supervised learning method Distillation with NO Labels, version 2 (DINOv2), is employed as a feature extractor to classify four types of acne—Acne fulminans, Acne nodules, Papules, and Pustules—using the “skin-90” dataset. The fine-tuning process is conducted through a Parameter-Efficient Fine-Tuning (PEFT) approach using Low-Rank Adaptation (LoRA) to adjust the model’s visual representations to the acne domain without updating all parameters in full, followed by integration with a classification head. The results show that the model achieves an accuracy of 90.70%, with precision, recall, and F1-score values of 90.64%, 90.68%, and 90.57%, respectively. The findings suggest that the proposed architectural design and training configuration are suitable for capturing relevant visual patterns of acne, while further validation is required to assess robustness across more diverse data distributions.
Co-Authors ., Ranny Agus, Agus Andhika Galuh Prabawati Arron, Rivaltino Asvian Sulaeman Bhustomy Hakim Chardaputeri, Gantari Kaunang, Fergie Joanda Lim Yong Teck Matheus Alvian Wikanargo Muh Rizky Ramadhan Muhammad Syukri Mustafa Novian Adi Prasetyo Ranny, Ranny Saputra, Dimas Sakti Reka Sinata, Frans Wikanargo, Matheus Alvian