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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization IJCIT (Indonesian Journal on Computer and Information Technology) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Linguistik Komputasional Jurnal Ilmu Teknik dan Komputer Jurnal Abdidas Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Universal Raharja Community (URNITY Journal) Jurnal Indonesia Sosial Teknologi Jurnal Pengabdian Masyarakat Nasional Engagement: Jurnal Pengabdian Kepada Masyarakat Journal Collabits JRTI (Jurnal Riset Tindakan Indonesia)
Afiyati, Afiyati
Universitas Mercu Buana
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Sentiment Analysis of X Users Toward Electric Motorcycles Using SVM and BERT Algorithms Calvin Adiwinata; Afiyati Afiyati
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26152

Abstract

This study presents a comparative analysis of Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis on electric motorcycles in Indonesia using data from the social media platform X, formerly known as Twitter. The dataset of 128,711 tweets collected between 2015 and 2024 was refined through systematic preprocessing, reducing the corpus to 38,954 entries after data cleaning, tokenization, and feature selection. The objective was to evaluate algorithm performance in classifying public sentiment, with metrics including accuracy, precision, recall, and computational efficiency. Results showed that SVM achieved higher overall accuracy 89.74% with strong precision for positive sentiment 91%, while BERT, specifically the IndoBERT variant, demonstrated superior recall for negative sentiment 91% despite slightly lower accuracy 87.90%, effectively capturing nuanced contextual language, such as sarcasm, informal expressions, and emotionally ambiguous statements that require deeper semantic understanding beyond literal word meanings. Computational analysis revealed that SVM required approximately 53 minutes of CPU training, compared to BERT’s 3.3 hours on GPU. The study suggests that SVM is optimal for rapid, resource-constrained applications, whereas BERT excels in detailed contextual analysis. These findings guide stakeholders in selecting algorithms based on analytical priorities, such as monitoring public reception or addressing consumer concerns
Pelatihan Dasar Keamanan Siber untuk Mengelola Resiko Digital di Pusat Data dan Informasi Pangan – Badan Pangan Nasional RI Misni Misni; Anis Cherid; Afiyati Afiyati
Jurnal Abdimas Indonesia Vol. 5 No. 3 (2025)
Publisher : Perkumpulan Dosen Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34697/jai.v5i3.1766

Abstract

Keamanan siber menjadi garis depan pertahanan data strategis di tengah gelombang digitalisasi yang kian masif. Ini terutama berlaku untuk lembaga pemerintah yang menangani informasi penting. Dengan mengelola data tentang produksi, distribusi, dan ketersediaan makanan, Pusat Data dan Informasi Pangan-Badan Pangan Nasional RI memainkan peran penting dalam memastikan ketahanan pangan. Sayangnya, ada perbedaan yang signifikan dalam kesadaran dan kesiapan untuk keamanan informasi. Ini merupakan ancaman nyata di era serangan siber yang semakin terarah dan canggih. Tim dari pengabdian kepada masyarakat menghadapi tantangan ini dengan membuat solusi yang mencakup transformasi total, bukan hanya pelatihan. Selama enam bulan, upaya kerja sama dan teknologi digunakan untuk meningkatkan keterampilan, membuat kebijakan, dan meningkatkan infrastruktur keamanan digital. Kegiatan ini tidak hanya meningkatkan pemahaman peserta tentang ancaman siber, tetapi juga meningkatkan komitmen institusional untuk menerapkan kebijakan keamanan informasi yang sesuai dengan standar internasional. Program ini menunjukkan bahwa membangun ketahanan digital memerlukan manusia, bukan alat semata. Pemahaman dan budaya keamanan meningkat, pertahanan institusi pun menjadi lebih kuat
Integration of artificial intelligence in e-voting systems for anomaly detection and prevention of electoral data manipulation Abdurrohman Abdurrohman; Afiyati Afiyati; Rasiban Rasiban
JRTI (Jurnal Riset Tindakan Indonesia) Vol. 11 No. 1 (2026): JRTI (Jurnal Riset Tindakan Indonesia)
Publisher : IICET (Indonesian Institute for Counseling, Education and Therapy)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/30036840000

Abstract

The increasing adoption of electronic voting (e-voting) systems has improved electoral efficiency and accessibility while simultaneously introducing new challenges related to cybersecurity and electoral data integrity. This study aims to examine the integration of Artificial Intelligence (AI) in e-voting systems for anomaly detection and the prevention of electoral data manipulation. Using a library research approach, data were collected through the analysis of books, scientific journals, research reports, and other relevant academic literature related to Artificial Intelligence, anomaly detection, and electoral data integrity. The findings indicate that AI-based anomaly detection mechanisms can effectively identify unusual patterns, suspicious activities, and potential manipulation attempts within electoral datasets. Furthermore, machine learning algorithms enable continuous monitoring and adaptive threat detection, overcoming several limitations of traditional security approaches. The study concludes that integrating AI into e-voting systems can strengthen electoral data integrity, improve security performance, and enhance public trust in digital electoral processes while supporting transparent, reliable, and accountable democratic governance.
Penerapan Algoritma Naïve Bayes Pada Analisa Sentimen Twitter Terhadap Opini Publik Badan Pangan Nasional Andhika Prima; Afiyati Afiyati
Jurnal Ilmu Teknik dan Komputer Vol. 9 No. 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jitkom.v9i1.007

Abstract

Penelitian ini fokus pada penerapan algoritma Naïve Bayes untuk menganalisis sentimen Twitter terhadap Badan Pangan Nasional (BAPANAS). Dalam era digital, media sosial, khususnya Twitter, menjadi saluran utama masyarakat untuk menyampaikan opini terkait instansi pemerintah. Algoritma Naïve Bayes digunakan untuk mengklasifikasikan sentimen menjadi positif atau negatif. Dengan langkah-langkah yang melibatkan crawling data, preprocessing, pelabelan data otomatis menggunakan InSet Lexicon, pembobotan kata dengan TF-IDF, data splitting, dan klasifikasi dengan algoritma Naïve Bayes. Hasil klasifikasi menunjukkan akurasi sebesar 79.7%, dengan presisi 78.6%, recall 74.0%, dan F1 score 76.2%. algoritma Naïve Bayes mengklasifikasikan sebanyak 1.093 data. Dari hasil tersebut, 453 sentimen positif (41.4%) sementara 640 sentimen negatif (58.6%) berdasarkan data testing sebanyak 20%.
Deep Learning-Based Autism Detection Using Facial Images and EfficientNet-B3 Hasanudin, Muhaimin; Afiyati, Afiyati; Budiarto, Rahmat; Wahab, Abdi; Jokonowo, Bambang; Indrianto, Indrianto; Yosrita, Efy; Hanifah, Nurul Afif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4574

Abstract

This study presents a novel deep learning approach for early detection of Autism Spectrum Disorder (ASD) using facial image analysis. Leveraging the EfficientNet-B3 model, the research addresses limitations in traditional diagnostic methods by autonomously extracting discriminative facial features associated with ASD. A balanced dataset of 2,940 facial images (1,470 autistic and 1,470 non-autistic children) from Kaggle was pre-processed to 200x200 pixels and evaluated under three dataset-splitting scenarios (80:10:10, 70:15:15, and 60:20:20) to assess generalisability. The model, trained with the Adam optimiser over 10 epochs, achieved optimal performance in the 80:10:10 scenario, with 84.67% precision, 84.35% recall, and 84.32% F1 score. Results demonstrate high confidence (>90% probability) in distinguishing autistic from non-autistic individuals on unseen data. The study underscores the potential of integrating deep learning into clinical decision-support systems for ASD detection, offering a robust, scalable, and efficient solution to improve diagnostic accuracy and reduce reliance on manual methods.
Co-Authors Abdi Wahab Abdul Karim Abdurrohman Abdurrohman Ahmad, Ersa Andhika Prima Anis Cherid Anny Kartika Sari Ardi, Liandy Hayanto Arrachman, Abdul Kholiq Azhari Azhari Bambang Jokonowo Calvin Adiwinata Efy Yosrita, Efy Fahrezi, Zidane Gian Maulana Hadi Santoso Hadi Santoso Hanif, Ilham Hanifah, Nurul Afif Hilyah Magdalena Indrianto Indrianto Irfan Rasyid Muchtar Karisma Riskinanti Kurnia Gusti Ayu Kurnia, Rizki Ade Lestari, Mulyati Lukman Hakim Mira Zulia Suriastuti Misni, Misni Mohamad Yusuf Muhaimin Hasanudin muhammad rizky, muhammad Naima, Faaza Ningrum, Rahma Farah Novrizal, Kevin Prima, Andhika Rachmita Maun Harahap Rahmat Budiarto Rasiban Rasiban Roza, Yuni Rudiarto, Sabar Saddam Wahib Rozaqi Sakhrassalam, Haytsam Siti Maesaroh Sukur, Daan Surya Aji Prasetio Syaban, Zulfikar Syafiq, Nur Muhammad Zihni Yuli yuli Astari Zelina, Nur