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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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Linguistik Komputasional Jurnal Ilmu Teknik dan Komputer Jurnal Abdidas Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Universal Raharja Community (URNITY Journal) Jurnal Indonesia Sosial Teknologi Eduvest - Journal of Universal Studies Jurnal Pengabdian Masyarakat Nasional Engagement: Jurnal Pengabdian Kepada Masyarakat Journal Collabits
Afiyati, Afiyati
Universitas Mercu Buana
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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Comparison of Linear Regression and Random Forest Algorithms for Premium Rice Price Prediction (Case Study: West Java) Irfan Rasyid Muchtar; Afiyati Afiyati
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 7 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i7.1184

Abstract

The staple food commodity that is crucial to the Indonesian society is rice. Rice often experiences fluctuations in prices. These fluctuations can be predicted using machine learning methods. The aim of this research is to evaluate the accuracy of machine learning algorithms in predicting the premium rice prices in the West Java Province, Indonesia. Two methods used in this study are Linear Regression and Random Forest. The dataset used consists of 6096 records from the Indonesian Food Commodity Management Agency. The evaluation results show that the Random Forest algorithm has an accuracy rate of 98.69%, while the Linear Regression algorithm has an accuracy rate of 95.08%. Based on these results, it is concluded that the Random Forest algorithm is more effective in predicting premium rice prices in the West Java Province compared to the Linear Regression algorithm.
Penerapan Algoritma Naïve Bayes Pada Analisa Sentimen Twitter Terhadap Opini Publik Badan Pangan Nasional Prima, Andhika; 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%.
Topic Modeling Analysis of Indonesia Food-Security News: Methods,Interpretations, and Trend Insights Afiyati, Afiyati; Rochmad, Imbuh; Budiyanto, Setiyo; Jokonowo, Bambang; Santoso, Hadi; Budiana, Kelik
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5784

Abstract

The critical problem for food-security stakeholders in Indonesia is the lack of scalable, quantitative methods to systematically distill dominant themes and evolving trends from vast volumes of news media, which severely hinders timely policy monitoring and responsive intervention. This study aimed to develop and validate a reproducible topic modeling pipeline specifically designed to uncover the latent thematic structure and quantify the temporal dynamics within Indonesian food-security news discourse. The research method is a comprehensive natural language processing pipeline applied to a curated corpus of 770 news documents spanning 2012 to 2025. The process involved languageadaptive preprocessing of Indonesian text, n-gram (1-2) vectorization to capture nuanced phrases, and training multiple Latent Dirichlet Allocation (LDA) models. The optimal model, with K=10 topics,was rigorously selected through a perplexity-based grid search across a range of potential topic numbers. The resulting topics were then qualitatively interpreted and manually labeled into policy-relevant themes by domain experts. Subsequently, we computed monthly topic intensity series to conduct a longitudinal analysis. The results of this research are that the pipeline successfully generated semantically coherent topics that aligned perfectly with core policy pillars, including availability, access, and utilization. Furthermore, the analysis revealed significant temporal shifts, sustained intensification of price and inflation-related discussions throughout the 2022-2024 period. This study conclusively demonstrates that unsupervised topic modeling can effectively transform unstructured news streams into actionable, quantifiable intelligence, thereby significantly enhancing situational awareness and supporting evidence-based decision-making for food security stakeholders.
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
Co-Authors Abdul Karim Ahmad, Ersa Alief Rama, Alief Ramadhan Dwi Putra Anis Cherid Anny Kartika Sari Ardi, Liandy Hayanto Arrachman, Abdul Kholiq Ayu, Kurnia Gusti Azhari Azhari Bambang Jokonowo Budiana, Kelik Calvin Adiwinata Fahrezi, Zidane Gian Maulana Hadi Santoso Hadi Santoso Hanif, Ilham Hilyah Magdalena Imbuh Rochmad Irfan Rasyid Muchtar Karisma Riskinanti Kurnia, Rizki Ade Lestari, Mulyati Lukman Hakim Mira Zulia Suriastuti Misni Misni Mohamad Yusuf muhammad rizky, muhammad Naima, Faaza Ningrum, Rahma Farah Novrizal, Kevin Prima, Andhika Rachmita Maun Harahap Riando, Dhafin Roza, Yuni Rudiarto, Sabar Saddam Wahib Rozaqi Sakhrassalam, Haytsam Setiyo Budiyanto Siti Maesaroh Sukur, Daan Surya Aji Prasetio Syaban, Zulfikar Syafiq, Nur Muhammad Zihni Yuli yuli Astari Zelina, Nur