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All Journal JURNAL PENDIDIKAN TAMBUSAI Jurnal Sains dan Teknologi Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Anggraini, Delia
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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 Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Analisis Citra Medis untuk Mendeteksi Diabetes Menggunakan Metode CNN(Convulutiona Neural Network) Anggraini, Delia; Maisyarah, Maisyarah; Sari Hasibuan, Maya; Pratika Siwi, Sindi; Fahreza Putra, Dafa; Khalil Gibran, M.
Jurnal Pendidikan Tambusai Vol. 9 No. 1 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i1.27716

Abstract

Pendeteksian dini terhadap penyakit diabetes menjadi kunci dalam meningkatkan kualitas hidup pasien dan mencegah komplikasi jangka panjang. Teknologi pengolahan citra medis berbasis kecerdasan buatan, khususnya metode Convolutional Neural Network (CNN), telah menunjukkan potensi besar dalam menganalisis dan mengklasifikasikan data visual dari tubuh manusia. Penelitian ini mengusulkan sebuah pendekatan otomatis untuk menganalisis citra medis, seperti gambar retina dan CT scan, guna mengidentifikasi indikasi diabetes. Dataset citra medis diolah melalui tahapan preprocessing, augmentasi, dan pelatihan menggunakan arsitektur CNN yang disesuaikan. Hasil eksperimen menunjukkan akurasi mencapai 94,2%, sensitivitas 91,7%, dan spesifisitas 95,5%.
Klasifikasi Komentar Kasar pada TikTok Menggunakan TF-IDF dan Logistic Regression Anggraini, Delia; Wahyudin, Rahmat; Wicaksana, Agum; ., Zulpadli; Zulnun, M. Ridho Azmuddin; Furqan, Mhd
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3906

Abstract

The increasing intensity of user interaction on the TikTok platform makes the comment section vulnerable to the emergence of rude comments, impolite speech, and negative verbal expressions that can reduce the quality of digital communication. The characteristics of TikTok language, which is informal, concise, and rich in slang variations and non-standard spelling, present challenges in the process of automatically identifying rude comments, especially in the Indonesian context. This study aims to develop and evaluate a binary classification model capable of distinguishing rude and non-rude comments on the TikTok platform using a text-based machine learning approach. The research method began with the collection of 650 Indonesian-language public comments from TikTok, which were then manually annotated into two classes: rude and non-rude comments. The labeled data were processed through preprocessing stages including text cleaning, case folding, slang normalization, repeated character reduction, tokenization, and stopword removal. Feature representation was carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) method with a combination of unigrams and bigrams, while the classification process used the Logistic Regression algorithm. The data were divided into training data and test data with a ratio of 80:20. The analysis techniques used included evaluating model performance using accuracy, precision, recall, and F1-score metrics. The results showed that the model achieved an accuracy of 87.4%, with precision, recall, and F1-score values ​​of 0.87 each, indicating good and balanced classification performance across both classes. These findings indicate that the combination of TF-IDF and Logistic Regression is effective as a baseline in classifying abusive Indonesian comments on the TikTok platform.
An Agent-Based Modeling Approach for Crowd Movement in Confined Spaces Anggraini, Delia; Khairani, Rika; Pangestu, Dimas
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.2006

Abstract

This study presents an agent-based modeling approach to analyze crowd movement and evacuation performance in confined spaces. The model simulates individual agents navigating toward a single exit while avoiding collisions under varying density conditions. Three evacuation scenarios were evaluated, consisting of 20, 40, and 60 agents within a confined environment measuring 10 × 8 meters. The simulation was executed using a discrete time step of 0.1 seconds, and performance was assessed based on evacuation time and collision frequency. The results indicate that increasing crowd density significantly affects movement efficiency. The 20-agent scenario achieved an average evacuation time of 6.42 seconds with 95.33 collision events. When the number of agents increased to 40, the evacuation time rose to 6.90 seconds with 391.77 collisions. The highest density scenario, consisting of 60 agents, produced an average evacuation time of 7.08 seconds and 890.73 collision events. These findings demonstrate that higher density levels lead to a disproportionate increase in interaction intensity and congestion, resulting in reduced evacuation efficiency. The study confirms that agent-based modeling is an effective approach for analyzing crowd dynamics in confined environments and provides a reproducible framework for evaluating evacuation performance under varying density conditions.
Co-Authors ., Zulpadli Fahreza Putra, Dafa Khairani, Rika Khalil Gibran, M. Maisyarah Maisyarah Mhd Furqan Pangestu, Dimas Pratika Siwi, Sindi Sari Hasibuan, Maya Wahyudin, Rahmat Wicaksana, Agum Zulnun, M. Ridho Azmuddin