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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika INTECOMS: Journal of Information Technology and Computer Science Progresif: Jurnal Ilmiah Komputer Journal of Applied Engineering and Technological Science (JAETS) Community Engagement and Emergence Journal (CEEJ) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Innovation Information Technology and Application (JINITA) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Minfo Polgan (JMP) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Wahana
Sri Handayani
Universitas Pembinaan Masyarakat Indonesia
Agriculture, Biological Sciences & Forestry 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 Public Health Social Sciences Other

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Estimasi Dimensi Kepribadian Melalui Analisis Citra Ekspresi Wajah Menggunakan Convolutional Neural Network Annas Prasetio; Sri Handayani
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6936

Abstract

Personality is a crucial aspect that influences a person's behavior, way of thinking, and interaction patterns in various situations. Advances in digital image processing and artificial intelligence technology offer opportunities for developing systems capable of automatically estimating personality dimensions through facial expression analysis. This study aims to build a personality dimension estimation model based on facial expression images using the Convolutional Neural Network (CNN) method. The research steps include collecting a dataset of facial images representing various expressions, image preprocessing, including face detection, image size normalization, and data augmentation, followed by training a CNN model to learn visual characteristics related to facial expressions. The resulting model is then tested using data not involved in the training process to measure the model's generalization ability. System performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results are expected to demonstrate that the Convolutional Neural Network approach is capable of effectively extracting visual features from facial expressions and can therefore be used as a basis for estimating personality dimensions. This research is expected to contribute to the development of computer vision and artificial intelligence technology, particularly in the fields of human behavior analysis, decision support systems, and more adaptive human-computer interaction applications. Keywords: Personality Estimation, Facial Expression Images, Digital Image Processing, Convolutional Neural Network, Computer Vision, Artificial Intelligence.
Deteksi Diabetes Menggunakan Analisis Citra Kuku Berbasis Vision Transformer CNN-LSTM Annas Prasetio; Sri Handayani
Progresif: Jurnal Ilmiah Komputer Vol 22, No 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3941

Abstract

Diabetes mellitus is a chronic metabolic disease that requires early detection to prevent complications. However, commonly used diagnostic methods are still invasive and require laboratory testing. This study aims to develop a diabetes detection model based on nail image analysis using the Hybrid Vision Transformer–Convolutional Neural Network–Long Short-Term Memory (Vision Transformer–CNN–LSTM) method as a non-invasive approach. The study included dataset collection, image preprocessing, including resizing, normalization, segmentation, dataset partitioning, model training, and evaluation using a confusion matrix. The Vision Transformer was used to capture a global image representation, the Convolutional Neural Network extracted local features, and the Long Short-Term Memory enhanced the feature representation before the classification process. Test results showed that the model achieved 93.33% accuracy, 91.89% precision, 94.44% recall, 93.13% F1-score, and an Area Under the Curve of 0.972. These results demonstrate that the proposed model is capable of accurately detecting diabetes and has the potential to be a fast, easy, and non-invasive alternative for initial screening based on nail images.Keywords: Diabetes mellitus; nail image; Vision Transformer; Convolutional Neural Network–Long Short-Term Memory; Early detection. AbstrakDiabetes mellitus menjadi penyakit metabolik kronis yang memerlukan deteksi dini untuk mencegah terjadinya komplikasi, namun metode diagnosis yang umum digunakan masih bersifat invasif dan memerlukan pemeriksaan laboratorium. Penelitian ini bertujuan mengembangkan model deteksi diabetes berbasis analisis citra kuku menggunakan metode Hybrid Vision Transformer–Convolutional Neural Network–Long Short-Term Memory (Vision Transformer–CNN–LSTM) sebagai pendekatan noninvasif. Penelitian dilakukan melalui tahapan pengumpulan dataset, preprocessing citra berupa resize, normalisasi, segmentasi, pembagian dataset, pelatihan model, dan evaluasi menggunakan confusion matrix. Vision Transformer dimanfaatkan untuk menangkap representasi global citra, Convolutional Neural Network mengekstraksi fitur lokal, sedangkan Long Short-Term Memory memperkuat representasi fitur sebelum proses klasifikasi. Hasil pengujian menunjukkan bahwa model menghasilkan accuracy 93,33%, precision 91,89%, recall 94,44%, F1-score 93,13%, dan Area Under Curve sebesar 0,972. Hasil tersebut menunjukkan bahwa model yang diusulkan mampu mendeteksi diabetes secara akurat serta berpotensi menjadi alternatif skrining awal berbasis citra kuku yang cepat, mudah, dan noninvasif.Kata kunci: Diabetes mellitus; citra kuku; Vision Transformer; Convolutional Neural Network–Long Short-Term Memory; Deteksi dini
Pembelajaran Matematika SD Berbasis Eco-Friendly Unplugged Coding Terintegrasi AI untuk Problem-Solving dan Computational Thinking Sri Handayani; Fider Lumbanbatu; Nurhalimah Nurhalimah
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16600

Abstract

Mathematics learning in elementary schools still faces challenges in simultaneously developing students' problem-solving and computational thinking skills, especially in schools with limited digital devices. This study aims to analyze the effect of elementary school mathematics learning based on eco-friendly unplugged coding integrated with Artificial Intelligence on students' problem-solving and computational thinking. The study used a quantitative approach with an explanatory survey design. Data were collected through questionnaires from 120 public elementary school students in Lubuk Pakam, then analyzed using Partial Least Squares-Structural Equation Modeling with SmartPLS software. The evaluation model included testing convergent validity, discriminant validity, construct reliability, coefficient of determination, and hypothesis testing through bootstrapping. The results showed that elementary school mathematics learning had a positive and significant effect on computational thinking with a path coefficient of 0.649, a t-statistic of 11.630, and a P value of 0.000. A positive and significant effect was also found on problem-solving, with a path coefficient of 0.623, a t-statistic of 10.527, and a P value of 0.000. These findings indicate that the integration of unplugged coding activities, an environmentally friendly context, and AI support can encourage problem decomposition, pattern recognition, algorithm development, debugging, and systematic mathematical problem solving. The research implications confirm that this model can be an innovative and inclusive learning alternative for elementary schools. Further research is recommended using experimental designs, control groups, larger samples, and testing the contribution of each learning component separately.
Co-Authors Adinda Silvana Dewi Akhyar Lubis Annas Prasetio Debby Endriani Dewi Mahrani Rangkuty Dwi Saraswati Fahmi Kurniawan Fajrul Malik Aminullah Napitupulu Fider Lumbanbatu Herman Mawengkang Ika Devi Perwitasari Juanda Hakim Lubis Muhammad Yusuf Parlagutan Lubis Nurhalimah Nurhalimah Nurmayana Puji Hariati Purwanto Siwi Purwarno, Purwarno Putra, Randi Rian Putri, Nadya Andhika Ramadha Yanti Parindori Randi Rian Putra Rian Farta Wijaya Suranto Suranto Virdyra Tasril Yuliska