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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika INTECOMS: Journal of Information Technology and Computer Science Jurnal Nasional Komputasi dan Teknologi Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal Of Computer Science And Technology Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Annas Prasetio
Universitas Pembinaan Masyarakat Indonesia
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Neuroscience Social Sciences Transportation Other

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Analisis Warna Kulit Menggunakan Citra Digital Berbasis Ruang Warna YCbCr Annas Prasetio; Yulia Utami
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9228

Abstract

Abstrak - Warna kulit manusia memiliki peranan penting dalam berbagai aplikasi berbasis citra digital, mulai dari sistem pengenalan wajah, pelacakan objek, kosmetik virtual, hingga aplikasi medis seperti deteksi penyakit kulit. Salah satu tantangan utama dalam mendeteksi warna kulit adalah keberagaman warna kulit antar individu, pengaruh pencahayaan, serta perbedaan kualitas kamera. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi metode segmentasi warna kulit berbasis ruang warna YCbCr yang mampu bekerja secara andal di berbagai kondisi pencahayaan dan latar belakang. Dataset yang digunakan terdiri dari 500 citra wajah dengan variasi etnis dan pencahayaan yang signifikan. Tahapan penelitian meliputi konversi ruang warna dari RGB ke YCbCr, segmentasi area kulit menggunakan teknik threshold pada kanal Cb dan Cr, serta evaluasi hasil dengan membandingkan area deteksi dengan ground truth yang telah dianotasi secara manual. Hasil eksperimen menunjukkan bahwa metode segmentasi berbasis YCbCr mampu memisahkan area kulit dengan tingkat akurasi sebesar 93,4%, precision sebesar 91,7%, dan recall sebesar 90,2%. Metode ini terbukti lebih stabil dibandingkan segmentasi menggunakan ruang warna RGB yang sangat dipengaruhi oleh intensitas cahaya. Selain itu, sistem ini memiliki kompleksitas komputasi yang rendah sehingga cocok diterapkan dalam aplikasi real-time. Penelitian ini memberikan kontribusi dalam pengembangan sistem deteksi warna kulit yang efisien, khususnya dalam pengolahan citra berbasis perangkat mobile dan sistem tertanam. Hasil yang diperoleh dapat menjadi landasan untuk pengembangan lanjutan, seperti klasifikasi jenis kulit, estimasi pigmen, atau analisis kondisi dermatologis secara otomatis.Kata Kunci: warna kulit; segmentasi citra; YCbCr; pengolahan citra digital; analisis warna; deteksi kulit. Abstract - Human skin color plays an important role in various digital image-based applications, ranging from facial recognition systems, object tracking, virtual cosmetics, to medical applications such as skin disease detection. One of the main challenges in detecting skin color is the diversity of skin color between individuals, the influence of lighting, and differences in camera quality. This study aims to develop and evaluate a skin color segmentation method based on the YCbCr color space that is able to work reliably in various lighting and background conditions. The dataset used consists of 500 facial images with significant ethnic and lighting variations. The research stages include converting the color space from RGB to YCbCr, segmenting the skin area using the threshold technique on the Cb and Cr channels, and evaluating the results by comparing the detection area with the ground truth that has been manually annotated. The experimental results show that the YCbCr-based segmentation method is able to separate skin areas with an accuracy level of 93.4%, a precision of 91.7%, and a recall of 90.2%. This method is proven to be more stable than segmentation using the RGB color space which is greatly influenced by light intensity. In addition, this system has low computational complexity so that it is suitable for real-time applications. This research contributes to the development of an efficient skin color detection system, especially in image processing based on mobile devices and embedded systems. The results obtained can be the basis for further development, such as skin type classification, pigment estimation, or automatic dermatological condition analysis.Keywords; skin color; image segmentation; YCbCr; digital image processing; color analysis; skin detection.
Implementasi Teknologi Informasi Melalui Program Kampus Berdampak Dalam Mendukung Desa Binaan Berkelanjutan Sri Handayani; Randi Rian Putra; Annas Prasetio; Rian Farta Wijaya
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 1 (2026): Juli 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i1.1430

Abstract

Program Kampus Berdampak merupakan salah satu upaya perguruan tinggi dalam meningkatkan kontribusi nyata kepada masyarakat melalui implementasi tridarma perguruan tinggi yang berorientasi pada penyelesaian permasalahan di lingkungan sekitar. Penelitian pengabdian ini bertujuan untuk mengimplementasikan teknologi informasi melalui Program Kampus Berdampak dalam mendukung pengembangan desa binaan yang berkelanjutan. Kegiatan dilaksanakan melalui pendekatan partisipatif dengan melibatkan dosen, mahasiswa, pemerintah desa, dan masyarakat sebagai mitra utama dalam proses identifikasi kebutuhan, perancangan solusi, implementasi, serta evaluasi program. Penerapan teknologi informasi dilakukan melalui pengembangan dan pemanfaatan sistem informasi desa, digitalisasi layanan administrasi, peningkatan literasi digital masyarakat, serta pemanfaatan media digital untuk mendukung promosi potensi desa dan pemberdayaan ekonomi masyarakat. Hasil pelaksanaan menunjukkan bahwa penerapan teknologi informasi mampu meningkatkan efektivitas pelayanan desa, memperluas akses informasi masyarakat, meningkatkan kompetensi digital warga, serta mendorong pengelolaan potensi desa secara lebih optimal dan berkelanjutan. Sinergi antara perguruan tinggi dan masyarakat melalui Program Kampus Berdampak diharapkan dapat menjadi model kolaborasi yang berkontribusi terhadap pembangunan desa berbasis teknologi dan mendukung pencapaian tujuan pembangunan berkelanjutan.
PENGEMBANGAN VIRTUAL TOUR INTERAKTIF BERBASIS WEB UNTUK EKSPLORASI KEINDAHAN DANAU TOBA Sri Handayani; Annas Prasetio; Randi Rian Putra
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 1 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/fayspp56

Abstract

Danau Toba merupakan salah satu destinasi wisata unggulan nasional yang memiliki potensi alam dan budaya yang sangat besar. Namun, keterbatasan akses informasi visual dan interaktif menjadi tantangan dalam memperkenalkan keindahan Danau Toba secara luas, khususnya kepada wisatawan domestik maupun mancanegara. Penelitian ini bertujuan untuk mengembangkan sebuah virtual tour interaktif berbasis web sebagai media digital inovatif untuk mengeksplorasi keindahan Danau Toba secara imersif dan informatif. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan tahapan analisis kebutuhan, perancangan sistem, pengembangan, pengujian, dan evaluasi. Teknologi yang diterapkan meliputi fotografi panorama 360 derajat, pemodelan multimedia interaktif, serta integrasi antarmuka web yang responsif. Hasil penelitian menunjukkan bahwa virtual tour yang dikembangkan mampu menyajikan pengalaman eksplorasi yang realistis, interaktif, dan mudah diakses, serta meningkatkan minat pengguna terhadap destinasi wisata Danau Toba. Uji kelayakan sistem berdasarkan aspek fungsionalitas, usability, dan kepuasan pengguna menunjukkan hasil yang sangat baik. Dengan demikian, virtual tour interaktif berbasis web ini berpotensi menjadi media promosi digital yang efektif dalam mendukung pengembangan pariwisata berkelanjutan di kawasan Danau Toba. Kata Kunci: Virtual Tour, Web Interaktif, Pariwisata Digital, Danau Toba, Multimedia Interaktif.    
Penerapan Digitalisasi Teknologi Informasi untuk Meningkatkan Kompetensi dan Karakter Siswa di SMK Mustafa Perbaungan: Pengabdian Annas Prasetio; Sri Handayani
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4631

Abstract

This community service activity aims to implement digitalization of information technology to improve academic competence and character building of students at SMK Mustafa Perbaungan. This activity is motivated by the limited use of digital technology in learning which impacts low participation, motivation, and strengthening of student character. The implementation method includes needs analysis, planning, training and mentoring the use of digital learning media, and evaluation. Digitalization is carried out through the use of information technology-based applications that support interactive, collaborative, and fun learning by actively involving teachers and students. The results of the activity show an increase in student understanding, skills, motivation, and activeness, the formation of positive character, and increased teacher competence in designing and implementing digital learning effectively, thus contributing positively to the quality of learning and strengthening student character at SMK Mustafa Perbaungan.
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
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
Development Of A Web-Based Student Graduation Information System For Vocational High Schools Using The Waterfall Method Muhammad Hari Hasibuan; Annas Prasetio; Riwanul Nasron; Arwin Putra; Ahmad Afif
Journal of Computer Science and Technology (JOCSTEC) Vol 4 No 3 (2026): JOCSTEC - September
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jocstec.v4i3.770

Abstract

Advances in information technology have driven digital transformation across many sectors, including education. One administrative service that is still largely handled conventionally is the announcement of student graduation results and the distribution of Graduation Certificates (Surat Keterangan Lulus/SKL), which makes the delivery of information relatively inefficient. This study aims to build a web-based Student Graduation Information System for vocational high schools (SMK) that simplifies graduation-data management for school administrators and gives students online access to their graduation information. The system was built using the Waterfall method, covering requirements analysis, design, implementation, testing, and maintenance, and was implemented with the Laravel framework, the PHP programming language, a MySQL database, and a Bootstrap interface. It serves two types of users: administrators, who manage graduation data, and students, who can check their graduation status without logging in by entering their National Student Identification Number (NISN). Students who pass can download their SKL directly in PDF format. The system was evaluated through Black Box Testing and the System Usability Scale (SUS). Testing showed that every system function operated as intended, while the usability evaluation returned an average SUS score of 84.5, placing it in the Excellent (Grade A) category. Overall, the system improves the efficiency of school administration, speeds up the delivery of graduation information, and supports the broader digitalization of educational services.
Co-Authors Ahmad Afif Arwin Putra Muhammad Hari Hasibuan Putra, Randi Rian Rian Farta Wijaya Riwanul Nasron Sri Handayani Sri Handayani Utami, Yulia