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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Paradikma: Jurnal Pendidikan Matematika JURNAL PENELITIAN SAINTIKA Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK JURNAL SAINS INDONESIA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Perspektif M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika JATI (Jurnal Mahasiswa Teknik Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Soft Computing Exploration J-Intech (Journal of Information and Technology) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Umum Pengabdian Masyarakat (JUPEMAS) Jurnal KALANDRA Journal of Education Transportation and Business International Journal of Educational Insights and Innovations (IJEDINS) Ulil Albab
Hermawan Syahputra
Jurusan Ilmu Komputer dan Elektronika, Universitas Gadjah Mada
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Website based classification of karo uis types in north sumatra using convolutional neural network (CNN) algorithm Purba, Boy Hendrawan; Syahputra, Hermawan; Idrus, Said Iskandar Al; Taufik, Insan
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.500

Abstract

Indonesia is one of the largest archipelagic countries in the world. It has abundant cultural diversity including nature, tribes. One of the tribes in Indonesia is the Batak Karo tribe. Batak Karo is a tribe that inhabits the Karo plateau area, North Sumatra, Indonesia. Batak Karo has various cultures, one of which is a traditional cloth known as uis. Unfortunately, the Karo Batak community, especially the younger generation, has insufficient knowledge of the types of uis. Thus, a solution that is easily accessible both in terms of time, cost and experts in recognizing Uis is needed. This research aims to build a website-based application that can classify the types of Karo Uis. This research uses Convolution neural network (CNN) using Alex Net architecture, to get the best model this research compares several hyper parameters, namely learning rate of 10-1 to 10-4, and data division with a ratio of 70:30 and 80:20. The best model falls on a ratio of 70:30 and a learning rate of 10-4 with an accuracy of 98%, and a validation accuracy of 99%, then the model is stored in h5 format in this study successfully builds and implements the model into a web-based application.
Motorcycle License Plate and Driver Face Verification Using Siamese Neural Network Model Pane, Yeremia Yosefan; S, Kana Saputra; Al Idrus, Said Iskandar; Syahputra, Hermawan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31750

Abstract

The security and efficiency of vehicle access management systems have become a primary concern for various institutions, including universities, offices, and public facilities. Effective access management not only enhances security but also improves the flow of incoming and outgoing vehicles, reduces congestion, and enhances user experience. This research aims to develop a vehicle plate detection system and driver face recognition using the Siamese Neural Network model to optimize traffic at the gate. The methods used include the application of deep learning algorithms, specifically the Siamese Neural Network, to verify the driver's face and the use of You Only Live Once (YOLO) to detect and recognize vehicle plates in real-time. Data was collected through direct capture with the researcher's camera. The model was trained and tested using a dataset containing images of vehicle license plates and driver faces. The results showed that the developed model was able to detect and recognize the vehicle plate and the driver's face with a fairly high accuracy, namely in the object detection results getting bounding box validation is 1.05 and class loss validation is 0.95, and 0.85 mAP. As well as in training using the Siamese Neural Network, the highest result is 0.82 with a learning rate of 10e-5 with 30 epochs. It is hoped that this system can be one of the innovations that can be applied in government agencies, universities, industries, etc.
Comparative Analysis of Model Architectures Using Transfer Learning Approach in Convolutional Neural Networks for Traditional Ulos Fabric Classification Abdullah, Taufik; Saputra S, Kana; Syahputra, Hermawan; Indra, Zulfahmi; Kartika, Dinda
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.719

Abstract

Ulos cloth is a traditional woven fabric of the Batak tribe in North Sumatra, valued for its aesthetic and symbolic significance in various ceremonies. The diversity of ulos motifs presents challenges in preservation due to their unique patterns and functions. This study aims to develop an accurate method for classifying ulos motifs using Transfer Learning on Convolutional Neural Network (CNN) architectures. Five popular models—VGG16, VGG19, MobileNetV3, Inception-V3, and EfficientNetV2—were evaluated on a dataset of 962 ulos images across six motif categories.The results show that Inception-V3 outperformed other models with an average validation accuracy of 98.13% and the lowest loss of 5.67%. Inception-V3 also demonstrated superior generalization, achieving the highest K-fold validation accuracy, while VGG16 and VGG19 exhibited overfitting at higher learning rates. Two-way ANOVA analysis confirmed significant performance differences among the models and highlighted the interaction between model type and training methods. This research recommends Inception-V3 as the optimal model for ulos motif classification, offering an efficient and reliable tool to support cultural preservation through advanced image recognition technology.
Identifikasi Tanda Tangan Dengan Menggunakan Metode Convolution Neural Network (CNN) Indriani.S, Dechy Deswita; Sinaga, Elya Juni Arta; Oktavia, Grace; Syahputra, Hermawan; Ramadhani, Fanny
J-INTECH (Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1273

Abstract

This research aims to develop and evaluate a Convolutional Neural Network (CNN) model for signature identification. The CNN method is chosen for its capability to extract and analyze complex visual features from signature images. The data used in this study consists of a collection of signature images divided into training and testing sets. The proposed CNN model comprises several convolutional, pooling, and fully connected layers optimized for classification tasks. Evaluation results indicate that the CNN model achieves excellent performance with an accuracy of 0.97, demonstrating high accuracy and precision in signature recognition. With these results, CNN proves to be an effective and reliable method for signature identification, making a significant contribution to the field of biometric identity verification. These findings open opportunities for further applications in security and authentication systems requiring automatic signature recognition.
DETEKSI OBJEK JAMUR PADA ROTI TAWAR SECARA REAL-TIME MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Lazuardi Harahap, Muhammad; Syahputra, Hermawan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.12999

Abstract

Penelitian ini mengembangkan sistem deteksi jamur pada roti secara realtime menggunakan Convolutional Neural Network (CNN) untuk mengatasi masalah cacat mikrobiologis yang umum dalam industri roti. Menggunakan metode Research and Development (R&D) dengan model CNN Transfer Learning MobileNetV2, penelitian ini mengolah 640 data primer yang diambil dari toko roti di Medan Marelan Pasar 1 menggunakan kamera 108 megapixel dengan lighting 50 watt berdiameter 18 inch. Dari pengujian 8 model dengan parameter berbeda, diperoleh hasil terbaik pada parameter 30000 num_steps dan 8 batch, menghasilkan akurasi 96.42% dan total loss 0.1181 dengan waktu training 2 jam 18 menit 36 detik. Pengujian pada 64 data (32 roti berjamur dan 32 tidak berjamur) menunjukkan model berhasil mendeteksi 30 dari 32 gambar roti berjamur dengan berbagai variasi, sementara semua gambar roti tidak berjamur terdeteksi dengan benar. Hasil ini membuktikan efektivitas implementasi CNN dalam mendeteksi jamur pada roti.
Deteksi Gerakan Kepala Secara Real-Time Menggunakan OpenCV Dan Python Imelda, Yusmita; Daulay, Leni Karmila; Purba, Desni Paramitha; Syahputra, Hermawan
Journal of Education Transportation and Business Vol 2, No 1 (2025): Juni 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetbus.v2i1.6528

Abstract

Perkembangan teknologi komputer dan kecerdasan buatan telah mendorong inovasi dalam sistem interaksi manusia-komputer, salah satunya adalah deteksi gerakan kepala secara real-time. Penelitian ini bertujuan untuk mengembangkan sistem yang mampu mendeteksi dan mengklasifikasikan orientasi kepala (pitch, yaw, dan roll) dengan memanfaatkan pustaka OpenCV dan Dlib dalam bahasa pemrograman Python. Sistem dirancang dengan mengimplementasikan teknik deteksi wajah, pelacakan landmark, dan estimasi pose kepala menggunakan fungsi solvePnP. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi orientasi kepala secara akurat dan responsif dalam waktu kurang dari 0.5 detik. Evaluasi kinerja menunjukkan tingkat konsistensi yang baik, meskipun terdapat kendala pada kondisi pencahayaan rendah dan kualitas kamera yang kurang optimal. Aplikasi ini berpotensi untuk diterapkan dalam berbagai bidang seperti alat bantu disabilitas, sistem kontrol berbasis gestur, dan keamanan. Dengan pengembangan lanjutan, sistem ini dapat menjadi solusi interaktif yang lebih inklusif dan intuitif.
Developing Flipped Classroom Learning Materials to Improve Mathematical Literacy and Learning Independence Nasution, Dinda Indriani; Dewi, Izwita; Syahputra, Hermawan
Jurnal Perspektif Vol 9 No 2 (2025): Jurnal Perspektif
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jp.v9i2.363

Abstract

This study aims to create a high-quality Flipped Classroom Model-based learning device to improve mathematical literacy and learning independence of seventh grade junior high school students on data centralization measurement material. This research method is a development research of the Analysis, Design, Development, Implementation, Evaluation (ADDIE) method. The results of this study are that the Flipped Classroom Model-based learning device is valid and practical; students' mathematical literacy skills increased by 0.65 which is classified as moderate; students' independent learning abilities increased by 0.23 which is classified as moderate. The Flipped Classroom Model is effective for learning Data Centralization Measurement. The Flipped Classroom Model functions well for learning data centralization measurements. More than 75% of learning objectives were achieved, more than 85% of students scored at least 75, more than 85% of students responded positively to the flipped classroom model-based learning device, and the use of time on the learning device did not exceed regular learning hours.
Penerapan Algoritma Caesar Cipher dan Metode Least Significant Bit Untuk Mengamankan Teks Dalam Vidio Pane, M Iqbal Anata; Taufik, Insan; Syahputra, Hermawan; Al Idrus, Said Iskandar; Niska, Debi Yandra
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 12 No. 1 (2025): Prosisko Vol. 12 No. 1 2025
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v12i1.9606

Abstract

Abstrak - Data digital adalah representasi informasi dalam bentuk angka atau simbol yang bisa diproses oleh komputer. Jenis-jenis data digital termasuk teks, gambar, audio, video, spreadsheet, presentasi, dan basis data. Ada data yang umum dan dapat diakses oleh banyak orang, serta data rahasia yang memerlukan perlindungan khusus saat ditransmisikan melalui internet. Penelitian ini bertujuan untuk mengamankan informasi dalam video menggunakan algoritma Caesar Cipher dan metode Least Significant Bit (LSB). Melalui analisis dan implementasi, penelitian ini menunjukkan bahwa kombinasi kedua teknik tersebut dapat efektif melindungi teks dalam video tanpa mengubah kualitas visualnya. Hasil penelitian ini memberikan sumbangan bagi pengembangan keamanan informasi digital serta memberikan dasar bagi penelitian lebih lanjut. Selain itu, tantangan utama dalam penelitian ini adalah memperhatikan kecepatan pengolahan video agar dapat diunggah dan diunduh dengan mudah oleh pengguna. Oleh karena itu, penelitian ini tidak hanya menawarkan solusi untuk masalah keamanan informasi, tetapi juga mempertimbangkan aspek praktis dalam penerapannya. Ke depannya, penelitian dapat mengeksplorasi integrasi metode keamanan yang lebih canggih dan efisien untuk meningkatkan perlindungan data digital. Secara keseluruhan, penelitian ini menyajikan solusi yang relevan dan berpotensi untuk diterapkan secara luas dalam konteks keamanan informasi digital yang terus berkembang.
Implementation of Convolutional Neural Network in Detecting Avocado Ripeness Level Luge, Miclyael; Indra, Zulfahmi; Syahputra, Hermawan; Al Idrus, Said Iskandar; S, Kana Saputra
Jurnal IPTEK Vol 29, No 1 (2025)
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2025.v29i1.6737

Abstract

Squeezing avocados to determine ripeness can cause physical damage or bruising, reducing the fruit’s quality and resulting in losses for sellers and buyers. This research aims to develop an Android-based mobile application to detect avocado ripeness based on skin color, avoiding physical damage to the fruit. The study uses three simple Convolutional Neural Network architectures to evaluate the algorithm’s ability to detect avocado ripeness. The dataset includes 385 images across four classes: immature, half-ripe, ripe, and overripe (74 images each), and an additional 89 images for the non-avocado class. The model was trained with learning rates of 0.001, 0.0001, and 0.00001. The architecture with the most convolutional layers achieved the best performance with a 0.001 learning rate, yielding a test accuracy of 94.15%, a test loss of 19.28%, and an F1-score of 94.0%. The best model was then converted to TFLite format and successfully integrated into an Android application that functions effectively.
PERBANDINGAN KINERJA KNN DAN SVM DALAM KLASIFIKASI SAMPAH ORGANIK DAN ANORGANIK MENGGUNAKAN EKSTRAKSI FITUR HOG DAN LBP Hidayatul Arifin, Muhammad; Amelia Vega S. Meliala, Ruth; Impana Manik, Kristin; Defiyanti, Aqilah; Syahputra, Hermawan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13972

Abstract

Pertumbuhan limbah yang semakin meningkat menimbulkan tantangan yang signifikan bagi upaya pelestarian lingkungan. Proses pemisahan sampah masih dilakukan secara manual dan sering kali tidak konsisten, yang merupakan kendala utama. Penelitian ini bertujuan untuk mengembangkan sistem yang menggunakan pengolahan citra untuk mengklasifikasikan sampah organik dan anorganik secara otomatis. Penelitian ini menggunakan dataset yang digunakan terdiri dari 1.800 citra dimana 900 organik dan 900 anorganik yang diekstraksi melalui Histogram of Oriented Gradients (HOG) dan Local Binary Pattern (LBP). Tahap preprocessing, yang mencakup pengubahan ukuran dan konversi ke grayscale. Selanjutnya, Principal Component Analysis (PCA) digunakan untuk mengurangi dimensi fitur HOG, kemudian digabungkan dengan fitur LBP dan diklasifikasikan menggunakan algoritma Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN). Hasil pengujian menunjukkan bahwa SVM dengan kernel RBF memiliki akurasi tertinggi sebesar 88,89%, sementara KNN dengan nilai k=5 memiliki akurasi sebesar 83,61%. Keunggulan SVM terletak pada kemampuan mereka untuk memaksimalkan margin pemisahan. Hasilnya menunjukkan bahwa metode penggabungan HOG dan LBP dengan klasifikasi berbasis SVM dapat meningkatkan akurasi pemisahan sampah secara otomatis. Hasil ini dapat mendorong upaya untuk mengurangi beban di TPA serta meningkatkan praktik daur ulang yang berkelanjutan.
Co-Authors Ade Amelia, Tasya Adhi Guna, Ekin Ahmad Andi Solahuddin Amelia Br Siregar, Ririn Amelia Vega S. Meliala, Ruth Andika Maulana, Sandy Angel Tumanggor, Asri Asrin Lubis Budi Akbar, Muhammad Daulay, Leni Karmila Davina, Sherly Dedy Husrizal Syah, Dedy Husrizal Dedy Kiswanto Defiyanti, Aqilah Delvin Ibo, Martince Deo Demonta Panggabean DIdi Febrian Drilanang, Mhd Ilyasyah Dwi Zahra Putri, Raisya E. Elvis Napitupulu, E. Elvis Elisabet Butarbutar, Lastri Farmawaty Tambunan, Vivielda Fauzi, KMS. Amin Fransiska Sihombing, Esra Hafiz, Alvin Hidayatul Arifin, Muhammad Husna Batubara, Shabrina Ida Ayu Putu Sri Widnyani Imelda, Yusmita Impana Manik, Kristin Indriani.S, Dechy Deswita Insan Taufik Irmaya, Nia Irya Shakila Syukron, Ananda Izwita Dewi Josafat Simanjutak, Todo Kana Saputra S Kartika, Dinda Lazuardi Harahap, Muhammad Luge, Miclyael Luthfiah, Dina Aulia Mahyuni Mahyuni Maulana, Raihan Maya Oktora Mukti Hamjah Harahap, Mukti Hamjah Nasution, Dinda Indriani Nico Pasaribu, Michael Niska, Debi Yandra Nurul Maulida Surbakti Oktavia, Grace Palendeo Sitepu, Kalpin Pane, M Iqbal Anata Pane, Yeremia Yosefan Panggabean, Suvriadi Panjaitan, Clara Kresensia Panjaitan, Nova Yanti Permata Putri Pasaribu, Yohanna Prana Walidin, Adamsyach Purba, Boy Hendrawan Purba, Desni Paramitha Ramadhan Manik, Albert Ramadhani, Fanny Rangkuti, Muhammad Aswin Riana, Ami Richi, Alfina Said . Iskandar Sembiring, Rinawati Sinaga, Elya Juni Arta Siregar, Putri Mayang Sari Solahudin, Ahmad Andi Sri Dewi Sriadhi Sriadhi, Sriadhi Sukma, Ayman Human Suleho, Febrina Veryawan, Veryawan Warjaya, Angga Wibowo, Aldiva winsyahputra Ritonga Yazid Noor, Muhammad Zulfahmi Indra, Zulfahmi Zulfahrizan, Atta