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Hermawan Syahputra
Universitas Negeri Medan
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IDENTIFIKASI JENIS REMPAH-REMPAH INDONESIA DENGAN CONVOLUTIONAL NEURAL NETWORK (CNN) MENGGUNAKAN ARSITEKTUR VGG16 Maulana, Raihan; Dwi Zahra Putri, Raisya; Ade Amelia, Tasya; Syahputra, Hermawan; Ramadhani, Fanny
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10138

Abstract

Indonesia merupakan negara yang kaya akan rempah-rempah alami, sebuah kekayaan alam yang telah menjadi bagian integral dari budaya dan ekonomi nasional. Rempah-rempah Indonesia tidak hanya digunakan dalam masakan sehari-hari tetapi juga diekspor ke berbagai negara, menjadikannya komoditas penting yang perlu dijaga keberadaannya dengan baik. Meskipun begitu, membedakan berbagai jenis rempah menjadi tantangan bagi sebagian masyarakat. Hal ini disebabkan oleh kurangnya pengetahuan masyarakat tentang bentuk asli rempah, serta minimnya keterlibatan langsung dalam pengolahannya. Kesulitan ini berdampak pada pengenalan dan penggunaan rempah yang kurang optimal, baik di tingkat rumah tangga maupun industri. Untuk mengatasi tantangan ini, penelitian ini bertujuan mengembangkan sebuah sistem berbasis teknologi yang dapat membantu masyarakat mengenali berbagai jenis rempah secara akurat. Sistem yang dikembangkan menggunakan Convolutional Neural Network (CNN) dengan arsitektur VGG16, yang dirancang untuk mengidentifikasi berbagai jenis rempah-rempah secara efektif dan efisien. CNN telah terbukti sebagai metode pembelajaran mendalam yang sangat efektif dalam mengklasifikasikan objek berdasarkan ciri-ciri visualnya. Dalam penelitian ini, dataset citra rempah terdiri dari tiga puluh satu kelas, masing-masing kelas memiliki 210 citra, dengan total 6510 citra. Model CNN yang digunakan dalam penelitian ini mengimplementasikan arsitektur VGG16, yang terdiri dari beberapa lapisan konvolusi untuk mengekstraksi fitur visual dari citra, diikuti oleh lapisan fully connected untuk melakukan klasifikasi. Hasil penelitian menunjukkan bahwa model CNN yang dikembangkan berhasil mencapai akurasi tertinggi sebesar 86,66% dalam mengklasifikasikan citra-citra rempah. Akurasi ini menunjukkan bahwa model mampu mengenali berbagai jenis rempah dengan cukup baik, meskipun terdapat beberapa kelas yang masih mengalami kesulitan dalam prediksi. Pendekatan ini tidak hanya memberikan solusi modern namun juga mudah diakses untuk mengenali rempah-rempah, sehingga dapat membantu masyarakat dalam membedakan jenis-jenis rempah secara lebih efektif dan efisien.
KLASIFIKASI CITRA SIMBOL MATEMATIKA MENGGUNAKAN METODE CONVOLUTION NEURAL NETWORK (CNN) Warjaya, Angga; Richi, Alfina; Syahputra, Hermawan; Ramadhani, Fanny
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10234

Abstract

Pemanfaatan notasi matematika sangat penting dalam menjelaskan konsep dan kerangka matematika, berfungsi sebagai alat dasar untuk komunikasi dan pemecahan masalah. Namun, gaya tulisan tangan yang beragam yang digunakan oleh individu menghadirkan tantangan unik dalam mengidentifikasi simbol matematika secara akurat karena perbedaan evolusi tulisan tangan dari waktu ke waktu. Munculnya teknologi pengenalan pola, terutama dalam pengenalan pola tulisan tangan, menekankan pentingnya mengembangkan aplikasi untuk mendeteksi dan menafsirkan simbol matematika tulisan tangan, dengan fokus pada penggunaan Convolution Neural Network (CNN) untuk kategorisasi otomatis, menampilkan tingkat akurasi yang menjanjikan. Studi ini menunjukkan kemampuan luar biasa model CNN untuk mengkategorikan simbol matematika dengan tingkat akurasi tinggi 99,25%, menunjukkan potensi signifikan metodologi CNN dalam mengklasifikasikan pola simbol matematika secara efektif dan perlunya eksplorasi lebih lanjut untuk meningkatkan kinerja klasifikasi dalam domain ini.
Development of Mathematics E-Comic Media Based On Problem Based Learning To Improve The Problem Solving Ability And Learning Interest of Students of Muhammadiyah Private Junior High School 16 Lubuk Pakam Irmaya, Nia; Syahputra, Hermawan; Lubis, Asrin
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 9 No. 3 (2024): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v9i3.665

Abstract

This research intends to (1) create valid, practical, and successful PBL-based E-Comics media; (2) evaluating Problem-Based Learning-based E-Comics media for problem-solving improvement; (3) examining student enthusiasm in learning with Problem-Based Learning e-comics medium. This study follows the ADDIE model—analysis, design, development, implementation, and evaluation. This study included SMP Muhammadiyah 16 Lubuk Pakam seventh-graders. RPP, LKPD, problem-solving ability exam, and student learning interest questionnaire are used to create e-comic media. The results showed that (1) Media E-Comics based Problem Based Learning (PBL) to improve problem-solving skills and interest in learning met valid criteria with a score of 4.69; (2) The practical criteria in the second practical test included observation of learning using E-Comics media with a score of 88.15%, teacher response of 87.94%, and student response of 90.75%; and (3) E-Comics media meets effectiveness criteria; (i) Problem-solving skills in the effectiveness test I rose by 0.68 with a medium category at pretest and posttest. Comparing posttest I and II values ​​showed that test II was more effective with N-gain 0.43 in the medium category; (ii) increased student interest in learning effectiveness test I with an average of 96.87%, with 8 excellent, 23 good, and 1 sufficient students. effectiveness test II followed with an average of 100%, with 17 excellent and 15 good students.
IDENTIFIKASI JENIS PENYAKIT PADA TANAMAN CABAI RAWIT MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DI DESA BINTANG KECAMATAN SIDIKALANG Josafat Simanjutak, Todo; Saputra S, Kana; Syahputra, Hermawan; Iskandar Al Idrus, Said; Febrian, Didi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12403

Abstract

Cabai rawit merupakan jenis tanaman terna atau setengah merdu, memiliki tinggi sekitar 50-120 cm dengan umur bisa mencapai 3 tahun, Prospek cabai rawit cukup menjanjikan untuk memenuhi kebutuhan domestik dan ekspor Namun, produksi justru menurun. Salah satu faktor penyebab rendahnya produksi tanaman cabai adalah adanya gangguan penyakit yang menyerang. Identifikasi penyakit tanaman menjadi langkah penting dalam pemeliharaan dan perawatan, termasuk pada cabai rawit.metode yang digunakan dalam penelitian ini adalah Metode CNN (Convolutional Neural Network) dengan LeNet-5 sebagai arsitekturnya.Penelitian ini berhasil mengembangkan sistem berbasis Convolutional Neural Network (CNN) menggunakan arsitektur LeNet-5 untuk mengidentifikasi dan mengklasifikasi enam kelas penyakit pada tanaman cabai rawit di Desa Bintang, Kecamatan Sidikalang, dengan kinerja yang cukup baik ditunjukkan oleh akurasi 86%, presisi 87%, recall 86%, dan f1-score 86%.Untuk meningkatkan performa sistem, disarankan untuk melakukan eksperimen lebih lanjut dengan mengoptimalkan hyperparameter seperti learning rate dan jumlah epoch, memperluas dataset dengan variasi citra, mengeksplorasi arsitektur model yang lebih modern seperti AlexNet atau ResNet, serta menggunakan perangkat keras dengan spesifikasi yang lebih tinggi untuk efisiensi dan kecepatan pemrosesan yang lebih baik.
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.
Automatic Waste Type Detection Using YOLO for Waste Management Efficiency Alfattah Atalarais; Kana Saputra S; Hermawan Syahputra; Said Iskandar Al Idrus; Insan Taufik
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.v4i2.770

Abstract

The management of waste in Indonesia is currently suboptimal, with only 66.24% being effectively managed, leaving 33.76% unmanaged. This highlights a significant challenge in waste management, primarily due to a lack of understanding in selecting appropriate waste types. Advances in deep learning and computer vision offer promising solutions to this issue. This study employs the YOLOv8l model, a well-regarded deep learning model for object detection, to develop an automated waste type detection system integrated with trash bins. The dataset comprises 2800 images across four classes, each containing 700 images, and is split with an 80:10:5 ratio for training, validation, and testing. Evaluation on test data yields a mean Average Precision (mAP) of 96.8%, indicating robust model performance in object detection. The model's accuracy is further validated with a score of 89.98%. Real-time testing conducted at Merdeka Park, Binjai, demonstrates the system's capability to detect waste with varying confidence levels, consistently above the 0.5 threshold. The highest confidence was observed in bottle detection at 0.94, and the lowest in cans at 0.64, underscoring the system's reliability across different detection scenarios within a 30cm range.
Implementation of MobileNet V3 In Classifying Butterfly Species with Android and Cloud Based Application Development Ihsan Zulfahmi; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
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.v4i2.797

Abstract

This research aimed to develop an Android application capable of classifying butterfly species using cloud computing and deep learning technologies. MobileNetV3-Large, a Convolutional Neural Network (CNN) architecture, was employed to process and classify six butterfly species. The dataset was divided into two ratios, 70:30 and 80:20, for training and testing. Evaluation results indicated that the optimal model was achieved with an 80:20 ratio, yielding an accuracy of 94% and precision, recall, and F1-Score values exceeding 90% for each species class. Google Cloud Platform (GCP) was utilized to manage and run the model using the Cloud Run service, enabling the application to function efficiently even with limited resources on Android devices. The application incorporates an encyclopedia of species and a camera scanning feature, making it a valuable educational tool
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.
Co-Authors Abil Mansyur, Abil Ade Amelia, Tasya Adhi Guna, Ekin Agus Harjoko Ahmad Andi Solahuddin Ahmad Hidayat Ajizah Siregar Aldiva Wibowo Alfattah Atalarais Amelia Br Siregar, Ririn Amelia Vega S. Meliala, Ruth Ami . Riana Andani D N Andika Maulana, Sandy Angel Tumanggor, Asri Angginy Akhirunisa Siregar Ani Sutiani Apiek Gandamana Arnita Arnita Asrin Lubis BORNOK SINAGA Bornok Sinaga Budi Akbar, Muhammad citra Daulay, Leni Karmila Davina, Sherly Dedy Husrizal Syah, Dedy Husrizal Dedy Kiswanto Defiyanti, Aqilah Delvin Ibo, Martince Deo Demonta Panggabean Dhea Putri Adriani DIdi Febrian Dina Aulia Luthfiah Drilanang, Mhd Ilyasyah Dwi Zahra Putri, Raisya E. Elvis Napitupulu E. Elvis Napitupulu, E. Elvis Edi Syahputra Edward Perdana Sinaga Elisabet Butarbutar, Lastri Fanny Rahmadani Farmawaty Tambunan, Vivielda Fauzi, KMS. Amin Fransiska Sihombing, Esra Hafiz, Alvin Harefa, Meilinda Suriani Hasratuddin Siregar Hidayatul Arifin, Muhammad Husna Batubara, Shabrina Ida Ayu Putu Sri Widnyani Ihsan Zulfahmi Ihsan Zulfahmi Ika Purnama Sari Imelda, Yusmita Impana Manik, Kristin Indriani.S, Dechy Deswita Insan Taufik Irhamna Irhamna Irmaya, Nia Irya Shakila Syukron, Ananda Iwan Jepri Izwita Dewi Josafat Simanjutak, Todo Kana Saputra S Karimuddin Hakim Hasibuan Kartika, Dinda Khairun Nadiah Kms. Amin Fauzi Lazuardi Harahap, Muhammad Lubis, M. Revano Ananda Luge, Miclyael Luthfiah, Dina Aulia M. Ari Maulana Mahyuni Mahyuni Martina Restuati Maulana, Raihan Maya Oktora MHD. Reza M.I. Pulungan Mia Yolanda Siregar Muhammad Febrilian Zulrahman Mukti Hamjah Harahap, Mukti Hamjah Nasution, Dinda Indriani Nico Pasaribu, Michael Niska, Debi Yandra Nova Yanti Panjaitan Nur Wahyuni Nurmala Berutu 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 Putri Mayang Sari Putri Mayang Sari Siregar R Givent A Simanjorang Ramadhan Manik, Albert Ramadhani, Fanny Rambe, Imelda Wardani Rangkuti, Muhammad Aswin Riana, Ami Richi, Alfina Said . Iskandar SANTI MARIA SIMARMATA Santi Maria Simarmata Sembiring, Rinawati Sinaga, Elya Juni Arta Siregar, Mochammad Gani Alfa Alkhoiri Siregar, Putri Mayang Sari Siti Nabila Panjaitan Solahudin, Ahmad Andi Sri Dewi Sriadhi Sriadhi, Sriadhi Steven Imanuel Naibaho Sukma, Ayman Human Suleho, Febrina Syamsah Fitri Syarief Afifi Sumantri Syawal Gultom Tri Bowo Atmojo Veryawan, Veryawan Waliyul M Siregar Warjaya, Angga Wibowo, Aldiva winsyahputra Ritonga Yazid Noor, Muhammad Zul Amry Zulfahmi Indra, Zulfahmi Zulfahrizan, Atta