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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal IPTEK Jurnal Informatika Upgris Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal CoreIT Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Teknik dan Informatika Jurnal Kridatama Sains dan Teknologi Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Sains dan Teknologi JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) TRIDARMA: Pengabdian Kepada Masyarakat (PkM) JATI (Jurnal Mahasiswa Teknik Informatika) INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Bulletin of Information Technology (BIT) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Pendidikan Matematika Malikussaleh Blend Sains Jurnal Teknik Jurnal Teknologi Informasi INPAFI (Inovasi Pembelajaran Fisika) Innovative: Journal Of Social Science Research JS (Jurnal Sekolah) Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Ilmu Komputer Journal of Informatics and Data Science (J-IDS) Jurnal Pendidikan IPA Indonesia SISFOTENIKA JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Kana Saputra S
Program Studi Ilmu Komputer, FMIPA, Universitas Negeri Medan
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Automasi Verifikasi Identitas Pengguna dalam Pengajuan Kartu Kredit Berbasis Biometrik Menggunakan ML Kit Reo Rizki Ananda; Arnita; S, Kana Saputra; Putri, Alsya Adelia; Neltriana Syafira; Sitompul, Sigun Putra Hasian
SISFOTENIKA Vol. 15 No. 1 (2025): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v15i1.526

Abstract

Kasus penipuan dan penyalahgunaan identitas dalam pengajuan kartu kredit semakin meningkat, menekankan perlunya sistem verifikasi identitas yang aman dan efisien. Penelitian ini mengembangkan sistem automasi verifikasi identitas berbasis biometrik menggunakan ML Kit untuk mendukung proses pengajuan kartu kredit. Metode waterfall digunakan untuk merancang sistem ini, meliputi analisis kebutuhan, desain, implementasi, dan integrasi. Sistem ini memanfaatkan biometrik wajah dan sidik jari untuk verifikasi awal saat pendaftaran guna mencegah akun ganda dan verifikasi lanjutan saat pengajuan kartu kredit untuk memastikan keaslian identitas pengguna. Hasil pengujian menunjukkan sistem ini mampu melakukan verifikasi secara real-time dengan akurasi tinggi, memberikan pengalaman pengguna yang praktis dan modern melalui perangkat mobile. Sistem ini meningkatkan keamanan dengan menggunakan data biometrik yang sulit dipalsukan, tetapi tetap menghadapi tantangan seperti ketergantungan pada perangkat tertentu dan kondisi lingkungan. Penelitian ini diharapkan menjadi solusi inovatif dalam meningkatkan kepercayaan dan efisiensi proses pengajuan kartu kredit berbasis biometrik.
Automatic Classifier of Road Condition and Early Warning System for Potholes Manurung, Jeremia; As, Mansur; Nasution, Hamidah; Al Idrus, Said Iskandar; Saputra S, Kana
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.v8i1.31866

Abstract

Damaged roads can have a negative impact on road users and can fatally cause accidents. One sign of a damaged road is the presence of holes in the road. This research aims to develop an Android application that can display the location of potholes and provide early warning to driver in Simalungun Regency - North Sumatra. This research implements the Convolutional Neural Network (CNN) algorithm using the transfer learning techniques on the pre-trained MobileNetV3 model for automatic classification of road conditions. The dataset used in the research consisted of 22.538 images which were divided into two classes, namely pothole and normal. This research uses dataset with a ratio of 60:20:20, 70:20:10 and 80:10:10. MobileNetV3 large variant with a dataset ratio of 60:20:20 shows the best value with an F1-Score of 0,9035. The model was further converted to Tensorflow Lite with an F1-Score of 0.8985. This research succeeded in implementing the trained and evaluated model along with early warning of potholes via audiovisual in Android application. Application functionality testing that is carried out using black box testing, showing that the application can run well.
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.
3D Application Development with Blender and Roblox Integration: A Case Study of the North Sumatera State Museum Malik Fajri, Maulana; Said Iskandar Al Idrus; Yulita Molliq Rangkuti; Kana Saputra S; Debi Yandra Niska
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.v4i2.752

Abstract

This research explores the potential of Metaverse and Immersive Space technologies to enhance virtual tourism experiences at the North Sumatra State Museum through the integration of Blender and Roblox Studio. The main focus is on developing complex and interactive metaverse content, as well as implementing an adaptive visit counting system. The methodology involves developing a 3D application using Blender for modeling and Roblox Studio for the virtual environment. Key results include the addition of Virtual Reality (VR) features, expansion of the virtual museum collection, and a continuous evaluation system based on user feedback. In conclusion, the integration of Blender and Roblox Studio proves effective in creating immersive virtual museum experiences, opening new opportunities in utilizing Metaverse technology to increase museum accessibility and offering innovative solutions for preserving and promoting cultural heritage through digital platforms.
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
Pembangunan Website untuk Penjadwalan Maintenance Menggunakan Algoritma Priority Schedulling Harahap, Muhammad Abarorya; Rangkuti, Yulita Molliq; AS, Mansur; Indra, Zulfahmi; Saputra, Kana
Jurnal Kridatama Sains dan Teknologi Vol 7 No 01 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i01.1504

Abstract

PTPN II Sugar Factory (PG.II) is a company that produces sugar which often experiences difficulties in producing sugar that is not time efficient due to frequent unexpected damage to the machine, which results in a reduced amount of time used to produce sugar. One of the causes of machine damage at PTPN II Kwala Madu is the absence of an information system about scheduling machine maintenance so that production machine damage occurs. The purpose of performing maintenance is so that the network distribution capability can meet the needs of the company, maintaining quality at the right level to meet what is needed by the production itself. Maintenance also aims to achieve the lowest possible cost level and avoid maintenance activities that can endanger the safety of the workforce or employees. help reduce usage or deviations beyond the limit and maintain the capital that has been invested during the specified time in accordance with the policies of the company or organization. The stages of this research are analyzing needs, designing / modeling a scheduling system with the Priority Schedulling algorithm, followed by programming, software testing and testing. Global system design using UML modeling language consisting of Usecase Diagram, Activity Diagram, Class Diagram, and Bari Diagram
PENGUATAN IMPLEMENTASI KURIKULUM MERDEKA BELAJAR-KAMPUS MERDEKA (MBKM) DI UNIVERSITAS NEGERI MEDAN Frisnoiry, Suci; Sinaga, Fajar Apollo; Siregar, Tiur Malasari; Saputra, Kana; Ramadhan, Taufiq
Jurnal Pendidikan Matematika Malikussaleh Vol. 5 No. 1 (2025): Jurnal Pendidikan Matematika Malikussaleh
Publisher : LPPM UNIMAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jpmm.v5i1.21184

Abstract

This study aims to evaluate the effectiveness of strengthening the implementation of the Independent Learning-Independent Campus (MBKM) Curriculum at Universitas Negeri Medan (Unimed). The research method employed is a qualitative-descriptive approach through observation, interviews, and document analysis. The findings indicate an improved understanding among lecturers regarding the MBKM concept, a more systematic curriculum document development, and enhanced collaboration with industry partners. Additionally, a more structured monitoring and evaluation system has been successfully developed to ensure the sustainability of the program. These findings suggest that the MBKM program can enhance the quality of higher education and better prepare graduates to face the challenges of the job market. Recommendations are provided to improve faculty training, digitize the MBKM system, and conduct periodic evaluations to align the curriculum with industry needs and national policies
KLASIFIKASI MOTIF PADA TENUN TRADISIONAL TARUTUNG MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN PERANCANGAN VISUAL PRODUCT GUIDE BERBASIS WEBSITE Eka Nainggolan, Rinay; Yandra Niska, Debi; Iskandar Al Idrus, Said; Saputra S, Kana
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.v9i2.13053

Abstract

Tarutung merupakan salah satu wilayah di Sumatera Utara yang masih aktif dalam melestarikan tradisi tenunnya. Desain kain tenun Tarutung terus berkembang dengan motif yang lebih bervariasi, mencerminkan dinamika budaya yang menggabungkan tradisi dan tren kontemporer. Hasil wawancara yang dilakukan peneliti dengan 25 pemasok songket di Tarutung menunjukkan 72% (18 orang) mengalami kesulitan mengidentifikasi jenis tenun, terutama penjual baru dan sales promotor yang perlu menjelaskan detail produk kepada pelanggan. Hasil wawancara juga menunjukkan 80% (20 orang) penjual kesulitan mengetahui kegunaan tenun karena keberagaman yang ada. Pembeli sering bertanya tentang penggunaan yang tepat, seperti untuk pakaian, upacara adat, keagamaan, atau dekorasi, sehingga penjual perlu memberikan informasi yang akurat. Kesulitan membedakan jenis tenun disebabkan oleh kemiripan pola akibat perbedaan satu atau dua lidi dalam proses mamutik (pembuatan motif). Penjual biasanya berdiskusi dengan sesama penjual atau memeriksanya ke pengrajin, yang tentunya memakan waktu lama. Ketidakakuratan informasi dapat mengurangi kepercayaan pelanggan, menurunkan pendapatan, dan mempersulit penentuan harga. Penjual harus memastikan informasi produk akurat untuk menghindari kerugian. Penelitian ini bertujuan untuk membangun model klasifikasi tenun Tarutung berdasarkan motif menggunakan metode Convolutional Neural Network (CNN), yang diintegrasikan ke dalam sebuah website untuk mengidentifikasi jenis kain yang diunggah pengguna. Selain itu, website juga menyediakan informasi relavan yang memudahkan akses pengguna ke berbagai fitur yang tersedia. Penelitian ini menghasilkan model CNN dengan akurasi sebesar 94%, precision 96%, recall 94%, dan F1-score 94%.
Co-Authors Adidtya Perdana, Adidtya Advis Ambrosius Sitohang, Yuda Afif Nashi Ulwan, Mhd Agus Buono Agus Kembaren Agus Waruwu, Stefen Al-Areef, M. Hafizh Alfattah Atalarais Alfin, Muhammad Amanda Fitria Amelia Br Siregar, Ririn Ananda Hafika, Rizky anastasya, disty Anggi Tasari Anti Nada Nafisa Arnita Azizi, Nur Azqal Azkia Bambang Suseno Budi Akbar, Muhammad Bush Henrydunan, John Chairunisah Chairunisah, Chairunisah citra, Citra Dewan Dinata Tarigan DIdi Febrian Dinda Farahdilla Dharma Dinda Kartika Eka Nainggolan, Rinay Erika Nia Devina Br Purba Fachry Abda El Rahman Fadhilah, Nazifatul Fadhillah, Mhd. Fahri Aulia Alfarisi Harahap Fajar Harahap, Muhammad Fajar Muharram Fajar Muharram Farhan Ramadhan, Haikal Fevi Rahmawati Suwanto Fitrahuda Aulia Fitri Aulia Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Habibi, Rizki Hafiz Harahap, Fauzan Hafiz, Alvin Haikal Al Majid, Muhammad Halimatun Nisa Harahap, Muhammad Abarorya Hasibuan, Hanisah Hermawan Syahputra Hutagalung, Arif qaedi Ida Ayu Putu Sri Widnyani Ihsan Zulfahmi Ilyasyah Drilanang, Mhd Imam Ahmad Impana Manik, Kristin Indriani, Dechy Deswita Insan Taufik Irham Ramadhani Irya Shakila Syukron, Ananda Jasmidi Jasmidi Jeremia Manurung Josafat Simanjutak, Todo Jufita Sari Sitorus Karimuddin Hakim Hasibuan Kartika, Dinda Khonofi, Khoidir Khusnul Arifin Khusnul Arifin Kurniawan, Catur Latifah Hasibuan, Najwa Lidia Pebrianti Lubis, Afiq Alghazali Luge, Miclyael Maharani, Raysa Malik Fajri, Maulana MANSUR AS Manurung, Jeremia Mhd Hidayat Mhd Hidayat Misgiya, Misgiya Mochammad Iswan Mochammad Iswan Perangin-Angin Mochammad Iswan Perangin-Angin Mohammed Hafizh Al-Areef Muhammad Affandes Muhammad Ardiansyah Muhammad Badzlan Darari Muhammad Usman Muslim Sinaga, Rizal Nadilla Putri, Rezkya Nasution, Hamidah . Neltriana Syafira Niska, Debi Yandra Nugraha, Zidan Indra Nur Hairiyah Harahap Nurul Adawiyah Putri Pane, Yeremia Yosefan Parapak, R Putri Angela Pinem, Josua Pittauli Ambarita Pizaini Pizaini Prana Walidin, Adamsyach Pratama, Ega Purwanto Putri, Alsya Adelia Putri, Rezkya Nadilla Raffi Akbar Tjg, Muhammad Raiyan Fairozi Ramadhan Manik, Albert Ramadhan, Sahrul Ramadhan, Taufiq Ramadhani, S.Pd., M.Pd, Irham Ratna Sari Dewi Reo Rizki Ananda Rifqi Maulana, Muhammad Rifqi Naufal, Muhammad Rizki Alfahri , Muhammad Ronaldo Mardianson Sinaga Rosyid Fauzan, Muhammad Ryan Ananda Nolly Said . Iskandar Sanjaya, Aditia Sanusi Sasalia S, Putri Setiawan, Abi Simanjorang, Rio Givent A Siregar, Angginy Akhirunnisa Siregar, Mochammad Gani Alfa Alkhoiri Siringoringo, Andi Roi Berlian Siti Rahmah Sitompul, Sigun Putra Hasian Sri Adelila Sari Sri Dewi Sri Wahyuni Suci Frisnoiry Syahri, Alfin Syarifuddin Syarifuddin Syawali, Yusfi Talib, Corrienna Abdul Tartiyoso, Seget Tiur Malasari Siregar, Tiur Malasari Tuti Hardianti Ulfa, Nadya Valentino, Nicholas Wahyu Tri Atmojo Wahyudi, Rizky Wisnu Ananta Kusuma Yanthy Leonita Perdana Simanjuntak Yazid Noor, Muhammad Yoakim Telaumbanua, Louders yola, beby Yulita Molliq Rangkuti Yulita Molliq Rangkuti Zaharani, Firna Zai, Samuel Anaya Putra Zulfahmi Indra, Zulfahmi Zulfahrizan, Atta