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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 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 Jurnal Teknologi Informasi 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) SAP (Susunan Artikel Pendidikan)
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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Pemberdayaan Pembatik Lokal melalui Pendampingan Desain Batik Digital Menggunakan Aplikasi Ambatig Kartika, Dinda; Niska, Debi Yandra; Misgiya, Misgiya; Nasution, Hamidah; Febrian, Didi; Habibi, Rizki; Saputra S, Kana; Atmojo, Wahyu Tri
Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): Edisi Maret 2026
Publisher : FKIP Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36709/amalilmiah.v7i1.602

Abstract

Program pengabdian ini bertujuan meningkatkan kapasitas perajin batik melalui pendampingan desain batik digital menggunakan aplikasi Ambatig untuk mempercepat perancangan dan menata konsistensi pengulangan motif tanpa menghilangkan identitas Batak–Melayu. Kegiatan dilaksanakan selama empat minggu melalui sosialisasi, pelatihan, penerapan teknologi, dan pendampingan dengan pemanfaatan pengaturan pola frieze (satu arah) dan kristalografi (dua arah). Evaluasi melibatkan 15 peserta menggunakan angket skala Likert pada lima poin serta observasi terhadap durasi pradesain, jumlah varian desain, dan kesalahan sambungan pola. Hasil menunjukkan rerata kepuasan peserta di atas skor 4 dengan sekitar 80% respons positif, terutama pada aspek kesesuaian gaya visual (4,5) dan kemudahan dipelajari (4,3). Secara operasional, waktu pradesain berkurang dari sekitar 90 menit menjadi 40–50 menit per varian, peserta mampu menghasilkan 2–3 varian desain dalam 90 menit, dan kesalahan sambungan pola berkurang. Program ini juga menghasilkan portofolio desain digital berisi parameter siap produksi. Dengan demikian, pemanfaatan Ambatig efektif meningkatkan efisiensi dan kerapian perancangan motif batik serta mendukung pelestarian dan keberlanjutan motif Batak–Melayu melalui dokumentasi digital dan penguatan kapasitas perajin lokal.
Optimalisasi Program Magang untuk Meningkatkan Kreativitas dan Inovasi Mahasiswa Tiur Malasari Siregar; Fajar Apollo Sinaga; Taufiq Ramadhan; Kana Saputra; Elfitra Elfitra; Yul Ifda Tanjung; Tangsi Tangsi
SAP (Susunan Artikel Pendidikan) Vol. 10 No. 3 (2026): SAP
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/sap.v10i3.696

Abstract

creativity and innovation so that graduates are better prepared to compete in the job market. This study aims to evaluate the effectiveness of the internship program at Medan State University and develop optimization strategies that focus on enhancing student creativity and innovation. This study was conducted using a mixed-method approach, namely a qualitative approach through interviews with students and industry representatives and observation. The quantitative approach through surveys and tests of student performance in the internship program. The research subjects included students and several partner companies where students underwent internships. The results showed that students involved in the internship program with an innovative project-based approach had higher levels of creativity compared to students who only carried out administrative tasks. Factors contributing to the program's success included the involvement of industry mentors, the integration of a problem-solving-based curriculum, and support facilities that foster innovation. The novelty of this research lies in the development of a creativity- and innovation-based internship program optimization model, which has not yet been widely implemented in Indonesian universities. This contributes to academic policy by improving the effectiveness of internship programs and strengthening university-industry collaborations to produce graduates better prepared to compete in the digital age.
Eye Disease Classification System Based on Fundus Images Using the InceptionV3 Architecture Annisa Aulia; Hermawan Syahputra; Yulita Molliq Rangkuti; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2263

Abstract

This study aims to develop an automated eye disease classification system based on retinal fundus images using the InceptionV3 deep learning architecture. The dataset consists of four classes: cataract, diabetic retinopathy, glaucoma, and normal, collected from public sources and clinical data. The proposed method applies several preprocessing techniques, including background segmentation, data augmentation, data normalization, and an 80:20 data split to improve model performance and generalization. Transfer learning is implemented by utilizing pretrained ImageNet weights and modifying the final layers to suit the classification task. The model is trained using the Adam optimizer with a learning rate of 0.001 and categorical cross-entropy loss function. Evaluation results show that the model achieves an accuracy of 96%, with average precision, recall, and F1-score values of 0.97, 0.96, and 0.97, respectively. The confusion matrix analysis indicates that most predictions are correctly classified, demonstrating strong performance across all classes. Furthermore, the model is successfully integrated into a web-based system that enables users to upload fundus images and obtain classification results automatically. These findings indicate that the proposed system can effectively assist in early detection of eye diseases and support clinical decision-making.
Face Recognition Motorcycle Rider Registration System for Rider Data Management Kana Saputra S; Insan Taufik; Irham Ramadhani; Putri Sasalia S; Yusfi Syawali; Dede Yusuf; Rezkya Nadilla Putri; Najwa Latifah Hasibuan; Fauzan Hafiz Harahap
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2157

Abstract

This research aims to develop a motorcycle rider registration system using facial recognition technology that can improve the efficiency of rider data management. This system is designed to identify and authenticate riders with high accuracy, thereby simplifying the registration and monitoring process. The methods used in this research include collecting rider facial data through cameras, image processing for feature extraction, and implementing a facial recognition algorithm. Testing was conducted in several locations with varying lighting conditions and viewing angles to ensure the system's robustness. The results show that the developed system is capable of achieving facial recognition accuracy of up to 95%. In addition, this system provides an intuitive user interface to facilitate the registration and data management process. With the implementation of this system, it is expected to reduce the time and costs required in managing motorcycle rider data, as well as improve safety and comfort while riding.
Classification of Purple Passion Fruit Ripeness Levels Using Convolutional Neural Network (CNN) Mochammad Gani Alfa Alkhoiri Siregar; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1787

Abstract

Passiflora edulis Sims (purple passion fruit) is a fruit that offers numerous health benefits and possesses high economic value. However, the manual assessment of ripeness by traders tends to be subjective and inconsistent, leading to post-harvest losses of up to 50%. This study developed a classification model for determining the ripeness level of purple passion fruit using a Convolutional Neural Network (CNN) and implemented it in a web-based application. The CNN model was designed to classify four ripeness stages (unripe, half-ripe, ripe, and rotten) with the addition of a non-passion-fruit class to enhance the system’s robustness. The dataset consisted of 2,000 images divided into five classes: four ripeness levels of purple passion fruit (unripe, half-ripe, ripe, and rotten) and one non-passion-fruit class as a comparator. All images were in JPG and PNG formats. The CNN architecture comprised four convolutional layers with 16, 32, 64, and 128 filters, respectively. Evaluation of various data-splitting ratios (80:20, 70:30, 60:40) and learning rates (0.001, 0.0001, 0.01) showed that the optimal configuration was achieved at a ratio of 80:20 with a learning rate of 0.001, resulting in a training accuracy of 96.72% and a testing accuracy of 95.76%, with a loss value of 0.1811. Validation using 5-Fold Cross Validation produced an average accuracy of 95.40%. The model was integrated into a web application developed using Flask and JavaScript, deployed on the PythonAnywhere cloud platform, enabling users to upload images and automatically obtain ripeness predictions to assist traders in sorting fruits more quickly and accurately.
English English Sri Adelila Sari; Jasmidi Jasmidi; Siti Rahmah; Kana Saputra S; Seget Tartiyoso; Bambang Suseno; Catur Kurniawan; Nadya Ulfa; Mhd. Fadhillah; Sahrul Ramadhan
Amaliah: Jurnal Pengabdian Kepada Masyarakat Vol 8 No 1 (2024): Amaliah: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPI UMN AL WASHLIYAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32696/ajpkm.v8i1.2861

Abstract

The revitalization of the SMK curriculum projects serves that students are not only ready to work but also be able to create jobs through entrepreneurship. SMK Students are provided additional hours of entrepreneurship coursework in order to foster creativity, innovation, and an entrepreneurial mindset among students. Furthermore, students are also required to possess a social entrepreneurial mindset, which encompasses a business development perspective that takes into account social, economic, environmental, and health factors. In this program, students are given training in making soap from used cooking oil. Used cooking oil is a waste that has promising business prospects. Appropriate processing of used cooking oil can yield valuable products that satisfy the needs of the community. As a result of this training, up to 97% of students have acquired greater understanding regarding the hazards and advantages associated with used cooking oil. Students possess advanced skills in producing soap products using used cooking oil. Furthermore, students develop a greater awareness regarding health and the environment.
3D Application Development with Blender and Roblox Integration: A Case Study of the North Sumatera State Museum Maulana Malik Fajri; 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
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 Annisa Aulia Anti Nada Nafisa Arnita Azizi, Nur Azqal Azkia Bambang Suseno Budi Akbar, Muhammad Bush Henrydunan, John Catur Kurniawan citra, Citra Dede Yusuf Dewan Dinata Tarigan DIdi Febrian Dinda Farahdilla Dharma Dinda Kartika Eka Nainggolan, Rinay Elfitra Elfitra Erika Nia Devina Br Purba Fadhilah, Nazifatul Fahri Aulia Alfarisi Harahap Fajar Harahap, Muhammad Fajar Muharram Farhan Ramadhan, Haikal Fauzan Hafiz Harahap Fevi Rahmawati Suwanto Fitrahuda Aulia Fitri Aulia Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Habibi, Rizki Hafiz, Alvin Haikal Al Majid, Muhammad Halimatun Nisa Harahap, Muhammad Abarorya Hasibuan, Hanisah Hermawan Syahputra Hutagalung, Arif qaedi 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 Jufita Sari Sitorus Karimuddin Hakim Hasibuan Khonofi, Khoidir Khusnul Arifin Khusnul Arifin Lidia Pebrianti Lubis, Afiq Alghazali Maharani, Raysa MANSUR AS Manurung, Jeremia Maulana Malik Fajri Mhd Hidayat Mhd Hidayat Mhd. Fadhillah Misgiya, Misgiya Mochammad Gani Alfa Alkhoiri Siregar 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 Nadya Ulfa Najwa Latifah Hasibuan 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 Putri Sasalia S Putri, Alsya Adelia Putri, Rezkya Nadilla Raffi Akbar Tjg, Muhammad Raiyan Fairozi Ramadhan Manik, Albert Ramadhan, Taufiq Ratna Sari Dewi Reo Rizki Ananda Rezkya Nadilla Putri Rifqi Maulana, Muhammad Rifqi Naufal, Muhammad Rizki Alfahri , Muhammad Ronaldo Mardianson Sinaga Rosyid Fauzan, Muhammad Ryan Ananda Nolly Sahrul Ramadhan Said . Iskandar Sanjaya, Aditia Sanusi Seget Tartiyoso Setiawan, Abi Simanjorang, Rio Givent A Siregar, Angginy Akhirunnisa Siringoringo, Andi Roi Berlian Siti Rahmah Sitompul, Sigun Putra Hasian Sri Adelila Sari Sri Adelila Sari* Sri Dewi Sri Wahyuni Suci Frisnoiry Syahri, Alfin Syarifuddin Syarifuddin Talib, Corrienna Abdul Tangsi Tangsi Taufiq Ramadhan Tiur Malasari Siregar Tiur Malasari Siregar, Tiur Malasari Valentino, Nicholas Wahyu Tri Atmojo Wahyudi, Rizky Wisnu Ananta Kusuma Yazid Noor, Muhammad Yoakim Telaumbanua, Louders yola, beby Yul Ifda Tanjung Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yusfi Syawali Zaharani, Firna Zai, Samuel Anaya Putra Zulfahmi Indra, Zulfahmi Zulfahrizan, Atta