Article Per Year (5 Year)
p-Index From 2021 - 2026
3.852
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Sinkron : Jurnal dan Penelitian Teknik Informatika Information System for Educators and Professionals : Journal of Information System Jurnal Sisfokom (Sistem Informasi dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Krea-TIF: Jurnal Teknik Informatika E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) Madaniya Jurnal Kajian Ilmiah Journal of Applied Computer Science and Technology (JACOST) Jurnal Atma Inovasia Prosiding University Research Colloquium INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Informatika Polinema (JIP) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Muhammad Resa Arif Yudianto
Universitas Muhammadiyah Magelang
Religion Humanities 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 Medicine & Pharmacology Social Sciences Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 27 Documents
Search

 1   2   3 
Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta Arya Prayoga; Maimunah; Pristi Sukmasetya; Muhammad Resa Arif Yudianto; Rofi Abul Hasani
Journal of Applied Computer Science and Technology Vol 4 No 2 (2023): Desember 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i2.486

Abstract

Batik is an Indonesian culture that has been recognized as a world heritage by UNESCO. Indonesian batik has a variety of different motifs in each region. One area that is famous for its batik motifs is Yogyakarta. Yogyakarta has a variety of batik motifs such as ceplok, kawung, and parang which can be differentiated based on the pattern. Yogyakarta batik motifs need to be preserved so they do not experience extinction, one way is by introducing Yogyakarta batik motifs. The recognition of Yogyakarta batik motifs can utilize technology to classify images of Yogyakarta batik motifs based on patterns using the Convolutional Neural Network (CNN). The Yogyakarta batik motif images used for classification totaled 600 images consisting of 3 different motifs such as ceplok, kawung, and parang. Image classification using CNN depends on the architectural model used. The CNN architecture consists of two stages, namely Convolutional for feature extraction and Neural Network for classification. The CNN architectural models made for the introduction of Yogyakarta batik motifs totaled 7 models which were distinguished at the feature extraction stage. The highest accuracy results in the classification of Yogyakarta batik motif images using CNN were obtained in the 6th model. The 6th model has an accuracy of 87.83%, an average precision of 88.46% and an average recall of 87.66%. The accuracy, precision, and recall values ​​obtained by the 6th model are above 80%, which means that the 6th model can classify Yogyakarta batik motifs quite well.
Object Recognition with SSD MobileNet Pre-Trained Model in the Cashier Application Burhanudin, Nazil Ilham; Laksito, Arif Dwi; Sidauruk, Acihmah; Yudianto, Muhammad Resa Arif; Rahmi, Alfie Nur
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1659

Abstract

Object recognition is a type of image processing technique that is frequently employed in current applications such as facial identification, vehicle detection, and automated cashiers. One issue with barcode and RFID cashier apps is that they cannot scan several products at the same time. The cashier application employing object identification using picture images is believed to be able to distinguish more than one object in order to speed up the transaction process. The usage of SSD pre-trained models with MobileNet architecture to detect items in automatic cashier applications is discussed in this paper. This study put the model to the test on three types of soft drink objects: coca-cola, floridina, and good day. A smartphone camera was used to collect the data, which totaled 203 images. The findings indicated that the product object identification method was 82.9% accurate, 97.5% precise, and 84.7% recall. The object recognition process takes between 365 and 827 milliseconds, with an average time of 695 milliseconds (0.69 seconds).
Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16 Irfan Rasyid; Yudianto, Muhammad Resa Arif; Maimunah; Tuessi Ari Purnomo
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12936

Abstract

The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.
Pengenalan Deteksi Wajah Artificial Intelligence dan Achievement Motivation Training untuk Siswa SMK Kuncup Samigaluh Sukmasetya, Pristi; Primadewi, Ardhin; Yudianto, Muhammad Resa Arif; Maimunah, Maimunah; Hasani, Rofi Abul; Nugroho, Setiya
Jurnal Atma Inovasia Vol. 4 No. 3 (2024)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

— Artificial Intelligence (AI) is a field of computer science aimed at developing machines capable of performing tasks that typically require human intelligence. In recent years, the development of AI has shown significant progress, and its use has expanded across various sectors, including education. The application of AI in education offers various opportunities and challenges, such as personalized learning and enhancing students' skills, but also presents challenges in technological adaptation and ethical understanding. This paper discusses the utilization of AI-based facial recognition technology at SMK Kuncup Samigaluh, with the goal of enhancing students' competence in information technology. This community service activity involves a series of structured stages, including initial planning, activity implementation, discussion and Q&A, as well as evaluation and feedback. The results of this activity indicate a significant improvement in students' understanding of AI and facial recognition technology, as evidenced by the increase in post-test scores compared to pre-test scores. With an interactive demonstrative approach, this activity successfully provided a positive impact on students' knowledge and interest in AI, and broadened their horizons regarding career opportunities in information technology.
The effect of Gaussian filter and data preprocessing on the classification of Punakawan puppet images with the convolutional neural network algorithm Kusrini, Kusrini; Arif Yudianto, Muhammad Resa; Al Fatta, Hanif
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3752-3761

Abstract

Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.
Uji Prototype Metode Design Thinking pada Penyebaran Informasi COVID-19 Hasani, Rofi Abul; Yudianto, Muhammad Resa Arif; Sukmasetya, Pristi; Febriyanto, Yusril
Jurnal Kajian Ilmiah Vol. 22 No. 2 (2022): May 2022
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/fa8q9e76

Abstract

The spread of information about COVID-19 spreads so fast that the information circulating in the community has not been confirmed. This causes excessive anxiety and fear. There are several approaches to getting things done like design thinking, design sprint, and lean UX. With the right approach, this kind of situation will be resolved. From these problems, in this research, problem-solving will be carried out using a design thinking method. Because design thinking is the best method for developing innovative products. The design thinking process consists of 5 stages: Empathize, Define, Ideate, Prototype, Test. This research has carried out all these stages. The prototyping process uses the Figma mirror application to produce a more real experience for users. Then the prototype was tested on 5 respondents. After the test is carried out, it shows the success data based on predetermined indicators in the form of task 1 success is 80%, task 2 success is 100% successful, task 3 success is 60%, task 4 success is 40%, task 5 success is 100% and task 6 has 60% success. so that the average success of the prototype made is 88%. This means that the prototype developed with the design thinking method is easy for users to use. However, there are still features that have a low yield of 40%. So in the next iteration, it is necessary to improve the interface display that focuses on the call doctor feature based on user feedback.
Analisis Perbandingan Ekstraksi Fitur Teks pada Sentimen Analisis Kenaikan Harga BBM Darmawan, Briga; Laksito, Arif Dwi; Yudianto, Muhammad Resa Arif; Sidauruk, Acihmah
Krea-TIF: Jurnal Teknik Informatika Vol 11 No 1 (2023)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v11i1.13819

Abstract

BBM merupakan bahan bakar yang digunakan kendaraan bermotor. Penggunaan BBM meningkat sejalan dengan pertumbuhan ekonomi di Indonesia. Kenaikan harga BBM di Indonesia menimbulkan berbagai macam pendapat di media sosial twitter melalui posting dan thread. Fokus penelitian ini melakukan analisis sentimen terhadap kenaikan BBM yang datanya didapat melalui twitter dengan jumlah 1667 data. Tujuan dari penelitian ini melakukan perbandingan metode ekstraksi fitur yang memiliki kinerja paling baik seperti TF-IDF, Bag of Word, dan FastText diuji dengan algoritma machine learning SVM. Untuk tahap penelitian yang pertama melakukan crawling data twitter, preprocessing data, ekstraksi fitur, pembuatan model dengan algoritma machine learning, dan kemudian dilakukan pengujian dan perbandingan model confusion matrix pada setiap ekstraksi fitur. Hasil dari penelitian ini menunjukkan bahwa penggunaan ekstraksi fitur BoW  memiliki kinerja lebih baik dibandingkan model ekstraksi fitur yang lain.
Peningkatan Kompetensi Digital Guru melalui Pelatihan Pembuatan Website dengan Google Sites di SMA Ma'arif 1 Yogyakarta Yudianto, Muhammad Resa Arif; Sari, Tika Novita; Nadhir Fachrul Rozam; Dzul Fadli Rahman; Masduki Zakarijah
Jurnal Atma Inovasia Vol. 5 No. 4 (2025)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jai.v5i4.11332

Abstract

Perkembangan teknologi digital saat ini menuntut semua sektor, termasuk pendidikan, untuk beradaptasi dan memanfaatkannya dalam proses belajar mengajar. Salah satu bentuk pemanfaatan teknologi di lingkungan pendidikan adalah kemampuan guru dalam membangun identitas profesional melalui website pribadi yang juga dapat digunakan sebagai media pembelajaran. Kegiatan pengabdian masyarakat ini dilakukan di SMA Ma’arif 1 Yogyakarta dan bertujuan untuk memberikan pelatihan kepada para guru dalam membuat website pribadi menggunakan platform Google Sites. Materi pelatihan meliputi konsep pembelajaran digital, perencanaan konten, serta praktik langsung membuat dan menyusun struktur website. Selain menjadi sarana branding diri, website yang dibuat juga diharapkan mampu menjadi repositori materi pembelajaran yang dapat diakses oleh siswa kapan saja dan di mana saja. Peserta pelatihan terdiri dari guru berbagai mata pelajaran, dan sebagian besar belum memiliki pengalaman membuat website sebelumnya. Hasil kegiatan menunjukkan peningkatan pengetahuan dan keterampilan guru dalam mengelola konten digital, serta antusiasme yang tinggi dalam mengikuti pelatihan. Kegiatan ini diharapkan dapat menjadi langkah awal dalam mendorong transformasi digital di sekolah, khususnya dalam memperkuat peran guru sebagai fasilitator pembelajaran berbasis teknologi. Kata Kunci—kompetensi digital, pelatihan guru, Google Sites, media pembelajaran, website pribadi
Object Recognition with SSD MobileNet Pre-Trained Model in the Cashier Application Burhanudin, Nazil Ilham; Laksito, Arif Dwi; Sidauruk, Acihmah; Yudianto, Muhammad Resa Arif; Rahmi, Alfie Nur
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1659

Abstract

Object recognition is a type of image processing technique that is frequently employed in current applications such as facial identification, vehicle detection, and automated cashiers. One issue with barcode and RFID cashier apps is that they cannot scan several products at the same time. The cashier application employing object identification using picture images is believed to be able to distinguish more than one object in order to speed up the transaction process. The usage of SSD pre-trained models with MobileNet architecture to detect items in automatic cashier applications is discussed in this paper. This study put the model to the test on three types of soft drink objects: coca-cola, floridina, and good day. A smartphone camera was used to collect the data, which totaled 203 images. The findings indicated that the product object identification method was 82.9% accurate, 97.5% precise, and 84.7% recall. The object recognition process takes between 365 and 827 milliseconds, with an average time of 695 milliseconds (0.69 seconds).
Klasifikasi Citra Candi Berdasarkan Tekstur Bentuk Menggunakan Convolutional Neural Network Devi Oktaviani; Muhammad Resa Arif Yudianto; Maimunah; Pristi Sukmasetya; Rofi Abul Hasani
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.4999

Abstract

Candi merupakan bangunan kuno peninggalan Hindu-Buddha yang terbuat dari batu yang digunakan sebagai tempat ibadah. Beberapa candi memiliki persamaan yang signifikan khususnya dari segi struktur bangunan pada candi tersebut. Penelitian ini mengeksplorasi penggunaan Convolutional Neural Network (CNN) dengan arsitektur ResNet-50 untuk mengklasifikasikan citra candi di Jawa Tengah, Indonesia. Pariwisata di provinsi ini memiliki nilai ekonomi dan kultural yang tinggi, terutama dengan warisan sejarah seperti Candi Borobudur dan Candi Mendut. Dengan banyaknya candi yang memiliki struktur serupa, penelitian ini memanfaatkan teknologi deep learning, khususnya CNN, untuk mengklasifikasikan citra berdasarkan tekstur bentuk. Dengan pengumpulan data menggunakan teknik scrapping, diperoleh 400 dataset citra dari kedua candi tersebut. Proses Image pre-processing melibatkan resizing, grayscaling. Pada penelitian ini dilakukan 2 jenis skenario pengolahan citra sebelum diproses dengan CNN yaitu menggunakan CLAHE dan deteksi tepi dengan metode Canny. Dua skenario tersebut dievaluasi, dan memperoleh akurasi tertinggi sebesar 95% untuk penggunaan CLAHE sedangkan saat menggunakan deteksi tepi Canny didapatkan akurasi sebesar 91,25%. Proses klasifikasi menggunakan arsitektur ResNet-50, dan hasilnya menunjukkan keunggulan penggunaan CLAHE dengan selisih akurasi 3,75% dibandingkan dengan deteksi tepi Canny. Penerapan model mencakup desain Graphical User Interface (GUI) untuk memudahkan pengguna dalam mengklasifikasikan citra candi. Hasil akhir menunjukkan bahwa CLAHE merupakan metode image pre-processing paling efektif untuk meningkatkan akurasi klasifikasi citra candi. Temuan ini memberikan kontribusi pada pemahaman tentang penerapan teknologi deep learning dalam mendukung identifikasi dan promosi warisan budaya, terutama di sektor pariwisata
Co-Authors Acihmah Sidauruk Agus Qomaruddin Munir Aldi Muhammad Nur Fadli Alfira Nisa Fadhilah Arazka Firdaus Anavyanto Ardhin Primadewi Arham Rahim Arif Dwi Laksito Arya Prayoga Bayu Agustian Bekti Wulandari Bonita Destiana Burhanudin, Nazil Ilham Darmawan, Briga Devi Oktaviani Dimas Sasongko Dzul Fadli Rahman Ema Utami Endah Ratna Arumi, Endah Ratna Fadhilah, Alfira Nisa Fadli, Aldi Muhammad Nur Febriyanto, Yusril Hanif Al Fatta Hanif Al Fatta Hasani, Rofi Abul Imam Saputra Imam Saputra Ira Nuryani Irfan Rasyid Irfan Rasyid Jalaluddin, Alif Kusrini Kusrini Kusrini, Kusrini Lusi Nurlatifah Maimunah Maimunah Maimunah, M Maimunah, Maimunah Masduki Zakarijah Meidar Hadi Avizenna Nadhir Fachrul Rozam Nadila Dwi Saputri Nuryani, Ira R Arri Widyanto Raden Kartika Satya Suryanegara Rahman, Dzul Fadli Rahmi, Alfie Nur Ramadhana Setiyawan Rasyid, Irfan Rofi Abul Hasani Sambodo Sriadi Pinilih Saputri, Nadila Dwi Satriyo Agung Dewanto Setiya Nugroho Sukmasetya, Pristi Suryanegara, Raden Kartika Satya Tika Novita Sari Tuessi Ari Purnomo Tuessi Ari Purnomo Uky Yudatama, Uky Yusril Febriyanto Zaenal Mustofa Zakariah, Masduki