Article Per Year (5 Year)
p-Index From 2021 - 2026
8.964
P-Index
This Author published in this journals
All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Education and Learning (EduLearn) JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Telematics and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal RESISTOR (Rekayasa Sistem Komputer) Journal of Computer Science and Informatics Engineering (J-Cosine) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Abdi Insani Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Fitri Bimantoro
Informatics Engineering Dept., Faculty Of Engineering, University Of Mataram
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

Published : 102 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 102 Documents
Search

 7   8   9   10   11 
Real Time Student Emotion Detection using Yolov5 Ulandari, Alisyia Kornelia; Bimantoro, Fitri; Wijaya, I Gede Pasek Suta
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25726

Abstract

The introduction of technology in the field of Education, especially in learner emotion detection plays an important role in the modern educational context. This research introduces the application of the YOLOV5 algorithm to detect learner emotions in real time during the classroom learning process. This research aims to see the performance of YOLOv5 in detecting student emotions by comparing YOLOv5 variants, namely YOLOv5m, YOLOv5n, YOLOv5l, YOLov5s, and YOLOv5x. The dataset used is a video recording of the learning process taken in classroom A3-02 in Building A, Informatics Engineering Study Program, Faculty, Engineering, University of Mataram, which is grouped into 3 classes, namely (Bored, Happy, and Neutral) with a total dataset of 451 images with dataset distribution divided into 87% training data, 8% validation data, and 4% testing data. Based on the tests conducted, YOLOv5m showed the best results with the highest accuracy reaching 89.60% on Mean Average Precision, with batch settings of 14 and epochs of 150. These results indicate that the YOLOv5 algorithm is effective in detecting learner emotions with a satisfactory level of performance and makes a significant contribution to learner emotion detection, underscoring the potential of this technology in enhancing interaction and learning in educational environments.
Arabic Character Recognition Using CNN LeNet-5 Satya Nugraha, Gibran; Suta Wijaya, I Gede Pasek; Bimantoro, Fitri; Yudo Husodo, Ario; Hamami, Faqih
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2422

Abstract

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.
Classification of Local Fruit Types using Convolutional Neural Network Method (Study Case: Lombok Island) Moh. Azzam Al Husaini; Ario Yudo Husodo; Fitri Bimantoro
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 8 No 2 (2024): Desember 2024
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v8i2.601

Abstract

Indonesia, with its natural beauty and abundant resources, has significant potential for producing food and horticultural crops, particularly on Lombok Island, West Nusa Tenggara. This region is crucial in supplying tropical fruits such as Mangosteen, Pisang Kepok, and Rambutan Lebak Bulus. However, the agricultural sector in NTB faces challenges in post-harvest handling, especially in classifying fruit ripeness, impacting distribution and supply sustainability. To address this, researchers developed a fruit classification model using digital image processing with the Convolutional Neural Network (CNN) method. This model serves as a preliminary step before creating a fruit maturity classification model. Evaluation results showed that the RGB format model achieved 95% accuracy, while the HSV format reached 97%. Comparing three models in HSV format revealed: the proposed model (0.97), MobileNetV2 (0.96), and ResNet50 (0.97). These results indicate that implementing this model could enhance post-harvest efficiency in NTB, ensuring better fruit supply management.
Pattern Recognition of Bima Script Handwritting using Convolutional Neural Network Method Ramdhani, Ghina Kamilah; Bimantoro, Fitri; Wijaya, I Gede Pasek Suta
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 3, August 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i3.1990

Abstract

Bima is one of the regions in West Nusa Tenggara Province. The Bima script is a cultural heritage used as a means of communication by the Bima community in the past. The decline in the use of the Bima script threatens cultural heritage. The government has addressed this issue by providing training to teachers to teach it in schools, but this has still been insufficient due to the limited number of teachers participating in the training. Therefore, one efficient method to assist with this issue is by leveraging modern technology, particularly through machine learning for handwriting recognition. This study aims to find the best CNN model for recognizing the Bima script with diacritics to help preserve Bima's cultural heritage through handwriting recognition. The CNN model is combined with hyperparameter tuning, and then testing is conducted in four different scenarios to evaluate the performance of each model architecture and hyperparameter variation to find the best combination. The dataset used is sourced from the Kaggle platform, and augmentation is performed to increase the total number of images to 6,750, with each image containing 75 images in 90 different classes. In this study, testing is done by dividing the dataset into training and testing sets in an 80:20 ratio. The test results show high performance, achieving an accuracy of 98.00%, precision of 98.19%, recall of 98.00%, and f1-score of 98.00% in scenario 4.
Classification of Diabetic Retinopathy Based on Fundus Image Using InceptionV3 Minarno, Agus Eko; Bagaskara, Andhika Dwija; Bimantoro, Fitri; Suharso, Wildan
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2155

Abstract

Diabetic Retinopathy (DR) is a progressive eye condition that can lead to blindness, particularly affecting individuals with diabetes. It is commonly diagnosed through the examination of digital retinal images, with fundus photography being recognized as a reliable method for identifying abnormalities in the retina of diabetic patients. However, manual diagnosis based on these images is time-consuming and labor-intensive, necessitating the development of automated systems to enhance both accuracy and efficiency. Recent advancements in machine learning, particularly image classification systems, provide a promising avenue for streamlining the diagnostic process. This study aims to classify DR using Convolutional Neural Networks (CNN), explicitly employing the InceptionV3 architecture to optimize performance. This research also explores the impact of different preprocessing and data augmentation techniques on classification accuracy, focusing on the APTOS 2019 Blindness Detection dataset. Data preprocessing and augmentation are crucial steps in deep learning to enhance model generalization and mitigate overfitting. The study uses preprocessing and data augmentation to train the InceptionV3 model. Results indicate that the model achieves 86.5% accuracy on training data and 82.73% accuracy on test data, significantly improving performance compared to models trained without data augmentation. Additionally, the findings demonstrate that the absence of data augmentation leads to overfitting, as evidenced by performance graphs that show a marked decline in test accuracy relative to training accuracy. This research highlights the importance of tailored preprocessing and augmentation techniques in improving CNN models' robustness and predictive capability for DR detection. 
IMPROVING SHOPPING EXPERIENCES AT NTB MALL THROUGH PERSONALIZED PRODUCT RECOMMENDATIONS USING CONTENT-BASED FILTERING Husodo, Ario Yudo; Bimantoro, Fitri; Agitha, Nadiyasari; Grendis, Nuraqilla Waidha Bintang
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4194

Abstract

NTB MALL, an e-commerce platform specializing in unique products from micro, small, and medium enterprises (MSMEs) in West Nusa Tenggara, faces challenges in providing personalized product recommendations due to the diversity of its product categories and consumer preferences. To address this, this study implements a content-based filtering (CBF) approach utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to enhance recommendation accuracy. The system analyzes product attributes and user interaction history to generate tailored suggestions. Experimental results indicate that cosine similarity outperforms Euclidean distance in recommendation precision, achieving an accuracy of 89% and a Mean Reciprocal Rank (MRR) of 95%. Furthermore, user feedback reveals that 93% of users found the recommendations highly relevant, 89% reported increased engagement, and 96% expressed satisfaction with the personalized shopping experience. This research provides a novel application of AI-driven recommendation systems in regional e-commerce marketplaces, demonstrating their potential to improve user experience and foster stronger connections between consumers and local producers.
Digital Business Model Training to Support the Development of a Modern Market for Local Street Vendors and Tourism Awareness Groups in the Mandalika Special Economic Zone: PELATIHAN MODEL BISNIS DIGITAL UNTUK MENDUKUNG TERCIPTANYA PASAR MODERN BAGI PEDAGANG KAKI LIMA DAN KELOMPOK SADAR WISATA PADA KAWASAN EKONOMI KHUSUS MANDALIKA Agitha, Nadiyasari; Husodo, Ario Yudo; Bimantoro, Fitri; Widiartha, Ida Bagus Ketut; Murpratiwi, Santi Ika
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 6 No. 1 (2025): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v6i1.1344

Abstract

Kawasan Ekonomi Khusus (KEK) Mandalika merupakan salah satu destinasi pariwisata prioritas yang mulai dikenal dunia karena penyelenggaraan MotoGP dan kegiatan balapan lainnya yang berstandar nasional maupun internasional. Dalam mendukung kegiatan tersebut, aktivitas jual beli berkembang pesat, terutama oleh Pedagang Kaki Lima (PKL). Namun, dalam praktiknya, beberapa PKL menjajakan dagangan mereka dengan cara memaksa dan kurang sopan, yang dapat mengganggu kenyamanan wisatawan serta menurunkan citra pariwisata KEK Mandalika. Untuk mengatasi masalah ini, dikembangkan model bisnis digital yang didukung oleh teknologi dari Dinas Perdagangan NTB, yaitu NTB Mall. Program pengabdian ini melibatkan beberapa tahapan: persiapan aplikasi NTB Mall, pelatihan literasi digital, pelatihan model bisnis digital, serta pelatihan pengelolaan pasar modern. Hasil pengabdian menunjukkan bahwa Kelompok Sadar Wisata (Pokdarwis) siap menjadi agent of change, terbukti dengan hasil pengujian System Usability Scale (SUS) sebesar 85, yang menunjukkan tingkat penerimaan aplikasi yang tinggi. Dampak lain yang terukur dari program ini mencakup peningkatan keterampilan digital bagi PKL, pemahaman strategi pemasaran digital, serta pertumbuhan ekonomi bagi para pedagang yang telah berpartisipasi. Selain itu, program ini juga berkontribusi dalam meningkatkan tata kelola kawasan KEK Mandalika dengan menata lokasi berdagang yang lebih rapi dan terorganisir, sehingga menciptakan lingkungan wisata yang lebih nyaman. Program ini juga mendorong peningkatan inklusi ekonomi bagi PKL melalui pemanfaatan teknologi digital, yang memungkinkan mereka untuk memperluas pasar dan meningkatkan daya saing di era digital.
Implementation of Digital Marketing Strategy for UMKM in Keroya Village, East Lombok through Optimization of Promotional Design Using Canva: IMPLEMENTASI STRATEGI DIGITALISASI MARKETING DI UMKM DESA KEROYA LOMBOK TIMUR MELALUI OPTIMALISASI DESAIN PROMOSI MENGGUNAKAN CANVA Akhyar, Halil; Bimantoro, Fitri; Dwiyansaputra, Ramaditia; Hamidi, Mohammad Zaenuddin; Maulana, Sutan Fajri; Rahayu, Susi
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 6 No. 1 (2025): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v6i1.1362

Abstract

Desa Keroya memiliki potensi pengembangan ekonomi karena memiliki banyak pelaku usaha mikro di sektor pertanian dan perkebunan. Upaya yang dilakukan untuk mendukung daya saing pelaku usaha mikro dengan mengadakan kegiatan pelatihan digitalisasi marketing. Kegiatan bertujuan untuk meningkatkan pemahaman dan keterampilan dalam strategi promosi menggunakan canva. Kegiatan ini mencakup pelatihan penggunaan aplikasi canva sebagai alat desain promosi serta pengenalan teknis pemasaran berbasis digital. Metode yang diterapkan dalam pelatihan meliputi pendekatan Participatory Rural Apprasial (PRA), Economic Empowerment Model, Technological Empowerment Model, dan edukatif. Hasil evaluasi menunjukkan bahwa pelatihan ini meningkatkan kesadaran peserta terhadap pentingnya digitalisasi marketing serta kemampuan peserta dalam merancang materi promosi secara mandiri. Namun, tantangan seperti keterbatasan perangkat teknologi dan tingkat literasi digital yang beragam menjadi hambatan. Banyak pelaku usaha yang masih kesulitan memanfaatkan platform digital secara optimal karena masih kurangnya pemahaman. Tanpa dukungan yang tepat, kesenjangan digital ini dapat mempengaruhi daya saing pasar yang semakin kompetitif. Oleh karena itu, diperlukan pendampingan berkelanjutan serta peningkatan akses teknologi guna memastikan keberlanjutan program ini. Kolaborasi antara akademisi, pemerintah desa, dan masyarakat menjadi factor kunci dalam mendukung pemberdayaan UMKM melalui digitalisasi marketing.
KLASIFIKASI IKAN CAKALANG DAN IKAN TONGKOL MENGGUNAKAN XCEPTION DAN MOBILENET Anugrah, Febrian Rizky; Bimantoro, Fitri; Wijaya, I Gede Pasek Suta
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 1 (2025): Maret 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i1.430

Abstract

This study compares the performance of two deep learning architectures, Xception and MobileNet, for classifying skipjack and mackerel tuna, with a focus on accuracy and computational efficiency. MobileNet achieved an impressive accuracy of 97%, with precision, recall, and F1-score all at 97%, and demonstrated a faster prediction time of 0.06 seconds, making it well-suited for real-time applications. In contrast, Xception achieved an accuracy of 93%, with a precision of 94%, recall of 93%, and an F1-score of 93%. However, its prediction time was slower at 0.13 seconds, indicating a higher computational complexity. Although Xception delivered substantial accuracy, MobileNet outperformed it in terms of efficiency, suggesting that MobileNet is better suited for applications with limited resources or time constraints. The results indicated that MobileNet's lightweight architecture makes it ideal for mobile or embedded systems. At the same time, Xception's more complex structure may be advantageous for tasks that require higher precision in image processing. This research makes a significant contribution to the development of deep learning-based methods for fish species classification, offering improvements in both accuracy and speed.
Comparative Analysis of ResNet-50 and VGG16 Architecture Accuracy in Garbage Classification System Yudhis, Putu Yudhis; Fitri Bimantoro; Regania Pasca Rassy
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 9 No 1 (2025): Juni 2025
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v8i1.620

Abstract

Population growth and urbanization have resulted in an exponential increase in waste generation, causing serious environmental and health risks. Garbage classification is essential to optimize the recycling process and minimize waste in landfills. In particular, Convolutional Neural Networks (CNN) and Deep Learning, has shown effectiveness for image classification systems such as waste sorting. This research addresses the gap in comparative analysis of CNN architectures for garbage classification by comparing the performance of VGG16 and ResNet-50. This study's objective is to identify the most effective architecture for categorizing six different categories of garbage: cardboard, glass, metal, paper, plastic, and trash. Using a dataset of 2,467 photos, the models were trained, validated, and tested using improved preprocessing and data augmentation techniques. The results showed that VGG16 obtained slightly greater accuracy (97%) than ResNet-50 (96%), indicating that VGG16 could be a better architecture for garbage classification systems. This study helps further development of automated waste sorting systems for recycling management, paving the way for more sustainable waste solutions. Hope for future research, this study can help in expanding the dataset, then using other architectures to improve the accuracy of the model, and help people to process garbage according to the type.
Co-Authors A.A.Sg. Mas Karunia Maharani Ade Ragil Purwandani Adi Sugita Pandey Afwani, Royana Agitha, Nadiyasari Agus Eko Minarno Ahmad Dia’ul Haqqi Ahmad Zafrullah Mardiansyah Aisyah, Yunda Akhyar, Halil Aldian Wahyu Septiadi Alif Sabrani Anita Rosana MZ Annisa Mujahidah Robbani Anugrah, Febrian Rizky Aohana, Mizanul Ridho Aprilla, Diah Mitha Aranta, Arik Arik Aranta Arik Aranta Ario Yudo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo, Ario Yudo Arrie Kurniawardhani arrie kurniawardhany, arrie Ayu Septya Maulani B. Nurwahyu Hairani Bagaskara, Andhika Dwija Baiq Rizki Putri Utami Budi Irmawati Chaerus Sulton Cokro Mandiri, Mochammad Hazmi Daniel Swanjaya Darmawan, Riski dina hastari Dina Juliani U M, Eka Ditha Nurcahya Avianty Dwiyansaputra, Ramaditia Ellysabeth Usmiatiningsih Fachry Abda El Rahman Fadilah . Fahmi Syuhada Faqih Hamami fathin zulian tsany Fernanda Dicky Ivansyah Fiena Efliana Alfian Fuad Fadlila Surenggana Fuad Fadlila Surenggana Gibran Satria Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Grendis, Nuraqilla Waidha Bintang Haidar Rahman Haidra Rahman Hamidi, Mohammad Zaenuddin Hanung Adi Nugroho Heri Wijayanto Hidhayah, Ratu Nisful Laily husnul khotimah I B K Widiartha I Gede Andika I Gede Pasek Suta Wijaya I Gede Putu Wirarama Wadashwara Wirawan I Gede Putu Wirarama Wedashwara W I Gede Wirarama Wedashwara W. I Putu Teguh Putrawan I Putu Teguh Putrawan I Wayan Agus Arimbawa I Wayan Agus Arimbawa, I Wayan Agus Ibrahim, Zaidah Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Imam Tantowi Isye Arieshanti Jatmika, Andy Hidayat Kansha, Lyudza Aprilia Lalu Zulfikar Muslim Lidia Ardhia Wardani Liza Yuliana Khairani Marcellino, Hendy Maulana Surya Negara Maulana, Sutan Fajri Mizanul Ridho Aohana Moh. Ali Albar Moh. Azzam Al Husaini Muhamad Irzan Muhammad Afif Ma'ruf Muhammad Daden Kasandi Putra Wesa Muhammad Edy Kurniawan Basri Muhammad Giri Restu Adjie Muhammad Hadi Muhammad Hadiasri Muhammad Khaidar Rahman Muhammad Sholihul Hamdi Muhammad, David Arizaldi Muntari Muntari Murpratiwi, Santi Ika Nanik Suciati Nazibullah Nazibullah Ni Nyoman Citariani Sumartha Nindya Alita Rosalia Noor Alamsyah Novanita Laylatul Husna Novita Nurul Fakhriyah Nugraha, Gibran Satya Nuraqilla Waidha Bintang Grendis Nurhaini Rahmawati Nurhalimah Nurhalimah Obenu, Juanri Priskila Patriaji Ibrahim Maulana Prof. I Gede Pasek Suta Wijaya Putu Wahyu Pratama Rabbani, Budiman Raihan, Muhammad Dzulhi Ramadhani, Rizky Insania Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramdhani, Ghina Kamilah Ramlah Nurlaeli Rani Farinda Regania Pasca Rassy Rijalul Imam Rina Lestari Riska Yulianti Rival Biasrori rizka amalia Rizki Rahmadi Rizqullah, Muhammad Naufal Robert Silas Kabanga Rosalina Rosalina Salma Nabilla Ulpa Salsabila Putri Rajani Said Satya Nugraha, Gibran Setiawan, Lalu Rudi Susi Rahayu Suwardiman Suwardiman Tazkiya Aulia Rachman Teguh Ardian Samudra Ulandari, Alisyia Kornelia Umbara Diki Pratama Wahyu Alfandi Wildan Suharso Yogi Permana Yudhis, Putu Yudhis Yudo Husodo, Ario Yufis Azhar Yunia Puspita Wulandari Zuhraini, Marlia Zul Rijan Firmansyah