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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro Lontar Komputer: Jurnal Ilmiah Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Education and Learning (EduLearn) Journal of Telematics and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal Sains Teknologi dan Lingkungan (JSTL) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Computer Science and Informatics Engineering (J-Cosine) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Multimedia 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 Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Gede Pasek Suta Wijaya
Informatics Engineering Dept., University Of Mataram, Jl. Majapahit 62 Mataram, Mataram INDONESIA
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Public Health Social Sciences Other

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Klasifikasi Citra Lubang pada Permukaan Jalan Beraspal dengan Metode Convolutional Neural Networks (CNN): Image Classification of Potholes on Paved Road Surfaces with the Convolutional Neural Networks (CNN) Method Ni Nyoman Citariani Sumartha; I Gede Pasek Suta Wijaya; Fitri Bimantoro; Gibran Satya Nugraha
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 8 No 1 (2024): Juni 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.v8i1.557

Abstract

A pothole is a bowl-shaped indentation in the road surface, less than 1 meter in diameter. The presence of potholes on the highway can endanger the safety of road users, so repairs need to be done as soon as possible. Images of potholed roads have high complexity, variations consisting of color contrast, hole size, presence of puddles or not, lighting when taking pictures, background and others. For this reason, an approach is needed that can classify images with a high degree of variation by extracting the important information contained in them. Judging from the potential success of using the Convolutional Neural Networks (CNN) approach in identifying images of potholes that will be reported for entry into the Public Works Service's road improvement record, the authors propose the idea of "Pothole Image Classification on Asphalt Road Surfaces with the Convolutional Neural Networks (CNN) Method”.
The Palmprint Recognition Using Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 Architecture Aprilla, Diah Mitha; Bimantoro, Fitri; Suta Wijaya, I Gede Pasek
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7577

Abstract

The palmprint is a part of the human body that has unique and detailed characteristics of the pattern of palm lines, such as the length and width of the palm (geometric features), principal lines, and wrinkle lines. It began to be developed as a tool for recognize a person. The palmprint dataset used comes from Kaggle, namely BMPD. The palmprint images in this dataset were taken in 2 sessions. In the first session, there was not much variation in rotation compared to the second session. This research uses Convolutional Neural Network (CNN) models with Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 architectures to see the best performance. The results of this research showed that the MobileNet model had the best performance with an accuracy of 96.6% and a loss of 14.3%. For Precision results of 94%, Recall 96%, and F1-Score 94%. Meanwhile, Xception obtained an accuracy of 88.3% and a loss of 52.9%, VGG16 70.8% and a loss of 109.8%, ResNet50 5.8% and a loss of 307.9%, and EfficientNetB0 3.3% and a loss of 340.1%.
MULTICLASS CLASSIFICATION OF SOUND HEALING WITH K-NEAREST NEIGHBOR ALGORITHM Ramadhani, Cipta; Suksmadana, I Made Budi; Ariessaputra, Suthami; Suta Wijaya, I Gede Pasek
DIELEKTRIKA Vol 8 No 2 (2021): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

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Abstract

Sound healing can be described to the practice of sound vibrations in an individual body directly to bring about a state of harmony and healing. In many ancient country, sound healing was used as a part of medicine and healing ritual. In this paper, we propose K-Nearest Neighbors (KNN) method to categorize the type of sound healing. Acoustic Sound for Wellbeing (ASW) such as Drums, Gongs, Chimes and Singing Bowls are used as dataset for KNN algorithm. The KNN algorithm is applied to classify The ASW dataset in multi class classification tasks. In our model, KNN gave the best performance measurement for 2 Classes classification. the value of Accuracy, Precision and recall are higher than 0,87. Meanwhile, The confusion matrix for 3 classes presented the lowest point from all experimental setting. Furthermore, confusion matrix for 4 classes showed some anomaly.
Temu Kembali Citra Menggunakan Metode Local Binary Pattern Rotation Invariant (Lbprot) dan Cosine Distance Similarity Zuhraini, Marlia; Wijaya, I Gede Pasek Suta; Bimantoro, Fitri
DIELEKTRIKA Vol 9 No 1 (2022): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

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Abstract

Image retrieval is a method of searching for an image by comparing the query image with the image in the database. One of the important things in image retrieval is the feature extraction process. Currently, the feature extraction method needed is reliable in recognizing images that are rotated at various angles or invariant to rotation such as the Local Binary Pattern Rotation Invariant (LBPROT) method. In addition to the feature extraction method, what is also important is the distance measurement method. The distance measurement method used is the Cosine Distance Similarity method. the combination of these two methods resulted in the highest average precision and recall at a ratio of 70%:30% of 98.06% and 96.78% for normal images and for images that were rotated at an angle of 90o of 97.08% and recall of 96,70%. In addition, testing on rotational images produces the lowest average precision and recall at an angle of 45° at 0%.
EAR DISEASE CLASIFICATION USING DEEP LEARNING WITH XCEPTION AND MOBILENET-V2 ARCHITECTURE Setiawan, Lalu Rudi; Wijaya, I Gede Pasek Suta; Bimantoro, Fitri
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 6 No 2 (2024): September 2024
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v6i2.426

Abstract

Hearing loss is a significant global health problem, with a high prevalence in Indonesia. Limited access to ENT specialists, especially in remote areas, causes delays in diagnosis and treatment of ear diseases. This research aims to develop an early diagnosis system for ear diseases using deep learning. The proposed method applies Xception and MobileNet-V2 Convolutional Neural Network (CNN) architecture with hyperparameter optimization using Bayesian Optimizer. The dataset consists of 1,101 images covering 20 types of ear diseases, collected using an endoscope ear cleaning kit at Mataram University Hospital. The dataset was divided into 60% training data, 20% validation data, and 20% test data. Xception recorded the best performance with accuracy, precision, recall, and f1-score of 0.911, 0.166, 0.166, and 0.151, respectively. The best model performance was obtained on MobileNet-V2 with the application of Bayesian Optimizer, resulting in the best hyperparameters at Unit Dense 174, Dropout Rate 0.2, and LXceptionearning Rate 0.003. This scenario resulted in an increase in accuracy, precision, recall, and f1-score compared to the scenario without hyperparameter search of 0.004, 0.010, 0.018, and 0.012, respectively. This research demonstrates the potential of deep learning in improving early diagnosis of ear diseases.
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.
Perancangan Mesin Klasifikasi Menggunakan Particle Swarm Optimization: Designing A Classification Machine Using Particle Swarm Optimization Made Agus Dwiputra; I Gede Pasek Suta Wijaya; Ramaditia Dwiyansaputra
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.v8i2.614

Abstract

Designing an effective classification engine is very important in various pattern recognition and machine learning applications. In this research, the Particle Swarm Optimization (PSO) algorithm is applied for the development of classification engines on various datasets. PSO is a population-based optimization method inspired by the behavior of flocks of birds or fish, which is effectively used to find optimal solutions in large search spaces. This research aims to develop a classification model by using Particle Swarm Optimization (PSO) as a training element to determine weights and biases. To test the performance on several different datasets, namely on a dummy multi-class dataset, Sasak Aksara image dataset, and the well-known Iris dataset. In the Sasak Aksara data, Discrete Cosine Trasnform (DCT) is used as feature extraction with the aim of reducing computation time. The results show that PSO can be used in the implementation of several datasets used, in the classification of dummy data, iris data, and Sasak Aksara image data. The model achieved 100% accuracy, precision, recall, and F1-Score on dummy data and iris data. However, on the Sasak Aksara image dataset, the performance of the model decreased with accuracy only reaching 65%, precision 50%, recall 32%, and F1-Score 39%. This research contributes in demonstrating the effectiveness of PSO in optimizing Perceptron models on simpler datasets and highlights the need for further development to handle more complex datasets.
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.
UI/UX Website Inventory of NTB Province Central Statistics Agency using User Centered Design Method: UI/UX WEBSITE INVENTORI BADAN PUSAT STATISTIK PROVINSI NTB MENGGUNAKAN METODE USER CENTERED DESIGN Rahmatin, Baiq Anggita Arsya; Wijaya, I Gede Pasek Suta; Putra, Chairul Fatikhin
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.v6i1.1351

Abstract

Badan Pusat Statistik (BPS) Nusa Tenggara Barat adalah lembaga pemerintah non kementerian yang bertanggung jawab langsung kepada presiden. Saat ini, pengelolaan inventarisasi barang di BPS masih menggunakan metode manual, yang sering menyebabkan kesalahan pencatatan, seperti duplikasi data atau ketidaksesuaian antara data fisik dan catatan. Selain itu, proses pencatatan memakan waktu hingga 2-3 hari untuk menyelesaikan inventarisasi satu ruangan, yang menghambat produktivitas karyawan.. Oleh karena itu, terbentuk ide untuk mendesain sistem informasi pengelolaan inventori berbasis website untuk BPS. Metode User Centered Design (UCD) dipilih untuk melakukan perancangan desain sistem informasi tersebut karena metode ini terfokus pada kebutuhan pengguna sehingga desain yang dirancang sangat membantu pengguna dalam pelaksanaan tugas inventarisasi. Sistem yang dirancang diuji dengan menggunakan metode System Usability Scale (SUS), metode ini dipilih karena dapat memberikan nilai untuk mengukur usability dari sebuah sistem yang dirancang. Pada pengujian menggunakan SUS kali ini melibatkan 5 responden dari instansi BPS, desain yang telah dibuat mendapatkan nilai sebanyak 88 yang berarti desain yang telah dibuat termasuk dalam tingkat kategori acceptability berupa acceptable, grade scale berupa B, dan adjective rating kategori excellent. Terdapat saran yang diberikan juga yaitu berupa tambahan beberapa fitur yang dapat menunjang kinerja dari sistem informasi inventori yang dibuat.
Co-Authors Adi Sugita Pandey Afwani, Royana Agitha, Nadiyasari Ahmad Musnansyah Ahmad Zafrullah Mardiansyah Akhyar, Halil Albar, Moh. Ali Aldian Wahyu Septiadi Andy Hidayat Jatmika Anita Rosana MZ Annisa Mujahidah Robbani Anugrah, Febrian Rizky Aprilla, Diah Mitha Aranta, Arik Ariessaputra, Suthami Arik Aranta Arik Aranta Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo, Ario Yudo Ariyan Zubaidi Ariyan Zubaidi Awaluddin Ayu Rezki Azizah Arif Paturrahman Belmiro Razak Setiawan Budi Irmawati Budi Irmawati Bulkis Kanata Chaerus Sulton Chandra Adiguna Chandra Adiguna Cipta Ramadhani Darmawan, Riski David Arizaldi Muhammad Dedi Ermansyah Dina Juliani U M, Eka Ditha Nurcahya Avianty Dwitama, Aditya Perwira Joan Dwiyansaputra, Ramaditia Eet Widarini Fa'rifah, Riska Yanu Fachry Abda El Rahman Fadilah . Fahmi Syuhada Faqih Hamami Farhan Yakub Bawazir Fiena Efliana Alfian Firdaus, Asno Azzawagaam Fitrah, Muhammad Dinul Fitri Bimantoro Gibran Satria Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gou Koutaki Gunawan Haidra Rahman Halil Akhyar Hamidi, Mohammad Zaenuddin Hendy Marcellino Heri Wijayanto Heri Wijayanto Heri Wijayanto Hidayat, Lalu Ramdoni I B K Widiartha I Gde Putu Wirarama Wedaswhara W. I Made Budii i Suksmadana I Made Subiantara Putra I Putu Teguh Putrawan I Wayan Agus Arimbawa I Wayan Agus Arimbawa I Wayan Agus Arimbawa, I Wayan Agus Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Nyoman Tegeh Adnyana Imam Arief Putrajaya Jayusman, Dirga Kadriyan, Hamsu Kansha, Lyudza Aprilia Keeichi Uchimura Keiichi Uchimura Keiichi Uchimura L. A. Syamsul Irfan Lalu Sweta Arif Lalu Zulfikar Muslim Lidia Ardhia Wardani Made Agus Dwiputra Mayzar Anas Maz Isa Ansyori Mega Laely Moh Ali Albar Moh. Ali Albar Muhamad Nizam Azmi Muhamad Syamsu Iqbal Muhammad Daden Kasandi Putra Wesa Muhammad Husnul Ramdani Muhammad Khaidar Rahman Muhammad Mukaddam Alaydrus Muhammad Naufal Rizqullah Muhammad Syulhan Al Ghofany Mulyana, Heru Murpratiwi, Santi Ika Mustiari, Mustiari Ni Nyoman Citariani Sumartha Ni Nyoman Kencanawati Nisa, Aisyah Khairun Novian Maududi Novita Nurul Fakhriyah Nugraha, Gibran Satya Nurhalimah Nurhalimah Obenu, Juanri Priskila Pahrul Irfan Pahrul Irfan Pandu Deski Prasetyo Putra, Chairul Fatikhin Rahmatin, Baiq Anggita Arsya Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramdhani, Ghina Kamilah Ramlah Nurlaeli Rani Farinda Reza Rismawandi Rina Lestari Riska Yulianti Ristirianto Adi Romi Saefudin Rosalina Rosalina Salsabila Putri Rajani Said Santi Ika Murpratiwi Saputra, Muhammad Harpan Teguh Satya Nugraha, Gibran Selvira Anandia Intan Maulidya Setiawan, Lalu Rudi Siti Faria Astari Sri Endang Anjarwani Sri Endang Anjarwani Sri Endang Arjarwani Suhada, Destia Suksmadana, I Made Budi Sulfan Akbar Syaifullah Syaifullah Topan Khrisnanda Tri Erna Suharningsih Ulandari, Alisyia Kornelia Wahyu Alfandi Widodo, Agung Mulyo Wirarama Wedashwara Wisnujati, Andika Yogi Permana Yudo Husodo, Ario Zafrullah, Ahmad Zakiyah Rahmiati Zubaidi, Ariyan Zuhraini, Marlia Zul Rijan Firmansyah