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All Journal 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 Sains Teknologi dan Lingkungan (JSTL) 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

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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”.
User Requirement Analysis dalam penerapan metode User Centered Design sebagai pendukung kebutuhan UI/UX dalam aplikasi NTB Mall Agitha, Nadiyasari; Ario Yudo Husodo; Fitri Bimantoro
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 7 No 2 (2023): Desember 2023
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v7i2.576

Abstract

User requirement analysis merupakan satu hal yang penting dilakukan dalam pembuatan sebuah user interface/user experience (UI/UX) menggunakan metode User Centered Design (UCD). Hal ini dikarenakan pada tahapan ini terdapat identifikasi dan dokumentasi kebutuhan pengguna. NTB mall adalah e-commerce pertama milik pemerintah daerah NTB dengan tujuan menjual produk unggulan daerah dari Usaha Mikro, Kecil Menengah (UMKM), Pedagang Kaki Lima (PKL) dan dibantu dengan pemantauan oleh Kelompok Sadar Wisata (Pokdarwis). Dalam pembuatan UI/UX NTBMall, diperlukan user requirement analysis yang kuat untuk mendapatkan penggunaan NTBMall yang sesuai dengan kebutuhan pengguna. User requirement analysis dibagi menjadi beberapa tahapan secara berurutan. Penggunaan user requirement analysis telah terbukti menghasilkan UI/UX yang menarik, dibuktikan dengan hasil pengujian SUS bernilai 72.82 yang mengartikan bahwa user telah puas dengan aplikasi NTBMall.
Pelatihan Desain Grafis Untuk Masyarakat Pelaku Wisata Di Lombok: Graphic Design Training for Tourism Communities In Lombok Widiartha, Ida Bagus Ketut; Afwani, Royana; Bimantoro, Fitri; Husodo, Ario Yudo; Agitha, Nadiyasari
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 4 No. 2 (2023): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v4i2.1113

Abstract

Lombok merupakan salah satu tujuan wisata utama yang ada di Indonesia, keindahan alamnya tidak kalah dengan Bali yang sudah terlebih dahulu terkenal di Manca Negara. Keberagaman budaya dan produk lokal yang dimiliki juga sangat banyak dan mendukung peningkatan jumlah kunjungan wisata. Promosi yang dilakukan oleh pemerintah tidak dapat mengakomodir semua obyek wisata dan mempromosikan produk lokal dari masyarakat tersebut, karena seiring dengan kesadaran masyarakat tentang pentingnya pariwisata dalam meningkatkan kesejahteraan, banyak sekali muncul obyek wisata baru dan produk- produk baru yang tidak tersentuh oleh promosi pemerintah. Dan promosi yang dilakukan secara konvensional membutuhkan biaya yang sangat mahal. Diera digital saat ini media sosial, memegang peranan sangat penting dalam mempromosikan sesuatu. Selain kemampuannya untuk mempromosikan produk ataupun obyek, media sosial juga bisa digunakan untuk membuat personal branding yang pada hilirnya dapat mendatangkan keuntungan materi. Untuk membuat konten yang menarik perlu pelatihan ketrampilan kepada masyarakat untuk dapat meningkatkan kemampuannya dalam membuat konten yang ditampilkan dalam media sosial sehingga lebih banyak orang melihat dan berkomentar yang pada akhirnya dapat menjadi media promosi yang murah
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%.
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

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

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%.
Implementasi Convolutional Neural Network pada Multi-label Classification Wajah Manusia Berdasarkan Usia, Gender, dan Ras Maulana Surya Negara; Muhamad Irzan; Ahmad Dia’ul Haqqi; Fitri Bimantoro
DIELEKTRIKA Vol 11 No 2 (2024): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v11i2.389

Abstract

Klasifikasi wajah manusia merupakan bidang penelitian penting dalam pengenalan pola dan computer vision, dengan fokus pada informasi seperti jenis kelamin, usia, ras, dan ekspresi wajah. Penelitian ini bertujuan untuk meningkatkan klasifikasi multi-label wajah manusia menggunakan convolutional neural network (CNN). Metode tradisional seperti Local Binary Pattern (LBP) dan Random K-Nearest Neighbor (KNN) menunjukkan keterbatasan dalam akurasi dan ketergantungan pada ekstraksi fitur manual, sementara metode CNN yang lebih modern menunjukkan peningkatan akurasi yang signifikan. Penelitian ini bertujuan meningkatkan klasifikasi multi-label wajah manusia berdasarkan usia, gender, dan ras menggunakan convolutional neural network (CNN). Menggunakan dataset UTKFace, model CNN diuji dengan berbagai arsitektur dan teknik augmentasi data. Hasil terbaik menunjukkan akurasi 82.98% untuk usia, 90.36% untuk gender, dan 79.48% untuk ras. Penggunaan augmentasi data dan peningkatan jumlah filter CNN secara signifikan meningkatkan akurasi model. Meskipun ada tantangan dalam mengklasifikasikan usia "teenager" dan ras "Indian" serta "Others" akibat distribusi data yang tidak seimbang, hasil ini menunjukkan potensi besar CNN dalam klasifikasi multi-label wajah manusia. Pengembangan lebih lanjut direkomendasikan dengan fine-tuning arsitektur CNN dan eksplorasi metode augmentasi data serta transfer learning.
PENGENALAN POLA SUKU KATA AKSARA BIMA DENGAN BARIS TANDA BUNYI MENGGUNAKAN EKSTRAKSI CIRI MOMENT INVARIANT DENGAN METODE ANN Rizqullah, Muhammad Naufal; Dwiyansaputra, Ramaditia; Bimantoro, Fitri
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 6 No 1 (2024): March 2024
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v6i1.188

Abstract

The Bimanese script is one of the archipelago's cultural heritage that needs to be preserved. The problem arose when some Bimanese people doubted the existence of the Bimanese script. Therefore, it is essential to safeguard the Bimanese script and learn the Bimanese script starting from reading and then understanding the letters. After that, add a line of sound marks to entirely understand the Bimanese script's meaning. This study aims to build an Artificial Neural Network (ANN) model to recognize the Bimanese Script Syllable Pattern with Sound Sign Lines by using Moment Invariant feature extraction. Before doing the training, first, determine the parameters on the ANN using the Tuning Hyperparameter, in the test, using a dataset of 2250 images of the Bimanese script. Based on the results of the tests carried out based on the optimal parameters, the accuracy is 77.59%, precision is 78.44%, recall is 77.61%, and F1-Score is 77.33%. Then for testing using K-Fold cross-validation, the best results were obtained using K = 9 with a ratio of 8:1 where the resulting accuracy was 79.74%. Overall the results of this study are expected to preserve the Bimanese script and are developed more widely.
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.
Co-Authors A.A.Sg. Mas Karunia Maharani Ade Ragil Purwandani Adi Sugita Pandey Afwani, Royana Agitha, Nadiyasari Agus Eko Minarno Agustini, Latifa Zahra Ahmad Dia’ul Haqqi Ahmad Zafrullah Mardiansyah Aisyah, Yunda Aldian Wahyu Septiadi Ali Albar, Moh Alif Sabrani Aliyah Fajriyani Alwi Pratama Anita Rosana MZ Anjarwani, Sri Endang 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 Effendy, Michael 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 Gerald Dennaya HD, Muh. 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 Halil Akhyar Hamidi, M. Zaenuddin Hamidi, Mohammad Zaenuddin Hanung Adi Nugroho Hendrawan 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 Prasetyo, Andrie Ridzki Prof. I Gede Pasek Suta Wijaya Putu Wahyu Pratama Rabbani, Budiman Raihan, Muhammad Dzulhi Ramadhani, Rizky Insania Ramadian Ridho Illahi 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 Sitti Latifah 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 Zafrullah M., Ahmad Zubaidi, Ariyan Zuhraini, Marlia Zul Rijan Firmansyah