Article Per Year (5 Year)
p-Index From 2021 - 2026
2.47
P-Index
This Author published in this journals
All Journal EMITTER International Journal of Engineering Technology Padjadjaran Journal of Dentistry JOIV : International Journal on Informatics Visualization INOVTEK Polbeng - Seri Informatika Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Riyanto Sigit, Riyanto
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Usability testing of “smart odontogram” application based on user’s experience Brahmanta, Arya; Maharani, Aulia Dwi; Dewantara, Bima Sena Bayu; Sigit, Riyanto; Sukaridhoto, Sritrusta; Fadhillah, Excel Daris
Padjadjaran Journal of Dentistry Vol 34, No 2 (2022): July
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/pjd.vol34no2.36566

Abstract

ABSTRACTIntroduction: Collecting dental data for odontogram in medical records is done chiefly conventionally and causes a lot of human errors. Disadvantages of the conventional method can be overcome by developing a server-based system to store medical information equipped with embedded artificial intelligence (AI), which can identify the patient’s dental condition using an intra-oral camera with the help of Deep Learning algorithms. It is essential to evaluate the usability of this application to adapt to user needs. This study aimed to know the user’s experience in using this application and also provide information for improvements of the application. Methods: This is quantitative descriptive research with 15 users (dentists) as the respondent. The questionnaire was used to measure the user’s experience using this application. The user’s experiences measured are effectivity, efficiency, and satisfaction.  Results: The highest scores of respondents on the three variables are extremely efficient, effective, and satisfied (9 people). The lowest score is slightly efficient and neutral on the efficiency and effectiveness variables (0 people). In the satisfaction variable, the lowest score is slightly satisfied (0 people). Conclusions: The Usability Testing of the “Smart Odontogram” Application based on User’s Experience showed a good result in 3 variables: effectiveness, efficiency, and satisfactionKeywords: smart Odontogram; medical record; application; usability testing; user’s experience
Face Recognition for Logging in Using Deep Learning for Liveness Detection on Healthcare Kiosks Ryando, Catoer; Sigit, Riyanto; Setiawardhana, Setiawardhana; Sena Bayu Dewantara, Bima
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.9.1.2759

Abstract

This study explores the enhancement of healthcare kiosks by integrating facial recognition and liveness detection technologies to address the limitations of healthcare service accessibility for a growing population. Healthcare kiosks increase efficiency, lessen the strain on conventional institutions, and promote accessibility. However, there are issues with conventional authentication methods like passwords and RFID, such as the possibility of them being lost, stolen, or hacked, which raises privacy and data security problems. Although it is more secure, face recognition is susceptible to spoofing attacks. In order to improve security, this study integrates liveness detection with face recognition. Data preparation is done using deep learning algorithms, namely FaceNet and Multi-task Cascaded Convolutional Neural Networks (MTCNN). Real-time authentication of persons is verified by the system, which provides correct identification of them. Techniques for enhancing data help the model become more accurate and robust. The system's usefulness is shown by the outcomes of the experiments. The VGG16 model outperforms alternative designs like MobileNet V2, ResNet-50, and DenseNet-121, achieving 100% accuracy in liveness detection. Face recognition and liveness detection together greatly improve security, which makes it a dependable option for real-world healthcare applications. Through the ability to differentiate between genuine and fake faces and foil spoofing efforts, facial liveness detection may boost security. This study offers insights into building biometric systems for safe and effective identity verification in the healthcare industry.
Implementation of Portable Ultrasound for Heart Disease Detection Using Cloud Computing-Based Machine Learning Sigit, Riyanto; Rika Rokhana; Setiawardhana; Taufiq Hidayat; Anwar; Jovan Josafat Jaenputra
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.904

Abstract

Heart disease remains one of the leading causes of death globally, including in Indonesia. Cardiovascular disease is the leading cause of death worldwide, resulting in a significant number of fatalities. In Indonesia, access to specialized heart examination services is limited, requiring patients to visit large hospitals equipped with specialized facilities. Echocardiographic examinations using ultrasound can measure various heart parameters, such as hemodynamics, heart mass, and myocardial deformation. Portable ultrasound devices have emerged, enabling flexible and effective heart examinations. These devices capture video data of the patient's heart condition. The data undergoes image preprocessing involving median filtering, high-boost filtering, morphological operations, thresholding, and Canny filtering. Segmentation is performed using region filters, collinear filters, and triangle equations. Tracking utilizes the Optical Flow Lucas-Kanade method, and feature extraction employs Euclidean distance and trigonometric equations. The classification stage uses Support Vector Machine (SVM). Video data is transmitted via a mobile application to the cloud, where all stages from preprocessing to classification are conducted on cloud servers. The classification results are then sent back to the mobile application. The proposed model achieved an accuracy rate of 86% with a standard deviation of 0.09, indicating that the detection system performs effectively.
Online Terrain Classification Using Neural Network for Disaster Robot Application Sanusi, Muhammad Anwar; Dewantara, Bima Sena Bayu; Setiawardhana; Sigit, Riyanto
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3132

Abstract

A disaster robot is used for crucial rescue, observation, and exploration missions. In the case of implementing disaster robots in bad environmental situations, the robot must be equipped with appropriate sensors and good algorithms to carry out the expected movements. In this study, a neural network-based terrain classification that is applied to Raspberry using the IMU sensor as input is developed. Relatively low computational requirements can reduce the power needed to run terrain classification. By comparing data from the Accelerometer, Gyroscope, and combined Accelero-Gyro using the same neural network architecture, the tests were carried out in a not moving position, indoors, on asphalt, loose gravel, grass, and hard ground. In its implementation, the mobile robot runs over the field at a speed of about 0,5 m/s and produces predictive data every 1,12s. The prediction results for online terrain classification are above 93% for each input tested.
Deteksi Kondisi Gigi Manusia pada Citra Intraoral Menggunakan YOLOv5 Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Dewantara, Bima Sena Bayu; Brahmanta, Arya
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3355

Abstract

Proses identifikasi dan pencatatan rekam medis pada praktik kedokteran gigi masih dilakukan secara manual. Akibatnya, proses tersebut memakan waktu yang cukup lama. Pada penelitian ini metode deteksi objek dimanfaatkan untuk membantu dokter melakukan identifikasi pada gigi pasien. YOLOv5 dipilih untuk dilatihkan pada dataset citra intraoral dengan lima kelas kondisi gigi (normal, karies, tumpatan, sisa akar, dan impaksi). Dataset yang digunakan berjumlah 1.767 data citra intraoral yang diambil dan dilabeli oleh dokter gigi. Dataset dibagi menjadi tiga bagian, 10% digunakan untuk data testing dan 90% digunakan untuk data training dan validation. Dilakukan komparasi performa berdasarkan nilai metrik evaluasi terhadap tiga jenis model YOLOv5 (S, M, L). Dari hasil pelatihan, YOLOv5 M sebagai model terbaik mendapatkan nilai mAP sebesar 84%, dan 82% nilai akurasi testing. Penelitian ini telah memenuhi tujuan utama untuk membangun sebuah model deep learning yang robust untuk mendeteksi dan mengklasifikasi beberapa kondisi gigi pada manusia.
Verifikasi Wajah untuk Menghitung Jumlah Transaksi Pengunjung Menggunakan Metode Deep Metric Learning Maulana, Rifqi Affan; Sigit, Riyanto; setiawardhana, setiawardhana
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8922

Abstract

This research carries the theme of facial recognition to detect visitors' faces by counting the number of times visitors make transactions. The objective of this research is to develop and implement a face verification system for public purposes, such as commercial purposes. One potential application of this system is in the realm of promotions, where it could be utilized to track the number of transactions conducted by visitors. The method employed utilizes deep metric learning (DML) to generate a model capable of verifying various facial images through the Convolutional Neural Network (CNN) architecture, which is designed to train human face image data. The triplet loss method is employed in training data due to its recognition as a more flexible approach in utilizing labels (in the form of face images) to facilitate comparison with the detected face images. The model employed for face recognition applications is facenet, a system that has been demonstrated to achieve a high degree of accuracy. The research's output is an application capable of swiftly and precisely verifying facial images of visitors and calculating the number of visitor transactions. The number of visitor transactions can subsequently be utilized as a promotional or discount strategy in commercial services.
Classification of Intraoral Images in Dental Diagnosis Based on GLCM Feature Extraction Using Support Vector Machine Romadhon, Nur Rizky; Sigit, Riyanto; Dewantara, Bima Sena Bayu
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3051

Abstract

This study aims to develop an AI-based diagnostic tool for classifying dental conditions and tooth types to enhance the accuracy and efficiency of dental diagnostics. Manual documentation and diagnosis in dentistry are often prone to errors, inefficiencies, and delays, leading to adverse patient outcomes. Leveraging digital image processing and machine learning, this research addresses these challenges by automating the classification process. Dental imaging data were collected from the Dental and Mouth Hospital (RSGM) of Nala Husada Surabaya, Indonesia, comprising 3,910 images categorized into dental conditions (1,767 images) and tooth types (2,143 images). The dataset was preprocessed through resizing, grayscale conversion, histogram equalization, and median filtering. Texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM), and classification was performed using Support Vector Machine (SVM), K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest algorithms. The SVM algorithm achieved the highest accuracy of 54.24% for dental conditions and 41.49% for tooth types, outperforming other methods. However, the overall performance was suboptimal, primarily due to dataset limitations, reliance on GLCM for feature extraction, and insufficient preprocessing. The results highlight the potential of AI-based tools in dentistry but also underscore the need for improvements in dataset diversity, advanced feature extraction methods, and hyperparameter optimization. Future research should focus on expanding the dataset, exploring deep learning-based feature extraction, and employing robust evaluation strategies to enhance model performance. This study lays the groundwork for developing a more reliable and efficient AI-based diagnostic tool, ultimately improving patient outcomes and streamlining clinical workflows in dentistry.
Real-Time Tuberculosis Bacteria Detection Using YOLOv8 Sigit, Riyanto; Yuniarti, Heny; Karlita, Tita; Kusumawati, Ratna; Maulana, Firja Hanif
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3147

Abstract

Tuberculosis (TB) is a contagious disease caused by the bacterium Mycobacterium tuberculosis. If not adequately managed, TB can become a fatal, life-threatening condition. In Indonesia, TB remains a critical public health issue, with millions affected and the country ranking third globally in TB cases, following India and China. Symptoms of TB include persistent cough lasting more than three weeks, hemoptysis (bloody sputum), fever, chest pain, and night sweats. The widely used diagnostic method in Indonesia is the Ziehl-Neelsen stained sputum smear technique, which processes sputum samples with specific reagents, allowing acid-fast bacilli to be visualized through microscopic examination. However, this process is labor-intensive and time-consuming, often requiring between half an hour and several hours for an accurate diagnosis. To address these challenges, there is a crucial need to develop technology that accelerates the TB diagnosis process, facilitating easier labor for healthcare workers. This study focuses on employing YOLOv8 to automate the detection of acid-fast bacilli. The system acquires sputum sample images from a microscope, and the acquired data is then used to train the model for detecting tuberculosis bacteria. The proposed real-time approach, employing the YOLOv8 algorithm, has demonstrated adequate performance for one of our specialized models, achieving a precision score of 0.88, a recall score of 0.77, and an F1 score of 0.82. This research aims to enhance TB case detection and increase treatment coverage, thereby improving overall public health outcomes in Indonesia.
PENDETEKSIAN SINYAL JANTUNG PQRST DENGAN CHIP BIOPOTENSIAL DAN TELEPON SELULER TIGA LEAD Rochmad, Moch.; Kemalasari, Kemalasari; Sigit, Riyanto
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 7, No 1 (2023): SEMNAS RISTEK 2023
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v7i1.6274

Abstract

Serangan jantung masih menjadi salah satu penyakit penyebab kematian di seluruh dunia. Banyak penelitian yang dilakukan untuk membuat modul akuisisi sinyal EKG (Electro Kardio Graf) yang portable dan akurat sebagai salah satu solusi bagi permasalahan penyakit jantung. Modul yang portable dan mudah digunakan dapat dimanfaatkan oleh pasien secara mandiri untuk mengetahui sedini mungkin adanya gangguan pada kerja jantung. Alat akuisisi sinyal EKG yang dibuat ini akan menghasilkan sinyal EKG yang dapat dimanfaatkan untuk merekam data sinyal jantung pasien. Penelitian ini dilakukan mulai April 2022 sampai November 2022. Alat ini terdiri dari rangkaian biopotensial sebagai penguat sinyal dari sensor elektroda, lalu LPF (Low Pass Filter) dan penguat noninverting ditambahkan untuk menghilangkan noise dan menguatkan sinyal sebelum masuk pada tahap selanjutnya. Dibutuhkan ADC (Analog to Digital Converter) dan mikrokontroller dalam satu modul Arduino dan chip bluetooth untuk komunikasi dengan telepon seluler. Dibutuhkan perangkat lunak pada telepon seluler untuk menampilkan grafik EKG dan menyimpannya. Pengujian akan di lakukan di beberapa lead EKG untuk mendapatkan kondisi sinyal jantung yang akurat untuk dianalisa berikutnya.Tujuan penelitian ini untuk diterapkan pada masyarakat luas untuk check up kesehatannya, sehingga masyarakat pengguna peralatan ini dapat merasakan pelayanan kesehatan dengan murah, data hasil pengukuran terekam dan langsung diinformasikan.
Human Bone Age Estimation of Carpal Bone X-Ray Using Residual Network with Batch Normalization Classification Nabilah, Anisah; Sigit, Riyanto; Fariza, Arna; Madyono, Madyono
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1024

Abstract

Bone age is an index used by pediatric radiology and endocrinology departments worldwide to define skeletal maturity for medical and non-medical purposes. In general, the clinical method for bone age assessment (BAA) is based on examining the visual ossification of individual bones in the left hand and then comparing it with a standard radiographic atlas of the hand. However, this method is highly dependent on the experience and conditions of the forensic expert. This paper proposes a new approach to age estimation of human bone based on the carpal bones in the hand and using a residual network architecture. The classification layer was modified with batch normalization to optimize the training process. Before carrying out the training process, we performed an image augmentation technique to make the dataset more varied. The following augmentation techniques were used: resizing; random affine transformation; horizontal flipping; adjusting brightness, contrast, saturation, and hue; and image inversion. The output is the classification of bone age in the range of 1 to 19 years. The results obtained when using a VGG16 model were an MAE value of 5.19 and an R2 value of 0.56 while using the newly developed ResNeXt50(32x4d) model produced an MAE value of 4.75 and an R2 value of 0.63. The research results indicate that the proposed modification of the residual training model improved classification compared to using the VGG16 model, as indicated by an MAE value of 4.75 and an R2 value of 0.63.
Co-Authors ., Justiawan Achmad Basuki Ali Ridho Barakbah Anwar Ari Setiawan Arna Fariza Arya Brahmanta, Arya Aulia Dwi Maharani Bima Sena Bayu Dewantara Fadhillah, Excel Daris Fakhri, Haidar Huda, Achmad Torikul iwan Syarif Jovan Josafat Jaenputra Kemalasari Madyono, Madyono Makarim, Ahmad Fauzi Maulana, Firja Hanif Maulana, Rifqi Affan Mochamad Mobed Bachtiar, Mochamad Mobed Nabilah, Anisah Pamenang, Muhammad Unggul Perdanasari, Lukie Rahagiyanto, Angga Ratna Kusumawati Rika Rokhana Rochmad, Moch. Rokhana, Rika Romadhon, Nur Rizky Ryando, Catoer Sanusi, Muhammad Anwar Sena Bayu Dewantara, Bima Setiawardhana Setiawardhana, Setiawardhana Taufiq Hidayat Tita Karlita Wibowo, Galih Hendra Yuniarti, Heny Zainal Arief Zamzami, Amran