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All Journal Bulletin of Electrical Engineering and Informatics Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah FIFO INOVTEK Polbeng - Seri Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Cendikia Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Social Responsibility Projects by Higher Education Forum Jurnal Pengabdian Masyarakat Nasional
Rinto Priambodo
Universitas Mercu Buana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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Pelacakan Lokasi Pasien berbasis Internet of Things untuk Sistem Pendukung Layanan Kesehatan Ibu dan Anak Rinto Priambodo; Trie Maya Kadarina
Jurnal Inovtek Polbeng Seri Informatika Vol 5, No 2 (2020)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v5i2.1509

Abstract

Aplikasi berbasis Internet of Things (IoT) di bidang kesehatan memberikan kemudahan dalam melakukan pemantauan terhadap kondisi pasien. Sejumlah perangkat sensor dapat mengukur dan mengirimkan data kondisi pasien beserta lokasinya. Dari data tersebut dokter maupun paramedis kemudian dapat melakukan analisis dalam waktu nyata dari jarak jauh sehingga kondisi pasien dapat selalu terpantau dan pendeteksian dini terhadap kondisi darurat dapat dilakukan. Rekomendasi tindakan terkait kondisi dan lokasi pasien dapat diberikan dengan lebih tepat. Begitu pula dalam kasus pelayanan kesehatan ibu dan anak, adanya informasi lokasi akan memudahkan tidak hanya dokter maupun paramedis dalam penentuan tindakan tapi juga membantu pasien dalam melakukan tindakan secara mandiri jika diperlukan. Dengan demikian pengolahan dan penyajian data lokasi pasien yang baik dalam pelayanan kesehatan ibu dan anak sangat dibutuhkan. Aplikasi Elasticsearch, Logstash, dan Kibana (ELK) merupakan sebuah teknologi yang memiliki performa yang sangat baik dalam mengumpulkan data log dan data lainnya yang berasal dari berbagai sumber secara kontinyu dalam jumlah yang sangat besar dan menampilkannya dalam bentuk grafik dan peta. Penelitian ini bertujuan untuk mengembangkan sebuah sistem yang dapat menampilkan hasil pencatatan kondisi dan lokasi dari sejumlah pasien dalam waktu yang nyata menggunakan aplikasi ELK untuk kebutuhan pelayanan kesehatan ibu dan anak.
Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network Zendi Iklima; Trie Maya Kadarina; Rinto Priambodo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 4 No 4 (2022): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v4i4.255

Abstract

A Convolutional Neural Network (CNN) classifier is generally utilized to classify an image tensor according to the mapped labels. The simplification of the classifier causes CNN to be often used to classify images, especially in the biomedical field. Thus, CNN is widely used to classify computer tomography (CT) and chest X-ray (CXR) images against the mapped labels. Several transfer learning models were implemented to classify CXR images for preliminary detection of COVID-19 infection, e.g., ResNet, Inception, Xception, etc. However, a transfer learning model has a maximum and minimum input resolution. Thus, the computational cost tends to be huge and unable to be optimized. Therefore, A custom CNN model can be a solution to reduce computational costs by configuring the feature extraction layers. This study proposed an efficient reduction of feature extraction for COVID-19 CXR namely Depthwise Separable Convolution Network. Furthermore, numerous strategies were adopted to lower the computational cost while retaining accuracy, including customizing the Batch Normalization (BN) layer and replacing the convolution layer with a separable convolution layer. The proposed model successfully reduced the feature extraction represented by the decreases in trainable parameters from 28.640 trainable parameters to 4.640 trainable parameters. The depthwise separable convolution effectively retains the performance accuracy 72.96%, loss 12.43%, recall 74.67%, precision 77.67%, and F1-score 75.33%. The CXR augmentation is also successfully increase the performance accuracy 74.55%, loss 11.37%, recall 77.67%, precision 79.56%, and F1-score 78.33%.
Aplikasi Jurnal Harian Covid-19 untuk Pemantauan Isolasi Mandiri (PISOM) Mutiara Anggun; Margaretha Amanda; Tri Ahmad Sandi; Rinto Priambodo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022945162

Abstract

Penyakit Coronavirus 2019 (Covid-19) merupakan penyakit infeksi virus SARS-CoV-2 yang mempengaruhi sistem pernapasan pada tubuh manusia dan dapat menular. Kasus Covid-19 di Indonesia semakin meningkat dari waktu ke waktu sehingga membutuhkan perhatian secara khusus. Pada kasus pasien yang terkonfirmasi “Tanpa Gejala” dan “Gejala Ringan” maka pasien tersebut harus melakukan isolasi mandiri di rumah. Pasien yang melakukan isolasi mandiri tersebut harus dipantau oleh petugas kesehatan melalui telepon. Hal ini menimbulkan masalah baru dikarenakan jumlah petugas yang terbatas untuk melakukan pemantauan pada pasien. Dari permasalahan tersebut, penulis membuat penelitian yang menghasilkan sebuah aplikasi Jurnal Harian Pasien Covid-19 untuk Pemantauan Isolasi mandiri (PISOM)”. Adapun tujuan dari aplikasi ini adalah untuk membantu petugas kesehatan dalam memantau pasien yang melakukan isolasi mandiri di rumah dan diharapkan data yang dihasilkan dapat dijadikan bahan penelitian lebih lanjut oleh pihak-pihak terkait. Aplikasi ini berbasis sistem operasi Android pada pasien dan web pada petugas kesehatan sehingga dapat mempermudah para pengguna dalam pengoperasian dan dapat diakses di manapun dan kapanpun. Dengan adanya aplikasi ini diharapkan dapat mempermudah dan membantu pasien dalam menjalani isolasi mandiri dan petugas kesehatan yang melakukan pemantuan.
Dental caries classification using depthwise separable convolutional neural network for teledentistry system Trie Maya Kadarina; Zendi Iklima; Rinto Priambodo; Riandini Riandini; Rika Novita Wardhani
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4428

Abstract

Caries may be halted or reversed in their progression by early detection, better hygiene habits, and coadministered drugs. The major clinical procedures for identifying dental caries are visual-tactile examination and dental radiography. However, due to their location, approximate caries exceedingly difficult to detect and affect the clinical assessment. Incorrect interpretations may also hinder the diagnostic procedure. Computational approaches and technology can be used to help dentists assess caries. Teledentistry has the ability to improve dental health care by providing access to dental care services from a remote location. Teledentistry helps identifying various stages of caries lesions using neural network and devices connected to the internet. This research develops an image classification for teledentistry systems using depthwise separable convolutional neural network. The trainable parameters reduction of depthwise separable convolution (DSC) successfully reduces the computational cost of conventional convolutional neural networks (CNN). As a result, the DSC model is reduced by 91.49% when compared to the traditional CNN model. Several DSC models improve conventional CNN accuracies in the training, validation, evaluation, and testing stages.
Sosialisasi Pemanfaatan Bahan Ajar Berbasis Multimedia Bagus Priambodo; Nur Ani; Sarwati Rahayu; Rinto Priambodo; Inge Handriani; Anita Ratnasari; Yuwan Jumaryadi; Nia Rahma Kurnianda
Journal of Social Responsibility Projects by Higher Education Forum Vol 4 No 2 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v4i2.4187

Abstract

Today's rapidly developing technology makes it easier for humans to do their work. Education is the main thing in educating children in a country so that they can perform well and in accordance with normative values. In the world of education we can do many things as a source of learning not only books, but also by utilizing the environment around us. Apart from the environment, we can also use digital media as a source of learning to make it easier. The development of a curriculum has always been the main focus and continues to be carried out to improve and respond to community needs so that the socialization of the use of multimedia applications is felt to be very useful for improving the quality of learning media. In this community service activity, we conducted socialization regarding the use of several multimedia applications at the Early Childhood Education Cluster Activity Center (PKG) in Sukaraja District. Based on the results of the questionnaire given after the activity, the activities carried out were very useful to support the teaching activities carried out.
Dental Caries Segmentation using Deformable Dense Residual Half U-Net for Teledentistry System Iklima, Zendi; Trie Maya Kadarina; Priambodo, Rinto; Riandini, Riandini; Wardhani, Rika Novita; Setiowati, Sulis
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.511

Abstract

Clinical practitioners’ workload and challenges are significantly reduced by classifying, predicting, and localizing lesions or dental caries. In recent research, a high-reliability diagnostic system within deep learning models has been implemented in a clinical teledentistry system. In order to construct an efficient, precise, and lightweight deep learning architecture, it is dynamically structured. In this paper, we present an efficient, accurate, and lightweight deep learning architecture for augmenting spatial locations and improving the transformation modeling abilities of fixed-structure CNNs. Deformable Dense Residual (DDR) enhances the efficacy of the residual convolution block by optimizing its structure, thereby mitigating model redundancy and ameliorating the challenge of vanishing gradients encountered during the training stages. DDR Half U-Net presents notable advancements to the simplified U-Net framework across three pivotal domains: the encoder, decoder, and loss function. Specifically, the encoder integrates deformable convolutions, thereby enhancing the model's capacity to discern features of diverse scales and configurations. In the decoder, a sophisticated arrangement of dense residual connections facilitates the fusion of low-level and high-level features, contributing to comprehensive feature extraction. Moreover, the utilization of a weight-adaptive loss function ensures equitable consideration of both caries and non-caries samples, thereby promoting balanced optimization during training.
A simplified dental caries segmentation using Half U-Net for a teledentistry system Kadarina, Trie Maya; Iklima, Zendi; Priambodo, Rinto; Riandini, Riandini; Wardhani, Rika Novita; Setiowati, Sulis; Jusoh, Mohd Taufik
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.005

Abstract

High-reliability diagnostic equipment efficiently supported by a computer-based diagnostics system. For instance, a computational approach establishes a model that can diagnose diseases. Artificial intelligence has been applied to aid in the field of medical imaging. Classification, prediction, and localisation of lesions or dental caries greatly minimise the load and difficulties for clinical practitioners. In this study, U-Net architectures are simplified to propose the feature reduction of the decoder layers. This simplification of U-Net architectures is utilised for segmented dental caries images. This paper simplified the U-Net decoder layers into the level of blocks Half-UNet () and Half-UNet (). The Half-UNet structural model surpasses the U-shaped structural model in terms of efficiency and segmentation capabilities. The simplification of the UNet architecture outperformed using Half-UNet 0.83% of the dice coefficient. The Half-UNet design is able to preserve model performance in segmenting actual images and ground truth against expected ground truth.
SOSIALISASI DAN PEMANFAATAN APLIKASI KESEHATAN KEPADA MASYARAKAT Yuwan Jumaryadi; Nur Ani; Rinto Priambodo; Anita Ratnasari; Sarwati Rahayu; Siti Maesaroh; Andi Nugroho
Jurnal Pengabdian Masyarakat Nasional Vol 4, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v4i2.27206

Abstract

Salah satu faktor yang mempengaruhi keberhasilan penerapan aplikasi kesehatan di masyarakat adalah kemampuan masyarakat dalam menerima penggunaan aplikasi kesehatan. Keberhasilan implementasi aplikasi kesehatan harus mampu mengintegrasikan teknologi kesehatan digital dengan kebijakan pemerintah. Untuk memastikan masyarakat dapat menerima dan menggunakan teknologi kesehatan digital, maka perlu didukung oleh perguruan tinggi untuk memberikan peningkatan pengetahuan dan kemampuan menggunakan aplikasi kesehatan dengan baik kepada masyarakat, terutama pada masyarakat dengan kondisi demografi yang kurang ideal, seperti rendahnya literasi digital. dan buruknya akses terhadap kesehatan. Bintaro merupakan salah satu kelurahan di Jakarta Selatan yang memiliki visi untuk meningkatkan pelayanan kesehatan masyarakat dengan memaksimalkan penerapan teknologi. Dengan kondisi geografis dan demografi penduduk yang cukup menantang dalam menerima teknologi baru, maka diperlukan kegiatan yang dapat meningkatkan pemahaman dan pengetahuan untuk menggunakan aplikasi Kesehatan Masyarakat sebagai upaya meningkatkan penerimaan pelayanan kesehatan secara cepat dan memadai. Oleh karena itu, tim dosen Fakultas Ilmu Komputer Universitas Mercu Buana Jakarta melaksanakan kegiatan pengabdian masyarakat untuk memberikan sosialisasi penggunaan aplikasi Kesehatan untuk membantu mendukung program pemerintah dan juga program Kementerian Kesehatan dalam hal kesehatan digital transformasi.
Co-Authors Alfi Nurfazri Andi Nugroho Andi Yuni Lusfiani Anita Ratnasari Bagus Priambodo Dzikry Ahmad Fauzy Inge Handriani Iskandar Iskandar Jepry Rahmadhan Jumaryadi, Yuwan Jusoh, Mohd Taufik Kusmiati Kusmiati Kusmiati Kusmiati Margaretha Amanda Mutiara Anggun Naldi Nashih Ulwan Neneng Hasanah Nia Rahma Kurnianda Nur Ani Nur Ani Riandini Riandini Riandini, Riandini Rika Novita Wardhani Rika Novita Wardhani Sarwati Rahayu Sarwati Rahayu Siti Maesaroh Suhari Pratama Sulis Setiowati Supriyadi Supriyadi Tri Ahmad Sandi Trie Maya Kadarina Yaya Sudarya Triana Zendi Iklima