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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JFA (Jurnal Fisika dan Aplikasinya) Journal of Information Systems Engineering and Business Intelligence Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Magister Pendidikan IPA Darmabakti Cendekia: Journal of Community Service and Engagements Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters ABSYARA: Jurnal Pengabdian Pada Masyarakat Medika Respati : Jurnal Ilmiah Kesehatan

Riries Rulaningtyas

Biomedical Engineering, Faculty Of Science And Technology, Universitas Airlangga, Surabaya, Indonesia

Author-ID : 2457782

Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Classification of neovascularization using convolutional neural network model Wahyudi Setiawan; Moh. Imam Utoyo; Riries Rulaningtyas
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.11604

Neovascularization is a new vessel in the retina beside the artery-venous. Neovascularization can appear on the optic disk and the entire surface of the retina. The retina categorized in Proliferative Diabetic Retinopathy (PDR) if it has neovascularization. PDR is a severe Diabetic Retinopathy (DR). An image classification system between normal and neovascularization is here presented. The classification using Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine, k-Nearest Neighbor, Naïve Bayes classifier, Discriminant Analysis, and Decision Tree. By far, there are no data patches of neovascularization for the process of classification. Data consist of normal, New Vessel on the Disc (NVD) and New Vessel Elsewhere (NVE). Images are taken from 2 databases, MESSIDOR and Retina Image Bank. The patches are made from a manual crop on the image that has been marked by experts as neovascularization. The dataset consists of 100 data patches. The test results using three scenarios obtained a classification accuracy of 90%-100% with linear loss cross validation 0%-26.67%. The test performs using a single Graphical Processing Unit (GPU).

An improvement of Gram-negative bacteria identification using convolutional neural network with fine tuning Budi Dwi Satoto; Imam Utoyo; Riries Rulaningtyas; Eko Budi Khoendori
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14890

This paper proposes an image processing approach to identify Gram-negative bacteria. Gram-negative bacteria are one of the bacteria that cause lung lobe damage-bacterial samples obtained through smears of the patient's sputum. The first step bacterium should pass the pathogen test process. After that, it bred using Mc Conkey's media. The problem faced is that the process of identifying bacterial objects is still done manually under a fluorescence microscope. The contributions offered from this research are focused on observing bacterial morphology for the operation of selecting shape features. The proposed method is a convolutional neural network with fine-tuning. In the stages of the process, a convolutional neural network of the VGG-16 architecture used dropout, data augmentation, and fine-tuning stages. The main goal of the current research was to determine the method selection is to get a high degree of accuracy. This research uses a total sample of 2520 images from 2 different classes. The amount of data used at each stage of training, testing, and validation is 840 images with dimensions of 256x256 pixels, a resolution of 96 points per inch, and a depth of 24 bits. The accuracy of the results obtained at the training stage is 99.20%.

Transfer learning with multiple pre-trained network for fundus classification Wahyudi Setiawan; Moh. Imam Utoyo; Riries Rulaningtyas
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14868

Transfer learning (TL) is a technique of reuse and modify a pre-trained network. It reuses feature extraction layer at a pre-trained network. A target domain in TL obtains the features knowledge from the source domain. TL modified classification layer at a pre-trained network. The target domain can do new tasks according to a purpose. In this article, the target domain is fundus image classification includes normal and neovascularization. Data consist of 100 patches. The comparison of training and validation data was 70:30. The selection of training and validation data is done randomly. Steps of TL i.e load pre-trained networks, replace final layers, train the network, and assess network accuracy. First, the pre-trained network is a layer configuration of the convolutional neural network architecture. Pre-trained network used are AlexNet, VGG16, VGG19, ResNet50, ResNet101, GoogLeNet, Inception-V3, InceptionResNetV2, and squeezenet. Second, replace the final layer is to replace the last three layers. They are fully connected layer, softmax, and output layer. The layer is replaced with a fully connected layer that classifies according to number of classes. Furthermore, it's followed by a softmax and output layer that matches with the target domain. Third, we trained the network. Networks were trained to produce optimal accuracy. In this section, we use gradient descent algorithm optimization. Fourth, assess network accuracy. The experiment results show a testing accuracy between 80% and 100%.

Reconfiguration layers of convolutional neural network for fundus patches classification Wahyudi Setiawan; Moh. Imam Utoyo; Riries Rulaningtyas
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.1974

Convolutional neural network (CNN) is a method of supervised deep learning. The architectures including AlexNet, VGG16, VGG19, ResNet 50, ResNet101, GoogleNet, Inception-V3, Inception ResNet-V2, and Squeezenet that have 25 to 825 layers. This study aims to simplify layers of CNN architectures and increased accuracy for fundus patches classification. Fundus patches classify two categories: normal and neovascularization. Data used for classification is MESSIDOR and Retina Image Bank that have 2,080 patches. Results show the best accuracy of 93.17% for original data and 99,33% for augmentation data using CNN 31 layers. It consists input layer, 7 convolutional layers, 7 batch normalization, 7 rectified linear unit, 6 max-pooling, fully connected layer, softmax, and output layer.

Pengaplikasian Optimasi Neural Network oleh Algoritma Genetika pada Pendeteksian Kelainan Otak Stroke Iskemik sebagai Media Pembelajaran Dokter Muda Riries Rulaningtyas
Jurnal Fisika dan Aplikasinya Vol 3, No 1 (2007)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Pengidentikasian dan pendiagnosaan kelainan otak hasil rekaman MRI dilakukan oleh dokter spesialis. Pendiagnosaan citra MRI memerlukan ketelitian agar tidak terjadi kesalahan deteksi. Para dokter muda yang mengambil spesialis syaraf tentunya memerlukan banyak latihan untuk mendiagnosa citra MRI agar dapat memberikan hasil diagnosa yang tepat. Untuk membantu para dokter muda dalam mengidentikasi penyakit stroke iskemik, maka pada penelitian ini telah dibuat computer aided diagnose dengan menggunakan metode neural network yang dapat menggantikan peran dokter spesialis sehingga para dokter muda tersebut dapat berlatih mendiagnosa kelainan otak stroke iskemik secara mandiri. neural network yang digunakan menerima inputcitra MRI yang mengalami pengolahan citra terlebih dahulu yang meliputi proses scanning, proses grayscale, high pass lter, segmentasi, dan normalisasi level grayscale tiap segmen citra. Untuk meningkatkan kinerja neural network dilakukan optimasi jumlah node dan beban yang digunakan yaitu dengan menggunakan optimasi algoritma genetika. Hasilnya neural network mampu mendeteksi 100% data baru, error neural 0,000089 dan fungsi tness algoritma genetika 0,000076.

IMPLEMENTATION OF ICT BASED PEDIATRIC TELEHEALTH CARE POSYANDU AS AUTOMATIC MONITOR AND IDENTIFICATION OF INFANT’S GROWTH AND DEVELOPMENT Riries Rulaningtyas; Abidah Alfi Maritsa; Weni Endahing Warni; Soegianto Soelistiono; Khusnul Ain; Amirul Amalia; Aji Sapta Pramulen; Yusrinourdi Muhammad Zuchruf; Hanif Assyarify; Miranda Syafira Widyananda; Muhammad Fadhel Maulama
Darmabakti Cendekia: Journal of Community Service and Engagements Vol. 2 No. 1 (2020): JUNE 2020
Publisher : Faculty of Vocational Studies, Universitas Airlangga

Background: Posyandu is one of the Indonesian government’s attempt in order to monitor and improve the health and life quality of the community, especially infant. However, the implementation of Posyandu is facing some issues such as low effectiveness and low accuracy during the data collecting process of the infant’s growth and development. Purpose: This study aims to develop an automatic telehealth care product in order to help to increase the effectivity and accuracy in the implementation of Posyandu. Methods: (1) Development of the Telehealth Care Posyandu Application, (2) Implementation of the application in the form of social service program. Result: (1) “Toddler” Telehealth Care Application based in Android and ICT was buith with artificial intelligence of Decision Tree and Random Forest method. Program testing was done with 97.89% accuration score from total 85 infant’s growth data. While from 47 questionnaire data of infant’s development, accuracy score of 83.33% was obtained. (2) Target’s respond on the Telehealth Care Posyandu Application shown the status of “Very Satistified” based on the score of 81% from the satisfaction survey. The satisfaction survey covered three aspects which are: System, User, and Interaction. Conclusion: “Toddler” Telehealth Care Posyandu Application was proven to has high accuracy, sensitivity, and sensitivity score and also resulted in “Very Satisfied” user respond.

Design Of Autofocus Microscope With Histogram Method For Tuberculosis Bacteria Observation Mohammad Kholil; Riries Rulaningtyas; Winarno Winarno
Indonesian Applied Physics Letters Vol. 1 No. 1 (2020): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v1i1.21331

This research was conducted to design an autofocus microscope with a histogram method that can observe Tuberculosis (TB) bacteria. The bacteria observed were preparations or phlegm preparations which had been stained with Ziehl Neelsen. The microscope is designed to be equipped with a program to control the focus motor that moves the microscope tube and the program to digitally display the image and histogram of TB bacteria. Histograms are analyzed based on intensity values spread between 0-255 and the entropy value is sought. The measurement results that have been carried out as many as 20 times the field of view of the TB bacteria show that the most focused areas have the highest entropy value with an accuracy level ranging from 81.90476% to 100% at 1000 times the magnification.

Tubule Formation Segmentation Of Histopathological Image Of Breast Cancer By Using Clustering Method Hadiyyatan Waasilah; Riries Rulaningtyas; Winarno Winarno; Anny Setijo Rahaju
Indonesian Applied Physics Letters Vol. 1 No. 1 (2020): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v1i1.21338

Histopathological assessment is one of the examinations that allows the classification of breast cancer based on its level. Histopathological assessment factors are based on tubule formation, nuclear pleomorphism, and the mitotic count. This study only focused on tubule formation. The tubule formation was represented by a lumen surrounded a nucleus. The segmentation of tubule histopathology of breast cancer method was using a combination of k-means clustering and graph cut. The image data used in this study were 15 images of breast cancer histopathology preparations using 5 variations in the number of clusters (k) in the k-means clustering method. The best results of tubule formation segmentation using k = 4, with an average value of balanced accuracy was 81.08% and the most optimal balanced accuracy results was 94.34%.

PID-Based Design of DC Motor Speed Control Irfan Irhamni; Riries Rulaningtyas; Riky Tri Yunardi
Indonesian Applied Physics Letters Vol. 2 No. 1 (2021): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v2i1.28297

DC motor is an easy-to-apply motor but has inconsistent speed due to the existing load. PID (Proportional Integral Differential) is one of the standard controllers of DC motors. This study aimed to know the PID controller's performance in controlling the speed of a DC motor. The results showed that the PID controller could improve the error and transient response of the system response generated from DC motor speed control. Based on the obtained system response data from testing and tuning the PID parameters in controlling the speed of a DC motor, the PID controller parameters can affect the rate of a DC motor on the setpoint of 500, 1000, 1500: Kp = 0.05, Ki = 0.0198, Kd = 0.05.

Pelatihan Rancang Bangun Alat Deteksi Kelelahan Berbasis Audiovisual untuk Meningkatkan Kualitas Kerja Dan Kesehatan di SMK 3 Pancasila Kecamatan Ambulu Kabupaten Jember Provinsi Jawa Timur Khusnul Ain; Riries Rulaningtyas; Alfian Pramudita Putra
Jurnal Pengabdian Magister Pendidikan IPA Vol 4 No 1 (2021)
Publisher : Universitas Mataram

Kelelahan adalah salah satu permasalahan serius yang sering dialami pekerja sehingga bisa mengancam nyawa jika kurang mendapat perhatian. Organisasi Buruh Dunia melaporkan sebanyak 2 juta/tahun pekerja melayang nyawanya akibat kecelakaan kerja yang disebabkan oleh kelelahan. Di Indonesia jumlah kecelakaan kerja mengalami peningkatan tiap tahunnya hingga 5%. Data dari BPJS ketenagakerjaan menunjukkan bahwa pada tahun 2016 terjadi 116.850 kasus kecelakaan kerja sedangkan pada tahun 2017 jumlah kasus meningkat menjadi 123.000 kasus. Banyak penelitian menunjukkan bahwa kelelahan adalah salah satu faktor yang berkontribusi sebagai penyebab kecelakaan. Salah satu cara untuk mengurangi resiko tersebut adalah mengukur kelelahan yang dialami pekerja. Kelelahan dapat dideteksi dengan mengukur waktu respon terhadap rangsangan yang diberikan. Waktu respon umpan balik sebagai tanggapan dari rangsangan yang diberikan merupakan parameter utama yang digunakan untuk menentukan tingkat kelelahan seseorang. Berdasarkan analisis situasi tersebut, maka melalui kegiatan pengabdian masyarakat Program Kemitraan Masyarakat ini, dapat diberikan bekal keahlian kepada siswa Sekolah Menengah Kejuruan (SMK) yang sudah memiliki bekal keilmuan elektronika dasar dan mikrokontroller untuk diberikan pelatihan pembuatan alat kesehatan dengan mempelajari dan mengembangkan instrumentasi medis sederhana berbasis elektronika dan mikrokontroller sederhana yaitu alat ukur tingkat kelelahan pekerja. Para siswa SMK diharapkan setelah lulus mampu mengembangkan produksi dan pengadaan alat kesehatan secara mandiri di Indonesia. Hasil kegiatan pengabdian masyarakat terlihat bahwa peserta pelatihan sangat antusias terhadap pelaksanaan kegiatan karena mendapatkan pengetahuan baru terkait dasar elektronika dan mikrokontroler

Co-Authors Abidah Alfi Maritsa Akila Firdausi Harahap Alfian Pramudita Putra Ama, Fadli Amillia Kartika Sari Amirul Amalia Amma, Fadli Anny Setijo Rahaju, Anny Setijo Aryati Aryati Budi Dwi Satoto Chandra Satria Arisgraha, Franky David Buntoro Kamandjaja Delfina Amarissa Sumanang Dewi, Rosa Amalia Eko Budi Khoendori Endah Purwanti Fitriyatul Qulub Franky Chandra Satria Arisgraha Hadiyyatan Waasilah Hanif Assyarify I Made Mas Dwiyana Prasetya Wibawa Imam Utoyo Indrawati Apriliyah Irfan Irhamni Katherine Katherine Katherine Kho Kezia Sarahsophia Immanuel Ryadi Khouliya Zalda Khusnul Ain Khusnul Ain Lina Choridah Miranda Syafira Widyananda Miranti Ayudya Kusumawardani Moh. Imam Utoyo Moh. Imam Utoyo Mohammad Kholil Muhammad Fadhel Maulama Muqmiroh, Lailatul Nafisa Rahmatul Laili Alami Nia Saurina Nita Lutfiyah Nur Chamidah Nur Rahma, Osmalina Osmalina Nur Rahma Pramudita, Alfian Pramulen, Aji Sapta Prihartini Widiyanti Quinolina Thifal Rahmatillah, Akif Rifai Chai Sayyidul Istighfar Ittaqillah Soegianto Soelistiono Solly Aryza Suprayitno Suprayitno Suryani Dyah Astuti Ummi Hanifah Wahyudi Setiawan Wahyudi Setiawan Wardhani, Inten Firdhausi Weni Endahing Warni Winarno Winarno, Winarno Yunardi, Riky Tri Yusrinourdi Muhammad Zuchruf

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