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All Journal ELKHA : Jurnal Teknik Elektro JFA (Jurnal Fisika dan Aplikasinya) Jurnal Pengabdian Magister Pendidikan IPA Indonesian Applied Physics Letters Indonesian Community Journal
Franky Chandra Satria Arisgraha
Biomedical Engineering Study Program, Department Of Physics, Faculty Of Science And Technology, Universitas Airlangga, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Public Health Social Sciences Other

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Desain dan Implementasi Visual Object Tracking Menggunakan Pan and Tilt Vision System Yunardi, Riky Tri; Mardhiyah, Ajeng W.; Yahya, M. Hafi; Arisgraha, Franky Chandra Satria
ELKHA : Jurnal Teknik Elektro Vol.11 No.2, October 2019
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2856.244 KB) | DOI: 10.26418/elkha.v11i2.34351

Abstract

Object tracking is a technique for detecting and following the moving object. It can be used to helping security officers to monitor the room that has a large monitoring working area. The aim of research is to design the visual system of object tracking by using pan and tilt vision system. The orientation of camera can move in vertically and horizontally path. Visualization program for this project is consist of motion detection, edge detection and center of mass. The detected object position can be used for controlling the pan and tilt at mechanical system which is mounted on the camera to track the moving object. The results of research show the design of object tracking can detecting and following walking human with an ideal distance of 6 meters and directional angular shift is 5 degrees on the visual resolution of 360 × 240 pixels
Desain Sistem Klasifikasi Kelainan Jantung menggunakan Learning Vector Quantization Endah Purwanti; Franky Chandra Satria Arisgraha; Pujiyanto Pujiyanto; Muhammad Arief Bustomi
Jurnal Fisika dan Aplikasinya Vol 9, No 2 (2013)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.44 KB) | DOI: 10.12962/j24604682.v9i2.841

Abstract

Electrocardiograph (ECG atau EKG) merupakan alat diagnosis yang mengukur dan merekam aktifitas listrik jantung. Analisis sinyal EKG sering digunakan untuk mendiagnosis beberapa jenis kelainan jantung. Pada penelitian ini, kami merancang sistem jaringan syaraf tiruan untuk klasifikasi citra elektrikardiogram. Metode pemrosesan citra digunakan untuk ekstraksi fitur citra EKG dan proses klasifikasi menggunakan learning vector quantization. Beberapa data elektrokardiogram digunakan sebagai data pelatihan dan pengujian jaringan klasifikasi. Tiga jenis kelainan jantung dapat dideteksi oleh sistem. Hasil simulasi menunjukkan bahwa akurasi algoritma klasifikasi adalah sebesar 89% yang terdiri dari 9 normal, 4 bradikardi, 8 takikardi dan 7 aritmia.
Application of Artificial Neural Network Method as A Detection of Blood Fat Disorders in Images of Complete Blood Examination Catharina Natasa Bella Fortuna; Franky Chandra Satria Arisgraha, S.T., M.T.; Puspa Erawati
Indonesian Applied Physics Letters Vol. 2 No. 2 (2021): December
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v2i2.31510

Abstract

Based on various epidemiological studies, it is stated that blood lipids are the main risk factor for atherosclerosis that leads to coronary heart disease. In patients with blood lipid disorders, red blood cells undergo deformability so that their shape is flatter than normal red blood cells, which are round. The research entitled Application of Artificial Neural Network Method as Detection of Blood Fat Abnormalities in Image of Complete Blood Examination Results was conducted to help facilitate laboratory examinations. This research hopes that it will provide appropriate early detection to support the expert diagnosis. This research consists of two stages. The first stage is digital image processing to obtain area, perimeter, and eccentricity features. These three features will be used as input to the Backpropagation Neural Network program as the second stage. At this stage, blood lipid abnormalities are detected from features that have been obtained from image processing. The accuracy of detecting blood lipid abnormalities with ANN Backpropagation is 85%.
Pelatihan Rancang Bangun Sistem Monitoring Kondisi Air Tambak Berbasis Internet of Things (IoT) di SMK Perikanan dan Kelautan Kecamatan Puger Kabupaten Jember Alfian Pramudita Putra; Riries Rulaningtyas; Franky Chandra Satria Arisgraha
Jurnal Pengabdian Magister Pendidikan IPA Vol 4 No 4 (2021)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (425.918 KB) | DOI: 10.29303/jpmpi.v4i4.1007

Abstract

Kualitas air tambak atau kolam budidaya ikan atau udang merupakan aspek eksternal yang harus diperhatikan. Permasalahan utama dalam kegagalan produksi ikan atau udang adalah buruknya kualitas air selama masa pemeliharaan, terutama pada tambak intensif. Sebagian besar pekerjaan monitoring telah dibantu teknologi informasi untuk memudahkan dalam pelaksanaan pemantauan. Salah satunya adalah dengan penggunaan Internet of Things (IoT). Sistem IoT ini dapat digunakan para petambak untuk memantau kondisi perarian tambak sehingga produksi mereka bisa meningkat. Melalui kegiatan pengabdian masyarakat Program Kemitraan Masyarakat ini, sistem yang dapat memantau suhu dan pH dari perariran secara kontinu telah dibuat dengan memanfaatkan IoT. Hal ini bermanfaat untuk para siswa SMK sehinga mereka dapat meningkatkan kemampuan di bidang teknologi yang tetap berkaitan dengan perikanan dan kelautan. Peserta pelatihan sangat antusias terhadap pelaksanaan kegiatan karena mendapatkan pengetahuan baru terkait mikrokontroler dan IoT. Selain itu, Siswa SMK dapat memiliki tambahan kemampuan dan pengetahuan yang berguna untuk bersaing di dunia kerja, khususnya pada era revolusi industri 4.0.
Classification of Pneumonia from Chest X-ray Images Using Keras Module TensorFlow Franky Chandra Satria Arisgraha, S.T., M.T.; Riries Rulaningtyas; Miranti Ayudya Kusumawardani
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48241

Abstract

Pneumonia is a respiratory disease caused by bacteria and viruses that attack the alveoli, causing inflammation of the alveoli. This study aims to examine the ability of the Convolutional Neural Network (CNN) model to classify pneumonia and normal x-ray images. The method used in this research is to construct a CNN model from scratch by compiling layers one by one with the help of the Keras TensorFlow module, which consists of a Convolution layer, MaxPooling layer, Flatten layer, Dropout layer, and Dense layer. Data used in this research is from Guangzhou Women and Children Medical Center, Guangzhou, China. The total data used is 200 images divided into 160 test data, 20 training data, and 20 validation data. From the results of the research conducted, the model has the fastest processing speed of 9.6ms/epoch with a total of 20 epochs. The model has the highest accuracy value of 77% in the training process and an accuracy value of 80% in the testing process. The highest sensitivity value is 1.54 in training and 1.6 in testing. The highest specificity value is 0.77 in training and 0.8 in testing. It can be said that the model can do good classification.
Detection of Throat Disorders Based on Thermal Image Using Digital Image Processing Methods Arisgraha, S.T., M.T., Franky Chandra Satria; Rulaningtyas, Riries; Purwanti, Endah; Ama, Fadli
Indonesian Applied Physics Letters Vol. 5 No. 1 (2024): June 2024
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v5i1.57073

Abstract

Throat disorders are often considered trivial for some people, but if they are not treated immediately they can result in more severe conditions and require a longer time to cure this disorder. Objective, safe and comfortable detection of throat disorders is important because throat disorders are an indication of inflammation which, if not treated immediately, can have negative consequences. This research aims to detect throat disorders based on thermal images using digital image processing methods. Image capture was carried out with the same color pallete range on the camera, namely 33°C-38°C. The image obtained is then cropped in the ROI, then the image is threshold with a gray degree of 190. Pixels that have a gray degree above 190 are converted to white, while those below the threshold are converted to black. Next, the percentage of each white and black area is calculated compared to the total ROI area. If the percentage of white area is greater than 38% compared to the area of "‹"‹the throat then it is identified as having a throat disorder, whereas if the percentage of white is less than 38% then it is identified as not having a throat disorder. The detection program created provides an accuracy of 87.5% on sample data of 8 test data.
Pemanfaatan Teknologi dalam Mewujudkan Sustainable And Productive Life Masyarakat Kelurahan Pradah Kalikendal, Kecamatan Dukuh Pakis, Surabaya Prihartini Widiyanti; Franky Chandra Satria Arisgraha; Fitriyatul Qulub
I-Com: Indonesian Community Journal Vol 4 No 4 (2024): I-Com: Indonesian Community Journal (Desember 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v4i4.5536

Abstract

One of the problems that exist in the community of Pradah Kalikendal Village, which is the largest sub-district in Dukuh Pakis District, Surabaya, is the waste of water that occurs during ablution and increasing chronic fatigue in the community which has an impact on reducing community productivity. Based on this, Airlangga University launched a community service program with the theme "Utilization of Technology in Realizing Sustainable and Productive Life in Pradah Kalikendal Village, Surabaya" which aims to increase water conservation and prevent fatigue and maintain community productivity through the application of technology, such as the use of automatic water taps. with a filter that can turn off when there is no object that stimulates the sensor and a fatigue detection tool to determine the individual's fatigue condition. This is necessary to prevent health problems and maintain the individual's fitness status. The program objectives are (1) water conservation: Using automatic sensor faucet technology to reducing water waste, especially in public service areas such as sub-districts, health centers and places of worship and (2) maintaining productivity: using a microcontroller-based fatigue detection tool to monitor the fatigue of people involved in intensive physical activity. This program involves Airlangga University students as part of the implementation of the Independent Learning Campus (MBKM) policy, which provides direct experience for students to interact with the community and solve real problems. This program takes the form of training on the use of technology-based tools in the form of filtered water taps for local residents, especially At-Taqwa mosque administrators and Dukuh Kupang Health Center medical personnel, for water conservation and the use of work fatigue detectors. Evaluation is carried out through questionnaires and community testimonials to measure the effectiveness of the program.
Inovasi Teknologi Otomatisasi Berbasis Mikrokontroler pada Budidaya Hidroponik dan Lele untuk Meningkatkan Ketahanan Pangan Masyarakat Kelurahan Kedurus Qulub, Fitriyatul; Rahma, Osmalina Nur; Arisgraha, Franky Chandra Satria; Andiani, Dhiela Huriyah; Wiratama, Dyo; Agathon, Daniel; Wijaya, Verlita; Habibullah, Alamah Ulzanati; PN, M. Rifqi Aditya
I-Com: Indonesian Community Journal Vol 5 No 4 (2025): I-Com: Indonesian Community Journal (Desember 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i4.8126

Abstract

Program pengabdian masyarakat dengan judul “Inovasi Teknologi Otomatisasi Berbasis Mikrokontroler untuk Optimalisasi Budidaya Hidroponik dan Lele sebagai Solusi Ketahanan Pangan di Kelurahan Kedurus” dilaksanakan untuk menjawab tantangan keterbatasan lahan dan fluktuasi pangan di wilayah perkotaan.  Kegiatan ini memadukan akuakultur lele dan hidroponik dalam ekosistem yang efisien dan ramah lingkungan. Tahapan pelaksanaan meliputi koordinasi dengan perangkat kelurahan dan kelompok masyarakat, sosialisasi, pre-test, serta pelatihan inti. Workshop otomasi pakan lele berbasis mikrokontroler menekankan prinsip desain, cara kerja, dan praktik langsung penggunaan teknologi untuk menekan biaya dan meningkatkan efisiensi, sedangkan materi hidroponik berkonsentrasi pada teknik pemilihan media, nutrisi, dan perawatan tanaman. Melalui diskusi, tanya jawab, dan praktik lapangan, partisipasi masyarakat sangat aktif. Hasil awal menunjukkan bahwa pengetahuan, keterampilan, dan kesadaran tentang pentingnya inovasi teknologi untuk kemandirian pangan telah meningkat. Diharapkan program ini akan menjadi model ketahanan pangan berkelanjutan di Kelurahan Kedurus dengan mendorong kelompok belajar dan rencana pendampingan berkelanjutan.
DEVELOPMENT OF A HEMOGLOBIN LEVEL PREDICTION MODEL BASED ON PHOTOPLETHYSMOGRAPH DATA USING EXTREME GRADIENT BOOSTING Arisgraha, Franky Chandra Satria; Ama, Fadli; Kusumo, Wirotomo Bayunoto Prono
Indonesian Applied Physics Letters Vol. 6 No. 1 (2025): Volume 6 No. 1 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i1.84086

Abstract

Anemia is a growing global health concern, driving the need for non-invasive detection methods. This research develops a non-invasive hemoglobin (Hb) level prediction model utilizing Photoplethysmography (PPG) signals and the Extreme Gradient Boosting (XGBoost) algorithm, addressing the limitations of conventional invasive and time-consuming approaches. PPG signals, captured by optical sensors (red 660 nm and infrared 880 nm) on the fingertip, monitor blood volume changes that correlate with Hb levels based on the Beer-Lambert Law. Following pre-processing of secondary data from 68 subjects (including missing value handling and gender encoding) and average Systolic Peak feature extraction, the XGBoost model was trained and evaluated. To enhance performance and overcome data limitations, data augmentation was implemented, expanding the sample to 204. Evaluation results demonstrate significant improvement: on the original data, the model achieved an MAE 0,0769, RMSE 0,1117, and R² 0,4954. For the post-augmentation, performance drastically improved to an MAE 0,0190, RMSE 0,0254, and the R² of 0,9724. This increased R² indicates the model's ability to capture 97,24%; of hemoglobin variability, while reduced MAE and RMSE signify higher prediction accuracy and better generalization, making this model reliable for non-invasive Hb prescreening and potentially supporting anemia diagnosis.
Co-Authors Agathon, Daniel Alfian Pramudita Putra Ama, Fadli Andiani, Dhiela Huriyah Catharina Natasa Bella Fortuna Endah Purwanti Fitriyatul Qulub Habibullah, Alamah Ulzanati Kusumo, Wirotomo Bayunoto Prono M. Arief Bustomi Mardhiyah, Ajeng W. Miranti Ayudya Kusumawardani Osmalina Nur Rahma PN, M. Rifqi Aditya Prihartini Widiyanti Pujiyanto Pujiyanto Puspa Erawati Riries Rulaningtyas Solly Aryza Wijaya, Verlita Wiratama, Dyo Yahya, M. Hafi Yunardi, Riky Tri