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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
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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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.
Design of a Fire Location Monitoring System Using Temperature and Smoke Detectors on Sea Ships Dr. Riries Rulaningtyas, S.T., M.T.; Indrawati Apriliyah; Winarno
Indonesian Applied Physics Letters Vol. 3 No. 2 (2022): December
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v3i2.40988

Abstract

A fire location monitoring system is designed in this study to determine the location of a fire on a ship. The inputs used are temperature detectors and smoke detectors. The fire location monitoring system is designed using raspberry pi as a mini pc, temperature detector, smoke detector, alarm and Lazarus as a user interface. The room used as the object of research consists of the control room, steering room, engine room and kitchen room. The type and number of detectors used vary depending on the design of the detector placement in each room. Based on the tests that have been carried out, the fire location monitoring system is able to detect a fire when the temperature or smoke detector is active. In addition, the system is able to show the location of detectors that actively detect fires accompanied by an alarm sound. The average performance of the system in detecting a fire is 93%.
RANCANG BANGUN PROTOTIPE 3 DIMENSI ORGAN MANDIBULA MENGGUNAKAN CITRA MEDIS RADIOLOGI Amillia Kartika Sari; Riries Rulaningtyas; Khusnul Ain; Suryani Dyah Astuti; Soegianto Soelistiono; David Buntoro Kamandjaja
Medika Respati : Jurnal Ilmiah Kesehatan Vol 17, No 4 (2022)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/mr.v17i4.762

Abstract

Latar belakang: Tumor pada mandibula dapat menyebabkan kecacatan tulang. hal ini memberikan dampak negatif pada kehidupan sosial penderita. Solusi pada kasus ini adalah operasi rekonstruksi mandibula. Untuk mengoptimalkan operasi tersebut salah satunya dapat digunakan prototipe 3D sebagai perencanaan pra-bedah. Tujuan: Penelitian ini berfokus pada proses pembuatan prototipe 3D yang menggunakan pencitraan dari modalitas CT-Scan. Hasil: Pembuatan prototipe 3D diawali dari akuisisi data citra CT-Scan yang selanjutnya dilakukan proses segmentasi citra dan visualisasi 3 dimensi, pada proses terakhir dilakukan pencetakan 3 dimensi. Prototipe 3D yang telah jadi dilakukan analisa kualitatif melalui pengukuran dimensi panjang di daerah ramus, angulus, dan body of mandible dan dibandingkan dengan hasil pengukuran organ mandibula cadaver. Didapatkan hasil rerata panjang ramus pada mandibula cadaver adalah 33,62±0,34 mm, sedangkan panjang ramus pada mandibula prototipe 3D adalah 32,98±0,44 mm. Nilai rerata pengukuran pada daerah angulus adalah 31,26±0,25 mm pada mandibula cadaver, dan nilai 31,23±0,22 mm pada mandibula protptipe 3D. Dan pengukuran pada daerah body of mandible  mandibula cadaver adalah 32,05±0,98mm, sedangkan apada mandibula prootipe adalah 32,06±1,03 mm, secara keseluruhan akurasi pada prototipe 3D sebesar 99,317%.  Kesimpulan: Penggunaan citra radiologi sebagai data awal untuk membuat prototipe 3 dimensi mandibula dapat dilakukan, pengukuran akurasi prototipe 3D harus dievaluasi untuk masing-masing tahap fabrikasi.
Detection of lung disease using relative reconstruction method in electrical impedance tomography system Lina Choridah; Riries Rulaningtyas; Lailatul Muqmiroh; Suprayitno Suprayitno; Khusnul Ain
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4940

Abstract

Lung disease can be diagnosed with the image-based medical devices, including radiography, computed tomography, and magnetic resonance imaging. The devices are very expensive and have negative effects. An alternative device is electrical impedance tomography (EIT). The advantages of EIT arelow cost, fast, real-time, and free radiation, so it is very appropriate to be used as a monitoring device. The relative reconstruction method has succeeded in producing functional images of lung anomalies by simulation. In this study, the relative reconstruction method was used to obtain functional images of four lungs conditions, namely a healthy person, patient with left lung tumor with organized left pleural effusion, one with pulmonary tuberculosis with right pneumothorax and one with pulmonary tuberculosis with left pleural effusion. The relative reconstruction method can be used to obtain functional images of an individual’s lung conditions by using expiratory-respiratory potential data with results that can distinguish between the lungs of a healthy person and a diseased patient, but the position of the lung disease may have less details. The potential data from comparison between the data of a patient and a healthy person can be used as a reference to obtain more accurate functional image information of lung disease.
Pelatihan pembuatan sensor medis berbasi IoT sebagai pengenalan smart medical devices Riries Rulaningtyas; Alfian Pramudita Putra; Osmalina Nur Rahma; Katherine Katherine; I Made Mas Dwiyana Prasetya Wibawa; Kezia Sarahsophia Immanuel Ryadi
ABSYARA: Jurnal Pengabdian Pada Masayarakat Vol 4 No 1 (2023): ABSYARA: Jurnal Pengabdian Pada Masyarakat
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/ab.v4i1.6989

Abstract

Cardiovascular disease (CVD) is a leading cause of death globally, resulting in approximately 17.9 million deaths each year (WHO, 2017), with estimates projecting a rise to 23.3 million deaths by 2030 (Pusdatin Kemenkes RI, 2014). Early detection of heart disease plays a crucial role in CVD prevention, with heart rate (bpm) being a key indicator to assess heart function, ranging from 60 to 100 beats per minute. To address the need for early detection, a practical heart rate monitoring device utilizing the Internet of Things (IoT) and Smart Medical Devices (SMDs) was developed. This research aimed to provide training on IoT-based heart rate detection to high school students in Trenggalek. The training encompassed lectures and hands-on practice, successfully enhancing participants' knowledge of IoT, as demonstrated by improved test scores. Moreover, the training resulted in a prototype of an IoT-based heart rate monitoring system that utilizes Arduino and a heart rate sensor. Post-training evaluations showed the majority of participants were satisfied with the quality of materials and organization, indicating the positive impact of this engagement on the partners. The results support the potential of this IoT training to equip high school students with essential skills, fostering self-reliance in medical device production and reducing dependence on imports in the face of ASEAN Economic Community challenges. Ultimately, this initiative contributes to building a competent healthcare workforce in Indonesia.
Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Sayyidul Istighfar Ittaqillah; Delfina Amarissa Sumanang; Quinolina Thifal; Akila Firdausi Harahap; Akif Rahmatillah; Alfian Pramudita Putra; Riries Rulaningtyas; Osmalina Nur Rahma, S.T., M.Si.
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.v4i1.48153

Abstract

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.
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.
Brain-computer interface-based hand exoskeleton with bidirectional long short-term memory methods Osmalina Nur Rahma; Khusnul Ain; Alfian Pramudita Putra; Riries Rulaningtyas; Khouliya Zalda; Nita Lutfiyah; Nafisa Rahmatul Laili Alami; Rifai Chai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp173-185

Abstract

It takes at least 3 months to restore hand and arm function to 70% of its original value. This condition certainly reduces the quality of life for stroke survivors. The effectiveness in restoring the motor function of stroke survivors can be improved through rehabilitation. Currently, rehabilitation methods for post-stroke patients focus on repetitive movements of the affected hand, but it is often stalled due to the lack of professional rehabilitation personnel. This research aims to design a brain-computer interface (BCI)-based exoskeleton hand motion control for rehabilitation devices. The Bidirectional long short-term memory (Bi-LSTM) method performs motion classification for the ESP32 microcontroller to control the movement of the DC motor on the exoskeleton hand in real-time. The statistical features, such as mean and standard deviation from the sliding windows process of electroencephalograph (EEG) signals, are used as the input for Bi-LSTM. The highest accuracy at the validation stage was obtained in the combination of mean and standard deviation features, with the highest accuracy of 91% at the offline testing stage and reaching an average of 90% in real-time (80%-100%). Overall, the control system design that has been made runs well to perform movements on the hand exoskeleton based on the classification of opening and grasping movements.
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.
Classification of endometrial adenocarcinoma using histopathology images with extreme learning machine method Rulaningtyas, Riries; Rahaju, Anny Setijo; Dewi, Rosa Amalia; Hanifah, Ummi; Purwanti, Endah; Rahma, Osmalina Nur; Katherine, Katherine
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp961-971

Abstract

As many as 70-80% of endometrial cancer cases are endometrial adenocarcinoma. Histopathological assessment is based on the degree of differentiation, into well-differentiated, moderate-differentiated, and poorly-differentiated. Management and prognosis differ between grades, so differential diagnosis in determining the degree of tumor differentiation is crucial for appropriate treatment decisions. Histopathological image analysis offers detailed diagnostic results, but manual analysis by a pathologist is very complicated, error-prone, quite tedious, and time-consuming. Therefore, an automatic diagnostic system is needed to assist pathologists in grading the tumor. This research aims to determine the degree of differentiation of endometrial adenocarcinoma based on histopathological images. The extreme learning machine (ELM) method performs image classification with gray level run long matrix (GLRLM) features and a combination of local binary pattern (LBP)-GLRLM features as input. Experimental results show that the ELM model can achieve satisfactory performance. Training accuracy, testing accuracy, and model precision with GLRLM features were 97.13%, 91.33%, and 80% and combined LBPGLRLM features were 91.03%, 71.33%, and 100%. Overall, the model created can determine the degree of tumor differentiation and is useful in providing a second opinion for pathologists.
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