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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Semantik Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika EMITTER International Journal of Engineering Technology Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri INOVTEK Polbeng - Seri Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JAVA Journal of Electrical and Electronics Engineering JAREE (Journal on Advanced Research in Electrical Engineering) Community Empowerment Jurnal Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Sewagati Jurnal Rekayasa elektrika
I Ketut Eddy Purnama
Institut Teknologi Sepuluh Nopember
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Analisis Kinerja Protokol Routing Reaktif dan Proaktif pada MANET Menggunakan NS2 Alamsyah; Eko Setijadi; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 2: Mei 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (927.423 KB)

Abstract

The development of mobile ad-hoc network (MANET) is becoming popular and interesting to research because it has a fast characteristic, cost-effective deployment, able to manage topology change independently, and can be applied to emergency locations such as forest fire detection, military operation, and health monitoring. However, the problems faced by MANET are dynamic network topology changes, limited energy consumption, and without the support of existing infrastructure. In order to overcome dynamic topology changes and to obtain reliable network quality, then routing protocol selection is critical in designing MANET. This study aims to analyze the performance of AODV, DSR, DSDV, and OLSR routing protocols based on the quality of service (QoS). Scenarios based on the number of vertices, packet size, a broad area of simulation, length of simulation, simulation speed, mobility model, and propagation model. The simulation has been done to produce four graphs, each of which describes the PDR, throughput, packet loss, and delay. The simulation results show that OLSR performs better than DSR, AODV, and DSDV in terms PDR, throughput, packet loss, and delay. OLSR average value in PDR by 39.997%, throughput by 417.383 Kbps, packet loss by 60.003%, and delay of 15.52 milliseconds.
Metode Kalibrasi Probe Ultrasonik dari Phantom Kawat Tunggal Menggunakan Algoritma Levenberg-Marquardt Tri Arief Sardjono; Eko Mulyanto Yuniarno; I Made Gede Sunarya; I Ketut Eddy Purnama; Mauridhi Hery Purnomo; Norma Hermawan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i3.6282

Abstract

A freehand three-dimensional (3D) ultrasound system is a method of acquiring images using a 3D ultrasound probe or conventional two-dimensional (2D) ultrasound probe to give a 3D visualization of an object inside the body. Ultrasounds are used extensively in clinical applications since they are advantageous in that they do not bring dangerous radiation effects and have a low cost. However, a probe calibration method is needed to transform the coordinate position into a 3D visualization display, especially for image-guided intervention. The current ultrasound probe calibration system usually uses the numerical regression method for the N-wire phantom, which has problems in accuracy and reliability due to nonlinear point scattered ultrasound image data. Hence, a method for ultrasound probe positional calibration of single-wire phantom using the Levenberg-Marquardt algorithm (LMA) was proposed to overcome this weakness. This experiment consisted of an optical tracking system setup, a 2D ultrasound probe with marker, an ultrasound machine, and a single-wire object in a water container equipped with a marker. The position and orientation of the marker in a 2D ultrasound probe and the marker in the water container were tracked using the optical tracking system. A 2D ultrasound probe was equipped with a marker connected wirelessly using an optical tracking system to capture the single-wire object. The resulting sequences of 2D ultrasound images were reconstructed and visualized into 3D ultrasound images using three transformations, ultrasound beam to ultrasound probe’s marker, single-wire phantom position to container’s marker, and the 3D visualization transformation. The LMA was used to determine the best optimization parameters for determining the exact position and representing that 3D visualization. The experiment result showed that the lowest mean square error (MSE), rotation error, and translation error were 0.45 mm, 0.25°, and 0.3828 mm, respectively.
Analisis Kinerja Protokol Routing AODV, DSR, dan OLSR pada Mobile Ad hoc Network Berdasarkan Parameter Quality of Service Alamsyah Zakaria; Eko Setijadi; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
Jurnal Rekayasa Elektrika Vol 14, No 3 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i3.9798

Abstract

MANET is autonomous, self-configured, and applicable to emergency locations such as forest fires, earthquakes, floods, and health monitoring. However, challenges and difficulties faced by the mobile ad-hoc network (MANET) is a dynamically built network system, without the support of infrastructure in communicating between one node and other nodes, and limited energy sources. To overcome MANET problems and to obtain optimal network quality, the selections of routing protocols and quality of service (QoS) are significant in MANET design. This study aims to analyze the performance of routing protocols: dynamic source routing (DSR), ad-hoc on demand distance vector (AODV) and optimized link state routing (OLSR) based on QoS. The analyzed QoS parameters include packet delivery ratio (PDR), packet loss, throughput, and delay. Simulation results using network simulator version based on the number of node densities indicate that OLSR has better performance compared to AODV and DSR regarding PDR, packet loss, throughput, and delay.
Detection of multi-class arrhythmia using heuristic and deep neural network on edge device Arief Kurniawan; Eko Mulyanto Yuniarno; Eko Setijadi; Mochamad Yusuf Alsagaff; Gijsbertus Jacob Verkerke; I Ketut Eddy Purnama
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1061

Abstract

Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick to implement the edge device. The outcomes are a prototype device cardiologists use as a supporting tool for analysing ECG signals, and patients can also use it for self-tests to figure out their heart health. To evaluate the performance of our edge device, we tested using the MIT-BIH database because other methods also use the data. The QVAT sensitivity and predictive positive are 99.81% and 99.90%, respectively. Our classifier's accuracy, sensitivity, predictive positive, specificity, and F1-score are 99.82%, 99.55%, 99.55%, 99.89%, and 99.55%, respectively. The experiment result of arrhythmia classification shows that our method outperforms the others. Still, for r-peak detection, the QVAT implemented in an edge device is comparable to the other methods. In future work, we can improve the performance of r-peak detection using the double-check algorithm in QVAT and cross-check the QRS complex detection by adding 1 class to the classifier, namely the non-QRS class.
Klasifikasi Citra Satelit menggunakan Lightweight Ensemble Convolutional Network Rachmadi, Reza Fuad; Prioko, Kentani Langgalih; Nugroho, Supeno Mardi Susiki; Purnama, I Ketut Eddy
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14346

Abstract

Citra satelit dapat digunakan salah satunya sebagai pengamatan kondisi atmosfer dan permukaan pada bumi. Dengan semakin berkembangnya teknologi citra satelit, waktu untuk pengambilan citra satelit menjadi lebih efisien. Makalah ini melakukan eksperimen menggunakan klasifier ensemble convolutional network untuk melakukan pengenalan kondisi atmosfer pada citra satelit. Empat buah arsitektur Convolutional Neural Network (CNN) digunakan dalam eksperimen ini, yaitu MobileNetV2, ResNet18, ResNet18Half, dan SqueezeNet. Keempat arsitektur CNN tersebut dipilih karena mempunyai jumlah parameter yang tidak terlalu besar (lightweight) serta dapat diterapkan pada banyak perangkat keras tertanam. Eksperimen yang dilakukan dengan menggunakan dataset USTC SmokeRS memperlihatkan bahwa klasifier ensemble memperoleh hasil yang baik dengan akurasi rata-rata tertinggi sebesar 97.06 %.
Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image Rumala, Dewinda Julianensi; Rachmadi, Reza Fuad; Sensusiati, Anggraini Dwi; Purnama, I Ketut Eddy
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.v12i2.853

Abstract

Achieving high accuracy with computational efficiency in brain disease classification from Magnetic Resonance Imaging (MRI) scans is challenging, particularly when both coarse and fine-grained distinctions are crucial. Current deep learning methods often struggle to balance accuracy with computational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear Convolutional Network designed to address this issue. Unlike traditional dual-network bilinear models, Lite-FBCN utilizes a single-network architecture, significantly reducing computational load. Lite-FBCN leverages lightweight, pre-trained CNNs fine-tuned to extract relevant features and incorporates a channel reducer layer before bilinear pooling, minimizing feature map dimensionality and resulting in a compact bilinear vector. Extensive evaluations on cross-validation and hold-out data demonstrate that Lite-FBCN not only surpasses baseline CNNs but also outperforms existing bilinear models. Lite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and 69.37% on hold-out data (a 3% improvement over the baseline). UMAP visualizations further confirm its effectiveness in distinguishing closely related brain disease classes. Moreover, its optimal trade-off between performance and computational efficiency positions Lite-FBCN as a promising solution for enhancing diagnostic capabilities in resource-constrained and or real-time clinical environments.
Segmentation of Facial Bones from Skull Point Clouds Based on Smoothed Deviation Angle Ulinuha, Masy Ari; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy; Hariadi, Mochamad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1464

Abstract

The human skull was the subject of study in various fields. Segmentation could be a basic tool for better understanding the skull. One of the most challenging tasks was facial bone segmentation. Our previous study had succeeded in segmenting facial bones from skull point clouds, however the quality of the results needed to be improved. In this paper, we proposed a new method to improve the results of facial bone segmentation from skull point clouds. The method consists of three stages: deviation angle extraction, smoothing, and thresholding. Each point in the point cloud was assigned a value based on the deviation angle. These values then went through a smoothing process to clarify the differences between the facial bone region and other regions. Next, thresholding was performed to divide the skull into two regions, namely facial bone and non-facial bone. The proposed method had succeeded in improving the quality of the segmentation results by achieving precision=0.931, recall=0.9854, and F=0.9573.
Adaptive Threshold Filtering to Reduce Noise in Elderly Activity Classification Using Bi-LSTM Rahayu, Endang Sri; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76064

Abstract

As the global population ages, there is an increasing need to provide better care and support for older individuals. Deep learning support to accurately predict elderly activities is very important to develop. This research discusses a new model integrating filtering techniques using adaptive thresholds with Bidirectional - Long Short-Term Memory (Bi-LSTM) networks. The problem of activity prediction accuracy, mainly due to noise or irrational measurements in the dataset, is solved with adaptive thresholds. Adaptive characteristics at the threshold are needed because each individual has different activity patterns. Experiments using the HAR70+ dataset describe the activity patterns of 15 elderly subjects and the gesture patterns of 7 activities. Based on body movement patterns, the elderly can be classified as using walking aids. The proposed model design obtains an accuracy of 94.71% with a loss of 0.1984.
Smart Home for Supporting Elderly Based On Ultrawideband Positioning System Muhtadin; Nazarrudin, Ahmad Ricky; Purnama, I Ketut Eddy; Fatichah, Chastine; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84186

Abstract

In 2017, the rate of dependency among the elderly was reported to be at 13.28%, which was problematic, due to the limited number of caregivers to assist them at all times. To address this issue, a robotic service and vital sign-based system were developed, but it was found to be insufficient for monitoring the activities of the elderly. Therefore, this study aimed to address the high dependency rates of elderly individuals who required constant support and care to survive by designing an ultrawideband-based positioning system. The system consisted of five sub-systems, including an indoor positioning system, a database system, a data processing system, an actuator system, and an application user interface. The system testing phase revealed several important findings, including that the position coordinates of the elderly were accurately read with differences of only 98.884 mm and 279.94 under Line of Sight and Non-Line of Sight conditions, respectively. Furthermore, the initial error rate of 164.39% was successfully reduced to only 1.096% by applying the average filter method in the data processing system. The actuator system also showed an impressive accuracy rate of 98% success, while the Android-based application user interface received a high user experience rate of 92.3%. Overall, these findings suggested that the ultrawideband-based positioning system had significant potential to support smart homes for the elderly and improve their quality of life.
Early Detection Depression Based On Action Unit and Eye Gaze Features Using a Multi-Input CNN-WoPL Framework Sugiyanto, Sugiyanto; Purnama, I Ketut Eddy; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84674

Abstract

Depression is a common mental disorder with significant life impact, including a high risk of suicide. Patients with depression attempt suicide five times more often than the general population. Self-reporting, subjective judgement and clinician expertise influence conventional diagnostic methods. For timely intervention and effective treatment, early and accurate diagnosis of depression is essential. This study proposes a framework called Multi-Input CNN-WoPL, a CNN-based method without a pooling layer that combines two features - action units and gaze - to improve accuracy and robustness in automatic depression detection. Pooling layer reduces spatial dimension of feature map, resulting in loss of information related to expression data, affecting depression detection result. The performance of the proposed method results in an accuracy of 0.994 and F1 score = 0.993, the F1 score value close to 1.0 indicates that the proposed method has good precision, recall and performance.
Co-Authors Abd Rahman Adhi Dharma Wibawa Adi Sutanto Ahmad Zaini Ahsan Ahsan Ait-Souar, Iliès Alamsyah Alamsyah - Alamsyah Alamsyah Andi Kurniawan Nugroho Arham Arham, Arham Arina Qona'ah Asayanda, Fikra Agha Rabbani Bernaridho Hutabarat, Bernaridho Boedinugroho, Hanny Budi Nur Iman Budi Santoso Catur Supriyanto Chastine Fatichah Dian Ratnawati Diana Purwitasari Dinar Mutiara Kusumo Nugraheni Effendy Hadi Sutanto Eka Dwi Nurcahya Eko Mulyanto Yuniarno Eko Mulyanto Yuniarno Elly Purwantini Endang Sri Rahayu Esther Irawati Setiawan Filiazsanti, Almira Firman Arifin Gijsbertus Jacob Verkerke Gijsbertus Jacob Verkerke Guruh Fajar Shidik Gusmaniarti, Gusmaniarti Handayeni, Ketut Dewi Martha Erli Hartarto Junaedi Hermawan, Norma Hernanda, Arta Kusuma Hidayat Arifin I Made Gede Sunarya Ida Hastuti Ima Kurniastuti Iman Fahruzi Ingrid Nurtanio Ismoyo Sunu Isturom Arif Jaya Pranata Joko Priambodo Juanita, Safitri Khakim Ghozali Kristian, Yosi Kurniawan, Arief Lilik Anifah Lukman Affandhy Lukman Zaman Margareta Rinastiti Masy Ari Ulinuha Mauridhi Heri Purnomo Mauridhi Heri Purnomo Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mira Candra Kirana Moch Hariadi Moch Hariadi Mochamad Hariadi Mochamad Hariadi Mochamad Yusuf Alsagaff Mochammad Hariadi Muhammad Anshari Muhammad Hariadi Muhtadin Muhtadin Muhtadin Mulyanto, Eko Munawir . Munawir Munawir Munir, M Syahrul Myrtati Dyah Artaria Nazarrudin, Ahmad Ricky Nofiandri Setyasmara Nursalam . Pramunanto, Eko Priambodo, Joko Prioko, Kentani Langgalih Pulung Nurtantio Andono Putu Gde Ariastita Putu Hendra Suputra R Dimas Adityo Rachmadi, Reza Fuad Raihan, Muhammad Reza Fuad Rachmadi Ricardus Anggi Pramunendar Rifky Octavia Pradipta Rika Rokhana Rika Rokhana Rima Tri Wahyuningrum Rima Tri Wahyuningrum Robby Aldriyanto Raffly Rokhana, Rika Rumala, Dewinda Julianensi Saiful Bukhori Saiful Bukhori Sensusiati, Anggraini Dwi Setijadi, Eko Slamet Hartono Stevanus Hardiristanto Stevanus Hardiristanto Stevanus Hardiristanto, Stevanus Sugiyanto - Supeno Mardi Susiki Nugroho, Supeno Mardi Suryo, Yoedo Ageng Terawan Agus Putranto Tita Karlita Tita Karlita Tita Karlita Tomoko Hasegawa Tri Arief Sardjono Wulandari, Ariani Dwi Yosi Kristian Yulis Setiya Dewi Zaimah Permatasari Zaman, Lukman