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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Kitektro Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Rekayasa elektrika Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics JURNAL REKAYASA KIMIA & LINGKUNGAN
Yudha Nurdin
Universitas Syiah Kuala
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Other

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The Development of Online Disaster Information System Using Location Based Service (LBS) Technology Nasaruddin Syafie; Yudha Nurdin; Roslidar Roslidar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 3, No 1: April 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1068.716 KB) | DOI: 10.11591/ijict.v3i1.pp47-58

Abstract

Indonesia is geographically located in the disaster vulnerable area and frequently hit by various disasters such as earthquake, flood, etc. So that Indonesian people aware that the disaster information system is very important. Thus, the development of information and communication technology application is needed for disaster management. For this purpose, this paper proposes on the development of online disaster information system based on location based service (LBS) technology by using short message service (SMS) gateway and global positioning system (GPS). Then,the web-based prototype of online disaster information system is designed and developed as the media to provide information of location and situation of the disaster area. Furthermore, a user interface is also designed and developed to transmit input data as the location information using manual SMS and automatically using smartphone based on SMS/GPS. The research method used in this research is a spiral method that begins with conceptual design, prototype development, application test, and evaluation. The results of this research are the web-based information system and the implemented user interface application (we called ASIKonLBS) for Android based smartphone. The online mapping of input data from smartphone to the web-based system has been tested. It shows that the disaster location information can be mapped to Google Maps timely and accurately that can be accessed using the Internet connection. The evaluation to mapping delay time shows that it is lower than the refresh time of the web-based system. Therefore, the proposed online system can be categorized as a real-time system.
RESTful web service usage for online exit-survey at syiah kuala university as data verification method Sayed Muchallil; Yudha Nurdin; . Ahmadiar; . Melinda
Proceedings of The Annual International Conference, Syiah Kuala University - Life Sciences & Engineering Chapter Vol 3, No 2 (2013): Engineering
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (118.716 KB)

Abstract

Many applications are developed and deployed in Syiah Kuala University main server. These applications and information system are built as tools to help the University’ daily activities. Most of these applications have its own database. As a result, data is inconsistent, and the worst is redundant data cannot be avoided. The idea behind of this research is to build one centralized data that can be used as baseline to other applications. Since the main data of Syiah Kuala University are located behind the proxy which is no internet direct access allowed to the data. The proposed method to answer this problem is touse web service as a gateway for data transfer. This technique keeps the database from direct external access but the data itself can be seen without knowing where the real data is. This method has been used for Online Exit-Survey to proof that the system can verify the students’ data. Some student cannot be identified because their data were empty, the other because the data in centralized database server were only prepared for undergraduate students, so that the post graduate and professional students cannot be verified. For undergraduate students this online exit-survey works fine without error on verification phase
RANCANG BANGUN PENGEMBANGAN PINTU OTOMATIS PENDETEKSI MASKER DAN SUHU TUBUH MENGGUNAKAN RASPBERRY PI 4 Teuku Radhi Muhammad Fitrah; Yudha Nurdin; Roslidar Roslidar
Jurnal Komputer, Informasi Teknologi, dan Elektro Vol 6, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/kitektro.v6i2.21428

Abstract

Pada masa pandemi COVID-19 saat ini, pemerintah memberlakukan peraturan yaitu ketika akan masuk ke dalam ruangan (khususnya gedung publik) diharuskan mematuhi protokol kesehatan berupa menggunakan masker dan dilakukan pengukuran suhu tubuh. Namun banyak dari masyarakat yang tidak mematuhi peraturan tersebut sehingga apabila memasuki suatu ruangan yang berisi banyak orang dan tanpa  protokol kesehatan akan berpotensi terpapar virus COVID-19. Salah satu solusi untuk mengimplementasikan protokol kesehatan tersebut adalah dengan menggunakan pintu otomatis yang dapat terbuka dengan sendirinya apabila seseorang memakai masker dan suhu tubuhnya kurang dari 38 ̊ C. Pada penelitian ini akan dibuat sebuah prototipe pintu yang mendeteksi penggunaan masker dan suhu tubuh dengan kamera dan sensor suhu tubuh. Penelitian ini menggunakan metode deep learning untuk mendeteksi masker dan pengukuran sensor suhu tubuh untuk mendeteksi suhu tubuh serta sebagai pemrosesan sensor, aktuator dan komponen lainnya digunakan raspberry pi 4. Hasil dari penelitian ini berupa prototipe pintu otomatis yang akan bekerja saat user berada pada posisi ≤ 6 cm, Adapun kondisi yang harus terpenuhi agar pintu terbuka adalah user memakai masker dan suhu tubuh 38 ̊ C maka pintu terbuka, user memakai masker dan suhu tubuh ≥ 38 ̊ C maka buzzer berbunyi dan pintu tidak terbuka, user tidak memakai masker dan suhu tubuh 38 ̊ C maka buzzer berbunyi dan pintu tidak terbuka, user tidak memakai masker dan suhu tubuh ≥ 38 ̊ C maka buzzer berbunyi dan pintu tidak akan terbuka. Adapun hasil akurasi deteksi masker tertinggi yaitu pada masker kn95 dengan akurasi 99.95 % dan pendeteksian suhu akurat pada jarak 2 cm yang menghasilkan galat 0.05%. Dengan demikian prototipe pintu otomatis telah diuji dan berjalan dengan baik mengikuti kondisi yang ditentukan.
Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi Kahlil Muchtar; Chairuman; Yudha Nurdin; Afdhal Afdhal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (861.037 KB) | DOI: 10.29207/resti.v5i1.2831

Abstract

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.
Implementation of Word Recommendation System Using Hybrid Method for Speed Typing Website Melinda; Maulana Imam Muttaqin; Yudha Nurdin; Al Bahri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4518

Abstract

Typing is one of the most frequently done activities in society therefore a medium is necessary to help train typing words that are often mistyped. Methods used in this research are the Content-Based Filtering Algorithm to gather the words that have a similar pattern to the words that are often mistyped based on the user's previous typing records and the Collaborative Filtering Algorithm that uses other users typing pattern to recommend the words. The result of this study shows the Collaborative Filtering Algorithm was able to gather words that are hard to type by the user with an accuracy of 49.2%, dan the Collaborative Filtering able to predict the score on how difficult for the user to type a word with the result of Root Mean Square Error (RMSE) value of 0.82 and with the Root Mean Square Percentage Error (RMSPE) value of 30% from the actual value, and a website which is the combination of the two algorithms with the result of 28% of the total word that is recommended was indeed difficult to type by the user with the typing speed of 103 WPM, and 72.3% for the user that has a typing speed of 39 WPM.
Implementation of System Development Life Cycle (SDLC) on IoT-Based Lending Locker Application Melinda Melinda; Shaquille Rizki Ramadhan Na; Yudha Nurdin; Yunidar Yunidar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5047

Abstract

Libraries are social institutions that provide information services that can be accessed publicly to meet the information needs of librarians. Based on the results of a survey conducted on 58 students who have visited the library of the Electrical and Computer Engineering Department of the Universitas Syiah Kuala, an information system was needed that provided research book information related to the author's name, year of writing, field concentration, and abstract of the research book, and there was a division of categories based on field concentration, and there was an online borrowing feature. Based on these problems, this study aims to implement an Android application system with IoT-based lending lockers using the SDLC (system development life cycle) prototyping method. This study produces an application with a locker-based online lending feature, several other features as the user desires, and one prototype lending locker. The locker-based online lending system integrated with ESP32-WROOM-32 can connect to Firebase storage and send locker key codes to Firebase so that the application can access them. Through experiments and tests conducted on the application, it is obtained that the application can access the locker key code and display it to the user. The application has also been validated using black-box and white-box testing and can be accepted by users based on the System Usability Scale (SUS) average score with a very feasible interpretation category.
Water Level Detection for Flood Disaster Management Based on Real-time Color Object Detection Saddami, Khairun; Nurdin, Yudha; Noviantika, Fina; Oktiana, Maulisa; Muchallil, Sayed
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1635

Abstract

Currently, the water level monitoring system for a river uses instruments installed on the banks of the river and must be checked continuously and manually. This study proposes a real-time water level detection system based on a computer vision algorithm. In the proposed system, we use color object tracking technique with a bar indicator as a reference’s level. We set three bar indicators to determine the status of the water level, namely NORMAL, ALERT and DANGER. A camera was installed across the bar level indicators to capture bar indicator and monitoring the water level. In the simulation, the monitoring system was installed in 5-100 lux lighting conditions. For experimental purposes, we set various distances of the camera, which is set of 40-80 centimeters and the camera angle is set of 30-60 degrees. The experiment results showed that this system has an accuracy of 94% at camera distance is in range 50-80 centimeters and camera angle is 60o. Based on these results, it can be concluded that this proposed system can determine the water level well in varying lighting conditions.
EEG Performance Signal Analysis for Diagnosing Autism Spectrum Disorder using Butterworth and Empirical Mode Decomposition Fathur Rahman, Imam; Melinda, Melinda; Irhamsyah, Muhammad; Yunidar, Yunidar; Nurdin, Yudha; Wong, W.K.; Zakaria, Lailatul Qadri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.788

Abstract

Electroencephalography (EEG) is a technique used to measure electrical activity in the brain by placing electrodes on the scalp. EEG plays an essential role in analyzing a variety of neurological conditions, including autism spectrum disorder (ASD). However, in the recording process, EEG signals are often contaminated by noise, hindering further analysis. Therefore, an effective signal processing method is needed to improve the data quality before feature extraction is performed. This study applied the Butterworth Band-Pass Filter (BPF) as a preprocessing method to reduce noise in EEG signals and then used the Empirical Mode Decomposition (EMD) method to extract relevant features. The performance of this method was evaluated using three main parameters, namely Mean Square Error (MSE), Mean Absolute Error (MAE), and Signal-to-Noise Ratio (SNR). The results showed that EMD was able to retain important information in EEG signals better than signals that only passed through the BPF filtration stage. EMD produces lower MAE and MSE values than Butterworth, suggesting that this method is more accurate in maintaining the original shape of the signal. In subject 3, EMD recorded the lowest MAE of 0.622 compared to Butterworth, which reached 20.0, and the MSE value of 0.655 compared to 771.5 for Butterworth. In addition, EMD also produced a higher SNR, with the highest value of 23,208 in subject 5, compared to Butterworth, which reached only 1,568. These results prove that the combination of BPF as a preprocessing method and EMD as a feature extraction method is more effective in maintaining EEG signal quality and improving analysis accuracy compared to the use of the Butterworth Band-Pass Filter alone.
Autism EEG Signal Pre-Processing: Performance Evaluation of MS-ICA and Butterworth Filter Mirza Rahmat, Muhammad; Nurdin, Yudha; Melinda, Melinda; Away, Yuwaldi; Irhamsyah, Muhammad; Wong, W. K
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.107

Abstract

Autism Spectrum Disorder (ASD) is a neurological condition characterized by challenges in communication and social interaction, accompanied by the development of repetitive behavioral patterns. Electroencephalography (EEG) is primarily used to assess brain function in children with Autism Spectrum Disorder (ASD), mainly due to its non-invasive nature and superior temporal resolution compared to other neuroimaging methods. However, EEG signals are often contaminated by biological artifacts, such as eye movements and muscle contractions, which can significantly distort analysis outcomes. Pre-processing is therefore required to increase the accuracy of the EEG signal before additional analysis. The goal of this study was to compare and evaluate the performance of two pre-processing techniques, the Butterworth Band-Pass Filter and Multiscale Independent Component Analysis (MS-ICA), using four different performance metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Signal-to-Noise Ratio (SNR). The Butterworth method has an MAE of 227.57, which is acceptable. However, it produced an MSE of 160,653.22, an RMSE of 394.49, and a maximum SNR of only 1.33 dB. MS-ICA performs far better with a best MAE of only 0.44, an MSE of 3.33, an RMSE of 1.76, and an SNR of 30.88 dB. Paired t-test (p < 0.05) was employed to determine statistical significance,  while Cohen's d was used to assess the practical significance of the results. The effect sizes of MAE (d = 1.60), MSE (d = 1.02), RMSE (d = 1.54), and SNR (d = -9.50) were all calculated as large. These findings demonstrate that MS-ICA offers both statistical advantages and strong practical usefulness for noise removal while preserving the structural integrity of the original EEG signals. Therefore, MS-ICA proves to be the best approach for pre-processing EEG signals to be used for analysis in children with ASD
Augmentation of Additional Arabic Dataset for Jawi Writing and Classification Using Deep Learning Razali, Safrizal; Muchtar, Kahlil; Rinaldi, Muhammad Hafiz; Nurdin, Yudha; Rahman, Aulia
Jurnal Rekayasa Elektrika Vol 20, No 1 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i1.33722

Abstract

This research aims to create an additional dataset containing Arabic characters for writing Jawi script and to train classification models using deep learning architectures such as InceptionV3 and ResNet34. The initial stage of the study involves digital image processing to obtain the additional Arabic character dataset from several sources, including HMBD, AHAWP, and HUCD, encompassing various connected and disconnected forms of Jawi script. Image processing includes steps such as preprocessing to enhance image quality, segmentation to separate Arabic characters from the background, and augmentation to increase dataset variability. Once the dataset is formed, we train the models using appropriate training data for each InceptionV3 and ResNet34 architecture. The classification evaluation results indicate that the model with ResNet34 architecture achieved the best performance with an accuracy of 96%. This model successfully recognizes Jawi script accurately and consistently, even for classes with similar shapes. The main contribution of this research is the availability of the additional Arabic character dataset that can be utilized for Jawi script recognition and performance assessment of various deep learning models. The study also emphasizes the importance of selecting the appropriate architecture for specific character recognition tasks. The research findings affirm that the model with ResNet34 architecture has excellent capability in recognizing the additional Arabic characters for writing Jawi. The results of this research have the potential to support further developments in Jawi character recognition applications and provide valuable insights for researchers in the field of character recognition sourced from Arabic characters. Dataset augmentation results can be accessed at https://singkat.usk.ac.id/g/En0skCKGAR
Co-Authors . Ahmadiar . Melinda . Roslidar Afdhal Afdhal Al Bahri Alhamdi, Muhammad Jurej Aulia Rahman Chairuman Cut Putri Agustina Cut Putri Agustina, Cut Putri Fathur Rahman, Imam Kahlil Muchtar Lailatul Qadri Zakaria Maulana Imam Muttaqin Melinda Melinda Mirza Rahmat, Muhammad Muchtar, Kahlil Muhammad Irhamsyah Nasaruddin Syafie Noviantika, Fina Oktiana, Maulisa Razali, Safrizal Riki Effendi Rinaldi, Muhammad Hafiz Saddami, Khairun Sayed Muchallil Sayed Muchallil Shaquille Rizki Ramadhan Na Syahiddin Syahiddin Syahiddin Syahiddin, Syahiddin Teuku Radhi Muhammad Fitrah Wahyu Rinaldi Wahyu Rinaldi Wong, W. K Wong, W.K. Wulan Windari Wulan Windari, Wulan Yunidar Yunidar Yunidar Yuwaldi Away