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Journal of Integrated and Advanced Engineering (JIAE)
Published by Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia
ISSN : 2774602X     EISSN : 27746038     DOI : https://dx.doi.org/10.51662/jiae
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering
Journal of Integrated and Advanced Engineering JIAE adalah jurnal ilmiah peer-review yang menerima makalah penelitian yang terkait erat dengan bidang Teknik, seperti Mekanik, Listrik, Industri, Sipil, Kimia, Material, Fisik, Komputer, Informatika, Lingkungan dan Arsitektur.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 7 Documents
 1 
Search results for , issue "Vol 3, No 1 (2023)" : 7 Documents clear
Counting Various Vehicles using YOLOv4 and DeepSORT Alfan Pahreza Kusumah; Dena Djayusman; Galih Rizki Setiadi; Ade Chandra Nugraha; Priyanto Hidayatullah
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.68

Abstract

The Ministry of Public Works and Public Housing (PUPR) conducted a traffic survey to determine the total number of vehicles and classify them according to the Bina Marga vehicle categorisation. The survey has thus far been carried out manually. As a result, surveys take a lot of time and money to perform. Additionally, as the survey scope grows, so will the requirement for surveyors. Therefore, a substitute that can execute the survey procedure automatically and with tolerable accuracy is required. One solution is to utilise deep learning technology to detect and categorise vehicles that can be used in apps. The program is designed as a web application that provides a summary of vehicle calculations and receives video data from traffic recordings. The deep learning model used is YOLOv4 which is trained to recognise vehicle classes following Bina Marga vehicle types. The model was trained and tested using the Python programming language and the Darknet framework on the Google Colab platform. The YOLOv4 and DeepSORT method with custom dataset reached a decent accuracy of 67.94%, considering the limited 1000 images used for training the model.
Identification of Whatsapp Digital Evidence on Android Smartphones using The Android Backup APK (Application Package Kit) Downgrade Method Deny Sulisdyantoro; Marza Ihsan Marzuki
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.70

Abstract

The use of WhatsApp for actions that lead to unlawful acts is a serious matter that needs to be proven in court. Android and the WhatsApp messaging application continue to update their features and security to provide maximum service and protection to its users, such as the WhatsApp database encryption using crypt14. With crypt14 encryption on the WhatsApp database, investigations of WhatsApp digital evidence against Electronic Evidence (BBE) require an acquisition and extraction method to identify artefacts relevant to digital evidence needs. The National Institute Standard Technology (NIST) reference methodology, from the collection, examination, and analysis to reporting stages, has become a widely used framework for digital forensics against BBE. The Android Backup Application Package Kit (APK) Downgrade method can decrypt the WhatsApp database crypt14 to become a solution that can be used in the framework of mobile forensics to answer the needs of investigations into certain criminal cases, including data that users have deleted. With the Cellebrite tools, the Android Backup Application Package Kit (APK) Downgrade method can identify approximately 651% more artefacts than the Android Backup and logical acquisition methods using the FinalData and MobilEdit tools.
Design and Simulation High Pass Filter Second Order and C-Type Filter for Reducing Harmonics as Power Quality Repair Effort in the Automotive Industry Mochamad Irlan Malik; Eko Ihsanto
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.79

Abstract

Electrical distribution is one of the most important parameters in industrial processes. Therefore, good power quality is needed as a supply to industrial machines. The use of industrial machines has an impact on the emergence of harmonics. As a result of the large Harmonics, the quality of power is getting worse, affecting productivity in the industry. Therefore, samples were taken using a Power Quality Analyzer on an 800 kVA transformer on the secondary side of the transformer to maximise the supply of electricity to consumers. Then obtained THDi Phase L1 of 23.1%, phase L2 of 24.7% and phase L3 of 21% and IHDi on the 5th order in phase L1 18.3%, phase L2 20.7% and phase L3 16.6% regarding (IEEE Std 3002.8-2018) and (SPLN D5.004-1:2012) the IHDi value should not be more than 7%. Then simulated using MATLAB/Simulink by designing the Second Order High Pass Filter and C-Type Filter. The results obtained by combining the two filters gained THDi results in the L1 phase at 2.53%, the L2 phase at 2.69% and the L3 phase at 2.22% and the IHDi at the 5th order of the L1 phase at 1.48%, the L2 phase 1.62% and L3 phase 1.33%.
Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition Ahmed Rimaz Faizabadi; Hasan Firdaus Mohd Zaki; Zulkifli Zainal Abidin; Muhammad Afif Husman; Nik Nur Wahidah Nik Hashim
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.84

Abstract

Machine vision will play a significant role in the next generation of IR 4.0 systems. Recognition and analysis of faces are essential in many vision-based applications. Deep Learning provides the thrust for the advancement in visual recognition. An important tool for visual recognition tasks is Convolution Neural networks (CNN). However, the 2D methods for machine vision suffer from Pose, Illumination, and Expression (PIE) challenges and occlusions. The 3D Race Recognition (3DFR) is very promising for dealing with PIE and a certain degree of occlusions and is suitable for unconstrained environments. However, the 3D data is highly irregular, affecting the performance of deep networks. Most of the 3D Face recognition models are implemented from a research aspect and rarely find a complete 3DFR application. This work attempts to implement a complete end-to-end robust 3DFR pipeline. For this purpose, we implemented a CuteFace3D. This face recognition model is trained on the most challenging dataset, where the state-of-the-art model had below 95% accuracy. An accuracy of 98.89% is achieved on the intellifusion test dataset. Further, for open world and unseen domain adaptation, embeddings learning is achieved using KNN. Then a complete FR pipeline for RGBD face recognition is implemented using a RealSense D435 depth camera. With the KNN classifier and k-fold validation, we achieved 99.997% for the open set RGBD pipeline on registered users. The proposed method with early fusion four-channel input is found to be more robust and has achieved higher accuracy in the benchmark dataset.
Design of Smart Shoes for Blind People Muhammad Aiman Mohd Razin; Muhammad Afif Husman; Siti Fauziah Toha; Aisyah Ibrahim
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.89

Abstract

Our daily lives depend heavily on our eyes. Eyesight is our most valuable gift, enabling us to see the world around us. However, some people suffer from visual impairments that hinder their ability to visualize such things. As a result, such people will experience difficulties moving comfortably in public places. One crucial aspect of mobile accessibility is detecting elevation changes. These include changes in the height of the ground or a floor, such as stairs, curbing, and potholes. They are common in both indoor and outdoor environments. People who are blind or visually impaired must detect these changes and assess their distance and extent to navigate them safely and effectively. Depth perception is essential to doing so and can be challenging for those with visual impairments. Therefore, this research aims to design a smart shoe that assists in climbing up and down the stairs using an IMU sensor to detect the user's movement. Before constructing a controller, the system is modelled using mathematical and physical modelling. Mathematical modelling is derived based on the mobility of people with visual impairment. The smart shoes are modelled in a 3D virtual world using the SolidWorks software. In addition, the shoe integrates with ultrasonic sensors whenever it detects any obstacles or barriers; they alert the users via vibration. This resulted in the intelligent shoes unlocking the heels whenever the low or high elevation was detected and vibrating if there was an obstacle. With the help of this device, the confidence level of people with visual impairment to walk independently will be improved.
Conversational Analysis Agents for Depression Detection: A Systematic Review Akeem Olowolayemo; Maymuna Gulfam Tanni; Intiser Ahmed Emon; Umayma Ahhmed; ‘Arisya Mohd Dzahier; Md Rounak Safin; Nusrat Zahan Nisha
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.85

Abstract

Depression is known as a non-cognitive disturbance that can be seen among different people all over the world. This pertains to disorders that have affected cognitions and behaviors that arise from overt disorders in cerebral function. It is more common for young adults to elderly people based on lifestyles, work pressure, personal problems, diseases, people who had strokes or hemorrhages, certain brain diseases, and paralysis. This paper is focused on reviewing the research papers previously done on detecting depression. Utilizing predefined search systems, we have gone through a couple of studies zeroing in on gloom and involved conversational information for location and conclusion. The objective of this research is to review large research studies on whether conversational agents can detect and diagnose depression by using smart texting analysis. The study was done by searching IEEE Xplore, Sci-hub, Doi, Scopus, and Pubmed using a predefined search strategy. This review was focused on studies that include the possibilities and steps of detecting depression and diagnosis that involved conversational data or analysis agents after assessing them by independent reviewers and relevancy for eligibility. After retrieving more than 117 references initially it was narrowed down to 95 references that were found relevant as most of them applied analytical techniques and technology-based solutions. Detecting depression and diagnosing it through smart texting analysis is a broad and emerging field and has a promising future but not every research studies were robust enough to get valid results in the end. This study aimed to keep the review as precise and informative as possible. 
Sentiment Analysis and Text Classification for Depression Detection Iffah Nadhirah Joharee; Nik Nur Wahidah Nik Hashim; Nur Syahirah Mohd Shah
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.86

Abstract

Depression is an illness that can harm someone's life. However, many people still do not know that they are having depression and tend to express their feelings through text or social media. Thus, text-based depression detection could help in identifying the early detection of the illness. Therefore, the research aims to build a depression detection that can identify possible depression cues based on Bahasa Malaysia text. The data, in the form of text, has been collected from depressed and healthy people via a google form. There are three questions asked which are “Apakah kenangan manis yang anda ingat?”, “Apakah rutin harian anda?” and “Apakah keadaan yang membuatkan anda stress?” which obtained 172, 169 and 170 responses for each question respectively. All the datasets are stored in a CSV file. Using Python, TF-IDF was extracted as the feature and pipeline into several classifier models such as Random Forest, Multinomial Naïve Bayes, and Logistic Regression. The results were presented using the classification metrics of confusion matrix, accuracy, and F1-score. Also, another method has been conducted using the text sentiment techniques Vader and Text Blob onto the datasets to identify whether depressive text falls under negative sentiment or vice versa. The percentage differences were determined between the actual sentiment compared to Vader and Text Blob sentiment. From the experiment, the highest score is achieved by AdaBoost Classifier with a 0.66-F1 score. The best model is chosen to be utilized in the Graphical User Interface (GUI).

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