cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Furizal
Contact Email
sjer.editor@gmail.com
Phone
+6282386092684
Journal Mail Official
sjer.editor@gmail.com
Editorial Address
Jl. Poros Seroja, Kesra, Kepenuhan Barat Sei Rokan Jaya, Kec. Kepenuhan, Kab. Rokan Hulu, Riau
Location
Kab. rokan hulu,
Riau
INDONESIA
Scientific Journal of Engineering Research
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31091725     DOI : https://doi.org/10.64539/sjer
Core Subject : Engineering,
Engineering
The Scientific Journal of Engineering Research (SJER) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal is committed to publishing high-quality articles in all fundamental and interdisciplinary areas of engineering, with a particular emphasis on advancements in Information Technology. It encourages submissions that explore emerging fields such as Machine Learning, Internet of Things (IoT), Deep Learning, Artificial Intelligence (AI), Blockchain, and Big Data, which are at the forefront of innovation and engineering transformation. SJER welcomes original research articles, review papers, and studies involving simulation and practical applications that contribute to advancements in engineering. It encourages research that integrates these technologies across various engineering disciplines. The scope of the journal includes, but is not limited to: Mechanical Engineering Electrical Engineering Electronic Engineering Civil Engineering Architectural Engineering Chemical Engineering Mechatronics and Robotics Computer Engineering Industrial Engineering Environmental Engineering Materials Engineering Energy Engineering All fields related to engineering By fostering innovation and bridging knowledge gaps, SJER aims to contribute to the development of sustainable and intelligent engineering systems for the modern era.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 16 Documents
 1   2 
A Bibliometric Analysis of Natural Language Processing and Classification: Trends, Impact, and Future Directions Setiawan Ardi Wijaya; Rahmad Gunawan; Rangga Alif Faresta; Asno Azzawagama Firdaus; Gabriel Diemesor; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.6

Abstract

This study presents a bibliometric analysis of Natural Language Processing (NLP) and classification research, examining trends, impacts, and future directions. NLP, a key field in artificial intelligence, focuses on enabling computers to process and understand human language through tasks such as text classification, sentiment analysis, and speech recognition. Classification plays a crucial role in organizing textual data, facilitating applications like spam detection and content recommendation. The research employs bibliometric analysis to evaluate publication trends, citation networks, and emerging themes from 1992 to 2025. Using data retrieved from Scopus, descriptive statistical analysis and bibliometric mapping with VOSviewer reveal key contributors, influential publications, and subject area distributions. Findings indicate a significant rise in NLP research, with deep learning models, particularly transformers, driving advancements in the field. The study highlights dominant research areas, including computer science, engineering, and medicine, and identifies leading countries in NLP research, such as the United States, China, and India. Additionally, ethical concerns, including bias and fairness in NLP applications, are discussed as critical challenges for future research. The insights derived from this analysis provide valuable guidance for researchers and policymakers in shaping the next phase of NLP development.
Traffic Management Analysis for Video Streaming Service Optimization Using Per Connection Queue (PCQ) Method Kariyamin; Deyti Lusty A’an; La Ode Alyandi; Adi Imantoyo
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.7

Abstract

Video streaming has become common in everyday life due to its ability to enhance information retrieval and provide an additional dimension to obtaining up-to-date information. However, these benefits are often accompanied by significant bandwidth demands, which can affect network performance. To overcome this challenge, efficient traffic management with separation between browsing and streaming traffic is required. This research addresses network performance issues caused by video streaming services by applying the Per Connection Queue (PCQ) method. This method optimizes streaming video quality while managing network traffic by separating traffic between web browsing and video streaming. The test results show that both types of networks exhibit relatively stable performance over different time intervals. The network without PCQ showed constant values in the measurement parameters, even at 720p video quality with a slight increase in packet loss. Similarly, the network with PCQ showed consistent performance at 240p and 360p video quality, with a slight increase in packet loss in scenario 3 with 720p video quality. The Average Index value of 3.666667 indicates that both have "Good" performance according to TIPHON standardization and can be considered comparable. This conclusion illustrates that the implementation of PCQ does not significantly affect network performance on packet loss, delay, and jitter measurements.
Trends and Impact of the Viola-Jones Algorithm: A Bibliometric Analysis of Face Detection Research (2001-2024) Wijaya, Setiawan Ardi; Famuji, Tri Stiyo; Mu'min, Muhammad Amirul; Safitri, Yana; Tristanti, Novi; Dahmani, Abdennasser; Driss, Zied; Sharkawy, Abdel-Nasser; Al-Sabur, Raheem
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.8

Abstract

The Viola-Jones algorithm remains a cornerstone in computer vision, particularly for object and face detection. This bibliometric study provides a comprehensive analysis of the algorithm’s academic impact and research trends, encompassing publication patterns, citation metrics, influential authors, and co-occurrence of keywords. The findings indicate a significant rise in research outputs and citations between 2016 and 2020, reflecting the algorithm's sustained relevance and application in various domains. Network visualization maps further reveal the algorithm's integration with diverse fields, including machine learning, image processing, and neural networks, emphasizing its versatility and adaptability to emerging technological challenges. Key research contributions include advancements in hybrid approaches, combining the Viola-Jones framework with techniques such as convolutional neural networks and HOG-SVM for improved detection accuracy. However, limitations such as computational inefficiency and sensitivity to environmental factors persist, presenting opportunities for innovation. This study concludes by highlighting future research directions, such as integrating deep learning and edge computing to enhance algorithmic performance in real-time and complex scenarios. This study provides a valuable reference for researchers and practitioners aiming to extend the Viola-Jones algorithm’s capabilities and applications by consolidating existing knowledge and identifying research gaps.
Comparison of Machine Learning Algorithms for Stunting Classification Yunus, Muhajir; Biddinika, Muhammad Kunta; Fadlil, Abdul
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): April
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.9

Abstract

Indonesia is one of the countries with medium stunting data over the past decade, around 21.6%. Stunting prevention is a national program in Indonesia, and stunting reduction in children is the first of the six goals in the Global Nutrition Target for 2025. Based on SSGI data in 2022, the prevalence of stunting in Gorontalo Province is 23.8% and is in the high category. Stunting prevention is an early effort to improve the ability and quality of human resources. This study compared two Machine Learning algorithms for stunting classification in children, namely the Naive Bayes method and Decision Tree C4.5 using Python by dividing the training and testing data a total ratio of 80:20. The performance of each algorithm was evaluated using a dataset of child health information based on z-score calculation data with a total of 224 records, consisting of 4 attributes and 1 label, namely gender, age, weight, height and nutritional status. The results of the research that have been conducted show that the Decision Tree C4.5 algorithm achieves the highest accuracy in the classification of stunting events with an accuracy of 87% while for the Naïve Bayes algorithm produces a low accuracy of 71% so that for this study the Decision tree C4.5 algorithm is the best algorithm for the classification of stunting events. These findings suggest this algorithm can be a valuable tool for classifying children's stunting.
Early Detection of Brain Tumors: Performance Evaluation of AlexNet and GoogleNet on Different Medical Image Resolutions Muis, Alwas; Rustiawan, Angga; Oyeyemi, Babatunde Bamidele; Syukur, Abdul; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 3 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i3.2025.10

Abstract

Early detection of brain tumors through medical imaging is crucial to improving treatment success rates. This study aims to classify brain tumors using two deep learning models, AlexNet and GoogleNet, by testing three image sizes. The dataset used consists of four classes: glioma, no tumor, meningioma, and pituitary. The test results show that the AlexNet model achieves the best accuracy of 98% at a resolution of 150x150, while GoogleNet shows stable performance with the highest accuracy of 96% at both 150x150 and 200x200 resolutions. The medium resolution (150x150) proves to be optimal for both models, providing the best balance between visual information and processing efficiency. This study highlights the potential use of AlexNet and GoogleNet in brain tumor classification, with opportunities for performance improvement through further development, such as ensemble techniques and the use of a larger dataset.
Implementation of Data Mining Using Simple Linear Regression Algorithm to Predict Export Values Fawait, Aldi Bastiatul; Rahmah, Sitti; Costa, Apolonia Diana Sherly da; Insyroh, Nazaruddin; Firdaus, Asno Azzawagama
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.11

Abstract

This study aims to analyze the trends in export value in East Kalimantan. The research utilizes secondary data sourced directly from the Central Statistics Agency of East Kalimantan Province. A simple linear regression algorithm for data mining is employed as the analytical method. The findings indicate a decline in East Kalimantan's export value from January 2022 to April 2024, as well as in the forecasted export value from May 2024 to December 2024. The prediction model achieved a Root Mean Square Error (RMSE) value of 3.182%, demonstrating a high level of accuracy in estimating export values. This research is expected to serve as a valuable reference for stakeholders in formulating strategies to enhance East Kalimantan's export performance and contribute to the region's future economic development.
Classification for Waste Image in Convolutional Neural Network Using Morph-HSV Color Model Fahmi, Miftahuddin; Yudhana, Anton; Sunardi; Abdel-Nasser Sharkawy; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.12

Abstract

Waste management is essential in preserving nature to be cleaner and more well-maintained. Waste management runs slower than the speed of waste accumulation. One reason is slow waste sorting. This problem can be overcome by building a learning machine that can sort the types of waste. The type of waste often separated in the first sorting is waste based on its type, namely organic and inorganic. The classification model used is the CNN with image processing Morph-HSV color model. The data obtained from Kaggle is collected and processed using Python. The processed image is trained using a CNN classification model. The results of this study are an accuracy of 99.58% and a loss of 1.57%. With this research, it is hoped that it can accelerate waste sorting performance using the most efficient ML based on image processing and its classification model.
Comparative Analysis of Hierarchical Token Bucket and Per Connection Queue Methods in Video Conferences Kariyamin; Alyandi, La Ode; A'an, Deyti Lusty; Suarti, Wa Ode Reni; Yapono, Putri; Tangaro, Diana May Glaiza G.; Talirongan, Florence Jean B.
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): April
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.13

Abstract

Video conferencing is a set of interactive telecommunication technologies that allow two or more parties in different locations to interact using audio and video simultaneously. In video conferencing tools, bandwidth management is needed to maintain the quality of data transmitted through bandwidth. The Hierarchical Token Bucket (HTB) method is a method that uses a hierarchical structure and priorities for the client so that the distribution of bandwidth can be adjusted. In contrast, the Per Connection Queue (PCQ) method is a method that applies bandwidth sharing so that the allocation of bandwidth can be done more evenly to all clients. The parameters used to determine the quality of service in both methods are throughput, packet loss, delay, and jitter. The test results showed that in the Zoom application, the HTB method had an average TIPHON Standard Index of 3.5, while the PCQ method was 3.75. However, in the TrueConf application, the HTB method has a TIPHON standard index of 3.75, while the PCQ method has a TIPHON standard index of 3.5. In the TrueConf application, the HTB method is superior, while in the Zoom application, the PCQ method is superior.
Semi-Supervised Learning for Retinal Disease Detection: A BIOMISA Study Nakib, Arman Mohammad; Shahed Jahidul Haque
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): April
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.14

Abstract

Proper immediate identification of Age-related Macular Degeneration (AMD) together with Central Serous Retinopathy (CSR) and Macular Edema (ME) is crucial for protecting vision. OCT imaging achieves better condition detection through automated model-based detection processes. The majority of studies in this domain utilize supervised learning because these approaches need large labeled dataset resources. The method confronts two essential obstacles due to limited medical data labeling quality, expensive expert training costs, and with irregular medical condition distributions. The considered factors limit practical implementation of these methods and their meaningful expansions. The study evaluates how semi-supervised learning techniques analyze retinal diseases in images that originate from the BIOMISA Macula database while providing diagnostic details about AMD, CSR, and ME in addition to Normal retinal results. SSL functions uniquely from fully supervised methods through its unique capability to process labeled and unlabeled data, which lowers manual annotation needs while improving generalized output performance. SSL delivers better results than traditional supervised learning practices through its ability to manage class irregularities and process extensive medical image files. The establishment of SSL as an attractive third option in medical settings with limited labeled data proves through research findings. The study provides insights regarding SSL use in diagnosis of retinal diseases alongside demonstrating its medical potential in healthcare environments. Future investigation designs improved deep learning algorithms which would enable higher system scalability and cost-effective diagnostics for ophthalmic disease systems.
A Novel Hybrid Framework for Noise Estimation in High-Texture Images using Markov, MLE, and CNN Approaches Kobra, Mst Jannatul; Md Owahedur Rahman; Arman Mohammad Nakib
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): April
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.25

Abstract

The assessment of complex noise in textured images requires a method which uses both Markov processes together with Maximum Likelihood Estimation and Convolutional Neural Networks. The evaluation of noise through traditional methods does not deliver acceptable results during preservation of image characteristics in areas with challenging texture patterns. Through Maximum Likelihood Estimation (MLE) probabilistic refinement together with Convolutional Neural Networks (CNNs) features the proposed model applies Markov processes to maintain spatial dependencies that provide accurate denoising with protected image quality. Using CNN-based denoising together with Gaussian filtering creates superior outcomes for imaging perception than individual methods during Edge Preservation Index (EPI) and Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) assessment. The experimental results show a 24.85 dB PSNR value together with 0.92 SSIM integrity and EPI quality of 0.85 for effective hybrid model noise reduction. The research utilizes Markov processes and MLE together with Convolutional Neural Networks to develop an all-encompassing approach for cleaning texturized complex images which could serve multiple image types including those from medical contexts and satellites and digital photographs.

Page 1 of 2 | Total Record : 16

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 1 No. 4 (2025): October Article in Process Vol. 1 No. 3 (2025): July Vol. 1 No. 2 (2025): April Vol. 1 No. 1 (2025): January