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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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2025): April" : 5 Documents clear
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.
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.
Nano-modified Bitumen Enhancing Properties with Nanomaterials Alam, Rafi Shahriar; Maynul, Md Omar Farkuq; Hossain, Sazib
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.v1i2.2025.26

Abstract

In the modern era, pollution from plastic waste has become a growing concern, particularly due to the widespread use of plastics like plastic bottles. This research explores a novel approach for recycling plastic waste by incorporating plastic bottles into bitumen for the enhancement of Hot Mix Asphalt (HMA). Alongside this, nano-materials such as carbon nanotubes (CNTs), graphene, nanosilica, and nanoclays were used to further improve the mechanical, rheological, and durability properties of the modified bitumen. The plastic waste, in the form of plastic bottles, was added in varying proportions (3%, 5%, 8%, 10%, and 12% by weight of total mix) to investigate its effect on bitumen’s performance. The study conducted a series of tests, including Dynamic Shear Rheometer (DSR), Rotational Viscosity, Penetration Test, Softening Point Test, and Scanning Electron Microscopy (SEM), to evaluate the rheological and mechanical properties. The results revealed that the incorporation of plastic waste significantly improved the bitumen’s resistance to rutting, cracking, and fatigue, while nano-additives further enhanced high-temperature stability and elastic recovery. As the percentage of plastic waste in the bitumen increased, improvements in resistance to aging and moisture susceptibility were observed. Additionally, the plastic-modified bitumen exhibited better stability, improved resilience to temperature fluctuations, and enhanced mechanical strength. These findings suggest that combining plastic waste and nano-materials in bitumen can contribute to more sustainable road infrastructure, reducing plastic pollution while improving the performance and longevity of asphalt pavements.

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