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Science, Technology, and Communication Journal
Published by Lembaga Studi Pendidikan and Rekayasa Alam Riau
ISSN : -     EISSN : 27748782     DOI : https://doi.org/10.59190/stc
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology
Sintechcom is a periodical publication that publishes scientific articles on research results in the fields of Basic Science, Engineering, and Telecommunications. Scopes of journal are: Chemistry and Chemical Engineering; Physics, Material Sciences, and Mechanical Engineering; Biology, Biological and Bio System Engineering; Food and Agriculture Engineering; Statistics and Mathematics; Computer Science and Computational Science; Earth Science and Engineering; Space Engineering; Electrical Engineering; Environmental Science and Soil Science; Telecommunication; Electronic and Optic Communication; Image Processing, Computer Vision and Pattern Recognition ; Energy Conservation and Renewable Energy; Information System and Artificial Intelligence.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 122 Documents
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Enhanced social media phishing detection model using LSTM and BERT Wenni Syafitri; Eddisyah Putra Pane; Edi Purwanto
Science, Technology, and Communication Journal Vol. 6 No. 2 (2026): SINTECHCOM Journal (February 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i2.360

Abstract

Phishing attacks are a major cyber threat, with more than 30% of incidents occurring via social media platforms, especially short message services. This study evaluates deep learning approaches for automated phishing detection using BERT and Hybrid (BERT-LSTM) architectures fine-tuned on 15950 annotated SMS. The BERT-only model achieved superior performance (F1 0.9928, recall 0.9952, AUC 0.999) with no statistically significant improvement from adding BiLSTM layers (0.0006). K-fold cross-validation demonstrated robust generalisation (coefficient of variation 0.10%). Dataset saturation analysis indicated that 15,950 SMS are sufficient for effective transfer learning. Mild overfitting (6.3x loss ratio) remained within acceptable bounds and did not affect validation metrics. The 1.77% false positive rate and 99.52% recall enable practical deployment for production phishing defence. Results demonstrate that transfer learning with BERT achieves production-grade performance while challenging conventional assumptions about architectural complexity.
Communicating customer value in sustainable fashion: Strategies and implications review for ethical consumerism Ikhma Zurani; Genny Gustina Sari; Mohd Rendy Samudra; Fibriyani Nur Aliya
Science, Technology, and Communication Journal Vol. 6 No. 2 (2026): SINTECHCOM Journal (February 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i2.363

Abstract

The fast fashion industry, with its penchant for consumerism, trash, and greenhouse gas emissions, is primarily responsible for environmental damage. Slow fashion is a movement that aims to promote more responsible consumerism through its emphasis on sustainable practices, ethical manufacture, and long-lasting design. In order to express sustainable ideals to consumers in the fashion industry, this study examines successful sustainability communication tactics. As a result of being impacted by honesty, equity, and repurposed goods, slow fashion customers place a premium on ethical business methods, environmental consciousness, and classic style. Constructal level theory, meanwhile, shows that while abstract messages reach more people, sustainability-conscious consumers are more receptive to concrete ones. However, despite the continued ineffectiveness of organisational green marketing, attitudes towards green products among Generation Z are driving sustainable behaviour. The environmental problems caused by rapid fashion necessitate more stringent regulations and consumer education, particularly in Vietnam. The findings of this study can help fashion industry stakeholders and policymakers tackle sustainability issues.
Optimization of plantar foot thermogram for diabetic foot ulceration early detection: An image enhancement approach Muhammad Nuril Huda; Aina Musdholifah; Aufaclav Zatu Kusuma Frisky
Science, Technology, and Communication Journal Vol. 5 No. 2 (2025): SINTECHCOM Journal (February 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v5i2.273

Abstract

Diabetes mellitus (DM) is a critical health condition caused by insulin production failure, leading to elevated blood glucose levels. DM often results in severe complications such as heart disease, stroke, and diabetic foot ulcers (DFU), which pose risks of infection and potential amputation. This study developed a machine learning model for early detection of diabetic foot ulcers, using thermogram images and the thermo dataset containing detailed foot temperature data. The multi-classifier model integrates CNNs for processing thermogram images and an ANN for tabular data analysis. Various image enhancement techniques were applied, including solarize, CLAHE, posterize, and gamma adjustment, to improve the visibility of key temperature distribution patterns. The results demonstrate that solarize consistently emerged as the most effective image enhancement method, significantly improving model performance across all evaluation metrics. Models enhanced with solarize achieved an impressive accuracy of 97.06%, alongside a perfect AUC score of 1,000. Additionally, the application of image enhancement techniques proved instrumental in reducing training and inference times, indicating computational efficiency. The integration of temperature data with enhanced thermogram images further boosted predictive accuracy while maintaining critical thermal information. This study underscores the transformative potential of image enhancement techniques, particularly solarize, in advancing the accuracy and efficiency of early detection models for diabetic foot ulcers. These findings contribute meaningfully to the development of medical imaging technologies, offering a robust framework for improving disease diagnosis and management.
Final assignment exam scheduling optimization using genetic algorithms with tournament selection techniques and violated directed mutation (VDM) Dian Meliani Kusuma Dewi; Aina Musdholifah
Science, Technology, and Communication Journal Vol. 5 No. 2 (2025): SINTECHCOM Journal (February 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v5i2.275

Abstract

Scheduling the final assignment exam is an important process that requires careful planning to ensure smooth implementation for each student. This process involves the stages of archiving final assignment submission files, determining supervisors and examiners, as well as preparing seminar and trial schedules. However, obstacles that often arise include conflicting schedules, long execution times, and low fitness values. To overcome this problem, the genetic algorithm approach is used to optimize scheduling. This algorithm can handle complex problems with a wide search space, although it has weaknesses in selecting appropriate parameters and the time required to reach the optimal solution. Genetic algorithm optimization techniques such as violated directed mutation (VDM) and tournament selection are used in this research. Previous research shows that VDM provides better results than other methods, while tournament selection improves the desired solution. It is hoped that the use of genetic algorithms with VDM and tournament selection will overcome the problem of conflicting schedules and increase the execution speed in final project exam scheduling.
Analysis of anemia disease in Pakistan using logistic regression Agnes Lee Si Tian; Kang Yuan Chin; Nik Azlin Nik Aziz; Norhaidah Mohd Asrah
Science, Technology, and Communication Journal Vol. 5 No. 2 (2025): SINTECHCOM Journal (February 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v5i2.281

Abstract

Anemia, a global health issue affecting over two billion individuals, is characterized by a deficiency in red blood cells or hemoglobin, impairing oxygen transport in the body. Early detection of anemia is critical, particularly in resource-constrained regions. This research aims to develop a robust anemia prediction model leveraging machine learning techniques and non-invasive data inputs, including red, green, and blue (RGB) pixel intensities and hemoglobin levels. The research focuses on three objectives which are to analyze the relationship between predictor variables and anemia status using a correlation heatmap, to assess the contribution of RGB pixel intensities and hemoglobin levels in predicting anemia using feature importance analysis, and to identify significant predictors through recursive feature elimination. The model, developed using logistic regression, achieved an exceptional accuracy of 99.33% and an AUC score of 1.00. The hemoglobin level emerged as the most significant predictor, showing a strong negative correlation of -0.84 with anemia status. This approach not only enhances understanding of anemia's determinants but also provides actionable insights for healthcare professionals to devise targeted therapies and public health measures. Addressing these risk factors is vital to improving health outcomes, particularly for vulnerable populations at higher risk of anemia.
Toddler nutritional status identification: Support vector machine (SVM) algorithm adoption Affan Asyraffi; Okfalisa Okfalisa; Fitri Insani; Surya Agustian; Riski Mai Candra
Science, Technology, and Communication Journal Vol. 5 No. 2 (2025): SINTECHCOM Journal (February 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v5i2.282

Abstract

Inadequate nutrition in toddlers can lead to health issues and adversely affect their growth, development, and cognitive capabilities. Consequently, it is essential to assess the nutritional status of toddlers to ascertain their health level. This study seeks to ascertain the nutritional health of toddlers utilizing the support vector machine (SVM) methodology, taking into account body weight (BB), height (TB), age, BB/TB ratio, Z-scores for BB/U, Z-scores for TB/U, and Z-scores for BB/TB. The data of 1458 toddlers were evaluated using the knowledge data discovery methodology. This study effectively categorized toddler nutrition into six classifications including malnutrition, undernutrition, adequate nutrition, overnutrition, risk of overnutrition, and obesity. Utilizing the confusion matrix methodology with an 80% training data to 20% test data ratio yields an accuracy of 89.04%. The SVM method is effectively utilized to ascertain the nutritional condition of toddlers, hence enhancing their growth and development.
Systematic literature review using deep learning in plant genomic prediction Rizki Darmawan
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.368

Abstract

Genomic prediction (GP) is a critical approach in plant breeding used to forecast agronomic traits based on genetic marker data. In recent years, machine learning and deep learning methods have been increasingly applied in genomic prediction to address the limitations of traditional methods such as genomic best linear unbiased prediction (GBLUP). This systematic literature review aims to evaluate research trends over the past decade (2015–2025) regarding the application of deep learning in genomic prediction for crops, encompassing publication trends, publication sources, dataset types, key research topics, methods/algorithms used, implementation schemes, and commonly used evaluation metrics. The study follows the PRISMA protocol, with literature searches conducted on the primary databases Scopus and SpringerLink. From thousands of identified articles, 43 studies meeting the inclusion criteria were selected. The data revealed a significant increase in publications since 2018, peaking in 2024. The majority of articles were published in open-access journals, with notable contributions in Frontiers in Plant Science, Scientific Reports, and  G3. The research covers various crop types, including wheat, maize, rice, soybean, sugarcane, and others, with diverse deep learning schemes such as convolutional neural networks (CNN), Ensemble Stacking, and Multi-Task Learning, often compared to conventional methods. The synthesis indicates that deep learning frequently enhances the accuracy of genomic prediction, particularly for complex multi-environment or multi-trait data. However, in some cases, classical linear/Bayesian models remain competitive. The application of deep learning in plant genomic prediction is rapidly advancing and demonstrates significant potential for improving crop selection performance.
QoS aware traffic shaping for TikTok live streaming over congested LAN using MikroTik queue tree Diah Risqiwati; Wiryawan Ananta Pratama Panigoro; Hanugra Aulia Sidharta
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.369

Abstract

Real time video streaming applications such as TikTok Live are highly sensitive to network instability, especially under bandwidth contention on shared access networks. This paper evaluates a Quality of Service (QoS)-aware traffic shaping scheme for TikTok live streaming in a congested local area network using a MikroTik router with a queue tree configuration. TikTok traffic is identified and assigned minimum bandwidth guarantees and highest priority, while non streaming traffic is treated as best effort. Network performance is assessed under two scenarios: baseline (without shaping) and experiment (with shaping), sing the TIPHON standard and four QoS parameters: throughput, delay, jitter, and packet loss. The experimental results show that although the average throughput only increases slightly from 70.67 Kbps to 80 Kbps and remains in the “Poor” category, the proposed scheme significantly improves temporal QoS metrics: average delay is reduced from 88.65 ms to 45.16 ms (“Very Good”), jitter decreases from 95.61 ms to 89.80 ms (“Good”), and packet loss drops from 7.66% to 3.78% (“Good”). These findings indicate that priority-based traffic shaping using a queue tree can effectively stabilize latency and data delivery for TikTok live streaming on bandwidth-limited networks without requiring capacity upgrades.
Security audit of a pharmacy information system using blackbox testing and CIA triad: A case study Rahmalia Syahputri; M Rivaldi Arwin Hadi Wijaya
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.370

Abstract

Pharmacy information systems are essential for managing drug inventory, sales, financial reports, and user administration, yet they are exposed to security risks like data manipulation, account misuse, and information leakage. This study integrates Blackbox Testing and the CIA Triad (Confidentiality, Integrity, Availability) to audit a pharmacy application. Testing employed 19 security scenarios, supported by tools such as SQLmap, Burp Suite, OWASP ZAP, and Apache JMeter to detect vulnerabilities without accessing source code. Results show that the system meets availability requirements and provides audit logging for user activity monitoring. However, confidentiality and integrity weaknesses were identified: input validation allowed illogical data like negative stock, potential SQL Injection existed on the login page, and password encryption was insufficient. Strengthening input sanitization, adopting strong encryption, and enhancing authentication are necessary to close security gaps and improve system reliability.
An IndoBERT-based framework for emotion classification in Indonesian song lyrics Agustar Alfonso; Fitri Insani; Okfalisa Okfalisa; Muhammad Fikry; Fitra Kurnia; Sri Wahyuni
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.372

Abstract

Emotion classification in song lyrics represented a significant research area within natural language processing, yet studies targeting Indonesian-language lyrics remained scarce due to the limited availability of labeled datasets and the absence of domain-specific models. This study developed and evaluated an emotion classification model for Indonesian song lyrics using fine-tuned IndoBERT-base-p2, a transformer-based language model pre-trained on a large Indonesian corpus. A dataset of 1,025 labeled lyric entries was compiled from Kaggle, Genius, and KapanLagi, covering four emotion categories: joy, sadness, fear, and anger. Preprocessing encompassed duplicate removal, case folding, structural marker removal, and non-alphabetic character cleaning. Nine fine-tuning experiments were conducted by systematically varying learning rate and dropout rate, with early stopping applied based on validation loss. The optimal configuration employed a learning rate of 3 × 10-5 and a dropout rate of 0.1, achieving 75.73% accuracy and 75.85% macro-averaged F1-score on the held-out test set. Joy and anger were classified most reliably, attaining F1-scores of 82.76% and 76.47% respectively, while sadness presented the greatest challenge, exhibiting the lowest precision of 64.10% alongside a recall of 80.65%, indicating a systematic tendency of the model to over-predict this class. These findings demonstrated that IndoBERT-base-p2, when fine-tuned with appropriate hyperparameter configuration, served as an effective approach for domain-specific emotion classification in Indonesian song lyrics.

Page 10 of 13 | Total Record : 122

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