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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,259 Documents
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Efficient CNN-Based Classification of SARS-CoV-2 Spike Gene Sequences Using Alignment-Free Encoding Anggarah, Rengga; Ernawati, Ernawati; Oktoeberza, Widhia KZ
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15691

Abstract

The COVID-19 pandemic caused by SARS-CoV-2 continues to challenge the global health system through the emergence of various variants with genetic characteristics that affect vaccine transmission and effectiveness. Conventional identification methods such as Whole-Genome Sequencing (WGS) have high accuracy but are constrained by significant cost and time. Most classification studies today still rely on complex hybrid architectures such as CNN-LSTM or image-based representations that increase computational load. This study aims to develop  an  efficient and lightweight pure Convolutional Neural Network model based on alignment-free encoding to classify five Variant of Concern (VOC) variants of SARS-CoV-2 (Alpha, Beta, Delta, Gamma, and Omicron) with an exclusive focus on the Spike gene sequence. The dataset consists of 5,000 Spike gene sequences that are represented using integer encoding and standardized with zero-padding. CNN  proposed Lightweight architecture  consists of four 1D convolution layers with a total of approximately 1.6 million parameters. The test results show that the model achieves excellent performance with an overall accuracy of 98.93%. The precision, recall, and F1-score values averaged 0.99, while the analysis of the ROC curve showed AUC values above 0.99 for all variants. This approach has proven to be efficient and effective, offering a fast, scalable, and resource-efficient solution to support real-time genomic surveillance systems in future pandemic mitigation.
Adoption of Artificial Intelligence in Vocational High Schools: A Systematic Review of Teachers’ Perspectives Mafrukhah, Erny; Lisana, Lisana
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15701

Abstract

The purpose of this study is to examine Vocational High School teachers' perceptions of the adoption of artificial intelligence (AI) in vocational education. Using a systematic literature review (SLR) approach with the PRISMA 2020 protocol, 1,346 articles were identified from six main databases: ACM, IEEE Xplore, ScienceDirect, Google Scholar, and Semantic Scholar. Of the 1,346 articles identified, 263 were excluded at an early stage because they did not meet basic criteria, such as authorship, publication language, and document type. A total of 1,083 articles were screened, yielding 208 reports for in-depth screening. After the screening and feasibility assessment, 29 studies were included in the final analysis. The analysis showed that 72% of studies reported positive results, 24% reported moderate results, and 3% reported exploratory results. The dominant factors influencing teachers' perceptions included infrastructure readiness, digital competence, institutional support, the relevance of the vocational curriculum, and ethical and privacy issues. These findings emphasize the need for a holistic strategy for implementing AI in vocational schools that encompasses teacher training, education policy, and ethical considerations.
Enhanced Stacked GRU Model for Monthly Rice Production Forecasting in Bali Province Gotama, I Gusti Agung Raditiya; Sudipa, I Gede Iwan; Brahma, Anak Agung Gede Raka Wahyu; Ariantini, Made Suci; Wulandari, Dewa Ayu Putri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15715

Abstract

Rice production has a seasonal pattern that depends on the planting cycle and environmental conditions, requiring forecasting methods that can accurately model temporal dynamics. This study aims to predict monthly rice production in Bali Province using the Stacked Gated Recurrent Unit (GRU) architecture. Monthly rice production data from 2018 to 2024 from the Central Statistics Agency (BPS) was used as the main source. The preprocessing stage includes data cleaning, Min-Max normalization, and feature engineering in the form of creating sin_month and cos_month features to capture seasonal patterns, as well as a 3-month rolling mean to extract short-term trends. The proposed stacked design with dual-layer GRU combined with seasonal features improves temporal pattern extraction compared to single-layer GRU baselines. The model was tested using three configurations, and Scheme 3 provided the best performance with an MAE value of 1610.21, an RMSE of 2055.90, and a MAPE of 14.29%, which is considered good accuracy. The model was able to follow seasonal production trends, including an increase at the beginning of the year and a decrease during the planting period. Long-term predictions for the next 12 months and quarterly forecasts per district/city also showed patterns consistent with historical data. The results of the study indicate that Stacked GRU is effective in forecasting seasonal rice production and can be used as a basis for decision support in food security planning in Bali.
Performance Evaluation and Optimization of an IoT-Based Fish Smoking Monitoring System for Ensuring Product Quality Syafirullah, Lutfi; Mahardika, Fajar; Purwanto, Riyadi; Prasetyanti, Dwi Novia
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15736

Abstract

Fish smoking is a widely used preservation method; however, the quality of smoked fish is highly dependent on the stability of temperature, humidity, and smoking duration. Manual control of these parameters has limitations and may reduce product quality. Existing studies on fish smoking monitoring systems primarily focus on temperature control without providing quantitative evaluation of how multi-parameter process stability affects product quality and shelf life. This study aims to design and implement an Internet of Things (IoT)-based monitoring system for fish smoking equipment to ensure the quality of smoked fish. The research method used is Research and Development (R&D), which includes needs analysis, system design, development, testing, and evaluation stages. The system integrates temperature and humidity sensors, a microcontroller, and an IoT platform for real-time monitoring. The test results show that the system is capable of monitoring the smoking chamber temperature within a range of 60–80 °C with an average error of ±1.5 °C compared to a standard measuring instrument, and maintaining an optimal temperature of 70 °C during the smoking process. Quality testing of the smoked fish indicates uniform doneness, a golden-brown color, firm texture, and an average moisture content reduction of 35%. Shelf-life testing shows that the smoked fish can last up to 7–10 days at room temperature and up to 21 days under cold storage without significant changes in aroma and texture. Unlike previous works, this study provides quantitative evidence that improved stability of multiple smoking parameters through IoT-based monitoring significantly enhances product quality consistency and extends the shelf life of smoked fish.
A Systematic Literature Review of Machine Learning for Endurance Running Performance Prediction Solang, Efraim William; Linawati, Linawati; Manuaba, Ida Bagus Gede; Setiawan, I Nyoman
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15743

Abstract

This study systematically reviews the application of machine learning methods for predicting running performance, with particular emphasis on short-middle distance events such as the 5 km. Although machine learning based performance prediction has been widely explored in endurance sports, a comprehensive review synthesizing models, predictors, and pipelines across running distances remains limited. The review followed the PRISMA 2020 framework. Articles published between 2020 and 2025 were retrieved from ScienceDirect, Google Scholar, and PubMed using predefined keyword combinations related to machine learning and running performance. Studies were included if they focused on running (excluding cycling, triathlon, or other sports), applied predictive modeling, and reported model evaluation metrics. A total of 26 studies met the inclusion criteria and were assessed using quality appraisal criteria inspired by TRIPOD and QUADAS-2. The analysis identified four main research themes: (1) application of machine learning models for running performance prediction, (2) physiological and anthropometric predictors, (3) non-physiological and contextual factors, and (4) personalized athlete training and monitoring. Ensemble learning models (Random Forest, XGBoost, LightGBM) consistently outperformed traditional linear regression by capturing non-linear interactions, while deep learning approaches (LSTM, GRU) demonstrated strong capability in modeling temporal training dynamics. A generalized machine learning pipeline for running performance prediction was also synthesized. This review contributes a structured framework that integrates modeling approaches, predictor categories, and evaluation strategies, and highlights research opportunities for explainable and personalized prediction systems, particularly for 5 km running performance.
An Integrated K-Means and Composite Risk Scoring Framework for Urban Dengue Vulnerability Mapping Alif, Moh. Fachri; Fahmi, Amiq
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15748

Abstract

The rising incidence of dengue hemorrhagic fever (DHF) in Indonesian urban areas highlights the urgent need for analytical frameworks capable of capturing spatial heterogeneity in vulnerability while supporting targeted public health interventions. However, most existing dengue vulnerability studies rely on clustering or indicator-based scoring in isolation, limiting interpretability and reducing their operational relevance for policy-driven decision making. This study explicitly addresses this gap by proposing an integrated spatial clustering and epidemiologically weighted composite risk scoring framework for urban dengue vulnerability mapping. Using Semarang Municipality as a case study, K Means based spatial clustering was combined with composite risk scoring to analyze dengue vulnerability across administrative subdistricts. Seven key indicators consisting of population density, area size, total population, morbidity, mortality, incidence rate, and health facility availability were processed through systematic imputation, normalization, and attribute selection to ensure data consistency and analytical robustness. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, after which K-Means clustering was applied to generate spatially coherent vulnerability groupings. A composite risk scoring mechanism was subsequently employed to classify regions into five operational risk categories: Low-Risk, Moderate-Risk, High-Risk, Very High-Risk, and Emergency-Priority. The results reveal clear structural differentiation in dengue vulnerability patterns, where Emergency-Priority and Very High-Risk clusters are not only characterized by elevated epidemiological indicators but also by constrained health service availability, amplifying outbreak susceptibility. Specifically, 13 subdistricts (7.5%) were identified as Emergency-Priority and 22 subdistricts (12.4%) as Very High-Risk, together accounting for approximately 20% of the study area. Beyond numerical classification, the integration of spatial clustering and composite risk scoring enhances interpretability by linking cluster structure with epidemiological severity and service capacity, thereby improving policy relevance compared to conventional clustering-only approaches. Validation through heatmap visualization, risk category distribution, and cluster ranking confirms the stability and interpretive clarity of the proposed framework. By moving beyond descriptive clustering toward an integrated analytical model, this study contributes a scalable and adaptive decision-support framework for dengue risk mapping. The findings provide actionable insights for policymakers, enabling evidence-based prioritization, optimized resource allocation, and the development of responsive intervention strategies to mitigate dengue burden in complex urban environments.
Hybrid Machine Learning Predictive Model for Resource Allocation Optimization and Project Risk Management Ismawan, Andika Noor; Wahyu, Sawali
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15773

Abstract

IT project management faces critical challenges related to inaccurate resource allocation estimation and project risk assessment, which complicates decision-making and threatens project performance. Although machine learning techniques have been widely adopted in this domain, existing studies predominantly rely on single models or simple ensemble strategies, limiting their ability to capture heterogeneous interactions among organizational, technical, and risk-related factors. This study proposes a hybrid machine learning–based decision support framework that integrates feature-level representation learning and probabilistic decision fusion. Gradient Boosting is reconceptualized as a feature selection and nonlinear interaction modeling mechanism, while Artificial Neural Networks generate latent feature embeddings representing complex project characteristics. These representations are fused through a Naive Bayes classifier to produce calibrated probabilistic predictions, supported by a weighted fusion strategy with F1-score–based threshold optimization to improve stability under imbalanced risk conditions. Experimental evaluation is conducted using 5,997 synthetic IT project records from PT Anugerah Nusa Teknologi. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Compared to standalone Gradient Boosting, Artificial Neural Network, and Naive Bayes models, the proposed hybrid framework consistently demonstrates superior predictive performance, achieving an accuracy of 0.85, an F1-score of 0.8485, and a ROC-AUC of 0.9050. Theoretically, this study contributes to project management research by demonstrating that IT project outcomes are more effectively modeled through multi-perspective learning rather than isolated predictors. Practically, the proposed framework provides actionable decision support to assist project managers in optimizing resource allocation and prioritizing risk mitigation under uncertainty.
Academic Performance Prediction from Student–VLE Bipartite Interaction Graphs Using Centrality Features A Comparative Study with Classical Classifiers Sumiati, Ai Irma; Hariguna, Taqwa; Barkah, Azhari Shouni
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15798

Abstract

The rapid growth of digital learning platforms has increased the availability of student academic records and fine-grained interaction logs, creating opportunities for Educational Data Mining (EDM) to support early academic monitoring. However, many predictive models still rely mainly on individual tabular attributes and underutilize relational signals embedded in learning interactions. This study proposes a graph-mining feature approach for predicting student academic performance using a bipartite Student–VLE interaction graph. Centrality measures—degree, weighted degree, HITS hub, PageRank, and eigenvector centrality—are extracted to form a centrality feature set and combined with standard student information features. Using the public OULAD dataset, we compare three supervised classifiers: Random Forest, Support Vector Machine, and XGBoost. Experiments show that adding the centrality feature set consistently and substantially improves performance across all models compared to baseline tabular features. On the test set, XGBoost achieves the strongest results with accuracy 0.842, ROC-AUC 0.922, PR-AUC 0.902, and MCC 0.684, while Random Forest is close behind (accuracy 0.834, ROC-AUC 0.916, PR-AUC 0.894, MCC 0.672). The SVM model also benefits (accuracy 0.800, ROC-AUC 0.869, PR-AUC 0.811, MCC 0.599), confirming the robustness of the graph-derived signal. Scientifically, this study provides empirical evidence that a multi-centrality representation offers more systematic and transferable predictive value than relying on a single graph metric, across multiple classical model families under the same evaluation protocol. These findings indicate that graph-mining centrality features capture complementary structural information about learning engagement that is not represented by tabular attributes alone, and they offer a practical, interpretable enhancement to classic EDM pipelines for academic performance prediction.
Experimental Characterization of ESP-Mesh Performance for Resilient Medical IoT Monitoring Systems Achyar, Zulfikar; Indrawati, Indrawati; Safar, Ilham; Wahyuni, Dewi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15817

Abstract

The reliability of medical Internet of Things (IoT) systems is critically dependent on network resilience, particularly in indoor hospital environments where conventional Wi-Fi infrastructures are vulnerable to single points of failure. Although ESP-Mesh has emerged as a promising self-healing communication protocol, its performance characteristics in medical IoT monitoring contexts remain insufficiently explored. This study aims to experimentally characterize the performance of ESP-Mesh networks for resilient medical IoT monitoring systems by analyzing multi-hop latency behavior, signal degradation, and communication stability under indoor medical-like conditions. A multi-parameter monitoring prototype integrating infusion volume, drip rate, and heart rate sensors was deployed as an experimental platform. Network performance was evaluated through controlled measurements of RSSI, end-to-end latency, and self-healing behavior, while MQTT was employed to assess cloud-based transmission efficiency. The results demonstrate that ESP-Mesh maintains stable self-healing communication with an average multi-hop latency of 0.714 s across distances up to 5 m, with latency increasing consistently as RSSI decreases. MQTT cloud transmission achieved a lower average latency of 0.247 s with zero packet loss, confirming its suitability for lightweight medical data delivery. Sensor evaluation revealed high accuracy for infusion volume monitoring (95.42%), while heart rate and drip rate measurements exhibited lower reliability due to signal interference and environmental sensitivity. These findings provide empirical insights into the performance limits and trade-offs of ESP-Mesh networks in medical IoT environments. The study confirms the feasibility of ESP-Mesh as a resilient communication backbone for medical monitoring, while highlighting the necessity of advanced signal processing to achieve clinical-grade sensing reliability.

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