cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Irpan Adiputra pardosi
Contact Email
irpan@mikroskil.ac.id
Phone
+6282251583783
Journal Mail Official
sinkron@polgan.ac.id
Editorial Address
Jl. Veteran No. 194 Pasar VI Manunggal,
Location
Kota medan,
Sumatera utara
INDONESIA
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 1,289 Documents
 125   126   127   128   129 
Comparative Academic Performance Prediction in Primary Schools Using Linear Regression and Random Forest Sembiring, Agustinus; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15953

Abstract

Predicting academic performance is an important aspect of data-driven decision making in education, particularly in primary schools where early identification of learning difficulties is crucial. This study compares the performance of Linear Regression and Random Forest Regression models for predicting students’ academic performance using an Educational Data Mining approach. The experiment uses the Students Performance Dataset from Kaggle, consisting of 1000 student records with eight predictor variables, including demographic and learning-related attributes. The target variable is the average score derived from mathematics, reading, and writing results. Model development and evaluation are conducted using Python in Google Colaboratory. Performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), while Random Forest is further optimized using GridSearchCV with 5-fold cross-validation. The results show that Linear Regression achieves the best performance (R² = 0.162, RMSE = 13.40, MAE = 10.49), outperforming both the default Random Forest (R² ≈ 0.000) and the tuned Random Forest (R² ≈ 0.112). Although the explained variance is relatively low, this finding indicates that simple demographic features provide limited predictive power for academic performance. A case study using a local dataset from a private primary school involving 132 sixth-grade students further confirms that Linear Regression performs more effectively than Random Forest for small and simple educational datasets. These results highlight the importance of aligning model selection with dataset characteristics in educational data mining.
Field Evaluation of an IoT-VFD Smart Ventilation System for Energy-Efficient Rice Seed Storage Riskiawan, Hendra Yufit; Anwar, Saiful; Setyohadi, Dwi Putro Sarwo; Arifin, Syamsul; Widiawan, Beni; Jannah, Annisa Nurul Hidayati; Setiawan, Akas Bagus
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15962

Abstract

Stable storage conditions are required in Rice Seed Storage to preserve seed quality and suppress fungal contamination, yet many warehouse ventilation systems still rely on inefficient on-off operation with limited responsiveness to changing temperature and humidity conditions. This study addresses the lack of integrated IoT-VFD control with field-validated energy and microclimate performance in seed warehouses. It proposes an IoT-based Ventilation Control architecture that combines ESP32, MQTT communication, and a Variable Frequency Drive to regulate a three-phase exhaust fan in both offline and online operating modes. The novelty of this work lies in integrating variable-speed control, real-time supervision, and field-based performance validation within a single seed warehouse deployment. The prototype was implemented in a 900 m3 warehouse at Politeknik Negeri Jember and evaluated through a 7-day field trial with continuous monitoring of temperature, humidity, and motor speed. The controlled system brought warehouse conditions closer to the intended storage setpoints and produced statistically significant improvements in both temperature and humidity (p < 0.001). Control performance was stable, with high target-hit accuracy and low RMSE, while energy testing showed lower electricity consumption than conventional non-VFD operation. Over an equivalent 2-hour operating period, energy use was reduced by 30.4%. The system also maintained 99.64% MQTT uptime, and no mold incidence was observed during controlled operation. These findings indicate that the proposed IoT-VFD architecture is a practical approach for improving microclimate stability, reducing energy use, and supporting fungus-preventive seed warehouse management.
Improving Multi-Class Public Complaint Classification with LSTM, Word2Vec, and Random Oversampling Nimasari, Azza; Saraswati, Galuh Wilujeng; Lutfina, Erba
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15975

Abstract

Digital transformation in the public sector encourages local governments to enhance service quality through online complaint management systems. However, the high volume of incoming complaints and significant data imbalance across 31 Organisasi Perangkat Daerah (OPD) pose challenges for efficient manual classification, often resulting in delays and misclassification. This study proposes an automated text classification model that integrates Long Short-Term Memory (LSTM), Word2Vec, and Random Oversampling (ROS), optimized using the Adam algorithm. The novelty of this research lies in the integration of sequential modeling and imbalance handling to address an extreme multi-class classification problem involving 31 OPD categories within a highly imbalanced dataset. The research stages include text preprocessing, word embedding construction using Word2Vec, data balancing through ROS, and model training using LSTM. Experimental results show that the proposed model achieves an accuracy of 0.72, with macro-average precision, recall, and F1-score of 0.67, 0.67, and 0.66, respectively. Considering the complexity of classifying 31 classes and the presence of severe data imbalance, the macro F1-score of 0.66 indicates that the model is reasonably effective in capturing classification patterns, although performance is not yet evenly distributed across all classes. Overall, the combination of LSTM, Word2Vec, and ROS demonstrates potential as a baseline approach for automating public complaint classification in complex multi-class scenarios. The proposed model can improve the accuracy and speed of complaint distribution to the appropriate OPD, thereby enhancing the efficiency and responsiveness of public service delivery compared to conventional manual methods.
Artificial Intelligence Usage Intention for Sustainable Development: A Neo ESG Perspective Using Hybrid Methods Nguyen, Vo Dinh Cao; Do, Huu Tam
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15985

Abstract

This study finds that the rapid development of artificial intelligence, together with the growing pressure to implement environmental, social, and governance principles, has driven firms to search for new models of sustainable governance. However, prior research has lacked empirical evidence on the role of artificial intelligence usage intention within a dynamic environmental, social, and governance framework and its interplay with social and environmental dimensions. To address this gap, the study reconceptualizes environmental, social, and governance by representing governance through artificial intelligence, the social dimension through diversity, equity, and inclusion, and the environmental dimension through exploitative green innovation and exploratory green innovation. Based on survey data from 357 firms, a hybrid methodological approach employing partial least squares structural equation modeling, artificial neural networks, and fuzzy set qualitative comparative analysis is applied. The results reveal that diversity, equity, and inclusion has the strongest effect on sustainable development (β = 0.533; t = 13.061; p < 0.001), followed by artificial intelligence, while exploitative green innovation plays a supportive role and exploratory green innovation shows no significant impact. Artificial neural networks validate these findings with stable predictive accuracy, while fuzzy set qualitative comparative analysis identifies multiple alternative pathways to sustainability (equifinality). The study contributes by positioning artificial intelligence as a new governance mechanism within environmental, social, and governance and highlighting the central role of diversity, equity, and inclusion, while also offering strategic guidance for integrating technological and social factors to foster sustainable development.
The Mapping Elementary School Digital Transformation Readiness through SERI for Roadmap Development Silalahi, Sondius Matogu Budiman; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15994

Abstract

Digital transformation has become a strategic priority in elementary education as schools are increasingly expected to integrate digital technology into teaching, assessment, and institutional management. However, previous studies on school digital readiness have generally focused on isolated aspects such as infrastructure, digital literacy, or leadership, without providing an integrated assessment model that simultaneously evaluates process, technology, and organisational dimensions in elementary school contexts. This study aims to assess the digital transformation readiness of an elementary school using the Smart Education Readiness Index (SERI). A descriptive quantitative case-study approach was employed by adapting the SERI assessment matrix into the elementary school context. The assessment covered three dimensions process, technology, and organisation through twelve indicators. Data were collected through a structured assessment matrix, supporting document review, and expert validation involving two educational technology experts. The results indicate that the school reached a moderate level of digital transformation readiness. The strongest indicators were specific or specialised skills (2.635), digital infrastructure readiness (2.634), digital interconnectivity (2.598), and organisational planning indicators (2.562), while the weakest indicators were assessment (1.708), policy guidance (1.708), general or transversal skills (1.744), and digital storage (1.852). Unlike previous studies that mainly assess digital readiness through separate technological or pedagogical indicators, this study applies a multidimensional institutional assessment framework. This study contributes by proposing a structured and adaptable assessment approach for elementary school digital transformation that supports the development of a more measurable and context-sensitive digital transformation roadmap.
A Statistical Benchmarking of Imbalance-Aware Ensemble Models for Cervical Cancer Prediction Sumarna, Sumarna; Astrilyana, Astrilyana; Sugiono, Sugiono; Wijaya, Ganda; Desvia, Yessica Fara
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15995

Abstract

Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide, particularly in developing countries. Early prediction through machine learning has the potential to support clinical decision-making; however, cervical cancer datasets often suffer from severe class imbalance, which reduces the ability of conventional models to correctly detect minority cases. This study aims to improve minority class detection in cervical cancer prediction by evaluating several imbalance-aware ensemble learning approaches. The proposed study compares five models, namely Random Forest (RF), SMOTE combined with Random Forest (SMOTE+RF), Balanced Random Forest (BRF), EasyEnsemble, and RUSBoost. The models were evaluated using 5-fold cross-validation with performance metrics including accuracy, recall, F1-score, and Area Under the Curve (AUC). Statistical validation was conducted using the Friedman test, followed by the Wilcoxon signed-rank test and Kendall’s W effect size analysis to assess the significance and magnitude of performance differences. Unlike prior studies that primarily focus on performance improvement, this study introduces a statistically rigorous comparative evaluation to assess both significance and practical effect of imbalance-aware ensemble methods. Experimental results show that imbalance-aware ensemble methods significantly improve minority detection compared to the baseline RF model. In particular, BRF achieved the highest AUC of 0.9469 with improved recall stability, while RUSBoost produced the highest F1-score of 0.7451. Although the Friedman test indicated no statistically significant difference among models (p = 0.2037), the Kendall’s W value of 0.297 suggests a small-to-moderate practical effect. These findings indicate that imbalance-aware ensemble learning can enhance the robustness of cervical cancer prediction models, particularly for minority class detection. The results highlight the importance of incorporating imbalance-handling strategies in medical prediction systems and suggest potential directions for future research in improving diagnostic decision-support models.
Improving Optic Disc and Optic Cup Segmentation with Flip-Gamma Augmentation and SegFormer Salamah, Fitri; Erwin; Desiani, Anita
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15996

Abstract

The Cup-to-Disc Ratio (CDR) is widely used as a diagnostic indicator for glaucoma, although variations and irregularities can influence its accuracy in the Optic Disc (OD) and Optic Cup (OC). To overcome this challenge, automated image segmentation is used. However, image segmentation is challenged by image blurriness, noise, and uneven illumination, which can affect segmentation quality and increase the risk of misdiagnosis. To address these challenges, this study applies a combined Flip-Gamma Augmentation and SegFormer approach for OD and OC segmentation. Flip-Gamma augmentation increases image diversity and improves image quality by adjusting brightness and contrast. Meanwhile, the SegFormer uses a Transformer-based backbone and efficiently extracts multi-scale features to enhance segmentation performance. Experimental results on the Drishti-GS dataset show that applying Flip-Gamma (δ = 0.8, 0.9, 1.1, 1.2) is associated with improved segmentation performance across all classes, with sensitivity (90-99%), DSC (90-99%), IoU (82-99%), and ROC (94-99%), indicating consistent segmentation of OD, OC and background regions. Furthermore, a one-sided Mann-Whitney U test indicates differences in performance compared to other augmentation methods. These findings suggest that the proposed augmentation strategy is beneficial for segmentation on the Drishti-GS dataset. However, further validation on larger and more diverse datasets is required to assess generalizability.
Real time weather forcasting with conditional CNN and TCN-BiLSTM Ensamble at Manokwari Lie, Ilham Tatayo; Naibaho, Julius Panda Putra; Kweldju, Alex De
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15999

Abstract

Short-term weather forecasting is fundamentally critical for disaster mitigation in dynamic tropical maritime regions. However, conventional numerical approaches suffer from high computational latency, and spatial deep learning models frequently experience severe performance degradation during nocturnal conditions due to the absence of illumination. This study aims to develop an adaptive, real-time multimodal weather nowcasting system that effectively prevents nocturnal predictive failure through a dynamic conditional ensemble architecture. The proposed framework integrates a Convolutional Neural Network (CNN) to extract optical features from a dataset of 2,515 localized sky images with a Temporal Convolutional Network and Bidirectional Long Short-Term Memory (TCN-BiLSTM) pipeline to process 15,111 corresponding meteorological time-series records from BMKG. To address visual ambiguity, the system strictly employs a day-night gating mechanism, deactivating the CNN at night to rely solely on thermodynamic data. Finally, the optimized model was deployed via TensorFlow.js for decentralized client-side browser inference. Experimental evaluations explicitly demonstrate that the conditional ensemble significantly outperformed all standalone models, achieving a peak accuracy of 92.49% and a Macro F1-score of 0.913 while successfully preserving a robust recall rate for precipitation events. Furthermore, the browser-based deployment completely eliminated server transmission bottlenecks, achieving sub-second warm-start inference latency across heterogeneous consumer devices. Ultimately, the conditional day-night modality gating mechanism effectively mitigates nocturnal visual degradation, proving that implementing this integrated architecture as a client-side web application is highly feasible for delivering instantaneous public weather warnings.
Decision Support System (DSS) for Rodenticide Selection using the TOPSIS Method Fitasari, Ayu Tri Nur; Lutfina, Erba; Saraswati, Galuh Wilujeng
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.16008

Abstract

Selecting an appropriate rodenticide is a critical decision in pest control operations, as each product differs in effectiveness, application cost, safety level, environmental impact, and resistance potential. In practice, rodenticide selection is often based on technician experience or habitual product use, which may result in subjective and less optimal decisions. This study aims to develop a decision support system for rodenticide selection using the TOPSIS method within a multi-criteria decision-making (MCDM) framework. The evaluation is conducted based on six criteria: effectiveness, application cost, safety derived from LD50 values, secondary poisoning risk, resistance potential, and application convenience. To improve the robustness of the decision-making model, this study incorporates an adaptive TOPSIS approach through scenario-based weighting and compares the results with the Simple Additive Weighting (SAW) method. The findings show that alternatives with a balanced performance in terms of safety and operational cost consistently achieve higher rankings, with Warfarin Bait and Zinc Phosphide appearing as top-performing options across different evaluation scenarios. In addition, the proposed model is implemented in a web-based system using a prototype development approach, enabling automated calculations and transparent ranking results. This study provides a structured and practical decision support model that integrates technical, economic, and environmental considerations to support more objective decision-making in pest control management.

Page 129 of 129 | Total Record : 1289

 125   126   127   128   129 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 2 (2026): Article Research April, 2026 Vol. 10 No. 1 (2026): Article Research January 2026 Vol. 9 No. 4 (2025): Articles Research October 2025 Vol. 9 No. 3 (2025): Article Research July 2025 Vol. 9 No. 2 (2025): Research Articles April 2025 Vol. 9 No. 1 (2025): Research Article, January 2025 Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024 Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024 Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024 Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023 Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023 Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023 Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022 Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022 Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022 Vol. 6 No. 1 (2021): Article Research Volume 6 Issue 1: January 2021 Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021 Vol. 5 No. 2B (2021): Article Research October 2021 Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 Vol. 5 No. 1 (2020): Article Research, October 2020 Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019 Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol. 3 No. 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol. 3 No. 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol 3 No 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol. 2 No. 2 (2018): SinkrOn Volume 2 Nomor 2 April 2018 Vol. 2 No. 1 (2017): SinkrOn Volume 2 Nomor 1 Oktober 2017 Vol. 1 No. 2 (2017): SinkrOn Volume 1 Nomor 2 April 2017 Vol. 1 No. 1 (2016): SinkrOn Oktober Volume 1 Edisi 1 Tahun 2016 More Issue