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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 1,361 Documents
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E-Book Design Analysis: The Effectiveness of Interactive Media in Understanding Characters and Ethnoscience Concepts Dede Latipah; Dewiantika Azizah; Mochamad Arief Mardiansah; Lucky Dewianti; Ade Tio Sopian
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.15091

Abstract

This research aims to develop a contextual-based interactive e-book integrating West Javanese folktales and ethnoscience concepts to enhance students’ understanding of natural phenomena in second-grade science at SDIT Abdurrahman Bin Arif, Curug, Bogor City. The study employed the ADDIE development model which includes Analysis, Design, Development, Implementation, and Evaluation stages. Data were obtained through expert validation, individual and small group testing, and effectiveness testing using pre-test and post-test instruments. Validation was conducted by media, language, and ethnoscience experts. The trial was performed on 29 second-grade students. The results showed excellent feasibility with expert assessments reaching over 90% in each category. The t-test analysis revealed a significant improvement in students’ learning outcomes after using the e-book (sig. 0.000 < 0.05). This indicates that the e-book was effective in increasing students' conceptual understanding of environmental changes through the integration of cultural narratives. The use of contextual e-books not only supports meaningful learning but also promotes character values such as responsibility and environmental awareness. Thus, the developed e-book is considered valid, practical, and effective for science learning in primary education
Multi-Device IoT Integration Using an API-Based Modular Architecture for Environmental Monitoring Systems Andi Marwan Elhanafi; Dedy Irwan; Kissi Lola Armedia Br Siregar
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.15970

Abstract

The Internet of Things (IoT) has increasingly played a significant role in the development of adaptive and real-time environmental monitoring systems. However, integrating multiple IoT devices remains challenging due to variations in data transmission intervals, communication protocols, and processing capabilities across devices. These differences often complicate system interoperability and data management within a unified monitoring platform. To address this issue, this study proposes an API-based modular architecture as a solution for integrating heterogeneous IoT devices in environmental monitoring systems. The proposed architecture separates core system functions into independent modules, including data acquisition, device management, and data visualization. The proposed architecture is evaluated through a multi-device environmental monitoring implementation configured with different logging intervals in order to assess communication performance and data consistency. The novelty of this study lies in its architectural approach to handling heterogeneous data transmission intervals in multi-device IoT environments using a modular API-based design. The experimental results indicate that the average communication latency is approximately 200ms, while the average daily data logging volume exceeds 3,500 entries per device. Furthermore, analysis of logging interval variations shows a time deviation of less than 3 seconds, which remains within the acceptable range for real-time environmental monitoring applications. The results demonstrate that the proposed architecture achieves success rate of over 97%, confirming the reliability of the proposed API-based modular architecture. Overall, the findings suggest that the modular API-driven architecture not only improves the flexibility and scalability of multi-device IoT integration but also maintains reliable data consistency and efficient communication performance.
Development Of Smartphone-Based Pornography Addiction Behavior Monitoring Application Litafira Syahadiyanti; Alhifny Wahid; Bondan Tiur Mahendra
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.15992

Abstract

The rapid growth of smartphone usage has increased access to various internet content, including pornography-related websites, particularly among adolescents. Existing solutions addressing this issue generally focus on psychological screening or access restriction through parental control systems, which either rely on subjective input or emphasize blocking mechanisms rather than providing insights into actual usage behavior. This study aims to develop MindSafe, a smartphone-based application for monitoring pornography-related domain access activity using a local VPN approach. The proposed system captures domain access activity in real time by intercepting DNS queries at the device level, classifies accessed domains using a blocklist-based approach, and records the results in a structured database. Unlike conventional parental control systems that focus on access restriction, this study introduces a monitoring-oriented approach that emphasizes real-time domain logging and statistical behavior visualization. The system was developed using Agile methodology and evaluated through functional testing and usability assessment. Functional testing confirms that all core features operate as expected. Usability evaluation using the System Usability Scale (SUS) involving 20 respondents resulted in an average score of 76.0, indicating acceptable usability. The results demonstrate that the system provides an objective and privacy-aware monitoring approach, offering a data-driven alternative to existing screening and blocking-based solutions.
Integration of Invisible Watermarking Based on a Hybrid DWT-SVD Approach in AI-Based Image Generators for Content Authentication Herlina Harahap; Imran Lubis
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16012

Abstract

The rapid advancement of artificial intelligence (AI) in the field of image generation has raised new challenges for digital content authentication and validity. AI-generated images are often indistinguishable from real photographs, creating potential risks of misuse in disinformation, visual manipulation, and copyright infringement. This study proposes the integration of an invisible watermarking method based on a hybrid of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) directly into the AI-image generation pipeline. The system is developed end-to-end with three main components: a Translator API to support Indonesian text inputs, an AI-image generator to create images from descriptive text, and a watermarking module to embed and extract hidden watermarks automatically. Experimental results confirm that the visual quality of watermarked images was preserved, with PSNR values consistently above 35 dB and SSIM ≥ 0.95, indicating that the watermark is imperceptible to human vision. Watermark extraction evaluation achieved a position accuracy of 59.43% after normalization and a subsequence accuracy of 80.20%, demonstrating reliable recognition of the embedded watermark sequence. Robustness tests under common manipulations such as JPEG compression, rotation, cropping, and noise addition showed that the watermark remained detectable, although accuracy decreased under extreme cropping. These findings demonstrate that the hybrid DWT–SVD method is effective for ensuring the authenticity of AI-generated content without compromising visual quality, while offering novelty through its integration into the generative pipeline and its support for local language inputs.
Implementation of CMM Method to Measure Maturity Level of SPBE in Palembang City Government Nyimas Hamidah Purnama Agustriani; Marsudi Wahyu Kisworo; Edi Surya Negara; Usman Ependi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16025

Abstract

The Palembang City Government faces challenges in synchronizing internal policies with the operational implementation of the Electronic-Based Government System (SPBE). This research aims to measure the maturity level of the Internal Policy Domain and the SPBE Governance Domain in the Palembang City Government using the Capability Maturity Model (CMM) method. Additionally, the study aims to identify gaps through gap analysis and formulate strategic recommendations to achieve the "Optimum" maturity level. The research focuses on evaluating 20 indicators covering the Internal Policy and SPBE Governance domains within the Palembang City Government. The maturity level analysis refers to the framework of Permenpan-RB No. 59 of 2020 and Menpan-RB Guideline No. 3 of 2024. The indicator assessment results show that the Internal Policy Domain reached an index of 4.1 (Very Good), while the Governance Domain obtained an index of 3.4 (Good). Overall, the Palembang City Government possesses a very strong regulatory foundation; however, governance effectiveness remains sectoral and is not yet fully aligned with technical implementation across all regional apparatus. It is recommended that the Palembang City Government strengthen cross-sector coordination through integrated SPBE budget synchronization and conduct periodic policy reviews accompanied by formal documentation to ensure the sustainable improvement of SPBE quality.  
Assessing Information System Acquisition and Implementation Using COBIT 4.1 in Housing Developers Dea Ramadhan; Hilyah Magdalena
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16039

Abstract

Housing development companies plan, construct, and market housing. As technology advances, information systems become crucial, especially in financial matters. However, no comprehensive analysis of financial information system implementation in this sector exists. This study employed a qualitative descriptive approach supported by quantitative assessment (mixed-method), combining observations and semi-structured interviews (qualitative) with a questionnaire-based maturity level calculation (quantitative). Data were collected from three respondents (Finance Manager, IT Staff, and Accounting Staff) directly involved in the system. The maturity level was determined using the COBIT 4.1 framework in the Acquire and Implement (AI) domain. Results show an average maturity of 3.08 Level 3 (Defined Process), meaning procedures are documented and structured. However, several subdomains remain below target AI5 gap 1.30 and AI7 gap 1.10 require improved implementation and control of financial information systems, while AI3 has the smallest gap (0.02). This study contributes to the academic community by (1) providing a detailed assessment of financial information system implementation in housing development companies, (2) identifying maturity gaps in each AI subdomain, and (3) offering practical recommendations for improving IT governance maturity. Routine maintenance, system monitoring, and infrastructure updates are still needed to ensure system stability. Overall, information systems management has not yet reached the targeted maturity level due to reliance on individuals, lack of formal training, and poor procedure communication. Continuous improvement is required to make information systems management more efficient and scalable.
A Multi-Model Time Series Framework for Forecasting Vietnam’s Tourism Revenue in the Post-COVID Recovery Era Ho Nhat Hiep; Nguyen Ngoc Xuan Quynh; Nguyen Thi Van Anh; Minh Ly Duc
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16045

Abstract

This study forecasts Vietnam’s tourism revenue in the post-COVID-19 recovery period using a multi-model time series framework. The dataset includes three groups (ID 292–294) covering tourism business performance, economic sectors, and regional revenue. Six forecasting models are applied and evaluated using MAPE, MAD, and MSD. Results show that decomposition and Holt–Winters achieve the best accuracy (e.g., MAPE as low as 18%), while moving average performs well in specific cases (MAPE ≈ 28%). Forecasts indicate that tourism revenue may nearly double by 2030, driven mainly by domestic demand and the non-state sector, although international tourism recovers more slowly.
Implementing Random Forest for Eye State Detection in an EEG-Based Brain-Computer Interface System Muhammad Alfathan; Tri Sugihartono
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16054

Abstract

Eye state detection using Electroencephalogram (EEG) signals is a growing research area in Brain-Computer Interface (BCI) systems, with practical implications for drowsiness monitoring and assistive technologies. However, EEG signals are highly susceptible to extreme outliers caused by muscle artifacts and electrode interference, which significantly degrade model performance when left unaddressed. Previous studies have largely overlooked explicit outlier handling strategies and often rely solely on accuracy as the evaluation metric, which is insufficient for imbalanced class distributions. This study aims to implement the Interquartile Range (IQR) Clipping method for outlier handling on EEG signals and develop a Random Forest classification model to distinguish open-eye and closed-eye states, evaluated through seven comprehensive metrics. The EEG-Eye-State dataset from the UCI Machine Learning Repository, consisting of 14,980 samples across 14 EEG sensor features, was used. IQR Clipping with bounds [Q1 − 1.5×IQR, Q3 + 1.5×IQR] was applied to all sensors, followed by StandardScaler normalization and an 80:20 Stratified Train-Test Split. A Random Forest model with 100 estimators and balanced class weights was trained and validated using Stratified 10-Fold Cross-Validation. IQR Clipping successfully handled 12,737 outlier instances across all sensors without discarding any samples. The model achieved an accuracy of 92.49%, Balanced Accuracy of 92.14%, ROC-AUC of 0.9791, PR-AUC of 0.9759, F1-Score Macro of 0.9236, Matthews Correlation Coefficient (MCC) of 0.8486, and Cohen Kappa of 0.8474. Cross-validation confirmed model stability with a mean accuracy of 92.86% ± 0.36% and ROC-AUC of 0.9809 ± 0.0020. Feature importance analysis identified sensors O1 (11.81%), P7 (10.59%), and F7 (10.15%) as the most dominant contributors. These results confirm that combining IQR Clipping with Random Forest produces a stable, accurate, and neuroanatomically interpretable model for EEG-based eye state classification, offering a strong foundation for real-world BCI and driver drowsiness detection systems.
CLASSIFICATION OF COFFE FRUIT DRYING USING VGG16 Annisa Diyan Novitasari; Yufis Azhar
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16055

Abstract

The drying process is a crucial stage in coffee post-harvest handling that directly affects the final product quality, especially in the specialty coffee segment. Assessment of the coffee fruit drying level in the field is still largely carried out visually and subjectively, which can potentially lead to inconsistent quality. This study aims to develop an automatic classification system for coffee fruit drying levels based on digital images using a deep learning method with the Convolutional Neural Network (CNN) VGG16 architecture. The dataset used consists of 561 coffee fruit images classified into three classes: Wet, Medium, and Dry. The preprocesssing stages include background removal, auto-cropping, and image standardization. Two models were developed: a baseline model without data augmentation and a model with data augmentation and selective fine-tuning on the final layers of VGG16. The evaluation results show that the baseline model achieved a validation accuracy of 83%, while the model with augmentation and fine-tuning improved the accuracy to 94%, accompanied by significant increases in precision, recall, and F1-score values. The proposed model also demonstrates a high and stable level of prediction confidence. These results prove that the VGG16 approach is effective for classifying coffee fruit drying levels and has the potential to be applied as an objective post-harvest quality control support system.
Comparative Analysis of Snort, Suricata, and Random Forest for Flood Detection Ichdan Maulana Nur Fazri; Ichsan Ibrahim
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16077

Abstract

Volumetric Denial of Service (DoS) attacks, particularly SYN Flood and ICMP Flood, remain critical threats to network availability. Signature-based NIDS tools such as Snort and Suricata are widely deployed, yet their trade-offs against machine learning approaches remain underexplored in simultaneous physical-environment studies. This study aims to quantify and compare the performance-accuracy trade-off of Snort 3, Suricata 7, and Random Forest for SYN/ICMP Flood detection on identical physical datasets. Experiments were conducted in a controlled physical laboratory using hping3-generated datasets: 28,930,364 ICMP packets (1.56 GB) and 1,532,301 SYN packets, each captured over 120 seconds. Both NIDS tools were tested in offline PCAP-replay mode. A Random Forest model was trained on 627,788 balanced samples using frame-level features, validated with 5-fold cross-validation. Results: Snort 3 achieved the highest throughput at 987,966 PPS (ICMP) and 240,908 PPS (SYN), while Suricata 7 demonstrated greater detection sensitivity with 148 alerts versus 36 matches in the ICMP scenario. The Random Forest classifier achieved Precision = Recall = F1-score = 1.00 on 125,558 test samples, confirmed by 5-fold cross-validation (99.98% ± 0.01%). Conclusion: A hybrid architecture combining signature-based NIDS as a first-line filter with Random Forest as a secondary validator represents the optimal configuration for volumetric DoS mitigation, balancing throughput and detection accuracy.

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