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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 1,289 Documents
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From Methodologies to Metrics: A Review of Aspect-Based Sentiment Analysis Approaches Esmaeel, Marwa; Taqa , Alaa
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.v10i2.15888

Abstract

Abstract: ABSA (Aspect-Based Sentiment Analysis) has been developed as a fine-grained sentiment analysis tool, which finds the sentiment towards a particular aspect, enabling more accurate sentiment mining in a variety of domains. Over the past decade ABSA research has transcended lexicon-driven and traditional machine learning methodology using deep learning and transformer-based pre-trained language models to generative large language models. Nevertheless, underlying issues remain: implicit aspect extraction, low cross-domain and cross-lingual robustness, dataset imbalance, and interpretability concerns of complex neural networks. In addition, the rapid scaling of ABSA subtasks has led to some fragmentation in methodological advances in earlier investigations. By methodically reviewing the development of methodological paradigms, benchmark datasets, and evaluation approaches, this review has offered a systematic and rigorous assessment of the literature on ABSA. Unlike previous reviews, the study adopts a holistic, task-aware view and makes a direct connection between ABSA subtasks and the accompanying modeling methodologies. The review explores new research directions such as explainable ABSA, meta-based learning frameworks, multilingual and low-resource modeling, and large language model integration, thus providing a structure toward the road to developing more resilient, interpretable, and generalizable ABSA systems.
Web3-Based Cyber Incident Reporting System With Smart Contracts and Non-Fungible Token Rewards Permana, Danang Juniar; Mahmud, Wildan; 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.v10i2.15898

Abstract

The rising frequency of cyber threats increases the need for incident reporting that is transparent, efficient, and privacy-preserving. This study designs and implements a hybrid Web2-Web3 cyber incident reporting prototype that anchors report references on a blockchain while storing full incident details off-chain, and explores non-fungible token (NFT) recognition incentives for reporters. Using an SDLC-based iterative prototyping approach, we built a React single-page application integrated with a Laravel REST API and MySQL for off-chain storage, and deployed Solidity smart contract modules on the Arbitrum Sepolia testnet to record report identifiers and UUID pointers (dataPointer) and to mint NFTs after administrative validation. We conducted black-box functional testing across core scenarios (submission, storage, pointer anchoring, validation, and minting) and a user acceptance study with 25 participants (15 cybersecurity students and 10 IT practitioners) using a 5-point Likert questionnaire. All tested scenarios executed as expected in the test environment, and on-chain events were traceable to corresponding backend records via transaction receipts and logged identifiers. The acceptance evaluation yielded an overall mean score of 3.4/5 (about 68%), indicating moderate acceptance and supporting the work as a prototype feasibility study rather than organizational-level generalization. The prototype demonstrates a practical workflow for hybrid incident reporting with transaction-level traceability and recognition incentives; future work should strengthen cryptographic binding (e.g., content hashing) and validate the approach with CSIRT stakeholders in operational settings.
Contextual Smart School Architecture Integrating SERI and TIER for Digital Transformation 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.v10i2.15910

Abstract

The digital transformation of elementary education has become an inevitable demand in the era of the Fourth Industrial Revolution. Nevertheless, schools in non-metropolitan regions continue to face persistent challenges, including limited infrastructure, low technology penetration, and insufficient policy support. This study aims to design a contextual smart school architecture by integrating the Smart Education Readiness Index (SERI) and the Transformation Impact and Essential Readiness (TIER) framework. A descriptive–qualitative approach, supported by quantitative survey data from 40 educators and education personnel, was employed to assess institutional readiness and formulate strategic priorities. The SERI assessment revealed an average digital readiness score of 3.12 (scale 0–4), with four dominant dimensions: Teaching and Learning Process (3.45), Assessment (3.28), Innovative Analysis (3.21), and IR 4.0 Policy (3.30). These dimensions were further validated through a Prioritisation Matrix weighted at 60% for cost factors, 20% for key performance indicators, and 20% for contextual proximity. The findings emphasize that effective digital transformation must leverage local strengths, be aligned with global frameworks, and be implemented through structured strategies. The key contribution of this research lies in the proposal of a modular, integrated, and sustainable smart school architecture model that can be replicated nationally to bridge global standards with local realities. This study provides both theoretical insights and practical implications for policymakers and educational leaders seeking to advance equitable digital transformation in non-metropolitan schools.
Multiclass SVM with Kernel Optimization for Schizophrenia Subtype Classification Using Clinical Symptom Records Rohman, Reisa Maulidya; Septiarini, Anindita; Tejawati, Andi
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.v10i2.15926

Abstract

Schizophrenia is a mental disorder that affects about 0.3% of the world population. It is characterized by a wide range of symptoms that form several subtypes. Overlapping symptoms and subjective clinical assessments may reduce consistency and make subtype classification challenging. Machine learning algorithms that use patients’ medical records offer a potentially objective approach for subtype classification. This study aims to classify four schizophrenia subtypes: paranoid, catatonic, undifferentiated, and residual, based on subtype labels recorded in the hospital using a multiclass SVM approach with kernel optimization. The dataset consists of 218 medical records of schizophrenia patients with 25 binary symptom variables used as input features. SVM was trained using two multiclass approaches, namely OAO and OAA. Evaluation was performed using five-fold stratified cross-validation. Performance was calculated using accuracy, macro-precision, macro-recall, and macro F1-score. Optimal performance was achieved using the OAA approach with an RBF kernel at C = 10 and gamma = 0.1. This configuration achieved an accuracy, macro-precision, macro-recall, and macro F1-score of 0.89, 0.90, 0.86, and 0.87, respectively. These results show that the multiclass approach, kernel functions, and parameter configuration influence classification performance. The proposed model may serve as a screening or decision-support tool to assist subtype identification based on clinical symptom records.  
Bidirectional Long Short-Term Memory for Early Detection of Running Injuries in Imbalanced Data David, David; Kurniawan, Defri
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.v10i2.15928

Abstract

Running-related injuries are a common sports health issue that can impair athletic performance and potentially terminate an athlete’s career. Early injury detection is therefore critical, as injuries are cumulative in nature and influenced by training load patterns over time. Consequently, data-driven predictive approaches based on time-series analysis are required to support athlete monitoring systems with a safety-oriented focus. This study aims to develop an efficient, accurate, and safety-first injury prediction model for running athletes. The study utilizes daily running activity time-series data obtained from Kaggle. The proposed model is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM) architecture to capture bidirectional temporal dependencies, combined with Focal Loss to address extreme class imbalance. In addition, domain-specific feature engineering is applied through the Acute:Chronic Workload Ratio (ACWR). Model performance is evaluated against tabular-data-based models, namely XGBoost and Balanced Bagging, across multiple experimental configurations. Experimental results indicate that the lightweight Bi-LSTM configuration achieves a Recall of 90.7%, outperforming the benchmark models while maintaining a competitive AUC. These findings demonstrate that sequential modeling is more effective in detecting rare injury events. Overall, this study confirms that Bi-LSTM-based sequential modeling is well suited for early detection of running injuries and suggests its potential applicability in athlete monitoring systems that prioritize safety.
Improving Brain Tumor Classification Performance Using EfficientNetB0 Integrated with CBAM Attention Mechanism At Taqwa, Abd Salam; Muh., Satriawan; Sheila Eunike, Kakisina; Puput, Kusuma Dewi; Fettyana; Ayutri, Wahyuni
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.v10i2.15931

Abstract

Accurate classification of brain tumors using magnetic resonance imaging (MRI) requires robust automated methods to support clinical diagnosis, particularly when tumor types present subtle visual distinctions. In this study, the Convolutional Block Attention Module (CBAM) is incorporated into the EfficientNetB0 architecture to improve feature representation for multi-class brain tumor classification. The performance of the proposed model is evaluated against the baseline EfficientNetB0 under identical training and testing conditions. EfficientNetB0 with CBAM achieves a training accuracy of 99.76% and a validation accuracy of 99.45%, with corresponding training and validation losses of 0.0085 and 0.0241. On an independent test dataset, the model attains a test accuracy of 99.25% and a loss of 0.0207. In comparison, the baseline EfficientNetB0 model attains a training accuracy of 52.68%, validation accuracy of 46.20%, and test accuracy of 43.32%, accompanied by significantly higher loss values. At the class level, the proposed model demonstrates macro-average precision, recall, and F1-score of 0.99, whereas the baseline model yields macro-average values of approximately 0.54 for precision and recall, and 0.50 for F1-score. Although CBAM integration increases computational time per evaluation step from 395 ms to 601 ms, the marked improvement in classification accuracy and error reduction underscores the value of attention mechanisms. These results demonstrate that attention-based feature refinement significantly enhances deep learning performance for medical image classification, particularly in multi-class brain tumor diagnosis.
Cross-Architecture Performance Evaluation of Transfer Learning Models for Multi-Class Vehicle Damage Severity Classification Ulumuddin, Mochammad Fatih; Pramana, Anggay Luri
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.v10i2.15939

Abstract

Automated vehicle damage classification supports objectivity and scalability in insurance claim processing and digital inspection systems; however, prior studies often report performance improvements without controlled experimental settings or statistical validation, limiting methodological reliability. This study establishes a statistically controlled cross-architecture evaluation framework to determine whether pretrained convolutional neural networks significantly outperform a custom baseline model in multi-class vehicle damage classification. A dataset of 891 labeled vehicle images categorized into heavy, medium, light, and normal damage was partitioned using stratified sampling (70% training, 15% validation, 15% testing). Four architectures Baseline (CustomCNN), VGG16, ResNet50, and MobileNetV2 were trained under identical preprocessing and optimization settings with two training durations (30 and 50 epochs). Five-fold cross-validation and paired t-test analysis were applied to assess statistical significance. At 30 epochs, MobileNetV2 achieved the highest accuracy (75.76%), while at 50 epochs VGG16 obtained the best performance (78.03%). Extending training duration did not produce statistically significant improvement (p > 0.05). Pretrained architectures significantly outperformed the baseline model, whereas ResNet50 did not demonstrate superior performance. The novelty of this study lies in its statistically validated comparative framework. Although limited by moderate dataset size and single-source imagery, the findings provide practical guidance for selecting efficient convolutional neural networks in vehicle damage classification systems.
Implementation of Semantic Search in an Academic Repository Using Sentence-BERT and FAISS Lubis, Ihsan; Lubis, Husni; Nur Wahidah, Inaya
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.v10i2.15940

Abstract

Academic repositories serve as centralized platforms for storing and managing scientific documents, including research papers, reports, and administrative records. Yet, traditional keyword-based search systems often struggle to deliver relevant results. These systems typically fail to capture the contextual meaning of user queries, which leads to mismatches when the query terms differ from those found in the documents. To overcome this limitation, this study introduces a semantic search approach for academic repositories by combining Sentence-BERT as the text embedding model with FAISS as the vector-based similarity search engine. In the proposed system, documents stored in a MySQL database are first preprocessed to remove HTML tags, then converted into semantic vector representations using Sentence-BERT. These vectors are indexed with FAISS, enabling fast and efficient similarity searches compared to conventional keyword matching. The system architecture integrates FastAPI as the backend service for indexing, searching, and evaluation, while CodeIgniter 4 functions as the frontend framework for document management by administrators and end users. Evaluation was carried out using three test sets, each containing ten queries. Performance was measured using Recall@K, normalized Discounted Cumulative Gain (nDCG), Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), and search latency. Experimental results show that the system achieved an average Recall@K of 0.61, a MAP of 0.39, and a No-Hit rate of 0.033, meaning all queries successfully retrieved results. Although the nDCG value declined in the third test set, the system consistently returned relevant documents.
Developing an Integrated Capital Assistance and Community Training System Using Agile Scrum Zuhdi, Ahmad Muzaki; 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.v10i2.15947

Abstract

Local governments increasingly require Cross-Agency Integration platforms to deliver transparent, auditable public services, yet capital assistance and community training programs are often managed through fragmented applications and manual workflows, leading to duplicated data, slow verification, and limited status traceability. This study develops an integrated capital assistance and community training system for local government using Agile Scrum, and evaluates its functional acceptance, usability, and security readiness to support Public Service Digitalization. Requirements were elicited through observation and interviews across three service-managing municipal agencies, while system governance and evaluation also involved the Communication and Informatics Office. The system was implemented as a web application with iterative sprints and backlog prioritization. Evaluation employed a User Acceptance Test (Likert 1–5, 10 items), System Usability Scale, and penetration testing using OWASP ZAP focusing on session management and HTTP security headers. Fifteen agency users participated in the evaluation. The system achieved 93% functional acceptance and a System Usability Scale score of 82.3, indicating excellent perceived usability. Security scanning found no high-risk issues, while medium- and low-risk findings were dominated by missing headers (Content Security Policy and X-Frame-Options) and incomplete cookie flags, which can be mitigated through standard hardening. The proposed platform improves cross-agency coordination and citizen-facing transparency while meeting usability expectations. Agile Scrum enabled rapid alignment with stakeholders and incremental quality improvements. Future work includes analytics, financial-system integration, and continuous security monitoring.
Analysis of the Effectiveness of a Music Learning Information System for Early Childhood (Golden Age) using the Technology Adoption Model (TAM) Safitri, Susan Juli; Rukhviyanti, Novi
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.v10i2.15951

Abstract

Advances in information technology have brought significant changes to the education sector, including in the learning process for early childhood. The integration of information systems into learning activities is no longer limited to administrative functions but also serves as a learning medium capable of enhancing the effectiveness of the learning process. This study aims to analyze the effectiveness of implementing a music learning information system and to examine the relationship between the quality of the information system and student learning outcomes. The research analysis framework is based on the Technology Acceptance Model developed by Fred D. Davis to explain how perceptions of ease of use and information quality influence users’ adoption of technology. This study employs a quantitative approach with a correlational design. The study population consists of students in the New Primary music program at Yamaha Forte Music Bandung, totaling approximately 800 students. The research sample was determined using purposive sampling, calculated as rxy = (nΣXY − (ΣX)(ΣY)) / √[(nΣX² − (ΣX)²)(nΣY² − (ΣY)²)], with a total of 63 students who actively supported the use of the digital learning system. Data collection was conducted via a Likert-scale questionnaire measuring three primary dimensions of the information system: usability, information quality, and learning impact. Validity was assessed using Pearson’s Product-Moment correlation, while reliability was evaluated using Cronbach’s Alpha > 0.70. The research results indicate that the effectiveness of the music learning information system falls into the “very effective” category, with an average score of 81.3%. The usability dimension received the highest score of 87.5%, followed by information quality at 84.2%, and learning impact at 81.3%. The results of the correlation analysis indicate a positive relationship between the quality of the information system and student learning behavior, such as increased practice discipline, learning motivation, and self-confidence in music.

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