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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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Evaluating Kubernetes Progressive Delivery in Constrained Environments Flagger vs. Argo Rollouts Gagah Syuja Saka Abdullah; Rama Aria Megantara
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.v10i3.16201

Abstract

Cloud‑native progressive delivery orchestrators reduce deployment risk by automating canary deployment rollback procedures, dramatically reducing mean time to recover from deployment failures. However, existing research predominantly evaluates these tools in hyperscale environments, masking the transient computational overhead they introduce in resource‑constrained edge deployments. This study empirically evaluates and compares the automated incident mitigation latency and computational resource volatility of Flagger and Argo Rollouts within a strictly resource‑constrained Kubernetes environment. A low virtual central processing unit Kubernetes testbed was provisioned using Talos Linux with strict hypervisor‑level central processing unit pinning, simulating edge computing conditions. Deterministic fault injection spanning four fault classes, two workload runtimes, and two network topology configurations was executed across thirty trials. A Shapiro-Wilk normality assessment, Welch t-test, Mann-Whitney U test, Cohen's d, and 95% confidence intervals were applied to compare temporal and computational metrics. Memory utilization remained statically bounded, averaging 24.01 megabytes for Flagger and 35.47 megabytes for Argo Rollouts. Under standard fault conditions, neither orchestrator demonstrated a consistent temporal advantage. However, under memory exhaustion progressing to CrashLoopBackOff, Argo Rollouts recovered in a mean of 29.67 seconds against Flagger's 166.79 seconds, a statistically significant 5.6-fold degradation with a large effect size. Argo Rollouts sustained transient central processing unit surges of 159 to 168 millicpu against Flagger's bounded ceiling of 17 to 18 millicpu. Progressive delivery automation introduces non‑negligible and fault-type-dependent computational overhead in resource‑constrained environments. Flagger is recommended for strict resource predictability in threshold-breach environments, while Argo Rollouts is recommended where broader fault-type resilience is operationally critical.
Semantic Embedding and Profile-Based Ranking for Automated Reviewer Recommendation Azisya Luthfi Bintang; Ida Nurhaida
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.v10i3.16208

Abstract

Manual reviewer assignment in peer review is difficult to scale because submission volumes grow faster than editors can inspect reviewer expertise, and reviewer profiles shift across topics and time. Existing automated approaches often rely on keyword or lexical matching, which cannot capture semantic similarity, and few combine dense retrieval with interpretable reviewer evidence. This study develops and evaluates an explainable reviewer recommendation system using a BERT-first Reciprocal Rank Fusion semantic-profile backend. The system retrieves candidate evidence using BERT and SPECTER2 semantic representations, extracts candidate reviewers from retrieved paper authors, and ranks them using fused retrieval evidence supported by frequency, h-index, and recency signals. The expertise-scoring component was evaluated using the Stelmakh/OpenReview benchmark, while end-to-end recommendation was evaluated on an OpenAlex citation-based proxy dataset using a validation split for configuration selection and a held-out test split for final reporting. SPECTER2 max pooling achieved a weighted Kendall tau loss of 0.22 on the Stelmakh/OpenReview benchmark, consistent with the public SPECTER2 baseline. On the held-out test split, the selected BERT-first RRF semantic-profile backend achieved the highest NDCG@10 of 0.2621, significantly outperforming BERT, SPECTER2-only, BM25, TF-IDF, and the previous profile-heavy backend. These findings indicate that rank-level fusion of complementary dense retrieval signals can improve reviewer candidate ranking while retaining interpretable profile evidence for editorial workflows. The local evaluation uses citation-based proxy relevance rather than true editorial assignments, so further validation using human-annotated reviewer data is needed.
Comparative Evaluation of IndoBERT-Based Architectures for Imbalanced Indonesian News Title Classification Erwin Sirait; Juni Ismail
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.v10i3.16209

Abstract

Stacking multiple imbalance-mitigation techniques on top of a pretrained transformer is widely assumed to compound their individual benefits, yet rigorous component-wise evidence for this assumption remains scarce in the Indonesian text classification literature. Four classification architectures are compared in this work on a publicly available Indonesian news title corpus. The working set contains 27,266 short headlines, drawn as a 30% stratified subsample from a cleaned corpus of 90,891 headlines, spread over nine target categories with a class ratio of 13.29. Three reference architectures are constructed: an LSTM trained from scratch with Random Oversampling, a bidirectional LSTM augmented with additive attention, and a fine-tuned IndoBERT on the oversampled training partition. A fourth architecture extends IndoBERT through three additions, namely learned attention pooling over contextual token embeddings, focal modulation applied on top of the cross-entropy term, and minority-class paraphrasing via Indonesian–English–Indonesian back-translation. Every configuration is evaluated through stratified 5-fold cross-validation, paired t-tests with Bonferroni correction across three comparisons, and McNemar tests on the held-out partition. The fine-tuned IndoBERT with Random Oversampling alone reaches the highest macro F1 of 0.837. By contrast, the combined configuration drops to 0.799, and statistical verification confirms that the gap is systematic rather than attributable to fold-level variation. A component-wise ablation isolates focal modulation as the principal driver of the decline, because it disturbs an already-balanced training distribution. The principal outcome of this study is empirical evidence indicating that composing several imbalance-oriented techniques on a pretrained transformer can yield adverse interactions rather than cumulative gains.
Intelligent Fault Diagnosis in Multi-Setpoint Water Level Systems Using LSTM-Autoencoder Muhammad Giriarda Abrari; Fitria Suryatini; Hasbi Fajrul Hakim
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.v10i3.16211

Abstract

Fault detection in multi-setpoint industrial process control systems is complicated by the fact that normal sensor behavior shifts substantially across operating points, making conventional threshold-based alarms and single-condition models unreliable when setpoints change frequently. Recent studies on LSTM-Autoencoder for industrial anomaly detection have demonstrated promising results, yet most are evaluated under fixed operating conditions and do not examine how feature engineering choices affect detection performance across diverse setpoints. This study aims to determine whether physics-informed derived features improve LSTM-AE fault detection performance in a real-time multi-setpoint water level control system, and whether the improvement holds under practical deployment conditions. The proposed framework augments seven raw PLC sensor readings with three derived variables: delta flow, level error, and frequency-per-flow and applies a per-setpoint windowing strategy to prevent cross-setpoint data contamination during training. An ablation study compares the eleven-feature model against a seven-feature baseline under three labeling scenarios reflecting varying preprocessing quality. The eleven-feature model achieves an AUC of 1.0000 and F1-score of 0.9993 under onset-cut evaluation, and reduces the false positive rate from 18.48% to 15.21% under corrected labeling while maintaining perfect recall. Real-time validation across thirty fault injection experiments confirms a 100% detection rate with a mean latency of 6.37 ± 2.04 seconds, 38.2% faster than the baseline. These results confirm that derived features meaningfully improve both classification quality and temporal detection performance, though adaptive thresholding at high-variability setpoints remains an open challenge for future work.
Explainable Hybrid XGBoost Fuzzy Logic Model for Accurate Anemia Risk Classification Jepri Banjarnahor; Natasya Sigalingging; Rio Brelly Pasaribu; Yessi Sesilia Sitompul; Jogi Devrant Sibarani
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.v10i3.16216

Abstract

Anemia remains a major global health concern that impairs oxygen transport and contributes to fatigue, cognitive decline, reduced productivity, and severe clinical complications. Although machine learning has shown promise for automated anemia detection, multiclass classification remains challenging due to class imbalance, overlapping hematological characteristics, and limited model interpretability. This study proposes an explainable hybrid framework integrating Extreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and Fuzzy Logic to improve anemia risk classification and clinical decision support. The publicly available SKILICARSLAN dataset containing 15,300 anonymized patient records across five anemia-related classes was utilized. Seven hematological parameters, namely hemoglobin (HGB), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red blood cell count (RBC), hematocrit (HCT), and ferritin, were employed as predictive features. The workflow comprised data auditing, stratified train–test splitting, Synthetic Minority Oversampling Technique (SMOTE), hyperparameter optimization, multiclass XGBoost modeling, SHAP-based explainability analysis, and fuzzy risk interpretation. Experimental results demonstrated 82.94% accuracy, 87.27% weighted precision, 82.94% weighted recall, and 84.78% weighted F1-score, with a mean cross-validation F1-score of 87.00%. The model further achieved a macro-average ROC–AUC of 0.81 and a weighted-average ROC–AUC of 0.90, indicating robust discriminative performance despite class imbalance. SHAP analysis identified HGB, ferritin, and RBC-related variables as the most influential predictors. Moreover, the fuzzy logic layer enhanced interpretability by translating model outputs into clinically meaningful risk levels. These findings demonstrate the potential of explainable hybrid intelligence for transparent and reliable anemia screening and decision-support applications.
Application of Sentence-BERT Embeddings for Semantic Deduplication of Industrial Material Records Seno Hardijanto Purnomo; Agung Triayudi
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.v10i3.16220

Abstract

Industrial material master data in Enterprise Resource Planning (ERP) and Enterprise Asset Management (EAM) systems accumulates duplicate records that distort inventory, procurement, and analytics. Traditional deduplication relies on string-similarity measures such as Levenshtein, Jaro–Winkler, and TF-IDF cosine, which can struggle on catalogs mixing Indonesian and English terminology—e.g. Valve versus Keran—and on paraphrastic variants with different word order or abbreviation style. This study formally specifies a semantic deduplication pipeline that encodes material descriptions as sentence embeddings using Sentence-BERT (SBERT) and compares them via cosine similarity, then diagnostically evaluates the extent to which SBERT improves over those baselines. Following Design Science Research, the pipeline specifies normalisation, encoding with a multilingual paraphrase-tuned SBERT variant, and pairwise comparison within candidate sets produced by hybrid blocking; the diagnostic evaluation reports scores on the raw descriptions to expose baseline behaviour before domain-specific harmonisation. A sample of 291,000 records from two Indonesian industrial power plants motivates the design. On a diagnostic set of 100 record pairs derived from existing engineer-annotated duplicate markers, Jaro–Winkler achieves F1 = 0.925 (precision 1.000, recall 0.860) and SBERT achieves F1 = 0.875 (precision 0.913, recall 0.840) at threshold τ = 0.65; qualitative analysis of twelve representative pairs further reveals that SBERT excels on structural paraphrase (cosine 0.73–0.88 where character-level methods score below 0.50), while Jaro–Winkler remains competitive on abbreviation, unit-standard, and cross-language pairs—particularly those involving Indonesian technical vocabulary under-represented in the model’s training distribution. The central finding is that Sentence-BERT complements rather than replaces string baselines, which motivates future work on multi-channel architectures combining textual semantics with structural context.  
Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection Systems Reza Nismara; Rama Aria Megantara
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.v10i3.16227

Abstract

Performance estimates of intrusion detection models may vary depending on how the training and testing data are separated. Although random split is commonly used in IDS experiments, network traffic often follows time-dependent patterns that differ from one day to another. This study compares random split, single temporal split, and rolling temporal split to examine whether random evaluation produces overly optimistic performance estimates. The CIC-IDS2017 dataset was used because it contains network traffic collected across several days and includes benign as well as malicious activities. The evaluation involved five classical learning models: Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, and Linear Support Vector Machine. The dataset was prepared by combining daily traffic files, removing irrelevant and invalid features, converting labels into binary classes, and applying consistent preprocessing for all models. Performance was measured using accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, training time, and prediction time. The results show that random split produced very high scores, with several models reaching F1-scores close to 1.0. In contrast, temporal evaluation caused a clear performance decrease, with single temporal F1-scores ranging from approximately 0.60 to 0.71, while rolling temporal validation showed that model performance varied across different chronological testing periods. These findings indicate that random split may overestimate IDS model performance because similar traffic patterns can appear in both training and testing data. Therefore, time-aware evaluation provides a more realistic strategy for assessing IDS model generalization.
IndoBERT-Based Sentiment Analysis of Indonesian Social Media Discourse on AI-Generated Images Halvino Iqbal Nataprawira; Ida Nurhaida
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.v10i3.16242

Abstract

The rapid emergence of generative artificial intelligence has disrupted creative ecosystems, prompting widespread discourse across Indonesian social media. However, the exact sentiment structure of this public reaction remains empirically unmapped due to the contextual complexities of informal language. The objective of this research is to evaluate the efficacy of contextual language models by fine-tuning IndoBERT and benchmarking it against classical machine learning classifiers—including Complement Naive Bayes, Logistic Regression, and Support Vector Machine—for classifying social media sentiment. A multi-platform dataset comprising 2,981 Indonesian-language posts from X, Reddit, and YouTube was collected and manually annotated into positive, neutral, and negative classes. To address inherent class imbalance, Synthetic Minority Oversampling Technique was applied to classical models, while class-weighted loss and Masked Language Modeling augmentation were utilized for IndoBERT. Performance was evaluated using macro-averaged F1-score across five repeated stratified random splits. IndoBERT achieved a mean macro-F1 of 0.7131 ± 0.0180, outperforming the best classical baseline by approximately 0.12, demonstrating a pronounced advantage in resolving ambiguous neutral discourse. Negative sentiment heavily dominated the corpus at 61.8%, reflecting a prevailing critical stance toward AI-generated imagery concerning ethical and copyright issues. Furthermore, evaluation variance across random seeds exceeded variance from augmentation strategies, indicating test set composition is a major performance determinant. In conclusion, this study establishes a robust empirical baseline for Indonesian sentiment analysis, proving transformer architectures superior for nuanced public opinion mining.
Graph-Based Hybrid GNN-Transformer for Imbalanced Credit Card Fraud Detection Muhammad Bayu Wijaya Putra; Rinto Priambodo
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.v10i3.16245

Abstract

Credit card fraud detection faces two major challenges: severe class imbalance and the limited ability of conventional feature-based models to capture relational patterns among transactions. This study proposes a graph-based Hybrid GNN-Transformer architecture for imbalanced credit card fraud detection by integrating transaction-level relational learning through k-nearest neighbor graph construction and feature-interaction learning through multi-head self-attention. The novelty of this study lies in combining graph-based transaction modeling and Transformer-based feature interaction within a unified architecture. Using the selected graph configuration  and validation-based threshold tuning, the proposed model achieved 79.71% precision, 74.32% recall, 76.92% F1-score, 96.06% ROC-AUC, and 68.65% PR-AUC. Compared with Logistic Regression, Random Forest, and Gradient Boosting baselines, the hybrid model showed competitive fraud detection sensitivity, although the baseline classifiers still achieved stronger overall F1-score and PR-AUC. Ablation results show that the hybrid architecture improves minority-class detection compared with single-branch variants by combining relational transaction information from the GNN branch and feature-interaction information from the Transformer branch. These findings indicate that graph-based hybrid representation learning is a promising direction for imbalanced fraud detection, while further optimization is still required to improve precision-recall balance and competitiveness against strong feature-based baselines.
Fish Disease Classification Using MobileNetV3Large Transfer Learning and Fine-Tuning Dela Fifi Lusiana; Ellya Helmud; Rahmat Sulaiman
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.v10i3.16246

Abstract

Fish diseases represent a major challenge in the aquaculture industry as this phenomenon frequently leads to significant economic losses. Manual disease identification requires specialized expertise and is time-consuming in the field. Therefore, this study aims to implement the MobileNetV3Large Deep Learning architecture to automatically identify eight types of fish conditions. This research dataset utilizes 2,400 digital images distributed evenly across eight fish condition categories. Each class consists of 300 image samples, including Bacterial Red disease, Aeromoniasis, Bacterial gill disease, EUS Disease, Fungal diseases Saprolegniasis, Parasitic diseases, White tail disease, and a Healthy Fish group. The dataset was sourced from https://www.kaggle.com/datasets/irfanulhuda/fish-disease-detection-dataset. These conditions include bacterial, fungal, viral, and parasitic infections, as well as healthy fish conditions. The research methodology applies Transfer Learning techniques combined with Fine-Tuning optimization on the last 70 layers. The methodology applies a transfer learning strategy with a data split of 80% for training, 10% for validation, and 10% for testing. This step was taken to adapt the model's weights to the visual characteristics of the fish disease images. The process was evaluated using the Adam optimization function and the Categorical Cross-Entropy loss function. Experimental results demonstrate highly superior model performance on the test data. The MobileNetV3Large model successfully achieved a test accuracy of 92.92% with a loss value of 0.2099. Furthermore, evaluation through the Confusion Matrix and ROC curves yielded an average AUC value of 1.00 across the majority of classes. This figure indicates that the model possesses exceptionally high discrimination capacity and sensitivity. In conclusion, the computational efficiency of the MobileNetV3Large architecture makes this system a highly potential solution. Researchers can implement this model on mobile devices to assist fish farmers in diagnosing diseases quickly and accurately directly at the aquaculture sites

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