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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 1,196 Documents
 116   117   118   119   120 
Hybrid GA–MILP Model for Community Building Retrofit Planning Towards Carbon Neutrality Aisyah, Chairini; Mestika, Adhita Nugraha
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15315

Abstract

Retrofitting community buildings is a key pathway toward carbon neutrality, yet most existing retrofit planning models lack adaptability to the diverse urban contexts of the Global South, where building typologies are heterogeneous and resources limited. Addressing this gap requires approaches that are both computationally efficient and context-sensitive. This study introduces a hybrid optimization framework that integrates Genetic Algorithm (GA) and Mixed-Integer Linear Programming (MILP) to tackle the multidimensional multiple-choice knapsack problem inherent in retrofit planning. The GA explores high-level system configurations, while MILP ensures precise component-level selection under budget and technical constraints. Compared to conventional single-method approaches, the hybrid GA–MILP achieves near-optimal emission reduction with reduced computation time and greater feasibility, offering a balanced trade-off between performance and scalability. Importantly, the framework demonstrates that medium-cost retrofit strategies provide the most cost-effective path to scalable carbon savings, making it highly relevant for resource-constrained urban environments. By situating retrofit planning within the realities of the Global South, this study advances methodological innovation and provides a robust decision-support tool aligned with sustainable development goals for inclusive and low-carbon urban futures.
A Disaster-Aware Traffic Assignment Model: Comparative Evaluation of Frank-Wolfe and Simulated Annealing Algorithms Suranto, Suranto; Siregar, Afrizal Rhamadan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15316

Abstract

Traffic assignment under disaster-induced disruptions poses unique challenges, as traditional models often overlook sudden capacity loss and unpredictable demand. This study introduces a disaster-aware Traffic Assignment Problem (TAP) model that integrates a modified Bureau of Public Roads (BPR) cost function, explicitly accounting for effective capacity changes during disasters. The Frank-Wolfe (FW) algorithm is applied to solve the model, chosen for its scalability and convergence properties. A comparative analysis with Simulated Annealing (SA) is also performed across various network sizes and disruption scenarios. Results show that FW consistently delivers near-optimal flow distributions with lower travel costs and faster convergence. While SA exhibits higher variability under tight capacity constraints, FW demonstrates robust stability, particularly in medium to large networks under moderate to severe disruptions. Flow patterns from FW highlight adaptive traffic redistribution, effectively bypassing congested or blocked links. This study is the first to systematically compare Frank-Wolfe and Simulated Annealing under disaster-induced TAP conditions with capacity degradation. Contributions include (1) formulating a disaster-aware TAP model, (2) applying FW to disrupted networks, and (3) validating through structured simulations. Findings suggest that FW offers a reliable optimization tool for real-time traffic reallocation, supporting resilient urban mobility in emergencies.
Cervical Cancer Classification Using Multi-Directional GLCM Shape-Texture Features in LBC Surmayanti, Surmayanti; Nozomi, Irohito; Aldi, Febri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15318

Abstract

Alsalatie, M., Alquran, H., Mustafa, W. A., Zyout, A., Alqudah, A. M., Kaifi, R., & Qudsieh, S. (2023). A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images. Diagnostics, 13(17), 2762. https://doi.org/10.3390/diagnostics13172762 Arbyn, M., Weiderpass, E., Bruni, L., Sanjosé, S. de, Saraiya, M., Ferlay, J., & Bray, F. (2020). Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. The Lancet Global Health, 8(2), e191–e203. https://doi.org/10.1016/S2214-109X(19)30482-6 Attallah, O. (2023). Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors. Applied Sciences, 13(3), 1916. https://doi.org/10.3390/app13031916 Chaddad, A., & Tanougast, C. (2017). Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer. Analytical Cellular Pathology, 2017(1), 8428102. https://doi.org/10.1155/2017/8428102 Díaz del Arco, C., & Saiz Robles, A. (2024). Advancements in Cytological Techniques in Cancer. In Handbook of Cancer and Immunology (pp. 1–46). Springer, Cham. https://doi.org/10.1007/978-3-030-80962-1_385-1 Garg, M., & Dhiman, G. (2021). A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Computing and Applications, 33(4), 1311–1328. https://doi.org/10.1007/s00521-020-05017-z Huang, X., Liu, X., & Zhang, L. (2014). A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation. Remote Sensing, 6(9), 8424–8445. https://doi.org/10.3390/rs6098424 Ikeda, K., Oboshi, W., Hashimoto, Y., Komene, T., Yamaguchi, Y., Sato, S., Maruyama, S., Furukawa, N., Sakabe, N., & Nagata, K. (2021). Characterizing the Effect of Processing Technique and Solution Type on Cytomorphology Using Liquid-Based Cytology. https://dx.doi.org/10.1159/000519335   Kaur, H., Sharma, R., & Kaur, J. (2025). Comparison of deep transfer learning models for classification of cervical cancer from pap smear images. Scientific Reports, 15(1), 3945. https://doi.org/10.1038/s41598-024-74531-0 Merlina, N., Noersasongko, E., Nurtantio, P., Soeleman, M. A., Riana, D., & Hadianti, S. (2021). Detecting the Width of Pap Smear Cytoplasm Image Based on GLCM Feature. In Y.-D. Zhang, T. Senjyu, C. SO–IN, & A. Joshi (Eds.), Smart Trends in Computing and Communications: Proceedings of SmartCom 2020 (pp. 231–239). Springer. https://doi.org/10.1007/978-981-15-5224-3_22 Mishra, G. A., Pimple, S. A., & Shastri, S. S. (2021). An overview of prevention and early detection of cervical cancers. Indian Journal of Medical and Paediatric Oncology, 32, 125–132. Plissiti, M. E., Nikou, C., & Charchanti, A. (2011). Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recognition Letters, 32(6), 838–853. https://doi.org/10.1016/j.patrec.2011.01.008 Raga Permana, Z. Z., & Setiawan, A. W. (2024). Classification of Cervical Intraepithelial Neoplasia Based on Combination of GLCM and L*a*b* on Colposcopy Image Using Machine Learning. 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 035–040. https://doi.org/10.1109/ICAIIC60209.2024.10463256 Rastogi, P., Khanna, K., & Singh, V. (2023, August 8). Classification of single‐cell cervical pap smear images using EfficientNet—Rastogi—2023—Expert Systems—Wiley Online Library. https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13418 Singh, D., Vignat, J., Lorenzoni, V., Eslahi, M., Ginsburg, O., Lauby-Secretan, B., Arbyn, M., Basu, P., Bray, F., & Vaccarella, S. (2023). Global estimates of incidence and mortality of cervical cancer in 2020: A baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. The Lancet Global Health, 11(2), e197–e206. https://doi.org/10.1016/S2214-109X(22)00501-0 Singh, T. G., & Karthik, B. (2023). Accurate Cervical Tumor Cell Segmentation and Classification from Overlapping Clumps in Pap Smear Images. In S. N. Singh, S. Mahanta, & Y. J. Singh (Eds.), Proceedings of the NIELIT’s International Conference on Communication, Electronics and Digital Technology (pp. 659–673). Springer Nature. https://doi.org/10.1007/978-981-99-1699-3_46 Strander, B., Andersson-Ellström, A., Milsom, I., Rådberg, T., & Ryd, W. (2007). Liquid-based cytology versus conventional Papanicolaou smear in an organized screening program. Cancer Cytopathology, 111(5), 285–291. https://doi.org/10.1002/cncr.22953 Wahidin, M., Febrianti, R., Susanty, F., & Hasanah, S. R. (2022, March 1). Twelve Years Implementation of Cervical and Breast Cancer Screening Program in Indonesia—PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC9360967/
Comparative Analysis of DNA Sequence Alignment Algorithms in SARS-CoV-2 Edi, Edi; Robet, Robet; Harahap, Nurhayati
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15323

Abstract

Sequence alignment is fundamental in bioinformatics, with Smith-Waterman (local) and Needleman-Wunsch (global) algorithms widely applied. However, comparative analyses on highly similar viral genomes such as SARS-CoV-2 remain scarce. This study systematically evaluated both algorithms using the first 5,000 nucleotides of two SARS-CoV-2 genomes (29,903 and 29,684 nt) under four parameter configurations: standard, low gap penalty, high gap penalty, and high match reward. Performance was assessed through alignment score, sequence identity, gap distribution, execution time, and parameter sensitivity. Both algorithms produced identical sequence identity (97.80%), with 4,943 matches out of 5,054 positions. Smith-Waterman consistently yielded higher alignment scores (12.6-112 points advantage), while Needleman-Wunsch was substantially faster (0.7752 vs 3.9014 s), showing 5.03 times greater computational efficiency. These findings indicate that both methods are reliable for highly similar viral sequences, with a trade-off between scoring precision and computational speed. This study provides the first parameter-sensitive comparison for full SARS-CoV02 genomes, emphasizing how parameter tuning can influence performance outcomes. A key limitation is that the analysis was restricted to the first 5,000 nucleotides, which may not capture variability across the complete genome.
Optimization of Machine Learning Models in Student Graduation Prediction Systems Using Ensemble Learning with PSO and SMOTE Hamdani, Hamdani; Susanti, Susanti; Lathifah, Lathifah; Anam, M. Khairul; Pradipta, Rahman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15335

Abstract

The timely graduation of students is a key metric in evaluating the academic effectiveness of higher education institutions. However, accurately identifying students at risk of delayed graduation remains challenging due to imbalanced data distributions and the instability of single-model prediction approaches. This study proposes an optimized ensemble-based machine learning system for predicting on-time graduation among university students. The model integrates C4.5, K-Nearest Neighbor (KNN), and Random Forest algorithms through a hard voting classifier, which is further optimized using Particle Swarm Optimization (PSO) to determine the most effective weighting configuration. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is implemented, ensuring balanced representation between timely and delayed graduates. A dataset of 809 student academic records from Universitas Sains dan Teknologi Indonesia (USTI) was used, and performance was evaluated using 5-fold cross-validation. The proposed ensemble model achieved an average accuracy of 93.70%, a precision of 0.94, a recall of 0.93, and an F1-score of 0.94, outperforming each individual classifier. These results confirm that the combination of ensemble learning, PSO-based optimization, and data balancing effectively improves both accuracy and model stability. The findings highlight the system’s potential as a reliable decision-support tool for educational institutions to anticipate delayed graduations and improve academic supervision strategies.
Comparative Analysis of SDLC and R&D Methods in System Development: A Case Study of Integrity Zone Management System Perdana, Adidtya; Dewi, Sri; Farhana, Nurul Ain; Febrian, Didi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15337

Abstract

This paper presents a comprehensive comparative analysis of Software Development Life Cycle (SDLC) and Research and Development (R&D) methodologies in system development, with a specific focus on their application to the Integrity Zone Management Information System. Through a systematic literature review and an in-depth case study analysis, this research examines the fundamental differences, strengths, and limitations of each methodology. The study identifies key dimensions for comparison including flexibility, risk management, innovation potential, documentation requirements, and stakeholder engagement. Findings reveal that while SDLC methodologies provide structure and predictability for well-defined requirements, R&D approaches offer greater innovation capacity for exploratory projects. The Integrity Zone Management Information System case demonstrates how hybrid approaches can leverage the strengths of both methodologies and improved stakeholder satisfaction by 94%. This research contributes to the theoretical understanding of system development methodologies and provides practical guidance for selecting appropriate approaches based on project context, objectives, and constraints. The paper concludes with recommendations for practitioners and suggestions for future research in methodological integration and adaptation.
A Hybrid Three-Term Conjugate Gradient Algorithm for Solving Unconstrained Optimization Problems Thanoon, Radhwan Basem
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15339

Abstract

In this paper, we introduce a novel hybrid three-term conjugate gradient algorithm referred to as THREER, designed to address unconstrained optimization problems. The proposed approach integrates the -parameter introduced by Al-Neami with an additional third component derived from a rate-based vector ​, resulting in a search direction that preserves and enhances key characteristics of traditional conjugate gradient methods. A rigorous theoretical investigation establishes that the algorithm satisfies the sufficient descent condition regardless of the line search technique employed. Furthermore, the global convergence of the method is guaranteed under commonly accepted assumptions. Extensive numerical experiments conducted on large-scale benchmark problems reveal that THREER achieves superior performance when compared with several classical algorithms, particularly in terms of iteration count and function evaluations. These results highlight the algorithm’s robustness, efficiency, and potential for solving high-dimensional optimization tasks.
Implementation and Evaluation of Artificial Neural Networks for Product Sales Prediction at Basmalah Stores Akkad, Muhammad Iqbal; Hariyadi, Mokhamad Amin; Almais, Agung Teguh Wibowo
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15341

Abstract

This study aims to develop a product sales prediction system for Toko Basmalah located in the Malang Regency area by utilizing the Artificial Neural Network (ANN) algorithm. A quantitative approach was employed, using time series sales data obtained from the Marketing Division of PT. Sidogiri Pandu Utama for the period of January 1, 2023, to December 31, 2024. The research stages included data collection and preprocessing, normalization using the min-max scaling technique, data splitting into training and testing sets, ANN model experimentation with various data compositions, and performance evaluation based on the Mean Squared Error (MSE) metric. The experiments were conducted five times using the Kaggle Editor platform. The results showed that the ANN-E model with a specific architecture achieved the lowest MSE value of 34.38%, making it the most optimal model for sales prediction. These findings are expected to assist in making better decisions regarding stock management, sales planning, and business strategies in the retail environment.
Integration Of Pca And K-Means Clustering For Staple Food Segmentation In Support Of National Food Policy Sipayung, Sardo; Hasugian, Paska Marto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15343

Abstract

This study aims to develop cross-provincial staple-food segmentation by integrating Principal Component Analysis (PCA) and K-Means to support policy formation. The dataset comprises 2023 staple-food consumption for 34 Indonesian provinces across six indicators from BPS/SUSENAS. All indicators were standardized using z-score, reduced via PCA, and the resulting component scores were used as inputs to K-Means. Three components (PC1–PC3) explained 73.86% of the variance and captured shifts between sweet/animal-based vs. plant foods, fatty or animal-based grains, and the energy contribution of fat. The optimal number of clusters was determined as k = 3, yielding Silhouette = 0.466 and DBI = 0.733, indicating sufficiently compact and well-separated groups. The results reveal three segments: the first group consists of 11 provinces that are predominantly plant-based with low sugar and low animal-based consumption; the second group includes 13 provinces characterized by high animal-based and high-fat consumption; and the third group comprises 10 provinces with low-fat diets and fresh plant-based consumption. Stability checks on initialization and a leave-one-feature-out procedure confirmed consistent assignments. This fills an empirical gap: to our knowledge, no prior research integrates PCA with K-Means for cross-provincial staple-food segmentation in Indonesia while also reporting internal validation. Practically, the study provides operational segmentation to support food-security interventions moving beyond composite indices toward actionable targeting for production support, supply/price stabilization, and improved nutritional access thereby reframing IKP/FSVA from index-ranking to evidence-based segmentation.
Addressing Class Imbalance in Stunting Classification Using SMOTE Enhanced Random Forest Belferik, Ronald; Sinaga, Frans Mikael; Ferawaty, Ferawaty; Manullang, Mangasa A.S.; Sinaga, Tetti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15349

Abstract

Stunting is a chronic nutritional problem that poses serious long-term effects on children’s health, including impaired physical growth, delayed cognitive development, and reduced productivity in adulthood. Early and accurate detection of stunting is therefore essential to support effective public health interventions and targeted policy implementation. However, one of the central challenges in developing machine learning models for this purpose is the presence of class imbalance in health-related datasets. Such imbalance frequently leads to biased classifiers that perform well on majority classes but fail to identify minority categories, reducing the overall reliability of the system. To overcome this issue, the present study utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the distribution of classes in a dataset containing 110,000 records. A Random Forest algorithm was then employed as the base classifier, with hyperparameter optimization carried out using the Optuna framework to ensure robustness and generalizability. The experimental results demonstrate that the combined application of SMOTE and Optuna significantly improved classification performance, producing the highest Macro Area Under the Curve (AUC) of 0.9972. This outstanding score indicates the model’s superior ability to distinguish nutritional status categories across both majority and minority classes. The study concludes that addressing data imbalance through oversampling is a fundamental methodological step in constructing fair and effective machine learning systems for stunting detection, ultimately contributing to improved health outcomes and evidence-based policy design.

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