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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
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ISO 27001 As Information Security Solution In Society 5.0 Era: Systematic Literature Review Nurbojatmiko, Nurbojatmiko; Karimiyah, Muhammad Sharhan Khatami; Asnadi, Nur Muhammad; Anisyah, Rifka
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14448

Abstract

In the era of Society 5.0, information security is an important issue along with the increasing use of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data. ISO 27001 acts as a globally recognized standard framework for managing information security. The ISO 27001 standard provides a systematic framework for identifying, assessing, and managing information security risks so as to ensure the integrity, confidentiality, and availability of data in an organization. This research aims to evaluate the implementation of ISO 27001 as an information security solution in the Society 5.0 era through a systematic literature review. Using the Systematic Literature Review (SLR) method, this research collects and analyzes relevant literature to identify benefits, challenges, and recommendations related to the application of ISO 27001 in an era of increasingly integrated technology. The results showed that the implementation of ISO 27001 in the Society 5.0 era proved to make a significant contribution in improving organizational information security. This is done through a PDCA (Plan-Do-Check-Act) approach that integrates information security policies into business processes, strengthens risk management, technology infrastructure, and human resource competencies. In conclusion, the implementation of ISO 27001 in the Society 5.0 era not only improves information security, but also supports the achievement of operational efficiency and organizational sustainability amid rapid technological developments.
Assessment Of IDW And ANN On Daily Rainfall Data Imputation in Semarang Central Java Suharmanto, Eko Taufiq; Supriyanto, Aji
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14452

Abstract

Rainfall plays a critical role in the global water and energy cycle, influencing surface water availability and recharge processes both spatially and temporally. Traditional rainfall data collection using ombrometers provides accurate live data, but often faces the challenge of missing data due to equipment failure or transmission, especially in agencies such as BMKG. This problem of missing data greatly impacts hydrological analysis and requires an effective data recovery process through imputation. This study aims to assess the accuracy of rainfall data imputation techniques using the Inverse Distance Weighting (IDW) and Artificial Neural Network (ANN) methods. In this study, we utilize data from 31 observation stations in Semarang City for more than three decades. The findings show that the spatial distribution of rainfall is variable and exhibits a cyclic pattern despite fluctuations. The ANN model performed very well in overcoming missing data, especially in the dry season with an RMSE of 0.9489 and a coefficient of determination (R2) of 0.9926. By demonstrating the superiority of the ANN model in accurately predicting rainfall, this study offers an effective approach to improve the quality of BMKG climate data. This is expected to support disaster mitigation decisions and sustainable development planning. This approach demonstrates that the selection of an appropriate method is critical for accurate and reliable analysis of rainfall time series data. In addition to making an academic contribution, these results also provide an alternative imputation method for various time series.
Effectiveness of Bi-GRU and FastText in Sentiment Analysis of Shopee App Reviews Rahmanda, Rayhan Fadhil; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14474

Abstract

E-commerce is proof of evolution in the economic field due to its flexibility to shop for various necessities of life anytime and anywhere. Shopee is one of the e-commerce platforms in demand by people from varied circles in Indonesia. Multiple reviews are shed publicly by Shopee users on the Google Play Store regarding shopping experiences, which can be positive or negative. This condition affects the decision of other users to shop at Shopee, thus impacting the increase or decrease in profits from Shopee itself. Therefore, user sentiment analysis is needed as a form of effort to maintain user trust in Shopee. This research aims to build a system to classify the sentiment of Shopee application users through reviews in the Google Play Store by utilizing the Bidirectional Gated Recurrent Unit (Bi-GRU) deep learning model. The dataset contains 9,716 reviews, including 3,937 positive and 5,779 negative sentiments. Several test scenarios were conducted to achieve the highest peak of performance, utilizing TF-IDF feature extraction, FastText feature expansion, and optimization using the Cuckoo Search Algorithm. Additionally, SMOTE resampling was utilized to correct the dataset’s uneven distribution. The combined test scenarios mentioned significantly improved the accuracy by 1.03% and F1-Score by 1.04% from the baseline, with the highest accuracy reaching 90.48% and the highest F1-Score of 90.16%.
Comparison of K-Nearest Neighbor, Naive Bayes, Random Forest Algorithms for Obesity Prediction Andani, Mia; Triloka, Joko; Irianto, Suhendro Yusuf; Nugroho, Handoyo Widi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14478

Abstract

Obesity is a global health problem that continues to increase and has serious impacts on physical and mental health. This research aims to predict a person's obesity status based on certain attributes using the K-Nearest Neighbor (KNN), Naive Bayes, and Random Forest algorithms. The dataset used was taken from the Kaggle platform with 2,111 data and 16 attributes, including gender, age, weight, height, frequency of consumption of high-calorie foods, physical activity, and water and vegetable consumption patterns. The research process follows the data mining stages, including business understanding, data understanding, data preparation, modeling, evaluation, and documentation. Experiments were carried out using RapidMiner with a cross-validation technique using 10 folds to measure overall model performance. The research results show that the Random Forest algorithm performs best in predicting obesity status compared to K-NN and Naive Bayes. Model evaluation using accuracy, precision, recall, and F1-score metrics shows significant results in distinguishing obesity categories. It is hoped that this research can contribute to the development of a machine learning-based health prediction system that can be used to support decision-making in the prevention and management of obesity.
Sentiment Analysis On Indonesian Tweets about the 2024 Election Sembiring, Alfan Ramadhan; Dewa, Chandra Kusuma
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14481

Abstract

This study investigates public sentiment on Indonesian Twitter regarding the 2024 General Election, employing machine learning and deep learning techniques, including Naïve Bayes, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The dataset was collected using a Tweet Harvest method with the keyword "Pemilu" and underwent preprocessing steps such as case folding, removal of symbols and URLs, stopword elimination, and tokenization to ensure data quality. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were applied to assess the models' effectiveness. Naïve Bayes achieved the highest accuracy of 64%, followed by SVM at 63%, LSTM at 60%, and GRU at 57%. The findings indicate that traditional models like Naïve Bayes and SVM perform effectively on smaller datasets with structured features, while deep learning models excel in capturing complex sequential dependencies. However, deep learning methods exhibited overfitting tendencies, indicating the need for better regularization and optimization techniques. Furthermore, it emphasizes the potential of integrating traditional algorithms with advanced methods to enhance sentiment classification accuracy and generalizability across diverse datasets.
The Enterprise Architecture for Enhanced Mutual Fund Service Integration in Digital Channel in the Banking Industry Akbar, Achmad Fathurrazi; Indrajit, Eko; Dazki, Erick
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14496

Abstract

The investment awareness among the Indonesian populace is rising, particularly during the Covid-19 pandemic. Mutual funds are the most prevalent investment vehicle due to their accessibility, requiring minimal cash, and offering competitive returns, as they are managed by seasoned investment professionals. This is due to the relative ease of investing in this instrument with accessible capital and competitive returns, as fund management is conducted by seasoned investment managers. Conversely, the rapid advancement and proliferation of technology present new challenges for firms in the financial sector, particularly in banking and financial technology, as they strive to innovate and provide convenient services that cater to the needs of customers and investors. This research seeks to develop Enterprise Architecture for the integration of Mutual Fund services into digital channels via mobile banking, utilizing the TOGAF framework as the foundational design approach, supplemented by SWOT analysis to assess the strategic position and market potential. The research employed a qualitative methodology utilizing the ArchiMate program to illustrate diagrams in Enterprise Architecture (EA) across the public banking sector. The research indicated that architectural design can facilitate improved data access and enhance adaptability to technology and market advancements, hence removing inefficiencies and streamlining the review process. In conclusion, the application of EA in the incorporation of mutual fund services into the digital banking platform will optimize the company's performance processes to attain objectives, while also enhancing agility in evaluating technological risks to facilitate the monitoring, metrics, and analysis of information technology, thereby ensuring the achievement of business goals.
Comparison of C4.5 & Random Forest Based on AdaBoost For Determining Loan Eligibility Customer Funds Lenny, Lenny; Violyn, Violyn; Ridwan, Achmad; Yennimar, Yennimar
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14499

Abstract

This research discusses the comparison between two data mining algorithms, namely Decision Tree C4.5 and Random Forest based on AdaBoost, in determining the creditworthiness of customer funds. The main objective of this research is to evaluate and compare the performance of the two algorithms in predicting loan eligibility based on customer data. Algorithm performance is measured using accuracy, precision, recall, and misclassification error metrics. The research results show that the AdaBoost-based Random Forest is superior with an accuracy of 78.86%, recall of 98.75%, and the lowest misclassification error of 21.14%. Meanwhile, Decision Tree C4.5 provides lower performance than AdaBoost-based Random Forest. This research recommends further exploration of other algorithms, such as Support Vector Machine (SVM) and Neural Networks, to obtain more optimal results in determining customer loan eligibility.
The Efficiency of Machine Learning Techniques in Strengthening Defenses Against DDoS Attacks, Such as Random Forest, Logistic Regression, and Neural Networks Z, Syauqii Fayyadh Hilal; Rushendra
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14502

Abstract

Distributed Denial of Service (DDoS) attacks are one of the most common cybersecurity concerns brought on by the quick development of digital technology. By flooding servers with too many requests, these assaults interfere with online services, highlighting the necessity of strong detection systems. Using the well-known CIC-DDoS2019 dataset, this study explores the use of machine learning algorithms—Random Forest (RF), Logistic Regression (LR), and Neural Networks (NN)—to improve DDoS assault detection. A comprehensive preprocessing procedure that comprised feature selection, normalization, and duplication removal was applied to dataset in order to ensuring optimal algorithm performance. With an accuracy of 97% on the entire test dataset and 99.13% on the training and validation datasets, RF showed exceptional performance. While NN successfully managed intricate data patterns, attaining an accuracy of roughly 94%, LR demonstrated impressive results with an accuracy of 98.65%. Because of its ensemble method, which minimizes overfitting and improves model generalization, the RF algorithm performed better than the others. This study highlights how machine learning may be used to solve practical cybersecurity issues by offering insightful information about how to optimize algorithms for real-time DDoS detection. The results improve the stability and resilience of digital infrastructures by aiding in the creation of effective intrusion detection systems. Future research can explore integrating advanced neural network architectures and hybrid methods to further improve detection rates and adaptability to evolving cyber threats.
Food Recipe Recommendation System with Content-Based Filtering and Collaborative Filtering Methods Widiantari, Ni Putu Triska; Suarjaya, I Made Agus Dwi; Rusjayanthi, Ni Kadek Dwi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14778

Abstract

Cooking your own food at home is a good step toward reducing fast food consumption. Fast food increases the risk of dangerous diseases. The diversity of recipe information available on the internet makes it difficult to choose recipes that match user preferences. Mobile technology can help with this by recommending recipes that better suit users' eating habits. This makes the transition to a healthier diet easier. Therefore, in this study, a recommendation system was developed that can recommend recipes based on the preferences of Android users. Two main recommendation methods are used in this study: content-based filtering and collaborative filtering. Using cosine similarity, a content-based recommendation system identifies the proximity between a recipe for food and its related context. The history of user comments on recipes serves as implicit feedback for the collaborative recommendation algorithm. This eliminates the need for explicit evaluations, such as ratings. This recommendation system generates recommendations in the form of the top ten food recipes with an evaluation matrix, referred to as NDCG@k and Hit-Ratio@k. The tests revealed that a content-based filtering technique may produce helpful recommendations, with the highest similarity score of 0.41 for the entry "chocolate cake that you can easily make at home." Meanwhile, in the collaborative filtering method using the Neural Collaborative Filtering (NCF) approach, the system shows consistent performance improvements, with the MAP@10 value increasing from 0.705 to 0.767 and the NDCG@10 from 0.78 to 0.83 after 10 training epochs. Keywords: Recommendation systems; content-based filtering; neural collaborative filtering; cosine similarity; implicit feedback
Hybrid Genetic Algorithm for Dynamic Portfolio Optimization Problems Nufus, Sarah Ayatun; Sutarman, Sutarman; Herawati, Elvina
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14868

Abstract

Dynamic portfolio optimization is a complex problem due to continuous changes in market conditions, demanding algorithms capable of effective adaptation. Genetic Algorithms (GA) are often used for optimization problems but may face limitations in convergence speed and solution precision. This research aims to develop and evaluate a Hybrid Genetic Algorithm (HGA) that integrates GA with the Hill Climbing local search method, and to compare its performance against standard GA in solving dynamic portfolio optimization problems with the objective of maximizing the Sharpe Ratio. A series of simulation-based experiments were conducted by varying key algorithmic and dynamic environment parameters. Simulation results indicate that HGA generally has significant potential to improve performance compared to standard GA. Consistently, HGA successfully achieved superior solution quality, both in terms of Offline Performance Solution Quality and Overall Best Fitness. Regarding robustness to dynamic changes, HGA also demonstrated a smaller impact from performance degradation and a more promising recovery capability after market environment changes. Although HGA's superiority in convergence speed is not always absolute and the implementation of Hill Climbing adds to the computational time per generation, the improvement in solution quality and robustness offered in many configurations can be considered a worthwhile trade-off, especially for complex dynamic portfolio optimization problems. These findings support the hypo that hybridizing GA with local search can provide a positive contribution, noting that careful parameter tuning is crucial for maximizing HGA's potential.

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