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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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Proposed Enterprise Architecture on System Fleet Management: PT. Integrasia Utama Wedha, Alessandro Benito Putra Bayu; Rahman, Ben; Hindarto, Djarot; Wedha, Bayu Yasa
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12387

Abstract

An information technology consulting firm that specializes in Global Positioning Systems provides fleet management services for many of its clients. The systems currently used by companies require more advanced modernization to ensure optimal service delivery. To overcome this challenge, a proposed enterprise architecture on system fleet management is presented in this paper. The proposed enterprise architecture is a comprehensive solution that includes the necessary hardware, software and operational processes to improve fleet management services. The proposed architecture is based on the Enterprise Architecture, which enables the integration of various systems and applications used by companies. The proposed architecture includes modules for vehicle tracking, fuel management, maintenance scheduling and driver performance monitoring. These modules work together to provide real-time data on fleet operations, enabling companies to make informed decisions regarding their fleet management services. The proposed architecture also incorporates an easy-to-use interface that simplifies the fleet management process and enhances customer satisfaction. The proposed system is scalable and easily adaptable to meet service requirements across multiple customers. In conclusion, the proposed enterprise architecture for system fleet management provides a comprehensive solution to the current challenges faced by companies as a corporate fleet service provider. The proposed architecture will improve service, reduce costs, and increase customer satisfaction.
TOGAF Framework For an AI-enabled Software House Crosley, Nathaniel; Indrajit, Richardus Eko; Dazki, Erick
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12390

Abstract

The integration of artificial intelligence (AI) in software development has revolutionized the industry, leading to faster and more accurate results. However, the implementation of AI requires a robust framework to ensure effective planning, design, implementation, and maintenance of AI-enabled software systems. The Open Group Architecture Framework (TOGAF) provides such a framework, enabling organizations to develop a structured and integrated approach to AI-enabled software development. In this journal, we present a case study of how a software house utilized the TOGAF framework to integrate AI in their software development processes. We discuss the challenges faced by the organization in the integration process and how the TOGAF framework provided a structured approach to overcome these challenges. We also highlight the benefits that the organization realized through the implementation of AI-enabled software systems. The case study presented in this journal demonstrates the applicability of the TOGAF framework in AI-enabled software development, and its potential to enhance the capabilities and competitiveness of software houses. The TOGAF framework provides a structured approach to the integration of AI in software development, ensuring that organizations can effectively leverage the benefits of AI while minimizing the associated risks and challenges.
Diagnostic on Car Internal Combustion Engine through Noise Sjah, William Surya; Rahman, Ben; Hindarto, Djarot; Wedha, Alessandro Benito Putra Bayu
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12392

Abstract

Internal Combustion Engines are known for their unique sound characteristics. Through these sound characteristics, an experienced car mechanic will be able to diagnose the type of engine damage just by listening to the sound. This reduces the need to disassemble components to pinpoint machine faults which also contributes to a significant reduction in overall repair time. The main aim of this paper is to build a process to identify faulty machines through engine noise analysis with visual data to determine machine faults at an early stage. By capturing various types of engine sounds, data visualization uses healthy engine sounds and broken engine sounds obtained from cars as well as various types of broken engine sounds that are usually found in vehicles. This audio data will be used in audio signal processing combined with a linear regression classification algorithm. Visualization data can distinguish various types of sounds that are commonly found in damaged or damaged engines such as clicks, ticks, knocks and other types of sounds to determine the types of damage that are usually found in internal combustion engines. The data used comes from Kaggle, which is public data which is widely used as general data for data science activities. Visually, data from vehicle engines can be seen from the data on which car brand is the best in terms of sound. The results using linear regression show the R-squared score (R^2) or also called the coefficient of determination reaching 91.95%.
Detects Damage Car Body using YOLO Deep Learning Algorithm Gustian, Yonathan Wijaya; Rahman, Ben; Hindarto, Djarot; Wedha, Alessandro Benito Putra Bayu
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12394

Abstract

This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.
Class Balancing Methods Comparison for Software Requirements Classification on Support Vector Machines Muhamad, Fachrul Pralienka Bani; Mulyani, Esti; Bunga, Munengsih Sari; Mushafa, Achmad Farhan
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12415

Abstract

Cost, time, and development effort can increase due to errors in analyzing functional and non-functional software requirements. To minimize these errors, previous research has tried to classify software requirements, especially non-functional requirements, on the PROMISE dataset using the Bag of Words (BoW) feature extraction and the Support Vector Machine (SVM) classification algorithm. On the other hand, the unbalanced distribution of class labels tends to decrease the evaluation result. Moreover, most software requirements are usually functional requirements. Therefore, there is a tendency for classifier models to classify test data as functional requirements. Previous research has performed class balancing on a dataset to handle unbalanced data. The study can achieve better classification evaluation results. Based on the previous research, this study proposes to combine the class balancing method and the SVM algorithm. K-fold cross-validation is used to optimize the training and test data to be more consistent in developing the SVM model. Tests were carried out on the value of K in k-fold, i.e., 5, 10, and 15. Results are measured by accuracy, f1-score, precision, and recall. The Public Requirements (PURE) dataset has been used in this research. Results show that SVM with class balancing can classify software requirements more accurately than SVM without class balancing. Random Over Sampling is the class balancing method with the highest evaluation score for classifying software requirements on SVM. The results showed an improvement in the average value of accuracy, f1 score, precision, and recall in SVM by 22.07%, 19.67%, 17.73%, and 19.67%, respectively.
Ranking Universities in Medan Using WoE and IV in Weighting of MAUT Fadilah, Putri Maulidina; Perdana, Adidtya; Farhana, Nurul Ain
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12532

Abstract

Throughout Indonesia, including Medan, the popularity of a university can be specified by the ranking of a university. There are five assessment components which important for ranking universities under the Ministry of Education, Culture, Research, Technology and Higher Education, such as the Quality of Human Resources, Institutional Quality, Quality of Student Activities, Quality of Research and Community Service, and Quality of Innovation. Multi Attribute Utility Theory (MAUT) is one of the decision support system (DSS) methods that can be used to calculate campus rankings. However, the researcher were determining the weight of MAUT method based on their preferences and it was subjective. Weight of Evidence (WoE) can be used to assign a numerical score to each category of independent variables that describes the strength of its relationship to the target variable. In selecting the independent variable that is most informative and relevant in predicting the target variable, Information Value (IV) can be used. Based on the results, college B is the most popular university out of ten universities in Medan, with the highest evaluation value 0.796296296 using MAUT method and 0.923794719 using MAUT method with WoE & IV. The last position is J college with the lowest evaluation value 0.1666666667 for MAUT method and 0.02540176 for MAUT method with WoE & IV. The weighting of MAUT method with WoE and IV produces more optimal evaluation value than the the original MAUT method.
Analysis of Network Attached Storage Performance with NFS Protocol in Integrated Business Start-Up Barovih, Guntoro; Surahmat, Surahmat; Febrianty, Febrianty
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12417

Abstract

The need for data storage due to individual and group computing work is increasing, so storage media with reliable capabilities and good performance are needed. The way that can be solution is shared storage, which is using one storage to be used together. One of the commonly used shared storage is NAS (Network Attached Storage). IBS (Integrated Business Start Up) uses NAS as a backup storage medium that will store important data that supports the performance of IBS services as a whole, so it is necessary to analyze to find out how the performance of the NAS system uses the NFS Protocol as the basis of the service. whereas in this study the focus of performance testing was carried out by looking at the results of measuring packet loss, throughput, CPU usage, and memory usage on the NAS server used. the performance level of the system used is running well, as seen from the results of the throughput test of 1.1 GB, packet loss of around 0%, CPU usage of 0.5%, and memory usage of 382.5Mb. The results of this performance test are also by Telecommunications standards and Internet Protocol Harmonization Through Networks
Application of Market Basket Analysis on Beauty Clinic to Increasing Customer’s Buying Decision Dio, Rafi; Dermawan, Aulia Agung; Putera, Dimas Akmarul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12421

Abstract

Beauty care and the need for cosmetics have become the lifestyle of modern women, especially in big cities. Public awareness to look beautiful makes modern women competing to take care of themselves to be more beautiful. The body care industry in Indonesia continues to grow. The growth has reached 6% and is predicted to continue to grow along with the high concern of Indonesian women in caring for their skin. To win the competition, companies need to know the market and consumer situation. One strategy that can be applied by the company is to use a promotional or advertising strategy. This research was conducted at the Ariana Audy beauty clinic in 2022 with the aim of identifying customer buying patterns which will then be used as reference material in the development of promotional menus for products and services offered by the beauty clinic. The approach used to design components on the promotional menu is Market Basket Analysis by applying the fp-growth algorithm using rapid miner software. Market basket analysis is focused on finding relationships between products based on customer purchases. The market basket analysis conducted resulted in 5 association rules that define consumer purchasing patterns for products and services provided by the Ariana Audy clinic. Through the 5 association rules formed, 3 promotional menus were produced, namely menu 1 consisting of baby skin crystal and oxy blue cream, menu 2 consisting of brightening cream products and sunscreen brightening, and menu 3 consisting of oxy jet peel and photodynamic therapy.
Crime of theft prediction using Machine Learning K-Nearest Neighbour Algorithm at Polresta Bandar Lampung Hermawan, Febry; Prianggono, Jarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12422

Abstract

The era of the industrial revolution 4.0 is a time where cyber and physical technology collaborate. This study aims to predict the types of theft crimes that occur in the Bandar Lampung Police area with the K-Nearest Neighbor algorithm, evaluate the prediction results and profiling the prediction results carried out by Bandar Lampung Police investigators in efforts to prevent and handle criminal acts of theft in the jurisdiction of the Bandar Lampung Police Lampung. The approach was carried out using the quantitative method of the K-Nearest Neighbor algorithm using the Rapidminer application by utilizing 1671 police report data from the Bandar Lampung Police and a questionnaire survey method conducted on 49 police investigators from the Bandar Lampung Police. Data collection techniques are carried out in a valid and reliable manner as a support for predictive validity. Based on the results of the classification and questionnaire, it was found that the majority of victims of the crime of theft were adult men who did not have a job and lived in urban areas. It was found that the majority of thefts occurred in parking lots in urban areas on Monday morning where the perpetrators used tools and targeted moving objects by tampering with locks which caused losses of around 10-50 million rupiah. This type of theft is theft by weighting (CURAT) which applies to Article 363 of the Criminal Code. The prediction results show that the neighboring value (K) and the distribution ratio of training and testing data are K=3 and 7:3, respectively. Predictions using K values and data sharing ratios show a high level of accuracy, namely 99. 20%. The results of the questionnaire show results that are in line with the results of the classification with an accuracy rate of the actual data of 75. 7122%. So by increasing the understanding skills of Bandar Lampung Police investigators using technology to predict the crime of theft, the number of theft crimes can be reduced.
Zero Knowledge Proof for SNAP (Standar Nasional OPEN API Pembayaran) in Indonesia Ramadhoni, Moehammad; Handri Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12423

Abstract

SNAP (Standar Nasional OPEN API Pembayaran) is an implementation of open banking for encouraging digital transformation in the banking industry. SNAP was submitted by several sub-working groups formed jointly by ASPI and the Bank of Indonesia. In the document Pedoman Tata Kelola (Bank of Indonesia, n.d.), there is already a customer data protection mechanism between the bank, the owner of Open API, and the user of Open API. However, there is no data protection process carried out by consumers so third parties, that use the Open API of the bank, do not need to know the customer's data. Based on the web3 protocol, users can store data and transmit only in encrypted form which can only be opened by calculating the data with a pre-agreed smart contract. Banks can work like a decentralized network on web3, where the process of calculating proof and witness is carried out by the bank. Proof and witness are calculated using a zero-knowledge proof protocol, making it difficult to duplicate. For this reason, we propose a new architecture using smart contracts between banks and customers using the ZK-SNARK method. Therefore, there is no significant performance difference between using ZK-SNARK and without ZK-SNARK in the API call process.

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