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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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Determination of MSMEs Business Feasibility Decisions using the Profile Matching Method Maulidah, Salsa Bila Jihan; Sudipa, I Gede Iwan; Fittryani, Yuri Prima; Widiartha, Komang Kurniawan; Winatha, Komang Redy
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13638

Abstract

Micro and Medium Enterprises (MSMEs) in Bali contribute to the local economy. When operating a business, it is crucial to evaluate the viability of MSME enterprises to enhance the calibre of business offerings and services. Nevertheless, the lack of competence to establish the parameters or criteria for evaluating the viability of a firm poses challenges for MSMEs in decision-making. This study presents a business feasibility assessment model utilising the Profile Matching method to aid in resolving issues and supporting Micro, Small, and Medium Enterprises (MSMEs) in making informed decisions for the long-term viability of their businesses. This study examines the feasibility of MSME businesses using the Profile Matching method. The method involves assessing 13 criteria and selecting from 10 alternatives. The process includes determining initial and target values, weighting criteria, grouping core and secondary factors, calculating total values, and ranking. The final results indicate which MSMEs are feasible and which ones require further evaluation. According to the calculations using the Profile Matching method, MSME 5 has a value of 27.80, indicating its feasibility.
Comparison of MAGIQ, MABAC, MARCOS, and MOORA Methods in Multi-Criteria Problems Muni, Gede Dharma Sahasra; Sudipa, I Gede Iwan; Meinarni, Ni Putu Suci; Wiguna, I Komang Arya Ganda; Sandhiyasa, I Made Subrata
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13639

Abstract

Determining the best alternative from many criteria is one of the core problems in decision making, both routine and non-routine problems. One of them is in the problem of determining egg suppliers. Eggs are one of the basic needs of the community so that the demand for eggs is always increasing, this makes the emergence of many egg agents in distributing and fulfilling needs. Selective and careful selection is needed in order to get a supplier that meets the desired expectations. Problems then arise in the selection of egg suppliers that are not in accordance with the expectations of the manager. In determining egg suppliers that have been carried out by UD Taluh Subur, only by means of a simple comparison between several factors such as price, production quantity, and quality without considering other factors. In addition to this, business managers have limited knowledge in statistical and business decision making. To optimize the supplier selection process, a Decision Support System can be used to help provide recommendations for selecting prospective suppliers of fixed eggs. Based on the situation of decision makers who have limited knowledge in statistical decision making, the MAGIQ method is suitable for weighting. To provide a more accurate ranking, additional methods such as the MABAC, MARCOS, and MOORA methods are used. The purpose of this research is to focus on which method is most recommended for the case study faced in the research based on the analysis results of the sensitivity test. The results of the sensitivity test show that the MAGIQ-MABAC method has the highest value of 4.42737%, then the MAGIQ-MOORA method with a value of 2.34415% and the MAGIQ-MARCOS method with a value of 0.45729%.
Integrating TOGAF and Big Data for Digital Transformation: Case Study on the Lending Industry Yudhistira, Andreas; Fajar, Ahmad Nurul
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13648

Abstract

In today’s digital era, the strategic integration of enterprise architecture frameworks with Big Data technologies is crucial in driving digital transformation, especially within the lending industry. This research aims to identify and analyze how The Open Group Architecture Framework (TOGAF) can be integrated with Big Data to enhance innovation, operational efficiency, and decision-making in the lending sector. This study examines Indonesian financial institutions using qualitative case studies, exploring the intricate practices, challenges, and benefits of the combination of TOGAF and Big Data. The qualitative methodology focuses on in-depth interviews and document analysis to gather contextual insights into the implementation dynamics and impacts of these technologies. Findings indicate that integrating TOGAF and Big Data not only streamlines workflows but also significantly enhances data security and risk management—critical elements in the lending industry. A vital outcome of this study is the development of a robust integration model that serves as a blueprint for companies in similar sectors to navigate their digital transformation journeys. Additionally, this research provides strategic recommendations to overcome integration and implementation challenges. These guidelines facilitate the transition to a more cohesive and strengthened digital architecture, equipping financial institutions to manage the complexities of modern digital economies effectively. Ultimately, this study delivers a comprehensive framework that enriches theoretical understanding and offers practical insights for effective technology integration in financial services.
Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm Melyani, Sri; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13651

Abstract

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.
Leveraging Enterprise Architecture to Empower KOMINFO's Business Core Operations: A PMO Perspective Purawidjaja, Ratna Amalia; Chudra, Glenny; Indrajit, Eko; Dazki, Erick; Yohannis, Alfa
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13656

Abstract

The Sky Bridge (Tol Langit) Program is an Indonesian government’s strategic project aimed at digital transformation in the 3T regions (Tertinggal, Terdepan, Terluar - Underdeveloped, Frontline, Outermost). It requires thorough planning and integrated management for its implementation. A specialized unit with a helicopter view perspective is needed to ensure and oversee the alignment of processes. This important role is managed by the Project Management Office (PMO). One of the challenges PMO faces in ensuring an end-to-end process alignment is identifying the appropriate digital resources to support the process. This is where the Enterprise Architecture (EA) framework plays a crucial role as a blueprint for the organization's digital landscape. This reference helps map out existing data, applications, and business processes. Having this blueprint allows PMO to have a holistic view and make targeted decisions. EA also helps identify existing applications that can be integrated with new programs, avoiding unnecessary duplication. The use of ArchiMate, a language for enterprise architecture modeling, assists PMOs in planning digital transformations considering all aspects - business needs, applications, and technology. In short, a well-defined EA framework empowers PMOs to navigate the complexities of digital transformation in the telecommunications sector to ensure the successful implementation of the Sky Bridge Program.
Analysis Of Improving Service Quality At The Ssctelkom Surabaya Institute Of Technology Using The Lean Six Sigma Method Rosyid, Ahmad Nur; Zunaidi, Rizqa Amelia; Dimyati, Aufar Fikri
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13657

Abstract

Student Service Centre (SSC) is a center that provides services and information to active students at InstitutTeknologi Telkom Surabaya (ITTS). ITTS provides SSC with academic, student, and faculty services to support its students' academic and non-academic development. One of the main services provided by SSC is the Active Certificate. However, SSC users need help obtaining the letter. This study aims to measure the quality of Active Certificate services using the Lean Six Sigma method and provide recommendations for improvement. The results showed that the quality of SSC services still needs to be improved, with a DPMO value of 289686, a sigma value of 2.07, and the highest negative gap in the Responsiveness dimension. The total Non Value Added time was obtained at 10 hours 31 minutes, and the total Value Added time was 4 hours 8 minutes. Proposed improvements include the deployment of QR Codes to provide information on document requirements and using Value Stream Mapping (VSM) to reduce the time spent on non-value added. Lean Six Sigma method can reduce the total value-added time and improve the efficiency of SSC services.
Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time Muliani, Sonia Sri; Sihombing, Volvo; Munthe, Ibnu Rasyid
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13658

Abstract

Almost all universities in Indonesia face the problem of a low number of students graduating on time. This will affect higher education accreditation. For this reason, universities must pay attention to the timely graduation of their students. The way that can be taken is to predict students' graduation on time. This research aims to predict students' timely graduations using a combination of exploratory data analysis and artificial neural networks. Exploratory data analysis is used to study the relationship between features that influence students' on-time graduation, while artificial neural networks are used to predict on-time graduation. This research goes through method stages, starting with determining the dataset, exploratory data analysis, data preprocessing, dividing training and test data, and applying artificial neural networks. From the research, it was found that Work features and GPS features greatly influence graduation on time. Students who study while working are less likely to graduate on time compared to students who do not work. Students who have an average GPS above 3.00 for eight consecutive semesters will find it easier to graduate on time. Meanwhile, Age and Gender features have no effect on graduating on time. With a percentage of 50% training data and 50% test data, epoch 100, and learning rate 0.001, the best network model was obtained to predict graduation on time with an accuracy rate of 69.84%. The research results also show that the amount of test data and the learning rate can influence the level of accuracy. Meanwhile, the number of epochs does not affect the level of accuracy.
Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method Ariani, Yayuk; Masrizal, Masrizal; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13659

Abstract

This student graduation rate research focuses on analyzing academic performance with the main aim of identifying and distinguishing between students who graduate on time and those who graduate late. The application of data mining techniques in this research uses the neural network method, which is expected to offer deeper insight into the factors that influence students' graduation times. In this study, the neural network method was used to classify graduation data from 150 students. The results of this analysis were very encouraging, with 149 students identified as graduating on time and one student graduating late. The level of accuracy achieved in this classification is 98%, which shows the effectiveness of the neural network method in processing and analyzing academic data. These results confirm that neural networks are a powerful and reliable tool for predictive tasks like this. The successful use of neural networks in this study also proves their potential in broader educational applications, particularly in optimizing educational and intervention strategies. By understanding the characteristics of students who graduate on time versus those who graduate late, educators and administrators can design more effective programs to support student success. This is important not only to improve graduation statistics, but also to improve the overall educational experience for students.
Application of MCDM-AHP and EDAS Methods for Selection of the Best Residential Locations Areas Akmaludin, Akmaludin; Sihombing, Erene Gernaria; Rinawati, Rinawati; Arisawati, Ester; Handayanna, Prisma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13661

Abstract

The population density has led to an expansion of the area where people live. This opportunity is exploited by housing developers to build many locations for the development of residential areas. The purpose of writing this paper is to provide proper consideration in housing selection which can be seen from various parameters as selection criteria. The method support that can be used in residential selection is the collaboration of the MCDM-AHP and EDAS methods. This method can be used as a recommendation against the concept of multi-criteria. The more criteria used, the higher the level of difficulty to support decision making. With the collaboration of the MDCM-AHP method, it can be used to provide an assessment of multi-criteria that have optimal values, while the EDAS method will be used as a strength in evaluating the selection of alternatives based on positive and negative distances for different types of criteria through normalized values. Determination of the weighting value of the criteria is obtained through the iteration stages using the mathematical algebra matrices method and proven by expert choice apps. The decision support results obtained provide a ranking value with the first priority being PR06 with an accumulative weight of 0.552 followed by the second and third ranks respectively PR04 and PR05 with a weight of 0.545 and 0.522 respectively. Thus supporting decision making with the recommendation of the MCDM-AHP and EDAS method collaboration can provide an optimal assessment of residential selection in a detailed and accurate manner.
A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production Aditya, Nanda; Munthe, Ibnu Rasyid; Sihombing, Volvo
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13666

Abstract

For countries with large populations, such as Indonesia, food security is a very important issue. The majority of Indonesia's population depends on rice as their main food, and paddy is one of the most widely cultivated food commodities. The very good and accurate national paddy production prediction results really support decisions regarding national paddy production targets for the coming period. Therefore, to ensure supply and price stability, paddy availability must be predicted. Many studies have used machine learning to predict crop yields. By learning important patterns and relationships from input data, machine learning can combine the advantages of other methods to make better predictions of paddy yields. The aim of this research is to conduct a comparative analysis between three machine learning algorithms, namely, random forest, decision tree, and k-nearest neighbors, in predicting paddy production. To determine which algorithm is the best, a model evaluation is carried out using the coefficient of determination (R2-score), mean absolute error (MAE), and mean squared error (MSE). This research goes through methodological stages, starting from collecting datasets, data preprocessing, training and testing split datasets, applying algorithms, and evaluating the model. From this research, results were obtained for the random forest algorithm with an R2-score of 82.38%, MAE of 261726.20, and MSE of 2.19495E+11. For the decision tree, the R2-score was 79.62%, MAE was 323257.99, and MSE was 2.49304E+11. Meanwhile, k-nearest neighbors obtained an R2-score of 76.25%, MAE of 318433.42, and MSE of 2.90577E+11. The results of this research show that the random forest algorithm is the best for predicting paddy production because it obtains a larger R2-score as well as smaller MAE and MSE results.

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