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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 54 Documents
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Search results for , issue "Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024" : 54 Documents clear
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.
Integrated MCDM-AHP and MABAC for Selection Head of Branch Offices Akmaludin, Akmaludin; Suriyanto, Adhi Dharma; Iriadi, Nandang; Widianto, Kudiantoro
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.v8i4.13669

Abstract

Leadership changes are very urgent in maintaining organizational stability. A good relay can build significant strength in carrying out organizational operational activities, of course this must be done with good selection. The purpose of this writing is to provide a consistent picture of the selection of branch heads in carrying out business competition which is measured based on the competencies possessed by the selected employees. The barometer is determined based on eight criteria as an assessment that is declared objective by the leadership, namely critical thinking, communication, analyzing, creative and innovation, leadership, adaptation, cooperation, and public speaking. The method used will be implemented in an integrated manner from the two MCDM-AHP methods and the MABAC method. These two methods have similar applications to the selection process. MCDM-AHP is used to select eight criteria as determinants of weighting and the MABAC method is used to determine the ranking process assessment for integrated decision making. The results obtained based on the weighted matrices of the branch head office selection process were measurably obtained, namely that the first priority was held by A11 with a weight of 1,406. The results of the integrity of both methods can provide evidence of decision support for the branch head selection process consistently with optimal results. The ranking system can be regulated and utilized for the purposes of selecting leaders to be placed in other positions.
Implementation Of Technology Towards The Merdeka Curricullum Doing Diagnostic Assessment For Student with Autism Spectrum Disorder In Preschool Level Suryadi, Yeanny; Yus, Anita; Milfayetti, Sri
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.v8i4.13862

Abstract

This research aims to developing an effective and applicable diagnostic assessment instrument that has been prepared based on the requirements of competency standards for graduates at the preschool institute. The instrument functions is to separate mild and moderate levels of the autism spectrum, for students with learning disabilities resembling autism spectrum symptoms in early childhood. This research used R and D methode from Borg and Gall with result is this application product containing a 23-item questionnaires that has been validated by material, language and media experts. Subject of this research is teachers of preschool institutions, and the objek is the instrument of diagnostic assessment wich researcher build. The practicality test results of this instrument have a percentage level of 92.52% in the 'very practical' category, with a validity test level of 84%, on the Likert scale showing the instrument is 'very feasible'.
Analytical Study Forecasting Students Using Random Forest and Linear Regression Algorithms Nurdin, Muhammad; Fauziah, Fauziah
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.v8i4.13886

Abstract

Forecasting new student admissions essential for higher education institutions as it helps them plan for staffing and budgetary needs. Accurate predictions are difficult due to factors like economic conditions, government policies, and University competition. This study aims to analysis forecasting at Nasional university using Random Forest and Linear Regression algorithms. By examining historical admission data, the research seeks to identify key factors influencing the number of accepted students. Methodology involves collecting data from past admissions and applying both Random Forest and Linear Regression to compare their performance. Preliminary results, based on parameters such as application form purchases from 2015 to 2023, form prices, accreditation, and leading study programs, suggest that Random Forest offers more stable and realistic predictions. Analysis for MAE, MSE, RMSE, MAPE, MAD suggests that Linear Regression is more accurate for this data. predicts closer to actual values with lower overall errors. This makes Linear Regression preferable as it provides more reliable predictions with less deviation compared to Random Forest. Looking at admissions forecasts for the next 5 years, Random Forest predicts a steady decrease from 4224 in 2024 to 4129 in 2028. In contrast, Linear Regression suggests a stable trend with slight annual dips, going from 4954 in 2024 to 4941 in 2028. Therefore, Linear Regression is a more stable and realistic choice compared to Random Forest for this forecasting task in this research.
Evaluation of Cluster Models for Creating Profiles of Home Buyers Dewi, Made Dhanita Listra Prashanti; Wasito, Ito
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.v8i4.13888

Abstract

The property industry in Indonesia is currently a dynamic and continuously evolving field, in line with rapid economic growth and urbanization. Shifts in lifestyle patterns, infrastructure development, and changes in government policies have had a significant impact on how properties are marketed in Indonesia. With a growing population and increasing purchasing power, the Indonesian property market is becoming more complex. Therefore, strategies are needed to segment consumer groups for effective marketing in the housing sector. This research will delve deeper into consumer segmentation in home selection, a technique that divides consumer diversity into distinct groups based on characteristics and behavior. By using an extensive dataset involving demographic data such as location, age, gender, occupation, and many other variables, clustering algorithms can uncover complex patterns to determine consumer segments in their home selection. The algorithms to be used for this study are K-Means clustering, the Gaussian Mixture model, and Hierarchical clustering. By using these three data clustering models, we can determine which algorithm produces the most ideal results for customer profiling. The results demonstrate that the K-Means algorithm outperforms the others in accurately identifying distinct consumer segments, hence producing customer profiles. Therefore, customer profiling can also be used by the marketing division as a tool to aid in promotions in order to better understand their target audience, hence creating a successful marketing campaign.
Rainfall Monitoring Using Aloptama Automatic Rain Gauge And The Network Development Life Cycle Method Nugroho, Kristiawan; Afandi , Afandi; Rokhayadi, Wakhid; Budiarto, Indri; Hermawan, Taufan
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.v8i4.13908

Abstract

Examining the role of rainfall data management in monitoring and reducing natural disasters. Between the observation post and the coordinating office of the Central Java Meteorology, Climatology and Geophysics Agency, there are problems in managing rainfall data. To increase the accuracy and efficiency of rainfall monitoring, the Central Java BMKG Coordinator has used various platforms that are considered very good, such as Grafana, Node-RED, Xampp, and MQTT. Previous research has shown that the use of the Automatic Rain Gauge (ARG) and the Network Development Life Cycle (NDLC) method is very effective in creating an accurate and reliable rainfall monitoring system. This research uses the NDLC model, which consists of analysis, design, prototype simulation, implementation, monitoring and management stages. It is hoped that the research results will help improve visual monitoring of rainfall in local areas and increase understanding of rainfall patterns, flood prediction, water resource management and mitigation measures. This will serve as a reference for governments and institutions working together to make decisions to avoid catastrophic climate change.
Performance Analysis of AODV and DSDV Routing Protocols for UDP Communication in VANET Bintoro, ketut Bayu Yogha; Marchenko, Michael; Saputra, Rofi Chandra; Syahputra, Ade
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.v8i4.13938

Abstract

In high-mobility Vehicular Ad hoc Networks (VANETs), maintaining a low Packet Loss Ratio and a high Packet Delivery Ratio (PDR) under UDP communication is crucial. This study compares the performance of Ad hoc On-Demand Distance Vector (AODV) and Destination-Sequenced Distance-Vector (DSDV) routing protocols in vehicular communications and networking using Network Simulator 3 (NS3) simulations. The research employs a simulation-based approach, leveraging NS3 and SUMO to analyze these protocols across different VANET scenarios, including free flow, steady flow, and traffic jams over varying time intervals (300 to 700 seconds). Our findings demonstrate that AODV outperforms DSDV. AODV maintained an average Packet Loss Ratio of 98% and achieved higher throughput, while DSDV experienced higher packet loss and lower throughput. Additionally, AODV exhibited lower end-to-end delay and a higher Packet Delivery Ratio compared to DSDV. These results indicate that AODV is better suited for UDP communication in VANETs, offering lower packet loss, higher throughput, and reduced delays. The study further emphasizes that AODV is preferable for UDP communication in VANETs due to its superior performance metrics. There is potential for further research in vehicular communications, such as integrating advanced hybrid routing protocols and exploring the effects of different traffic densities, vehicle types, and real-world environmental conditions. By investigating these factors, future studies can enhance the reliability and efficiency of VANET communications, contributing to the advancement of intelligent transportation systems.
A Comparative Study of Alternative Automatic Labeling Using AI Assistant Julianto, Indri Tri; Kurniadi, Dede; Balilo Jr, Benedicto B.; Rohman, Fauza
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.v8i4.13950

Abstract

The development of AI assistants has become increasingly sophisticated, as evidenced by their growing adoption in assisting humans with various tasks. In particular, AI assistants have demonstrated potential in the field of sentiment analysis, where they can automate the labeling of text data. Traditionally, this labeling process has been performed manually by humans or using tools like the VADER Lexicon. This study is imperative to evaluate the performance of AI Assistants in sentiment labeling, as compared to traditional human-based labeling and the application of the VADER sentiment analysis algorithm. The methodology involves comparing the labeling results of Gemini and You AI with those of human labeling and VADER. Performance is evaluated using the Naive Bayes and K- Nearest Neighbour algorithms, and K-Fold Cross Validation is employed for evaluation. The results indicate that the performance of both AI assistants can closely approximate the performance of human labeling. Gemini's best accuracy is achieved with the k-NN algorithm at 53.71%, while You AI's best accuracy is achieved with the Naive Bayes algorithm at 48.30%. These results are close to the accuracy of human labeling (61.12%) using the k-NN algorithm and VADER (54.29%) using the Naive Bayes algorithm. This suggests that AI assistants can serve as an alternative for text data labeling, as the differences in performance are not statistically significant.
Comparison Of Machine Learning Algorithms On Stunting Detection For 'Centing' Mobile Application To Prevent Stunting Sabilillah, Ferris Tita; Sari, Christy Atika; Abiyyi, Ryandhika Bintang; Susanto, Ajib
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.v8i4.13967

Abstract

Stunting is a growth disorder caused by chronic undernutrition, with long-term impacts on child health and development. In Indonesia, the prevalence of stunting reached 31.8% in children under five years old in 2018, indicating an urgent need for effective interventions. In an effort to address this issue, we developed a mobile application called Centing (Cegah Stunting) that utilizes machine learning for early detection and prevention of stunting. In this study, we compare the performance of four machine learning algorithms Logistic Regression, Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP) in detecting children's nutritional status based on a dataset from Kaggle with 121 thousand data and four main features: age, gender, height, and nutritional status. The experimental results show that SVM with RBF kernel and CNN achieved the highest accuracy of 98%, while Logistic Regression and MLP achieved 76% and 97% accuracy respectively. SVM with RBF kernel was chosen as the best model due to its high accuracy and efficiency in computation time. These findings suggest that the Centing application, with the implementation of SVM RBF, has significant potential in early detection and prevention of stunting, and makes an important contribution to improving child health in Indonesia.
An IT Governance Analysis in Interior Contracting Industry: A COBIT 2019 Approach Susatyo, R Wahyu Indra; Indrajit, Eko; Dazki, Erick
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.v8i4.13978

Abstract

The very rapid development of technology is currently having an impact on every industry, which must adapt by carrying out technological transformation to survive and have added value for customers. Many businesses, including interior contractors, use a variety of hardware and software, as well as information systems, to streamline their business processes. Under these conditions, the importance of strong IT governance to ensure that the implementation of IT investments continues to provide great benefits for the company's progress has been considered a top priority. This research explores how IT governance functions in this industry using COBIT 2019, a leading evaluation framework. The main areas of COBIT 2019 will be used to assess a company's IT capabilities. This study focused on an interior contractor company in Serpong, Indonesia, which was already using enterprise resource planning (ERP) and project management software. The analysis identified 12 out of 40 domains that need improvement to achieve certain target levels. These agreed targets aim to improve IT capabilities, such as reducing dependence on external vendors for system development and creating clear standards for managing technological change. Despite these recommendations, further investigation revealed a gap between the desired and current conditions. This research proposes solutions to bridge this gap, including achieving greater IT system independence and establishing clear guidelines for navigating technological advances.

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