Pub Date : 2024-01-10DOI: 10.33395/sinkron.v9i1.13335
Nur Azis, Purwo Agus Sucipto, Agus Herwanto, Era Sari Munthe, Dola Irwanto
After the covid-19 pandemic outbreak and the high uncertainty index during the covid-19 pandemic. The business world is experiencing a huge impact in addition to the sluggish interest of buyers is also limited in its movement. On this occasion, the researcher intends to provide an overview that can help business people, especially in purchasing goods that are useful for filling the stock of goods in the warehouse. To get maximum results and minimum error rate. Researchers use the Apriori Algorithm in analyzing stock items and use the Tanagra version 1.4 application. Research data used the sales history of the past 1 year here the data used is between May 2022 and April 2023. With a total itemset of 375. But after applying the Golden Rule (threshold), there are only 10 products with sales reaching 1623 items. This research produces a final ordered association based on the minimum support and minimum confidence that has been determined, namely 12 rules with a combination of 2 itemsets with a confidence value of 100%.
{"title":"Analysis of Goods Stock Using the Apriori Algorithm to Aid Goods Purchase Decision Making","authors":"Nur Azis, Purwo Agus Sucipto, Agus Herwanto, Era Sari Munthe, Dola Irwanto","doi":"10.33395/sinkron.v9i1.13335","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13335","url":null,"abstract":"After the covid-19 pandemic outbreak and the high uncertainty index during the covid-19 pandemic. The business world is experiencing a huge impact in addition to the sluggish interest of buyers is also limited in its movement. On this occasion, the researcher intends to provide an overview that can help business people, especially in purchasing goods that are useful for filling the stock of goods in the warehouse. To get maximum results and minimum error rate. Researchers use the Apriori Algorithm in analyzing stock items and use the Tanagra version 1.4 application. Research data used the sales history of the past 1 year here the data used is between May 2022 and April 2023. With a total itemset of 375. But after applying the Golden Rule (threshold), there are only 10 products with sales reaching 1623 items. This research produces a final ordered association based on the minimum support and minimum confidence that has been determined, namely 12 rules with a combination of 2 itemsets with a confidence value of 100%.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":" 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139627424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.33395/sinkron.v9i1.13260
Djarot Hindarto
Using qualitative methodology, this study investigates the effects that the digital revolution in corporate architecture has had on the apparel industry. In this article, digital technologies, like AI, big data analytics, and the Internet of Things, are the main points of emphasis. They have revolutionized business and operational practices, as well as marketing strategies in the sector. According to the findings of this study, the implementation of advanced technologies significantly contributes to the enhancement of operational efficiency, the introduction of innovative products, and the enhancement of the competitiveness of businesses. The research also highlights the impact that digital transformation has had on sustainability and personalization in the clothing production industry. It demonstrates that adopting an enterprise architecture that is aligned with digital technologies not only increases operational efficiency but also strengthens innovative and competitive capacity. Furthermore, this research acknowledges the significance of ethically responsible and transparent business practices in this digital era, as well as taking into consideration the effects that digital transformation has on society and the environment. The findings of this study provide industry stakeholders with a strategic perspective that can be utilized in the formulation of adaptive business strategies, the exploitation of opportunities, and the facing of challenges in the ever-changing business environment that is associated with the digital era
{"title":"Building the Future of the Apparel Industry: The Digital Revolution in Enterprise Architecture","authors":"Djarot Hindarto","doi":"10.33395/sinkron.v9i1.13260","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13260","url":null,"abstract":"Using qualitative methodology, this study investigates the effects that the digital revolution in corporate architecture has had on the apparel industry. In this article, digital technologies, like AI, big data analytics, and the Internet of Things, are the main points of emphasis. They have revolutionized business and operational practices, as well as marketing strategies in the sector. According to the findings of this study, the implementation of advanced technologies significantly contributes to the enhancement of operational efficiency, the introduction of innovative products, and the enhancement of the competitiveness of businesses. The research also highlights the impact that digital transformation has had on sustainability and personalization in the clothing production industry. It demonstrates that adopting an enterprise architecture that is aligned with digital technologies not only increases operational efficiency but also strengthens innovative and competitive capacity. Furthermore, this research acknowledges the significance of ethically responsible and transparent business practices in this digital era, as well as taking into consideration the effects that digital transformation has on society and the environment. The findings of this study provide industry stakeholders with a strategic perspective that can be utilized in the formulation of adaptive business strategies, the exploitation of opportunities, and the facing of challenges in the ever-changing business environment that is associated with the digital era","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139627524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.33395/sinkron.v9i1.13293
Hanapi Hasan, Asmar Yulastri, G. Ganefri, Tansa Trisna Astono Putri, Rizkayeni Marta
Entrepreneurs are critical to a country's economic progress and job creation. Few people felt schools have much to offer with business a generation ago. Students are expected to be an entrepreneur as the outcome of the course. The goal of this study is building a model to predict students' future employment, particularly in the field of entrepreneurship, using big data analysis and data mining. Various educational institutions can use data mining methodologies to identify hidden patterns in data contained in databases. The feature selection technique was utilised in this study to select and assess the significance of each element. The model was built using the final parameters determined by the feature selection technique (Correlation Based Feature Selection). Using the 10-fold cross validations for training and testing dataset distribution, the Naïve Bayes classifier was used to forecast the students' future of work. The dataset for the study was gathered from a student's performance report at Universitas Negeri Medan's engineering department. The effectiveness of using feature selection algorithms was compared to the effectiveness of not using feature selection algorithms, and the results are discussed. According to the findings of this study, the accuracy of Naïve Bayes with Correlation Based Feature Selection is 87.4%, which is higher than the model that did not use any feature selection. It was also discovered that the overall accuracy of the Correlation Based Feature Selection and Naïve Bayes Classifier models appears to be higher than that of the other treatments.
{"title":"Prediction of Student Entrepreneurship Future Work based on Entrepreneurship Course using the Naïve Bayes Classifier Model","authors":"Hanapi Hasan, Asmar Yulastri, G. Ganefri, Tansa Trisna Astono Putri, Rizkayeni Marta","doi":"10.33395/sinkron.v9i1.13293","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13293","url":null,"abstract":"Entrepreneurs are critical to a country's economic progress and job creation. Few people felt schools have much to offer with business a generation ago. Students are expected to be an entrepreneur as the outcome of the course. The goal of this study is building a model to predict students' future employment, particularly in the field of entrepreneurship, using big data analysis and data mining. Various educational institutions can use data mining methodologies to identify hidden patterns in data contained in databases. The feature selection technique was utilised in this study to select and assess the significance of each element. The model was built using the final parameters determined by the feature selection technique (Correlation Based Feature Selection). Using the 10-fold cross validations for training and testing dataset distribution, the Naïve Bayes classifier was used to forecast the students' future of work. The dataset for the study was gathered from a student's performance report at Universitas Negeri Medan's engineering department. The effectiveness of using feature selection algorithms was compared to the effectiveness of not using feature selection algorithms, and the results are discussed. According to the findings of this study, the accuracy of Naïve Bayes with Correlation Based Feature Selection is 87.4%, which is higher than the model that did not use any feature selection. It was also discovered that the overall accuracy of the Correlation Based Feature Selection and Naïve Bayes Classifier models appears to be higher than that of the other treatments.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"6 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-09DOI: 10.33395/sinkron.v9i1.13247
Djarot Hindarto
To guarantee consumer safety and meet quality expectations, accurate detection of meat quality is a critical component of the food industry. The objective of this research endeavor is to assess and contrast the fresh meat detection capabilities of two distinct artificial neural network architectures, denoted as Dense201 and VGG19. Automated systems that can identify vital qualities in fresh meat, including color, texture, and cleanliness, have become feasible due to the development of image processing technology. For this reason, however, there are still few direct comparisons between various architectures of artificial neural networks, particularly VGG19 and Dense201. Comparing and contrasting the performance of both models in identifying the quality of meat from visual images, this study attempts to fill this void. Utilizing a vast dataset containing a variety of fresh meats exhibiting substantial visible variations constituted the research methodology. The assessment was conducted by examining the efficacy of both models in determining the quality of meat using established performance metrics, including accuracy, precision, recall, and F1-score. Regarding the detection of fresh meat, it is anticipated that the findings of this study will offer a comprehensive understanding of the benefits and drawbacks associated with every artificial neural network architecture. Contributing to a greater comprehension of the application of precise and efficient meat detection technology, this study also furnishes the food industry with a foundation for determining which model best meets the requirements of meat quality detection on a larger production scale.
为保证消费者安全并满足质量期望，准确检测肉类质量是食品工业的关键组成部分。这项研究的目的是评估和对比两种不同的人工神经网络架构（Dense201 和 VGG19）的鲜肉检测能力。随着图像处理技术的发展，能够识别鲜肉重要品质（包括颜色、质地和清洁度）的自动化系统已经变得可行。然而，由于这个原因，各种人工神经网络架构之间的直接比较仍然很少，特别是 VGG19 和 Dense201。本研究试图通过比较和对比这两种模型在从视觉图像中识别肉质方面的性能来填补这一空白。研究方法是利用一个包含各种新鲜肉类的庞大数据集，这些肉类表现出明显的差异。通过使用既定的性能指标，包括准确度、精确度、召回率和 F1 分数，对两种模型在确定肉类质量方面的功效进行了评估。关于鲜肉检测，预计本研究的结果将有助于全面了解与每种人工神经网络架构相关的优点和缺点。这项研究有助于更好地理解精确、高效的肉类检测技术的应用，也为食品工业提供了一个基础，以确定哪种模型最能满足更大生产规模的肉类质量检测要求。
{"title":"Model Performance Evaluation: VGG19 and Dense201 for Fresh Meat Detection","authors":"Djarot Hindarto","doi":"10.33395/sinkron.v9i1.13247","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13247","url":null,"abstract":"To guarantee consumer safety and meet quality expectations, accurate detection of meat quality is a critical component of the food industry. The objective of this research endeavor is to assess and contrast the fresh meat detection capabilities of two distinct artificial neural network architectures, denoted as Dense201 and VGG19. Automated systems that can identify vital qualities in fresh meat, including color, texture, and cleanliness, have become feasible due to the development of image processing technology. For this reason, however, there are still few direct comparisons between various architectures of artificial neural networks, particularly VGG19 and Dense201. Comparing and contrasting the performance of both models in identifying the quality of meat from visual images, this study attempts to fill this void. Utilizing a vast dataset containing a variety of fresh meats exhibiting substantial visible variations constituted the research methodology. The assessment was conducted by examining the efficacy of both models in determining the quality of meat using established performance metrics, including accuracy, precision, recall, and F1-score. Regarding the detection of fresh meat, it is anticipated that the findings of this study will offer a comprehensive understanding of the benefits and drawbacks associated with every artificial neural network architecture. Contributing to a greater comprehension of the application of precise and efficient meat detection technology, this study also furnishes the food industry with a foundation for determining which model best meets the requirements of meat quality detection on a larger production scale.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139628572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-08DOI: 10.33395/sinkron.v9i1.13271
Rahmawati Putrianasari, E. K. Budiardjo, Kodrat Mahatma, Teguh Raharjo
Agile methods are becoming increasingly popular in modern corporate strategies, which represents a paradigm change in project management techniques. The concept of pragmatic agility has become essential for enterprises to manage the complexities of ever-changing contexts. However, some organizations—especially small ones with limited resources—face unforeseen difficulties while implementing Agile-Scrum software development. In order to clarify the challenges small businesses, encounter throughout this adoption process, this study combines ideas from fifteen studies into a thorough and systematic analysis of the literature. The issues that have been discovered may be categorized into four primary areas: technology, people, process, and organization, and agile techniques. Organizations are able to anticipate obstacles by using a comprehensive understanding provided by the methodical examination and classification of situations. This proactive approach is essential to preventing unfavorable outcomes, as those seen in the past when implementation errors were made worse by culture problems, insufficient support from upper management, and waning consumer cooperation. This research provides small firms with a navigational aid by synthesizing lessons from the literature, enabling them to plan an Agile-Scrum adoption process that is more smoothly executed. Organizations may enhance their preparation, protect themselves from frequent traps, and ultimately maximize the transformative potential of Agile techniques in their developmental undertakings by adopting these insights.
{"title":"Problems in The Adoption of Agile-Scrum Software Development Process in Small Organization: A Systematic Literature Review","authors":"Rahmawati Putrianasari, E. K. Budiardjo, Kodrat Mahatma, Teguh Raharjo","doi":"10.33395/sinkron.v9i1.13271","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13271","url":null,"abstract":"Agile methods are becoming increasingly popular in modern corporate strategies, which represents a paradigm change in project management techniques. The concept of pragmatic agility has become essential for enterprises to manage the complexities of ever-changing contexts. However, some organizations—especially small ones with limited resources—face unforeseen difficulties while implementing Agile-Scrum software development. In order to clarify the challenges small businesses, encounter throughout this adoption process, this study combines ideas from fifteen studies into a thorough and systematic analysis of the literature. The issues that have been discovered may be categorized into four primary areas: technology, people, process, and organization, and agile techniques. Organizations are able to anticipate obstacles by using a comprehensive understanding provided by the methodical examination and classification of situations. This proactive approach is essential to preventing unfavorable outcomes, as those seen in the past when implementation errors were made worse by culture problems, insufficient support from upper management, and waning consumer cooperation. This research provides small firms with a navigational aid by synthesizing lessons from the literature, enabling them to plan an Agile-Scrum adoption process that is more smoothly executed. Organizations may enhance their preparation, protect themselves from frequent traps, and ultimately maximize the transformative potential of Agile techniques in their developmental undertakings by adopting these insights.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"136 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139628942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-08DOI: 10.33395/sinkron.v9i1.13237
Nihayah Afarini, Djarot Hindarto
To conduct an exhaustive examination of airline passenger growth prediction methods, this study compares the performance of three distinct strategies: LSTM, Prophet, and Neural Prophet. To forecast passenger volumes accurately, the aviation industry needs robust prediction models due to rising demand. This research evaluates the performance of LSTM, Prophet, and Neural Prophet models in passenger growth forecasting by utilizing historical airline passenger data. A comprehensive examination of these methodologies is conducted via a rigorous comparative analysis, encompassing prediction accuracy, computational efficiency, and adaptability to ever-changing passenger traffic trends. The research methodology consists of various approaches for preprocessing time series data, engineering features, and training models. The findings elucidate the merits and drawbacks of each method, furnishing knowledge regarding their capacity to capture intricate patterns, fluctuations in passenger behavior across seasons, and abrupt shifts. The results of this study enhance comprehension regarding the relative efficacy of LSTM, Prophet, and Neural Prophet in prognosticating the expansion of airline passenger numbers. As a result, professionals and scholars can gain valuable guidance in determining which methodologies are most suitable for precise predictions of forthcoming passenger demand. This comparative study serves as a significant point of reference for enhancing aviation prediction models to optimize the industry's resource allocation, operational planning, and strategic decision-making.
{"title":"Forecasting Airline Passenger Growth: Comparative Study LSTM VS Prophet VS Neural Prophet","authors":"Nihayah Afarini, Djarot Hindarto","doi":"10.33395/sinkron.v9i1.13237","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13237","url":null,"abstract":"To conduct an exhaustive examination of airline passenger growth prediction methods, this study compares the performance of three distinct strategies: LSTM, Prophet, and Neural Prophet. To forecast passenger volumes accurately, the aviation industry needs robust prediction models due to rising demand. This research evaluates the performance of LSTM, Prophet, and Neural Prophet models in passenger growth forecasting by utilizing historical airline passenger data. A comprehensive examination of these methodologies is conducted via a rigorous comparative analysis, encompassing prediction accuracy, computational efficiency, and adaptability to ever-changing passenger traffic trends. The research methodology consists of various approaches for preprocessing time series data, engineering features, and training models. The findings elucidate the merits and drawbacks of each method, furnishing knowledge regarding their capacity to capture intricate patterns, fluctuations in passenger behavior across seasons, and abrupt shifts. The results of this study enhance comprehension regarding the relative efficacy of LSTM, Prophet, and Neural Prophet in prognosticating the expansion of airline passenger numbers. As a result, professionals and scholars can gain valuable guidance in determining which methodologies are most suitable for precise predictions of forthcoming passenger demand. This comparative study serves as a significant point of reference for enhancing aviation prediction models to optimize the industry's resource allocation, operational planning, and strategic decision-making.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"123 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139629102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.33395/sinkron.v9i1.13229
Djarot Hindarto
An innovative strategy for improving supervised learning by utilizing empirically enriched datasets through the application of generative style transfer techniques. Within the realm of artificial intelligence, supervised learning has emerged as a significant domain. However, the challenge of acquiring datasets that are both representative and diverse persists. To tackle this issue, this research integrates the notion of style transfer to broaden the range of data accessible for supervised learning models. This method employs the style transfer process to generate diverse style variations within the existing data. Incorporating various image variations enhances the dataset and enables the model to gain a deeper comprehension of the image's content. Experiments were performed utilizing a conventional dataset that was enhanced using a style transfer technique and subsequently inputted into a supervised learning model. The results demonstrate substantial enhancements in model performance, particularly in terms of its ability to generalize to new test data. This confirms the efficacy of this approach in enhancing the quality of supervised learning. These findings emphasize the significant potential of employing style transfer in dataset enrichment to improve and intensify model comprehension in managed learning scenarios, as well as its implications in the advancement of artificial intelligence technologies that are more flexible and capable of adjusting to various visual scenarios.
{"title":"Enhancing Supervised Learning through Empirical Enrichment Using Style Transfer Generative Datasets","authors":"Djarot Hindarto","doi":"10.33395/sinkron.v9i1.13229","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13229","url":null,"abstract":"An innovative strategy for improving supervised learning by utilizing empirically enriched datasets through the application of generative style transfer techniques. Within the realm of artificial intelligence, supervised learning has emerged as a significant domain. However, the challenge of acquiring datasets that are both representative and diverse persists. To tackle this issue, this research integrates the notion of style transfer to broaden the range of data accessible for supervised learning models. This method employs the style transfer process to generate diverse style variations within the existing data. Incorporating various image variations enhances the dataset and enables the model to gain a deeper comprehension of the image's content. Experiments were performed utilizing a conventional dataset that was enhanced using a style transfer technique and subsequently inputted into a supervised learning model. The results demonstrate substantial enhancements in model performance, particularly in terms of its ability to generalize to new test data. This confirms the efficacy of this approach in enhancing the quality of supervised learning. These findings emphasize the significant potential of employing style transfer in dataset enrichment to improve and intensify model comprehension in managed learning scenarios, as well as its implications in the advancement of artificial intelligence technologies that are more flexible and capable of adjusting to various visual scenarios.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"104 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139629543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.33395/sinkron.v9i1.13227
Kadek Oky Sanjaya, Kadek Noppi, Adi Jaya, Made Esa, Juana Arta, Ni Made, Sintha Maharani
The development of an Alumni Information System based on a Website is an effective solution in managing information and data regarding an institution's alumni. Issues related to non-systemic and manual information dissemination, as well as challenges in gathering alumni data, are expected to be resolved by this system. It is anticipated that this system will facilitate alumni in connecting and interacting. The aim of this research is to develop an effective and efficient alumni information system to enhance alumni engagement and participation in institutional activities. The research follows a waterfall model involving various stages, starting from needs analysis, design, implementation, testing, to maintenance. The developed alumni information system includes features such as alumni profiles, current news and information, job vacancies, and alumni activities. This system is implemented in the form of a website using the CodeIgniter framework. Testing results using black box testing indicate that this system effectively manages various data and information crucial for alumni. Alumni using this system can easily access and update their profile information, as well as connect with fellow alumni and the institution. For future research, it is hoped that a more flexible information system can be developed, perhaps in the form of a mobile-based application.
{"title":"Development of a Web-Based Alumni Information System at Universitas Hindu Indonesia","authors":"Kadek Oky Sanjaya, Kadek Noppi, Adi Jaya, Made Esa, Juana Arta, Ni Made, Sintha Maharani","doi":"10.33395/sinkron.v9i1.13227","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13227","url":null,"abstract":"The development of an Alumni Information System based on a Website is an effective solution in managing information and data regarding an institution's alumni. Issues related to non-systemic and manual information dissemination, as well as challenges in gathering alumni data, are expected to be resolved by this system. It is anticipated that this system will facilitate alumni in connecting and interacting. The aim of this research is to develop an effective and efficient alumni information system to enhance alumni engagement and participation in institutional activities. The research follows a waterfall model involving various stages, starting from needs analysis, design, implementation, testing, to maintenance. The developed alumni information system includes features such as alumni profiles, current news and information, job vacancies, and alumni activities. This system is implemented in the form of a website using the CodeIgniter framework. Testing results using black box testing indicate that this system effectively manages various data and information crucial for alumni. Alumni using this system can easily access and update their profile information, as well as connect with fellow alumni and the institution. For future research, it is hoped that a more flexible information system can be developed, perhaps in the form of a mobile-based application.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"106 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.33395/sinkron.v9i1.13074
Ridwan Setiawan, Asri Mulyani, Pipit Fitriani, Kharisma Wiati Gusti
Executive Information System is a type of system that provides information about reports generated by the system, assists executives in making necessary decisions, and provides easy access to information from both internal and external sources. This system aims to help specific organizations solve problems. The objective of this research is to design and develop a web-based executive information system that can provide access to student, faculty, and program data at the Faculty of Economics, Garut University, with data visualization in the form of graphs and numbers using the Scrum method. The Executive Information System can provide a real-time overview of data for executive-level individuals, namely the faculty leaders. Scrum is the development methodology used, with stages such as product backlog, sprint, daily scrum meeting, sprint review, and sprint retrospective. The results of this research have produced an Executive Information System that provides data on students, faculty, and programs. This system features functions such as filtering, drilldown, and importing. Testing results indicate the successful achievement of sprints on time or even ahead of schedule, and the team was able to meet targets in each sprint. In this research, the Scrum method has been effectively utilized in creating the executive information system. Therefore, this method can be employed to develop similar executive information systems in the future.
{"title":"Implementing Scrum in Executive Information System at University","authors":"Ridwan Setiawan, Asri Mulyani, Pipit Fitriani, Kharisma Wiati Gusti","doi":"10.33395/sinkron.v9i1.13074","DOIUrl":"https://doi.org/10.33395/sinkron.v9i1.13074","url":null,"abstract":"Executive Information System is a type of system that provides information about reports generated by the system, assists executives in making necessary decisions, and provides easy access to information from both internal and external sources. This system aims to help specific organizations solve problems. The objective of this research is to design and develop a web-based executive information system that can provide access to student, faculty, and program data at the Faculty of Economics, Garut University, with data visualization in the form of graphs and numbers using the Scrum method. The Executive Information System can provide a real-time overview of data for executive-level individuals, namely the faculty leaders. Scrum is the development methodology used, with stages such as product backlog, sprint, daily scrum meeting, sprint review, and sprint retrospective. The results of this research have produced an Executive Information System that provides data on students, faculty, and programs. This system features functions such as filtering, drilldown, and importing. Testing results indicate the successful achievement of sprints on time or even ahead of schedule, and the team was able to meet targets in each sprint. In this research, the Scrum method has been effectively utilized in creating the executive information system. Therefore, this method can be employed to develop similar executive information systems in the future.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"27 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139631210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.33395/sinkron.v9i1.13023
Annisya Hayati Suhendar, A. A. Rohmawati, Sri Suryani Prasetyowati
This study proposes utilizing the machine learning technique CART to classify the spread of dengue hemorrhagic fever (DHF). To expand the features used, the CART classification model was developed based on data collected over the previous 2 to 4 years. The data sources included the Bandung City Health Office for the cases of DHF, the Bandung Meteorology, Climatology and Geophysics Agency for the climate data, the Bandung City Central Statistics Agency for population and educational history data. The top-performing CART classification model over the past 2, 3, and 4 years achieved accuracies of 93%, 93%, and 90%, respectively. The models that exhibited the highest accuracy values and optimal number of feature extensions were chosen as the best ones. CART is among several machine learning techniques that can effectively measure the most impactful features during the classification process. The meteorological parameters were found to be irrelevant in the classification process. This study reveals that the population size, male population proportion, and educational attainment levels are the most impactful features in the classification of DHF spread in Bandung City. The research provides valuable insights into the classification of DHF spread in Bandung City through feature expansion.
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