Sinkron : jurnal dan penelitian teknik informatika
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Optimizing HEI On-Page SEO with Instagram: Owned vs. Paid Media (PMB UHW Perbanas Case)
No full textIn today's digital age, nearly every institution, including those in education, utilizes social media. Instagram, a leading social media platform, offers a wealth of features for sharing engaging visual content. To maximize the effectiveness of new student recruitment on Instagram, UHW Perbanas needs a clear understanding and implementation of paid and owned media marketing strategies. The next step is to compare content performance before and after implementing SEO strategies, both paid and organic. Marketing strategy analysis using content on the @pmb.uhwperbanas Instagram account has demonstrably built a positive image and attracted audience attention. Relevant, informative, and engaging content fosters audience interest, creates engagement, and increases brand awareness. This research suggests that utilizing paid advertising can significantly amplify the reach and impact of existing content. The results of content with organic Instagram show insight results of 1,232 reaches, 1,626 impressions, 133 interactions and 83 profile activities. The results of content with paid Instagram show insight results of 109,173 reaches, 177 post interactions, 1,619 profile activities and 987 advertisements. This data collection platform is obtained from the features owned by Instagram Business.The conclusion of this research highlights the effectiveness of a balanced paid and organic media strategy on Instagram. By leveraging keyword analysis results from an SEO tool, UHW Perbanas can craft compelling captions that optimize search content and drive new student admissions
Building Sustainable Communities: SIMARET Development for Financial Transparency with MDALC Approach
No full textThe increasing need for financial transparency and efficiency in community-level governance, particularly within Rukun Tetangga (RT) in Indonesia, calls for innovative solutions. This study presents the development of SIMARET, a mobile application designed to enhance the management of RT financial activities and resident participation, using the Mobile Application Development Life Cycle (MDALC) approach. The research aims to address the challenges of manual financial management, such as lack of transparency and difficulties in tracking funds and activities like neighborhood watch (Siskamling). SIMARET incorporates key features such as digital tracking of resident contributions (jimpitan), QR code-based attendance for Siskamling, and automated financial reports. The system was developed through MDALC’s structured phases: identification, design, development, testing, and deployment. Blackbox Testing and User Acceptance Testing (UAT) were conducted to ensure functionality and user satisfaction. The results show a high satisfaction rate of 97%, confirming that SIMARET simplifies financial administration and enhances community participation. The study also highlights the application’s contribution to the United Nations Sustainable Development Goals (SDG) 16 by promoting transparency and effective governance at the local level. Although SIMARET demonstrates significant potential, further research is recommended to improve its user interface design and expand its implementation in other communities
Implementation of the Agglomerative Hierarchical Clustering Method in Ordering Hijab Products
No full textThe ever-evolving internet technology has an impact on various sectors, including the hijab business, where the demand for hijab products is increasing through online transactions. This research was conducted at the Kinan Hijab Store in Kota Pinang, North Sumatra, with the aim of optimizing the management of hijab product stock. The problem faced is the imbalance in the stock of hijab products, where some hijab products have excess stock that are less in demand while popular hijab products often experience a shortage of stock. To solve this problem, the Agglomerative Hierarchical Clustering method is used to group hijab products based on sales data, product type, and price. This study uses hijab sales data from May to July 2024. After the clustering process, hijab products are grouped into two categories: "Popular" and "Less Desirable". The "Popular" category includes 190 products, while the "Less Desirable" category includes 983 products. Product stock in the "Popular" category will be increased by 50% of the average sales, while stock in the "Less Desirable" category will be reduced by 25%. the effectiveness of the Agglomerative Hierarchical Clustering (AHC) method in stock planning and management by showing that it improved the inventory allocation based on customer demand patterns. The clustering method categorized hijabs into two main groups: "Popular" and "Less Preferred", based on key sales metrics such as quantity sold, price, and total sales. The implementation of the stock plan is carried out based on the sales pattern of each hijab category. Overall, the application of this method not only helps stores in understanding customer purchasing patterns but also optimizes product availability, which can ultimately increase customer satisfaction
Web, E-Report Web-based E-Report Information System Design
No full textThe E-Report application information system is a system that can assist and facilitate users in creating performance reports for a company. In this study, a web-based e-report system was developed and designed for digital reporting by field employees at PT. In this study, a web-based e-report system was developed and designed for digital reporting by field employees at PT. Cipto Sarana Nusantara, which specifically operates in the gas pipeline installation sector. Previously, the company generated field worker performance reports through manual systems, in the form of hard copies or sent via chat applications. This led to poorly organized reports received by administrators. To address this issue, the author developed E-Report, a web-based application that aims to facilitate field workers and administrators in submitting and collecting reports. By utilizing this system, reporting performance significantly improves in terms of reporting speed, accuracy, and ease. By utilizing this system, reporting performance significantly improves in terms of reporting speed, accuracy, and ease. By utilizing this system, reporting performance significantly improves in terms of reporting speed, accuracy, and ease. Furthermore, this system enables the generation of daily, monthly, and yearly reports more efficiently and effectively
Forecasting Airline Passenger Growth: Comparative Study LSTM VS Prophet VS Neural Prophet
No full textTo 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
Depression Detection of Users in Social Media X using IndoBERTweet
No full textAccording to the Ministry of Home Affairs, the population of Indonesia stands at 273 million, Indonesia has approximately 167 million active subscribers to virtual entertainment platforms, including YouTube, Facebook, Instagram, and Twitter. The use of online entertainment is huge, particularly on Twitter, and has been associated with mental health implications, such as depression. This research objective is to do a comprehensive study about the IndoBertweet deep learning framework to investigate the prevalence of depression in social media, focusing on Twitter. Utilizing the DASS-42, the research estimates depression levels based on user interactions and reactions to tweets. The results of this research showed that the IndoBERTweet method achieved an accuracy rate of 82% in detecting depression using Twitter data. This research highlights the importance of intervention strategies to support the mental health of social media users, emphasizing the importance of proactive measures in addressing mental well-being issues in the digital space
Physical Activities Recommender System Based on Sequential Data Use K-Mean Clustering
No full textPhysical activities such as Exercise are essential in maintaining health and fitness, especially for those who adopt a healthy lifestyle. Irregularity in doing Exercise can hurt the body and health, especially if it is not done according to one's physical capacity. In the framework of this research, we developed a Recommender System that aims to provide exercise suggestions according to the user's preferences, especially in the categories of cycling, running, walking, and horse riding. The primary considerations of the variables include heart rate (Average Heart Rate) and pace (Speed Rate). This research approach uses the FitRec Dataset and applies the K-Mean Clustering Algorithm, with the support of APACHE SPARK, for large-scale data processing, given the large data size in the FitRec dataset. Grouping is done using the FitRec dataset and K-Mean. Users are grouped according to heart rate and pace information; this provides appropriate Exercise for users. The test results show that the proposed system performs well, as indicated by the silhouette score = 0.596, calinzski-harabaz score = 2133.09, and davies bouldin score = 0.480. These test metrics reflect the system's ability to cluster. Indirectly, the accuracy performance of the system is assessed through these metrics, showing good accuracy test results
Blockchain Utilization in Secure and Decentralized Web 3.0 Application Development
No full textThe implementation of blockchain technology in the creation of secure and decentralized Web 3.0 applications has grown in significance. Blockchain, an industry-spanning distributed ledger technology, has facilitated substantial advancements in information and communication technology, among others. Regarding Web 3.0, this study examines how the implementation of blockchain technology can enhance decentralization and security. By conducting a literature review, this study examines how the implementation of blockchain technology in the development of Web 3.0 applications significantly improves data security. Through the implementation of robust cryptographic features and distributed security principles, the outcomes demonstrate that blockchain can effectively safeguard data while it is being transmitted and stored via Web 3.0 applications. This is a crucial step in the direction of resolving the security issues that are frequently encountered in the digital environment of today. Furthermore, blockchain technology facilitates enhanced decentralization within Web 3.0 applications. Blockchain applications reduce their reliance on a central authority, thereby enhancing their resilience against single-system malfunctions and monopoly control. Furthermore, it facilitates the development of platforms that are more equitable and transparent, granting users greater authority over their data and interactions
C4.5 Forward Selection Based Algorithm For Class Level Classification Of Nurul Jadid Islamic Boarding School Students
No full textPesantren is an Islamic educational institution that plays a central role in the development of education in Indonesia. Although originally established for Islamic religious education (Pendidikan Agama Islam or PAI), pesantren has evolved into an educational institution that contributes to both scholarly and community service aspects. According to the regulations set by the Ministry of Religious Affairs of the Republic of Indonesia under Number 31 of 2020, pesantren is a community-based institution that upholds the teachings of Islam rahmatan lil'alamin (Islam as a blessing for all) and the noble values of the Indonesian nation. Pesantren education is efficient because it is conducted in a boarding school setting, which shapes the character of its students or 'santri.' However, the current method of determining the grade levels of santri is often inaccurate, relying solely on the average scores of entrance exams without considering essential aspects of subjects. This leads to a decrease in students' interest in learning and delays in achieving higher levels of education. By utilizing data mining techniques, such as the C4.5 algorithm based on Forward Selection, it is possible to address this issue and enhance the accuracy of placing santri into their appropriate grade levels at the Nurul Jadid Paiton Probolinggo pesantren. This improvement can make the pesantren education system more effective in managing student learnin
GOVERNANCE EVALUATION ELECTRONIC SECURITY SYSTEM (ESS) (Case Study: ABC Central Bank)
No full textAs we know, the role of the security system has a very important role for a state institution to provide security and comfort in carrying out its functions, such as the ABC central bank. A good security system is a security system that is supported by a reliable electronic security system and is composed of several components such as a CCTV monitoring system, Access Control System (ACS), Security Alarm System (SAS), and Fire Alarm System (FAS). This system is very necessary to provide support for the duties of these state institutions to protect devices, data and electronic infrastructure from potential threats and security risks. The main functions of electronic security systems include prevention, detection, response to incidents, and recovery after disturbances/disasters. For this reason, efforts are needed to provide an evaluation of the system maturity level and information security management as a form of risk management to maintain the continuity of system use. This research uses the INDEKS KAMI 4.1 to map ESS governance maturity and the OCTAVE Allegro method to analyze information security management. From the analysis carried out, it has been concluded that the ESS implementation has been operated well in accordance with the security system requirements and has reached a good level of governance maturity. Information security management analysis carried out using the OCTAVE Allegro method has succeeded in identifying information security management with the result that information security management has been implemented well. This is proven by the existence of indicators, namely CCTV recording data, log systems as information assets that have been managed and distributed according to authorit