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In today’s digital era, social media has become a new space for individuals to develop their personal creativity. This study analyzes the content strategy of the TikTok account @buiramira in building audience engagement and understanding the impact it has on the account’s image. The background of this research stems from the phenomenon of increasing social media usage, particularly TikTok, as a means of communication, entertainment, and promotional media. Using McGuire’s information processing theory, the study finds that followers’ perceptions of the TikTok account @buiramira as a source of information for thesis learning are very positive, making it suitable as a learning platform. The content presented not only provides technical knowledge but also shapes students’ attitudes and behaviors in facing the thesis process.This research employs a descriptive qualitative method  with data collection techniques including content observation, interviews, and documentation. The results show that the @buiramira account successfully builds strong interaction with its audience through consistent uploads, the use of a communication style that feels close to viewers, and the utilization of current trends. In addition, the study finds that audience engagement is strongly influenced by a combination of content creativity, relevant topic selection, and the account owner’s ability to maintain emotional closeness with followers. These findings emphasize the importance of well-planned digital communication strategies in managing social media accounts, so that they can provide benefits both personally and professionally.

This research presents the development of a transaction data protection mechanism for a bouquet sales application by utilizing the Advanced Encryption Standard (AES) algorithm. The rapid growth of digital commerce has led to an increase in online transactions, which in turn raises serious concerns regarding the security of sensitive transaction data. Information such as customer identities, order details, delivery addresses, and payment data are vulnerable to unauthorized access, data leakage, and manipulation if not properly secured. To address these issues, this study applies the AES-128 encryption algorithm using a 128-bit secret key to secure transaction data before it is stored in the system database. The encryption process follows the standard AES workflow, including key expansion, initial transformation, multiple encryption rounds, and a final transformation stage. Decryption is restricted exclusively to authorized users who possess the correct encryption key. The research methodology includes system analysis, AES integration into the application, and functional testing of the encryption and decryption processes. Data integrity is validated by comparing the original plaintext with the decrypted output, while system performance is evaluated based on processing time and decryption accuracy. Experimental results indicate that the average encryption and decryption time remains under 10 milliseconds per transaction, without affecting system performance. The findings confirm that AES-128 effectively enhances transaction data confidentiality and integrity in the bouquet sales application

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

Diarrhea and Tuberculosis (TB) remain significant public health problems in Indonesia, including Rembang Regency. This study aims to analyze the spatial distribution and temporal changes of Diarrhea and TB cases in Rembang Regency during 2023–2024 using a Geographic Information Sistem (GIS) approach. A descriptive-analytic method was applied using secondary data, including administrative boundary shapefiles, disease case data, and health facility data at the sub-district level. Spatial processing and analysis were conducted using QGIS to produce thematic maps and compare disease distribution patterns between years. The results indicate that Diarrhea and TB cases were unevenly distributed across sub-districts and formed distinct spatial patterns. Case changes between 2023 and 2024 varied spatially, reflecting dynamic disease trends. The integration of disease maps with health facility distribution shows that areas with high case numbers were not always supported by adequate health facilities. The implementation of GIS-based maps into a web platform enhances accessibility and supports spatial-based public health decision-making.

This study aims to develop a Student Progress Assessment Information System for PAUD Laskar Pelangi as a solution to various problems that arise in the academic data management process, which was previously carried out manually. The management of student grades, student data, class data, and subject data often encountered obstacles such as delays, recording errors, difficulty in searching for data, and inaccuracies in report preparation. To overcome these issues, a web-based information system was developed to manage all data in an integrated and more efficient manner. The system development method used the Website Development Life Cycle approach, and the application was implemented using the PHP programming language. The system is designed to enable the admin to manage student data, class data, subject data, grades, and automatically generate student grade reports. Database implementation was carried out to ensure that data is stored properly and can be accessed easily. Functional testing was conducted to ensure that all features operate according to user needs. The results of the study show that the developed information system is capable of improving administrative efficiency, accelerating the grade processing workflow, minimizing recording errors, and generating student grade reports quickly and accurately. Therefore, this system provides significant benefits for PAUD Laskar Pelangi in improving the effectiveness of academic administrative management and can be further developed according to the school's needs.

The public complaint system is an important facility for the police institution to improve the quality of public services, particularly in receiving, processing, and following up on various reports and complaints from the public. However, in practice, the complaint process is still largely carried out manually, such as through face-to-face interactions, paper-based forms, or direct communication that is not well documented. This gives rise to problems such as delays in handling, difficulties in tracking the history and status of reports, low work efficiency of officers, and limited transparency of information for reporters. These problems ultimately affect the level of public trust in the police institution. This study aims to design a web-based public complaint information system that supports a more structured, measurable, and transparent complaint management process. This system is designed by implementing the Kanban method as a workflow management tool that allows for prioritization, monitoring the handling process, and tracking the status of complaints in real time. The research methods include a literature review related to information systems and the Kanban method, direct observation of the existing complaint process, interviews with stakeholders, system requirements analysis, modeling using UML (Use Case Diagram, Activity Diagram, Sequence Diagram), and database design using Entity Relationship Diagram (ERD). The results of this research resulted in the design of a web-based public complaints information system that can facilitate complaint data recording, report management and status updates, task assignments to officers, performance monitoring, and complaint status notification to the public.

This research investigates the development of IT-based Automatic Identification System (AIS) data surveillance models supporting maritime safety through integration of advanced information technology, maritime engineering principles, and human factors optimization. AIS technology generates vast real-time vessel movement data creating unprecedented opportunities for safety enhancement through systematic surveillance, collision risk detection, traffic pattern analysis, and incident prevention, yet effectiveness depends critically on intelligent data processing algorithms, reliable IT infrastructure, and competent personnel capable of interpreting surveillance outputs and taking appropriate actions. Through qualitative analysis involving maritime safety authorities, vessel traffic service (VTS) operators, port authorities, marine engineers, IT specialists, data scientists, and maritime training institutions, this study examines how IT-based surveillance models incorporating pattern recognition, anomaly detection, predictive analytics, and crew-centered interfaces can transform maritime safety management from reactive incident response toward proactive risk prevention. Results demonstrate that intelligent AIS surveillance can identify 75-90% of high-risk situations 15-45 minutes before critical events, reduce collision risks by 60-80%, improve traffic management efficiency by 35-55%, and enhance crew situational awareness by 45-65% when integrated with appropriate training programs developing personnel competencies in data interpretation, system operation, and coordinated response. Key implementation challenges include data quality and completeness issues, computational infrastructure requirements, algorithm development complexity, personnel competency gaps requiring substantial training investments, organizational coordination barriers, and privacy/security concerns. Findings reveal that successful AIS surveillance implementation requires holistic sociotechnical approaches integrating IT systems engineering, maritime domain expertise, and human capability development through coordinated design, deployment, and training strategies. This research contributes to maritime safety literature by providing integrated frameworks for IT-based surveillance systems incorporating technical capabilities, operational requirements, and human factors supporting evidence-based safety management.

This study addresses the urgent need for digital transformation at Percetakan Rajawali, a printing company currently reliant on manual order processes, which leads to service delays, recording errors, and lack of transparency. The research aims to design and implement a web-based online ordering information system to facilitate customer orders, accelerate confirmation and order tracking, and improve the efficiency of order data management. Adopting the linear and sequential Waterfall methodology, the study progressed through stages of system analysis, design, coding, testing, and maintenance. The key findings include the successful design of a comprehensive system featuring product catalogs, an online ordering module with real-time cost calculation, integrated payment management, and an admin dashboard for order processing. The system is designed with user-centered and secure principles, expected to significantly enhance operational efficiency, service speed, and customer satisfaction by providing an integrated, transparent, and accessible online ordering platform.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Flooding is one of the most recurrent natural disasters in Central Java Province, particularly during the rainy season. Diverse geographical characteristics, high rainfall intensity, and rapid urban development contribute to the region’s high vulnerability to flood hazards. According to the Central Java Statistics Agency, a total of 414 flood events and 407,784 affected victims were recorded between 2019 and 2021. This study aims to develop a web-based Geographic Information System (GIS) capable of mapping the spatial distribution and impact levels of floods across Central Java. The methodology includes collecting flood event data from the Central Java Statistics Agency, processing spatial data such as administrative boundary shapefiles, performing attribute integration between spatial and non-spatial datasets, and creating thematic maps using QGIS. The visualization outputs were exported into an interactive web format using the qgis2web plugin and subsequently integrated into a website developed with HTML, CSS, and JavaScript. The results show that the GIS system successfully visualizes flood-prone areas, identifies regions with high flood intensity, and enables users to explore detailed information through interactive digital maps. Additional website features—such as historical flood data, statistical summaries, and descriptive impact indicators—enhance the system's usefulness for disaster analysis. This study demonstrates the crucial role of GIS in supporting disaster mitigation, spatial planning, and policy evaluation related to flood management. Future research is recommended to incorporate more recent datasets and additional non-spatial variables such as rainfall intensity and floodwater depth to improve the system’s analytical accuracy and comprehensiveness.

The Information System registration module for the Regional Taxpayer Identification Number (NPWPD) was developed using the Laravel framework and implemented by the Taxpayer Identification Agency (BPPKAD) at Kediri City. The system was designed to digitize the NPWPD registration process, which was previously done manually. This traditional approach often led to long queues, extended processing times, and, at times, errors in data entry. The new system offers several key advantages, including an online registration form that allows taxpayers to upload required documents such as photos of ID cards, business locations, and other necessary paperwork. Data validation is performed by officers to ensure accuracy, and automatic notifications are sent to taxpayers, informing them of the status of their applications. The implementation of this system has had several positive impacts, such as significantly improving the efficiency of administrative processes, reducing the manual workload for officers, and increasing transparency and accountability in public services. Moreover, it has improved customer satisfaction by providing faster, more accurate, and more responsive services. This system supports the creation of a streamlined, user-friendly, and effective method for taxpayers to register for NPWPD online, enhancing the overall quality of public sector service delivery.

The attendance recording system at PLN ULP Tanjungbalai still relies on manual, paper-based methods, resulting in delays in data recap, reduced efficiency, and a high potential for recording errors. This condition affects the accuracy of employee attendance information, which is essential for administrative activities and managerial decision-making. Based on these issues, this practical work aims to design and develop a web-based e-attendance application as a solution to enhance efficiency, processing speed, and the accuracy of attendance recapitulation. The system was developed using PHP as the programming language and MySQL as the database management system, following several stages including requirement analysis, system design using UML, and implementation of a web-based user interface. The application provides essential features such as user login, daily attendance recording, employee data management, attendance notes (permission, sickness, etc.), and automatic attendance report generation. The system is designed for two types of users—Admin and Employees—each with specific access rights. The implementation results indicate that the e-attendance application significantly improves the efficiency of attendance administration at PLN ULP Tanjungbalai. Data collection and recapitulation become faster, more structured, and less prone to errors, while also enabling administrators to monitor employee attendance in real time. Therefore, this web-based e-attendance application serves as an effective solution to support operational activities and enhance the quality of employee attendance management.

SMP Negeri 5 Merlung is a public junior high school in Merlung Subdistrict that has utilized the DAPODIK system for online data processing management, enabling efficient sending and receiving of information to the government. This research analyzes IT governance on the DAPODIK system using the COBIT 5 framework, specifically the MEA01 domain (Monitor, Evaluate and Assess Performance and Conformance), which focuses on monitoring and evaluating performance and conformance. The research background is based on the need to maximize the utilization of the system at the school level. The main objectives are to determine the current and expected capability levels, as well as to provide improvement recommendations to achieve higher process maturity. The research method applies Assessment Process Activities, covering observation, interviews, identification of findings, gap analysis, and recommendations. The results show that the current capability level is at level 3 (established process), while the expected capability level is directed toward level 4 (predictable process). The implications of these findings provide practical recommendations such as routine monitoring enhancements, staff training, and integration of automation tools to bridge the capability gap, thereby improving the effectiveness of IT governance at SMP Negeri 5 Merlung sustainably.

This research aims to deeply analyze the process mechanism for the management and issuance of the National Pollution Prevention Certificate (SNPP) by PT. Multi Jaya Samudera Belawan at the Port Authority and Harbormaster’s Office (KSOP) Belawan. The SNPP is a crucial document that certifies a vessel’s compliance with technical and administrative requirements regulated by national laws and international conventions, specifically concerning the prevention of marine environmental pollution. The research methodology employed is a qualitative descriptive case study, focusing on direct observation of the procedural stages and interviews with relevant parties within the shipping agency and the KSOP institution. Data were collected through field observation during the fieldwork period and literature study relevant to ship certification procedures. The findings indicate that the SNPP issuance process involves a series of structured stages, beginning with the application submission, verification of supporting document completeness, followed by a physical vessel inspection by KSOP inspection officers to ensure that all pollution prevention installations and systems function according to the required standards. The final stage involves issuing the certificate once all requirements are met. Despite the established procedures, this study identified several key challenges, including: (1) Administrative Delays, which can interfere with the processing schedule; (2) Lack of Understanding among business actors regarding the latest detailed procedures, often resulting in incomplete documentation; and (3) Limited Technical Support, encompassing digital information systems or other supporting facilities. The optimization of SNPP issuance heavily relies on solid coordination between the shipping agency, ship’s crew, and the regulator (KSOP). The implications of these findings highlight the importance of regular dissemination and socialization of the latest regulations and procedures, as well as enhancing the efficiency of digital administration systems to minimize waiting times and ensure vessel compliance with marine pollution prevention standards.  

This study aims to design and build a population distribution application in Demak Regency in 2025 using a Geographic Information System (GIS) approach. The study focuses on three main variables: population, population density, and population growth rate per sub-district. The author used the research method of collecting data and references that can later strengthen the results of this study and the application design using the waterfall model. Non-spatial data, namely data in the form of population information, was obtained from the Central Statistics Agency of Demak Regency, while spatial data is data related to regional administrative boundaries. Data processing was carried out using QGIS 2.18 through the stages of joining attributes, classification using the Natural Breaks (Jenks) method, and thematic map creation. The results show that population distribution is uneven. Demak Kota, Karangtengah, and Sayung sub-districts have the highest number and density, while coastal sub-districts such as Wedung and Bonang have low densities. The highest population growth rate is in Karangtengah sub-district at 0.8%. The application of GIS has proven effective in visualizing population distribution and supporting spatial-based regional development planning.  

The growth of e-commerce in Indonesia has led to an increase in product reviews, including for beauty products on Tokopedia and Shopee. These reviews serve as important sources of information to assess consumer satisfaction; however, manually analyzing thousands of reviews daily is impractical. This study applies Natural Language Processing (NLP) with Naive Bayes, C4.5, XGBoost algorithms to classify sentiment in Indonesian-language reviews. The dataset used consists of 76,256 reviews labeled as positive, negative, and neutral. The research stages include text preprocessing, feature representation using BoW and TF-IDF, data balancing through SMOTE, and model performance evaluation based on accuracy, precision, and recall. Differences in results among the algorithms were analyzed using ANOVA. The results show that Naive Bayes achieved the highest accuracy at 67.71%, followed by XGBoost at 65.91%, and C4.5 at 58.39%, with Naive Bayes performing best in identifying positive and negative sentiments, while XGBoost and C4.5 handled more complex data patterns effectively. These findings provide guidance for sentiment analysis in Indonesian and support businesses in obtaining automated insights from customer reviews to improve product quality and services.

This study examines the implementation of digital health services via the Mobile JKN application at the Payung Sekaki Community Health Center in Pekanbaru City and explores factors affecting service effectiveness, especially complaints about long waiting times. Using a descriptive qualitative approach based on the Mobile Health Acceptance Model by Handayani et al. (2021), the study focuses on five constructs: ease of use, system availability, system responsiveness, health workers’ digital skills, and user trust. The results show that Mobile JKN has significantly simplified administrative processes, accelerated registration, and improved service efficiency at the health center. Effectiveness is supported by factors such as the application’s general ease of use, faster queue data processing, and adequate digital skills among staff. Users also show a high level of trust, though queue time estimation still needs improvement. Despite these benefits, complaints about long waits persist due to patients who register online but still queue manually and misunderstandings between Mobile JKN and e-Puskesmas queue numbers. Late patient arrivals also contribute to delays. Overall, Mobile JKN proves effective in enhancing digital health services, but further optimization is needed through better socialization of service procedures, accurate queue information, and improved system integration to maximize the advantages of digitalization.

Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.

This study aims to develop and implement a web-based Library Information System as a means to enhance literacy within the Kampoeng Baca Pelangi (KBP) Community. The system is designed to simplify library data management processes that were previously performed manually, including book cataloging, member registration, borrowing, returning, and the generation of operational reports. The development process follows the Waterfall method, starting from requirement analysis, system design, implementation, up to system testing. The results indicate that the system successfully meets the primary needs of the library administrators, based on feasibility testing involving internal KBP respondents. Evaluation through a Likert-scale questionnaire produced a score of 94%, indicating that the system falls into the “Highly Feasible” category for use. Features such as book data management, borrowing/return transaction processing, collection search, and report generation are proven to enhance the efficiency and accuracy of library operations.

The distinctive markings on the fingers are known as biometrics. The fingerprint attendance method uses biometrics. Without developing a usable fingerprint tool, research on fingerprint attendance is restricted to processing attendance data. As a result, an Arduino-based fingerprint approach was used to construct an attendance tool. The education sector is where SMK Negeri 1 Indralaya Utara. The school's student attendance system is still operated manually. In this study, the system uses fingerprints and camera recording to track students' locations. Using an Arduino Mega as a microcontroller, this tool will display student attendance information on an LCD, save it on an SD card module, and send the ESP32CAM-generated photographs over Telegram. The literature review is the first step in the research process, followed by hardware and software design. Following completion, the tool is put through testing to evaluate its functionality. Each of the ten participants scanned their thumb and index finger. The thumb and index finger have an average of five tests, with a 0.28% mistake rate, depending on the number of trials. Each component's error percentage is less than 1% while still falling within the 5% tolerance limit. The image test results indicate two conditions: 20 cm and greater. The ESP32CAM will activate when we take a picture at a distance of 20 cm.