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CV Mitra Grafika is a printing company where warehouse inventory management is still conducted manually, often resulting in data entry errors and delayed item distribution. To address these issues, a web-based Warehouse Management System (WMS) is needed to assist in stock monitoring, item transactions, and reporting processes. This study aims to design and develop a WMS website using the CodeIgniter 3 (CI3) framework and the SB2 Admin interface template. The development methodology used is the waterfall model, consisting of requirements analysis, design, implementation, and testing. The system includes key features such as item data management, warehouse transactions, reporting, and user role-based access (admin, staff, manager, director). The results indicate that the system successfully improves warehouse operational efficiency, accuracy, and structure.

Manual stock management systems are still commonly used in electrical equipment sales businesses, often leading to problems such as inaccurate data recording, delayed reporting, and potential data loss. To overcome these issues, a web-based inventory management information system was developed to automate the recording of incoming goods, outgoing goods, damaged items, and lost items. A case study was conducted at Toko Dhanisa Mandiri Jaya, which previously relied on manual bookkeeping using physical logs. The system was developed using the Waterfall method, involving stages such as requirements analysis, system design, implementation, testing, and maintenance. Data collection was carried out through observation, interviews, and literature review. The system includes key features such as inventory data management, transaction processing, reporting, and user management. The implementation results indicate improved operational efficiency, reduced recording errors, and the availability of real-time inventory data to support managerial decision-making. This system is expected to help the store modernize its business processes with better accuracy and control.

The advancement of information technology has encouraged companies to enhance operational efficiency through computerized systems, particularly in human resource management. PT Volar Mekanikal Teknologi x Luminous Creation faces challenges in manual attendance recording, unsystematic leave management, and a payroll system that lacks integration. Therefore, this study aims to design and implement a web-based Employee Management Information System using the Extreme Programming (XP) methodology. The system focuses on three main features: attendance tracking using GPS and selfie verification, online leave applications, and automated payroll calculation integrated with attendance data. The system development process includes planning, design using Unified Modeling Language (UML), coding with the Laravel framework and MySQL database, and testing through the Black-Box method. The implementation results show that the system effectively improves efficiency and accuracy in employee management, while also supporting the company’s operational activities in a more professional and structured manner.  

 In today's digital era, efficient and accurate inventory management is crucial to support operational effectiveness in business sectors, including retail optics stores. Optik Retna, a retail optical business, previously utilized Microsoft Excel for stock management, which led to various limitations such as restricted multi-user access, risk of data loss, and lack of real-time reporting. To address these challenges, a web-based inventory system was designed and implemented using the Agile methodology. The system was developed using HTML, CSS, JavaScript, PHP, and MySQL, and includes key features such as inventory tracking, expense and return management, transaction recording, sales reporting, and user administration. The implementation results demonstrate that the system enhances work efficiency, simplifies the recording of incoming and outgoing goods, and supports faster and more accurate decision-making at Optik Retna. Therefore, this system provides a practical contribution to the digital transformation of small and medium-sized enterprises in the optical sector.

An effective project management system is a crucial need in software development and other IT projects. This study aims to design and evaluate a web-based project management information system with integration of collaboration, scheduling, and reporting features based on real-time data. This study adopts a combined approach from three previous studies, namely a NodeJS-based collaboration system, a CodeIgniter project management system, and a PERT-based evaluation approach. The implementation results show increased teamwork efficiency, reporting accuracy, and managerial ability to anticipate project obstacles. This system is suitable for application in various scales of IT projects.

This study aims to evaluate user sentiment toward the Liputan6.com application available on the Google Play Store. In the digital era, user reviews serve as a significant indicator in assessing the quality of an application. However, the inconsistency between rating scores and review content renders manual analysis less objective. To address this issue, a machine learning approach was adopted by comparing two algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). A total of 2,500 reviews were collected through a web scraping process and automatically labeled based on the rating (positive if ≥ 3, negative if < 3). The data preprocessing stages included cleaning, case folding, tokenizing, stopword removal, and token filtering. Subsequently, word weighting was carried out using the TF-IDF method, followed by classification using 10-Fold Cross Validation in RapidMiner. The evaluation results indicate that, in the positive class, NB demonstrated superior precision (89.47%), whereas SVM achieved higher recall (98.94%) and F1-score (90.96%). In the negative class, SVM performed better in terms of precision (66.15%), while NB attained higher recall (65.65%) and F1-score (36.34%). Further evaluation based on AUC and accuracy positioned SVM in the good category (AUC 0.842; accuracy 83.82%), while NB was categorized as fail (AUC 0.505; accuracy 60.87%). Overall, SVM is considered to be more effective than NB.

This study aims to find out the code of ethics in the accounting profession in Indonesia, consisting of the influence of the code of ethics and the application of the code of ethics on the accounting profession in Indonesia. This study uses the Systematic Literature Review (SLR) method. SLR is a research technique that is carried out to evaluate, analyze, collect and maintain information from several article sources obtained from websites such as google scholar as many as 30 articles. The result of this study is that a code of ethics is very necessary in the accounting profession, especially in Indonesia. Based on the influence of the code of ethics in Indonesia, it increases public trust and regulates behavior professionally. And based on the application of the code of ethics, the Indonesian accountant profession can prevent unethical practices and abuse of office as well as strengthen accountability and transparency.    

The development of information technology has encouraged government agencies to adopt digital systems to improve the quality of public services. One of these innovations is the Waste Bank Management Information System application (SIMBA), which is used by the Lombok Tengah Environment Agency. This research aims to analyze the usability of the SIMBA application based on three main dimensions of the Webqual 4.0 method, namely usability, information quality, and service interaction quality. The method used is a quantitative approach through the distribution of questionnaires to 51 respondents who are users of the application. Each dimension is measured using a five-point Likert scale to obtain users' perceptions of the quality of the application. The results of the study show that the score for the usability dimension is 188, information quality is 192, and service interaction is 188, with a total Webqual score of 189. This score falls into the 'good' category, indicating that the SIMBA application has met user needs in terms of usability, information quality, and service interaction quality. This research is expected to serve as an evaluation material for the development of environmental service information systems in the future.  

This study uses a qualitative method to analyze content trend strategies in interactive digital marketing that contribute to building consumer loyalty. In today’s highly competitive digital era, companies are required to create strong engagement with consumers through digital platforms such as social media, websites, and applications. Content trends like storytelling, user-generated content, live streaming, and influencer usage have proven to attract audiences and encourage ongoing interaction. This research explores how these trends are applied by various brands and how consumers respond to these approaches over the long term. The findings show that message consistency, emotional value in content, and interactivity are key factors in forming emotional bonds that impact loyalty. Furthermore, two-way engagement between consumers and brands through comments, likes, and sharing features strengthens this relationship. By understanding consumer digital preferences and behaviors, content strategies can be dynamically adapted to maintain loyalty while expanding the relevant market reach.

In the past, toys were identified solely as instruments of entertainment for children. Adults were generally considered inappropriate to own or play with toys. Over time, toys have evolved into collectibles that have artistic, historical, and investment value for people of all ages. This study aims to analyze Pop Mart's marketing strategy in shaping impulsive buying behavior. The study was conducted using descriptive qualitative methods. The research data was obtained from digital searches and observations from various official sources and related official websites. The results of the study show that Pop Mart, as a leader in this industry, has successfully implemented marketing strategies that utilize collaborations with artists, the blind box concept, and the principle of scarcity to create a unique and attractive shopping experience for its consumers. Collaborations with artists play an important role in building brand identity and increasing product appeal. By offering limited editions of popular characters such as Molly, Labubu, and SkullPanda, Pop Mart has been able to attract collectors and art enthusiasts who have an emotional connection to these works. This has led to increased brand loyalty and purchase frequency. The blind box concept creates an element of surprise that triggers excitement when opening the packaging, thereby encouraging repeat purchases. Additionally, the scarcity marketing strategy, combined with the application of FOMO behavior, successfully creates a sense of urgency among consumers. Products with limited editions and purchase quantity restrictions create exclusivity, making consumers feel they must act quickly to obtain the product before it sells out.

This study aims to analyze the strategy of improving the quality of public services through digital innovation in Sunter Agung Urban Village. In the era of globalization and rapid development of information technology, public services are expected to be more responsive, transparent, and efficient. The research used a descriptive qualitative approach with data collection through documentation studies and in-depth interviews with urban village officials and service users. The results showed that the use of digital innovations such as web-based service applications, online queuing systems, and document digitization has had a positive impact on improving service quality, including accelerating administrative processes and increasing community satisfaction. However, various obstacles are still found, including limited network infrastructure, suboptimal human resource capacity in mastering technology, and low digital literacy among the community, especially the elderly. For this reason, the proposed quality improvement strategy includes strengthening institutional capacity through digital training for apparatus, developing technological infrastructure, and digital literacy campaigns involving the wider community. This research is expected to serve as a reference for local governments in developing inclusive and sustainable digital-based public services.

This study aims to analyze market sentiment towards Gold Financing Products (PKE) in Islamic banking before and after the Trump Effect phenomenon using the text mining method. This technique involves extracting information from unstructured text data to then be visualized and analyzed using the Natural Language Processing (NLP) approach and a RoBERTa-based classification model. Data was collected through web scraping from the X application with the help of API and processed using Google Colab. From a total of 4,074 tweets analyzed, it was found that the majority of public sentiment was neutral (59%), followed by negative (24%) and positive (17%). This reflects the public's tendency to discuss informatively rather than emotionally, although there was a spike in negative sentiment in certain periods indicating sensitivity to global dynamics, especially the impact of the Trump Effect on gold prices. The resulting wordcloud reveals key topics such as gold prices, buying and selling activities, and institutions such as Pegadaian Syariah and BSI. Terms such as "sharia", "riba", and "principles" emphasize the importance of Islamic financial values ​​in public perception. The results of this study indicate that text mining-based sentiment analysis is effective in capturing the dynamics of public opinion in real-time and can be a strategic tool for Islamic financial institutions in responding to market changes.

Maintaining optimal temperature in server rooms is critical to ensure the reliability and longevity of IT equipment. Overheating can lead to system failure, data loss, and increased maintenance costs. This study presents the design and implementation of an IoT-based temperature monitoring application tailored for server room environments. The system utilizes a DHT22 temperature and humidity sensor connected to an ESP32 microcontroller, which transmits real-time data to a cloud-based dashboard via Wi-Fi. The data is visualized through a web interface that allows administrators to monitor room conditions remotely and receive alerts when temperature thresholds are exceeded. The application was tested in a simulated server room environment over a two-week period. Results demonstrated stable performance with accurate temperature readings and reliable alert delivery. The system's scalability, low cost, and ease of deployment make it a practical solution for data centers, schools, or small businesses. This IoT-based approach offers an efficient and automated method to enhance server room monitoring and improve response time in managing thermal risks.

Digital learning applications like HeyJapan are increasingly popular. User reviews on platforms such as Google Play Store contain valuable information on user perceptions and experiences. To process this information systematically, this study employs a Natural Language Processing (NLP) approach to analyze sentiment toward the HeyJapan application. Data was collected using web scraping techniques with Python and the google play scraper library, resulting in 1,000 latest user reviews. The analysis included data collection, preprocessing, sentiment labeling using TextBlob, visualization, modeling with Logistic Regression, and evaluation. After preprocessing, 923 valid reviews were classified into three sentiment categories based on polarity which are positive, neutral, and negative. Results showed 71.4% of reviews positive, 26.1% neutral, and 2.5% negative. Visualizations in pie charts and word clouds provided an overview of user perceptions. Modeling with TF-IDF and Logistic Regression achieved 88% accuracy with the highest f1-score in the positive sentiment category. Evaluation indicates the model is fairly reliable in classifying sentiments, especially for positive and neutral categories, though negative sentiment classification needs improvement. This study shows the NLP approach can evaluate user perceptions of educational applications based on reviews and serve as a basis for improving foreign language learning app quality.

Handwritten digit recognition is one of the key challenges in the field of digital image processing and artificial intelligence, with significant potential in various applications such as automatic form input systems, handwritten data correction, and attendance systems based on handwriting. This study aims to develop a web-based information system capable of automatically recognizing handwritten digits using the K-Nearest Neighbors (KNN) classification method. The system is designed through several main stages, including image preprocessing (conversion to grayscale, thresholding, and image size normalization), feature extraction using the zoning technique, and classification using the KNN algorithm. This research utilizes the MNIST dataset, which contains thousands of handwritten digit images ranging from 0 to 9. The system is developed using the Google Colab platform, supported by Python libraries such as OpenCV, NumPy, and Scikit-learn. Test results show that the system can achieve an accuracy of over 90% at certain K values, indicating that the KNN method is quite effective and efficient in recognizing handwritten digit patterns. This system is expected to be applicable for various digitization needs of handwritten numbers in education, administration, and information technology sectors

The rapid development of financial technology (fintech), particularly digital wallet applications like OVO, has significantly transformed transaction patterns in society. However, issues such as server instability and unsatisfactory user experiences frequently emerge on social media platforms. This study aims to analyze user sentiments toward OVO on platform X (formerly Twitter) by comparing the performance of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Data were collected through web scraping from 1,000 Indonesian-language tweets containing the keyword "OVO." The research methodology included text preprocessing (data cleaning, tokenization, stopword removal), feature extraction using TF-IDF, and sentiment classification (positive, negative, neutral). Evaluation results demonstrated that SVM achieved the highest accuracy of 85.2%, while Naïve Bayes reached 78.5%. SVM also outperformed in precision (87%) and recall (83%) due to its ability to handle non-linear data. These findings provide actionable recommendations for OVO developers to enhance server stability and features based on user feedback. Additionally, this study serves as a reference for future sentiment analysis research employing algorithmic comparisons.

Micro, Small and Medium Enterprises (MSMEs) in the Social Service of the North Sumatra Province Batubara Regency are business operators engaged in the business sector, which touches the interests of the community around the Regency of Coal. MSMEs have an important position, not only in employment and public welfare, in many ways they are the glue and stabilize the problem of social inequality. MSMEs are currently very developed. However, the current problem that arises is that the Chair of the UMKM in Coal is having difficulty monitoring MSME social funding assistance provided to communities around Coal, the lack of public understanding of the reporting of the social assistance fund accountability and due to these inaccessible location conditions that triggered the social services difficult to monitor. then we need an information system that is able to do data collection, management, monitoring the use of aid funds. In addition, the Social Service can directly monitor the responsibility of MSMEs in managing aid funds.The results of this study are a monitoring information system application that replaces the previously running process, in this application makes it easy for the recipient to know the amount of assistance received and in making reports can be directly accessed online.

In today's digital era, the application of digital marketing is the main key in increasing the sales volume of various products, including ornamental corals. This research aims to find out how CV Rezky Bahari Makassar applies digital marketing to increase sales of ornamental corals. Through a qualitative approach of exploratory case study type, data collection through interviews, observation, and documentation. data analysis methods using descriptive qualitative. The results showed that CV. Rezky Bahari Makassar has implemented various digital marketing strategies, including social media marketing, such as WhatsApp, Facebook, Instagram, Website and Email Marketing, Integrated digital marketing strategies focus on attractive visual content, technological innovations such as 360-degree videos and direct interaction with customers to increase the visibility and accessibility of its products to consumers. The implementation of this strategy resulted in a significant increase in sales of ornamental corals. Despite facing challenges such as a decline in revenue due to the Covid-19 pandemic in 2020, the company managed to return to a positive growth path through digital marketing strategies, with sales revenue reaching its highest peak in 2023. This research provides insights into effective digital marketing practices in the ornamental coral industry and suggests that the integration of various digital channels can result in increased sales.

The development of technology in the retail world drives the need to create more efficient solutions in transaction management and stock of goods. Kasirku is a web-based cashier application designed for small and medium-sized stores. This application provides three main features, namely Transactions, Stock Items, and Sales Reports, with additional additional transaction features, discounts, taxes, add items, and QR scanners to simplify store operations. The Transaction feature assists users in recording sales efficiently, complete with options for adding items, applying discounts, and automatic tax calculation. The Stock Item feature allows store owners to monitor stock items in real-time, reducing the risk of out-of-stocks or recording errors. Sales Reports available in various formats, such as PDF or Excel, help store owners conduct more in-depth sales analysis. The development of the Kasirku application was conducted using the waterfall approach, which includes several steps: requirements analysis, design, implementation, and testing. The results of the testing process show that this application can increase efficiency in recording transactions, minimize errors in stock management, and produce precise reports. The QR Scan feature is proven to speed up the item input process when the store is busy, providing additional flexibility to users. This app has successfully become a reliable solution for store owners to manage their stores more easily and quickly.

An expert system is a computer program designed to imitate the decision-making ability of an expert in a particular field, combining knowledge and rules obtained from experts to diagnose problems, provide advice, or make complex decisions, especially in dental diseases, so this requires the design of an expert system application for website-based dental disease diagnosis with a UML model for users who support the diagnostic process using the certainty factor method, in terms of the explanation of the certainty factor which has a concept based on symptoms and diagnoses from the weight of an expert's value and the user's value, then calculated with the formula in CF, the final result is the creation of a website-based dental disease diagnosis expert system.