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Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

Indonesia is a country with a high level of disaster vulnerability, influenced by tectonic activity and its tropical climate. This study uses the K-Means clustering method to identify and group disaster-prone areas based on the level of vulnerability. The data used included average temperature (Tavg) and rainfall (RR) which were processed using Python. The analysis process includes data collection, pre-processing, determination of key features, and evaluation of clustering quality using the Elbow and Silhouette Score methods. The results of the grouping show two main patterns, namely flood-prone areas and drought-prone areas. These findings are expected to support the government in more effective and data-based disaster mitigation planning.  

Penelitian ini bertujuan untuk menganalisis distribusi rata-rata dan simpang baku Produk Domestik Regional Bruto (PDRB) per provinsi di Indonesia pada tahun 2021. PDRB merupakan indikator penting dalam menggambarkan kondisi ekonomi suatu wilayah. Data PDRB per provinsi dianalisis menggunakan statistik deskriptif, termasuk perhitungan rata-rata dan simpang baku, untuk mengidentifikasi perbedaan kontribusi ekonomi antar provinsi. Hasil penelitian menunjukkan bahwa DKI Jakarta memiliki rata-rata PDRB tertinggi, mencerminkan perannya sebagai pusat ekonomi nasional. Provinsi-provinsi dengan rata-rata PDRB menengah, seperti Jawa Barat, Jawa Timur, dan Jawa Tengah, menunjukkan kontribusi signifikan yang didorong oleh sektor industri, perdagangan, dan pertanian. Sebaliknya, provinsi-provinsi dengan rata-rata PDRB rendah, seperti Papua Barat, Gorontalo, dan Maluku Utara, cenderung bergantung pada sektor primer. Analisis simpang baku menunjukkan adanya variasi signifikan dalam kontribusi ekonomi antar provinsi, dengan DKI Jakarta memiliki simpang baku tertinggi, sementara provinsi seperti Bengkulu dan Maluku Utara menunjukkan distribusi ekonomi yang lebih merata. Temuan ini memberikan wawasan penting untuk kebijakan pembangunan ekonomi yang lebih inklusif dan merata di Indonesia.

Motion detection is an important process in computer vision to analyze activities in videos. This study implements a simple system to detect motion in video files using Python and the OpenCV library. The system works by comparing consecutive frames in a video to detect changes and mark areas that experience motion. The implementation shows satisfactory results on various sample videos. This study provides a solution that is easy to implement and can be used in applications such as video analysis and computer-based monitoring.

In recent years, virtual assistants have become an integral part of everyday life, simplifying routine tasks and allowing users to focus on more important matters. This research aiming to design GiggleGate, a virtual desktop assistant integrated with both face and speech recognition technology to enhance authentication security. The objective is to develop an authentication system that not only verifies user identity but also provides a more intuitive experience and seamless interaction. The research employs a development methodology to create and implement the system, which integrates face recognition via OpenCV and speech recognition via a Python library. The findings indicate that the integration of these technologies enhances security and user experience by offering dual-factor authentication. The system is expected to contribute to more secure and accessible virtual assistant applications, offering both a practical and efficient solution for users. The implications of this study suggest that the combination of face and speech recognition can provide an effective means to protect user privacy and improve the overall functionality of desktop assistants.

The growing number of vehicles in major cities has posed significant challenges in parking lot management. Motorists often have difficulty finding empty parking slots quickly, which not only wastes time but also aggravates traffic congestion and increases air pollution. This research develops a Python-based smart parking system by utilizing the OpenCV library to detect the status of parking slots in real-time. The system uses a camera as the main sensor and processes the image using techniques such as grayscale, Gaussian blur, and adaptive threshold to identify the parking slot status, whether empty or occupied, with good accuracy. The parking slot coordinate data is stored in CSV format to ensure efficient data management. Experimental results with video recordings show that the system is able to operate well in various parking conditions. The system proved to be cost-effective and easy to implement, making it an ideal solution for parking managers who want to improve management efficiency without being burdened with high costs. This research offers a practical solution to help motorists and parking managers optimize parking space usage, reduce search time, and minimize negative impacts such as congestion and carbon emissions.

This research focuses on the development of automatic waste classification by applying the Convolutional Neural Network (CNN) method in a web-based application. This system is designed to help the waste management process through automatic sorting between organic and inorganic waste, so that it can support recycling efforts and reduce environmental impacts. In its application, this application utilizes the CNN algorithm to analyze images and recognize the type of waste with good accuracy. The development uses technologies such as Python and OpenCV to ensure efficient processing of image data, with the CNN model trained using a dataset of 22,564 images. Test results show excellent accuracy, reaching 99.27% for organic waste and 98.72% for inorganic waste.

This study discusses the implementation of computer vision technology for face detection in photos using two sample images with variations in lighting and face pose. The developed system combines the Viola-Jones algorithm and Convolutional Neural Networks (CNN) to enhance resilience against lighting and face orientation variations. Experimental results show high accuracy even with only two sample images. This research also develops preprocessing techniques to handle extreme lighting conditions and demonstrates efficient implementation using Python and OpenCV.  

The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.    

The research focuses on creating an automated attendance system using face recognition through the Convolutional Neural Network (CNN) approach at IPB University's Vocational School. The current manual attendance methods show limitations, such as potential inaccuracies in recording and the risk of cheating, like attendance proxies. To overcome these challenges, this study applies the CNN approach with Python and OpenCV, enabling automatic face detection and recognition for students. The system accurately logs attendance by identifying faces in real time. Testing indicates that the system records attendance reliably, whether with a single individual or with multiple faces present within a single frame.

Objective: analyze the modulation scheme that can intelligently select the appropriate modulation model for service conditions to obtain a high Signal to Noise Ratio, as well as throughput efficiency on wireless networks through the DNN approach. Method: this study uses simulations with the Python language, through AI-Driven on BPSK, QPSK, 16-QAM, and 64-QAM modulation, to determine the SNR and Quality of Service (QoS) produced, both through conventional approaches and Deep Neuro Network (DNN). Researh Finding: AI-Driven modulation used for Cognitive Cellular Networks (CCN), through Deep Neuro Network designed to intelligently classify and select the appropriate modulation model to be applied, shows significant improvement in throughput efficiency, QoS and has the ability to adapt to the environment in dynamic networks. Conclussion: AI-Driven using Deep Neuro Network is able to dynamically adapt to determine the selected modulation model, according to the user's environmental conditions, increase spectrum efficiency and throughput, and increase SNR which can automatically increase the efficiency of network usage.

This study analyzes the utilization of Python as a tool for calculating function limits, with an emphasis on the application of the SymPy and NumPy libraries. The flexibility of Python allows researchers to perform mathematical calculations efficiently, employing both the symbolic approach provided by SymPy and the numerical methods offered by NumPy. SymPy facilitates the management of complex mathematical expressions and produces accurate symbolic results, while NumPy provides speed and efficiency in executing numerical computations. With active community support and regular updates to the libraries, Python proves to be a robust and flexible environment for research in the field of mathematics. Findings from this study indicate that the combination of these two libraries not only enhances the accuracy of limit calculations but also accelerates the research process, making it a relevant choice in both academic and practical contexts..

This research uses a quantitative approach with the help of Python to solve infimum and supremum problems involving measurements, calculations and data analysis to draw conclusions. This research process consists of several stages: problem identification, modeling a set of numbers or functions, and implementing algorithms in Python to calculate the infimum and supremum. The calculation results are compared with manual analytical solutions to ensure accuracy and efficient use of Python. Sets as a basic concept in programming allow organizing data and logical operations more efficiently. These findings show that Python is not only effective in calculating supremum and infimum, but also speeds up the solution process compared to manual methods. The results of the program execution show that the analyzed set has an infimum fan supremum which is in accordance with the theory, where set 1 has an infimum of 2, set 2 has a supremum of 5, and set 3 has an infimum of 1 and a supremum of 4.

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.

Mathematics is a science that is structured deductively and systematically. Proof in Mathematics is important because it can enable critical thinking logically, and the truth of a hypothesis can be tested.  Mathematical induction is a proof method that has 2 steps, namely basis and induction. With advances in technology today, there are many applications that can make this proof easier, such as Python. This research uses quantitative and qualitative approaches to prove the effectiveness of using Python compared to manual proof. The results show that Python not only speeds up work but also minimizes errors that could occur if done manually. With this research we recommend further exploitation of mathematical induction in other programming applications.

This research aims to analyze the ability of Unimed FMIPA Mathematics students in solving absolute value inequalities with the help of the Python program. The problem faced is that students often have difficulty solving inequality problems manually. The purpose of this research is to see the effectiveness of using Python in helping to solve absolute value inequalities and how it affects understanding of mathematical concepts. The method used was qualitative research with a sample of 20 fifth semester students of the Mathematics Study Program, FMIPA, Medan State University. Students are given absolute value inequality problems to solve manually and with the help of Python. Data was collected through online tests and questionnaires using Google Form. The research results show that the majority of students feel that using Python is very helpful in solving absolute value inequalities. As many as 95% of students consider Python to be effective in making it easier to solve mathematical problems and increasing understanding of the concept of absolute value.

This study aims to optimize marketing strategies through RFM (Recency, Frequency, Monetary) analysis on a retail transaction dataset obtained from Kaggle. The dataset contains 64,682 transactions from 5,242 SKUs involving 22,625 customers over one year. Data cleaning and RFM analysis were conducted to segment customers based on recency, frequency, and monetary values. The findings reveal that customers were segmented into groups such as Champions, Loyal Customers, and At Risk. These segments provide valuable insights for developing targeted marketing strategies, such as loyalty programs for high-value customers and retention campaigns for at-risk customers. The study demonstrates that RFM analysis is effective in identifying valuable customer segments and optimizing marketing efforts based on customer behavior. This approach can increase customer retention and improve the return on investment (ROI) in marketing campaigns.

This research aims to develop an application for recommending safe and suitable skincare products for pregnant women. The application is built using the prototype method and implemented with the Python programming language using the Flask framework. The study encompasses product categories from Drw Skincare, including serums, toners, shampoos, body care products, facial washes, and creams. The system provides options for users to select categories of skincare ingredients they desire. The application issues warnings about skincare ingredients that should be avoided during pregnancy, such as retinol and specific acids. The end result is a list of recommended skincare products containing ingredients that align with the user's selected criteria. Each product is accompanied by comprehensive information about its ingredients and advice. Users can utilize search and advanced filtering features to find skincare products based on product types and brands. The application offers clear user guidance and an intuitive interface, enabling users to navigate and use the application comfortably. It is expected that this application can assist pregnant women in choosing safe and effective skincare products while providing a better understanding of skincare during pregnancy.

Big Data Analytics has gained significant popularity in recent years, with many companies integrating it into their information technology roadmaps to enhance business performance. However, surveys indicate that Big Data Analytics demands substantial resources, including technology, costs, and talent, which often leads to failures in the initial stages of implementation. This study proposes a VGG6 architecture approach, intended to provide a framework for the initial implementation of Big Data Analytics. The study's outcomes include the implementation of the VGG6 architecture for processing images of aromatic plants using Python. Furthermore, this approach enabled the development of a Minimum Viable Product (MVP) solution that adheres to general Big Data principles, such as the 3Vs (Volume, Velocity, and Variety), and encompasses key technological components: 1) Data Storage and Analysis, 2) Knowledge Discovery and Computational Complexity, 3) Scalability and Data Visualization, and 4) Information Security.

Combinatorial optimization is a fundamental area in operations research and computer science, focusing on identifying optimal solutions from a finite set of possibilities. This study explores the integration of branch and bound methods with heuristic algorithms to address optimization problems in graph theory and discrete mathematics. Python was employed for algorithm implementation due to its flexibility and comprehensive computational libraries, enabling efficient data analysis and visualization. Several benchmark problems were examined, including the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and Graph Coloring. Simulations were conducted using datasets of varying sizes (small, medium, and large) to evaluate performance across different scales. The results demonstrate that the hybrid approach achieves a balance between solution quality and computational efficiency, outperforming brute-force methods in terms of speed while maintaining near-optimal accuracy. Tabulated results and graphical comparisons highlight the reduction in computation time and improved scalability of the proposed method. The findings suggest that combining systematic search strategies with heuristics offers a practical and effective framework for solving complex combinatorial optimization problems. Recommendations for future research include testing scalability with larger datasets, incorporating advanced metaheuristics, and applying the approach to real-world domains such as logistics and network design.