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This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

This study analyzes the effect of operating costs, production costs, and sales volume on net profit in pharmaceutical companies listed on the Indonesia Stock Exchange (IDX) for the period 2021-2024. Using a quantitative method with panel data regression analysis, this study took a sample of 11 companies and secondary data from financial reports. The results of the hypothesis test show that operating costs, production costs, and sales volume partially have a positive and significant effect on net profit. These findings are consistent with existing literature and indicate that efficient cost management and increased sales volume are crucial factors in maximizing profitability in the pharmaceutical sector. Furthermore, this research is also relevant to Agency Theory, which suggests that management, as agents, must manage costs and sales transparently to align their interests with those of shareholders, ultimately leading to the sustainable increase of company value. This study contributes to understanding key factors driving financial performance in the industry.

This study aims to reveal the effect of production costs, operational costs and sales volume on financial performance at CV. Salim Media Indonesia for the 2021-2023 period. This thesis uses a quantitative approach using the multiple regression statistical analysis method partially and simultaneously with 35 samples. The results of the study indicate that production costs have an effect on financial performance at CV. Salim Media Indonesia for the 2021-2023 period partially. Operational costs have an effect on financial performance at CV. Salim Media Indonesia for the 2021-2023 period partially. Sales volume has an effect on financial performance at CV. Salim Media Indonesia for the 2021-2023 period partially. Based on the calculated F value > F table and the significant value < a , it is concluded that Ho is rejected and Ha is accepted, meaning that Production Costs and Operational Costs together or simultaneously have an effect on financial performance at CV. Salim Media Indonesia for the 2021-2023 period.