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The Indonesia Pintar Program (PIP) is an educational assistance program for students from underprivileged families, but determining the eligibility of recipients still faces obstacles in the form of subjectivity and data imbalance. This study aims to classify the eligibility of high school students receiving PIP in Jambi City using data mining methods. The SMOTE technique was applied to overcome class imbalance, and Gain Ratio feature selection was used to determine important attributes. The dataset used consisted of 19,596 student data with a training data distribution of 70% and testing data of 30%. The classification process used the Naïve Bayes, Decision Tree (J48), and Random Forest algorithms with the Use Training Set, 5-Fold, and 10-Fold Cross Validation testing schemes. The results show that SMOTE improves model performance, but feature selection in some cases reduces accuracy. Overall, Random Forest without feature selection provides the best results with an accuracy of 93.33% and is recommended as the most effective model for objectively determining PIP recipient eligibility.

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 creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.

In Indonesia, micro, small, and medium enterprises (MSMEs) have long been recognized as a very important business sector due to their various real roles in the economy. However, MSMEs often face many obstacles in running their businesses. More incentive support from various parties, especially the government, is needed for the development of MSMEs. In order to overcome the problems faced by MSMEs and support their sustainability, a KKU (Business Field Study) activity was carried out by Group 15 at the KKU-11 of the Institute of Technology and Business (ITB) Adias Pemalang at the MSME “Ev_Kids Product.”After conducting an observation phase, several problems faced by the SME were identified, including the lack of social media for marketing, failure to calculate the Cost of Goods Sold (COGS) and maintain simple accounting records, absence of a business logo, banners, promotional image designs, organizational structure, business profile, product catalog, stamps, receipt books, and product attributes. Additionally, the SME had never produced a production video or promotional video.After conducting the observation phase, KKU actors formulated several business development assistance programs, namely creating social media accounts, calculating the Cost of Goods Sold (COGS), creating simple bookkeeping, creating a business logo, banners, promotional image designs, organizational structure, business profile, product catalog, stamps, note books, and product attributes in the form of hangtags. They also created production videos and promotional videos.

This research is purposely conducted to evaluate and compare the effectiveness, efficiency, applicability  and appropriateness of modern firefighting systems installed on board ships. As a result, the review attempt to  ascertain the significance of firefighting systems on ships in terms of their safety, types, structure, principles as  well as installations. In this way, the review tries to justify the importance of these systems on board ships. The design of this survey revolves around three basic elements that incorporate the complete firefighting  procedure. These components include: modern technology of firefighting, different types of systems that are  used on board ships and people traits in terms of drilling, firefighting training to deal with any incident of fire.  In the previous decade, the globe has experienced massive destructions, harms and injuries resulting from fire incidents on ships. This has triggered and ignited enhanced fabrication and staging of new practices,  technologies and inventions aimed at efficiently deal with fire incidents so as to support the safekeeping and  reliability on these fire systems on board ships. Equally, through this technological enhancement, the  firefighting systems have been designed to facilitate rapid detection capable of differentiating between real  smokes or flames indicators in case of a fire event on board ship. In recent times, these technological  developments have been viewed as strategies that help ship owners to ensure better protection of the crew’s life,  ship’s inventories as well as minimising on the possible losses that occur as a result of fire events on board ship.  Therefore, with the review of the recent or latest methods and technologies for speedy fire detection on ships,  the review demonstrates enhancement attributes, features and qualities of these new systems. Additionally, the  paper critically evaluates the components of these firefighting systems, as well as looking at their competences,  capabilities, benefits and applicability on board ships.

This study investigates the impact of board characteristics on the financial performance of non-financial companies listed in the JII70 index in Indonesia. Motivated by the ongoing debate on the effectiveness of corporate governance mechanisms in enhancing firm outcomes, particularly within Sharia-compliant markets, this study focuses on three key board attributes: board size, board independence, and female representation on the board. Using a quantitative causal approach and panel data from 25 companies over the period 2020–2023, the study employs a fixed effect model to evaluate the relationship between board structure and financial performance measured by Return on Assets (ROA). The results show that board size has a positive and significant effect on firm performance, indicating that larger boards may enhance oversight capacity and provide broader resources beneficial to strategic decision-making. Conversely, board independence and board female representation do not exhibit significant effects on financial performance, suggesting that their roles may be more symbolic or constrained by institutional and contextual factors in the sampled companies. These findings highlight the importance of understanding corporate governance not merely in structural terms, but in relation to functional effectiveness and contextual maturity. The study offers implications for regulators, companies, and governance reform initiatives, particularly regarding strengthening substantive roles of independent and female commissioners in improving firm performance within Sharia-compliant markets.

The development of value-added corn-based beverages representation an emerging opportunity within the agribusiness sector, particularly for student-led entrepreneurial initiatives seeking to utilize local agricultural resources. This study examines consumer perceptions of Zeagrain corn milk, a sweet-corn-derived drink positioned as a healthy, practical, and affordable alternative within the ready-to-drink market segment. The research aims to identify key attributes influencing consumer interest, evaluate perceived product performance, and assess the potential competitiveness of Zeagrain as a new agribusiness product. Data were collected using an online quantitative survey targeting respondents aged 17–35 years who represent potential urban consumers. The questionnaire measured demographic characteristics, consumption patterns of similar beverages, perceived importance and performance of product attributes, and purchase intention. The findings are expected to provide insights into consumer expectations regarding taste, packaging, nutritional value, and price. The results further serve as empirical input for improving product development strategies and enhancing the market readiness of Zeagrain. Ultimately, this study contributes to strengthening the commercialization prospects of corn-based beverages and supports innovation efforts among young agripreneurs seeking to optimize local commodities.  

Laskar Buah is a modern retail chain specializing in the sale of fresh fruit. At present, the company operates one hundred branches across ten regencies. One of its outlets, Laskar Buah Ngumpakdalem, ranks among the top three branches in terms of transaction volume; however, it has received a considerable number of customer complaints regarding the quality of service provided. This situation has prompted management to conduct a thorough evaluation of the store’s service quality.This study was conducted with the aim of analyzing and evaluating service quality using the Importance–Performance Analysis (IPA) method based on the Retail Service Quality Scale (RSQS). The results of the IPA analysis were subsequently used as a foundation for determining priority areas for service improvement.The findings reveal that four service attributes fall within Quadrant B, indicating that they should be prioritized for immediate improvement. These attributes include the cleanliness of the shopping area, store layout, employee product knowledge, and product quality. Additionally, twelve attributes fall under Quadrant C, where performance should be maintained due to their already strong results. On the other hand, eleven attributes fall into Quadrant A, meaning they are considered lower  priority, while one attribute is located in Quadrant D, suggesting that Laskar Buah Ngumpakdalem is providing excessive performance in that particular aspect.

This study aims to determine the optimal pectinase enzyme concentration and hydrolysis duration for the most preferred physical, chemical, and organoleptic characteristics of kweni mango syrup. The research was conducted from October 2024 to August 2025 at the Integrated Laboratory and Basic Science Laboratory of the Agricultural Institute (INTAN) Yogyakarta using a Completely Randomized Design (CRD) with a factorial pattern consisting of two factors and two replications. The first factor was the pectinase enzyme concentration (0%, 0.25%, 0.50%, and 0.75%), and the second factor was the hydrolysis duration (0, 30, and 60 minutes). Observation parameters included yield, vitamin C, viscosity, pH, total soluble solids, and organoleptic attributes. Data were analyzed using ANOVA at a 5% significance level followed by Duncan’s Multiple Range Test (DMRT). The results showed significant effects of both pectinase enzyme concentration and hydrolysis duration on yield, viscosity, total soluble solids, and organoleptic characteristics (color, taste, aroma, and overall acceptability), while no significant effects were observed on vitamin C content and pH. The best formulation was obtained using 0.50% pectinase enzyme concentration with a hydrolysis duration of 30 minutes.

Axiology is a branch of philosophy that examines values ​​in human life, such as goodness, truth, beauty, and the benefits of knowledge. In the context of Islamic education, axiology plays a crucial role in determining the direction and goals of education, namely, to develop individuals who are balanced intellectually, morally, and spiritually, known as insan kamil (the perfect human being). Values ​​in axiology are divided into two primary sources: divine values ​​derived from God's revelation and His attributes, and human values ​​derived from human experience and civilization. These two types of values ​​serve as the foundation for developing a comprehensive Islamic education. The axiology of Islamic education emphasizes not only the attainment of knowledge but also the formation of character and morals in students. Teachers and educational institutions act as agents of values, instilling ethics, aesthetics, and socio-political awareness based on Islamic teachings. By integrating these values, Islamic education aims to produce a generation that is not only intellectually intelligent but also possesses noble morals and is capable of making a positive contribution to society and global civilization. Axiology, therefore, serves as a crucial foundation for realizing a meaningful and humanity-oriented education.

The selection of a thesis topic that aligns with students’ interests and competencies often poses a challenge in academic environments. Inappropriate topic selection can lead to decreased motivation and delays in completing the final project. This study aims to develop a thesis topic recommendation system based on a genetic algorithm that considers students’ interests and academic abilities. The data used include grades from core courses, results of research interest questionnaires, and a list of thesis topics provided by academic supervisors. Each topic is represented as a chromosome, while the fitness function is calculated based on the level of compatibility between student attributes and topics. The selection process employs the roulette wheel method, with single-point crossover and random mutation to generate an optimal solution population. The test results show that the recommendation system based on the genetic algorithm achieves an accuracy rate of 86.7%, higher than the keyword-matching method, which only reaches 71.2%. Therefore, this approach is proven effective in assisting students to determine thesis topics that are suitable, objective, and efficient.

This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.

The present study was conducted to assess consumer acceptability of taro-based kue talam supplemented with dragon fruit peel and to describe the ingredient composition and nutritional content of the preferred formulation. An experimental design comprising three formulations was employed. The resulting products were subjected to an organoleptic evaluation by untrained panelists, covering the attributes of color, aroma, taste, texture, and overall acceptability. The findings indicated that Formula A received the highest preference scores. The bottom layer of Formula A consisted of 650 g taro, 130 g dragon fruit peel, 250 ml coconut milk, 110 g tapioca flour, 230 g rice flour, 250 g granulated sugar, ½ tsp salt, and 1 tsp vanilla. The top layer comprised 350 ml coconut milk, 25 g granulated sugar, 30 g tapioca flour, 60 g rice flour, and ½ tsp salt. Nutritional analysis per 100 g of product revealed carbohydrate and protein contents of 48.45% and 2.515%, respectively. The study concludes that taro-based kue talam enriched with dragon fruit peel has promising acceptability and may be considered a potential innovation in local tuber-based food products. Further research is recommended to include cost analysis for pricing determination.

This study aims to implement an information retrieval system for cryptocurrency data using an attribute-based approach integrated with the Vector Space Model (VSM). The primary objective is to develop a system capable of retrieving the most relevant digital asset information according to specific search attributes, including positive sentiment, price fluctuation, and prediction confidence level. The research adopts a descriptive qualitative method combined with an experimental approach to evaluate the retrieval performance of the cosine similarity algorithm on normalized numerical data. Data preprocessing and attribute weighting were conducted to ensure consistency and improve retrieval accuracy. The experiment demonstrates that the proposed system achieves a Precision@5 value of 1.0, which indicates that all top-five retrieved results are fully relevant to user queries. These findings validate the effectiveness of the attribute-based VSM in analyzing multidimensional cryptocurrency datasets. Overall, this research contributes to the advancement of information retrieval applications in the cryptocurrency domain, particularly for supporting data-driven decision-making and intelligent financial analysis.

This study investigates the influence of Brand Image, Product Quality, and the use of Social Media on consumers’ purchasing decisions to Wardah facial cream. Using a quantitative method with purposive sampling technique, this study involved 120 Wardah product users in the West Semarang area. Data were collected through an online questionnaire and processed using SmartPLS 4.0. The results indicate that product quality and social media usage have a positive and significant effect on purchasing decisions, whereas brand image shows a positive but insignificant effect. This suggests that consumers tend to prioritize tangible attributes such as product quality and active engagement through social media over brand perception The model explains 75.2% of the variance in purchasing decisions (Adjusted R² = 0.752), highlighting the strong explanatory power of the variables examined. Practically, the findings suggest that marketers should prioritize product quality and optimize social media campaigns to boost customer engagement and loyalty, while reassessing brand perception strategies.

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.

Selecting a faculty that aligns with students’ interests and talents is a strategic step in determining the success of higher education and future career paths. However, most vocational high school (SMK) students still face difficulties in identifying the most suitable faculty due to the lack of data-driven analysis. This study implements the C4.5 classification algorithm within data mining techniques to build an automatic and measurable faculty recommendation system. The dataset consists of attributes such as SMK major, interest level, aptitude test results, academic grade average, and gender, with the output being the recommended faculty. The C4.5 algorithm was chosen for its ability to generate a transparent and interpretable decision tree, which helps both guidance counselors and students understand the rationale behind the recommendations. The experimental results show that the constructed classification model achieved an accuracy rate of 88%, based on cross-validation testing using data from 12th-grade students. The implementation of this system is expected to serve as an objective tool in the faculty selection process and to promote a data-driven decision-making approach in secondary education environments.

This study aims to investigate the impact of packaging type and storage temperature on the physical, chemical, and sensory properties of chili blocks during storage. Additionally, the study aims to identify the optimal packaging and storage temperature combination, as well as to characterize the quality attributes of chili blocks. The experimental design employed was a Completely Randomized Design (CRD) in a factorial arrangement with two factors: packaging type and storage temperature. In this study, using aluminum foil as primary packaging, PP plastic packaging, and paper as secondary packaging, and 3 treatment storage temperatures, each repeated as many as 3 trials, so that it has 18 experimental units. Data analysis using ANOVA with Duncan's Multiple Range Test (DMRT)at a significant level of 5% using the SPSS18 program. The results showed that secondary packaging types and storage temperatures did not significantly affect physical properties (moisture content) and organoleptic properties (color, aroma, texture), but significantly affected the chemical properties (pH, vitamin C, ash content) and secondary packaging types the good for chili block is alufo + plastic packaging and refrigerator storage temperature (10oC), and good chili block characteristics are: Refrigerator storage (temperature 10oC) with alufo + plastic packaging: lowest physical properties (moisture content) 27.29, chemical properties (lowest pH 5.23, Vitamin C 12.91 and ash content 6.35).

The rapid growth of online transportation services has significantly transformed urban mobility patterns, including in Abepura District, Jayapura City. This study is grounded in the concept of smart mobility, which emphasizes technological integration, efficiency, and accessibility within the smart city framework. The theoretical foundation draws on consumer preference theory and the Customer Satisfaction Index (CSI) model. A quantitative approach was applied through questionnaires distributed to 100 respondents, supported by secondary data on digital infrastructure and local transport regulations. The analytical methods included conjoint analysis to identify user preferences, CSI analysis to assess smart mobility readiness, and spatial analysis to map infrastructure support. The findings indicate that fare and safety are the most influential attributes shaping user preferences, followed by application usability, transport mode, and travel time. Maxim emerged as the most widely used application, followed by Grab and Gojek. The CSI score reached 77.60%, categorized as “highly ready,” though gaps remain in intermodal integration and waiting time efficiency. Spatial analysis confirmed that the coverage of 16 BTS towers in Abepura adequately supports online transportation operations. In conclusion, online transportation services in Abepura District demonstrate strong readiness to support the implementation of smart mobility, yet further improvements are needed in modal integration and operational efficiency to ensure sustainable and inclusive urban mobility.

Earnings management is an action that can affect the quality of a company's financial information. As the highest leader, the CEO plays a critical role in strategic decision-making, including in earnings management practices. This study aims to examine the influence of CEO characteristics—namely age, education level, and tenure—on earnings management in food and beverage sub-sector companies listed on the Indonesia Stock Exchange for the 2019–2023 period. A quantitative approach is employed using secondary data from annual reports of 21 companies, with a total of 99 firm-year observations. The data were analyzed using multiple linear regression with leverage, profitability, and sales growth as control variables. The results show that CEO age has a negative effect on earnings management, CEO tenure has a positive effect, while CEO education level shows no significant effect. These findings indicate that the personal characteristics of CEOs influence a company’s tendency to engage in earnings management. This study provides insights for investors, management, and regulators to consider CEO attributes when assessing the risk of financial reporting manipulation.