https://repository.umindanao.edu.ph/handle/123456789/2289 Wed, 20 May 2026 09:45:44 GMT 2026-05-20T09:45:44Z https://repository.umindanao.edu.ph/handle/123456789/2327 Hedonic preference of airline passengers Caliao, Jeziel V.; Yumul, Emmanuel; Tamayo, Adrian; Murcia, John Vianne; Delima, Allemar Jhone P. The purpose of this study is to determine which airlines Davao City-bound and outbound passengers prefer. Passenger information was acquired via survey questionnaires employing the Likert scale. In this study, non-experimental quantitative research employing a casual research method was employed. Using descriptive and informal research methodologies, this study investigated whether demographic variables and airline service quality influence the airline service choices of Davao City passengers. This investigation employs artificial neural network research to determine which factors influence airline preference. The majority of air carrier respondents are between the ages of 21 and 35, are predominantly female, and are employed by private companies. The airline preferences of passengers are influenced by price, cabin services, flight schedule, safety, on-time performance, and employee conduct. Important airline demographics included age, occupation, and sex. Price is the most significant criterion when selecting an airline, followed by flight schedule, on-time performance, and employee conduct. Flights should offer discounts to students and senior citizens. They may adjust fares based on the findings. To attract a broader age range of customers, airlines should strategically organize their marketing and focus on specific market niches. To increase sales, airlines may consider offering discounted, low-cost, or one-peso fares during certain times. To assist time-sensitive travelers in reaching their destinations in the event of airline delays or cancellations, alternative flights are advised. To gain a deeper understanding of airline carrier selection, research should be extended, employing a larger sample or alternative statistical methods. This study employs a non-experimental quantitative approach and artificial neural networks to analyze how demographic factors and service quality metrics specifically identifying price and scheduling as the most critical drivers influence the airline preferences of travelers in Davao City. Sun, 05 Feb 2023 00:00:00 GMT https://repository.umindanao.edu.ph/handle/123456789/2327 2023-02-05T00:00:00Z https://repository.umindanao.edu.ph/handle/123456789/2326 Organic phase change materials as thermal energy storage for automated solar seawater desalination system Jimenez, Cyril Jefferson Calape; Magallanes, Jose Fernando Bactol; Tabay, Carl James Hermoso; Genobiagon, Cresencio Pombo Jr A prototype of an automated solar seawater desalination system equipped with thermal energy storage was designed and developed to analyze experimentally the effect of adding organic phase change materials (palm wax and beeswax) to the desalination system’s efficiency. The desalination machine was set up with two configurations: with organic and without organic PCMs. While the setup without a PCM can produce an average of 30.8 mL per day and convert 2.056% of the total volume of input water to an output peak of 48.5 mL using solar energy, the setup with a PCM can produce an average daily water production of 68.9 mL and convert 4.6% of the input water volume with the highest record output of 104.5 mL compared to configurations without PCM, the organic PCM system with a 1:1.2 mixture of beeswax and palm wax can produce an average of 123.7%. A prototype solar seawater desalination system with thermal energy storage was developed using organic phase change materials (palm wax and beeswax). The PCM-enhanced setup significantly boosted efficiency, producing up to 104.5 mL daily and achieving a 123.7% increase compared to the non-PCM configuration. Mon, 01 Jan 2024 00:00:00 GMT https://repository.umindanao.edu.ph/handle/123456789/2326 2024-01-01T00:00:00Z https://repository.umindanao.edu.ph/handle/123456789/2325 Predictive soil-crop suitability pattern extraction using machine learning algorithms Soberano, Kristine T.; Pisueña, Jeffric S.; Tee, Shara Mae R.; Arroyo, Jan Carlo T.; Delima, Allemar Jhone P. Machine learning has experienced notable advancements in recent times. Furthermore, this field facilitates the automation of human evaluation and processing, leading to a reduced demand for manual labor. This research paper employs data mining techniques and Knowledge Discovery in Databases (KDD) to conduct an evaluation and classification of various algorithms for pattern extraction and soil suitability prediction. The study utilizes experimental data, data transformation, and pattern extraction techniques on diverse soil samples obtained from different regions of Negros Occidental, Philippines. Specifically, the Naive Bayes, Deep Learning, Decision Tree, and Random Forest algorithms are selected for the classification and prediction of soil suitability based on the available datasets. The assessment of soil-crop suitability is based on data sourced from the Philippine Rice Research Institute, considering 14 parameters including inherent fertility, soil pH, organic matter, phosphorus, potassium, nutrient retention (CEC), base saturation, salinity hazard, water retention, drainage, permeability, stoniness, root depth, and erosion. The findings indicate that the Random Forest algorithm achieved the highest accuracy rate at 94.6% and the lowest classification error rate at 5.4%, suggesting a high level of confidence in the model's predictions. The model's predictions reveal that most soil samples in the area are only marginally suitable for banana, maize, and papaya crops. Furthermore, the study demonstrates that the majority of soil samples have a low fertility rating, which significantly impacts crop suitability. The information obtained from this study can serve as a basis for local farmers to develop improved soil management programs aimed at ensuring more productive soil. Simultaneously, it can contribute to active soil protection initiatives addressing issues such as acidity and salinity in Negros Occidental, Philippines. This research paper investigates the use of machine learning algorithms, specifically identifying Random Forest as the most effective, to analyze soil data from various regions in the Philippines and predict its suitability for crops like banana, maize, and papaya. Tue, 04 Apr 2023 00:00:00 GMT https://repository.umindanao.edu.ph/handle/123456789/2325 2023-04-04T00:00:00Z https://repository.umindanao.edu.ph/handle/123456789/2324 A data-driven approach in predicting scholarship grants of a local government unit in the Philippines using machine learning Fajardo, Reban Cliff A.; Yara, Fe B.; Adeña, Randy F.; Hernandez, Michael Kevin L.; Arroyo, Jan Carlo T. Inefficient, tedious, and outdated processes in resource allocation are some of the common hurdles educational institutions and agencies face in managing scholarship grants and selecting potential grantees. In response to the challenge, this study developed a predictive model utilizing a range of machine learning algorithms; by leveraging algorithms like Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machine, and Multilayer Perceptron, the study aimed to enhance the selection process for scholarship programs to match applicants with the most suitable scholarship based on their individual backgrounds and qualifications. A number of measures, including accuracy, precision, recall, and F1-score, were used to assess the performance of the models. Results revealed Logistic Regression as the best-performing model in terms of overall accuracy and balance between precision and recall. Moreover, the Support Vector Machine, Naive Bayes, and Random Forest models demonstrated competitive performance, while the Multilayer Perceptron exhibited the lowest performance among others. This study developed machine learning models to improve scholarship selection, testing algorithms like Naïve Bayes, Random Forest, Logistic Regression, SVM, and MLP. Logistic Regression achieved the best overall performance, offering a more accurate and balanced approach to matching applicants with suitable scholarships. Mon, 01 Jan 2024 00:00:00 GMT https://repository.umindanao.edu.ph/handle/123456789/2324 2024-01-01T00:00:00Z