Publication Search

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

This community service was carried out at SMAS PGRI 1 Mojokerto City with the main goal of improving student discipline through the application of Japanese-style discipline culture. This activity is designed in a structured manner to have a real and sustainable impact on student behavior and the integrity of educational institutions. The method used is a participatory and applicative approach, so that students not only passively receive information, but also actively engage in the process of learning, reflection, and discipline practice. This approach emphasizes the direct involvement of students in activities that foster awareness, responsibility, and positive habits that support the creation of a conducive learning environment. The results of the implementation of activities are divided into three main categories. First, the short-term results seen immediately after the activity are increased students' motivation and understanding of the importance of discipline. Second, the medium-term results that appear within 1-3 months are in the form of real behavioral changes, such as punctuality, neatness, and compliance with school rules. Third, long-term results that contribute to strengthening the integrity of the institution as a school with a high discipline image. Thus, this community service is expected to be able to become a model for implementing a discipline culture that is effective, sustainable, and relevant to the needs of education in Indonesia.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

This study aimed to improve the speaking skills of sixth-grade students at SDN Negeri Kelapa Dua Wetan 01 Pagi in using the Simple Past Tense pattern (was/were) through the implementation of the Role Playing method. The research was conducted in two cycles involving 32 students as the subjects. The research method used was Classroom Action Research, consisting of the stages of planning, action implementation, observation, and reflection in each cycle. Data were collected through oral pre-tests and post-tests, as well as observations of students’ learning activities during the lessons. The results showed a significant improvement in students’ speaking skills. Their average score increased from 60.3 on the pre-test to 74.1 on the post-test. The percentage of students who achieved the Minimum Mastery Criteria also rose from 31% to 75%. The Role Playing method proved effective in enhancing students’ accuracy in using was/were, improving speaking fluency, and boosting self-confidence in oral communication. In conclusion, the application of the Role Playing method had a positive and significant impact on English learning outcomes among the sixth-grade students at SDN Negeri Kelapa Dua Wetan 01 Pagi. It is recommended that teachers continue to apply this method consistently, and that schools provide additional supporting facilities to optimize the learning process. For future studies, teachers and students are encouraged to expand the variety of role-play scenarios and include a control group to obtain a deeper and more comprehensive understanding of the effectiveness of the Role Playing method.