International Journal of Innovation Research in Education, Technology and Management

Factors Increasing Net Income in The Company; Systematic Literatur Review

Sat, 28 Feb 2026 00:00:00 +0000

This study aims to identify factors that influence the increase in net profit in companies through a Systematic Literature Review approach. Increasing net profit is one of the main objectives of the company, which affects investment decisions and business sustainability. Given the importance of understanding these factors, this research is relevant to provide a comprehensive overview of the elements that affect company profitability. This topic was chosen because although many studies have examined the factors of net profit, there is no research that combines various industry sectors and external factors that affect the financial performance of companies. This research was conducted by collecting and analyzing relevant articles from various scientific journals from 2003 to 2024, by identifying internal factors such as company size, capital structure and operational efficiency, as well as external factors such as macroeconomic conditions, government policies and competition. The results of the analysis show that both internal and external factors significantly affect a firm's net profit, with firm size and operational efficiency having a greater impact. The findings address the issues explored by providing strong evidence regarding the role of external factors that are often overlooked in previous studies. This research makes a significant contribution in understanding the factors that influence net profit, as well as providing practical recommendations for managers to optimize the company's financial performance.

K-NN Based Prediction of AI Tool Utilization by Non-Technical University Students

Tengku Akhsay, Hetty Rohayani — Sat, 28 Feb 2026 00:00:00 +0000

The increasing integration of Artificial Intelligence (AI) tools, particularly ChatGPT, into higher education necessitates a deeper understanding of their adoption patterns among non-technical students. While AI offers significant benefits for learning and academic tasks, its utilization varies across disciplines, with non-technical fields often exhibiting lower adoption rates. This study addresses the critical need to predict AI adoption among students in non-technical majors such as Business, Education, Humanities, and Social Sciences. We employ the K-Nearest Neighbor (K-NN) algorithm to classify and forecast the likelihood of these students using ChatGPT for academic purposes. The dataset, comprising survey responses from 48 non-technical students, includes attributes like AI knowledge level, frequency of personal and academic AI use, and interest in AI careers. After rigorous data preprocessing, including encoding and normalization, the dataset was split into training (70%) and testing (30%) sets. The K-NN model, with an optimized K-value determined through cross-validation, utilized Euclidean distance for classification. Our findings indicate that approximately 39.6% of non-technical students are predicted to utilize AI tools like ChatGPT for their academic activities, closely aligning with actual survey responses. This research provides valuable insights for educational institutions to tailor teaching methods, offer targeted support, and develop relevant digital literacy programs, ensuring AI becomes an inclusive and empowering educational tool for all students.

A EDUCATORS CONTRIBUTION TO EMPLOYEE PERFORMANCE FOR LEARNING

Sat, 28 Feb 2026 00:00:00 +0000

The purpose of this research is to obtain information about the efectiveness of school management in the place i work as an ASN PPPK educator who teaches the subject of geography using the 2013 curriculum or more known as kurtilas. This research started in may 2024. In the learning process teachers are seen as having an important role, especially in helping students to develop their potential in cognitive, affective and psychomotor abilities, teachers also strive to arouse curiosity, encourage independence and accuracy of intellectual logic, and create conditions for success in learning. Teacher performance can be seen and measured based on the competency specifications/criteria that each teacher must have.

The educators who are the objects of the research are educatiors from senior high school 10 Tangerang district and the musyawarah of geography teachers in Tangerang district (MGMP Geography) from state secondary and secondary schools on the privatw one that is in Tangerang district. The method used in this research was a questionnaire method or data collection via the google form and whatsapp applications also a questionnaire which was share using a hardcopy specifically for educators at sma negeri 10 Tangerang district. This method was used to get the actual state his and research result described. Data collection techniques used through observation and documentation. The informants in this case are the teachers of Tangerang district 10 state high school and teachers who joined in the teacher’s for the geography subject of Tangerang district. In this conclusion it can be conclued that the effectiveness of school management will be good and orderly if educators are able to cooperate with each other and create a confortable atmosphere in the teacher’s room for fellow educators or other school citizens.

EXPERT SYSTEM FOR DIAGNOSING DISEASES IN CUCUMBER PLANTS USING FORWARD CHAINING METHOD

ilham assidiq, Hetty Rohayani, Hendra Kurniawan — Sat, 28 Feb 2026 00:00:00 +0000

The purpose of this research is to develop an expert system to diagnose plant diseases in cucumber using the forward chaining method. The agricultural sector, particularly vegetable cultivation, faces great challenges due to the spread of diseases that reduce productivity and economic value. Expert systems mimic human expertise to accurately diagnose diseases and provide practical solutions by providing effective recommendations. The forward chain, rule-based reasoning approach, ensures systematic analysis to derive conclusions from known facts, thereby improving diagnostic accuracy. The focus of this research is to identify common diseases in cucumber plants and encode the expertise into a functional system. The development of the system involved gathering knowledge from agricultural experts, creating rules, and implementing them in a user-friendly interface. Preliminary results show the high accuracy and potential of the system to help farmers quickly diagnose diseases and take preventive measures. This paper contributes to sustainable agriculture by integrating an expert system to effectively address plant health issues. Future enhancements may include real-time monitoring and integration with IoT devices.

Literature Review: Implementation of the Naive Bayes Algorithm for Classification in Various Fields of Data Mining

Aprilia Nurfazila, hetty rohayani — Sat, 28 Feb 2026 00:00:00 +0000

The significant increase in data volume across various sectors demands efficient, accurate, and adaptive classification methods. The Naive Bayes algorithm is one of the probabilistic classification techniques widely used in data mining due to its model simplicity and its capability to handle high-dimensional data. This study aims to systematically review the application of the Naive Bayes algorithm for data classification in various sectors in Indonesia through a Systematic Literature Review (SLR) approach. Data were obtained from scientific journals published in the last five years (2019–2024) relevant to the topic and analyzed using qualitative descriptive methods. The review results show that Naive Bayes is widely applied in the fields of health, education, social sciences, economics, and technology. Most studies report high accuracy rates, particularly in text classification and imbalanced dataset cases. However, the limitation of this algorithm lies in the assumption of attribute independence, which is often not met in real-world cases. Therefore, several studies combine Naive Bayes with other methods to improve performance. This study provides a comprehensive overview of the strengths and weaknesses of Naive Bayes and serves as a reference for selecting appropriate classification methods in future data mining applications.