Abstract:This research aims to understand the influence of Sales Promotion, Utilitarian, Self-Esteem, and Hedonic Motive on Purchase Decisions with Impulse Buying and Behavior Intention as intervening variables in Shopee. The research…
earch method used is quantitative with a descriptive approach using a Likert scale. Using the SEM data analysis method. The population of this study is Shopee application users in Indonesia, especially generation Z (1997-2012), with 285 respondents taken using non-probability sampling techniques. The research results show that Sales Promotion, Utilitarian, Self-Esteem, and Hedonic Motive have a positive effect on Purchase Decision. Apart from that, Sales Promotion also has a positive effect on Impulse Buying and Behavior Intention. The implication of this research is that companies can utilize the Self-Esteem motive to improve purchasing decisions. Personalized product recommendations and price offers that match consumer preferences can be implemented to increase consumer satisfaction and trust. Implementing algorithms can help companies provide better shopping experiences, improve purchasing decisions, and strengthen consumer behavioral intentions
Abstract:The rapid growth of the hotel industry requires hotels to improve operational planning, one of which is by forecasting room reservations. Inaccurate forecasting may cause an imbalance between room availability and customer…
er demand. This study aims to compare the Weighted Moving Average and Single Exponential Smoothing algorithms in forecasting room reservations at Raz Hotel and Convention Medan using historical data from January 2025 to May 2026. The research method consisted of data collection, forecasting using both algorithms, and accuracy evaluation through Mean Absolute Deviation, Mean Squared Error, and Mean Absolute Percentage Error. The results indicate that the Single Exponential Smoothing algorithm achieved a Mean Absolute Percentage Error of 15.24%, which is lower than the 15.63% obtained by the Weighted Moving Average algorithm. Furthermore, the Single Exponential Smoothing algorithm predicted 835.90 room reservations for June 2026. Therefore, it can be concluded that the Single Exponential Smoothing algorithm provides better forecasting accuracy and is more suitable for predicting room reservations at Raz Hotel and Convention Medan.
Abstract:Academic achievement is a measure of students' learning outcomes, encompassing aspects of knowledge and skills. Academic performance serves as a crucial indicator in evaluating students' learning progress. MAS Al-Wasliyah…
h Petatal is committed to providing quality education but still faces limitations in applying technology to evaluate student learning. The current evaluation process relies on teachers' subjective assessments, which restricts the information about students' progress. Therefore, the implementation of machine learning is proposed as a solution to enhance objectivity in student learning evaluation through more effective data processing. The method used is the K-Means Clustering algorithm, which can group or classify data based on specific patterns. This study aims to evaluate the extent to which machine learning can process student learning evaluation data through the analysis results obtained from the clustering process, which are then used as benchmarks to improve the evaluation system and provide feedback for students needing improvement in their academic performance. The data used comprises students' grades from the odd semester of the 2024/2025 academic year, with a total of 210 data points. The clustering results produced three clusters: the "good" cluster with 60 students, the "average" cluster with 99 students, and the "low" cluster with 51 students.
Abstract:Bread product sales have become an important aspect of the bakery business, influenced by fluctuations in demand that are not easily predictable. Efficient sales management requires a deep understanding of sales patterns.…
. This study aims to optimize bread product sales by using the K-Means Clustering algorithm to analyze daily sales performance at Toko Roti Amin. The data used includes sales volume and transaction frequency for bread products, consisting of 356 data points. The results show that the bread products can be grouped into three clusters: 129 data in the “Good Sales” cluster, 28 data in the “Moderate Sales” cluster, and 199 data in the “Low Sales” cluster. These findings assist bakery owners in managing stock, production planning, and more targeted marketing strategies. Although there are limitations in using K-Means Clustering, such as dependence on the initial centroid selection, this study proves that applying this technique can enhance inventory management and maximize profit in the bakery business.
Abstract:The development of information technology provides solutions for increasing efficiency and accuracy in decision-making, such as in determining students eligible for BLT at SD Swasta IT ABI Husni. This study aims to implement…
ment the Naive Bayes algorithm to support a more objective BLT recipient selection process. The method used is CRISP-DM, starting from understanding the problem, data preparation, to model implementation. The data analyzed included type of residence, KPS recipients, parents' income, KIP recipients, number of siblings, distance from home and reasons for eligibility for BLT used were data from students of SD Swasta IT ABI Husni in the odd semester of 2024/2025, with a total of 137 data. The results of the study showed that the Naive Bayes algorithm was able to achieve an accuracy level of 98% with precision and recall of up to 100%, proving the effectiveness of the model in minimizing classification errors. In conclusion, the use of the Naive Bayes algorithm can help make decisions that are more targeted, transparent, and fair in the distribution of BLT.
Abstract:This research is motivated by the large number of prospective students who simply choose a major when they want to enter a vocational school without considering their abilities. The Decision Tree or C45 method is used because…
cause it is able to make decision trees that are easy to describe, and has a level of efficiency in handling discrete and numeric attribute data. While the Naive Bayes method is used because it has a high accuracy of results. This research was conducted based on data from students of SMK Muhammadiyah 10 Kisaran which contained questions about feelings of wrong majors, interests, and determinants of other majors. Data is divided into 2 labels, namely free labels (y) and bound labels (x). Followed by dividing the dataset into training data and testing data with a ratio of 70:30 in both methods to get the level of accuracy. From the results given, it can be seen that the C45 algorithm has an accuracy of 85% and the Naive Bayes algorithm has an accuracy of 26%. This shows that the C45 algorithm is more effective in classifying the available datasets compared to the Naive Bayes.
Abstract:The use of smartphones in Indonesia has been steadily increasing each year. In the era of the Fourth Industrial Revolution, smartphones have become a lucrative business sector, leading to intense market competition. Consequently,…
equently, smartphone companies must pay closer attention to the market segmentation desired by consumers. Data mining is the process of discovering significant relationships and patterns by analyzing large datasets using statistical and mathematical techniques. This study aims to identify and analyze the market segments of Android smartphone users among students at STMIK Royal. The data used in this research were collected from 122 student respondents. The study employs clustering using the K-means algorithm. The resulting data modeling will categorize market segments into several clusters. This segmentation yields three clusters: Cluster 1 (features), consisting of 36 respondents who prioritize price, battery, camera, and warranty; Cluster 2 (product), with 49 respondents who value all attributes except warranty; and Cluster 3 (superiority), comprising 37 respondents who prioritize camera, brand, and RAM.
Abstract:This study aims to explore the impact of social media use on the mental health of university students. Utilizing a qualitative approach, this study analyzes data from in-depth interviews with 10 students from various universities…
versities in Indonesia. Thematic analysis reveals that social media use contributes to various aspects of mental health, including anxiety, depression, and self-image disorders. The study identifies that interactions on social media often trigger social comparison and unrealistic expectations about personal life, contributing to a decline in psychological well-being. However, the results also indicate that social media can be a useful tool for supporting mental health, particularly through support communities and awareness of mental health issues. The researchers suggest a need to increase awareness of healthy and constructive social media use among students. This study also emphasizes the importance of mental health education and intervention programs in educational institutions. These results provide insights for policymakers, educators, and mental health practitioners in developing effective strategies to support the mental health of students in the digital era. This study paves the way for further research on the specific impact of social media features and algorithms on student mental health.
Abstract:The advancement of digital technology has encouraged the adoption of Artificial Intelligence (AI) in online counseling services to enhance the accessibility and quality of mental health care. The emergence of AI through…
chatbots, virtual counselors, and digital mental health platforms presents various opportunities, challenges, and ethical implications that require comprehensive examination. This study aims to analyze the opportunities offered by AI, the challenges associated with its implementation, and the ethical implications of its use in online counseling services. The study employed a literature review method with a qualitative approach, examining 15 scientific articles published between 2016 and 2026. Data were analyzed using content analysis through the synthesis of findings from previous studies. The results indicate that AI has the potential to expand access to counseling services, improve service efficiency, support preliminary assessments, and facilitate the delivery of more personalized and data-driven interventions. However, the use of AI continues to face several challenges, including limitations in understanding human emotions and social contexts, the risk of inaccurate responses, algorithmic bias, and the potential for user dependency on technology. The most prominent ethical concerns involve privacy protection, data security, algorithmic transparency, system accountability, and professional responsibility. This study concludes that AI is more appropriately positioned as a supportive tool or co-counselor for professional counselors rather than a replacement for human practitioners. The novelty of this study lies in its integration of the opportunities, challenges, and ethical implications of AI into a comprehensive analytical framework.
Abstract:The advancement of digital technology has accelerated the adoption of Artificial Intelligence (AI) in e-counseling services to address the growing demand for mental health support in the digital era. The increasing number…
r of clients, counselors’ limited availability, and the challenges associated with conducting psychological assessments through online platforms have created a pressing need for innovative solutions. This study aims to examine the role of AI as a co-counselor, evaluate the effectiveness of its integration into e-counseling practices, and identify the challenges associated with its implementation. The study employed a Narrative Literature Review method by critically analyzing relevant scholarly publications. The findings indicate that AI functions as a collaborative partner that assists counselors in assessment processes, data analysis, emotion detection, service documentation, and the continuous monitoring of clients’ psychological conditions. The integration of AI has been shown to enhance the efficiency, accessibility, and overall quality of digital counseling services. However, its implementation continues to face significant challenges related to data privacy, information security, algorithmic bias, and limitations in understanding cultural contexts and human empathy. The novelty of this study lies in its comprehensive conceptualization of AI as a co-counselor that supports the entire e-counseling service cycle through a collaborative model integrating the analytical capabilities of technology with the humanistic competencies of counselors, thereby fostering more effective, ethical, and inclusive counseling services.