Abstract:This study aims to analyze the effect of Fear of Missing Out (FOMO), hedonic shopping, and self-control on impulse buying among students who use Shopee, as well as to examine the role of self-control as a moderating variable.…
able. This research uses a quantitative approach with a survey method involving students of the Faculty of Economics and Business, Universitas Negeri Gorontalo, class of 2022, as respondents. Data collection was conducted using questionnaires distributed through forms and Google Forms. Data analysis was carried out using SPSS and SEM-PLS 4.1 to test the relationships among variables. The results show that Fear of Missing Out (FOMO) and hedonic shopping have a positive effect on impulse buying. This indicates that digital social pressure and pleasure-driven motivation are the main factors encouraging students to engage in impulsive purchases. In contrast, self-control does not affect impulse buying, suggesting that students’ ability to regulate themselves is not yet strong enough to reduce impulsive consumption behavior. This condition is influenced by the characteristics of students who do not yet have a stable income, so self-control has not developed optimally in financial management due to the absence of income.
Abstract:The This study is based on the phenomenon of increasing investment losses and FOMO (Fear of Missing Out) in Indonesia, with illegal investment losses amounting to IDR 139 trillion and FOMO among young people rising to 80%…
% by 2024. This trend aligns with the growing number of investors and digital literacy in Indonesia, particularly in Java, which has the highest concentration of investors. The large population of Generations Y and Z in West Java serves as the subject of this research, highlighting the gap between financial literacy (56.10%) and financial inclusion (88.31%). This reinforces the urgency of this study. Information disclosure is considered crucial in reducing information asymmetry and managing risk in investment decision-making. The main objective of this study is to examine the direct and indirect effects of digital literacy, financial literacy, gender, and FOMO on stock investment decisions, as well as to test the role of information disclosure. A quantitative approach is used, with a questionnaire distributed to 443 respondents from Generations Y and Z in West Java, all of whom have investment experience in stocks. The purposive sampling technique was used, and data analysis was conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS) to test model validity, reliability, and relationships between variables. The results show that digital literacy, financial literacy, gender, and FOMO significantly affect stock investment decisions. Information disclosure mediates the relationship between financial literacy, gender, and FOMO on investment decisions but does not mediate the relationship between digital literacy and investment decisions. Furthermore, information disclosure positively influences stock investment decisions, emphasizing the importance of transparency. This study contributes to the development of a theoretical model that highlights the role of market discipline through information disclosure. Practically, the findings can guide OJK and companies in designing digital-financial literacy programs and improving information transparency to prevent investment fraud and increase investor confidence. The study suggests that investors should enhance their understanding of investment risks and critically assess available information. Limitations include the focus on Generations Y and Z in West Java using purposive sampling, and the exclusion of other factors like education. The self-report quantitative method may lead to bias, and cross-sectional data does not capture changes in investment behavior over time. Future research is recommended to expand the demographic sample, include additional variables, and use a mixed-method approach for more comprehensive results.
Abstract:Digital technologies are changing rapidly, and this has changed the financial services industry, especially how people decide where to invest their money. This study looks at how digital literacy, financial literacy, and…
fear of missing out (FOMO) affect how much risk young investors are willing to take when investing with information disclosure as a mediating variable. The research data is taken from 447 investors of Gen Y and Z in West Java and used Partial Least Squares-Structural Equation Modeling (PLSSEM). The results show that being financially literate makes people more likely to search for financial information, but it also makes them less willing to take risks when investing. This suggests that people who know a lot about money are more careful when they invest. On the other hand, FOMO has a positive effect on both information searching and risk tolerance, showing how emotions can affect people online. Digital literacy helps people be more willing to take risks, but it does not have a big effect on how much information they search. These results show how important cognitive and emotional factors are in determining how people act when it comes to IT-driven finances. The study helps with responsible digital transformation by showing how important it is for individuals to be ready to navigate fintech ecosystems. It also gives regulators, platform providers, and educators ideas on how to promote more informed and resilient investment practices.
Abstract:Shopee, as one of the e-commerce establishments established in 2015 and part of the Sea Group in Southeast Asia, has succeeded in creating a safe, user-friendly and fast online shopping platform. With strong operations in…
n seven markets in Southeast Asia, Shopee not only provides a platform for buyers to find products, but also provides training and support to their sellers. The rapid growth of e-commerce in Indonesia is also influenced by factors such as the increase in population, increasing smartphone and internet users, and the development of financial technology companies. The convenience of online shopping encourages consumers to buy various desired products so that they are encouraged to behave consumptively. This then stimulates the phenomenon of impulse buying in consumers. Currently, economic activities are dominated by Generation Z's behavior to carry out activities such as scrolling, online shopping, and making transactions. Some marketplaces have not paid attention to the positive emotions of consumers with the Generation Z group in the target market, hedonic shopping motivation, fear of missing out, and shopping lifestyle that can trigger impulsive purchases which are part of the consideration of the preparation and strategy of innovative marketing targets according to the needs of market psychology. This study aims to find out how much respondents assess hedonic shopping motivation, fear of missing out, and shopping lifestyle, find out the influence of hedonic shopping motivation, fear of missing out, and shopping lifestyle on impulse buying generation Z, find out the influence of positive emotions on impulse buying generation Z, to know the influence of hedonic shopping motivation, fear of missing out and shopping lifestyle on impulse buying through positive emotions. This study uses a quantitative method. The sampling technique uses purposive sampling. The data obtained was 385 respondents by distributing questionnaires online. The respondents in this study are Generation Z Shopee users in Indonesia. Data analysis was conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS) which was then processed with SMARTPLS. The results of this study are hedonic shopping motivation, fear of missing out, shopping lifestyle, positive emotion have a significant positive effect on impulse buying.
Abstract:This research discusses the influence of social media content on the emergence of Fear of Missing Out (FOMO) behavior and its connection to increasing consumerism in Generation Z. This generation grew up in a digital ecosystem…
system saturated with visualizations of ideal lifestyles disseminated through platforms like TikTok, Instagram, and Twitter. Constant exposure to such content creates anxiety about missing out on seemingly important social experiences. FOMO then triggers consumptive actions, not due to functional needs, but because of symbolic impulses and social pressure to remain relevant within digital communities. This study employs a descriptive qualitative method with in-depth interviews of informants aged 20–26 years. The analysis results show that content such as unboxing, product reviews, and personal lifestyles have a significant impact on creating social pressure and shaping consumption-based identity. These findings underscore the importance of digital literacy and critical awareness so that Generation Z does not get trapped in a cycle of impulsive consumption, which poses risks to both mental and financial health. As an alternative, the Joy of Missing Out (JOMO) approach is introduced to build healthier and more authentic digital relationships.
Abstract:This study examines the transformation of digital marketing communication that positions visual elements as the primary component in event promotion on social media, particularly Instagram. This shift is driven by changes…
s in audience behavior, which has become more responsive to visual stimuli than to textual information. The research problem focuses on how visual elements are constructed as an effective persuasive communication strategy to capture attention, evoke emotions, and influence audience perceptions and interests through the peripheral route based on the Elaboration Likelihood Model framework. The study employs a qualitative approach using content analysis of 11 Instagram posts from @colorrunfestivalid selected through purposive sampling, supported by observation and interviews. The findings indicate that visual dominance reaches 82%, characterized by the use of bright colors, expressions of happiness, and interactive activities that effectively generate emotional appeal and rapid audience engagement. Most audiences pay greater attention to visuals than to text and demonstrate a willingness to participate after viewing the experiential representations presented. The psychological effect of fear of missing out further reinforces this response. The persuasion process is found to occur predominantly through the peripheral route, marked by low cognitive elaboration and a high reliance on visual cues. The study concludes that visuals function as the primary determinant of effective digital persuasive communication, as they simultaneously construct meaning, emotion, and experiential expectations. The novelty of this research lies in affirming visuals as the core of digital persuasion strategies through the integration of emotional, social, and psychological dimensions, while also reinforcing the dominance of the peripheral route as a key mechanism in contemporary media consumption.
Abstract:The rapid advancement of digital technology has brought significant changes to various aspects of life, including how individuals interact and access information. Young people, particularly university students, are among…
the most affected by these changes. One phenomenon that has emerged alongside the increasing use of social media is Fear of Missing Out (FoMO)—a feeling of anxiety or worry about being left out of trends, information, or social activities experienced by others. This phenomenon not only affects individuals psychologically but also has implications for students’ consumer behavior. This study aims to analyze the influence of FoMO on students’ consumptive behavior and to identify the mediating or reinforcing factors that shape this relationship. The research employs a qualitative approach, using data collection techniques through literature review from various recent and relevant sources. The findings indicate that FoMO has a significant correlation with the rise of consumptive behavior among students, especially in the context of purchasing symbolic goods that enhance one’s self-image on social media. Key factors influencing the relationship between FoMO and consumptive behavior include the level of digitalization, social dynamics such as peer pressure, and low self-control. Furthermore, the study finds that financial literacy plays an important protective role. Students with higher levels of financial literacy tend to be more capable of managing the consumptive urges triggered by FoMO. This research contributes new insights into the psychological impact of social media on students’ economic behavior. The novelty of the study lies in its in-depth analysis of the link between FoMO and student consumer behavior in the digital era, as well as the identification of relevant mediating factors. Therefore, the results of this study are expected to serve as a foundation for developing more adaptive digital and financial literacy education programs to meet the challenges of the technological era.
Abstract:Abstract: SMP Muhammadiyah 5 Samarinda still relies on manual evaluation with limited data analysis tools in predicting student academic achievement. This study aims develop a system for predicting the learning achievement…
nt of students at SMP Muhammadiyah 5 Samarinda using the Naive Bayes classification method. The dataset used consists of 192 student exam scores covering academic scores, attendance, parents’ education and income, and living conditions as independent variables, while the dependent variable is the achievement label (achieved or not achieved). The preprocessing stage includes label normalization, feature selection, and median imputation to handle missing data. The dataset was divided into 75% training data and 25%. The model was implemented as a pipeline consisting of a median imputer and a Gaussian Naive Bayes classifier. The evaluation results showed that the model achieved an accuracy of 79.2%, with a perfect recall value (1.00) in the high-achieving class and (0.64) in the low-achieving class. This shows that the model is quite effective in identifying high-achieving students. The trained model was then integrated into a Flask-based web application, which enables online predictions through a simple form interface, facilitating contextual interpretation. This system is expected to assist in educational decision-making by helping teachers identify students’ achievement levels early on and design more targeted learning interventions.
Keywords: academic performance; educational data mining; naive bayes; prediction system; student achievement
Abstrak: SMP Muhammadiyah 5 Samarinda masih bergantung pada evaluasi manual dengan alat analisis data terbatas dalam melakukan prediksi prestasi akademik siswa. Penelitian ini bertujuan mengembangkan sistem prediksi prestasi belajar siswa SMP Muhammadiyah 5 Samarinda menggunakan metode klasifikasi Naive Bayes. Dataset yang digunakan terdiri atas 192 data nilai ujian siswa yang mencakup skor akademik, kehadiran, pendidikan dan pendapatan orang tua, serta kondisi tempat tinggal sebagai variabel independen, sedangkan variabel dependen berupa label prestasi (berprestasi atau tidak berprestasi). Tahap preprocessing meliputi normalisasi label, seleksi fitur, serta imputasi median untuk menangani data yang hilang. Dataset dibagi menjadi 75% data latih dan 25%. Model diimplementasikan dalam bentuk pipeline yang terdiri atas median imputer dan Gaussian Naive Bayes classifier. Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 79,2%, dengan nilai recall sempurna (1,00) pada kelas berprestasi dan lebih rendah (0,64) pada kelas tidak berprestasi. Hal ini menunjukkan bahwa model cukup efektif dalam mengidentifikasi siswa berprestasi. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi web berbasis Flask, yang memungkinkan prediksi secara daring melalui antarmuka formulir sederhana untuk mendukung interpretasi kontekstual. Sistem ini diharapkan dapat membantu untuk pengambilan keputusan dalam pendidikan dengan membantu guru mengidentifikasi tingkat prestasi siswa sejak dini dan merancang intervensi pembelajaran yang lebih terarah.
Kata kunci: prestasi akademik; penambangan data Pendidikan; naive bayes; sistem prediksi; prestasi siswa
Abstract:Abstract: Stroke is one of the leading causes of death and disability in various parts of the world, including in Indonesia. Along with the development of digital technology, the use of Machine Learning in the health sector…
tor is growing, one of which is in an effort to predict the occurrence of stroke. This study aims to implement the Logistic Regression algorithm in predicting the likelihood of a person having a stroke based on data from the Brain Stroke dataset. The research process includes data preprocessing (missing value handling, normalization, and label encoding), dividing the data into 80% training data and 20% test data, as well as model training. The model was then evaluated using several measures such as accuracy, precision, recall, F1-score, and ROC-AUC, as well as a confusion matrix. The results of the study showed that Logistic Regression was able to provide stroke classification results with an accuracy of 82.4%, precision of 80.1%, recall of 78.6%, F1-score of 79.3%, and a ROC-AUC value of 0.87. Then, the model is integrated into applications that use Streamlit, so it can be used interactively to predict stroke risk in new data. The results of this study show that the combination of Machine Learning and web-based applications has the potential to support efforts to detect early stroke risk.
Keywords: logistic regression; machine learning; prediction; streamlit; stroke.
Abstrak: Stroke adalah salah satu penyebab utama kematian dan kecacatan di berbagai belahan dunia, termasuk di Indonesia. Seiring perkembangan teknologi digital, penggunaan Machine Learning dalam bidang kesehatan semakin berkembang, salah satunya dalam upaya memprediksi terjadinya penyakit stroke. Penelitian ini bertujuan untuk mengimplementasikan algoritma Logistic Regression dalam memprediksi kemungkinan seseorang mengalami stroke berdasarkan data dari dataset Brain Stroke. Proses penelitian meliputi preprocessing data (penanganan missing value, normalisasi, dan label encoding), membagi data menjadi 80% data latih dan 20% data uji, serta pelatihan model. Model kemudian dievaluasi menggunakan beberapa ukuran seperti akurasi, precision, recall, F1-score, dan ROC-AUC, serta confusion matrix. Hasil penelitian menunjukkan bahwa Logistic Regression mampu memberikan hasil klasifikasi penyakit stroke dengan akurasi sebesar 82,4%, precision 80,1%, recall 78,6%, F1-score 79,3%, dan nilai ROC-AUC sebesar 0,87. Kemudian, model tersebut diintegrasikan ke dalam aplikasi yang menggunakan Streamlit, sehingga dapat digunakan secara interaktif untuk memprediksi risiko stroke pada data baru. Hasil penelitian ini menunjukkan bahwa kombinasi Machine Learning dan aplikasi berbasis web berpotensi mendukung upaya deteksi dini risiko stroke.
Kata kunci: logistic regression; machine learning; prediksi; streamlit; stroke.
Abstract:Abstract: Recognizing one's interests and talents early on is crucial in guiding an individual toward a prosperous future. While distinct, interests and talents share a close relationship. Interest denotes a genuine attraction…
action to something without external pressure, and when consistently nurtured, it evolves into a skill or talent. Machine learning, specifically utilizing the SVM algorithm with the RBF kernel, can be applied to categorize interests and talents. Prior to SVM modeling, conducting Exploratory Data Analysis (EDA) is imperative for scrutinizing interests and talents. This analysis facilitates the identification of variables, enabling the elimination of missing values and ensuring the selection of appropriate interest and talent variables. The primary objective is to achieve optimal accuracy in modeling the classification of interests and talents. The insights gained from this research contribute to the creation of an application designed for categorizing interests and talents within SDN XYZ school. This application is designed for student use, assisting them in making informed decisions about their future education and career paths
Keywords: exploratory data analysis; interests and talents; machine learning; SVM Algorithm
Abstrak: Mengenali minat dan bakat seseorang sejak dini sangat penting dalam membimbing individu menuju masa depan yang sukses. Meskipun berbeda, minat dan bakat memiliki hubungan yang erat. Minat mengindikasikan ketertarikan yang tulus terhadap sesuatu tanpa tekanan eksternal, dan ketika terus-menerus dibina, berkembang menjadi keterampilan atau bakat. Pembelajaran mesin, khususnya dengan menggunakan algoritma SVM dan kernel RBF, dapat digunakan untuk mengelompokkan minat dan bakat. Sebelum pemodelan SVM, melakukan Analisis Data Eksploratif (EDA) sangat penting untuk mengkaji minat dan bakat. Analisis ini memfasilitasi identifikasi variabel, memungkinkan penghilangan nilai yang hilang, dan memastikan pemilihan variabel minat dan bakat yang tepat. Tujuan utamanya adalah mencapai akurasi optimal dalam pemodelan klasifikasi minat dan bakat. Temuan dari penelitian ini berkontribusi pada pengembangan aplikasi yang ditujukan untuk mengkategorikan minat dan bakat di sekolah SDN XYZ. Aplikasi ini dirancang untuk digunakan oleh siswa, membantu mereka membuat keputusan yang terinformasi mengenai pendidikan dan karier masa depan mereka.
Kata kunci: Algoritma SVM; exploratory data analysis; machine learning; minat dan bakat