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EFFECT OF MARKETING MIX AND NATIONAL CULTURE ON CONSUMER BUYING INTENTION FOR MUSIC PRODUCTS: A CASE STUDY OF INDONESIAN AND INDIAN CONSUMERS Oswari, Teddy; Kusumawati, Reni Diah; Yusnitasari, Tristyanti; Shukla, Vijay Kumar
ASEAN Marketing Journal Vol. 11, No. 1
Publisher : UI Scholars Hub

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Manuscript type: Research articles.Research Aims: To determine the effect of marketing mix and culture on consumer buying intention on music product.Design/methodology/approach: The research conducted a different test to determine there are whether or not differences in consumer behavior in Indonesia and in India.Research Findings: The results of this study can provide information to music industry about the factors that can attract consumer buying intention of music products, so the music industry can determine the right product marketing strategy.Theoretical Contribution/Originality: This research was conducted by analyzing the perceptions of consumers from Indonesia and India by paying attention to internal and external factors such as the culture of Indonesia and India.Practitioner/Policy Implication: Reducing piracy of music works, with the implementation of laws that can ensnare piracy that occurs.Research limitation/Implications: The research is only seen from consumer perceptions, while the perceptions of company or music industry are still not done.

Consumer Perception of Agribusiness E-marketplace Opportunities in Indonesia Kusumawati, Reni Diah; Oswari, Teddy; Yusnitasari, Tristyanti; Dutt, Himanshu
Majalah Ilmiah Bijak Vol. 19 No. 1: March 2022
Publisher : Institut Ilmu Sosial dan Manajemen STIAMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31334/bijak.v19i2.1934

E-commerce for agribusiness products began to develop in Indonesia in 2015, but it is still very small in number when compared to products from other industries. E-marketplace allows farmers to reach a wider range of consumers, so that everyone who has internet access has the opportunity to be able to enjoy fresh agricultural products. The success of an e-marketplace is supported by the good performance of the web, so that consumers will feel comfortable in searching for products and making transactions. This study discusses the performance of e-marketplaces, and consumer perceptions to determine consumer purchase intentions of agricultural product e-marketplaces. The population in this study are consumers who have used the agricultural product e-marketplace. The data were obtained through a questionnaire, then a classic assumption test was carried out to determine the appropriateness of the statements used in the questionnaire and the distribution of the data used. Furthermore, the data will be analyzed using multiple linear regression. The results show that partially only the convenience of the website from the e-marketplace performance indicators that affects consumer purchase intentions of agricultural products, while other variables do not affect consumer purchase intentions of e-marketplaces that offer agricultural products. Simultaneously, the results of the study indicate that e-marketplace performance, perceived ease of use, and perceived benefits influence consumer purchase intentions in agricultural product e-marketplaces. The contribution of the influence of the independent variables to the dependent variable is 17.9%, and the rest is influenced by other variables not included in the study.

Strategi Komunikasi Pelestarian Budaya Tari Tradisional Jaipong di Era Modernisasi pada Sanggar Eschoda Management Ohorella, Noviawan Rasyid; Rahel Dwi Natalia; Dyah Anggraini; Yusnitasari, Tristyanti
CARAKA : Indonesia Journal of Communication Vol. 5 No. 2 (2024): Caraka : Indonesia Journal of Communication
Publisher : Indonesian Scientific Journal (Jurnal Ilmiah Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/caraka.v5i2.186

Penelitian ini bertujuan untuk mengetahui strategi komunikasi dalam Sanggar Eschoda Management sebagai upaya dalam pelestarian budaya tari tradisional Jaipong, serta bagaimana peran anggota sanggar dan masyarakat mempertahankan warisan budaya di tengah arus modernisasi. Metode yang digunakan dalam penelitian ini adalah metode pendekatan kualitatif deskriptif dengan paradigma konstruktivisme. Teori yang digunakan pada penelitian ini adalah teori perencanaan komunikasi. Teknik pengumpulan data pada penelitian ini yaitu melalui observasi langsung, wawancara mendalam, dokumentasi dan studi pustaka. Hasil penelitian dari penelitian ini sudah sesuai dengan asumsi dasar teori perencanaan komunikasi, bahwa perencanaan yang kompleks serta motivasi yang cukup besar akan menguatkan tujuan. Sanggar Eschoda Management menggunakan media sosial dan media konvensional sebagai strategi dalam pelestarian budaya tari tradisional Jaipong. Strategi komunikasi yang digunakan oleh Sanggar Eschoda Management sudah cukup efektif, Sanggar Eschoda Management dengan sasaran khususnya generasi muda dan masyarakat luas sebagai upaya pelestarian budaya di era modernisasi. Strategi lain yang digunakan adalah dengan membuat suasana sanggar tersebut menjadi nyaman serta aktif mengikuti lomba serta tampil pada acara kesenian dan acara pemerintahan, dengan demikian Sanggar Eschoda Management menjadi wadah dalam pelestarian budaya tari tradisional Jaipong di era modernisasi.

Market Segmentation for Local Product Marketing Strategy Using K-Means and Dempster-Shafer Algorithm Implementation Hendrawan, Satya Arisena; Yusnitasari, Tristyanti; Oswari, Teddy
Journal Innovations Computer Science Vol. 4 No. 1 (2025): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i1.244

Market segmentation represents a critical challenge in local product marketing, particularly when addressing complex consumer behavior patterns and uncertain classification environments in today's digital economy. This research develops and validates a hybrid model integrating K-Means Clustering with Dempster-Shafer theory to enhance segmentation accuracy and reliability for local product markets. The K-Means algorithm groups consumers based on demographic, psychographic, and behavioral characteristics, while Dempster-Shafer theory quantifies uncertainty and provides confidence measures for segment assignments. Data collection involved comprehensive consumer surveys and transaction records from 2,847 participants across multiple local product categories over a 12-month period. The hybrid model achieved superior performance with 87.5% accuracy, 85.3% precision, 86.1% recall, and 85.7% F1-score, representing improvements of 5.4% over standard K-Means and 8.2% over hierarchical clustering methods. Four distinct market segments were identified: Young Urban Professionals (28%), Value-Conscious Families (35%), Traditional Loyalists (22%), and Digital Natives (15%), each exhibiting unique purchasing patterns, digital engagement levels, and price sensitivity characteristics. Cross-validation yielded a consistency score of 0.91 with segment stability demonstrated through 8.3% churn rate and conflict measure K = 0.12, indicating substantial agreement among evidence sources. The methodology successfully addresses uncertainty in consumer classification while providing actionable insights for targeted marketing strategies, pricing optimization, and customer retention programs. Local product marketers can implement this framework to develop evidence-based marketing approaches that accommodate both traditional and digital consumer preferences, enabling competitive positioning in increasingly complex market environments. The research establishes a scalable and practical solution for small to medium enterprises seeking sophisticated market analysis capabilities without requiring extensive computational infrastructure or technical expertise.

Development of A Deep Learning Model for Mental Health Classification and Early Screening through Draw a Person (DAP) Test Images Nurasiah, Nurasiah; Mutiara, Achmad Benny; Yusnitasari, Tristyanti; Asmarany, Anugriaty Indah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.700

Mental health, as defined by the World Health Organization (WHO), is a fundamental aspect of overall well-being. The increasing complexity of modern society, coupled with rising levels of competition and stress, significantly impacts individuals’ mental health. The DAP test is a psychological assessment tool that uses human figure drawings to gain insights into an individual’s personality and mental condition. YOLO (You Only Look Once) is a deep learning algorithm based on Convolutional Neural Networks (CNNs) designed for real-time object detection. This study utilizes a DAP image dataset contributed by adolescents aged 12 to 16 years to develop a model for detecting and classifying objects in DAP images using the YOLOv8 algorithm. Optimal training results were achieved after 150 epochs, yielding a Precision of 0.821, Recall of 0.799, and mAP50 of 0.88. The model evaluation demonstrated an F1-Score of 0.78, indicating a balanced performance between Precision and Recall. Psychological analysis was conducted based on symptoms extracted from the characteristics of DAP images. Mental health conditions were classified according to severity levels consisting of minor, medium, and serious, based on weighted symptomatology derived from DAP image characteristics. The successful development of this model highlights its capability to classify various mental health conditions based on psychological analysis of DAP images. The findings suggest that mental health classification using DAP test images has the potential to support early screening and psychological assessment by providing an innovative and objective approach to identifying psychological indicators.

Market Segmentation for Local Product Marketing Strategy Using K-Means and Dempster-Shafer Algorithm Implementation Hendrawan, Satya Arisena; Yusnitasari, Tristyanti; Oswari, Teddy
Journal Innovations Computer Science Vol. 4 No. 1 (2025): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i1.244

Market segmentation represents a critical challenge in local product marketing, particularly when addressing complex consumer behavior patterns and uncertain classification environments in today's digital economy. This research develops and validates a hybrid model integrating K-Means Clustering with Dempster-Shafer theory to enhance segmentation accuracy and reliability for local product markets. The K-Means algorithm groups consumers based on demographic, psychographic, and behavioral characteristics, while Dempster-Shafer theory quantifies uncertainty and provides confidence measures for segment assignments. Data collection involved comprehensive consumer surveys and transaction records from 2,847 participants across multiple local product categories over a 12-month period. The hybrid model achieved superior performance with 87.5% accuracy, 85.3% precision, 86.1% recall, and 85.7% F1-score, representing improvements of 5.4% over standard K-Means and 8.2% over hierarchical clustering methods. Four distinct market segments were identified: Young Urban Professionals (28%), Value-Conscious Families (35%), Traditional Loyalists (22%), and Digital Natives (15%), each exhibiting unique purchasing patterns, digital engagement levels, and price sensitivity characteristics. Cross-validation yielded a consistency score of 0.91 with segment stability demonstrated through 8.3% churn rate and conflict measure K = 0.12, indicating substantial agreement among evidence sources. The methodology successfully addresses uncertainty in consumer classification while providing actionable insights for targeted marketing strategies, pricing optimization, and customer retention programs. Local product marketers can implement this framework to develop evidence-based marketing approaches that accommodate both traditional and digital consumer preferences, enabling competitive positioning in increasingly complex market environments. The research establishes a scalable and practical solution for small to medium enterprises seeking sophisticated market analysis capabilities without requiring extensive computational infrastructure or technical expertise.

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