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The Influence of Viral Marketing and Price on Purchasing Decisions Through Customer Trust: Case Study of the Skincare Brand Skintific Wulandari, Natasya; Arafah, Willy
International Journal of Business, Law, and Education Vol. 5 No. 2 (2024): International Journal of Business, Law, and Education
Publisher : IJBLE Scientific Publications Community Inc.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56442/ijble.v5i2.721

Abstract

This study aimed to explore the impact of viral marketing, pricing strategies, and consumer trust on purchasing decisions regarding products released by Skintific. The approach utilized in this study was quantitative, with a sample size of 90 respondents who had already made purchases of Scientific products. Analysis conducted using SEM-PLS revealed that both viral marketing and consumer trust in Scientific products positively influenced consumers' willingness to transact, while pricing did not significantly affect purchase decisions. Additionally, it was found that other factors such as gender, age, and duration of consumer engagement with social media also had an impact
Implementation of Convolutional Neural Networks (CNN) for Crowd Counting in Shopping Mall Environments Prihandoko, P; Wulandari, Natasya; Eska, Juna
IJISTECH (International Journal of Information System and Technology) Vol 8, No 4 (2024): The December edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i4.377

Abstract

Accurate crowd counting is crucial in public spaces such as shopping malls to ensure safety and optimize resource management. This article explores the use of Convolutional Neural Networks (CNN), specifically a modified VGG16 architecture, for real-time crowd counting in shopping mall environments. Using a dataset collected from various crowd scenarios, the model was trained and tested using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the proposed model is effective, achieving higher accuracy compared to traditional methods, thanks to advanced feature extraction techniques. This research offers a robust and scalable solution to enhance security and improve crowd management in commercial spaces.
Implementation of Convolutional Neural Networks (CNN) for Crowd Counting in Shopping Mall Environments Prihandoko, P; Wulandari, Natasya; Eska, Juna
IJISTECH (International Journal of Information System and Technology) Vol 8, No 4 (2024): The December edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i4.377

Abstract

Accurate crowd counting is crucial in public spaces such as shopping malls to ensure safety and optimize resource management. This article explores the use of Convolutional Neural Networks (CNN), specifically a modified VGG16 architecture, for real-time crowd counting in shopping mall environments. Using a dataset collected from various crowd scenarios, the model was trained and tested using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the proposed model is effective, achieving higher accuracy compared to traditional methods, thanks to advanced feature extraction techniques. This research offers a robust and scalable solution to enhance security and improve crowd management in commercial spaces.
Community commitment to manage forests that integrate al-'urf values in reducing tenurial conflicts from an islamic legal perspective Hamka, Hamka; Satriani, Indra; Raziq, Abd.; Atika, Nur; Eril, Eril; Wulandari, Natasya; Harmilawati, Harmilawati
Asy-Syari’ah : Jurnal Hukum Islam Vol. 11 No. 1 (2025): Asy-Syari'ah: Jurnal Hukum Islam, Januari 2025
Publisher : LP3M Universitas Islam Zainul Hasan Genggong

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research examines the community's commitment to managing forests by integrating al-'Urf values to reduce tenure conflicts among forest area communities. This research contributes to discussions regarding forest conservation from an Islamic law perspective. This research is a qualitative study using observation, interviews and documentation methods in collecting data, namely going directly to the field which is the object of research. This research shows that it is important to have community commitment in managing forests by integrating al-'urf values. The local community's perception of forests is different from that of the government, where the government bases its designation on references to legislation that they themselves have made in the past. Community commitment in managing forests using the al-'urf approach plays an important role in reducing conflict and resolving or opening management and ownership rights to land or forests. Islamic law in this context focuses on forest management efforts based on the Koran, hadith and expert opinions in order to achieve the common interests and welfare of the community of all parties involved, whether local, government or other parties with an interest in forest preservation. Community efforts to reduce tenure conflicts through Islamic Law with the 'urf approach can resolve land disputes that are not only based on individual or group rights, but also consider the long-term impact on shared prosperity that prioritizes the principles of justice, sustainability and social harmony, with the aim of achieving a solution that benefits all parties without harming any party.