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Contact Name
Ahmad Ashifuddin Aqham
Contact Email
ahmad.ashifuddin@gmail.com
Phone
+6285885852706
Journal Mail Official
febri@stiestekom.ac.id
Editorial Address
Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern Jl. Diponegoro No.69, Dusun I, Wirogunan, Kec. Kartasura, Kabupaten Sukoharjo, Jawa Tengah 57166 ; Email : lppm@stie-trianandra.ac.id
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Jawa tengah
INDONESIA
Education: Jurnal Sosial Humaniora dan Pendidikan
ISSN : 28282620     EISSN : 28282612     DOI : 10.51903
Pendidikan usia dini. Pendidikan Dasar. Pelajaran kedua. Pendidikan yang lebih tinggi. Pendidikan Karakter. Pendidikan non formal. Pendidikan Informal. Pendidikan Inklusi, dan Pendidikan Luar Biasa Lainnya (Bencana, Kemasyarakatan, Anti Korupsi, Bela Negara, dll). Sosial Humaniora Hukum
Arjuna Subject : Umum - Umum
Articles 93 Documents
Budaya Konsumtif dan Identitas Sosial: Studi Budaya terhadap Perilaku Konsumen Fashion Lokal Diana, Anita Nur; Oktaria, Novita
Education : Jurnal Sosial Humaniora dan Pendidikan Vol. 5 No. 3 (2025): November: Jurnal Sosial Humaniora dan Pendidikan
Publisher : Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/7sp8tg45

Abstract

This study examines the relationship between consumerist culture and social identity formation in the consumption of local fashion among urban youth in Indonesia. Employing a qualitative approach with a phenomenological design, data were collected through in-depth interviews with 10 urban consumers of local fashion aged 20–35 years who are active social media users. Thematic analysis revealed four main findings: support for local products, fashion consumption as a representation of identity and social status, the strong influence of social media on consumption preferences, and consumerism as part of everyday lifestyle practices. The results indicate that local fashion consumption is predominantly driven by symbolic motives related to self-image and cultural affiliation, while functional considerations such as comfort and quality remain secondary. This study contributes to cultural consumption studies by highlighting how local fashion serves as a medium for negotiating social identity in the digital era, particularly among urban Indonesian youth.
Pengaruh Algoritma TikTok terhadap Pola Konsumsi Konten Generasi Z di Indonesia: Studi Analisis Perilaku dan Strategi Engagement Putri, Anindya Saraswati; Prakoso, Bayu Ramadhan
Education : Jurnal Sosial Humaniora dan Pendidikan Vol. 5 No. 3 (2025): November: Jurnal Sosial Humaniora dan Pendidikan
Publisher : Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/q72r8m61

Abstract

TikTok, one of the most popular platforms, has earned Generation Z's affection in Indonesia. Through personalization, the app's intelligent algorithms force its users to consume content and interact. Even though such algorithms contain elements that may influence user consumption and interaction, the research done locally on the subject is rather scarce. A major concern in this study is how the TikTok algorithm impacts content consumption and engagement strategies used by Generation Z in Indonesia. The study followed a quantitative survey method among purposively sampled 450 active TikTok users of Generation Z. The data collection involved an online Likert scale questionnaire, which was processed through multiple linear regression analysis. It was found that the TikTok algorithm does affect patterns of content consumption significantly (β = 0.512; R² = 0.262) and engagement strategies (β = 0.431; R² = 0.186). 78.4% of respondents used the For You page to discover various content within the entertainment category (67.3%) as a prime focus, followed by light education (45.8%) and those that show viral trends (42.7%). The study ends up entering the literature by trying to put together technical algorithm analysis with behavioral analysis of digital consumption in the case of Indonesia's generation Z, thereby enriching the literature in algorithmic media theory. While most users engage with the content by liking videos (85.1%), fewer users share videos (39.6%): the fact is that users mostly engaged in the study using only one interaction form. An example of these practical implications is how the platform developers, marketers, and policy-makers will set about optimizing retention strategy and creating engagement with millennial or young users.  
Optimalisasi Artificial Intelligence dalam Pembelajaran Adaptif: Studi Kasus Platform EdTech Berbasis Data di Sekolah Menengah Permata, Citra Anggun; Nugroho, Doni Pratama
Education : Jurnal Sosial Humaniora dan Pendidikan Vol. 5 No. 3 (2025): November: Jurnal Sosial Humaniora dan Pendidikan
Publisher : Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/5gn23z75

Abstract

The rapid development of Artificial Intelligence (AI) has created new opportunities for adaptive learning in secondary education, particularly through data-driven Educational Technology (EdTech) platforms. However, empirical evidence on effective AI optimization in formal secondary schools in Indonesia remains limited. This study aims to examine the optimization of AI-based adaptive learning by identifying best practices, implementation barriers, and key success factors. A mixed-methods case study was conducted across five secondary schools in three Indonesian provinces. Quantitative data were collected from 500 students and analyzed using t-tests, ANOVA, and Pearson correlation, while qualitative insights were obtained from teachers, principals, and EdTech developers through thematic analysis. The findings show a 12.4% increase in average examination scores and an improvement in material completion rates from 74.2% to 88.6% within six months of AI implementation. A strong positive correlation was also found between AI usage intensity and academic achievement (r = 0.62, p < 0.01). This study contributes a practical evaluation framework for assessing the readiness and effectiveness of AI-driven adaptive learning based on real-world school data, offering actionable implications for educators, EdTech developers, and policymakers.

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