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All Journal JTT (Jurnal Teknologi Terpadu) Journal of Humanities and Social Studies
Azra, Muhammad Azyumardi
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Arts Humanities Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Negotiating The Identity Of Punk Children Amidst Social Diversity In The City Of Semarang Azra, Muhammad Azyumardi; Khoir, Tholkhatur
JHSS (JOURNAL OF HUMANITIES AND SOCIAL STUDIES) Vol 9, No 2 (2025): Journal of Humanities and Social Studies
Publisher : UNIVERSITAS PAKUAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/jhss.v9i2.11963

Abstract

This research examines the identity negotiation process of punk children in the city of Semarang as part of subcultural dynamics in the context of a pluralistic urban society. Punk children are positioned as a marginalized group that faces stigma and social exclusion, but also shows adaptive capacity through various resistance strategies and community solidarity. Using a qualitative approach with phenomenological methods, data was collected through participant observation, in-depth interviews and documentation. The research results show that punk children negotiate their identity through symbolic performativity, situational adaptation, use of digital space, as well as social and artistic activities that build dialogue with the wider community. Community and internal solidarity play an important role in forming and maintaining collective identity amidst external pressures. This study confirms that subcultural identities are dynamic, and punk subculture is an alternative form of expression that contributes to the city's social diversity. These findings highlight the importance of a humanist approach in understanding the existence of subcultures as a legitimate part of urban society.
PREDIKSI GANGGUAN PANIK MENGGUNAKAN KNOWLEDGE DISCOVERY IN DATABASE DENGAN ALGORITMA GRADIENT BOOSTING Maulizidan, Muammar Ramadhani; Hermanto, Muhammad Lucky; Ardhillah, Onky; Azra, Muhammad Azyumardi; Purba, Kevin Agustin; Zidan, Umar Rahman; Tania, Ken Ditha; Meiriza, Allsella
Jurnal Teknologi Terpadu Vol 13, No 2 (2025): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v13i2.2518

Abstract

In an effort to enhance the diagnosis and intervention of panic disorder, this study develops a predictive model for determining the severity level of panic disorder using the Knowledge Discovery in Databases (KDD) approach. The dataset comprises variables such as age, gender, personal and family history, current stressors, symptom severity, impact on daily life, demographics, medical history, psychiatric history, substance use, coping mechanisms, social support, and lifestyle factors. The Gradient Boosting algorithm was employed to analyze the data and uncover complex patterns among the variables. The results indicate that the proposed model is capable of classifying the severity of panic disorder with high accuracy, aligning with findings from previous studies that utilized similar approaches. Other research also supports the effectiveness of machine learning algorithms in predicting panic attacks using data from wearable devices and mobile applications. These findings are expected to contribute to the development of decision support systems in the field of mental health. 
Co-Authors Ardhillah, Onky Hermanto, Muhammad Lucky Ken Ditha Tania Khoir, Tholkhatur Maulizidan, Muammar Ramadhani Meiriza, Allsella Purba, Kevin Agustin Zidan, Umar Rahman