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All Journal Journal of Applied Data Sciences ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) BEAM
Asep Sutarman
University of Muhammadiyah Prof. Dr. HAMKA, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Social Sciences

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Community Service Strategies through Education and Innovation in Rare Earth Elements Downstreaming Rusmin Saragih; Imeldawaty Gultom; Elisa Ananda Natalia; Jonathan Parker; Asep Sutarman; Paroli Paroli
ADI Pengabdian Kepada Masyarakat Vol 6 No 1 (2025): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v6i1.1341

Abstract

The downstreaming of Rare Earth Elements (REE) in Indonesia has significant potential to support national industrialization but faces challenges in environmental management and community involvement. Communities in REE-producing areas generally lack sufficient understanding of the ecological impacts and economic opportunities related to downstreaming activities. This community service program aims to enhance local community capacity through education and innovation in environmental management and sustainable utilization of REE downstreaming potential. The program applied a qualitative approach through socialization, counseling, group discussions, and community mentoring in potential REE-producing areas. It was supported by policy analysis and participatory observation to assess community understanding and engagement. The community service activities resulted in improved public awareness of environmental impacts and REE waste management. Communities became more active in promoting environmentally friendly practices and initiated simple innovations to support sustainable downstreaming. The conclusion of this community service program shows that education and innovation are effective strategies to strengthen community roles as active partners in supporting sustainable REE downstreaming. This approach can serve as a collaborative model between government, industry, and communities to promote environmentally conscious development of strategic natural resources.
Real Time Audience Analytics Using Machine Learning to Measure Listener and Viewer Cultural Engagement Asep Sutarman; Felix Sutisna; Dimas Aditya Prabowo; Kgomotso Moyo
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/beam.v1i1.131

Abstract

The rapid evolution of digital media has transformed audience interaction, yet traditional metrics like views and likes fail to capture the nuanced emotional and cultural dynamics of broadcast content. This study develops a real-time audience analytics framework using machine learning to measure deep cultural engagement and emotional resonance within digital media environments. Adopting a hybrid methodological approach, the research integrates Natural Language Processing (NLP) with qualitative interpretation. The system processes live interaction data, employing sentiment analysis and pattern recognition to categorize audience responses into complex emotional and cultural engagement tiers beyond simple polarity. Findings demonstrate that the machine learning model effectively identifies real-time shifts in audience sentiment, revealing how specific cultural cues trigger heightened engagement and collective emotional responses. This research advances audience analytics by bridging the gap between computational speed and qualitative depth, offering a scalable model for broadcasters and researchers to understand the cultural impact of digital content as it happens.
Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction Chandra Lukita; Lalu Darmawan Bakti; Umi Rusilowati; Asep Sutarman; Untung Rahardja
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.131

Abstract

This research aims to predict and analyze customer churn using Data Mining and Machine Learning methods. The background of this research is based on the importance of understanding the factors that influence customer decisions to churn, as well as improving the effectiveness of customer retention strategies in a business context. The method used in this research involves the use of a customer bank dataset that includes information about customers who left in the past month, services registered by customers, customer account information, and demographic info about customers. The factors most influential to churn were identified through heatmap analysis, including MonthlyCharges, PaperlessBilling, SeniorCitizen, PaymentMethod, MultipleLines, and PhoneService. This research compares the performance of several machine learning algorithms, including Random Forest, Logistic Regression, Adaboost, and Extreme Gradient Boosting (XGBoost), to predict customer churn. Accuracy metrics and confusion matrix results are used to evaluate the performance of these algorithms. The results showed that XGBoost proved to be the best algorithm in predicting customer churn with high accuracy. The factors that have been correctly identified do not provide missed precision, showing a significant influence on customer churn decisions. The novelty and uniqueness of this research lies in focusing on the factors that have the most influence on customer churn and comparing the performance of machine learning algorithms. This research provides more specific and relevant insights for companies in developing effective customer retention strategies. However, this research has some limitations. One of them is the use of a dataset limited to a customer bank, so the generalizability of the findings of this research may be limited to that business context. In addition, other factors that are not the focus of this research may also contribute to the prediction of customer churn.
Co-Authors Chandra Lukita Dimas Aditya Prabowo Elisa Ananda Natalia Felix Sutisna Imeldawaty Gultom Jonathan Parker Kgomotso Moyo Lalu Darmawan Bakti Paroli Paroli Rusmin Saragih Umi Rusilowati Untung Rahardja