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All Journal Eastasouth Journal of Effective Community Services The Es Accounting and Finance West Science Social and Humanities Studies
Afrizal Afrizal
STIE Eka Prasetya
Humanities Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Pemberdayaan Pelaku UMKM Dalam Rangka Meningkatkan Kelangsungan dan Mengembangkan Usaha Bakmie Purnama Muh. Aniar Hari Swasono; Rizal Bakti; Yusniar Yusniar; Montaris Silaen; Afrizal Afrizal
Eastasouth Journal of Effective Community Services Vol 2 No 01 (2023): Eastasouth journal of Effective Community Services (EJECS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/ejecs.v2i01.141

Abstract

Pengabdian ini fokus pada upaya pemberdayaan pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) dalam konteks pengembangan bisnis Bakmie Purnama. Tujuan utama dari Pengabdian ini adalah untuk menganalisis strategi pemberdayaan yang diterapkan untuk meningkatkan kelangsungan dan pengembangan usaha Bakmie Purnama. Metode Pengabdian melibatkan wawancara mendalam dengan pemilik usaha, analisis data kualitatif, serta pengamatan langsung terhadap operasional usaha. Melalui program pemberdayaan yang terfokus, termasuk pelatihan keterampilan, bimbingan pengelolaan usaha, serta dukungan dalam hal pemasaran dan pengembangan produk, Pengabdian ini menunjukkan bahwa pelaku UMKM Bakmie Purnama mampu mengatasi berbagai tantangan bisnis dan meningkatkan daya saing mereka di pasar. Hasil analisis menunjukkan peningkatan dalam efisiensi operasional, kualitas produk, dan efektivitas pemasaran. Hasil Pengabdian ini memberikan pandangan mendalam tentang pentingnya pemberdayaan pelaku UMKM dalam menghadapi persaingan bisnis yang semakin kompleks. Pengalaman Bakmie Purnama menjadi contoh nyata tentang bagaimana kombinasi strategi pemberdayaan dan upaya pengembangan dapat memengaruhi pertumbuhan bisnis yang berkelanjutan dan menguntungkan.
The Effect of Artificial Intelligence Adoption, Machine Learning, and AI Ethics on Product Innovation in Start-ups in Bogor Afrizal Afrizal; Hildawati Hildawati; Sehan Rifky; Arnes Yuli Vandika
West Science Social and Humanities Studies Vol. 2 No. 05 (2024): West Science Social and Humanities Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsshs.v2i05.906

Abstract

This research investigates the influence of artificial intelligence (AI) adoption, machine learning (ML) integration, and AI ethics on product innovation within Bogor's startup ecosystem. A quantitative approach was employed, collecting data through an online survey from 180 startups. Structural equation modeling with Partial Least Squares (PLS) 3 was utilized for data analysis. The results reveal significant positive relationships between AI adoption, ML integration, AI ethics, and product innovation. AI adoption and ML integration positively impact product innovation, while adherence to ethical AI practices also plays a crucial role. These findings highlight the importance of leveraging AI technologies responsibly and ethically to drive innovation within startup ecosystems. Policymakers, entrepreneurs, investors, and other stakeholders can utilize these insights to foster a conducive environment for sustainable growth and innovation in Bogor's startup community.
The Impact of Data Volume and Analytical Complexity in Big Data Technology on Financial Performance Prediction in Financial Companies in Indonesia Tanti Widia Nurdiani; M. Anas; Afrizal Afrizal; Indra Sulistiana
The Es Accounting And Finance Vol. 2 No. 01 (2023): The Es Accounting And Finance (ESAF)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esaf.v2i01.155

Abstract

This quantitative research explores the intricate relationship between data volume, analytical complexity, and their combined impact on financial performance prediction across 25 Indonesian financial companies. This study utilizes multiple regression analysis and statistical techniques using IBM SPSS Statistics version 26 to uncover the dynamics at play. The findings show that data volume and analysis complexity have a significant and positive role in improving financial performance prediction. The symbiotic relationship between these factors underscores the importance of adopting a holistic approach to data management and analysis. The results of this study have profound implications for financial professionals, data scientists, and decision makers, as it provides a roadmap for utilizing Big Data technology for more accurate and informed decision making in the Indonesian financial sector.
Co-Authors Arnes Yuli Vandika Bakti, Rizal Hildawati Indra Sulistiana M. Anas Montaris Silaen Muh. Aniar Hari Swasono Sehan Rifky Tanti Widia Nurdiani Yusniar Yusniar