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All Journal Jurnal Ilmu Sosial dan Humaniora Jurnal Akuntansi Profesi EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis Jurnal Ilmiah Wahana Akuntansi SENTRALISASI Alignment: Journal of Administration and Educational Management International Journal of Social Science and Business Aptisi Transactions on Management JIA (Jurnal Ilmiah Akuntansi) Jurnal Bisnis dan Akuntansi JURNAL MANAJEMEN DAN BISNIS INDONESIA Business Management Journal Program Studi Manajemen Jurnal Sains Manajemen dan Bisnis Indonesia The Indonesian Journal of Accounting Research BUDGETING : Journal of Business, Management and Accounting JRAK (Jurnal Riset Akuntansi dan Bisnis) (e-Journal) Journal of Management - Small and Medium Enterprises (SME's) Journal of Economics and Business UBS Jurnal Pengabdian Masyarakat Indonesia juremi: jurnal riset ekonomi Jurnal Abdi Masyarakat Indonesia ASERSI : Jurnal Akuntansi Terapan dan Bisnis Accountia Journal (Accounting, Trusted, Inspiring and Authentic Journal) Jurnal Pengabdian Masyarakat Manage Jurnal Pengabdian Masyarakat IPTEKS
DWI CAHYONO
Universitas Muahammadiyah Jember
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Membangun Pemahaman Keuangan Untuk Mencegah Korban Pinjol Pada Mahasiswa Universitas Muhammadiyah Jember Dwi Cahyono; Radina Ayuningtyas; Ananda Dwi Maulidiah; Kamilatul Jannah; Aurelia Trya Febri Nur Aisiyah; Zico Is Mirza Wijaya; Evi Lestari
Jurnal Pengabdian Masyarakat IPTEKS Vol. 10 No. 1 (2024): JURNAL PENGABDIAN MASYARAKAT IPTEKS
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/jpmi.v10i1.2150

Abstract

Dalam era digital yang telah berkembang pesat, teknologi finansial menjadi semakin penting bagi kehidupan sehari - hari, termasuk Financial Technology (FinTech) yang menggabungkan inovasi keuangan dengan teknologi modern. Salah satu jenis FinTech yang populer di Indonesia adalah Peer To Peer (P2P) Lending atau pinjaman online (pinjol). Artikel ini membahas kegiatan Pengabdian Kepada Masyarakat (PKM) yang dilakukan oleh Tim PKM Universitas Muhammadiyah Jember untuk meningkatkan pemahaman keuangan mahasiswa yang berguna untuk mencegah agar tidak ada korban pinjaman online (pinjol). Hasil dari kegiatan PKM yang dilakukan oleh Mahasiswa Universitas Muhammadiyah Jember menunjukkan bahwa pelatihan dan sosialisasi dengan topik mengenai dampak negatif pinjol serta pengelolaan keuangan yang baik, sangat bermanfaat bagi mahasiswa. Partisipasi mahasiswa dalam kegiatan PKM dinilai baik, sehingga diharapkan kegiatan pembinaan dan pelatihan ini terus berlanjut untuk meningkatkan kemampuan mahasiswa dalam mengelola keuangan dengan baik. Dengan demikian, artikel ini dapat memberikan kontribusi dalam upaya mencegah mahasiswa dari menjadi korban pinjaman online melalui pemahaman keuangan yang lebih baik
Artificial Intelligence Driven Predictive Risk Management in Green Technology Investment Paroli Paroli; Agung Rizky; Qurotul Aini; Dwi Cahyono; Jonathan Parker; Untung Rahardja
APTISI Transactions on Management (ATM) Vol 10 No 2 (2026): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cz38xg11

Abstract

This study explores predictive risk management in green technology investments by leveraging Artificial Intelligence (AI) to address uncertainties associated with sustainable projects. As global financial institutions and governments increasingly allocate capital toward renewable energy, smart infrastructure, and low-carbon innovation, investors face multidimensional risks, including market volatility, technological failure, and regulatory change. Therefore, this research aims to develop an AI-driven predictive framework capable of identifying, analyzing, and forecasting potential investment risks in green technology portfolios to support informed decision-making. The study employs a quantitative approach using machine learning algorithms, including Random Forest, Gradient Boosting, and Neural Networks, trained on historical financial indicators, environmental performance metrics, and policy datasets. Each algorithm is selected based on its strengths: Random Forest for robustness, Gradient Boosting for predictive accuracy, and Neural Networks for capturing complex nonlinear relationships. A comparative perspective is used to highlight their tradeoffs, followed by feature importance analysis and predictive validation through cross-validation and evaluation metrics such as accuracy, precision, and RMSE. The findings show that the proposed model improves early risk detection compared to conventional statistical models, highlighting the effectiveness of machine learning in handling complex sustainability data. Furthermore, it identifies key risk determinants and enhances predictive reliability. Consequently, integrating AI-based predictive analytics into green investment strategies can strengthen risk mitigation, improve investor confidence, and support sustainable financial decision-making.
Co-Authors Abadi Sanosra Achmad Syahfrudin Zulkarnnaeni Ade Rani Aprillia Afroh, Ibna Kamilia Fiel AGUNG MULYONO Agung Rizky Aliyatus Syamilah Ananda Dwi Maulidiah Anistiana Amaliyatus Ari Sita Nastiti Atmanegara, Stivani Yanti Aulia Rachmaniyah Aurelia Trya Febri Nur Aisiyah Ayu Tinis Khaulia Ayuk Firdawati Bela Anggardini Catur Indah Sulistiyorini Citra Dwi Agustin Dea Alvionita Azka Denia Ratna Sari Devi Wulandari Devita Eka Lestari Didik Eko Pramono Dino Angga**, Budi Santoso* Dyah Probowulan Erika Puspita Sari Erike Wulandari Evi Lestari Faruq Syah Permana Putra Fathushobi Ridwan Rulistia Adji Fitriya, Elok GALUH SYAH PUTRA Gardina Aulin Nuha Haris Hermawan Heni Dwi Susanti Iid Pua Wadjo Imam Ghozali Indra Fatmawati Intan Sarifa Nuraeni Jonathan Parker Juli Rima Afiani Kamilatul Jannah La Ode Muhammad Ady Ardyawan Leni Siti Rukmana Deffi Lina Amelia Linda Indrawati Maharani, Astrid Malik Abdul Jabbar Moch Anas Kurniawan Muh. Bayu Muhammad Bayu Muhammad Bayu Muhammad Fathorossi Muhammad Luqman Hakim Muhammad Luqman Hakim, Muhammad Luqman Muhammad Yazir Nabila Zubaidi Nadhif Qotrunnada Ni Nyoman Putu Martini G Nina Martiana Nopa Dwi Saputra Nursaid Nursaid Nurul Qomariah Octavia Paramitha Paroli Paroli Prijono, Wiwin Puguh Probowulan, Diyah Putri Gusti Aulia Qurotul Aini Radina Ayuningtyas Rasid Rasid Rendy Mirwan Aspirandi Rika Astutik S, Sya?roni Siti Juariyah Slamet Wijiono Sukma Lesmana Susbiyani, Arik Suwarno Suwarno Sya’roni S Toni Herlambang Triono Triono Untung Rahardja Wijiono, Slamet Wiwin Puguh Prijono Zico Is Mirza Wijaya