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All Journal JSEH (Jurnal Sosial Ekonomi dan Humaniora) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JATI (Jurnal Mahasiswa Teknik Informatika) Islam Universalia : International Journal of Islamic Studies and Social Sciences Jurnal E-Komtek Jurnal Penelitian Pendidikan Fisika Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) sudo Jurnal Teknik Informatika Governors Science and Technology: Jurnal Pengabdian Masyarakat Kemakmuran Hijau: Jurnal Ekonomi Pembangunan
Saputra, Rizal
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Exploring green investment interest in Islamic finance: The case of Indonesian Green Sukuk among university students Sukmaningrum, Lutfiah; Saputra, Rizal; Ariyanto, Muhammad Helmi
Kemakmuran Hijau: Jurnal Ekonomi Pembangunan Vol. 2 No. 1: (February) 2025
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jekop.v2i1.2025.2269

Abstract

Background: The escalating frequency of natural disasters in Indonesia, particularly hydrometeorological hazards, underscores the urgency of sustainable financing instruments that integrate environmental responsibility with Islamic ethical principles. The Indonesian Green Sukuk (IGS) represents such an instrument, yet little is known about the factors influencing young investors’ interest in it. Building on the Theory of Consumption Value (TCV), which comprises functional, social, religious, emotional, and knowledge dimensions, this study examines how these values shape students’ green investment interest. Methods: A quantitative correlational design was employed using cross-sectional data collected from 178 Universitas Gadjah Mada (UGM) students through an online questionnaire with a five-point Likert scale. Data were analyzed using ordinal logistic regression to test the influence of value dimensions, complemented by cluster tests to identify demographic variations. Findings: The results show that among the five TCV dimensions, only knowledge value significantly affects investment interest (odds ratio = 3.22, p < 0.05). Functional, social, religious, and emotional values were not significant. Cluster tests further reveal variations in investment interest based on gender, faculty affiliation, and income level, suggesting that demographic factors also play a role. Conclusion: Investment interest in IGS is primarily driven by cognitive understanding of its mechanisms, benefits, and sustainability impacts, rather than by functional, social, religious, or emotional considerations. Enhancing financial literacy is therefore essential to increase students’ engagement with sustainable Islamic financial instruments. Novelty/Originality of this article: This study contributes to the literature by applying TCV in the context of Islamic green finance among university students, a demographic often overlooked in prior research. It introduces the Integrated Greenvestment Class as an innovative literacy program to foster a generation of young investors who are financially literate, sustainability-oriented, and guided by Islamic ethical values in their financial decision-making.
Sistem Klasifikasi Perubahan Lahan pada Ekosistem Pesisir Berbasis Deep Learning Menggunakan Citra Sentinel-2: Studi Kasus: Tanjung Tiram, Sulawesi Tenggara Nurfadilah, Annisa; Saputra, Rizal; Sarita, Muhammad Ihsan
sudo Jurnal Teknik Informatika Vol. 5 No. 1 (2026): Edisi Maret
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v5i1.1635

Abstract

Perubahan tutupan lahan pada ekosistem pesisir merupakan isu lingkungan global yang signifikan karena berdampak langsung terhadap fungsi ekologis dan keberlanjutan hayati. Tanjung Tiram, Sulawesi Tenggara, merupakan kawasan pesisir yang rentan terhadap degradasi akibat tekanan aktivitas antropogenik, sehingga memerlukan pendekatan pemantauan yang otomatis dan akurat. Penelitian ini mengembangkan sistem klasifikasi dan kuantifikasi perubahan tutupan lahan pesisir yang berfokus pada ekosistem mangrove, terumbu karang, dan padang lamun. Data utama menggunakan citra Sentinel-2 Level-2A yang diproses melalui Google Earth Engine, dengan penerapan arsitektur Deep Learning hybrid UNet–ResNet34 untuk melakukan segmentasi semantik tingkat piksel. Hasil penelitian menunjukkan kinerja klasifikasi yang kuat dengan nilai akurasi sebesar 84,31% untuk mangrove, 95,99% untuk terumbu karang, dan 89,41% untuk padang lamun. Kemampuan segmentasi model dinilai representatif dengan capaian Mean IoU sebesar 0,6124 secara keseluruhan. Analisis multi-temporal periode 2018–2025 mengungkapkan dinamika spasial yang signifikan, ditandai dengan peningkatan luas mangrove sebesar 35,53% (95,20 ha), namun terjadi penurunan luas terumbu karang dan padang lamun masing-masing sebesar 32,39% (29,20 ha) dan 26,68% (98,76 ha). Temuan ini menegaskan bahwa integrasi Sentinel-2 dan Deep Learning mampu menyediakan informasi spasial yang andal untuk mendukung pengelolaan pesisir berkelanjutan. Sistem ini dapat diadopsi oleh pemerintah daerah sebagai platform pemantauan ekosistem pesisir secara real-time guna mendukung pengambilan keputusan konservasi yang responsif dan berbasis data.
Co-Authors Ade Adriani Angraeni, Anggie Ariyanto, Muhammad Helmi As'Ad, Akmal EMI SALMAH Faa'iza, Nabiila Nuur Humairah Arta, Karuni Hunaidah, Hunaidah Imron , Arizal Mujibtamala Nanda Kelana, Mohammad Aquarisandy Arsa Khotimah, Dwi Ayu Usnul Kusumawati, Niky Dwi Mariska, Linda Mulyatno, Roni Najmi, Muhamad Nazaruddin, Said Ngii, Katharina Nugroho, Hanif Pandu Nurfadilah, Annisa Palupi, Dwi Ayuning Pradana, Adelia Septa Rizki, Ardelia Astriany Rufa Mongkito, Vivi Hastuti Sabrina, Dinar Saprudin Saprudin Saputra, Rahmat Sarita, Muhammad Ihsan Sujadi Sujadi Sukmaningrum, Lutfiah Suwignyo, Rigel Altaira Widyaningsih, Adinda Resti Yuriansa, Adelfa