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All Journal Journal Of Agribusiness, Social and Economic Jurnal Riset Multidisiplin : Agrisosco Sinergi Aksi Nyata Cendekia
Aditya Arief Rachmadhan
Agribusiness Study Program, Faculty Of Agriculture, Universitas Pembangunan Nasional “Veteran” East Java, Indonesia
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Penerapan Experiential Learning untuk Meningkatkan Kompetensi dan Refleksi Mahasiswa pada Pelatihan Mining Data Makro Intan Mega Maharani; Dita Rosyita; Aulia Nurul Hikmah; Putra Astaman; Aditya Arief Rachmadhan; Akbar Hariputra; Dita Atasa; Annisa Vira Widayanti
Sinergi Aksi Nyata Cendekia Vol 1, No 1 (2025): Agustus
Publisher : Lembaga Penelitian, Pengembangan, Pemberdayaan Potensi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.6131/sancaka.v1i1.173

Abstract

Penelitian ini bertujuan untuk menganalisis efektivitas pelatihan data mining makro berbasis experiential learning dalam meningkatkan kompetensi konseptual, kemandirian, dan refleksi mahasiswa untuk menutup kesenjangan antara tuntutan Outcome-Based Education (OBE) dan kesiapan mahasiswa. Kegiatan ini merupakan bagian dari program pengabdian kepada masyarakat yang dirancang dengan pendekatan student-centered learning (SCL). Data diperoleh melalui pre-test, post-test, dan kuesioner reflektif. Analisis menggunakan tiga indikator utama: Normalized-Gain (N-Gain), Reflective Learning (RL), dan Experiential Learning Mapping (ELM). Hasil menunjukkan peningkatan signifikan pada pemahaman konseptual (N-Gain = 1,00), sedangkan nilai RLII sebesar 3,29 mengindikasikan refleksi moderat. Temuan ini menegaskan bahwa experiential learning efektif dalam memperkuat kompetensi kognitif, tetapi optimalisasi fase reflektif diperlukan agar pembelajaran menjadi transformasional dan berkelanjutan.
Prevalensi Praktik Child Labor pada Usahatani Kopi di Kelompok Tani Bumi Rahayu, Kabupaten Trenggalek Aditya Arief Rachmadhan; Intan Mega Maharani; Dita Rosyita; Putri Nurmalitasari; Ekamonika Manihuruk
Journal of Agribusiness, Social and Economic Vol. 6 No. 1 (2026): Journal of Agribusiness, Social and Economic (JASE)
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jase.v6i1.8564

Abstract

he sustainability of the coffee agribusiness is determined not only by economic and environmental dimensions but also by social sustainability, including the protection of children from child labor. This study aims to identify the forms and characteristics of children's involvement in coffee farming and to evaluate whether such involvement meets the child labor criteria established by the International Labour Organization (ILO). This study employed a descriptive-explanatory research design. Data were collected through questionnaires and interviews with six coffee farmers who were members of the Bumi Rahayu Farmer Group. The results revealed that the prevalence of child labor in coffee farming within the Bumi Rahayu Farmer Group was 16.67%, indicating a relatively low occurrence. Children's involvement was primarily intended to assist their parents, learn coffee cultivation practices, and compensate for family labor shortages, reflecting the concepts of child participation and children's work. Nevertheless, children were also involved in hazardous tasks and experienced disruptions to their educational activities. Based on the ILO criteria, these practices were therefore classified as child labor.
Utilizing ChatGPT as a Large Language Model for Qualitative Decision Tree Modeling: A Proof-of-Concept for Strengthening Food Security in Indonesia Aditya Arief Rachmadhan; Prasmita Dian Wijayati; Annisa Vira Widayanti; Akbar Hariputra; Rizki Puspita Dewanti
Jurnal Riset Multidisiplin Agrisosco Vol 4, No 1 (2026): Vol 4 No 1 April 2026
Publisher : Lembaga Penelitian, Pengembangan, Pemberdayaan Potensi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61316/jrma.v4i1.229

Abstract

This study explores the use of Artificial Intelligence, specifically ChatGPT as a large language model, in constructing a qualitative decision tree to support food security analysis in Indonesia. Framed within the FAO’s four-pillar approach—availability, access, utilization, and stability—the research adopts a methodological proof-of-concept design and does not rely on primary or secondary empirical datasets. Instead, the analysis is based on AI-generated reasoning derived from structured prompts, which are systematically organized into a conceptual decision tree framework. Validation is conducted through interpretative comparison with established theoretical frameworks and national policy documents, rather than empirical testing or expert elicitation. The resulting model provides a structured representation of strategic pathways and potential policy options, highlighting the advantages of AI-assisted modeling in terms of speed, scalability, and integrative synthesis of knowledge. However, the model remains qualitative and exploratory, with limitations related to contextual specificity, potential bias, and the absence of real-time data. The findings suggest that AI can function as a complementary analytical tool for structuring policy-relevant insights, although its application requires careful validation and should not be interpreted as evidence of policy effectiveness.
Co-Authors Akbar Hariputra Annisa Vira Widayanti Aulia Nurul Hikmah Dita Atasa Dita Rosyita Dita Rosyita Ekamonika Manihuruk Intan Mega Maharani Intan Mega Maharani Prasmita Dian Wijayati Putra Astaman Putri Nurmalitasari Rizki Puspita Dewanti