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Syarif Hidayat Amrullah
Biologi, Fakultas Sains Dan Teknologi, UIN Alauddin Makassar
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Journal : Jurnal Biogenerasi

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Analisis Komparatif Keragaman Serangga Tanah Diurnal pada Perkebunan Kopi Berdasarkan Prediksi AI dan Eksplorasi Lapangan Afandi, Aril; Winarno, Winarno; Suhada, Suhada; Maharani, Annisa Lidya; Safitri, Anggi; Saputri, Nur Ayu; Rhamadaningtyas, Nabila Aulia; Soegiharto, Yolande Cathleya; Apriani, Vivin; Fitrisyah, Asyifa Zahara; Pratama, M. Idris Afta; Vega, Cindy Ameliya; Pawaka, Arrahmaan Syah; Saputra, Rama Arsalta Bara; Amrullah, Syarif Hidayat; Parabi, M. Iqbal; Rustiati, Elly Lestari; Pratami, Gina Dania; Permatasari, Nindy; Priyambodo, Priyambodo
Jurnal Biogenerasi Vol. 10 No. 4 (2025): Volume 10 nomor 4 tahun 2025 Terbit Oktober-Desember 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/biogenerasi.v10i4.7121

Abstract

Soil insects play a crucial role in maintaining ecosystem balance and supporting soil fertility, particularly within coffee plantation ecosystems. This study aims to analyze the diversity of soil insects by comparing results from artificial intelligence (AI)-based predictions and field explorations to obtain a comprehensive understanding of community structure. The research was conducted in a smallholder coffee plantation located in Wiyono Village, Pesawaran Regency, Lampung. Field data were collected using the pitfall trap method, while AI-based predictions were generated utilizing a dataset derived from 14 relevant scientific publications. Data analysis employed the Shannon-Wiener diversity index (H′) to evaluate differences between predicted and observed results. The findings revealed that the AI-based prediction estimated an H′ value of 1.787 (moderate diversity), whereas the field exploration yielded an H′ value of 0.428 (low diversity). This discrepancy is influenced by dataset limitations, species dominance, and selectivity inherent in the sampling method. The results highlight the importance of integrating AI-based predictive approaches with field validation to enhance the accuracy of biodiversity assessments. This study contributes to the development of AI-driven prediction models and supports sustainable management of coffee plantation ecosystems.
Co-Authors 'Aliyah, Siti Hamidatul A. Ridha, Ariati Adnan, Nur Afandi, Aril Alir, Rahmat Fajrin Andini M, Sri Apriani, Vivin Ar, Riski Amaliah Aswar Rustam Aswar, Asrul Cambaba, Sunarti Delfiah, Fila Devi Armita Dirhamzah Dirhamzah Dirhamzah Dirhamzah, Dirhamzah Elly Lestari Rustiati Febriyanti Fikri, Muh Fitrisyah, Asyifa Zahara Gina Dania Pratami, Gina Dania Hafizhah Al Amanah Hamka Hamka, Hamka Harun, Hadrian Rukmana Harun, Hadriyan Rukmana Hasyimuddin Hasyimuddin Hidayat, Bahrul Irmawati Irmawati Juhari, Damraeni Kahby, Icha Ayunita Khusnul Mar'iyah Lilis Minarseh Maharani, Annisa Lidya Masriany, Masriany Muhammad Isbatullah Ningsih, Sulastri Ayu Nurannisa, Nurannisa Nurhalisa, Ince St Nurman Nurman Pangngan, Sofi Bella Sari Parabi, M. Iqbal Pauline Destinugrainy Kasi Pawaka, Arrahmaan Syah Permatasari, Nindy Pratama, M. Idris Afta Priyambodo Priyambodo Rahmat Fajrin Alir Rhamadaningtyas, Nabila Aulia Rita Harnita Rukmana, Rusmadi Rusmadi Rukmana Rusmadi Rukmana Safitri, Anggi Saputra, Rama Arsalta Bara Saputri, Nur Ayu Sijid, St. Aisyah Soegiharto, Yolande Cathleya St Aisyah Sijid St. Aisyah Sijid Suhada Suhada, Suhada Suhaeni Suhaeni Syahri, Khairul Tri Sutriani Syam Vega, Cindy Ameliya Widya Yanti, Widya Winarno Winarno Wirawan, Hadi Purnama Yunadia Yunadia Yunadia Zahara, Syahnas Ainun Zulkarnain Zulkarnain Zulkarnain Zulkarnain Zulkarnain Zulkarnain