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All Journal Jurnal Biogenerasi BIO-EDU: Jurnal Pendidikan Biologi Biofarm : Jurnal Ilmiah Pertanian Kenanga : Journal of Biological sciences and Applied Biology
Afandi, Aril
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Education Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Jurnal Biogenerasi

 1 
AMPLIFIKASI GEN N-METHYLTRANSFERASE KOPI ROBUSTA DI PERKEBUNAN KOPI RAKYAT WIYONO LAMPUNG: AMPLIFIKASI GEN N-METHYLTRANSFERASE KOPI ROBUSTA DI PERKEBUNAN KOPI RAKYAT WIYONO LAMPUNG Lestari, Septi Wahyu; Rustiati, Elly Lestari; Priyambodo, Priyambodo; Wahyuningsih, Sri; Sandra, Shifa; Winarno, Winarno; Thesalonika, Natasya; Febriansyah, Muhammad; Ashari, Minanti Mayda; Andriyani, Yuliana; Afandi, Aril; Maharani, Annisa Lidya; Pratiwi, Dian Neli; Srihanto, Eko Agus; Suhada, Suhada; Permatasari, Nindy
Jurnal Biogenerasi Vol. 10 No. 2 (2025): Volume 10 no 2 periode februari - september 2025 ( continues)
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/biogenerasi.v10i2.5544

Abstract

Kopi robusta (Coffea canephora) merupakan salah satu tanaman perkebunan yang umum ditanam di Indonesia karena memiliki nilai ekonomis tinggi. Kabupaten Pesawaran menjadi salah satu daerah di Lampung yang banyak mengusahatanikan kopi robusta dalam skala perkebunan rakyat. Isolasi dan amplifikasi gen menjadi langkah awal dalam analisis molekuler untuk mengetahui keragaman gen pada kopi robusta. Penelitian ini bertujuan untuk menganalisis hasil isolasi dan amplifikasi DNA daun kopi robusta dari perkebunan kopi rakyat Pesawaran, Lampung. Isolasi DNA dari 5 sampel daun kopi robusta telah dilakukan mengacu pada protokol kit isolasi DNA dari Geneaid Genomic DNA Mini Kit (Plant). Hasil ekstraksi kemudian diamplifikasi menggunakan primer N-methyltransferase dengan metode Polymerase Chain Reaction (PCR). Visualisasi hasil ekstraksi dan amplifikasi dilakukan menggunakan elektroforesis gel agarosa 1%. Visualisasi atas hasil ekstraksi telah menunjukkan diperolehnya ekstrak DNA yang baik. Visualisasi atas hasil amplifikasi menunjukkan telah diperolehnya fragmen DNA sepanjang 600bp.
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 Apriani, Vivin Ashari, Minanti Mayda Eko Agus Srihanto, Eko Agus Elly Lestari Rustiati Febriansyah, Muhammad Fitrisyah, Asyifa Zahara Gina Dania Pratami, Gina Dania Lestari, Septi Wahyu Maharani, Annisa Lidya Parabi, M. Iqbal Pawaka, Arrahmaan Syah Permatasari, Nindy Pratama, M. Idris Afta Pratiwi, Dian Neli Priyambodo Priyambodo Rhamadaningtyas, Nabila Aulia Safitri, Anggi Sandra, Shifa Saputra, Rama Arsalta Bara Saputri, Nur Ayu Soegiharto, Yolande Cathleya Sri Wahyuningsih Suhada Suhada, Suhada Syarif Hidayat Amrullah Thesalonika, Natasya Vega, Cindy Ameliya Winarno Winarno Yuliana Andriyani