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Hendi Hendra Bayu
Universitas Muhammadiyah Bangka Belitung
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Dinamika Sifat Kimia Tanah dan Implikasinya terhadap Kesuburan pada Lahan Reklamasi Bekas Tambang Timah Reny Setyasih Widodo; Hendi Hendra Bayu; Andesta Granitio Irwan
Jurnal Sains Agro Vol 11, No 1 (2026): Jurnal Sains Agro
Publisher : Universitas Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36355/jsa.v11i1.2006

Abstract

Penelitian ini bertujuan untuk menganalisis dinamika sifat kimia tanah berdasarkan variasi umur reklamasi pada lahan eks tambang timah di wilayah operasional PT Timah Tbk Area Belitung serta mengevaluasi implikasinya terhadap kesuburan tanah. Penelitian dilakukan menggunakan pendekatan kronosekuens pada empat tingkat umur reklamasi (0, 6, 8, dan 10 tahun). Sampel tanah diambil pada kedalaman 0–20 cm dan dianalisis terhadap parameter pH, C-organik, N-total, P₂O₅ tersedia, K₂O potensial, kation dapat tukar, kapasitas tukar kation (KTK), dan tekstur tanah. Hasil penelitian menunjukkan bahwa kandungan C-organik, N-total, serta KTK cenderung meningkat seiring bertambahnya umur reklamasi, meskipun nilai pH tetap berada pada kategori masam. Ketersediaan fosfor menunjukkan fluktuasi yang dipengaruhi oleh kondisi kemasaman tanah. Secara umum, umur reklamasi berkontribusi terhadap perbaikan sifat kimia tanah, namun tingkat kesuburan tanah masih tergolong rendah hingga sedang. Hasil ini menunjukkan bahwa proses pemulihan tanah pada lahan eks tambang timah memerlukan waktu yang panjang serta pengelolaan lanjutan untuk mencapai kondisi kesuburan optimal.
Pendeteksi Penyakit Daun Kentang Menggunakan Algoritma Convolutional Neural Network (CNN) Arvi Pramudyantoro; Muhamad Kurniawan; Hendi Hendra Bayu
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4740

Abstract

Potato leaf disease is one of the main problems in potato cultivation because it can reduce plant quality, decrease crop yield, and cause economic losses for farmers. Manual disease detection still has limitations because it depends on farmers’ experience and is prone to errors, especially when disease symptoms have similar visual characteristics. This study aims to apply the Convolutional Neural Network (CNN) algorithm to predict potato leaf diseases based on digital images. The dataset used in this study was obtained from Kaggle and consisted of 1,500 potato leaf images divided into three classes: healthy leaves, early blight, and late blight. The research stages included dataset collection, data splitting into training, testing, and validation data, CNN modeling using Jupyter Notebook, model training with 50 epochs, model evaluation using a Confusion Matrix, and model implementation into a web-based system using Flask. The test results show that the CNN model was able to classify potato leaf diseases with an accuracy of 97%. These results indicate that CNN is effective in recognizing visual patterns in potato leaf images, such as color changes, spots, and leaf damage. This study is expected to serve as a basis for developing an early detection system for potato leaf diseases that is faster, more accurate, and easier for farmers to use.
Co-Authors Andesta Granitio Irwan Arvi Pramudyantoro Muhamad Kurniawan Reny Setyasih Widodo