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All Journal AKSIOMA : Jurnal Sains Ekonomi dan Edukasi SISFOTENIKA
Weli Zuandi
Universitas Nahdlatul Ulama Kalimantan Barat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Klasifikasi Citra Kebakaran Hutan Menggunakan Arsitektur ResNet-50 Berbasis Transfer Learning: Forest Fire Image Classification Using ResNet-50 Architecture Based on Transfer Learning Aditya Pratama; Candra Gudiato; Denny Primanda; Weli Zuandi; Wahyu Prayitno
SISFOTENIKA Vol. 16 No. 2 (2026): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v16i2.707

Abstract

Kebakaran hutan dan lahan (karhutla) merupakan bencana lingkungan serius yang memerlukan sistem deteksi dini secara akurat dan efisien guna meminimalisir dampak kerusakan. Penelitian ini bertujuan untuk menguji keandalan dan efisiensi arsitektur ResNet-50 berbasis Transfer Learning dalam mengklasifikasikan citra digital kebakaran hutan ke dalam dua kelas biner (wildfire dan nowildfire). Dataset sekunder yang diunduh dari repositori terbuka Kaggle terlebih dahulu melalui tahap pra-pemrosesan, mencakup pemeriksaan integritas data (data integrity check) untuk menangani file gambar yang rusak/korup, pengubahan ukuran citra (resizing) menjadi 128 × 128 piksel, normalisasi nilai piksel, serta teknik augmentasi data acak. Dataset dibagi secara terpisah dengan proporsi rasio 70% data latih (30.250 citra), 15% data validasi (6.300 citra), dan 15% data uji (6.300 citra). Arsitektur ResNet-50 dikombinasikan dengan classification head buatan (Global Average Pooling 2D, Dense Layer 256 unit dengan ReLU, Dropout 0.4, dan Output Sigmoid) serta dilatih selama 5 epoch menggunakan optimizer Adam (default learning rate 1 × 10⁻3) dan fungsi kerugian Binary Cross-Entropy Loss. Evaluasi terhadap 6.300 citra uji independen menunjukkan bahwa model mampu mencapai Akurasi Keseluruhan sebesar 88%. Model terbukti memiliki sensitivitas tinggi terhadap kelas kebakaran (wildfire) dengan Recall sebesar 0.90 dan F1-Score sebesar 0.90 (berhasil mengenali 3.141 dari 3.480 citra kebakaran secara tepat). Pengujian Receiver Operating Characteristic (ROC) Curve menghasilkan nilai Area Under Curve (AUC) sebesar 0.92, yang menegaskan kemampuan pemisahan kelas (class separation ability) yang sangat unggul. Hasil ini membuktikan bahwa pendekatan Transfer Learning berbasis ResNet-50 sangat efektif dan efisien untuk diterapkan sebagai mesin pemrosesan visual utama pada sistem peringatan dini kebakaran hutan
Evaluasi Pengolahan Air Lindi di Area Penimbunan Tailing, Fly Ash, Bottom Ash Berdasarkan Parameter pH, TSS dan Fe (Studi Kasus pada Salah Satu Industri Pengolahan Bauksit di Kalimantan Barat) Nini Malasari; Weli Zuandi; Rizky Putranto
AKSIOMA : Jurnal Sains Ekonomi dan Edukasi Vol. 3 No. 8 (2026): AKSIOMA : Jurnal Sains, Ekonomi dan Edukasi
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/aksioma.v3i8.2843

Abstract

This study aimed to evaluate the effectiveness of the leachate treatment system at a tailings, fly ash, and bottom ash disposal facility of an alumina processing company based on pH, Total Suspended Solids (TSS), and iron (Fe) parameters. A descriptive quantitative research method was employed by collecting leachate samples from the inlet and outlet for five consecutive days. The pH and TSS parameters were measured directly in the field, while Fe concentrations were analyzed in an accredited laboratory. Treatment effectiveness was assessed by comparing water quality before and after treatment in accordance with the effluent quality standards stipulated in the Indonesian Ministry of Environment Regulation No. 34 of 2009. The results showed that the leachate pH was successfully reduced from 12.6–12.7 to 7.2–8.8, meeting the applicable quality standards. TSS concentrations also decreased from 239–798 mg/L to 11–385 mg/L, with an average removal efficiency of 77.47%, and most samples complied with the regulatory standards. In contrast, Fe concentrations increased from 0.42 mg/L to 22.1–30.5 mg/L after treatment, exceeding the maximum allowable limit of 5 mg/L. This increase indicates that the treatment system was ineffective in controlling Fe concentrations, which was likely caused by the remobilization of Fe(OH)₃ precipitates or chemical reactions occurring during the neutralization process. Overall, the leachate treatment system was effective in reducing pH and TSS but requires improvement in Fe removal, particularly during the neutralization and polishing pond stages.
Co-Authors Aditya Pratama Denny Primanda Gudiato, Candra Nini Malasari Rizky Putranto Wahyu Prayitno