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All Journal Aceh International Journal of Science and Technology Jurnal Rekayasa elektrika Jurnal Pertambangan dan Lingkungan Acta Geoscience, Energy, and Mining
Mulkal, Mulkal
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science

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Penggunaan Machine Learning Algoritma Support Vector Machine (SVM) Untuk Mengidentifikasi Kadar Pasir Besi di Kabupaten Aceh Besar Kana, Muhammad Rizki; Rahmi, Nadhiratur; Mulkal, Mulkal
JURNAL PERTAMBANGAN DAN LINGKUNGAN Vol 5, No 1 (2024): Juni 2024
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpl.v5i1.23216

Abstract

Seiring dengan perkembangan zaman, teknologi eksplorasi berkembang dengan sangat pesat. Salah satunya ialah penerapan machine learning dalam kegiatan eksplorasi. Penggunaan machine learning memungkinkan untuk mendapatkan sebaran mineral pasir besi pada lokasi tertentu dengan menambahkan berbagai parameter yang berpengaruh sehingga mendapatkan output berupa keputusan terkait keterdapatan dan kadar mineral pasir besi pada daerah tersebut. Dalam hal ini, penelitian yang dilakukan hanya berfokus pada algoritma Support Vector Machine (SVM). SVM merupakan salah satu algoritma machine learning yang digunakan untuk tugas-tugas klasifikasi dan regresi. Oleh karena itu, penelitian ini dilakukan untuk mendapatkan model algoritma SVM yang dapat digunakan untuk mengidentifikasi kadar pasir besi dengan menambah beberapa parameter pendukung seperti data jarak titik sampel terhadap pantai, jarak titik sampel terhadap sungai, jarak titik sampel terhadap sesar, nilai pixel, data ketinggian, temperatur, data curah hujan, dan jenis batuan penyusun. Hasilnya grafik regresi linear menunjukkan hubungan nilai kadar Fe prediksi dari model SVM dan kadar Fe aktual, dimana nilai Root Mean Square Error (RMSE) adalah 0,076 dan nilai r2 adalah 0,705. Artinya nilai model algoritma SVM yang dibuat memiliki tingkat kesalahan yang kecil dan korelasi antar data yang kuat sehingga algoritma tersebut dapat dijalankan untuk mengidentifikasi kadar pasir besi.
Soil Quality Investigation of an Abandoned Mine Area Using Geochemical and Geospatial Approach in Jantang Village Aflah, Nurul; Mulkal, Mulkal; Muchlis, Muchlis; Harisman*, Hendra; Alisastromijoyo, Alisastromijoyo; Lubis, Mirna Rahmah; Anggraini, Jessica
Aceh International Journal of Science and Technology Vol 11, No 1 (2022): April 2022
Publisher : Graduate School of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.11.1.23323

Abstract

The physical and chemical environmental impact in a mining area is inevitable, particularly for open pit mining areas. The impact could affect soil and water quality where mining activities, such as land clearing, blasting and hauling, occur. Thus, environmental monitoring in mining areas should be taken to measure the impact of mining activity for reclamation purposes. The objective of this research focuses on the measure of environmental impact on soil quality in terms of the nutrient content in an abandoned mine area at Jantang village, Lhoong, Aceh Besar. The research was conducted by collecting 15 soil samples, followed by laboratory analysis using atomic absorption spectrophotometry to investigate sampled 'soil's nutrients which are pH, Carbon (C-organic), Nitrogen (N-total), Phosphor (P-availability), and Ferro substance (Fe-concentration). In addition, to estimate the soil properties at locations outside the sampling area, a spatial interpolation method called inverse distance weight with an optimum power was used. The result shows that the soil is acidic, with low C-organic in the range of 0.02%1.84%, N-total 0.02%0.16%, and P-availability 0.55%3.75%. In contrast, the Fe-concentration is very high, at 30003400 ppm.
Pengembangan plugin QGIS untuk estimasi debit limpasan pada tambang terbuka Ginting, Sutradara; Oktarini*, Yoessi; Mulkal, Mulkal
Acta Geoscience, Energy, and Mining Vol 3, No 4 (2024): December 2024
Publisher : Departemen Teknik Kebumian Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/actaGEM.v3i4.42913

Abstract

Musim hujan di Indonesia dapat memengaruhi operasional industri pertambangan. Curah hujan yang tinggi seringkali menyebabkan terhambatnya proses pengangkutan material tambang, terutama di daerah-daerah dengan infrastruktur yang terbatas. Genangan air yang muncul akibat curah hujan yang deras dapat memperburuk situasi ini, menghalangi akses ke lokasi tambang atau jalur transportasi. Air di area tambang umumnya berasal dari air limpasan yang muncul akibat adanya hujan. Air limpasan harus dipertimbangkan agar pencegahan dapat dilakukan. Penelitian ini bertujuan untuk mengembangkan sebuah Plugin untuk melakukan estimasi debit limpasan dengan mudah dan cepat. Hasil penelitian menunjukkan waktu yang dibutuhkan untuk melakukkan pemrosesan berkisar antara 5,60 6,21 detik dengan dua tipe program komputer. Plugin juga menghasilkan estimasi debit limpasan di area studi kasus sebesar 2,80 m3/detik.
Automating Mining Surface Monitoring using SpatioTemporal Asset Catalog(STAC): A Spectral Index Approach with Sentinel-2 Satellite Imagery Mulkal, Mulkal; Oktarini, Yoessi
Jurnal Rekayasa Elektrika Vol 21, No 3 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v21i3.42536

Abstract

Mining activities significantly impact the environment, necessitating effective, continuous monitoring. Traditional surface monitoring methods are often costly and labor-intensive. This study proposes an automated workflow using the SpatioTemporal Asset Catalog (STAC) and Sentinel-2 satellite imagery to monitor mining surface changes. By calculating the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Bare Soil Index (MBI), the workflow identifies land cover changes within mining concessions. The system was Implemented in Python environment using libraries such as PySTAC, PySTAC Client, Xarray, Rioxarray, Geopandas, Dask, and Numpy. The mining surface change was analyzed using the regression line gradient of each spectral index. Results show active mining sites exhibit an NDVI slope lower than -1, indicating rapid conversion of vegetation to non-vegetative land due to land clearing activities. Conversely, the positive NDWI trend indicates increased water coverage from land excavation, while the MBI trend is the weakest, suggesting limited sensitivity to surface changes in mining areas. To evaluate the accuracy of the results, manual verification was conducted. The analysis revealed that 3 out of 25 mining concessions were incorrectly classified, resulting in an overall accuracy of 88%.
Co-Authors Alisastromijoyo, Alisastromijoyo Anggraini, Jessica Ginting, Sutradara Harisman*, Hendra Kana, Muhammad Rizki Mirna Rahmah Lubis Muchlis Muchlis Nurul Aflah Oktarini*, Yoessi Oktarini, Yoessi Rahmi, Nadhiratur