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"This paper attempts to clarify the position of geological and mining conditions at Ombilin in comparison with those in Australia and the UK by examining both the technical strategy and its efficiency. A summary of this review is made to facilitate judgments on long wall layouts and strata control matters chosen during the stage of mine planning and design for purpose of the underground coal mine development in Indonesia."

"A potential coal deposit has been identified during detailed coal exploration campaign carried out by the Directorate General of Mines coordinating with the NEDO in Napal, Central Sumatera. Eight coal seams were found in the Upper Miocene formation, with horizontal dips. Considering the fact, a big open pit coal mining operation is possible to be planned in Napal."

"[ABSTRAKPertambangan mineral dan batuan merupakan salah satu sektor strategis dalam perekonomian dunia. Nilai guna yang tinggi serta diharuskannya suatu perusahaan tambang untuk menetap dalan jangka waktu yang lama di dalam wilayah suatu negara menjadikan sektor ini menjadi sangat strategis baik secara ekonomi maupun politik. Sektor pertambangan mineral dan batuan seperti layaknya pertambangan migas, umumnya masih dikuasai oleh MNCs dan perusahaan pertambangan dari negara maju, sementara sumber daya mineral dan batuan mayoritas berada pada negara berkembang yang miskin teknologi. Hal ini menciptakan suatu kondisi dimana negara berkembang sering merasa dirugikan dengan kondisi dimana mereka hanya dapat memproduksi barang tambang mentah dan diharuskan membeli kembali hasil olahan dari barang tambang mentah yang berasal dari negara mereka. Hal ini pula yang membuat banyak negara berkembang mengeluarkan kebijakan yang bercorak resource nationalism guna mengejar kepentingan ekonomi maupun kepentingan politik.Indonesia merupakan salah satu negara penghasil komoditas tambang mineral dan batuan yang cukup besar. Sedikitnya dua puluh komoditas tambang mineral dan batuan diproduksi oleh Indonesia. Namun sebagaimana negara berkembang lain, sektor pertambangan mineral dan Batuan Indonesia masih didominasi oleh MNCs dan perusahaan pertambangan Asing. Hal yang mengejutkan kemudian dilakukan oleh Pemerintah Indonesia dimana pada periode tahun 2009 hingga tahun 2014, pemerintah Indonesia mengeluarkan kebijakan yang bercorak resource nationalism. Pergeseran kebijakan ini menjadi suatu hal yang menarik dimana sebelumnya kebijakan di sektor tersebut cenderung bercorak liberalis. Penelitian ini bertujuan untuk mengetahui penyebab dilakukannya pergeseran kebijakan di sektor pertambangan mineral dan batuan Indonesia menuju penerapan prinsip-prinsip resource nationalism pada periode tersebut.

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ABSTARCTMineral and ores mining is one of the most strategic sectors in world economy. High value of usage and the obligation of the mining companies to stay in other country's sovereign territory for a long period of time makes this sector very strategic economically and politically. Mineral and ores mining just like oil and gas mining, is usually dominated by MNCs and mining companies from the developed countries, while the mineral and ores resources is often located in developing countries that are lacking in technology to exploit it. This creates condition where the developing countries often feels at disadvantages, because they can only produce the raw materials, and in return they have to purchase the processed products which originally made from the raw materials from their own countries. This also causes many developing countries create policies that are associated with resource nationalism to pursue economic ends and also political ends.Indonesia is one of the biggest mineral and ores commodities producing countries. At least twenty kind of mineral and ores commodities are produced by Indonesia. However, just like many other developing countries, mineral and ores mining sector in Indonesia is still dominated by MNCs and foreign mining companies. In the period between 2009 and 2014, Indonesian government created some shocking policies in their mineral and ores mining sector that associated with resource nationalism. This political shift towards more resource nationalist policies is interesting to observe because previously mineral and ores mining policies in Indonesia is considered to be more liberal. This research aims to analyze what causes the shift on indonesian mineral and ores mining sector towards resource nationalism on that period;Mineral and ores mining is one of the most strategic sectors in world economy. High value of usage and the obligation of the mining companies to stay in other country's sovereign territory for a long period of time makes this sector very strategic economically and politically. Mineral and ores mining just like oil and gas mining, is usually dominated by MNCs and mining companies from the developed countries, while the mineral and ores resources is often located in developing countries that are lacking in technology to exploit it. This creates condition where the developing countries often feels at disadvantages, because they can only produce the raw materials, and in return they have to purchase the processed products which originally made from the raw materials from their own countries. This also causes many developing countries create policies that are associated with resource nationalism to pursue economic ends and also political ends.Indonesia is one of the biggest mineral and ores commodities producing countries. At least twenty kind of mineral and ores commodities are produced by Indonesia. However, just like many other developing countries, mineral and ores mining sector in Indonesia is still dominated by MNCs and foreign mining companies. In the period between 2009 and 2014, Indonesian government created some shocking policies in their mineral and ores mining sector that associated with resource nationalism. This political shift towards more resource nationalist policies is interesting to observe because previously mineral and ores mining policies in Indonesia is considered to be more liberal. This research aims to analyze what causes the shift on indonesian mineral and ores mining sector towards resource nationalism on that period, Mineral and ores mining is one of the most strategic sectors in world economy. High value of usage and the obligation of the mining companies to stay in other country's sovereign territory for a long period of time makes this sector very strategic economically and politically. Mineral and ores mining just like oil and gas mining, is usually dominated by MNCs and mining companies from the developed countries, while the mineral and ores resources is often located in developing countries that are lacking in technology to exploit it. This creates condition where the developing countries often feels at disadvantages, because they can only produce the raw materials, and in return they have to purchase the processed products which originally made from the raw materials from their own countries. This also causes many developing countries create policies that are associated with resource nationalism to pursue economic ends and also political ends.Indonesia is one of the biggest mineral and ores commodities producing countries. At least twenty kind of mineral and ores commodities are produced by Indonesia. However, just like many other developing countries, mineral and ores mining sector in Indonesia is still dominated by MNCs and foreign mining companies. In the period between 2009 and 2014, Indonesian government created some shocking policies in their mineral and ores mining sector that associated with resource nationalism. This political shift towards more resource nationalist policies is interesting to observe because previously mineral and ores mining policies in Indonesia is considered to be more liberal. This research aims to analyze what causes the shift on indonesian mineral and ores mining sector towards resource nationalism on that period]"

"Opportunities for use of video conferencing in education and communication to the mining and metallurgy industry in Australia and nearby countries are discussed. In particular, experiences gained at the University of Queensland from presenting advanced short courses, postgraduate subjects, under-graduate teaching, interaction with overseas countries and management of research at remote mining sites are examined. Some barriers to usage the system are highlighted."

"This dissertation discusses about the tin mining in Bangka community in Penagan village, Province of Bangka-Belitung Islands, particularly on relations of strength in the activity structure of tin mining performed by the miners (individuals or groups) in organizing the control, exploitation, management and distribution of the resources in the area. Tin mining activities performed by Bangka people in Penagan is called TI forming inseparable part of Bangka people life. In these activities, the miners are connected in the context of relation of power based on the strength of each miner. The relation of strength and powers is not only demonstrated by the statue and role between Boss and subordinates in TI activities but also among TI with other miners interested in tin resources.In this dissertation, i will show the relation of power in the social structure being the function of mutual relations of strength among the miners based on the capabilities and capacity of each miner in such structure. The powers of each miner includes capital and symbols of relaionship regularly reproduced to strengthen the existing relation. Mutual relation is functionally empowered by the miners to maximize the economic benefit from available resources in the walks of life of the ccommunity. Tin mining structure is a siymbiotic and mutual cooperation network (mutual and profit exchange) primarily among the miners based on economic benefit of tin resource, where the fund will then become collective standard of the miners and business groups. The strength held by each miner in the context of tin resources control and tin mining activities indicate no dualism of the strength of the miner binding of free. In term of mining context, the existing strength is independent or binding. There is no miner who can directly control by the power it owns in tin mining activities. This is attributable to the fact that tin, as a resource in social system of tin mining is considered as collective resources.Research methodology used in this dissertation is qualitative research method with observation technique and in-depth interview. Specifically, it uses ethnography method resulth in deep and holistic examination on tin mining performed by Bangka community in Penagan. The period of data collection began from the early 2005 until August 2006. I use the approach referring to the relations of power and strength among the miners in the tin mining structure which includes control, management and organizing of tin resources. The relation of powers among the miners is based upon the strength of the miners to influence and or to control other miners."

"Industri pertambangan merupakan salah satu sektor pekerjaan paling berbahaya di dunia karena memiliki tingkat kecelakaan kerja yang tinggi, terutama kecelakaan fatal. Dalam 23 tahun terakhir, industri pertambangan mencatat 30.327 kecelakaan, dengan 357 berakhir dengan kematian pekerja. Machine learning dapat digunakan untuk memecahkan permasalahan dunia nyata yang kompleks, termasuk klasifikasi derajat cedera akibat kecelakaan kerja di industri pertambangan bawah tanah. Penelitian ini menggunakan metode machine learning Whale Optimization Algorithm Support Vector Machine (WOA-SVM), dengan Whale Optimization Algorithm (WOA) berperan sebagai optimizer untuk parameter model Support Vector Machine (SVM). Derajat cedera dibagi menjadi tiga kelas berdasarkan pengaruhnya terhadap produktivitas pekerja, yaitu no days away from work (NDAFW), days away from work (DAFW), dan disability or fatality (DF). Data yang digunakan berasal dari Mine Safety and Health Organization milik pemerintah Amerika Serikat sebanyak 28.520 kejadian kecelakaan dalam rentang 1 Januari 2000 hingga 31 Desember 2023. Sebelum mengimplementasikan model machine learning, dilakukan data preprocessing yang meliputi pembersihan data, transformasi data, sampling data, encoding data, penyeimbangan data, dan seleksi fitur. Kinerja model WOA-SVM dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score dengan berbagai proporsi splitting data train dari 50% hingga 90%, serta mempertimbangkan waktu komputasi. Setelah itu dilakukan komparasi model WOA-SVM dengan model SVM tanpa optimisasi. Hasil komparasi menunjukkan bahwa model WOA-SVM lebih unggul dibandingkan model SVM, dengan keunggulan pada metrik konfusi, akurasi, presisi, recall, F1-score, serta memiliki waktu komputasi yang lebih cepat. Model WOA- ix Universitas Indonesia SVM memiliki nilai akurasi, presisi, recall, dan F1-score tertinggi pada proporsi 70:30, masing-masing sebesar 82,4153 %, 82,1255%, 82,4153%, dan 82,0812%.

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The mining industry is one of the most dangerous employment sectors in the world due to its high rate of workplace accidents, particularly fatal ones. Over the past 23 years, the mining industry has recorded 30,327 accidents, with 357 resulting in worker fatalities. Machine learning can be employed to address complex real-world problems, including the classification of injury severity resulting from workplace accidents in the underground mining industry. This study utilizes the Whale Optimization Algorithm Support Vector Machine (WOA-SVM) method, with the Whale Optimization Algorithm (WOA) acting as an optimizer for the parameters of the Support Vector Machine (SVM) model. The severity of injuries is divided into three classes based on their impact on worker productivity: no days away from work (NDAFW), days away from work (DAFW), and disability or fatality (DF). The data used comes from the Mine Safety and Health Organization's, managed by the U.S. government, encompassing 28,520 accident incidents from January 1, 2000, to December 31, 2023. Before implementing the machine learning model, data preprocessing was conducted, including data cleaning, data transformation, data sampling, data encoding, data balancing, and feature selection. The performance of the WOA-SVM model was evaluated using accuracy, precision, recall, and F1-score metrics with various train data splitting proportions ranging from 50% to 90%, while also considering computational time. A comparison was then made between the WOA-SVM model and the non-optimized SVM model. The comparison results indicated that the WOA-SVM model outperformed the SVM model, with superiority in confusion metrics, accuracy, precision, recall, F1-score, and having the fastest computational time. The WOA-SVM model has the highest accuracy, precision, recall, xi Universitas Indonesia and F1-score values at a 70:30 ratio, which are 82.4153%, 82.1255%, 82.4153%, and 82.0812%, respectively."