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"Kawasan Gunung Endut merupakan kawasan yang tersusun atas batuan beku plutonik Tersier dan batuan beku vulkanik Kuarter yang diduga merupakan hasil proses subduksi tektonik yang terjadi di selatan Jawa. Penelitian ini bertujuan untuk mengetahui jenis batuan beku dan menginterpretasikan jenis magma, evolusi magma, dan tatanan tektonik yang membentuk batuan beku di daerah penelitian. Metode yang digunakan dalam penelitian ini adalah analisis petrologi, analisis petrografi, dan analisis geokimia batuan beku menggunakan XRF. Batuan beku Gunung Endut terdiri dari beberapa satuan batuan beku yaitu batuan beku lava basaltik/andesitik, batuan beku intrusi tanggul basaltik/andesitik, dan batuan beku intrusi stok basaltik/andesitik. Analisis petrografi batuan beku Gunung Endut menunjukkan bahwa batuan lava beku terdiri dari porfiri andesit, sedangkan batuan beku intrusi terdiri dari porfiri diorit dan porfiri dasit. Tekstur mikro dari plagioklas yang terdapat pada setiap sampel menunjukkan bahwa batuan beku Gunung Endut telah mengalami pencampuran magma dengan magma primitif. Berdasarkan diagram TAS, batuan lava beku tersusun atas andesit, dasit, traki-andesit, andesit basaltik, trakhdasit trakit, dan basaltik trachy-andesit. Batuan beku intrusi terdiri dari batuan beku andesit, trachy-andesite, basaltic andesite, dan trachy trachydacite. Deret magma pembentuk batuan beku Gunung Endut merupakan gugus magma kalk-alkali yang menopang lingkungan tektonik pembentuk batuan beku Gunung Endut yaitu batas kontinen aktif.

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The Mount Endut area is an area composed of Tertiary plutonic igneous rocks and Quaternary volcanic igneous rocks which are thought to be the result of a tectonic subduction process that occurred in southern Java. This study aims to determine the types of igneous rocks and interpret the types of magma, magma evolution, and tectonic arrangements that form igneous rocks in the study area. The methods used in this research are petrological analysis, petrographic analysis, and geochemical analysis of igneous rocks using XRF. Mount Endut igneous rock consists of several igneous rock units, namely basaltic/andesitic lava igneous rock, basaltic/andesitic embankment intrusion igneous rock, and basaltic/andesitic stock intrusion igneous rock. Petrographic analysis of the Gunung Endut igneous rock shows that the igneous lava rock consists of andesite porphyry, while the intrusive igneous rock consists of diorite porphyry and dacite porphyry. The micro texture of the plagioclase contained in each sample shows that the igneous rock of Mount Endut has undergone magma mixing with primitive magma. Based on the TAS diagram, igneous lava rock is composed of andesite, dacite, trachy-andesite, basaltic andesite, trachyde-tracheal and basaltic trachy-andesite. Intrusion igneous rock consists of igneous andesite, trachy-andesite, basaltic andesite, and trachy trachydacite. The magma series that forms the igneous rocks of Mount Endut is a calc-alkaline magma group that supports the tectonic environment that forms the igneous rocks of Mount Endut, which is the active continent boundary."

"The intrusive rocks of Gunung Parang and Gunung Bongkok appear to be boss shape. The intrusion lie physiographically at the Bogor zone, Purwakarta area, West Java. Petrographic features show that the rocks have good porphyritic textures, and primary minerals of plagioclase, pyroxene, and hornblende as phenocryst are in groundmass made of crystallites plagioclase. This ranges from calcic andesine, and subordinate sodic to calcic labradorite, especially in the intrusive rocks of Gunung Bongkok. Chemical analyses reveal that the parent magma of these rocks is high-alumina basalt derived from olivine tholeiites fractionation at the depth of 44 km and the temperature 1066 degree Celcius while the depth of parent magma is calculated about 175 km to the depth of the undergoing Benioff zone."

"Pada penelitian ini berkaitan dengan penerapan kemajuan kecerdasan artifisial dengan menggunakan algoritma You Only Look Once (YOLO) dalam tugas deteksi dan segmentasi pada bidang geologi yaitu untuk identifikasi mineral dengan menggunakan data petrografi. Data yang digunakan untuk proses pelatihan model deteksi dan segmentasi berjumlah 500 gambar sayatan tipis batuan beku. 500 gambar sayatan tipis, dilakukan proses anotasi secara manual dan membagi data tersebut ke dalam set pelatihan, set validasi, dan set prediksi. Pada 3 set tersebut, jumlah kelas mineral yang teranotasi adalah 6 yaitu kelas mineral plagioklas, biotit, horblend, piroksen, alkali-feldspar, dan kuarsa. Teknik augmentaasi yang diterapkan untuk mengatasi keterbatasan dataset pada penelitian ini adalah augmentasi geometri (model 1) dan mosaik (model 2). Model dengan augmentasi mosaik, menjadikan model dengan kinerja yang baik dalam tugas deteksi dan segmentasi mineral, dikarenakan augmentasi mosaik menghasilkan 1 image patch memiliki 4 variasi gambar sayatan tipis, sehingga model tersebut memiliki nilai mAP = 82.3% sedangkan model dengan augmentasi geometri nilai mAP 67.5%. Empat kelas mineral yang memiliki nilai mAP diatas 70% pada mode pelatihan dan validasi adalah mineral plagioklas, biotit, alkali-feldspar, dan piroksen. Diharapkan dari penelitian ini dapat membantu identifikasi mineral dalam sayatan tipis dengan lebih efisien dan akurat.

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This research is related to the application of advances in artificial intelligence using the You Only Look Once (YOLO) algorithm in detection and segmentation tasks in the field of geology, namely for mineral identification using petrographic data. The data used for the detection and segmentation model training process consisted of 500 thin section images of igneous rock. 500 thin section images were annotated manually and divided the data into a training set, validation set and prediction set. In these 3 sets, the number of annotated mineral classes is 6, namely the mineral classes plagioclase, biotite, horblend, pyroxene, alkali-feldspar, and quartz. The augmentation techniques applied to overcome the limitations of the dataset in this research are geometric augmentation (model 1) and mosaic (model 2). The model with mosaic augmentation is a model with good performance in mineral detection and segmentation tasks, because mosaic augmentation produces 1 image patch with 4 variations of thin section images, so the model has a mAP value = 82.3% while the model with geometric augmentation has a mAP value of 67.5%. The four mineral classes that have mAP values above 70% in training and validation mode are the minerals plagioclase, biotite, alkali-feldspar, and pyroxene. It is hoped that this research can help identify minerals in thin sections more efficiently and accurately."