Monitoring Forest Cover Loss Due to The Impact of Mining Activities Using Google Earth Engine in West Aceh Regency

In recent years, forest areas in Aceh Province, especially in West Aceh Regency, have experienced a significant decline in forest cover. This can negatively impact biodiversity, community quality of life, and natural disaster risk, exacerbating global climate change. The fact that West Aceh is one of the regencies with the highest number of gold and coal mining companies in Aceh is a significant concern when assessing whether mining activities impact forest cover loss. This study aims to monitor and quantify forest cover change and the impact of mining activities in Aceh Barat from 2019 to 2024. The research methodology included the use of Google Earth Engine (GEE) for Sentinel-2 satellite image analysis by calculating the Normalized Difference Vegetation Index (NDVI); NDVI values above 0.7 were classified as forest, values below 0.7 as non-forest, and negative values as water bodies. The analysis showed that forest cover loss in West Aceh reached 13030.84 ha over the last five years at an average rate of 2606.17 ha/year. Illegal gold mining activities contributed 12.8%, legal coal mining 10.2%, and legal gold mining 7.3% to forest cover loss, while non-mining factors caused 69.7%. This study presents a cost-effective forest monitoring method that supports biodiversity protection and improved forest management policies in mining areas

Optimizing Mechanical Properties of Al6063 Aluminum Alloy through Silicon Weight Percent Variation and Heat Treatment at the Propeller Shaft Materials Casting

Ship propulsion relies significantly on the efficiency of its components, with the shaft propeller playing a pivotal role in navigating vessels through water. Traditionally constructed from steel, this study explores an unconventional approach by employing an Aluminum Alloy base material, specifically the 6063 alloy, for the propeller shaft model. The material's mechanical properties become a crucial focus, prompting a detailed investigation into the impact of silicon and magnesium elements through a meticulous heat treatment process. The experimental procedure involves heating the Al6063 alloy to 790C, transitioning to a completely liquid state, and subsequently incorporating silicon and magnesium at specific temperatures. The stirring process, executed with a mechanical stirrer, sets the stage for the alloy's casting into a mold under pressure. Post-casting, the propeller shaft undergoes a comprehensive heat treatment regimen, including solution treatment, quenching, and artificial aging. The study's findings showcase a remarkable reduction in porosity with increasing silicon elements, reaching its lowest point at 4% wt Silicon. Tensile tests demonstrate a direct correlation between silicon addition and increased stress values, with the highest stress observed at 4% wt Silicon. Concurrently, hardness values ascend proportionally with silicon inclusion, peaking at 4% wt Silicon. The thorough analysis presented here highlights the effectiveness of silicon elements in enhancing the mechanical characteristics of the shaft propeller made of aluminum alloy, which bodes well for future developments in ship propulsion technology

Analysis of Illegal Gold Mining (PETI) Impact on The Environment with TDS, TSS, Mercury and Cyanide Parameters in Water and Sediment of Cikaniki River

Gold production from artisanal mining extraction reaches 120 tons annually, providing significant environmental and economic impacts for the community. The processing method used triggers environmental pollution, because it produces tailings in the form of metal mercury and cyanide. This research was conducted at the location of post Illegal gold mining (PETI), although PETI activities have been disciplined, but based on the characteristics of mercury which is difficult to dissolve in water, easily binds to suspended solids and easily deposited to the bottom of the waters, can pollute river sediments. The purpose of the study was to determine the levels of Total Dissolved Solid (TDS), Total Suspended Solid (TSS), mercury and cyanide in the Cikaniki River Watershed, based on Government Regulation No. 22 of 2021. The purposive sampling method was used in determining the sampling location at 3 observation stations for surface water and sediment, namely station 1 area where is former gold processing. Station 2 river body where former PETI produces mercury waste, station 3 is a place where there is no gold processing. TDS and TSS measurements using the gravimetric method, mercury and cyanide levels using ICP-OES. The results of laboratory sediment analysis of 3 observation location in Cisarua Village, Curug Bitung Village, and Lukut Village, for the TDS and TSS parameters, mercury was detected at the highest level at point 3 in Lukut Village. Luku Village is the most downstream location of the Cikaniki River which is located very far form the peoples gold processing site. This concludes that after PETI activities occur, the distribution of mercury (Hg) waste spread to the most downstream areas of the Cikaniki River is always present even though its presence is still below environmental quality standards

Implementation of Deterministic and Multimineral Method in Petrophysical Analysis for Identifying Low Resistivity Reservoir in Tesla Field, Air Benakat Formation, South Sumatera Basin

The Tesla field is located in the South Sumatra Basin, where there is the Air Benakat Formation, with the constituent rocks being dominated by alternating sandstone and claystone so that it is a shaly sand environment with the potential to become a low-resistivity hydrocarbon reservoir. Hydrocarbon reservoirs generally have a resistivity log value of more than 10 m; when a hydrocarbon reservoir has a low-resistivity value between 0.5 - 5 m, it is referred to as a low-resistivity hydrocarbon reservoir. Initially, deterministic analysis was carried out to calculate the petrophysical parameters of the potentially low-resistivity reservoirs. However, the results show a low validation value of petrophysics parameters, such as effective porosity and water saturation, when compared to the DST data, so a multimineral analysis is carried out to increase the validation value of the petrophysical parameters. The use of the multimineral method has produced the petrophysics parameter closer to DST Data when compared to the petrophysics parameter produced by the deterministic method in Tesla Field. The formation analysis shows that the low resistivity reservoir in the Tesla Field is caused by the grain size of the sandstone, which is very fine so that it can bind water significantly (irreducible water), abundant shale content, and distributed by lamination of shale, dispersed shale, and structural shale as well as the presence of conductive glauconite minerals

Study Comparison Deep Learning and Support Vector Machine for Face Mask Detection

Deep Learning (DL) and Support Vector Machine (SVM) was used for a plethora number of researches lately. Deep Learning works by representing data in layers of learning layers so that the representation becomes more meaningful, and Support Vector Machine tries to find the hyperplane that maximizes the margin between the hyperplane and the closest data points from each class so that the classification becomes more accurate. Both algorithms have proven to be powerful tools for any classification problem specially to classify or identify image patterns. However, the performance of machine learning algorithms can be affected by any factor, thus sometimes we found several algorithms that are generally known to be powerful, even showing unsatisfactory results. The purpose of this study is to compare the ability of classification methods Deep Learning and Support Vector Machine to detect face mask. Face mask detection has gained significant attention and importance in the context of public health and safety, particularly during the COVID-19 pandemic. The study revealed that Deep Learning algorithm performed better than the Support Vector Machine Algorithm and showed excellent performance in all four metrics. In particular, the Deep Learning algorithm achieved an average Sensitivity/Recall rate of 92%, a Specificity rate of 95.44%, a Precision rate of 95.28%, and an Accuracy rate of 93.72%

Microplastics in Landfill Environments: Distribution, Characteristics, and Risks from Gampong Jawa, Indonesia

Landfills are generally considered the ultimate solution for waste management. However, the degradation process of plastic waste in landfills causes the release of microplastic particles into the surrounding environment and threatens human health. The distribution and properties of microplastics in four environment matrices, soil, leachate, river water, and well water surrounding the landfill, are examined in this study. Sampling was conducted at the inflow and outflow areas of the leachate ponds., The soil at the top (05 cm) and bottom (520 cm), upstream and downstream surface water adjacent to the landfill, and community wells within a radius of fewer than 700 meters from the landfill. Microplastic analysis used a gradual extraction method with saturated NaCl for density separation, 30% hydrogen peroxide for organic matter degradation, and 0.05 M FeSO4 as a catalyst. Physical character identification of microplastics using a microscope showed microplastic contamination at all study sites. The results showed an abundance of microplastics was found in well water samples (808 to 979 items/L), leachate (209 to 757 items/L), surface water (6.29 to 7.2 items/L), and soil (23,340 to 23,420 items/kg). Types of microplastics found consist of fragments, fibers, films, pellets, foam, and rods. The size of microplastics found ranged from 1.897 m to 1,642.79 m. Fourier Transform Infrared spectroscopy examination identified polyethylene terephthalate (PET) plastic compounds in soil and leachate materials. The high concentration of microplastics in well water indicates potential groundwater contamination from landfill activities that may impact the surrounding community. This study provides preliminary insights into how landfills may contribute to environmental microplastic contamination. It paves the way for further research to develop mitigation strategies

Long-term Monitoring of Low-cost Seismometers: Consistency Analysis of The Instrument

Instruments have become an essential part of conducting a study or research. With the aid of instruments, the measurement process can be faster, more efficient, and more accurate. However, an instrument also has a limited service life. Over time, the performance of the instrument will degrade. Therefore, the equipment must be regularly maintained and calibrated periodically. This research aims to test the measurement consistency of a low-cost seismometer (RS-3D). The approach involves long-term measurements to assess the instrument's stability in taking measurements. The measurement data is then processed and presented as frequencies using spectrum analysis. The research findings indicate that the instrument's consistency is generally good, with an average standard deviation of 0.18 and a coefficient of variation of 5%. Additionally, 95% confidence interval calculations yielded values of 2.520.02 for measurements at RKD, 3.040.05 for measurements at GLT-USK, and 3.30.04 for measurements at GFT-USK. Data validation was performed using the equations from building codes, showing that the difference between the measured microtremor frequency and the empirical equation was less than 1, indicating good measurement results. The conclusion drawn from this study is that a higher standard deviation value indicates a more distributed data spread, signifying less consistent research data. Conversely, a lower standard deviation indicates that the data is more concentrated around the mean value, indicating more consistent measurement results. Moreover, with previous studies having conducted validation and consistency testing, it is hoped that both tests will be routinely performed during instrument maintenance

Structural Health Monitoring by Identification Dynamic Properties and Load Rating Factor at Multi-span Prestressed Concrete Girder Bridge

It is crucial to perform routine bridge maintenance in order to evaluate the structure's current state. As a result, it is possible to guarantee that the bridge structure can offer services that are both comfortable and secure. The bridge structure being able to reach the service life as planned is another goal that can be accomplished. Visual inspection or the use of some currently popular sensors can be used to monitor the condition of the bridge. The dynamic properties of a structure including modal frequency and mode shape will be used to determine the structure's present and potential future conditions. Using a velocitymeter, vibration data collection is conducted as the first step. The next step is analyzing data to determine natural frequency. The fundamental frequency of the Tugu Suharto bridge structure in Semarang was determined to be 3.995 Hz. Future bridge structure condition monitoring can be done using frequency data and finite element model. The condition of bridge infrastructure in the future for one city is an important thing that must be considered. Some bridges are classified as structurally deficient, and many bridges are nearing the end of their design lives. The next generation of Semarang highway bridges is currently being designed and built, but existing bridges still need to be maintained through proper inspection and load rating. In order to incorporate structural modeling, instrumentation, and nondestructive testing into the design, construction, and management of bridges, this study proposes an objective load rating protocol. Using information gathered from structural health monitoring (SHM), a baseline structural model is developed and verified. The load rating factors of the bridge are then determined using the structural model under both real-condition and simulated damaged conditions

Recycling of Disposable Face Mask: Experimental Studies on Different Types of Polymer Mixture

The COVID-19 pandemic has caused significant impacts on the environment since the use of disposable face masks leads to the accumulation of plastic waste. In this study, a two-step extrusion and injection molding was performed to manufacture polymer blends consisting of 80% used face mask and 20% fraction of one of these recycled polymer mixtures: polypropylene (PP), high-density polyethylene (HDPE), and PET. ASTM D256 standard was used to evaluate the mechanical properties of the resulting polymer blend materials, while the physical performance was assessed by analyzing the shrinkage. It was found that adding other polymeric mixtures could not enhance the mechanical properties of pure disposable face masks, as measured by the impact strength. However, incorporating the recycled polymer into the face mask mixture is revealed to decrease shrinkage. Observation of the morphology surface of the fracture impact specimen using a Scanning Electron Microscope (SEM) confirmed the less miscibility within the recycled polymer/face mask. The blend, which contains recycled PET, showed the lowest percentage of shrinkage. Taking advantage of its recyclability characteristic, this current work may provide an alternative approach for using the disposable face mask in low load-bearing applications

Variations in Site Conditions and Blast Geometry on The Formation of Toxic Gas (Fumes) in Open-Pit Coal Mining

The blasting activity generates one of the effects in the form of toxic gases (fumes) that can disturb living beings around them. Fumes formation is formed by the reaction of the explosive material not in a zero oxygen balance condition, and is influenced by several factors including the condition of the blast hole, rock moisture content, blast hole temperature and relative humidity, sleep blast, explosive material ratio, and poor confinement stemming. This study investigates the variations in site condition and blast geometry on the formation fumes in open-pit coal mining. This research was conducted at the coal mine of Kaltim Prima Coal (PT KPC) to quantitatively measure the levels of toxic gas (fumes) resulting from blasting activities. In-situ measurements were conducted using a gas detector suspended above a drone. From the measurement results, it was found that blasting activities at the PT KPC coal mine produce CO and NO2 gases in toxic gas visual conditions at Levels 0 and 1A. The CO gas levels resulting from blasting activities ranged from 60.34 to 324.79 ppm, and the NO2 gas levels ranged from 0.3 to 2.11 ppm. From the trial results, by altering the explosive material ratio, toxic gas visual conditions were observed at Level 2A with CO gas levels of 360.29 ppm and NO2 gas levels of 3.16 ppm. The formation of CO and NO2 gases from blasting is influenced by the blast hole temperature and humidity, as well as differences in explosive material ratios. Based on the gas CO and NO2 level measurements, according to the threshold values with the maximum exposure level for humans over a 15-minute period for both gases, it was determined that workers could safely return to the blasting site in less than 1 minute