Universitas Ahmad Dahlan Journal
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Analysis of subsurface structure in ulakan tapakis district Padang Pariaman regency using refraction seismic method
Get PDFAnalysis of Subsurface Structure in Ulakan Tapakis District, Padang Pariaman Regency Using Refraction Seismic Method. This study aims to analyse the subsurface structure in Ulakan Tapakis District, Padang Pariaman Regency using the seismic refraction method. Data acquisition used the in-line configuration technique on eight passes. The data obtained form of wave traveltime as a function of distance. Data processing the refraction seismic method is based on the arrival time of the first wave, by picking the first break to obtain a travel time graph and using time-term inversion to obtain a 2-D seismic cross section. The results showed that the subsurface structure in the study area consisted of 2 layers based on differences in wave propagation velocity on each path. Wave velocities in the first layer of 252 m/s to 425 m/s are interpreted as layers of weathered bedrock, topsoil, unsaturated sand and gravel mix at depths of 1 to 6 meters. The second layer has wave velocities of 503 m/s to 665 m/s interpreted as layers of alluvium, saturated sand and gravel at depths greater than 6 meters. The subsurface structure in this area is included in the primary structure with rock formation units in the form of surface Alluvium (Qal) deposits which are suspected of experiencing subsurface weathering. This research makes a significant contribution to mitigating the risk of earthquake disasters by providing an understanding of the potential associated hazards
Perceived organizational support and gratitude towards employee organizational commitment
Get PDFThis study aims to examine the influence of perceived organizational support (POS) and gratitude on the organizational commitment of employees at RSUD dr M.Y. The sampling technique used was quota sampling, with 103 employees participating as subjects in the study. The instruments utilized included organizational commitment scale, perceived organizational support scale, and gratitude scale. Data analysis employed multiple linear regression. The results indicate a significant influence of both perceived organizational support and gratitude on organizational commitment at RSUD dr M.Y. Specifically, perceived organizational support does not significantly affect organizational commitment, whereas gratitude has a highly significant impact on organizational commitment. The conclusion drawn from this research is that higher levels of POS and gratitude jointly correspond to higher organizational commitment, and vice versa. However, independently, high or low levels of POS do not influence organizational commitment. Furthermore, higher levels of gratitude correlate with increased organizational commitment, while lower levels of gratitude correlate with decreased organizational commitment
The dynamics of online sexual grooming: From comfort to insecurity in victims' experiences
Get PDFThe prevalence of sexual violence has been rising, particularly with the increasing reports of online sexual abuse. This study aims to examine the experiences of victims subjected to online sexual grooming, a form of sexual violence occurring in digital spaces. The research focuses on the manipulative tactics employed by perpetrators to exploit their victims. Using a qualitative approach and a phenomenological study design, the research involved two participants who had been victims of online sexual grooming by unknown perpetrators, with no prior face-to-face contact. Data were collected through interviews, and analysis followed the stages of data reduction, data presentation, and drawing conclusions. Findings reveal that prior to the grooming process, participants encountered the perpetrators via social media and engaged in frequent conversations. Throughout the grooming process, perpetrators employed various manipulative techniques such as conversational manipulation, sustained contact, secrecy, sexualization, compliments, erratic moods, and other grooming behaviors. The emotional impact of online sexual grooming on the victims included both positive and negative feelings, with the latter predominating during the grooming experience
Antibacterial activity of guava leaf ethanolic extract (Psidium guajava L.) nanosuspension against Escherichia coli bacteria
No full textDiarrhea is a condition where a person has bowel movements three or more times a day, with consistent stools. One of the common bacteria that causes diarrhea is Escherichia coli. Empirical and preclinical studies have demonstrated the effectiveness of guava leaves (Psidium guajava L.) in treating diarrhea due to their tannin content. Nanosuspension formulations can be created to simplify the use of guava leaves for medicinal purposes. This study aims to investigate the efficacy of guava leaf extract, both in its natural form and as a nanosuspension preparation, against Escherichia coli. Additionally, the study aims to characterize the guava leaf extract nanosuspension used in the experiment. The technique used to make nanosuspension involves ionic gelation methods by using chitosan as a polymer, and subsequent characterization of the resulting product includes organoleptic testing, specific weight, pH, sedimentation volume, and viscosity. After the characterization of the guava leaf nanosuspension, it was found that the optimal formula had a particle size of 245.7 nm at a concentration of 0.01%, a polydispersion index of 0.406, and a zeta potential of +26.9 mV. Guava leaf ethanol extract 1% has a diameter of the inhibitory zone of 4.05±0.45 mm. However, the nanosuspension form of P. guajava L at a concentration of 0.01% has an inhibitory zone diameter of 11.45±0.64 mm. The nanosuspension formulation using P. guajava L has met the evaluation requirements and has antibacterial activity against E. coli bacteria
In silico study of Sambiloto (Andrographis paniculata) compounds from GC-MS and LC-MS/MS as alpha-glucosidase and DPP-4 enzyme inhibitor
Get PDFDiabetes mellitus is a group of metabolic diseases characterized by hyperglycemia, impaired insulin secretion, and insulin action. To overcome this disease, some people treat it with natural ingredients. Sambiloto (Andrographis paniculata) is reported to have a wide range of pharmacological activities, one of which is anti-diabetic. Sambiloto showed activity in lowering blood glucose which has the potential as an antidiabetic. Computational methods, such as molecular docking, can increase the effectiveness and reduce the cost of searching for new active compounds. The purpose of this study was to determine the component compounds contained in the ethanol extract of Sambiloto and obtain the potential compounds to inhibit the alpha-glucosidase and DPP-4 enzymes as anti-diabetics with molecular docking method. Sambiloto leaves were macerated for 3 x 24 hours using ethanol 96% as a solvent and concentrated with an evaporator. Sambiloto extract was analyzed using LC-MS, and GC-MS. In-silico analysis includes geometry optimization and molecular docking methods. Preparation of the test ligands was carried out by the ChemBioDraw Ultra and ChemBio3D applications, then optimization by Gaussian 09 application. The crystal structures of the target proteins used were those with PDB ID 5NN8 for alpha-glucosidase and 2QOE for DPP-4. Molecular docking was performed using Autodock 4.2.3 application. From analysis with LC- MS/MS and GC-MS methods, 18 compounds were identified. Molecular docking was performed on the identified compounds. The results of molecular docking showed that the compound S17 (11-(P- Bromoanilino)-5H-Dibenzo [B,E] [1,4] Diazepine), S1 (andrographolide) and S2 (andrographanin) have the potential to inhibit the activity of alpha-glucosidase enzyme; on the other hand S17 (11-(P-Bromoanilino)-5H-Dibenzo [B,E][1,4]Diazepine) and S5 (andrographolactone) have the potential to inhibit the activity of DPP-4 enzyme. These compounds have the potential to inhibit alpha- glucosidase and DPP-4 enzymes which act as antidiabetics
Gastroprotective activity of Banana peel (Musa paradisiaca var. sapientum) methanol extract purified on aspirin-induced gastric ulceration in Rats
Get PDFBanana (Musa paradiasiaca var. sapientum) is the world's most popular fruit-bearing crop, with rising consumption and waste. This study aimed to measure the metabolite compound and evaluate the gastroprotective properties of a banana peel-purified methanol extract. Animals test used in this study were divided into six groups: Group One received NaCMC 0.5%, Group Two received sucralfate, Group Three received aspirin 1000 mg/kg body weight, and groups four, five, and six received PBP at doses of 200 mg/kg body weight, 400 mg/kg body weight, and 600 mg/kg body weight, respectively, for seven days. Except for group 1, all groups were induced with aspirin at 1000 mg/kg body weight on the eighth day. The result of this study exhibited banana peel containing total phenolic, flavonoid, and tannin compounds with concentrations of 33.45 mg GAE/g, 19.92 mg QE/g, and 0.16 %, respectively. The results showed that pure extract of Musa paradiasiaca var. sapientum fruit peel can reduce the incidence of gastric ulcers by decreasing the ulcer index (p<0.05).. The results suggested that Musa paradisiaca var. sapientum peel has a gastroprotective effect against aspirin-induced gastric ulceration
Determinants of Sugar Imports, Sugar Consumption and Production in Indonesia (2000 – 2019 Study Case)
Get PDFIndonesia was a prime sugar exporter country in the past, but since year 1967 has been importing sugar from other countries. Sugar import volume has been increasing every year so Indonesia has become second largest sugar importer in the recent years. Based on those problems, this research aims to analyze the factors that affect Indonesian sugar imports. The data used for analysis is secondary data in the form of time series in the range of 20 years (2000 – 2019), which were collected from various related agencies. Data analysis uses Seemingly Unrelated Regression (SUR), which was used to analyze the effect of sugar production, GDP, sugar consumption, domestic sugar prices, International sugar prices, and rupiah exchange rate on the volume of Indonesian sugar imports. The result shows that only sugar consumption affects significantly sugar imports, while sugar production, GDP, domestic sugar prices, international sugar prices, and the rupiah exchange rate do not significantly affect sugar imports. Sugar consumption is affected by GDP and domestic sugar prices, while sugar production is affected by domestic sugar prices. In addition, sugar imports volume shows that trend imports grew positively with an estimated trend of 197.978 tons per year in the 2000 – 2019 period. According to the sugar import trend, can be concluded that there will be growth of sugar import volume in the coming years. Based on SUR analysis, sugar imports growth is caused by consumption growth and consumption growth is affected by the growth of GDP and the decrease in domestic sugar prices. One policy that can be implemented by the government to resolve the sugar import problems is the policy on sugar prices, because high sugar prices decrease sugar consumption, and on the other hand also increase domestic sugar production
Adaptive Traffic Light Signal Control Using Fuzzy Logic Based on Real-Time Vehicle Detection from Video Surveillance
Get PDFIntersections often become the focal points of congestion due to poor traffic signal management, reduced productivity, increased travel duration, gas emissions, and fuel consumption. Existing traffic light systems maintained constant signal duration regardless of traffic situations, resulting in green signals for lanes with no vehicle queues that increased waiting times in other lanes. Therefore, a real-time traffic signal optimization system using Fuzzy Logic control, utilizing vehicle queue and flow rate real-time data from video surveillance, is needed. This research used recorded video from surveillance cameras in Banten Province, Indonesia, during daylight conditions. Vehicle queues and flow rate data were used as parameters to determine traffic light signals. The YOLO algorithm obtained these parameter values, then served them as inputs for the Fuzzy Logic system to determine signal duration. The accuracy of the traffic situation estimation system fluctuated within a range of 40% to 100%. Simulation results showed an improvement of approximately 18% by evaluating the total number of vehicles that exited the queue and reduced vehicle waiting time by about 21% compared to the existing system on intersection efficiency. Consequently, the proposed system can reduce pollution and fuel consumption, contributing to urban sustainability and public well-being enhancement. Despite the improvements over the previous systems, the accuracy of the vehicle detection system may vary with traffic density based on the extent of occlusions present, which is an area that needs further refinement. This research's contributions include utilizing real-time video footage from surveillance cameras above traffic lights to obtain real traffic conditions and identify potential errors such as occlusion of overlapping vehicle due to very congested roads. Another contribution is the adjustment of the Fuzzy membership function based on the vehicle detection system's ability to ensure precise determination of green signal duration, even when the input data contains errors
Detection of Eight Skin Diseases Using Convolutional Neural Network with MobileNetV2 Architecture for Identification and Treatment Recommendation on Android Application
Get PDFSkin diseases are common in Indonesia due to the tropical climate, high population density, and low public awareness about skin health. These diseases are often caused by infections, chemical contamination, or other external factors and typically develop internally before becoming visible, with contact dermatitis being the most frequently reported condition. To address this issue, this research proposes the use of Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) with the MobileNetV2 architecture, to detect eight types of skin diseases, namely cellulitis, impetigo, athlete's foot, nail fungus, ringworm, cutaneous larva migrans, chickenpox, and shingles. MobileNetV2 was chosen for its efficiency and high accuracy in mobile applications. The methodology involves developing a detection system using CNN MobileNetV2, integrated into an Android application to identify skin diseases and provide treatment recommendations. The dataset was collected, labeled, resized, and normalized to meet the model requirements. After training, the model was tested using a separate dataset to ensure its generalization ability and was finally integrated into the Android application. This application allows users to detect skin diseases and receive treatment advice directly. The research results show that the CNN MobileNetV2 model achieves high accuracy in classifying the eight types of skin diseases, with stable performance over several training epochs. Evaluation of the test dataset revealed an overall accuracy of 97%, with high precision, recall, and F1-score for all disease classes. The application achieved an accuracy of 84% on general data, demonstrating its practical utility. However, the need for real-time updates of treatment information was identified as a limitation. This research advances skin disease detection technology and improves public access to accurate healthcare services. Future studies should focus on real-time treatment information updates and expanding the range of detectable diseases to enhance skin disease application
Exploring Energy Data through Clustering: A Hyperparameter Approach to Mapping Indonesia's Primary Energy Supply
Get PDFThe rapid economic growth and population development in Indonesia have significantly increased the demand for energy, presenting complex challenges in managing the primary energy supply due to geographical variability and dispersed natural resources. This study addresses these challenges by applying clustering techniques with a hyperparameter approach to explore and map Indonesia's primary energy supply. The research contributes to the field by offering an effective method for analyzing energy data patterns and optimizing energy management. Secondary data on energy production, consumption, and distribution from reliable sources such as the Ministry of Energy and Mineral Resources were collected and analyzed. Various clustering algorithms, including K-Means, Fast K-Means, X-Means, and K-Medoids, were applied to identify energy supply patterns across different regions. The Davies-Bouldin Index was used to evaluate the effectiveness of the clustering algorithms. The results indicate that distance measures such as Euclidean Distance and Chebychev Distance consistently show excellent clustering performance. The study found that the choice of distance measure significantly impacts the clustering quality. The insights gained from this analysis provide valuable information for stakeholders involved in energy planning and policy-making, enhancing the efficiency and sustainability of energy management in Indonesia. This research establishes a foundation for further detailed and holistic energy data analysis, supporting better decision-making in energy planning and development