International Journal on Advanced Science, Engineering and Information Technology
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2006 research outputs found
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Organizational Resistance to Technology Diffusion: The Case of IPv6
Get PDFIP address is an essential protocol to identify every connected device to the Internet uniquely. IPv6 was developed as a long-term solution to overcome IPv4's shortcomings. However, IPv6 adoption is still very rare. Organizations tend to resistance to adopting and implementing IPv6 on their network. This study aims to develop and test a model of organizational resistance to IPv6, an Internet Protocol (IP) intended to replace IPv4, the widely used incumbent. This exploratory mixed-methods study analyzed interview data from Indonesian organizations, supplemented with insights from prior literature, to identify factors of organizational resistance to IPv6. A subsequent survey of Indonesian organizations was conducted to assess the relationship of each factor with IPv6 resistance. The survey data was then rigorously analyzed using PLS-SEM. While IPv6 is typically portrayed as an essential Internet infrastructure development, Indonesian organizations perceive it as unnecessary and threatening. A Structural Equation Model of IPv6 Resistance was developed and posits that although perceived threat, perceived lack of need, and environmental influences all influence organizational resistance to IPv6, switching costs and satisfaction with current technology have no impact. This study has practical implications for organizations that aim to promote IPv6 diffusion; promotion strategies should address the key factors identified in this study. While prior models of technology resistance have focused on individual-level resistance to technologies promoted from within the organization, this study focuses on organizational-level resistance to technology promoted by sources external to the organization and hence makes a new theoretical contribution
IoT Implementation for Server Room Security Monitoring Using Telegram API
Get PDFThis research aims to create a system that can monitor server room security by utilizing IoT technology, controllers, sensors, actuators, and Telegram API. The system made includes security for the indoor and outdoor parts. The method used in this study is experimental. The output obtained by the user is telegram text messages, photos, and video. The main controller on this system is the Raspberry Pi. This study showed that if the server room temperature is >230C, the system will send a message via Telegram. If the PIR sensor_1 or PIR sensor_2 detects an intruder or the measured distance i
Delay Time (δt) and Polarization Direction (φ) Analysis Based on Shear Wave Splitting (SWS) Method
Get PDFThe information of dominant polarization direction and the mapping of fracture intensity are among the most important informations during the monitoring of geothermal field reservoir evaluation, as an effort to develop geothermal energy production. The appearance of geothermal reservoir fractures caused by fluid injection and the production activity resulting in the decreased pore pressure and appearance of open weak zone. The micro-earthquake activity around the area can represent these fractures that appear in the geothermal reservoir. Shear Wave Splitting (SWS) analysis can be done based on the polarization of S wave through the anisotropy medium recorded by seismograph. There are two parameters related to Shear Wave Splitting: the polarization direction (φ) related to the micro fracture direction with its delay time (δt), showing the fractures density and its permeability area. The result of Shear Wave Splitting Analysis of the field X geothermal shows that two dominant polarization directions are NW-SE and NE-SW. It is caused by the fractures around the X field geothermal with similar fractures direction, and it is compatible with the distribution micro-earthquake hypocenter of the previous study. Based on the map of fractures intensity, the value range shows a relatively dense intensity value around 6.6 – 8.0 ms/km. The high value of intensity fractures indicates a high value of anisotropy around the area, and it is also confirming the presumption of the high permeability potential of the X geothermal field
Software Traceability in Agile Development Using Topic Modeling
Get PDFTracing the implementation of requirements for making better software identifies whether the application fulfils users' desires; progress of development; problematic areas in the testing process, and how far those apply to the source code. In this paper, the software development method we studied was the agile method, Extreme Programming (XP). The artifacts in the agile approach considered vital include the requirement documents, test documents, and source codes. We used Topic Modelling to map the content similarities from those documents to make trace links. The three topic modelling methods we compared consist of Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF). The NMF method proved itself the most stable, with an accuracy value of 67% for the requirement, 59% for testing, and 48% for defect lists. The second application results proved more accurate with 70%, 79%, and 54%. Although NMF lost to LSA in the second application (LSA achieved an accuracy of 79%, 84%, and 56%), the precision and recall values showed almost similar results. We successfully found the link in the source code based on keywords extracted from each topic. This research provides a way of explaining the requirement in detail, simplifying it for tracing purposes such as the consistent use of terms, technical details inclusion, and mentioning all the variables involved. In the future, sentence structure and synonyms need recognition as part of pre-processing to build better trace links
Identification of Landslide Area Using Geoelectrical Resistivity Method as Disaster Mitigation Strategy
Get PDFExtreme weather triggers high-intensity rainfall, and it triggers land movement that eventually becomes landslides. The water of rain will enter to the ground through the rock gaps and accumulate along the landslide area to reduce effective stress and reduce the shear strength of the soil. Morphologically, Sukaresmi Village, Cisaat Sub-District, Sukabumi Regency is located at the foot of Mount Gede with a bumpy surface relief. This condition is one factor that triggers landslides because the soil is prone to movement. This research aims to identify the field slope zone for landslide prediction in the Sukaresmi village, hoping that the surrounding community could anticipate further landslides. The research was carried out using the Geoelectrical Resistivity method of the Schlumberger configuration as many as eight measuring points with 1 m electrode spacing. This research indicates that the subsurface conditions are divided into three constituent rocks: Clay, Tuff, and Volcanic Breccia. The field slide zone is located between the Tuff rock and turf layer at a depth of 4-7. 5 m long, 82 m (Line 1), and 40 m (Line 2), with a resistivity value range of 56-158 Ωm. The efforts that the local government can make to anticipate the condition of the building to remain safe include analyzing soil stability, strengthening slopes, and making retaining walls to increase the value of the safety factor
Genetic Variability Analysis of Phaius spp Orchid based on RAPD Markers
Get PDFPhaius spp live in tropical forests with high humidity due to deforestation, which has resulted in the extinction of many endemic orchid species, especially orchids Phaius spp, so it needs to be conserved through a plant breeding program. There are not many research reports on the genetic diversity of orchids Phaius spp, even though the study of genetic diversity plays an important role in plant breeding programs and genetic conversation. This study aims to determine the genetic diversity of Phaius spp. This research was carried out using molecular markers RAPD (Random Amplified Polymorphic DNA). DNA from Phaius spp was extracted and then amplified by PCR using 6 RAPD primers. The results of this study found that four species of orchids of the genus Phaius spp (Phaius tankervillae (1); Phaius montanus (2); Phaius collasus (3); and Phaius amboinensis (4)) amplified using 6 RAPD primers to produce 37 DNA bands with a size of 250-1600 bp, and produce 100% polymorphic bands The genetic similarity coefficient of the 4 orchid species ranges from 0.19-0.53 and the genetic variability ranges from 47%-81%
Interesterification Process of Palm Oil Using Base Catalyst: The Effect of Stirring Speed and Type of Catalyst on Kinetic Energy and Dipole Moment
Get PDFMaking biodiesel which has so far been carried out, is a transesterification process with glycerol by-products. Glycerol has a low economic value and is usually only disposed of as waste. An alternative process for producing biodiesel with more valuable by-products is interesterification. The by-product of the interesterification reaction is triacetin which is widely used in the chemical, food, and pharmaceutical industries. The operating conditions of the interesterification reaction were the reaction temperature of 60â°C, the molar ratio of palm oil: methyl acetate = 1: 6, the reaction time of 1 hour, catalyst type (KOH, NaOH), stirring speed (200, 300, 400, 500, 600 rpm) and catalyst mass (0.25, 0.5, 0.75% wt. oil). From the analysis and calculation, the highest FAME yield was 57.30% at reaction temperature 60â°C, the molar ratio of palm oil: methyl acetate = 1: 6, reaction time 1 hour, KOH catalyst, stirring speed of 300 rpm, and catalyst mass 0.75% wt oil. From the calculation of ChemDraw software for the triglyceride + methyl acetate + KOH system had a kinetic energy of 3,670 kJ/kmol and a dipole moment of 20,330 debyes, whereas the triglyceride + methyl acetate + NaOH system had a kinetic energy of 2,977 kJ/kmol and a dipole moment of 11,457 debyes so that the KOH catalyst was superior in terms of reactivity and solubility. Biodiesel produced had an acid value of 0.3927 mg KOH/gr and met ASTM D664 for a maximum acid value of 0.5 mg KOH/gr
Produced Water Treatment using Electrocoagulation Combination Method with Aluminum (Al) and Iron (Fe) Electrodes and Activated Carbon Adsorption Treatment
Get PDFOil is the main source of energy and income for various countries today, and its production has become one of the most important industrial activities in the 21st century. Besides being produced, the oil and gas industry also has a problem with a large volume of waste, and 80% of the liquid waste produced is water, which is also referred to as produced water. Produced water is a by-product of oil and gas processing. This water is different from water because it contains hazardous chemicals and other elements in oil and gas. In this study, a combination of electrocoagulation processes using aluminum (Al) and iron (Fe) electrodes with filtration treatment using activated carbon from a coconut shell, comparing the performance of three processes: electrocoagulation process and adsorption and combination of the electrocoagulation-adsorption process with a continuous process. Electrocoagulation is a process using designed electric currents such as voltmeter circuits with voltage variations of 3, 6, 9, and 12 V, and time variations of 30, 60, 90, 120, and 150 minutes with Al and Fe electrodes were then carried out by their adsorption process using activated carbon from coconut shell waste. The main advantage of this method is the relatively short contact time, and electrode material is easily obtained, and this method is proposed as a substitute for a coagulation system with alum/alum material. The results showed that the decrease in the optimum decrease in COD (98.39%) from the initial content of 737.57 mg/L to 11.90 mg/L, TDS (93.54%) from the initial content of 16,610 mg/L to 1,073 mg/L ammonia (75.16%) from the initial content of 24.24 mg/ L to 6.02 mg/L, oil content (97.56%) from the initial content of 364.2 mg/L to 8.9 mg/L, and phenol (92.5%) from the initial content of 1.20 to 0.09 mg/L. With the optimum voltage parameters at 12 V and a time of 150 minutes. The result achieved in this process is the combination of electrocoagulation and adsorption obtained at 12 V for 150 minutes able to reduce the produced water so that it meets the quality standard by the regulation of the state minister of the environment concerning wastewater quality raw for businesses or oil and gas as well as geothermal activities
Machine Learning Model for Sentiment Analysis of COVID-19 Tweets
Get PDFCovid-19 pandemic presents unprecedented challenges and enormously affects different aspects of individuals' lives worldwide. The implementation of different prevention measures, the economic and social disruption, and the significant rise in the mortality rate greatly affect the peoples' spectrum of emotions. Sentiment analysis, an important branch of artificial intelligence, uses machine learning techniques to understand public perspectives and gain more insights into how they think and feel. During the pandemic, sentiment analysis increasingly contributes towards making appropriate decisions. This research aims to analyze the public sentiment related to Covid-19 by exploring social perceptions shared on Twitter, one of the most ubiquitous social networks. This goal was achieved by building a machine learning model using a dataset of Covid-19 related English tweets. Different combinations of machine learning classification algorithms (Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB)) and feature extraction techniques (Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram) were built and applied to the dataset for binary (positive, negative) and ternary (positive, negative, and neutral) classifications. A comparative study for the performance of the different models was then conducted, and the results concluded that XGB classification algorithm with unigram and bigram for binary classification achieved the highest accuracy of 90%. This sentiment analysis model can assist countries and governments in measuring the impact of the pandemic and the applied prevention measures on people's emotional and mental health and take early actions to reduce their impact or prevent them from becoming severe cases
IoT Network of Sensor Array for Intrusion Detection and Diagnosis of Electrical Systems
Get PDFModern buildings consist of various equipment, including heating, ventilation, air conditioning (HVAC), and lighting. All equipment can be monitored and managed by the building management system. All of these components can be damaged due to prolonged use, misconfiguration, and network connection problems. Equipment breakdown affects maintenance costs and, in particular, energy efficiency. This study aims to develop a monitoring system of the current consumption of lighting (lamps) by light detection and current consumption of air conditioning (AC) by room temperature detection using Internet of Things (IoT) implementation. Hardware design consists of a power supply circuit, installing an ACS 712 current sensor, LDR sensor, the temperature sensor of DHT22, and thermal sensor of LM35. While the software design consists of a diagram flow for the current sensor, light sensor, temperature sensor reading program, program on the display board, and a web server design. The detection of current, lamplight, room temperature, and thermal cable is carried out to determine errors that occur in electrical equipment. Monitoring the consumption of lighting flows by detecting lamp light and air conditioning current consumption by detecting room temperature is done through the Firebase web server using a computer or smartphone. The results showed that the built system could monitor current consumption, detect lamplight, and detect room temperature in real-time. This system can be used to detect faulty electrical equipment and determine its position so that repairs can be carried out immediately. However, the type of damage has not been identified