ORCID Profile
0000-0002-6074-5114
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Publisher: Science Publishing Corporation
Date: 20-07-2018
Abstract: Wireless Sensor Networks (WSNs) is a collection of devices and sensor nodes connected with wireless network and communicate with one another via radio signals. Sensor in WSN is an autonomous (self-configuring) device used to sense the light, heat, motion, moisture and pressure etc that communicate with their neighbor nodes. Node placement is a technique that places the nodes effectively in the specified network environment. In WSN basically, wireless sensor network includes different topologies namely star, point-to-point, ring, bus, mesh and hybrid. In recent years, research has been carried out on different node placement strategies and produced different results based on its performance that includes power distribution and energy consumption of sensors. Energy consumption and network lifetime are considered to be the critical issues as the nodes are powered by the batteries which have finite energy reservoirs. In this paper, three different node placements namely Random, Uniform and Grid with respect to AODV (Ad hoc On-Demand Distance Vector) protocol is evaluated in order to analyze the energy factor during wireless communication. The performance metrics used to measure the analysis are Energy Consumption Average Jitter, Average End-to-End Delay, Average Throughput and Average Packet Delivery Ratio. The comparison results suggests that Grid node placement performs well in grid scenarios and shows best for specific performance metrics.
Publisher: Conscientia Beam
Date: 21-09-2021
DOI: 10.18488/JOURNAL.89.2021.71.36.49
Abstract: This paper examined how firm-level idiosyncratic risk varies over time. It affected initial public offering (IPO) in the presence of pump-and-dump and flipping trends during the early trading of IPO stocks in the Indonesia Stock Exchange. The paper used the IPO data taken from 181 companies during the year 2015-2019. It revisited the relationship between Cumulative Abnormal Return thirty-days (CAR30D) and Cumulative Abnormal Return five-days (CAR5D) and the Characteristics (IPO Floating shares, IPO Fund and Price) and Macroeconomics Condition (Inflation rate). It also used the cointegration analysis and VECM model. The paper found that Both LnFloat and LnPrice had causal evidence in the long-run causality or short-run with Cumulative Abnormal Return thirty days (CAR30D). We also noted that idiosyncratic risk exposure depends on IPO characteristics. It was crucial for firms going public in hot-issue markets, undervalued IPOs, and high idiosyncratic-risk issues. The model suggested that those series should cointegrate firstly. However, the variable of LnIPOFund had causal evidence in the short-run causality only.
Publisher: EconJournals
Date: 20-08-2021
DOI: 10.32479/IJEEP.11212
Publisher: Conscientia Beam
Date: 08-02-2022
Abstract: Customer's creditworthiness was becoming more crucial for ESCO. Machine learning was used to predict the creditworthiness of clients in retrofit financing processes. Machine learning was used to predict the creditworthiness of clients in ESCO financing processes. This research aimed to develop a retrofitting scoring model to leverage a machine learning and life cycle cost analysis (LCCA) to evaluate alternative financing for Energy Efficiency Saving in Indonesia. The model was built on the Logistic Regression model and Artificial Neural Networks model of machine learning. The model was developed and tested using the Python algorithm, and the proposed model's efficiency was demonstrated. The logistic regression calculations showed that the accuracy value of prediction data with test data was 88.3562 % and 87.67% for Artificial Neural Networks and Logistic Regression models. The prediction rate result that refers to the correct predictions among all test data for Artificial Neural Networks and Logistic Regression model was 92.20% and 91.98%, respectively. Meanwhile, the percentage of customers who were correct to all customers predicted to default was 94.41% for Artificial Neural Networks and 93.81% for the Logistic Regression model. Credit models were helpful to evaluate the risk of consumer loans. Finally, the quality and performance of these models were evaluated and compared to identify the best one. The logistic regression and neural network models obtained were good and very similar, although the neural network was slightly better.
Publisher: Wiley
Date: 2015
DOI: 10.1002/14651858
Publisher: Science Publishing Corporation
Date: 22-05-2018
DOI: 10.14419/IJET.V7I2.29.13326
Abstract: Many companies search and develop new innovations as pivotal activities for better approaches to fulfill the existing customer’s requirements but failed to serve the future, service innovations that produce new markets. The absorptive capacity variables utilize the new external information and new knowledge in relation to the company innovativeness into the new product, new services, new processes, and new systems for internationalization achievement. This study investigates several factors that affect the service innovation capability based on s les of 38 Indonesian SMEs. We presume that our s les previously have a potential absorptive capacity as a basis to enhance their innovation capabilities and company’s performance. We test the hypotheses on several s les of Indonesian SMEs, using multivariate OLS regression analysis and Multi Discriminant Analysis. The results indicate that the combined factors of “number sales & marketing”, “product launching” and the “customer involvement” have a significant effect on “internationalization” in as a representation of service innovation capability. By keeping these "science-push" and "demand-pull” of SMEs on innovation as a top priority, the innovation opportunity will be alive and better practice under AEC implementation.
Publisher: IOP Publishing
Date: 06-2022
DOI: 10.1088/1755-1315/1041/1/012024
Abstract: For decades, Indonesia’s Economic Development had achieved great high growth rates by using more labor input, capital investment, physical and human, and fostering competition. Over time, Indonesia’s energy intensity, which historically always been high compared to other industrialized economies. This study aimed to analyze decomposition factors of energy consumption by analyzing the link between the other nine factors and household energy use. The Logarithmic Mean Divisia Index were used to identify the determinant variables that drove a household’s energy usage. This study used a Time-series VECM model for prediction model analysis. The Logarithmic Mean Divisia Index (LMDI) and decomposition index were used to identify the determining technological, economic, and social factors driving a household’s energy consumption evolution. We analyzed the outcomes after applying the LMDI. We provided econometric in idual time series VEC models, which we used for three targeting models: social, economic, and technical factors. We discovered that economic variables were the most significant factors that aggravated the change in household energy consumption based on LMDI. Technical factors, on the other hand, were the lowest. We found that the government might reduce its energy use over time by improving technological factors. Energy efficiency had been a cornerstone in managing growing energy use. The declining relevance of fewer labor-force in the previous decade was reflected in the negative indication of social factors at home. We discovered that the models were more stable while using Time Series VECM.
Publisher: EconJournals
Date: 27-09-2022
DOI: 10.32479/IJEEP.13426
Abstract: This research investigated consumer behavior patterns on energy conservation and energy efficiency. We investigated the factors that influence the rebound effect based on common characteristics and Socio-economic household characteristics in urban communities. This paper also explored the conservation of electricity through the idea of "nudge." The authors used the word "encouragement" in the chosen architectural program, designed to reduce household electricity consumption so that the rebound effect did not occur in household consumption. By utilizing the 'social norms and curtailment,' households given a "nudge" can save more on their electricity consumption costs. The condition of "nudge" that we included was through information on the costs incurred by households, 'social norms and curtailment,' and energy efficiency through replacing conventional l s with energy-efficient l s. The research took place in urban areas in the Bantul, Jogjakarta, between December 2021 and April 2022. Sixty-two respondents ide into two groups of households, i.e., observe households (self-selected) and control households (randomly selected). Both observe and control households must have an active electricity account for at least one year and have owned a house between 50 and 200 square meters. The model used a t-paired s le through the "Non-Equivalent Groups Design" (NEGD) framework for the two comparisons. The research found that 'social norms and curtailment' can reduce household electricity consumption, has cost savings over electricity consumption on average to 16.3049% for three months, and no rebound effect on savings happened.
Publisher: Science Publishing Corporation
Date: 08-08-2018
DOI: 10.14419/IJET.V7I3.21.17160
Abstract: The IFRS-16 will shortly be implemented worldwide, where the operating lease is effectively removed, and all operational leases are qualified as debt. The operational lease is a major source and is important for off-balance sheet financing in Indonesia. Therefore, the implementation of IFRS-16 will probably hurt Indonesian business. The contract and/or service level agreements on the retrofits for the energy efficiency product can relieve the business owner from cash flow and new debts. This study established a framework for a critical comparative study compare and assess the possibility of the retrofit financial practices under the current taxes regime, and the possibility upon IFRS-16 implementation to better understand the favorable measures for retrofit practitioners and future customers to handle the disputes. Triangulate the findings of comparative study and questionnaire survey to develop recommendation for the future researchers, practitioners and the government. The result indicates that the benefits of the retrofits financing implementation beat the cost and complexity posed by the enactment of new IFRS standards. Thus, the impact of the IFRS-16 and taxation are being considered as a part of business constraints that should be addressed together and able to manage the disputes over misperception on retrofits aspects with taxes officers.
Publisher: IOP Publishing
Date: 06-2022
DOI: 10.1088/1755-1315/1041/1/012086
Abstract: Increased energy efficiency led to increased economic growth, the energy used in the economy, and overall production. Unfortunately, the rebound effect and carbon emissions were adverse effects on the final result of energy efficiency. Indonesia relied on new energy efficiency technologies to reduce its energy intensity while avoiding the rebound effect. Hence, the other strategy to stimulate increased energy efficiency in the economic sector was an essential part of government policy overcoming resource constraints. This paper used the logarithmic-mean Divisia index decomposition and KAYA identity to recognize the determinant factors of carbon emissions and rebound effect changes in Indonesia. According to the research findings, substantial energy rebounds in Indonesia have revealed that energy efficiency gains in Indonesia may be related to the rebound effect. Indonesia sought to go in this direction, with the national energy policy aiming to lower energy efficiency by 1% each year to stimulate energy savings in all sectors. This paper advocated enhancing energy efficiency, notably in the building sector’s plan to encourage energy efficiency measures.
Publisher: EconJournals
Date: 19-01-2022
DOI: 10.32479/IJEEP.12629
Abstract: For decades, the subsidy had prompted excessive and wasteful while offering little motivation to boost energy efficiency or reduce domestic greenhouse gas emissions. This paper aimed to measure household subsidy energy by examining the relationship between the other ten variables. The Logarithmic Mean Divisia Index (LMDI) and decomposition index were deployed to recognize the determinant effects that drive household's subsidy energy consumption. This study also presented an ARDL model applied. The robustness of the Granger Causality, Long-run, and Short-run causality during 1990-2017 was assessed. Based on LMDI, we found out that Population, Income Per Capita, Ratio National Renewal Energy over Fuel Fossil, Gross Capital Stock, Urban Household Consumption, and Ratio Household Subsidy were the positive factors that aggravate the change in household energy subsidy. The negative sign of Ratio National Energy Intensity effect, Ratio Fossil Renewal Energy effect, Ratio Capital Labour substitution, and Ratio Household over Labour Force signified the decreasing significance of less household energy subsidy. On the ECM, we identified a negative sign speed-of-adjustment and significant at 1%. It implied that all the ten variable effects were converging in the long run after an experience shocks. The equation parameters were considered stable since the CUSUM gets inside the two critical lines. Additional RESET test of the stability to ascertain whether the estimated model was linear or correctly specified has been performed.
Publisher: Editorial Universidad de Almeria
Date: 14-10-2021
Abstract: Companies can form their own "ESCO model" with their capitals. Unfortunately, customer's creditworthiness was becoming more crucial for ESCO. Machine learning was used to predict the creditworthiness of clients in ESCO financing processes. This research aimed to develop a scoring model to leverage a machine learning and life cycle cost analysis (LCCA) to evaluate alternative financing for Energy Saving in Indonesia. The results of calculations using multinomial logistic regression showed that the accuracy value of prediction data with test data was 88.3562 %. The prediction rate result that refers to the percentage of correct predictions among all test data was 91.67%, and False Positive Rate (FPR) was 39.44%. The True Positive Rate was called Recall or 'Sensitivity Rate' as it was defined as several positive cases that were correctly identified (TPR) was 92.20%. We found the machine learning methods for creditworthiness prediction in retrofitting projects were fresh and worth a shot. It was hoped that this new practice would grow in popularity and become standard among ESCOs. Unfortunately, current machine-learning-based creditworthiness scoring practices lacked explain ability and interpretability. Unfortunately, ESCO must penalize the retrofitting project. As a result, since retrofitting was a new industry, the credit approval process was challenging to communicate to consumers. The most important thing for ESCO to deal with the project was to have a friendship and know-how with the client. Research from these case studies led to a clearer understanding of the factors affecting all parties' decisions to implement and continue with their ESCO project.
No related grants have been discovered for eka sudarmaji.