Journal of Applied Engineering and Technological Science (JAETS)

The Impact of Compressor Degradation on The Optimized Fleet Compositions, Optimized Thermal Efficiencies, and The Operations & Maintenance Cost of Fleets of a Reheat Engine Running on Associated Gas Fuel

Mafel Obhuo — 2023-06-05

Associated gas is a viable source of fuel for industrial gas turbines. Flaring of this fuel resource has not only resulted in environmental pollution and deterioration but also huge energy and economic loss. TURBOMATCH, a Cranfield University performance simulation software was used in modeling a hypothetical but realistic 296MW reheat gas turbine engine.The study was carried out using one clean fleet and three degraded fleets – the optimistic, medium, and pessimistic. Optimization of the fleet compositions and thermal efficiencies were achieved using Genetic algorithm. Detailed operations and maintenance costs analysis for the various fleets were carried out. .Results from the optimization show the optimized fleet compositions, from the various fleets and their turbine entry temperatures for 20 years life span of the project. the result from the 11th to the 20th year of the project, only one unit of engine was left due to engine divestment. Results of the optimized efficiencies for all the fleets show a gradual reduction in optimized efficiencies over the years of the project. Similarly, for all the scenarios considered, from the 11th to the 20th year of the project, with only one unit of engine left, the optimized efficiency trend is observed to be Clean > Optimistic > Medium > Pessimistic.Results from the fleets operations and maintenance costs show that the clean, optimistic, medium, and pessimistic degraded fleets have total operations and maintenance costs to be 1.224, 1.242, 1.265, and 1.297 billion US dollars respectively. Engine degradation resulted to 1.4%, 3.3%, and 5.9% increase in the operations and maintenance costs of the optimistic, medium, and pessimistic degraded fleets respectively.The results, approach and methodology presented in this paper would be a very useful decision-making tool for investors and governments who would want to invest in the economic utilization of associated gas using gas turbines.

Integrated Application-Based Digital Learning Technology in Successful Learning Activities During the Pandemic

Mahsusi Mahsusi — 2023-06-05

This study aimed to look at madrasah's efforts in developing a learning system through "digital learning." This was done by examining the digital learning system implemented at MAN in terms of its advantages and disadvantages. The method used in this research was descriptive qualitative, with the data collection techniques employed were direct observation and interviews. Meanwhile, the research location is four MAN in Jakarta and Tangerang area, including. The four madrasahs that became the research locations were MAN Insan Cendekia Serpong (South Tangerang), MAN 1 Tangerang, MAN 4 Jakarta, and MAN 13 Jakarta. The researcher conducted interviews with school parties, namely teachers and principals. The results showed that three madrasahs have well-managed e-learning systems. In this case, they are MAN IC Serpong, MAN 1 Tangerang, and MAN 4 Jakarta. The e-learning system used by each madrasah is E-Learning Madrasah which can be accessed using the web and applications from Play Store. Meanwhile, MAN 13 Jakarta still needs to have its e-learning system and use standard features such as zoom, G-meet, etc. Basically. Madrasahs such as MAN IC Serpong, MAN 4 Jakarta, and MAN 1 Tangerang have registered to utilize the digital learning platform with all the features provided. Meanwhile, MAN 13 Jakarta has yet to register, so it cannot utilize the features contained in the digital learning platform. This research implies that madrasahs have innovated well in digital learning implementation so that learning during the pandemic can run well and smoothly. This research further contributes theoretically and practically to the world of education, especially for madrasas regarded as "second-class" schools. In general, this research contributes to the development of the world of digital learning by providing a valuable platform through the various features provided

Analysis of Fire Simulation on Polyurethane Foam Using FDS in a University Meeting Room

Pratomo Setyadi — 2023-06-05

The polyurethane foam material is commonly used and marketed in two forms, namely flexible and rigid. Flexible foam is used as a cushion, with various applications for commercial products such as chair support. Therefore, this study aims to describe the occurrence of a fire situation in a university room filled with many polyurethane foam chairs. It also aims to provide awareness regarding potential flame hazards, by using a fire modelling method with FDS. The results showed that fires on PU foam materials produced a high HRR and a wide spread of flame and smoke. From this context, the harmful effects of the fire on the room occupants were emphasized. The results obtained are expected to support the theory of compartment fire, flame distribution in solid materials, PU Foam inferno behaviour, etc. It is also expected to provide additional fire protection and evacuation training for room occupants.

Using Holt Winter 2 Variable Modelling To Analyze The Potential Combining Of Zakat Collection In Three Countries In Southeast Asia As One Business Centre

Muhammad Marizal — 2023-06-05

The Covid 19 outbreak has taught businesses all over the world that they must have a business that can not only survive but also thrive in the face of the pandemic. The income from the Zakat, which is one of Islam's teachings about stable business, tends to rise despite the pandemic. As most Muslim countries on the planet, Indonesia, Malaysia, and Singapore have shown that their Zakat Organization, which is utilized to gather gifts from the public every year, expanded dramatically during the pandemic. The primary focus of this investigation will be the data on annual income from Indonesia, Malaysia, and Singapore, three of the world's most Muslim nations. The fact that some businesses will always be loyal to the government is the foundation of the statistical modeling theory that aims to predict the annual zakat income in five and ten years. Holt Winter's statistical modeling of two variables will be used to guarantee accurate forecasting. It accurately generates comparable annual data for the three nations. Likewise, that might act as a strong starting point for using measurable displaying to gauge the zakat gathered in the resulting five and a decade. The zakat revenue will continue to significantly rise in each nation as the study concludes. This result also indirectly demonstrated that businesses in every nation will be able to combine zakat as a blessing without experiencing deflation in Southeast Asia

Comparison Analysis of Brain Image Classification Based on Thresholding Segmentation With Convolutional Neural Network

Alwas Muis — 2023-06-05

Brain tumor is one of the most fatal diseases that can afflict anyone regardless of gender or age necessitating prompt and accurate treatment as well as early discovery of symptoms. Brain tumors can be identified using Magnetic Resonance Imaging (MRI) to detect abnormal tissue or cell development in the brain and surrounding the brain. Biopsy is another option, but it takes approximately 10 to 15 days after the inspection, so technology is required to classify the image. The goal of this study is to conduct a comparative analysis of the greatest accuracy value attained while classifying using segmentation with thresholding versus segmentation without thresholding on the CNN method. Images are assigned threshold values of 150, 100, and 50. The dataset consists of 7023 MRI scans of four types of brain tumors: glioma, notumor, meningioma, and pituitary. Without utilising thresholding segmentation, the classification yielded the highest degree of accuracy, 92%. At the threshold of 100, classification by segmentation received the highest score of 88%. This demonstrates that thresholding segmentation during CNN model preprocessing is less effective for brain image classification

Does The Indonesian National Standard (SNI) for Products in SMES Influence Cleaner Production Practices? A Snapshot of Best Practices from Yogyakarta, Indonesia

Muhammad Imron Rosyidi — 2023-06-05

This article investigates the extent to which the Indonesian National Standard (SNI) for products adopted and applied by small and medium enterprises (SMEs) in Yogyakarta, Indonesia, can encourage them to effectively implement a quality management system (QMS) within their internal organizations, which eventually affects and benefits their process performance and clean production practices. Survey data collected from 44 respondents in 12 SMEs with SNI-certified products were processed and examined using descriptive analysis and regression analysis. The results showed that these could implement QMS effectively partly because it is a requirement for the SNI certification of the proposed product. The effectiveness of QMS implementation affects the achievement of process performance in that it can reduce the number of defective products, process costs, and process cycle times. According to the respondents, an effective QMS makes every activity and action taken in the production process more environmentally friendly and leads to cleaner production practices. These findings can help further research determine the model's feasibility to design and develop a better framework that promotes QMS and clean production practices, especially among SMEs in Indonesia.

Regression Model-Based Short-Term Load Forecasting for Load Despatch Centre

Saikat Gochhait — 2023-06-05

Forecasting load is an integral part of the planning, operation, and control of power systems. This paper is part of a research effort aimed at developing better energy demand forecasting models for load dispatch centers (LDCs) in Indian states as part of an ambitious project utilizing artificial intelligence-based load forecasting models. In this paper, we present a half hourly load forecasting method for the energy management system of the project that will be used at 33 /11 kV and 0.415 kV substations with good accuracy. The paper uses the half-hourly load consumption dataset collected from MSEDCL for Maharashtra from July 1, 2020 through August 31, 2022. This paper evaluates 24 regression model-based half hourly based load forecasting algorithms for ALE PHATA load based on the load consumption dataset and the collected meteorological dataset. The 24 models in MATLAB Regression belong to five types of regression models: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), Ensemble of Trees, and Neural Networks. As a consequence of their nonparametric kernel-based probabilistic nature, the GPR family of models demonstrates the best load forecasting performance. Least squares estimation was used to determine the regression coefficients. There is a direct correlation between load in an electrical power system and temperature, due point, and seasons, as well as a correlation between load and previous load consumption. Therefore, the input variables are Wet Bulb Temperature at 2 Meters (C), Dew/Frost Point at 2 Meters (C), Temperature at 2 Meters (C), Relative Humidity at 2 Meters (%), Specific Humidity at 2 Meters (g/kg) and Wind Speed at 10 Meters (m/s). The mean absolute percentage error and the R squared are used to validate or verify the accuracy of the model, which is shown in the results section. Based on this study, two GPR models are recommended for load forecasting, the Rational Quadratic GPR and the Exponential GPR and Exponential GPR as final model.

Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

Taufik Hidayat — 2023-06-05

The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.

Study of Contract Change Order (CCO) on Implementation Time in Building Construction Project

Tutang Muhtar Kamaludin — 2023-06-05

The aim of this research is to identify the main causes and effects of CCO in the construction of the Palu State Islamic University (IAIN) campus II building project for the Faculty of Tarbiyah and Teacher Training and the Faculty of Sharia and Islamic Economics Lecture Building. Tasks included conducting a field survey via a questionnaire and interviews with main parties; project owner, contractors and consultants who are directly related to the project. It was determined that the top three causes of CCO from project owner, contractor and consultant. The contractor has do CCO at the beginning of the project of 70% respondent, while consultant has changed contract order at mid-project implementation of 50% respondents and 70% at the end project implementation from the contractor. The results that are quite important from this study are the discovery of volume estimation errors and changes in work methods. Errors in estimating volumes are caused by the planning consultant's inaccuracy in planning so that there is a significant difference in volume from the initial contract with conditions in the field so that some work items experience an increase or decrease in volume

The Potential of Sawdust and Coconut Fiber as Sound Reduction Materials

Joseph Nyumutsu — 2023-06-05

In this study, biodegradable materials that could be utilized to reduce noise were examined. Sound absorption test was conducted with an impedance tube. Sawdust, coconut fiber, and expansive clay were used to create test samples. Noise reduction coefficient results for sawdust and expansive clay mixture ranged from 0.24 to 0.62. A mixture of coconut fiber and expansive clay recorded in noise reduction coefficient between 0.31 and 0.58. Coconut fiber mixed with expansive clay recorded noise reduction coefficient ranging from 0.31 to 0.58. The study findings suggests that these materials have good acoustic properties and can therefore be used as alternative noise reduction materials. These findings have important implications in reducing environmental pollution if adopted in the development of noise reducing materials.

Optimized Artificial Neural Network for the Classification of Urban Environment Comfort using Landsat-8 Remote Sensing Data in Greater Jakarta Area, Indonesia

Nurwita Mustika Sari — 2023-06-05

The development of computer vision technology as a type of artificial intelligence is increasing rapidly in various fields. This method uses deep learning methods based on artificial neural networks, a well-performed algorithm in multi-parameter analysis. One of the development of computer vision models and algorithms is for a thematic digital image classification, such as environmental analysis. Remote sensing based digital image classification is one of the reliable tools for environmental quality analysis. This study aims to perform neural network optimization for the analysis of the urban environment comfort based on satellite data. The input data used are 4 types of geobiophysical indexes as urban environmental comfort parameters derived from cloud-free annual mosaics Landsat-8 remote sensing satellite data. The results obtained in this study indicate that the 1 hidden layer neural network architecture with 16 neurons for the classification of urban environmental comfort and 10 other land cover classes is quite good. The result of the classification using this optimized artificial neural network shows that the distribution of classes is very uncomfortable which dominates the Greater Jakarta area and its surroundings. For other classes in the study area, some are uncomfortable and rather comfortable. By using this method, we obtained a fast classification training time of 18 seconds for 145 iterations to achieve an RMS Error of 0.01, and has a fairly high classification accuracy overall 89% with a Kappa coefficient of 0.88, while the 2 hidden layer neural network architecture does not succeed in achieving convergence

Failure Investigation of Blank Holder Force (BHF) Control in The Outside Bracket For Front Seat

Rofan Yulian Romansyah — 2023-06-05

This study investigated the failure of the Blank Holder Force (BHF) control in the outside bracket for the front seat. The production process involved progressive dies consisting of nine stations: first pierce, first trim, second trim, idle, flange, idle, second pierce, idle, and parting. However, at the 7th-9th station, the pilot hole in the product deforms into an oval shape, which is undesirable. Gemba-Kaizen methods were used in this study, and primary data were collected by comparing the design and actual progressive dies. The results showed that product defects are primarily caused by an unbalanced BHF and inadequate piercing clearance. A uniform distribution of force during the forming process is obtained by reducing the spring number on the blank holder. This reduces the force generated during the process. Furthermore, the die clearance was increased from 0.01 mm to 0.1 mm, making press and die alignment less critical and requiring less cutting and stripping forces

Mitigation of Environmental Damage Through Natural Resources Management Contracts (Eco-Contract Perspective)

Hengki Firmanda — 2023-06-05

Environmental damage by extractive activities is caused by the formulation of policies that have not placed nature and the environment as legal subjects. Policy formulation in the form of a contract of work tends to accommodate human interests and is profit-oriented. This study aims to examine strategies for mitigating environmental damage caused by PT Freeport’s mining activities based on an eco-contract perspective. PT Freeport’s contract of work is the object of analysis in this research, while data on environmental damage are obtained from national media. Theoretical-reflective approach is used in analyzing the data to formulate mitigation strategies. The results showed that the mitigation of environmental damage in the eco-contract perspective emphasizes the natural relationships and interactions between all components of the ecosystem. In other words, mitigation of environmental damage requires equal treatment between the environment and humans which also indicates equal rights. In fact, force majeure conditions and natural disasters are still viewed from a human perspective, so that efforts to identify and socialize environmental damage only focus on human aspects rather than nature and the environment. PT Freeport’s contract of work has not accommodated mitigation efforts of environmental damage that ecologically oriented. In addition, the weak mapping, control, and socialization of PT Freeport’s mining activities have implications for the lack of efforts to mitigate environmental damage.

Identification Of Covid-19 Patients' Effect On Education Outcomes In Islam Majority Student Using Spatial Analysis

Kharil Anwar — 2023-06-05

The COVID-19 epidemic has had an impact on the educational landscape, particularly the move to a remote learning model utilizing internet media. This system has so many issues that we need to do an extensive educational assessment of the subject. In order to create an educational map of the mathematics learning scores of the Islam Majority Student population during the COVID-19 epidemic in SMP Pekanbaru City, Indonesia, this study used spatial analysis. The distribution of the number of patients who tested positively was related to the geographical analysis of the learning score. The majority of the city's western and eastern regions had few patients and did not improve the score for mathematics education, according to a comparison of the two maps. On the other hand, a small percentage of the northern and western regions revealed that the few patients raised the Mathematics education score. A tiny portion of the southern region discovered that the score for mathematics education fell as the proportion of positive patients rose. Furthermore, the fewest patients are found in tree-lined, deserted locations, yet there are still few schools there. In Pekanbaru City, the majority of the schools are still located in urban areas devoid of pleasant open spaces.

A Critical Study on Group Key Management Protocols and Security Aspects For Non-Networks

Rituraj Jain — 2023-06-05

The rise in internet usage and advanced communication systems has led to an increase in security issues. The need for more robust and flexible secure communication has led to the introduction of mobile non-network multicast communication systems like MANET or VANET. Multicasting is increasingly being used for group-oriented applications such as video conferencing, interactive games, TV over Internet, e-learning, etc. To address the security concerns, this paper highlighted the confidentiality, authentication, and access control for non-network multicast communication systems like MANET or VANET. For this, paper explores the group key management protocols. The paper concluded that centralized and asymmetric group key management protocol (GKMP) is most effective for designing secure, and efficient communication models for non-networks. The key findings of the paper are that in group key management protocols (GKMPs) for multicast communication systems adoption of asymmetric GKMPs provides better security, and reduces computational overhead. Therefore, this paper help to improve the robustness and security of multicast communication systems and meet the growing demands of group-oriented applications over the internet.

Transport Demand Management Strategy Priority Assessment Based on Expert Judgment

Nindyo Cahyo Kresnanto — 2023-06-05

The main problem of transportation is the very high growth of vehicles causing congestion, resulting in various derivative impacts such as pollution, fuel waste, time value, and other environmental problems. This problem can be solved by Transportation Demand Management (TDM). TDM is a combination of various strategies, which strategy should be chosen whose priority depends on the conditions of each region. This research was conducted in a medium-scale city by determining the priority of TDM using the Analytical Hierarchy Process (AHP) tool. The final result of the judgment is the priority weight of the TDM strategy that will be applied with a CR value of < 10%, namely the pull strategy. This strategy is represented by improving public transport services and infrastructure (especially the integration of public transport services). This study shows that the strategy group with a high AHP Consensus Index (ACI) score means a high consensus among experts.

Identifying Factors for The Success of Halal Management Practices in Leather Industry

Tengku Nurainun — 2023-06-05

The need to apply halal management practices to non-food industries today is still merely seen as a necessity to meet the requirements of Islamic rules. Meanwhile, this approach has demonstrated that it can improve organizational efficacy in a variety of contexts. This study seeks to investigate the depth of halal principles implementation among leather industries and comes up with strategies for how small and medium-sized enterprises (SMEs) in the leather industry can use halal management practices to move toward halal certification and enhance its performance. An exploratory-descriptive approach was used to get the current state of halal practices among leather industry SMEs through interviews and survey questionnaires. Five stakeholders were interviewed in a semi-structured manner. A survey questionnaire was distributed to 127 SMEs in the leather industry center of Sukaregang, Garut, Indonesia. This paper discusses the key factors of halal implementation and determines which halal practices need more emphasis. The result showed that the current knowledge, awareness, and implementation of halal requirements among leather SMEs in Indonesia are still low. An action plan for the industry, authority, and supplier was provided. The implication of this research can contribute to the leather industry players that intent to implement halal management system effectively and stakeholders in making decision to accelerate halal certification process.

Countenance Evaluation of Virtual Reality (VR) Implementation in Machining Technology Courses

Waskito Waskito — 2023-06-05

This study aims to evaluate whether virtual reality (VR) learning media can be used in Machining Technology courses which are practical learning but implemented virtually. The research using the Stake Countenance evaluation method was carried out at the Department of Mechanical Engineering FT-UNP in the July-December 2021 semester with 60 students as research subjects. This study was mix method by using sequential explanatory design. which is the collection of quantitative and qualitative data that is carried out sequentially. Data related to the antecedents, transaction, and outcomes phases were collected using questionnaires, interviews, and observations. The research begins with developing VR media that is implemented to learning materials in the field of Machining Technology and then applied to learning. Then first stage is carried out using quantitative then the next stage or the second stage is carried out using qualitative. The result of research showed that this VR application can help students understand the theory of introducing machine tool operations but have not been able to run machine. This study imply that students’ learning process should be enjoyable and also influence existing practices of Student-Centered Learning. The novelty of this study showed the evaluation result of technology, especially virtual reality can be implemented in the practice learning course, it can be reference for educator to consider implementing technology in practice learning. This study will contribute to existing knowledge and various instructional method that can be implemented by educator

Application of Spatial Analysis to Eliminating Radicalism in Madrasah Schools

Afrizal Mansur — 2023-06-05

The attack on the twin towers of the World Trade Center (WTC) on September 11, 2011 in New York, United States caused madrasas to be considered Islamic schools that gave birth to a radical generation. Madrasas' efforts to improve this negative image by improving the quality of education, especially 10 years after the incident, have succeeded in making Madrasas the schools of choice for students in Indonesia. This study focuses on analyzing the rise of Madrasah Tsanawiyah in the city of Pekanbaru, especially on students' mastery of knowledge for the last 4 years (2016, 2017, 2018 and 2019) by means of spatial analysis. The progress of science in Madrasas can be seen from the mapping of the value of knowledge, especially in the downtown area which is complete with various facilities and activity centers in Pekanbaru. The performance of some madrasas is almost the same as SMA in terms of mastery of knowledge. This study leads to an important analysis that the Pekanbaru madrasa as a Muslim-majority city has succeeded in making madrasas the main choice of parents to equip their children with religious and scientific education, which indirectly proves that madrasas do not provide space for the formation of radical Islamic generations, on the contrary. Madrasas have succeeded in forming a generation of Muslims who have good religious and scientific knowledge

Social Commerce Purchase Intention Factors in Developing Countries : A systematic literature review

Adi Suryaputra Paramita — 2023-06-05

Over the last decade, research on social commerce has grown exponentially, reflecting the widespread adoption of social commerce strategies and practices. Social commerce encompasses a broad range of distinct concepts. Recent reviews of the literature detail the numerous factors of social commerce adoption. This paper has an objective to investigate the important factor of social commerce adoption in developing countries. 149 articles from high quality repository collected to be review, in this study the systematic literature review conducted through Kitchenham methodology which consist of developing research question, determining the sources as well as research string, categorizing inclusion and exclusion criteria, choosing the primary studies, extracting the data then synthesizing the data. After careful quality assessment process, 49 articles selected to process in depth review. The result of this study found that there ae several important factors and lead by trust factor for social commerce adoption in developing countries.

Twitter Data Analysis and Text Normalization in Collecting Standard Word

Arif Ridho Lubis — 2023-06-05

is one of the most important data sources in social data analysis. However, the text contained on Twitter is often unstructured, resulting in difficulties in collecting standard words. Therefore, in this research, we analyze Twitter data and normalize text to produce standard words that can be used in social data analysis. The purpose of this research is to improve the quality of data collection on standard words on social media from Twitter and facilitate the analysis of social data that is more accurate and valid. The method used is natural language processing techniques using classification algorithms and text normalization techniques. The result of this study is a set of standard words that can be used for social data analysis with a total of 11430 words, then 4075 words with structural or formal words and 7355 informal words. Informal words are corrected by trusted sources to create a corpus of formal and informal words obtained from social media tweet data @fullSenyum. The contribution to this research is that the method developed can improve the quality of social data collection from Twitter by ensuring the words used are standard and accurate and the text normalization method used in this study can be used as a reference for text normalization in other social data, thus facilitating collection. and better-quality social data analysis. This research can assist researchers or practitioners in understanding natural language processing techniques and their application in social data analysis. This research is expected to assist in collecting social data more effectively and efficiently.

Financial Viability of Business Models For Engineered Vertical Hydroponics Systems For Sustainable Onion Production in The Philippines

Kenneth L. Armas — 2023-06-05

This study aimed to explore the financial, socio-economic, and environmental benefits of sustainable onion production using vertical farming and hydroponic systems, and to identify key factors affecting the viability of these business models. Data were collected through a survey of onion farmers and producers in the Philippines, and analyzed using descriptive and inferential statistics. Results showed that sustainable onion production using vertical farming and hydroponic systems has the potential to generate higher income for farmers, increase employment opportunities, improve food security, enhance market competitiveness, and promote environmental sustainability. Key financial factors affecting viability included production costs, market prices, yield, labor costs, energy costs, capital costs, financing costs, taxes and regulatory costs, and maintenance and repair costs. Recommendations for optimizing financial viability include reducing production costs, diversifying income streams, and improving market competitiveness. Overall, this study suggests that sustainable onion production using vertical farming and hydroponic systems is a viable and promising approach to achieving socio-economic and environmental sustainability in the agricultural sector.

Sensitivity Study of The Effect Polymer Flooding Parameters to Improve Oil Recovery Using X-Gradient Boosting Algorithm

Tomi Erfando — 2023-06-05

Implementation of waterflooding sometimes cannot increase oil recovery effectively and requires additional methods to increase oil recovery. Polymer flooding is a common chemical EOR method that has been implemented in the last few decades and provides good effectiveness in increasing oil recovery and can reduce the amount of injection fluid injected into the reservoir. Seeing the success of polymer flooding in increasing oil recovery, it is necessary to know the parameters that influence the success of polymer flooding so that it can be evaluated and taken into consideration in creating a new scheme to increase oil recovery with polymer flooding. The parameters tested in this study include Injection Rate, Injection Time, Injection Pressure, Adsorption, Inaccessible Pore Volume, Residual Resistance Factor. This research uses the X-Gardient Boosting Algorithm to look at the most influential parameters in polymer flooding. The parameters that most influence the performance of polymer flooding on the value of oil recovery with the importance level of each parameter in this study are injection time of 0.452632, injection rate of 0.430075, injection pressure of 0.064662, Adsorption of 0.025564, RRF of 0.021053, IPV of 0.006014 and produce accurate predictive modeling using x-gradient boosting where with 3 variations of the comparison ratio of training and testing data obtained at a ratio of 0.7 : 0.3 obtained an R2 train of 0.9886 and an R2 test of 0.9645, a ratio of 0.8 : 0.2 obtained an R2 train of 0.9891 and an R2 test of 0.9579, and a ratio of 0.9: 0.1 obtained R2 train of 0.9890 and R2 test of 0.9660.

Automatic Classification of Desmids using Transfer Learning

Rajmohan Pardeshi — 2023-06-05

This research paper presents a novel approach to classifying microscopic images of desmids using transfer learning and convolutional neural networks (CNNs). The purpose of this study was to automate the tedious task of manually classifying microscopic algae and improve our understanding of water quality in aquatic ecosystems. To accomplish this, we utilized transfer learning to fine-tune 13 pre-trained CNN models on a dataset of five categories of desmids. We evaluated the performance of our models using several metrics, including accuracy, precision, recall, and F1-score. Our results show that transfer learning can significantly improve the classification accuracy of microscopic images of desmids, and efficient CNN models can further enhance performance. The practical implications of this research include a more efficient and accurate method for classifying microscopic algae and assessing water quality. The theoretical implications include a better understanding of the application of transfer learning and CNNs in image classification. This research contributes to both theory and practice by providing a new method for automating the classification of microscopic algae and improving our understanding of aquatic ecosystems

The Development of Exhaust Fan Housing With Ceiling Mounting For High Rise Buildings by Using DFMA

Agri Suwandi — 2023-06-05

Design for manufacturing and assembly (DFMA) is widely applied in many industries to optimize the manufacturing and assembly process at the early stage of design, with the aides of the CAD model. Many researchers apply the DFMA to increase assembly efficiency, by decreasing the number of parts from a product, decreasing the manufacturing cost, and reducing assembly time. Therefore, this research applies DFMA to develop exhaust fan housing with ceiling mounting for high rise building type with the same purpose, and at the same time to justify that the method can overcome the problem of assembly time in a production line. Both designs from before and after the application of DFMA, are being compared by using finite element simulation and experimental. The simulation employs stress analysis, to predict the strength of those designs. While the experimental uses a manufacturing cost survey, real assembly time survey and failure test to show the advantages of DFMA design results. The research result shows that the DFMA method can decrease the manufacturing cost by 0.44%, and the assembly time by up to 2%, and able to withstand the entire mass of the ceiling mounting fan.

A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer

Tati Erlina — 2023-06-05

In Indonesia, assistance for small enterprises has grown in recent years. However, a monitoring system is required to support these enterprises and ensure their expansion and survival. Using a single-board computer and the YOLO algorithm, we construct a visitor tracking system in this study to meet this demand. To capture objects and categorize them as human or non-human, we employ the YOLOv4-tiny model, which has a mAP of 89.21%. Human visitors are welcomed with the use of a speaker. A telegraph bot that notifies the owner of the retail establishment of the visitor's presence also makes the presumption as to whether the visitor is a potential customer or an intruder. Our research demonstrates that the created monitoring system effectively recognizes and categorizes visits, enabling retail store owners to make defensible choices regarding visitor interaction and security precautions. Small business owners can save personnel costs while still maintaining high levels of client engagement and security. The theoretical application of this research is the creation of a visitor monitoring system that is affordable and may be used in small enterprises, particularly in Indonesia. The practical ramifications of our research include the possibility for small retail business owners to boost profits by lowering labor expenses while raising customer satisfaction and security. The importance of our study lies in its role in creating a monitoring system that will support small enterprises and increase their sustainability.

The Durability of Stone Matrix Asphalt (SMA) Mixtures Designed Using Reclaimed Asphalt Pavement (RAP) Aggregates Against Floodwater Immersion

Edi Yusuf Adiman — 2023-06-05

The durability of asphalt mixtures against floodwater immersion can serve as a reference to anticipate potential road damage. Moreover, Reclaimed Asphalt Pavement (RAP) materials have been discovered as a substitute for aggregate materials in road pavement due to their environmental friendliness and cost-effectiveness. Therefore, this research aimed to assess the durability of asphalt mixtures produced using RAP aggregate materials against floodwater immersion for 1, 2, 4, and 8 days. The process involved using Stone Matrix Asphalt (SMA) mixtures with a proportion of 33% RAP aggregate as test specimens. The Marshall test conducted on the asphalt mixture produced an optimum asphalt content (OAC) value of 6.1%. Moreover, the durability of the mixture reduced up to the 8th day of immersion with a residual strength value of 86.29%. It was also discovered that the reduction in the durability value of the mixture produced using 33% RAP aggregate was almost similar to the application of 100% new aggregate (non-RAP). This means RAP aggregate materials are feasible as an environmentally friendly substitute in the mixture of road pavement.

The Cuckoo Optimization Algorithm Enhanced Visualization of Morphological Features of Diabetic Retinopathy

Dafwen Toresa — 2023-06-05

This research compares strategies for identifying diabetic retinopathy (DR) using fundus image and discusses the efficiency of various image pre-processing techniques to enhance the quality of fundus images. Fundus images in medical image processing often suffer from non-uniform lighting, low contrast, and noise issues, which necessitate image pre-processing to enhance their quality. The study evaluates the effectiveness of several optimization techniques in selecting the best technique for identifying DR. One of the image pre-processing techniques compared in the study involves comparing negative images, dark contrast stretch, light contrast stretch, and partial contrast stretch, which are then evaluated using standard performance metrics such as NIQE, PNSR, MSE, and entropy. The results are further optimized using the Cuckoo Search Algorithm. The proposed technique produces better image quality improvements in several performance metrics, such as MSE, NIQE, PSNR, and entropy. Bright Contrast Stretch outperforms other techniques in NIQE Mean 5.2850, Entropy 5.0193, NIQE Standard deviation 0.2261, and Entropy 0.2612.

Adoption and Implementation of Self-Development IT Applications : An Empirical Study of State Islamic Higher Education Institutions in Indonesia

Muhammad Qomarul Huda — 2023-06-05

Implementing IT innovation in organizations is a complex and challenging process that affects organizational problems. The process involves many interacting factors and actors; hence this situation is difficult to control. This problem demonstrates the need to understand researchers' perceptions of IT adoption and implementation. This study aims to explore in depth the adoption and implementation of self-development IT applications (SDIT) in Islamic-based Higher Education Institution (IHEI) in Indonesia. The IT Adoption and Implementation Framework (Irawan et al., 2018) was applied as a lens to investigate the case. We conducted in-depth interviews with key informants involved during the adoption and implementation process in the organization. Interviews were transcribed and analyzed using thematic analysis. Certain Focus Group Discussion (FGD) studies and specific interviews with key informants representing three levels of management explained that mediating factors such as post-implementation interventions, subjective norms, and facilitating conditions influence success in adopting and implementing IT innovations in such cases. This study concludes that managerial interventions play an important role in reducing resistance from authoritarian approaches to mandating use and serve as a determinant of its sustainability in the future. These findings have significant implications for understanding how to achieve success in IT adoption and implementation in the post-implementation phase by providing empirical evidence. Theoretically, this study contributes to IT adoption and implementation frameworks by identifying the active role of critical actors in the adoption and implementation of IT applications in higher education institutions.

Classification Academic Data using Machine Learning for Decision Making Process

Elin Haerani — 2023-06-05

One of the qualities of higher education is determined by the success rate of student learning. Assessment of student success rates is based on student graduation on time. Sultan Syarif Kasim State Islamic University Riau is one of the state universities in Riau, with a total of 30,000 students. Of all the active students, there are some who are not. Students who are not active in this case will affect the timeliness of their graduation. The university always evaluates the performance of its students to find out information related to the factors that cause students to become inactive so that they are more likely to drop out and what data affect students being able to graduate on time. The evaluation results are stored in an academic database so that the data can later be used as supporting data when making decisions by the university. This research used data science concepts to explore and extract data sets from databases to find models or patterns, as well as new insights that can be used as tools for decision-making. After the data was explored, machine learning concepts were used to identify and classify the data. The method used was the Decision Tree Method. The results of the study found that these two concepts can provide the expected results. Based on the test results, it is known that the attribute that influences the success of student studies is the grade point average (GPA), where the accuracy of the maximum recognition rate is 88.19%.

Keywords : Data science; Decision Tree; Graduate on Time; Machine Learning;

Youtube For Developing Technological Skill

Muhammad Kristiawan — 2023-06-05

There are many teachers do not have skill on operating computer specially to make materials interesting for their students. This study was to improve the technological skills of students, particularly those who teach online. In this study, we attempted to determine how students may utilize YouTube as a medium to strengthen their technological skills. This study was carried out by us with the help of students who took the Education Management Course as the subject of study. The data collection technique used in this study was observation sheets, which were then used to answer the formulation of how the student responses related to the video-making project in the Education Management Course uploaded on YouTube; then, to answer the formulation of how the student’s activities on Education Management, we asked students to complete their video projects and upload them on YouTube; and to answer the formulation of whether the use of YouTube can develop technological skills of students, observation sheets was used. This study concludes that respondents are able to edit and upload video about Education Management on YouTube.

Favorite Book Prediction System Using Machine Learning Algorithms

Dersin Daimari — 2023-06-05

Recent years have seen the rapid deployment of Artificial Intelligence (AI) which allows systems to take intelligent decisions. AI breakthroughs could radically change modern libraries' operations. However, introducing AI in modern libraries is a challenging task. This research explores the potential for smart libraries to improve the caliber of user services through the use of machine learning (ML) techniques. The proposed work investigates machine learning methods such as Random Forest (RF) and boosting algorithms, including Light Gradient Boosting Machine (LGBM), Histogram-based gradient boosting (HGB), Extreme gradient boosting (XGB), CatBoost (CB), AdaBoost (AB), and Gradient Boosting (GB) for the task of identifying and classifying Favorite books and compares their performances. Comprehensive experiments performed on the publicly available dataset (Art Garfunkel's Library) show that the proposed model can effectively handle the task of identifying and classifying Favorite books. Experimental results show that LGBM has achieved outstanding performance with an accuracy rate of 94.9367% than Random Forest and other boosting ML algorithms. This empirical research work takes advantage of AI adoption in libraries using machine learning techniques. To the best of our knowledge, we are the first to develop an intelligent application for the modern library to automatically identify and classify Favorite books