Engineering Headway Vol. 13

Abstract: Boilers in the manufacturing industry have an important role in the production process. Boilers can produce steam, which is used in the production process. Steam produced by boilers can also be used as a power plant where the steam flow is used to drive a turbine. Several manufacturing industries that utilize boilers include the food, beverage, petrochemical, and fertilizer industries. Boiler B 03, which operates in one of the subsidiaries of PT Pupuk Indonesia Group, is the focus of this research because of an issue in which this boiler cannot produce steam according to its design capacity. Based on the problem, through this research an investigation will be carried out to improve the reliability of the B 03 boiler system and what components cause the boiler cannot operate according to design capacity. A boiler that cannot produce steam according to its design capacity can be ascertained to have low efficiency in manufacturing. Boiler B 03 is included in the category of water tube boilers. In this type of boiler, the water or steam pipe is in the pipe with the combustion flame being outside. The method used to find the root of the problem to improve the performance of the boiler system is by using the failure mode effect & criticality analysis (FMECA). By using the FMECA method, it is hoped that all problems in the B 03 boiler can be identified and, as a result, the company can determine the priority of repairs to the boiler components and the company can obtain recommendations regarding steps in improving the boiler production system.

Abstract: The increasing use of Unmanned Aerial Vehicles (UAVs) in various applications, such as military operations and civilian tasks, has created a demand for efficient algorithms to plan their mission. This study explores the use of the Bees Algorithm (BA), a nature-inspired optimisation method, to solve two main problems in UAV mission planning: the UAV collision avoidance problem (UCAP) and the Asymmetric TSP (ATSP). Results of comparison between the proposed algorithm and other metaheuristic algorithms indicate that BA can produce competitive solutions. However, the study also found that the BA had a deviation of 10% on ATSP instances, whereas it was able to achieve similar deviation when solving symmetrical TSP of 200 dimensions. This highlights the need for further improvement of the neighbour search mechanism in the BA for better accuracy on the problems.

Abstract: Donations are needed to help victims of natural disasters. To optimize the amount of donations, charitable organizations need to know the level of importance of the factors that influence the intention to donate. In this study using Fuzzy AHP to prioritize existing factors. Several previous studies have often used multi-criteria methods for factor problems. However, several previous methods have not been applied to donation intentions, which is novelty in this study. In this study, there is an order of factors from the most important intention to donate to disasters online. The first is Trust and Transaction Security. The second is Interactivity, Disclosure and Online News. The third order is Subjective Norms and eWoM, while the last order is Personal Financial Status. The results of this study are useful for managers of charitable organizations to find out the factors that are important in causing people's intentions to donate. In addition, it is also useful for knowledge in the field of multi-criteria decision analysis in the application of fuzzy AHP to rank the factors that influence online donations.

Abstract: Consuming halal products has become part of today's lifestyle. In addition, product halal assurance is one of the basic requirements for Indonesian products to penetrate the global halal market. The government continues to encourage the existence of a new Halal Inspection Agency as a form of strengthening halal product guarantees as well as accelerating the development of the halal industry in Indonesia. The purpose of this study is to evaluate the efficiency of performance in each product group segment at one of the Halal Inspection Agency using Data Envelopment Analysis. Performed for 9 segments of the main product groups using input and output variables. The input variables are what non-profit organizations used to produce output such as the number of employees, while the output variables are a measurement of the output provided by the organizations. The finding shows that of 9 product segments, 2 were identified as being efficient and others are still inefficient. By using the CCR model, this study proposed the cause of inefficiency. This research also provides slack and radial movement analysis. The methods and the results of this study can serve as a model for researchers and practitioners to follow when evaluating efficiency in non-profit organizations.

Abstract: In recent years, palm oil has become an essential vegetable since it is the most produced and consumed. The growth of the palm oil industry and global expansion of the plantations pose economic, social, and environmental issues. These conditions have caused a rise in calls for sustainable palm oil production, primarily motivated by worries regarding the consequences. This study aims to identify factors influencing sustainability development in the palm oil industry. The results indicate that waste management, green transportation, and policy and regulation are the most important aspects for achieving sustainability in the palm oil industry. The internal economics dimension of the palm oil industry has become a priority aspect of implementing sustainability. In addition, economic benefit, productivity, and efficiency factors must take precedence for the palm oil sector to be sustainable. The findings can help palm oil supply chain actors and decision maker in preparing sustainable development strategies.

Abstract: Oil palm has become the world’s leading vegetable oil with a tremendous increase in plantations and production. Thailand is among the three largest producers of oil palm. To enhance the oil palm producing potential competitively, the oil palm industry in Thailand has to improve the efficiency of production management among Thai farmers. This work aimed to identify important factors affecting oil palm cultivation based on machine learning and statistical inference methods. The proposed models were evaluated on a data set collected from the local community group for oil palm cultivation and production in Surat Thani and Nakhon Si Thammarat provinces, Thailand. The seedlings’ source and the age of oil palm seedlings were the most significant features according to the analysis.

Abstract: A brain tumor is an uncontrolled and unstructured growth of brain cells inside the skull. Most of the time, tumors are misclassified due to the complexity of the lesion and as a result, the survival rate of the patient effects adversely. Magnetic resonance imaging (MRI) is usually used to identify various types of brain tumors. Due to the advancement in computer-aided systems and bio-informatics ML and DL algorithms has been applied to assist neurologist in decision-making. However, the current techniques are error-prone and time-consuming. Therefore, a Bi-fold CNN model for tumor detection and classification has been proposed with validation accuracies of 99.13% and 98.10% respectively. The proposed system consists of two ends-to-end connected CNN models, where the first has been used to detect tumors while the second is used to classify them into glioma, meningioma, and pituitary tumors. The respective accuracies on blind testing are 99.36% and 97.35%. The model has been compared with ResNet50, Xception, InsepectionV3, and EfficeintNetV2S. The comparison results showed the novelty and superiority of our proposed system. The publicly available dataset has been used in this research. In addition, due to its structural specifications, it has less computational complexity as compared to the existing methods.

Abstract: Stroke is the second factor of mortality in the world. According to the World Health Organization (WHO), stroke is an acute brain dysfunction. The effects of stroke are disability and mortality. Therefore, this is a concern for world health. In early 2019, the Pandemic Covid-19 attacked the world and caused many mortalities. Especially, people who have complications with diseases such as heart attack, stroke, and asthma. The purpose of this research is to predict stroke diseases with input parameters (age, glucose level, heart rate, and BMI) and to test the accuracy of the system. Moreover, analysis of the management of stroke patients’ strategy. ANFIS is a combination of ANN and FIS. It can construct a network realization of IF/THEN rules. This method was used by many researchers to predict and test the accuracy of the system. According to the result, the error of this system is 0.04 and the accuracy is 94%. Thus, it was good for predicting stroke diseases. According to the severity of the stroke, there are stroke management strategies that can be conducted by the patients; self-management and medical management. For self-management, problem-solving, goal setting, decision-making, and coping skills can help recovery. On the other way, there are five categories for medical management; stroke acute care, reperfusion, rehabilitation, cognitive decline, and neuroprotection and repair.Your manuscript will be reduced by approximately 20% by the publisher. Please keep this in mind when designing your figures and tables, etc.

Abstract: This study assessed the auto-mechanics' four dimensions of mental workload and posture in an automobile repair shop in San Jose Occidental Mindoro, Philippines, with issues in increasing body pains and headaches affecting efficiency and performance. Five participants were observed from January-March 2022. Due to the limitations of the NASA-TLX in evaluating the mental workload, the authors used the new Carmen-Q questionnaire. Results revealed that the auto-mechanics had been exposed to an average of twenty-nine (29) in the cognitive dimension, eighteen point five (18.5) in the temporal dimension, seventeen point five (17.5) in the emotional dimension, and seventeen points seventy-five (17.75) in performance dimension, which means that the auto-mechanic's mental workload is in the medium range and preventive measures must be done. Previous research also revealed that mental workload is linked with poorer posture; thus, this study is the first to combine mental workload with posture risk assessment. RULA and REBA techniques have been utilized to evaluate the posture, and results discovered that the risk index for tire repair and installation is two (2.00) and three (3.0) for the engine repair and installation, which means that the risk is high and change in posture is required. Statistical analysis also described that the cognitive and emotional dimension has a significant relationship with each other. In contrast, the different dimensions have no significant relationship, such as: cognitive and temporal dimensions, cognitive and performance dimensions, temporal and emotional dimensions, temporal and performance dimensions, and emotional and performance dimensions. In conclusion, this study can be considered a basis for helping the automobile repair shop design the tasks for the auto-mechanics as well as guidance in improving the working conditions and a tool for evaluating cognitive state in a critical working environment.