Engineering Headway Vol. 6

Abstract: Preparation of Edible Film with the Addition of Red Ginger Extract (Zingiber Officinale Var. Rubrum) and Its Application to Tomato (Lycopersicum esculentum) has been carried out. The purpose of this study was to analyze the optimum concentration of adding red ginger extract to edible films on the physical and mechanical properties and to analyze the optimum concentration of adding red ginger extract to edible films on the shelf life of tomatoes. The working principle of making edible films is by varying the red ginger extract 0; 0.25; 0.50; 0.75 and 1% were analyzed by testing the thickness, tensile strength, elongation, young's modulus, and WVTR as well as analyzing the addition of red ginger variations 0; 0.25; 0.50; 0.75 and 1% for testing the shelf life of tomatoes in the form of texture tests and FTIR tests. The addition of red ginger variations had a significant effect (p<0.05) on the thickness, tensile strength, elongation, and modulus of young edible film at the optimum concentration of 0.50% with a thickness value of 0.100 mm, tensile strength 4.696 Mpa, elongation 0.194%, modulus young 26.68 Mpa, and WVTR 15.85 g/m2.hour.

Abstract: Making edible films from potato starch by adding glycerol from used cooking oil with various concentrations of 10, 20 and 30% (v/w starch). This study aims to determine the physical properties of edible films made using these materials and the effect of adding variations in glycerol concentration on the physical properties of the resulting edible films. The physical property tests carried out included thickness, tensile strength, elongation and WVTR (Water Vapor Transmission Rate) tests as well as to see the effect using the pearson correlation test on SPSS version 25. The physical properties of edible film made from potato starch raw materials with the addition of variations in glycerol concentration produced successive thicknesses respectively 0.0670; 0.0674 and 0.0818 mm. Tensile strength respectively 24.237; 14.929 and 3.417 N/m2. Consecutive elongation 2.4064; 5.6768 and 20.096 %. As well as WVTR 5.05556 respectively; 6.38426 and 8.36574. The addition of variations in glycerol concentration to the physical properties of the edible film showed that the addition of glycerol did not significantly affect the thickness, but greatly affected the tensile strength, elongation and WVTR of the resulting edible film.

Abstract: Research on making edible films from kepok banana peels with glycerol-sorbitol plasticizer aims to analyze the mechanical properties and functional groups of edible films from kepok banana peels with a comparison of the concentration of the plasticizer combination of glycerol-sorbitol. This research consisted of 3 stages, namely making kepok banana peel starch, making edible films, and characterizing kepok banana peel starch and edible films. The yield of kepok banana peel starch obtained was 11.94%. Edible film production was carried out with variations in the glycerol-sorbitol concentration ratio of 100:0, 75:25, 50:50, 25:75 and 0:100 on a basis of 40% (w/w) of total starch weight. Functional group analysis of kepok banana peel starch and edible film was carried out with Fourier Transform Infrared (FTIR). Testing the characteristics of the edible film includes thickness, tensile strength, elongation, and Young's modulus. The results showed that edible film was successfully made with mechanical properties that tended to comply with the Japanese Industrial Standard (JIS) 1975 on the parameters of thickness, tensile strength, and young's modulus.

Abstract: Cancer is the second most common cause of death in the world. WHO notes, deaths caused by cancer will reach 10 million cases in 2021. Of many cancers, breast cancer is a cancer with the most cases. Early diagnosis of breast cancer plays an important role in the treatment process. Various imaging methods, including magnetic mammography, are used to diagnose breast cancer. With the help of machine learning, the process of diagnosing breast cancer with mammography images is more precise and accurate. Various machine-learning methods have been developed by researchers to diagnose breast cancer. Among them is a deep learning method that can achieve good feature representation and can solve the problem of image classification and object localization. Through a systematic literature review, this research collects and analyzes related studies regarding the classification of breast cancer that have been done previously. Several aspects that will be evaluated include the methods used, data sources used, and accuracy of the method used. This research is expected to provide clear knowledge about the advantages and disadvantages of using artificial intelligence techniques for breast cancer classification. The results of this study can provide insight for researchers and medical practitioners in the further development and application of deep learning methods in the diagnosis and classification of breast cancer.

Abstract: This study used the Morris-Lecar (ML) neuron model coupled with Short-Term Plasticity (STP) to simulate neuronal connectivity and synaptic patterns. We analyze this neural network synchronization activity, examined the post-synaptic conductance patterns in the modelled neural network, investigated the dynamics of the neural network membrane potentials in the synchronous state, and analyze the Short-Term Plasticity (STP) synaptic transmission patterns by varying the inter-neuron connection probability for both inhibitory (pi) and excitatory (pe). This computational-based study was executed using Brian2 Simulator. The results revealed that the higher the connection probability, the more connections and synapses are formed. The greater value of pe, the more synchronous the neural network activity. In contrast, the higher value of pi, the less synchronous the neural network activity. A synchronous neural network implies that the spikes occur coincidentally, where coincidental spikes lead to easily detectable membrane potentials and postsynaptic conductance. Furthermore, spikes affect the release of neurotransmitters, thereby affecting synaptic transmission patterns. We further determined the frequency of this neural network synchronization.

Abstract: Implant brachytherapy is one of the therapies for Low Dose Rate (LDR) in prostate cancer. This type of therapy uses a source with low energy and penetration rate so that the organs around the prostate do not receive much of the absorbed dose. This study uses a calculation method Monte Carlo N-Particle 6 (MCNP-6) to calculate the interaction of photons with organ materials. The main objective of this research is to determine the number of seeds and the ideal source activity to achieve the optimal absorbed dose in the prostate and provide the minimum absorbed dose in the surrounding organs. Organs around the prostate include the testes, small intestine, descending colon wall, descending colon, sigmoid colon wall, bladder wall, and bladder. This study uses two types of radioisotope sources namely ¹⁰³Pd and ¹³¹Cs which each has a photon energy of 21 keV and 30 keV. Variations made is the addition of the number of seeds from 60 to 100 at intervals of 8 seeds symmetrically away from the center of the prostate and variation of source activity from 0.1 mCi to 0.6 mCi at intervals of 0.1 mCi for each type of source. Results of this study obtained the relationship that the more the number of seeds the greater the dose received by the prostate and surrounding organs, as well as the addition of source activity. The effect of increasing the number of seeds can increase the absorbed dose more significantly than the effect of adding activity to the organs around the prostate. Optimal absorbed dose for ¹⁰³Pd is 125 Gy and 115 Gy for ¹³¹Cs. Based on the simulation results with MCNP-6, it is obtained that the ideal combination for the optimal absorbed dose is obtained from the source ¹⁰³Pd is the number of seed 60 with 0.3 mCi activity, and source ¹³¹Cs is the number of seed 76 with an activity of 0.5 mCi. Source ¹⁰³Pd provides a lower absorbed dose to the organs around the prostate compared to the source ¹³¹Cs.

Abstract: The consumption of coffee has its health benefits and its risks, one of the risks is mostly related to cardiovascular diseases. One of the diseases is hypertension which is considered “the silent killer” as it is a serious condition which promotes other complications and typically has no symptoms for a period of time until it has done significant damage. Acute hypertension can lead to a stroke. It is a very serious medical condition where the blood flow to the brain is poor, causing the death of cells within the brain. Some medications, surgeries and other healthcare programs are capable of controlling stroke, but stroke still remains to be the main cause of death and disability in Indonesia. However, provided that the consumption is restrained, multiple studies show that coffee consumption actually can reduce the risk of getting a stroke, by consuming between 2 to 4 cups of coffee per day. Additionally, coffee can reduce the likelihood of blood clots from forming and is likely to alter the blood vessel physiology. Therefore, the current project will explore the possibility of utilization of bioactive compounds other than caffeine from coffee beans that can be implemented in a form of supplements to help in treating patients both with stroke symptoms and during the recovery phase. Protein docking analysis is an alternative way to search and predict for drug discovery. Through protein docking analysis we can gain information on the bioactive compounds and their respective interactions with the target. Based on the virtual screening pipeline, it is predicted that Dehydrokahweol could elicit possible lead for the anti-stroke activity.

Abstract: License plates play an important role in vehicle identification in a variety of applications such as traffic safety, parking management, and traffic enforcement. In this study, we propose the development of license plate recognition applications using optical character recognition (OCR) and convolutional neural network (CNN) techniques. The OCR method is used to recognize characters on license plates and the CNN method is used to recognize license plates in images. The purpose of this research is to develop a system that can automatically recognize and recognize license plates in images. The OCR method accurately recognizes the characters on the license plate. Additionally, the CNN method is employed to detect license plates with good accuracy, even in various formats of license plates. The proposed methods in this research are implemented in the form of an application using the Python programming language. The application takes vehicle images as input and produces text recognition of the license plate as output. Furthermore, the application can also display additional information such as date, time, location, and detected vehicle type. Through this research, it is expected that the license plate recognition application using OCR and CNN methods can contribute to improving efficiency and reliability in automatic license plate recognition. Moreover, this application also has the potential to be used in various security applications, traffic monitoring, and automatic vehicle recognition

Abstract: Landsat satellite images are images that represent the ocean and land areas of the earth. Image data can be used for various purposes such as environmental analysis, remote sensing, mapping, and others. However, the quality of Landsat imagery is often unsatisfactory due to interference or noise from sources such as sensors, transmission, atmosphere, and storage. Therefore, they can reduce the contrast, sharpness, and information of landsat satellite images. Some of these disturbances prevent people from obtaining clear geographical locations. In order to overcome this problem, an effective and efficient method of Landsat satellite image quality improvement is needed. This research uses an image improvement method, namely discrete cosine transformation. The discrete cosine transformation method is used to reduce image noise by dividing it into each basic element. The method can perform the calculation process metematically and applicatively in the process of Landsat satellite image improvement. The processed results obtained are used to design and implement Landsat satellite image enhancement using the discrete cosine transformation method.

Abstract: There is a great deal of uncertainty regarding the factors that influence their final year grade, which includes their entry qualification. This paper investigates the impact of entry qualification and pre-university CGPA on student performance at the university level. Entry qualifications are critical for educational institutions or educational providers to ensure the quality of the graduates. The goal of this study is to analyze and compare performance of Bachelor of Science (Industrial Statistics) with Honours (BWQ) students. Total of 54 students were selected form the Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia (UTHM). The students are coming from Malaysian Higher School Certificate (STPM) and Malaysian Matriculation Programme. Paired t test and Z test were carried out to analyze the impact of pre-university’s CGPA and each semester’s GPA as well as impact of entry qualification towards their final year grade. Classification and Regression Tree (CART), K-Nearest Neighbors and Naïve Bayes were used to develop and predict the students’ performance. The findings show that there is no relation between the result obtained from previous semester towards the next semester. Meanwhile, students from STPM outperform Matriculation in terms of their GPA per semester, pre-university CGPA as well as their final CGPA. The K-Nearest Neighbors and Naïve Bayes models have been documented as the most efficient data mining techniques in predicting student performance with the highest percentage of accuracy of 100%.