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Performances of academic departments using data envelopment analysis (DEA) approach
Full text linkUniversities are the backbone of a country's development. Public universities are subsidised by the government, with funds coming from taxpayers’ contributions. It is therefore important that public universities utilise their resources efficiently. This study evaluates the performance of five academic departments for the period 2016-2018. The tool used to evaluate efficiency is Data Envelopment Analysis (DEA) with CCR output-oriented model using a software DEA-Solver-LV version 8. The secondary data of the three input variables are the number of academic staff, the number of non-academic staff and capital grants. The three output variables used in this study are the number of publications, the number of PhD students and the number of undergraduate graduates. The results show that the departments that are really efficient in the years 2016 to 2018 are the academic departments B and E. The average efficiency score from 2016 to 2018 is 0.972436, 1.0000 and 0.990989, respectively, which shows that the performance of departments in general has been somewhat inconsistent over the three years
Estimating technical efficiency of crude palm oil in malaysia
Full text linkThe main purpose of this study is to apply parametric techniques in evaluating the technical efficiency (TE) of crude palm oil (CPO) production by the states in Malaysia. To achieve this, the parametric stochastic frontier analysis (SFA) approach was applied. This study involves a panel data consisting of 12 CPO producing states in Malaysia, over a 18 year time period from year 1999 to 2016. The output variable chosen was the annual CPO production and the input variables considered were plantation area, fruit mill capacity, labour and time variable. We found fruit mill capacity, labour and time as input variables that significantly affect the level of CPO output. Plantation area was proven to be statistically insignificant. Technical efficiency was found to be increasing over time. It was also found that the inefficiencies in the industry were mainly caused by ‘pure’ technical inefficiency rather than scale inefficiency. The overall mean TE of SFA is 0.79. Selangor is the top efficient state according to SFA. We concluded that the state of Malacca is overall the least efficient state due to their low ranking
Home credit default risk assessment using embedded feature selection and stacking ensemble technique
Full text linkThe objective of this study is to evaluate and compare the accuracy of typical credit assessment techniques, particularly Logistic Regression, Gradient Boosting, Random Forest (RF), Extra Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Cat Boost. Furthermore, the study involves stacking ensemble learning with feature selection based on embedded techniques. This research utilized a data set sourced from Kaggle, namely the Home Credit Default Risk gathering data. The results of the study indicate that the reached accuracies were as follows: Logistic regression - 92.02%, XGB - 92.01%, LGBM - 92.09%, RF - 92.07%, and CB - 92.06%. Additionally, while stacking with XGB, RF, and LGBM models, and utilizing the final logistic regression estimator 92.01%, the accuracy does not show any improvement when compared to the usual algorithm. It is even lower than the LGBM accuracy results. However, the findings of this study demonstrate better rates of accuracy in comparison to other previous research conducted by researchers, regardless of that used the same dataset. However, study Mahmudi et al. in 2022 performs better than it in terms of accuracy using oversampling approaches. This finding provides evidence that the accuracy of the model is affected by the quantity of features that are examined. The level of accuracy will be better the more optimally chosen features are for examination
Improvement accuracy of gradient boosting in app rating prediction on google playstore
Full text linkGoogle Playstore is a platform that provides various useful applications for smartphone users, especially Android users. But in reality, users are often faced with many choices of applications with various features and functions in the Play Store itself. Rating applications on the Google Play store help users evaluate and choose applications that suit their needs. The purpose of this research is to optimize accuracy in predicting app ratings on the Google Play store using the Gradient Boosting algorithm. This research uses publicly accessible data on the Kaggle platform. The research process includes data collection, pre-processing, Data Spliting, algorithm modeling, and model evaluation. Apart from using the Gradient Boosting algorithm, this research also applies and optimizes other algorithms such as XGBoost, KNN, Logistic Regression, Decision Tree, Random Forest, LightGBM, AdaBoost, and SVM to predict app ratings on Google Playstore. By implementing and optimizing these algorithms, this study succeeded in achieving an accuracy of 92.62%, with MAE 0.311, RMSE 0.467, and R-square 0.144 using the Gradient Boosting algorithm. This research contributes to the development of better prediction methods in the mobile application industry and provides new insights regarding the factors that influence app ratings on the Google Play store
The effect of autoclaving method on sago starch as a functional food source: a review from a gastronomy perspective
No full textStarch is a carbohydrate that is a polymer of glucose, and consists of amylose and amylopectin. Starch is found abundantly in the gastrointestinal tract and is slightly fermented by the intestinal microflora. RS is often identified as a food starch that cannot be digested in the small intestine so that it functions properly for the health of the human body. This study aims to investigate the effect of autoclaving method on sago starch as a functional food source, with emphasis on gastronomic aspects. Through this approach, we can gain a more comprehensive understanding of the potential of sago starch in providing healthy and tasty food for consumers. This research method was carried out under the condition of modification of sago starch by autoclaving-cooling including modification cycles, among others: no cycle, one cycle, 2 cycles, and 3 cycles. The results showed T0 starch showed 1.26% and 3 cycles showed 5.62%. The best modification of RS starch is shown in the cycle process 3
Measuring the usability effectiveness of using card menus and tree menus in school web applications
Full text linkThe aim of this research is to measure the usability effectiveness of a web application by using card menus and tree menus using user-friendly criteria and access speed as indicated by the number of clicks made by the user. The method used in this research is the Task-centered User Interface method, where this method allows for planning and evaluating the arrangement of the interface according to user needs. There are four stages in this method, including user identification by conducting needs analysis, the second phase is user interface design. The third phase is the implementation of the card menu and tree menu design, and the fourth face is testing the usability and effectiveness requirements. From the research that has been carried out regarding measuring the effectiveness of using card menus, it is more effective to use than tree menus because you can directly lift the menu and access it. Meanwhile, for usability, the card menus have a higher usability index than the tree menus. Meanwhile, for usability measurements carried out by direct observation and distributing questionnaires, the resulting percentage of user understanding, ease, and speed for the card menu display was 87% and for the tree menu was 60% so that the card menu display was more accepted by users than the tree menu. The new thing provided by the results of this research is in the form of suggestions that can be used by web application developers to use the right type of menu in building web-based applications with the same specifications as in the case of school finance applications
Classification of travel class with k-nearest neighbors algorithm using rapidminer
No full texthe tourism industry in Indonesia plays an important role in the national economy. The selection of travel class according to the needs and budget of tourists is an important aspect in the tourism industry. This research aims to develop a travel class classification model using dummy datasets and the K-Nearest Neighbors (KNN) algorithm with RapidMiner software. The travel class dummy data set was obtained from the internet and modified according to research needs. The KNN algorithm was used to classify new travel classes based on previously classified dummy data. These dummy data were preprocessed and analyzed using RapidMiner software. The performance of the KNN model was evaluated using accuracy, precision, recall and F1-score. The results showed that the KNN algorithm with the values k = 1-2, k = 3-6, k = 8-10, k = 11-14 and k = 15 resulted in accuracy of 35.71%, 39.29%, 48.26%, 46.43% and 50.00%, respectively. This shows that the KNN algorithm with a value of k=15 produces the highest accuracy that can be effectively used to classify new travel classes based on dummy data
Off-Grid Emergency Tent Solutions with a Compact Solar Photovoltaic System
Full text linkIn recent years, solar photovoltaic (PV) systems have seen increased utilization for powering a diverse range of applications, from residentials and buildings to off-grid installations, highlighting their growing importance in the global transition towards sustainable development using renewable energy sources. This paper proposes the development of a compact solar PV system for emergency tent systems to aid preparations and rapid response efforts during disasters and unforeseen events. The system aims to support local authorities and communities in mitigating disaster risks due to power outages. The design and implementation of an emergency tent prototype measuring 3´3 m2, with a power consumption of 0.36 kW/day using a 30 W solar PV panel and battery storage capacity of 40 Ah. The system is sufficient for powering the basic functions of dc bulbs and fans as the primary load
A comparison study of patients attendance to klinik rawatan keluarga pergigian (krkg) hospital universiti sains malaysia (Hospital USM): A study from the year 2017 to the year 2020
Full text linkThe study aimed to compare the patient's attendance to Klinik Rawatan Keluarga Pergigian (KRKG), Hospital Universiti Sains Malaysia (Hospital USM) from the year 2017 to the year 2020. Data was collected starting from January 2017 till December 2020. Data was plot according to the trendline for each year, and the mean of patient's attendance is being calculated, recorded, and present using a simple bar means. The equation for the particular year is being estimated and compared to the reference category using multinomial regression. From the analysis, it was found that the year 2020 having a decreasing [F-Stat (df)=6.786(1.759,19.349); p < 0.05] in the trendline of patients who attended KRKG, this is due to the global coronavirus pandemic. The finding had found that the year 2020 having has a significant decrease as compared to the previous year [F-Stat (df)=6.786(1.759,19); p < 0.05]. In the multinomial regression analysis, the estimates for the parameter can be identified compared to a baseline category. The finding had shown that the patient's attendance to KRKG is less due to the Covid-19 pandemic
An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network
Full text linkThere are many types of orchids in Indonesia, such as Phalaenopsis Amabilis (Moon Orchid), Cattleya, etc. Because the shape and color of each orchid flower looks the same, a system is needed that can classify orchid flowers. In this research, we will use a system using a Convolutional Neural Network with ResNet50 architecture to classify orchid species. There are 4 types of orchids that will be used, namely Moon Orchids, xDoritaenopsis Orchids, Cattleya Orchids, and Coelogyne Pandurata Orchids (1000 datasets for each type). The aim of this research is to implement deep learning using the Convolutional Neural Network method combined with the ResNet50 architecture and identifying the types of orchid flowers and calculating accuracy when identifying orchid flower types. This research uses 4000 orchid image datasets, with a data split of 80:20 so that 800 images are used as training data and 200 as test data. ResNet50 uses a confusion matrix evaluation, namely Accuracy, Precision, Recall, Specificity and F1-score with epochs 10, 20, 30, 40. From the research that has been carried out, it produces the highest accuracy on Test Data with the 30th epoch, reaching 98.87%. and the lowest accuracy on Test Data with the 10th epochs which produces an accuracy of 97.75%