JOURNAL OF EDUCATION AND SCIENCE

Laboratories are an essential part of the educational process in university faculties because
of their importance in delivering scientific and applied material to students. There is great
importance to the availability of safety conditions, professional staff, technical
capabilities, and other laboratory requirements. Given the importance of quality in
educational laboratories, the ranking of its must be done frequently. The evaluation
information must be sound and available when needed. The objective of this research is to
create an electronic Laboratory Quality Ranking (LabQR) program that simulates the
paper evaluation process. The program converts all documents to electronic
documentation and extracts the final score of the required conditions. Also, all the
attached documents were saved in electronic databases, such as course certificates for
employees and administrative orders. The program also provides ease of searching,
retrieval, and archiving. The expansion and flexibility features have been considered in the
program by adding other colleges, departments, and universities.

Food products are an essential source of human life, so they have a very important place,
and it will be important to monitor and determine their quality in a short time. Our study
deals with a very important and indispensable food product, which is the milk product,
which is considered the main and important element in people’s lives, especially children,
because it is the main source of their growth, building their bones, and strengthening.
Because it is a highly perishable product, it must be monitored and its specifications must
be monitored, because any gram of milk that is of low or poor quality may cause tons of
milk to spoil, and also cause major financial losses. Therefore, a study was conducted to
determine the quality of dairy product through machine learning algorithms (ML), which
are support vector machine (SVM) algorithm, nearest neighbors (KNN) algorithm,
decision tree (DT) algorithm and Bagging algorithm using milk dataset taken from data
warehouse Kaggle. This data consists of 1059 samples and seven features. The proposed
models were trained and tested with the aim of finding the best and most accurate model
for detecting milk quality and were evaluated using the evaluation metrics: accuracy,
precision, recall, f1_score and confusion matrix. According to the evaluation results three
models: SVM, KNN, and DT outperformed Bagging algorithm, as they obtained the
highest level for all metrics 100%. The SVM algorithm was the most efficient because its
execution time was 0.146 seconds, which was less than the other models.

State Universities and Private Universities compete fiercely to produce quality
students in line with the development of the world of education in Indonesia. Universities
strive to improve quality and provide the best education to students and the number of
students who graduate on time or not. In this research, a comparative test of the
performance of the accuracy values of the K-Nearest Neighbor algorithm and the Support
Vector Machine was carried out as a classification method for predicting the study period
of students in the Bachelor of Law study program, Faculty of Law. Law, Sebelas Maret
University, Surakarta, Indonesia using the RapidMiner application. In this study, a
comparison of two classification methods was used, namely K-Nearest Neighbor and
Support Vector Machine with 433 student data used. The data is divided into 70% training
data and 30% test data. The test results for the highest K-NN prediction accuracy value
were at K=5, namely 98.45%. While for the Support Vector Machine method, the
accuracy value using the SVM model was 96.90%. Therefore, the results of this research
are included in the good category in producing high accuracy, so that the contribution of
the K-NN modeling research results using the value K=5 is getting the best accuracy
compared to the SVM method using the SVM in predicting student study periods. class of
2021, Bachelor of Law study program, Faculty of Law, Sebelas Maret University

This research paper includes the design and implementation of a system for mining
student and patient data at the College of Dentistry at the University of Mosul using the
Microsoft SQL Server database management system to design and implement the database
system and WEKA program for database mining, and the Microsoft Visual C# .NET 2012
language was used to program system interfaces. The main steps of the database included
analysis, design and implementation, and the mining process included seven steps; data
collection, data preprocessing, data exploration, data transformation, data modeling,
evaluation, and deployment. The database mining process was divided into two parts; the
first part is a smart cluster process for students of the Faculty of Dentistry for the fourth
and fifth stages on laboratories (i.e. the number of chairs available for each laboratory)
using three famous algorithms (Canopy, K-Means, EM), the second part is the process of
classifying patients into four classes according to the type of treatment that each patient
needs using three also famous algorithms (SVM, Naïve Bayes, Random Forest). ). After
applying the system to the real data of the College of Dentistry at the University of Mosul,
it was found that the best cluster algorithm is K-Means and the best classification
algorithm is Naïve Bayes.

In this research, we presented a new method for collecting data and performing
calculations to obtain the required results differently from what have been studied before.
In the beginning, we introduce new types of soft sets in soft topological spaces, explaining
many properties such as soft ii-dense sets, soft ii-connectedness, soft ii-perfection, and
soft ii-compactness. We proved that any soft set (S,E) could be soft ii-connected in (X,τ,E)
when it is soft ii-connected in a soft partial sub-space (

In this work, the energy levels of the ground state band (GSB), β-band and -
bands for 158-160Er isotopes were calculated using the Interacting Boson Model(IBM-1),
the Semi-Empirical Formula (SEF) and the New Empirical Equation (NEE). All three
models were developed by IBM. In order to complete this study, the above-mentioned
models and equations were applied. The results of the GSB, β-band and -bands showed
that IBM-1, SEF, NEE, and the available experimental data are all in agreement with
certain variations this was shown by the results of the GSB. The NEE calculations have a
higher level of compatibility with the experimental data when compared to both the IBM-
1 calculations and the SEF calculations. According to the findings of this research, the
SEF and NEE equations are both capable of describing the energy spectra of Er isotopes
in comparison to IBM-1. The electric quadrupole transition probabilities B(E2) transitions
which obtained through IBM-1 for the above isotopes were calculated and found to be in
agreement with the available experimental data, On the other hand, Isotopes of Er exhibit
behavior that is consistent with a rotational SU(3) transition.

Appropriate selection of features may lead to the specificity of classification methods
and identify the most critical features from all sparse or dense impact data using a filter
based on the recognition selection method characterized. Filtration is used to reduce
sample complexity, improve the clarity of viscous samples, and reduce background
signals, resulting in increased signal-to-noise ratios in analytical tests. Depending on the
filtration method applied, particles are separated based on properties such as size. This
study assessed the impact of filter selection and the variation in the number of projections
on the final reconstructed artificial phantom images. Utilizing image reconstruction
techniques, it delves into the application of mathematical transforms, including Radon and
Fourier, to improve image quality and resolution, particularly in medical imaging
modalities such as CT and MRI. The research predominantly focuses on the application of
the Filtered Back Projection (FBP) algorithm to reconstruct images from changing
numbers of projections. The results underscore the main role of filter choice in removing
noise, with the Ramp filter presenting the most promising results. The investigation
concludes that reducing the number of projections results in a decline in image contrast
and an increase in image noise.

Behavioral diagrams in Unified Modeling Language reflect the interaction between
system components and give a comprehensive description and visualization of the system
during the design phase. One of the most important behavioral diagrams is the sequence
diagram which describes the chronological sequence of events between the components of
the system. The process of extracting information and metrics from a sequence diagram is
time-consuming so creating a special tool to help developers obtain information from the
sequence diagram has become necessary because of the great advantages and ease it
provides. This paper aims to build a tool that extracts information from the sequence
diagram, creates a table that includes this information, and then calculates three categories
of metrics related to the sequence diagram which are size, complexity, and level of detail.
These categories include 15 metrics to give quantitative values that indicate software
quality which is used to estimate the schedule, cost, effort, and other resources in the
software development process. As a case study, the hotel reservation system is adopted
and constructed as two versions of sequence diagrams for comparison purposes. The
results showed a quantitative measurement of small and unnoticeable differences between
the two diagrams.