Naive Method Research Articles

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

Estimating the remaining useful life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine’s condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time-series sensor measurements serves as the input, initially processed by the bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods.

Improvement of support vector machine for predicting diabetes mellitus with machine learning approach

<p>The prevalence of diabetes is currently increasing worldwide, including in Indonesia, due to the increasing levels of stress and lack of physical activity that led to obesity and related complications such as hypertension. However, only about 25% of diabetes patients are aware of their condition. Therefore, this study aims to find an algorithm that can help predict with better accuracy using the diabetes mellitus dataset obtained from Kaggle. To obtain information about the accuracy level of diabetes diagnosis, the data will be processed using two methods, namely support vector machine and naive bayes. To obtain the most accurate results, we optimize each variant and parameter of every algorithm used. The best method in this study was produced by the support vector machine method with a radial basis function (RBF) kernel, which achieved an accuracy level of 98.25%, superior to the naive bayes method which obtained the highest accuracy of only 77.25%. Additionally, this study also applied the proposed method using the diabetes mellitus dataset from LAB01 DAT263x taken from the Kaggle website. The results of the experiment indicate that the suggested model outperforms other methods in terms of performance, with a tendency for high accuracy generated in every experiment for all datasets.</p>

Prediction of electric power performance of the exhaust waste heat recovery system of an automobile with thermoelectrical generator under real driving conditions by means of machine learning algorithms

In internal combustion engines, approximately 40% of the thermal power obtained from fuel burning is thrown out to the environment from the exhaust system. Implementations of waste heat recovery systems with thermoelectrical generator over exhaust system have become widespread as it is the waste heat resource with the highest temperature in a vehicle. During literature research, experiments related to waste heat recovery under real road conditions are very few and no study on estimation of system performance by machine learning algorithms has been found; therefore, Toyota Corolla has designed an air-cooled waste heat recovery system using 3 thermoelectrical generators for the exhaust system of the automobile. During the road tests, temperature and electric power generation values obtained as per gear, vehicle speed and engine speed have been recorded. In the study, a dataset consisting of 10 attributes was created with each record as a result of the path test. Using this dataset, Random Forest, Support Vector Machine (SVM) and Naive Bayes machine learning algorithms estimated the electrical power to be generated from the thermoelectric generator recovery system. In the study, 7 electric power classification estimates were made. In the estimation process, 76% of the dataset was used for training and 24% was used for testing. In terms of estimation success; an estimation success of 96.6% has been achieved by Random Forest method; 94.6% by Support Vector Machine (SVM) method; and 76.7% by Naive Bayes method. The results show that prospective electricity generation estimation can be achieved with high level of accuracy.

Optimizing Driving Completeness Prediction Models: A Comparative Study of YOLOv7 and Naïve Bayes at Institut Teknologi Sumatera

The number of vehicles in Indonesia is increasing every year. The number of motor vehicle accidents in 2022 will be more than 100,000. It is hoped that several regulations regarding motorbike rider equipment will increase awareness of rider safety. By utilizing image recognition technology developed with artificial intelligence, it is possible to create digital image processing models or images that are fast and accurate for detecting driving equipment. The object detection model developed uses a dataset in the form of images of motorists who want to enter ITERA through the main gate. The object detection model will also be integrated with the classification model to create a program that can detect motorbike rider equipment, such as mirrors, helmets, not wearing a helmet, shoes, not wearing shoes, open clothes, and closed clothes. After detecting motorized rider equipment in the classification area, the results will be transferred to a classification model to determine the level of safety for motorized riders, either insufficient or sufficient safety. The test results show that the optimal object detection model was found at an epoch value of 70 with a batch-size of 16, producing a mAP value of 0.8914. The optimal classification model uses the naive Bayes method which has been trained with a dataset of 62 data and achieves an accuracy of 94%.

ANALYSIS OF THE EXISTENCE OF THE ACCOUNTING PROFESSION IN THE ERA OF SOCIETY 5.0 WITH THE NAIVE BAYES METHOD

Society 5.0 has opened a new phenomenon that allows humans to use modern-based science (Artificial Intelligence) for human needs with the goal of human life more easily. This phenomenon opens up various new possibilities and risks simultaneously. These changes have had a significant impact on the development of the accounting profession, resulting in fewer human resources needed in business, including accounting staff. This research aims to 1) Apply sentiment analysis to the existence of the accounting profession in the era of society 5.0. 2) Application of the naive Bayes method to classify sentiment analysis for the existence of the accounting profession in the era of society 5.0. 3) Testing the naive Bayes algorithm in sentiment analysis for the existence of the accounting profession in the era of society 5.0. This research design uses quantitative research, analysis of student sentiment on the existence of the accounting profession was carried out using data from 432 student opinions from several campuses in the East Java region. Test data using the Naïve Bayes algorithm with Term-Frequency Inverse Document Frequency feature selection and the Lexicon method. The results of this research are divided into five parts consisting of the first part 424 students' opinions on the level of accuracy (78%), the second part 427 students' opinions on the level of accuracy (62%), the third part 429 students' opinions on the level of accuracy (47%), the fourth part 425 students' opinions level of accuracy (56%), fifth part 429 student opinions level of accuracy (52%). It was found that the number of positive sentiments was more dominant than negative or neutral sentiments

ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.

Analysis of Student Satisfaction with the Quality of Education Services and Lecturer Performance Using the Survey and Naive Bayes Methods

This research aims to evaluate the level of student satisfaction with the quality of educational services and the assessment of faculty performance at STMIK Tegal through a survey method and the application of Naive Bayes. The research sample consists of 100 active students from different study programs. Survey results were processed using the Naive Bayes method to assess the impact of variables related to the quality of educational services and faculty performance on the level of student satisfaction. The findings of the analysis indicate that the quality of educational services has a positive effect on student satisfaction, while faculty performance also contributes positively. This research has practical implications for improving the quality of educational services and faculty performance at STMIK Tegal. Infrastructure and facilities supporting the teaching and learning process can be enhanced, and faculty members can receive training and guidance to improve their teaching quality. In the long term, this can enhance the institution's reputation and overall student satisfaction. This study has limitations related to the limited sample size and the use of non-random sampling techniques. Therefore, future research can expand the sample size and use more representative sampling techniques to enhance the accuracy of research results

Perbandingan Kinerja K-Nearest Neighbors dan Naive Bayes Untuk Klasifikasi Perilaku Nasabah Pada Pembayaran Kredit Bank

:Credit customers are people who use banking services or other financial services. to use bank money in its business activities, so it expects that the bank's credit can meet business capital needs. To reach information to increase profits and reduce company losses, we need a method that can provide knowledge to support the company's data. Research data can be obtained from processing classification data from credit customer data that are categorized as potential or not potential in the next credit grant. Data processing can be done using machine learning, namely classification techniques. This technique will produce a predictive churn model to determine which customer categories belong to a group. potential smooth or jammed. The Naive Bayes method was chosen because it can produce maximum accuracy with little training data. Meanwhile, the K-Nearest Neighbor method was chosen because it is robust against noise data. The performance of the two methods will be compared, so that it can be seen which method is better in classifying documents. The results obtained show that the Naive Bayes method has better performance with an accuracy rate of 70%, while the K-Nearest Neighbor method has a fairly low accuracy rate of 40%. Thus, it can be seen the accuracy value displayed by applying the classification algorithm. K-Nears Neighbors and Naïve Bayes. Parameter category. which in this study are account numbers, names of debtors, collectibility in the categories: current, DPK (on special mention), substandard, doubtful, loss. Then clarified with a description of the type of loan, collectability of ADK (computer data archive), type of business.

ANALISIS KEPUASAN MAHASISWA TERHADAP SISTEM PEMBELAJARAN ONLINE PADA MASA PANDEMIC COVID 19 DI STMIK AKAKOM DENGAN METODE NAIVE BAYES

In accordance with the circular of the Minister of Education and Culture of the Republic of Indonesia regarding Circular Letter Number 4 of 2020 concerning the Implementation of Educational Policies in the Emergency Period for the Spread of Corona Virus Disease (COVID-19), the learning system implemented is online. 
 STMIK AKAKOM is one of the universities that has also implemented online lectures since the government's policy for the home learning system was established. Various efforts have been made by STMIK AKAKOM to carry out the online learning process during this covid-19 pandemic. 
 Therefore, researchers conducted a study that aims to analyze the success of the online learning system for students conducted at STMIK AKAKOM with the Naive Bayes method approach, using 4 (four) criteria, namely Communication, Building a Learning Atmosphere, Assessment of Students, and Delivery of Lecture Materials. The level of satisfaction assessment using a Likert scale (likert scale) 5 points with the same interval. starting from point 1 (one) which states very less, to point 5 (five) which states very well. Based on the results of calculations that have been carried out, it can be seen that the classification of testing data from respondents numbered R89 to R93 for online learning systems during the Covid 19 pandemic is satisfied.

Multidimensional Analysis of the Number of Passengers Transported by Air in Europe Between 2019-2022 in Terms of Economic Security

The article presents a multidimensional comparative analysis of the number of passengers transported by air in Europe between 2019-2022 in terms of economic security. Data for the study was taken from Eurostat and categorized line and bar graphs were used. The conducted research shows that in 2021, in each of the considered European countries, an average of 138% of the number of passengers of 2020 were transported by air. In 2022, an increase to 230% of the number of passengers of 2021 was observed. The forecasting of the number of passengers conducted by air transport for 2023 in 29 European countries under consideration was made. A naive method was used for the forecasting. In 2023, the largest number of passengers will be transported in Spain, followed by Germany in the second place and France as third. The sum of the forecast of the number of passengers transported for 2023 in 29 European countries under consideration will equal 1 401 839 218 people and will be higher than in 2022 by 296 222 604 people.

Timeliness Of Spp Payments At Smk Tritech Infomatika Using Naive Bayes Algorithm

SMK Tritech Informatika is one of the vocational schools located on Jalan Bhayangkara Medan. Smk Tritech plays an important role in education. However, many schools are late in financing their operations. So there are many problems related to school operational payments such as tuition payments. SPP payment is an important problem at Smk Tritech Infomatika because many of the students are late in paying SPP even though the payment deadline has been set. Therefore, it is necessary to evaluate the SPP payment. To overcome this problem, it is necessary to detect the factors that cause late tuition payments using data mining. The data mining technique used is classification with the Naive Bayes algorithm method.

Classification Of Maternal Health Risk Using Three Models Naive Bayes Method

Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.

IMPLEMENTASI ALGORITMA NAIVE BAYES CLASSIFIER MENGGUNAKAN FEATURE FORWARD SELECTION DALAM MEMPREDIKSI KETEPATAN MASA STUDI MAHASISWA

Students are one of the important pillars in the life cycle of a higher education institution. One indicator of the success of a study program can be seen from the accuracy of the student's study period. The accuracy of a student's study period refers to the time schedule that the student must achieve from entering the study program to graduating, according to the time span determined by the university. At the Diploma Three (D-III) level, it is said to graduate on time if you have completed the equivalent of three academic years of study and not graduate on time if you have completed more than three academic years. Study. This research aims to influence the quality of study programs, so that it is used as an assessment criterion for accreditation by (BAN-PT). The results of this research are presented in accordance with the research conducted. The data used in this research is academic data for students of the Informatics Management Study Program (D-III) Class 2020 – 2022. The categories used are gender, major, IPS 1 to IPS 4, credits, and GPA. The test results using 129 training data and 40 test data obtained an accuracy of 97.50% using the Naive Bayes method with forward selection. There were 12 students who made late predictions and 28 students made correct predictions. So it can be stated that the Naive Bayes process model is suitable for use in determining good decision results in terms of prediction and classification.

Hough transform and line rotation on analog gauge reading: A case study

While digital meters can automatically communicate with a database to store reading values, many industries still utilize analog meters which are not economically or physically viable to replace. Instead, computer vision modules may be attached to cameras to automatically read values and record them. This paper tests three implementations of gauge reading, two simple Hough line and circle transform methods and one lightweight naive line rotation method. A dataset was created for testing purposes, consisting of 46 images from various sources and variations including pointer rotations, binarizations, and text or logo removal. Results showed the line rotation technique substantially more robust and accurate than both Hough line implementations. Two major obstructions were detected: pointer tails and dense text/logos, and their removal via photoshop tools improved the average accuracy to roughly 1 degree from ground truth. This is accurate enough to replace human readers in most imprecise situations and is lightweight enough to function under nearly all circumstances. Future research seeks to validate these findings further by testing line rotation on more varied gauges.

Nonparametric Regression Analysis of BE4DBE2 Relationship with n and z Variables using Naive Bayes and SVM Classification on Nuclear Data

This research article describes several analyses of nuclear data using various statistical methods. The first analysis uses linear regression to investigate the relationship between the independent variables (n and z) and the response variable (BE4DBE2). The second analysis uses a nonparametric regression model to overcome the assumptions of normality and linearity in the data. The third analysis uses the Naive Bayes method to classify nuclear data based on variables n and z. The fourth analysis uses a decision tree to classify nuclear data based on the same variables. Finally, the article describes an SVM analysis and a K-means analysis to classify and group nuclide data. The article presents clear and organized descriptions of each analysis, including visual representations of the results. The findings of each analysis are discussed, providing valuable insights into the relationships between the variables and the response variable. The article demonstrates the usefulness of statistical methods in analyzing nuclear data.

Analisis Sentimen terhadap Penyelenggaraan Sea Games 2023 Kamboja pada Twitter Menggunakan Algoritma Naive Bayes

Southeast Asian Games or SEA Games is a Southeast Asian sporting event held every 2 years, where the participants are 11 member countries of the Association of Southeast Asian Nation (ASEAN). Cambodia was chosen as the host for the 2023 SEA Games. The implementation of the SEA Games in Cambodia experienced many controversies ranging from the inverted Indonesian flag to leaking lodging rooms for athletes. Social media Twitter became one of the places for netizens to express their opinions about the implementation of the SEA Games in Cambodia. This study aims to determine the level of tendency of positive, negative and neutral opinions through the sentiment analysis process. The sentiment analysis process is carried out using the Naive Bayes method, through five main stages, namely Data Selection, Preprocessing, Transformation, Data Mining, and Evaluation. The data used comes from Twitter users who use the hashtag "SEA Games Cambodia" then obtained data as many as 1595 tweets. The results of this study describe the results of Naive Bayes implementation and performance testing using confusion matrix obtained accuracy 66%, precision 70%, recall 66%, and f1-score 61%. and also obtained the results of the tendency of public opinion sentiment on Twitter with positive results as much as 49%, then negative results as much as 40% and neutral results as much as 11%.

UNBALANCED MULTICLASS CLASSIFICATION WITH ADAPTIVE SYNTHETIC MULTINOMIAL NAIVE BAYES APPROACH

Opinions related to rising fuel prices need to be seen and analysed. Public opinion is closely related to public policy in Indonesia in the future. Twitter is one of the media that people use to convey their opinions. This study uses sentiment analysis to look at this phenomenon. Sentiment is divided into three categories: positive, neutral, and negative. The methods used in this research are Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours, and Adaptive Synthetic Random Forest. The Adaptive Synthetic method is used to handle unbalanced data. The data used in this study are public arguments per province in Indonesia. The results obtained in this study are negative sentiments that dominate all provinces in Indonesia. There is a relationship between negative sentiment and the level of education, internet use, and the human development index. Adaptive Synthetic Multinomial Naive Bayes performed better than other methods, with an accuracy of 0.882. The highest accuracy of the Adaptive Synthetic Multinomial Naive Bayes method is 0.990 in Papua Barat Province.

Development of tomato maturity level prediction model based on portable visible spectrometer and machine learning

A tomato is classified as a fruit, which level of maturity is determined by its color. Upon distribution, tomatoes require sorting based on their ripeness level. Generally making improvements done conventionally with the human eye. This method has the disadvantage that the results are subjective. One way that can be used to measure the ripeness level of tomatoes is using a spectroscopic sensor. Spectroscopic sensors can predict the level of ripeness and its contents automatically. This study uses machine learning to create a model to classify ripeness level and predict firmness, total dissolved solids (TDS), and total acid in tomatoes. This study used tomatoes with 3 categories of maturity. Tomatoes were tested non-destructively, namely measuring firmness, total dissolved solids content, and total acid. The data obtained were processed using the Partial Least Square Regression method to predict firmness, TDS, and total acid, while the maturity level used the Naive Bayes method. The data processing results to predict the level of maturity using Naive Bayes obtained a success rate of 100%. While for the predictions of firmness, TDS, and total acid had R2 training and R2 testing, namely 0.685 and 0.678, 0.534 and 0.521, and 0.352 and 0.349, respectively.

Pemanfaatan Teknologi Computer Vision Untuk Implementasi Deteksi Masker Menggunakan Metode Supervised Learning

The use of masks is still very strict in public places, especially in hospitals, this is solely done to prevent the spread of the corona virus again. The purpose of this research is to assist examination workers or health protocol workers in supervising the use of masks in public places. Mask detection is a solution to this problem, by utilizing computer vision technology and applying supervised learning algorithms. For this mask detection classification method, this system uses the Naive Bayes method. The output of this mask detection system is planned to distinguish people wearing masks and not wearing masks, by giving red labels to people who are not wearing masks and green labeling to people wearing masks. The distance aspect is used in testing this mask detection system, the system is able to work well by getting an error rate presentation below 2% and getting the highest accuracy of 100% with an average percentage value of 98%. On the other hand, there are still weaknesses in this system, the use of brown masks that are in harmony with skin color can worsen the results of the classification system

Generation matrix: An embeddable matrix representation for hierarchical trees

Starting from the local structures to study hierarchical trees is a common research method. However, the cumbersome analysis and description make the naive method challenging to adapt to the increasingly complex hierarchical tree problems. To improve the efficiency of hierarchical tree research, we propose an embeddable matrix representation for hierarchical trees, called Generation Matrix. It can transform the abstract hierarchical tree into a concrete matrix representation and then take the hierarchical tree as a whole to study, dramatically reducing the research complexity. Mathematical analysis shows that Generation Matrix can simulate various recursive algorithms without accessing local structures and provides a variety of interpretable matrix operations to support the research of hierarchical trees. Applying Generation Matrix to differential privacy hierarchical tree release, we propose a Generation Matrix-based optimally consistent release algorithm (GMC). It provides an exceptionally concise process description so that we can describe its core steps as a simple matrix expression rather than multiple complicated recursive processes like existing algorithms. Our experiments show that GMC takes only a few seconds to complete a release for large-scale datasets with more than 10 million nodes. The calculation efficiency is increased by up to 100 times compared with the state-of-the-art schemes.