Penentuan Rekomendasi Pelatihan Pengembangan Diri  Bagi Pegawai Negeri Sipil Menggunakan Algoritma C4.5 Dengan Principal Component Analysis  Dan Diskritisasi

Rahmawan, Hanif

doi:10.33365/jtk.v14i1.531

Cited by 3 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Normalization is performed to change the data value into a data range between 0 to 1 [16]. d. Data discretization Data discretization is used to change continuous features into discrete ones [17]. Discretization used in this study is a type of equal frequency, which divides data or determines intervals based on the same frequency.…”

Section: B Change Class Labelmentioning

confidence: 99%

Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio

Meidina,

Abidin

2023

Recursive J. of Informatics

View full text Add to dashboard Cite

Abstract. Purpose: This study aims to apply feature selection particle swarm optimization (PSO) and gain ratio to the naïve Bayes algorithm and gauging the level of accuracy before and after applying PSO feature selection and gain ratio to the naïve Bayes algorithm in the diagnosis of heart disease.Methods/Study design/approach: Data collection is done by using taking the Cleveland dataset obtained from the UCI machine learning repository. The data used in this study were 303 samples. The data is processed using the preprocessing stage. The naïve Bayes algorithm is used for a classifier, while PSO and gain ratio for feature selection.Result/Findings: The results of the study revealed that the classification accuracy of the naïve Bayes algorithm without the application of feature selection in the Cleveland dataset is 86.88%, while the results of the classification accuracy of the naïve Bayes algorithm after applying PSO and gain ratio in the Cleveland dataset is 93.44%. Application of PSO and gain ratio as feature selection algorithms can improve classification accuracy by 6.56%.Novelty/Originality/Value: This study combines the PSO feature selection and gain ratio on the naïve Bayes algorithm using the Cleveland dataset. The research model that was carried out was enriched by carrying out the preprocessing stages, namely data cleaning, changing the number of class labels, data normalization, and data discretization. This study shows that using a combination of the PSO feature selection algorithm and the gain ratio gives better accuracy to the naïve Bayes algorithm in diagnosing heart disease.

show abstract

Section: B Change Class Labelmentioning

confidence: 99%

Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio

Meidina,

Abidin

2023

Recursive J. of Informatics

View full text Add to dashboard Cite

show abstract

“…Principal Component Analysis (PCA) adalah salah satu metode yang populer digunakan untuk mereduksi dimensi citra (Vidal et al, 2018) (Rahmawan, 2020) (Andrian, 2019) dari ruang citra sehingga basis baru dapat menggambarkan model yang khas/unik dari citra tersebut (Yuliana & Nurhaida, 2018). PCA diciptakan pada 1901 oleh Karl Pearson (Vidal et al, 2018)…”

Section: Pca (Principal Component Analysis)unclassified

“…Prosedur kunci dalam PCA didasarkan pada tranformasi Karhumen-Loeve. Principal Component Analysis diaplikasikan pada variabel prediksi saja dengan mengabaikan variabel target (Rahmawan, 2020). Principal Component Analysis sering digunakan dalam mereduksi data.…”

Section: Pca (Principal Component Analysis)unclassified

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Rambe

Tanjung

Muhathir

2022

JITE

View full text Add to dashboard Cite

Pattern recognition is one of the sciences used to classify things based on quantitative measurements of the main features or properties of an object. Pattern recognition has been widely used in various fields of research. One of the pattern recognition that is often discussed is facial recognition. The face is one of the human biometrics that is often used as the main information of a person. Face recognition is a field of research with many applications in applications such as attendance, population data collection, security systems, and others. The research utilizes feature extraction of PCA (Principal Component Analysis), and K-NN (K – Nearest Neighbor) with variations of the distance formula by applying facial recognition attendance at the Safiatul Amaliyah School. This research is expected to get accurate results in detecting, recognizing, and comparing a person's face with a small error rate. The distance formula with accuracy level is presented with the equation Cityblock < Euclidian < Minkowski < Chebychev. The effect of applying the variation of the distance formula on the performance of the facial attendance recognition model is not too big, but it is better.

show abstract

“…Additionally, the algorithm is used to aid students in selecting elective courses [8] and optimize product placement on shelves based on customer search frequency levels [9]. While [10] compared three methods for determining selfdevelopment training, namely the C4.5 algorithm, the combination of PCA and C4.5, and the combination of C4.5, discretization, and PCA. The test results show that the combination of PCA, discretization, and C4.5 gives better performance than the other two methods with an average accuracy rate of 86.6%.…”

Section: Introductionmentioning

confidence: 99%

“…After reviewing the literature, it is evident that association techniques are used in several fields, ranging from recommending sales strategies [2], [6], [9], [11], book recommendations [3], [4], study plan recommendations [5], [8], and even violence prevention recommendations [7]. However, research on training recommendations using data mining is very limited [10]. Therefore, the researcher opted to use association data mining techniques with the Apriori method to design training recommendations.…”

Section: Introductionmentioning

confidence: 99%

Employee Education and Training Recommendations using the Apriori Algorithm

Arief Wibowo,

Vasthu Imaniar Ivanoti,

Megananda Hervita Permata Sari

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data of education and training implementation in 2022 at the Central ICT Department in MoF, only 27.35% of employees participated in education and training according to proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or the substitute participants that is not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the apriori algorithm to analyze historical data of employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resource Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance.

show abstract

Penentuan Rekomendasi Pelatihan Pengembangan Diri Bagi Pegawai Negeri Sipil Menggunakan Algoritma C4.5 Dengan Principal Component Analysis Dan Diskritisasi

Cited by 3 publications

References 7 publications

Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio

Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Employee Education and Training Recommendations using the Apriori Algorithm

Contact Info

Product

Resources

About