ANALISIS PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) DALAM PENILAIAN KINERJA KARYAWAN SATPAM PT. SINAR PANDAWA

SRI HANDAYANI HARAHAP, NPM 2209100120 (2026) ANALISIS PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) DALAM PENILAIAN KINERJA KARYAWAN SATPAM PT. SINAR PANDAWA. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja algoritma Naïve Bayes dan Support Vector Machine (SVM) dalam penilaian kinerja karyawan satpam berdasarkan data kuisioner yang telah dikumpulkan secara kuantitatif, serta memberikan solusi yang objektif dalam proses pengambilan keputusan. Pendekatan yang digunakan berfokus pada pengolahan data secara sistematis untuk menghasilkan model klasifikasi yang akurat dan dapat diandalkan. Tinjauan pustaka dalam penelitian ini mengacu pada konsep Data Mining, khususnya teknik klasifikasi menggunakan algoritma Naïve Bayes dan SVM yang telah banyak digunakan dalam berbagai penelitian sebelumnya. Selain itu, teori- teori pendukung terkait pengolahan data, evaluasi model, serta penerapan Machine Learning dalam penilaian kinerja juga menjadi landasan dalam penelitian ini. Pada tahap analisis dan perancangan, dilakukan proses pembersihan data, transformasi, serta pembagian dataset untuk membangun model yang sesuai dengan kebutuhan penelitian. Selanjutnya, dirancang alur proses pengolahan data menggunakan aplikasi RapidMiner untuk mengimplementasikan kedua metode yang digunakan. Hasil dan pembahasan menunjukkan bahwa kedua algoritma mampu melakukan klasifikasi dengan baik, di mana Naïve Bayes menghasilkan nilai probabilitas yang digunakan dalam penentuan kelas, sedangkan SVM mampu membentuk batas pemisah (hyperplane) yang optimal; berdasarkan evaluasi model seperti akurasi, precision, recall, dan F1-score, salah satu metode menunjukkan performa yang lebih unggul dalam mengklasifikasikan data kinerja karyawan. Kesimpulan dari penelitian ini menunjukkan bahwa kedua metode memiliki keunggulan masing- masing dalam proses klasifikasi, namun terdapat satu algoritma yang lebih efektif berdasarkan hasil evaluasi yang diperoleh. Dengan demikian, hasil penelitian ini diharapkan dapat menjadi referensi dalam penerapan metode klasifikasi untuk penilaian kinerja karyawan secara lebih objektif dan sistematis. Kata Kunci: Naïve Bayes, Support Vector Machine, Klasifikasi, Penilaian Kinerja, Data Mining ================================================================================================= This study aims to analyze and compare the performance of the Naïve Bayes algorithm and Support Vector Machine (SVM) in assessing the performance of security guards based on quantitatively collected questionnaire data, and to provide objective solutions in the decision-making process. The approach used focuses on systematic data processing to produce an accurate and reliable classification model. The literature review in this study refers to the concept of Data Mining, specifically classification techniques using the Naïve Bayes algorithm and SVM, which have been widely used in various previous studies. In addition, supporting theories related to data processing, model evaluation, and the application of Machine Learning in performance assessment also form the basis of this study. In the analysis and design stage, the process of data cleaning, transformation, and dataset division were carried out to build a model that suits the research needs. Next, a data processing process flow was designed using the RapidMiner application to implement both methods used. The results and discussion show that both algorithms are capable of performing good classification, where Naïve Bayes produces probability values used in class determination, while SVM is able to form optimal separating boundaries (hyperplanes). Based on model evaluations such as accuracy, precision, recall, and F1-score, one method demonstrated superior performance in classifying employee performance data. The conclusion of this study indicates that both methods have their respective advantages in the classification process, but one algorithm is more effective based on the evaluation results obtained. Therefore, the results of this study are expected to serve as a reference in the application of classification methods for more objective and systematic employee performance assessments. Keywords: Naïve Bayes, Support Vector Machine, Classification, Performance Assessment, Data Mining

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Naïve Bayes, Support Vector Machine, Klasifikasi, Penilaian Kinerja, Data Mining===============Naïve Bayes, Support Vector Machine, Classification, Performance Assessment, Data Mining
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	02 Jun 2026 03:51
Last Modified:	02 Jun 2026 03:51
URI:	http://repository.ulb.ac.id/id/eprint/2456

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