Metode Naïve Bayes Untuk Penilaian Kinerja Dosen Universitas Islam 45 Bekasi

2018 ◽  
Vol 6 (2) ◽  
pp. 201-210
Author(s):  
Haryono Haryono
Keyword(s):  
Data Mining ◽  
Quality Assurance ◽  
Performance Appraisal ◽  
Naive Bayes ◽  
Value Added ◽  
Naïve Bayes ◽  
Internal Quality ◽  
Appraisal System ◽  
A Value ◽  
Bayes Algorithm

Abstract   Each semester of each lecturer Universitas Islam 45 Bekasi is evaluated by the quality assurance office (KPM) by collecting important documents such as SK, certificates and journals, but the results of lecturer performance assessment are often complained by lecturers. Problems that are complained of them are many lecturers complain of getting minus results there is also a score of 0 or empty even more value obtained lecturer in the previous semester that can be used as a value added for next semester assessment can be lost or not counted for the next semester. By utilizing the data at Quality Assurance Center of Universitas Islam 45 Bekasi is expected to be able to measure internal quality continuously. This study aims to implement naïve Bayes algorithm on lecturer performance appraisal system at Quality Assurance Office of Universitas Islam 45 Bekasi. The method used is qualitative and the algorithm used is Naïve Bayes while the application used is WEKA. Accuracy Information from the implementation of this system can be used by the management of Universitas Islam 45 Bekasi in making decisions.   Keywords: Data Mining, Lecturer's Performance, Naïve Bayes     Abstrak   Setiap akhir semester tiap-tiap dosen Universitas Islam 45 Bekasi dievaluasi kenerjanya oleh kantor penjaminan mutu (KPM) dengan mengumpulkan berkas-berkas penting seperti SK, sertifikat-sertifikat maupun jurnal, namun hasil penilaian kinerja dosen sering dikeluhkan oleh dosen-dosen.  Masalah-masalah yang dikeluhkan tersebut diantaranya adalah banyak dosen mengeluh mendapatkan hasil minus ada pula yang mendapatkan nilai 0 atau kosong bahkan nilai lebih yang didapat dosen pada semester sebelumnya yang dapat digunakan sebagai nilai tambah untuk penilaian disemester selanjutnya bisa hilang atau tidak terhitung untuk semester selanjutnya. Dengan memanfaatkan data pada Pusat Penjaminan Mutu Universitas Islam 45 Bekasi, diharapkan dapat dilakukan pengukur kualitas internal secara berkelanjutan. Penelitian ini bertujuan mengimplementasikan   algoritma naïve Bayes pada sistem penilaian kinerja dosen di Kantor Penjaminan Mutu Universitas Islam 45 Bekasi. Metode yang digunakan adalah kualitatif dan algoritma yang digunakan adalah Naïve Bayes sedangkan aplikasi yang digunakan adalah WEKA. Keakuratan Informasi dari implementasi sistem ini dapat digunakan oleh pihak manajemen Universitas Islam 45 Bekasi dalam mengambil keputusan.   Kata kunci: Data Mining, Kinerja Dosen, Naïve Bayes

scholarly journals The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Tech-E ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Rino Rino
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Set ◽  
A Value ◽  
C4.5 Algorithm ◽  
Calculation Results ◽  
Mining Methods ◽  
Bayes Algorithm

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the Naïve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the Naïve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the Naïve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the Naïve Bayes algorithm in predicting heart disease.

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

scholarly journals Optimasi Model Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Hartatik Hartatik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Prediction Models ◽  
Naive Bayes ◽  
Program Planning ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Naive Bayes Method

<p>Abstrak :</p><p>Prediksi tentang status kelulusan mahasiswa menjadi persoalan tersendiri di perguruan tinggi. Perguruan tinggi utamanya di era Big Data sangatlah penting untuk melakukan prediksi perilaku akademik mahasiswa aktif sehingga dapat di ketahui kemungkinan mahasiswa bisa studi secara tepat waktu serta dapat diketahui langkah preventive dalam membuat prpgram perencanaan. Salah satu cara yang digunakan adalah teknik data mining yaitu menggunakan Algoritma <em>naive bayes</em>. Algoritma <em>Naive bayes</em> merupakan salah satu metode yang digunakan untuk memprediksi kelulusan mahasiswa.  Peneliti  dalam hal ini menerapkan  metode  <em>Naive bayes</em> menggunakan parameter Indeks prestasi kumulatif( IPK) dan membandingkan dengan menggunakan prediksi <em>naive bayes methods</em> berdasarkan parameter IPK dan sosial parameter yaitu jenis kelamin dan status tinggal. Dalam penelitian ini menggunakan parameter akademis  dan dilakukan optimasi menggunakan parameter sosial yang melekat pada mahasiswa. Berdasarkan hasil evaluasi untuk mendapatkan akurasi, hasil dari penelitian ini mendapatkan nilai akurasi untuk metode <em>Naive bayes</em>  sebesar 75% dan akurasi untuk model prediksi dengan parameter sosial  sebesar 85% dengan selisih akurasi 10%.</p><p>__________________________</p><p>Abstract : </p><p><em>Predictions about a student's graduation status are a problem in college. Major tertiary institutions in the era of Big Data are very important to predict the behavior of active students so that they can find out the possibility of students in a timely manner and can determine preventive steps in making program planning. One method used is data mining techniques using the Naive bayes Algorithm. The Naive bayes algorithm is one of the methods used to predict student graduation. Researchers in this case applied the Naive bayes method using the cumulative achievement index (GPA) parameter and compared using the prediction of the Naive bayes method based on the GPA parameters and social parameters, namely gender and status. This study uses academic parameters and is carried out optimally using social parameters inherent in students. Based on the results of the evaluation to get an accuracy value, the results of this study get an accurate value for the Naive bayes method of 75% and accurate for prediction models with social parameters of 85% with a difference of 10%.</em></p>

scholarly journals Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes

2019 ◽  
Vol 1 (01) ◽  
pp. 25-34
Author(s):  
Ade Riani ◽  
Yessy Susianto ◽  
Nur Rahman
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Disease ◽  
Chest Pain ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Mining Method ◽  
The World ◽  
Bayes Algorithm ◽  
Exercise Induced

Heart disease is a disease with a high mortality rate in the world of health. The disease is usually rarely realized the cause. However, there are several parameters that can be used to predict whether a person has a risk of heart disease or not. As for this study, researchers will use several indicators including Age, Sex, Chest pain type, Trestbps, Cholesterol, Fasting blood sugar, Resting ECG, Max heart rate, Exercise-induced angina, Oldpeak, Slope, Number of vessels coloured, and Thal This research will perform calculations using the Data Mining method with the Naive Bayes Algorithm. The results of this study get an accuracy of 86% for the 303 datasets tested. 

scholarly journals Analisis Kelayakan Lokasi Promosi Dalam Penerimaan Mahasiswa Baru (PMB) Dengan Algoritma Naïve Bayes & Decission Tree C4.5

Kilat ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 169-178
Author(s):  
Wulan Wulandari
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Tertiary Institution ◽  
Public And Private ◽  
Measurement Results ◽  
Bayes Algorithm ◽  
The City ◽  
New Student

Competition for new student admissions in every public and private tertiary institution is currently growing rapidly every year, some spend a lot of money on promotional activities, to assist institutions / institutions in obtaining recommendations for the feasibility of promotion locations based on several measurement criteria using the classification algorithms contained in data mining . The algorithm used to compare the measurement of the feasibility of the promotion location of the city and district of Bekasi is Naïve Bayes and Decission Tree C4.5 using four parameters including the number of students in one sub-district, the number of students in one sub-district, the distance of location and last year's enthusiasts using 35 regions / sub-districts in Bekasi city and district.  measurement results using the rapidminner, the accuracy value of the Naïve Bayes algorithm is 91.43% and the Decission Tree C4.5 is 94.29%.

scholarly journals Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor

2020 ◽  
Vol 7 (4) ◽  
pp. 737
Author(s):  
Sitti Aliyah Azzahra ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Emotional Expressions ◽  
Tourist Attraction ◽  
Test Results ◽  
Tourist Attractions ◽  
Bayes Algorithm ◽  
Labeling Method ◽  
The City

<p class="Abstrak">Wisatawan seringkali mencari informasi tentang obyek wisata pada situs web seperti TripAdvisor. Situs web TripAdvisor memiliki fitur bagi penguna terdaftar untuk memberi ulasan tentang objek wisata dalam kategori kuliner dari berbagai negara. Ulasan tersebut bisa digunakan wisatawan sebagai pertimbangan sebelum mendatangi objek wisata kuliner yang ingin dituju. Komentar atau ulasan yang ada di situs TripAdvisor dapat dianalisis untuk mengetahui nilai sentimen dari suatu obyek wisata yang diulas. Hasil analisis itu dapat bermanfaat bagi pengelola tempat wisata, pengusaha kuliner maupun bagi wisatawan lain. Ada tantangan yang ditemukan saat analisis sentimen dilakukan pada kalimat ulasan yang mengandung ikon emosi atau <em>emoticon</em>, karena ulasan dapat mengandung arti sentimen yang berbeda antara kalimat dengan ekspresi emosi yang ada. Penelitian ini berisi analisis ulasan tentang kuliner kota Bandung pada situs TripAdvisor yang mengklasifikasi sentimen menjadi tiga kelas. Penelitian ini menggunakan teknik klasifikasi data mining dengan <em>algoritme Naïve Bayes</em> dikombinasi dengan metode pelabelan multi aspek yang disertai konversi ikon emosi pada teks ulasan. Selain itu, analisis dilakukan pada bobot ulasan berdasarkan jumlah kontribusi pemberi ulasan di web TripAdvisor. Hasil pengujian menunjukkan bahwa penggunaan seluruh kombinasi metode tersebut dalam proses klasifikasi sentimen mampu menghasilkan nilai akurasi sebesar 98,67%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Tourists often look for information about attractions on websites such as TripAdvisor. The TripAdvisor website has a feature for registered users to provide reviews about attractions in the culinary category from various countries. These reviews can be used by tourists as a consideration before visiting culinary attractions to be addressed. Comments or reviews on the TripAdvisor site can be analyzed to determine the sentiment value of a tourist attraction being reviewed. The results of the analysis can be useful for managers of tourist attractions, culinary entrepreneurs and for other tourists. There are challenges that are found when sentiment</em><em> </em><em>analysis is carried out on review sentences that contain emotion icons or emoticons, because reviews </em><em>may</em><em> contain different sentiment meanings between sentences and existing emotional expressions. This study contains a review of the culinary analysis of the city of Bandung on the TripAdvisor site which classifies sentiments into three classe</em><em>s</em><em>. This study uses data mining classification techniques with the Naïve Bayes algorithm combined with a multi-aspect labeling method accompanied by the conversion of emotional icons in the review text. In addition, the analysis is carried out on the weight of the review based on the number of contributing reviewers on the TripAdvisor web. The test results show that the use of all combinations of these methods in the sentiment classification process is able to produce an accuracy value of 98.67%.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Adatbányászati technikák alkalmazása magyar vállalkozások adatait tartalmazó adatbázison Microsoft Excel 2007-ben

2010 ◽  
Vol 5 (1-2) ◽  
pp. 229-233
Author(s):  
György Hampel ◽  
Zoltán Fabulya ◽  
Elemérné Nagy
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Other ◽  
Annual Income ◽  
Data Mining Technique ◽  
Microsoft Excel ◽  
Mining Technique ◽  
Bayes Algorithm ◽  
Main Activity

Using a simple data mining technique, the Analyze Key Influencers, in Excel 2007 Data Mining Add-ins, we searched for relationship among the seat (county and town), the form of business, the main activity, the number of employees and the annual income of the Hungarian companies. This technique uses the Naive Bayes algorithm. According to the used method the seat has no influencers. Most of the main activities have no influencers, but some activities (82 out of 495) have relationship with the other criteria, mainly with the form of business. The form of business (all 30 categories), the number of employees (17 of 18 categories) and the annual income (all 9 categories) are each others key influencers. Cramer's association was used to check the results of the data mining. The Cramer contin-gency coefficient showed similar results as the data mining, but the results also indicated that the strength of the association was less than moderate in all cases. The highest associa-tion were between the annual income and the number of employees (0.46, moderate asso-ciation), the main activity and form of business (0.36, moderate association) and the annual income and the form of business (0.27, low association).

scholarly journals Prediksi Angka Kelahiran Bayi Pada Desa Tridaya Sakti Dengan Menggunakan Algoritma Naive Bayes

2020 ◽  
Vol 1 (2) ◽  
pp. 77-88
Author(s):  
Nur Isnaini Parihah ◽  
Sari Hartini ◽  
Juarni Siregar
Keyword(s):  
Data Mining ◽  
Population Growth ◽  
Naive Bayes ◽  
Large Population ◽  
Naïve Bayes ◽  
Training Data ◽  
Birth Rates ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Infant Birth

The birth rate is something that can affect the increase in population growth. Large population is a burden for development. According to Malthus's Theory which states that a large population growth is not the welfare that is obtained but rather poverty will be encountered if the population is not well controlled. The number of baby births in Tridaya Sakti Village is increasing every year. Therefore Data Mining using the Naive Bayes algorithm can help in the calculation of predicting infant birth rates in Tridaya Sakti Village. Data Mining in predicting the number of infant birth rates aims to determine the number of infant birth rates for the coming year using the Naive Bayes algorithm. By looking at the prediction patterns of each variable and testing training data on testing data. It is hoped that the Naive Bayes algorithm can solve the problem in Tridaya Sakti Village in handling and overcoming the calculation of infant birth rates and can help the Tridaya Sakti Village in regulating population growth in the coming years. The results obtained from the data that have been taken and calculated by Data Mining using the Naive Bayes algorithm produce an information that can be used as a reference to find out the number of births. Performance and time in data processing are more effective and efficient as well as more accurate and accurate predictions of the number of baby births.   Keywords: Naive Bayes, Birth of a Baby, Prediction   Abstrak   Angka kelahiran merupakan suatu hal yang dapat mempengaruhi peningkatan pertumbuhan penduduk. Jumlah penduduk yang besar merupakan beban bagi pembangunan. Menurut Teori Malthus yang menyatakan bahwa pertumbuhan jumlah penduduk yang besar bukanlah kesejahteraan yang didapat tapi justru kemelaratan akan ditemui bilamana jumlah penduduk tidak dikendalikan dengan baik. Jumlah angka kelahiran bayi di Desa Tridaya Sakti setiap tahunnya semakin bertambah. Maka dari itu Data Mining dengan menggunakan algoritman Naive Bayes dapat membantu dalam perhitungan memprediksi angka kelahiran bayi di Desa Tridaya Sakti. Data Mining dalam memprediksi jumlah angka kelahiran bayi bertujuan untuk mengetahui jumlah angka kelahiran bayi tahun yang akan mendatang mengunakan algoritma Naive Bayes. Dengan melihat pola prediksi dari setiap variabel dan melakukan pengujian data training terhadap data testing. Diharapkan algoritma Naive Bayes ini dapat menyelesaikan permasalahan di Desa Tridaya Sakti dalam menangani dan mengatasi perhitungan angka kelahiran bayi dan dapat membantu pihak Desa Tridaya Sakti dalam mengatur pertumbuhan jumlah penduduk tahun yang akan mendatang. Hasil yang diperoleh dari data yang sudah diambil dan dihitung dengan Data Mining mengunakan algoritam Naive Bayes menghasilkan sebuah informasi yang dapat digunakan sebagai acuan untuk mengetahui jumlah angka kelahiran bayi. Kinerja dan waktu dalam proses pengolahan data lebih efektif dan efesien serta dari prediksi jumlah kelahiran bayi lebih tepat dan akurat. Kata Kunci: Naive Bayes, Kelahiran Bayi, Prediks  

scholarly journals PENERAPAN DATA MINING TERHADAP DATA COVID-19 MENGGUNAKAN ALGORITMA KLASIFIKASI

2021 ◽  
Vol 21 (1) ◽  
pp. 44-52
Author(s):  
Rizka Dahlia ◽  
Nanik Wuryani ◽  
Sri Hadianti ◽  
Windu Gata ◽  
Arina Selawati
Keyword(s):  
Data Mining ◽  
South Korea ◽  
Respiratory System ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
The World ◽  
Bayes Algorithm

Coronavirus 2019 or more commonly referred to as COVID-19 is a type of virus that attacks the respiratory system. Until now the number of spread and the number of deaths caused by this virus continues to increase. As of April 21, 2020, based on data from the WHO, the total number of cases infected with this virus reached 2,397,217 with 162 deaths from all over the world. For South Korea itself, as of March 21, 2020, the total number of infected cases was 10,683 with a total of 237 deaths. In this study, researchers conducted data processing on the spread of COVID-19 in South Korea with Rapidminer using a classification algorithm, namely Naïve Bayes, C4.5, and K-Nearest Neighbor by performing the stages of selection, preprocessing, transfotmating, data mining and interpretation or evaluating the quality of the best accuracy of 80.79% with AUC of 0.881 achieved by the Naïve Bayes algorithm. The distribution of the data found that the influential attribute of the isolated class factor from the patient contained in the sex attribute where more women experienced isolation. Keywords— COVID-19, data mining, classification, C4.5, Naïve Bayes, K-NN

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