scholarly journals PENGGUNAAN METODE PROFILE MATCHING DAN NAÏVE BAYES UNTUK MENENTUKAN STARTING ELEVEN PADA SEPAK BOLA

2020 ◽  
Vol 14 (2) ◽  
pp. 59-68
Author(s):  
Fabio Fahri Pratama ◽  
Youllia Indrawaty Nurhasanah
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Original Position ◽  
Identification Process ◽  
Ideal Position ◽  
Profile Matching ◽  
Player Selection ◽  
Selection Of ◽  
The Ideal

Abstrak - Pemilihan pemain starting eleven atau kesebelasan dan formasi tim dengan komposisi pemain yang tepat dalam olahraga sepak bola merupakan hal yang penting untuk meningkatkan performa permainan sebuah tim. Pelatih terkadang memilih pemain starting eleven tidak secara objektif, dikarenakan dibutuhkan keahlian dan kejelian dalam menilai kemampuan seseorang. Guna memudahkan pemilihan pemain dalam starting eleven maka dibangun sistem untuk membantu pelatih memilih posisi I  deal bagi pemain dan memilih pemain secara objektif agar meningkatkan kualitas pemilihan pemain, baik dari penempatan posisi ideal pemain maupun pemilihan pemain sebagai starting. Sistem ini akan menerima input berupa nilai atribut kemampuan dan kondisi pemain yang akan diproses untuk menghasilkan output berupa rekomendasi pemain untuk dijadikan starting eleven. Dalam proses menentukan pemain, nilai atribut kemampuan pemain dilakukan proses Profile Matching (PM) untuk menentukan posisi ideal bagi pemain, dari tiap kelompok posisi dilakukan proses identifikasi menggunakan Naïve Bayes (NB) untuk menentukan pemain yang cocok untuk dijadikan starting eleven. Pengujian rekomendasi posisi dilakukan dengan membandingkan posisi asli pemain dengan posisi hasil rekomendasi dengan hasil akurasi sebesar 65%, sedangkan pengujian pemilihan starting eleven dilakukan menggunakan game Football Manager dengan melakukan pertandingan dengan pemilihan pemain secara default dan pemilihan pemain hasil rekomendasi masing-masing sebanyak sepuluh kali melawan tim dengan komposisi pemain yang sama, hasil dari pertandingan tersebut dihitung selisih (%) dari rata-rata rating pemain. Hasil yang diberikan setelah digunakan perekomendasian pemilihan pemain kenaikan rata-rata rating tim hanya naik sebesar 0.98%. Abstract - The selection of starting eleven players and team formations with the correct composition of players in soccer is important to improve the performance of a team. Coaches sometimes choose not starting players objectively, because it takes expertise and foresight in assessing one's abilities. In order to facilitate the selection of players in the starting eleven, a system was built to help the coach choose the ideal position for the players and choose players objectively to improve the quality of player selection, both from placing the player's ideal position and selecting players as starting. This system will receive input in the form of the ability and condition attribute values ​​of the player which will be processed to produce output in the form of a player's recommendation to become the starting eleven. In the process of determining the players, the value of the attributes of the player's ability is carried out the Profile Matching (PM) process to determine the ideal position for the players, from each group of positions the identification process is done using Naïve Bayes (NB) to determine the suitable players to be the starting eleven. Position recommendation testing is done by comparing the original position of the player with the position of the recommended results with an accuracy of 65%, while testing the selection of the starting eleven is carried out using the game Football Manager by playing matches by selecting players by default and selecting the results of the recommendation players ten times each against the team with the same player composition, the result of the match is calculated as a difference (%) from the average player rating. The results given after using the player selection recommendation increase the team's average rating to only increase by 0.98%.

Case Based Reasoning And Naive Bayes Implementation In Laptop Purchasing Recommender System

2021 ◽  
Vol 9 (3) ◽  
pp. 411
Author(s):  
I Gusti Ngurah Agung Dharmawangsa ◽  
I Wayan Supriana
Keyword(s):  
Recommender System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
The Right ◽  
Case Based ◽  
Selection Of ◽  
The Ideal

Purchasing a new laptop will be difficult if we do not know what the ideal laptop specification for our needs. Especially with a wide selection of laptops. From this problem, system that can give a recommendation to choose the right laptop based on purchaser’s specification choice is needed. This research using two method, Case Based Reasoning and Naive Bayes. The concept of Case Based Reasoning is the process of solving new problems based on the solutions of similar past problems, While Naive Bayes assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive bayes will be implemented in retrive process of case based reasoning. The recommender system utilizing 7 feature, Kecepatan Processor, Kapasitas Ram, Tipe Grafis, Ukuran Layar, Ukuran Harddisk, Kecepatan Layar, and Harga. The percentage of respondents who said the system was successful in providing the right recommendations was 70 percent of the total respondents.

scholarly journals Penerapan Metode Naive Bayes Dalam Pemilihan Kualitas Jenis Rumput Taman CV. Rumput Kita Landscape

2018 ◽  
Vol 9 (2) ◽  
pp. 162-171
Author(s):  
Sri Rahayu ◽  
Anita Sindar RMS
Keyword(s):  
Decision Making ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Air Humidity ◽  
Bayes Method ◽  
Market Prices ◽  
Decision Making System ◽  
Quality Ranking ◽  
Selection Of

Penataan taman yang menarik, sejuk dan indah memerlukan budget yang tinggi.  Dari beragam jenis rumput, umumnya Rumput Mini ditanam untuk mempercantik rumah atau bangunan. Para pengelola jasa taman menentukan kualitas rumput dari pengalaman sehari-hari. Ini menunjukkan belum adanya pemanfaatan sistem komputer dalam pemilihan jenis rumput taman yang berkualitas, menyebabkan terjadi kesalahan dalam menentukan kualitas rumput terbaik. Dalam permasalahan ini metode Naïve Bayes digunakan sebagai Sistem Pengambil Keputusan (SPK). Naïve bayes merupakan metode pengklasifikasian ada tidaknya ciri tertentu dari sebuah kelas. Empat kriteria pemilihan kualitas jenis rumput taman yaitu suhu udara, curah hujan, kelembapan udara dan harga pasar. Hasil perangkingan dari R1, R2, R3, R4, R5, R6, R7 menunjukkan R6: Rumput Golf= 0.4705882353;  R7: Rumput Swiss= 0.4705882353 merupakan rumput yang memiliki Kualitas Baik.   Kata Kunci: Pemilihan Rumput, Kualitas, Ranking, Naïve Bayes   Abstract An attractive, cool and beautiful garden arrangement requires a high budget. Of the various types of grass, generally Mini Grass is planted to beautify your home or building. The managers of garden services determine the quality of grass from everyday experience. This shows that there is no use of computer systems in the selection of quality garden grass types, causing errors in determining the best quality of grass. In this problem the Naïve Bayes method is used as a Decision Making System (SPK). Naïve Bayes is a method of classifying the presence or absence of certain characteristics of a class. Four criteria for selecting the quality of garden grass types are air temperature, rainfall, air humidity and market prices. The ranking results of R1, R2, R3, R4, R5, R6, R7 indicate R6: Golf Grass = 0.4705882353; R7: Swiss grass = 0.4705882353 is a grass that has good quality.    Keywords: Selection Of Grass, Quality, Ranking, Naïve Bayes

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

scholarly journals Sistem Pendukung Keputusan Pemilihan Tenaga Kerja Indonesia (TKI) Berdasarkan Kualitas Untuk Menentukan Tempat Kerja Menggunakan Metode Profile Matching (Studi Kasus: PT Adila Prezkifarindo Duta)

2020 ◽  
Vol 1 (2) ◽  
pp. 142
Author(s):  
Dasarius Gulo
Keyword(s):  
Decision Making ◽  
Migrant Workers ◽  
Selection Process ◽  
Matching Method ◽  
Matching Process ◽  
Profile Matching ◽  
Long Time ◽  
Support Decision Making ◽  
Selection Of ◽  
The Ideal

In the process of selecting Indonesian Workers (TKI) based on quality at PT. Adila Prezkifarindo Duta is classified as still manual, where there is not yet a system for selecting quality migrant workers so it requires a long time for its assessment and the selection process is less effective. To support decision making in the selection of qualified Indonesian Workers (TKI) to make it easier by using a decision support system. One method used in the selection of qualified Indonesian Workers is the Profile Matching method. The profile matching method is a decision-making mechanism by assuming that there is an ideal level of predictor variables that must be met by applicants, rather than the minimum level that must be met or passed. In the profile matching process a process will be compared between individual competencies into standard competencies so that different competencies can be identified (also called Gap). The smaller the gap produced, the greater the weight value. In matching this profile, the selected TKI candidates are Indonesian Workers who are closest to the ideal profile of a qualified TKI.

scholarly journals Analisis Metode K-Nearest Neighbors (K-NN) Dan Naive Bayes Dalam Memprediksi Kelulusan Mahasiswa

2021 ◽  
Vol 3 (2) ◽  
pp. 107-113
Author(s):  
Kartarina Kartarina ◽  
Ni Ketut Sriwinarti ◽  
Ni luh Putu Juniarti
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Entry And Exit ◽  
Accuracy Rate ◽  
Bayes Method ◽  
K Nearest Neighbors ◽  
Bayes Methods ◽  
Accuracy Test ◽  
Student Graduation ◽  
Selection Of

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

Naive Bayes Classification Algorithm Based on Optimized Training Data

2012 ◽  
Vol 490-495 ◽  
pp. 460-464 ◽  
Author(s):  
Xiao Dan Zhu ◽  
Jin Song Su ◽  
Qing Feng Wu ◽  
Huai Lin Dong
Keyword(s):  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Training Data ◽  
Large Scale Data ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Effectiveness Threshold ◽  
Selection Of

Naive Bayes classification algorithm is an effective simple classification algorithm. Most researches in traditional Naive Bayes classification focus on the improvement of the classification algorithm, ignoring the selection of training data which has a great effect on the performance of classifier. And so a method is proposed to optimize the selection of training data in this paper. Adopting this method, the noisy instances in training data are eliminated by user-defined effectiveness threshold, improving the performance of classifier. Experimental results on large-scale data show that our approach significantly outperforms the baseline classifier.

scholarly journals Word sense disambiguation to improve precision for ambiguous queries

2012 ◽  
Vol 2 (4) ◽  
Author(s):  
Adrian-Gabriel Chifu ◽  
Radu-Tudor Ionescu
Keyword(s):  
Information Retrieval ◽  
Ad Hoc ◽  
Naive Bayes ◽  
Word Sense Disambiguation ◽  
Naïve Bayes ◽  
Word Sense ◽  
Interdisciplinary Approaches ◽  
Sense Disambiguation ◽  
Ranking Technique

AbstractSuccess in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.

scholarly journals Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease

2019 ◽  
Vol 2 (2) ◽  
pp. 56-60
Author(s):  
Irfan Santiko ◽  
Ikhsan Honggo
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Progressive Decline ◽  
Bayes Algorithm ◽  
Test Algorithm ◽  
Selection Testing ◽  
Selection Of

Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.

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