scholarly journals DETEKSI PENYALAHGUNAAN NARKOBA DENGAN METODE TWIN BOUNDED SVM

2021 ◽  
Vol 15 (4) ◽  
pp. 753-760
Author(s):  
Berny Pebo Tomasouw ◽  
Yopi Andry Lesnussa
Keyword(s):  
Drug Use ◽  
Test Results ◽  
Classification Problems ◽  
Accuracy Rate ◽  
Testing Data ◽  
Rbf Kernel

Twin Bounded SVM (TB-SVM) is an improvement of the Twin SVM method and has advantages in classification problems compared to standard SVM. In this research, linear TB-SVM and nonlinear TB-SVM methods will be applied to detect drug use based on 23 symptoms experienced. The training and testing data is divided into three partition data schemes (60/40 scheme, 70/30 scheme and 80/20 scheme) in order to determine the best level of accuracy that can be obtained. The test results show that the nonlinear TB-SVM with the RBF kernel has a better accuracy rate than the linear TB-SVM, that is 80% at 60/40 scheme, 90% at 70/30 scheme, and 95% at 80/20 scheme.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Prediction of Rotor Burst Using Strain-Based Fracture Criteria to Comply With the Engine Airworthiness Regulation

2020 ◽  
Author(s):  
Huihui Li ◽  
Kaiming Wang ◽  
Chuncheng Zhang ◽  
Weiguo Wang ◽  
Guoguang Chen
Keyword(s):  
Test Data ◽  
Aircraft Engine ◽  
True Stress ◽  
Average Stress ◽  
Civil Aviation ◽  
Test Results ◽  
Fracture Criteria ◽  
Testing Data ◽  
Physical Essence ◽  
Dimensional Growth

Abstract Relative to the rotor overspeed compliance governed by civil aviation airworthiness regulation, nowadays Area-Average Stress method is commonly used approach. However, in order to effectively apply the Area-Average Stress method in analyzing burst speed, large amount of testing data is needed to define an important element of this method: a correction factor. This prerequisite hinders the use of this method for many companies which have limited test data. Meanwhile, analysis of rotor burst speed based on Strain-based Fracture Criteria using true stress-strain curves and burst tests has been done on the LPT rotor, and a work procedure obtaining the most critical burst speed for certification is proposed. The analysis results, which had a good correlation with test results, showed that Strain-based Fracture Criteria can accurately predict the burst speed considering the most adverse combination of dimensional tolerances, temperature, and material properties, and rotor dimensional growth under the overspeed condition. Both are required by the aircraft engine airworthiness overspeed regulation. Compared to the Area-Average Stress method, Strain-based Fracture Criteria reflects the physical essence of the rotor burst more realistically and can be simply verified without requiring too much test data, therefore it has a good application prospect in the aircraft engine airworthiness.

scholarly journals Jaringan Saraf Tiruan Untuk Memprediksi Tingkat Pemahaman Sisiwa Terhadap Matapelajaran Dengan Menggunakan Algoritma Backpropagation

2017 ◽  
Vol 1 (1) ◽  
pp. 24 ◽  
Author(s):  
Solikhun Solikhun ◽  
M. Safii ◽  
Agus Trisno
Keyword(s):  
Prediction Accuracy ◽  
Teaching And Learning ◽  
Learning Algorithm ◽  
Learning Activities ◽  
Guidance And Counseling ◽  
Test Results ◽  
Accuracy Rate ◽  
Several Variables ◽  
The Subject ◽  
Backpropagation Learning

Prediction of students 'understanding of the subject is important to know the extent to which the students' understanding of the subjects presented by educators when teaching and learning activities and to determine the ability of educators in delivering subjects. Artificial Neural Network to predict the level of students' understanding of subjects using backpropagation learning algorithm uses several variables: Knowledge, skills / abilities, assessment and workload and guidance and counseling. Backpropagation learning algorithm is applied to train eight indicators to predict the level of students' understanding of the subjects. The test results obtained by the student's understanding level prediction accuracy rate of 90% with a 6-5-1 architecture.

scholarly journals Klasifikasi Kakao Berbasis e-nose dengan Metode Neuro Fuzzy

2018 ◽  
Vol 8 (1) ◽  
pp. 49
Author(s):  
Ikhsan Nur Rahman ◽  
Danang Lelono ◽  
Kuwat Triyana
Keyword(s):  
Feature Extraction ◽  
Electronic Nose ◽  
Extraction Process ◽  
Differential Method ◽  
Test Results ◽  
Optimal Parameters ◽  
Accuracy Rate ◽  
Gas Sensor Array ◽  
Neuro Fuzzy

During this time to clasify quality of cacao based on color and aroma involving human taster. But this cacao tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of cacao is determined by volatile compounds such aldehid and alcohol. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the neuro fuzzy training to obtain optimal parameters. The parameters have been optimized is then tested on cacao. Based on test results, neuro fuzzy can clasify samples with 95,21% accuracy rate so that the clasification of cacao quality with electronic nose using neuro fuzzy has been successfully carried out.

scholarly journals Analysis of Personality Characteristic Using the Naïve Bayess Classifier Algorithm (Case Study Official Twitter of Basuki Tjahaja Purnama's and Anies Baswedan)

2019 ◽  
Vol 7 (2) ◽  
pp. 14
Author(s):  
Ireicca Agustiorini Harsehanto ◽  
M. Didik R. Wahyudi
Keyword(s):  
Social Media ◽  
Big Five ◽  
Personality Characteristic ◽  
Training Data ◽  
Big Five Personality ◽  
Personality Theory ◽  
Test Results ◽  
Bayes Classifier ◽  
Testing Data

Abstract - This research uses data from social media Twitter based on the results of tweets from user_timeline @basuki_btp and @aniesbaswedan. This study uses 2100 tweet data. Data that has been collected is then pre-processed first and labeled manually. The next process is classification using the Naïve Bayess Classifier Algorithm using the Big Five Personality Theory. Based on the test results using 500 tweet data as training data and 1600 tweet data as testing data. The classification results obtained by using the Naïve Bayes Classifier Method and grouped in the "Big Five" personality groups: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism on tweet data in Indonesian.

scholarly journals Helmet Monitoring System using Hough Circle and HOG based on KNN

2021 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Rachmad Jibril Al Kautsar ◽  
Fitri Utaminingrum ◽  
Agung Setia Budi
Keyword(s):  
Police Officers ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Histogram Of Oriented Gradient ◽  
Surveillance Camera ◽  
Testing Data ◽  
Motorized Vehicles ◽  
Traffic Operation

 Indonesian citizens who use motorized vehicles are increasing every year. Every motorcyclist in Indonesia must wear a helmet when riding a motorcycle. Even though there are rules that require motorbike riders to wear helmets, there are still many motorists who disobey the rules. To overcome this, police officers have carried out various operations (such as traffic operation, warning, etc.). This is not effective because of the number of police officers available, and the probability of police officers make a mistake when detecting violations that might be caused due to fatigue. This study asks the system to detect motorcyclists who do not wear helmets through a surveillance camera. Referring to this reason, the Circular Hough Transform (CHT), Histogram of Oriented Gradient (HOG), and K-Nearest Neighbor (KNN) are used. Testing was done by using images taken from surveillance cameras divided into 200 training data and 40 testing data obtained an accuracy rate of 82.5%.

scholarly journals C5.0 Algorithm Implementation on Web-Based Software and Usability Evaluation

SISTEMASI ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 394
Author(s):  
Deny Jollyta
Keyword(s):  
Data Mining ◽  
Decision Makers ◽  
Classification Algorithms ◽  
Test Results ◽  
Classification Problems ◽  
Process Data ◽  
Web Based ◽  
Classification Technique ◽  
Software Classification ◽  
Algorithm Implementation

AbstrakPerangkat lunak merupakan alat bantu yang memudahkan pengguna dalam pengolahan data dengan cepat dan tepat. Para pengambil keputusan membutuhkan alternatif perangkat lunak yang dapat digunakan setiap saat dengan teknik klasifikasi data algoritma C5.0 sesuai kriteria yang diinginkan. Namun perangkat lunak yang ada umumnya terdiri dari sejumlah teknik dan belum dapat digunakan secara online. Sebagai salah satu algoritma klasifikasi yang popular dalam ilmu data mining, C5.0 dapat memberikan hasil yang lebih baik. Penelitian bertujuan untuk membangun perangkat lunak yang dapat melakukan klasifikasi data menggunakan algoritma C5.0 berbasis web. Perangkat lunak dapat digunakan oleh siapa saja, terutama para pengambil keputusan. Penelitian ini juga dilengkapi dengan pengujian perangkat lunak usability sebelum digunakan. Hasil pengujian memperlihatkan bahwa perangkat lunak yang dibangun dapat diterima dengan nilai usability 76,892% dan berada pada predikat Baik. Diharapkan melalui penelitian ini, dapat memberikan alternatif perangkat lunak yang mampu menyelesaikan masalah klasifikasi menggunakan algoritma C5.0.Kata kunci: perangkat lunak, klasifikasi, algoritma c5.0, usability AbstractSoftware is a tool that makes it easy for users to process data quickly and precisely. Decision makers need an alternative software that can be used at any time with the C5.0 algorithm data classification technique according to the desired criteria. However, the existing software generally consists of a number of techniques and cannot be used online. As one of the popular classification algorithms in data mining science, C5.0 can provide better results. This study aims to build software that can classify data using the web-based C5.0 algorithm. Software can be used by anyone, especially decision makers. This research is also complemented by testing Usability software before used. The test results showed that the software built can be accepted with a Usability value of 76.892% and is in the Good predicate. It is hoped that through this research, it can provide alternative software that is able to solve classification problems using the C5.0 algorithm.Keywords: software, classification, c5.0 algorithm, usability

scholarly journals PENILAIAN ESAI JAWABAN BAHASA INDONESIA MENGGUNAKAN METODE SVM - LSA DENGAN FITUR GENERIK

2012 ◽  
Vol 5 (1) ◽  
pp. 33 ◽  
Author(s):  
Rama Adhitia ◽  
Ayu Purwarianti
Keyword(s):  
Support Vector Machine ◽  
Assessment Process ◽  
Support Vector ◽  
Test Results ◽  
Accuracy Rate ◽  
Wrong Answer ◽  
Index Term ◽  
Automatic Text Classification ◽  
Index Terms ◽  
Automatic Text

Paper ini mengkaji sebuah solusi untuk permasalahan penilaian jawaban esai secara otomatis dengan menggabungkan support vector machine (SVM) sebagai teknik klasifikasi teks otomatis dengan LSA sebagai usaha untuk menangani sinonim dan polisemi antar index term. Berbeda dengan sistem penilaian esai yang biasa yakni fitur yang digunakan berupa index term, fitur yang digunakan proses penilaian jawaban esai adalah berupa fitur generic yang memungkinkan pengujian model penilaian esai untuk berbagai pertanyaan yang berbeda. Dengan menggunakan fitur generic ini, seseorang tidak perlu melakukan pelatihan ulang jika orang tersebut akan melakukan penilaian esai jawaban untuk beberapa pertanyaan. Fitur yang dimaksud meliputi persentase kemunculan kata kunci, similarity jawaban esai dengan jawaban referensi, persentase kemunculan gagasan kunci, persentase kemunculan gagasan salah, serta persentase kemunculan sinonim kata kunci. Hasil pengujian juga memperlihatkan bahwa metode yang diusulkan mempunyai tingkat akurasi penilaian yang lebih tinggi jika dibandingkan dengan metode lain seperti SVM atau LSA menggunakan index term sebagai fitur pembelajaran mesin. This paper examines a solution for problems of assessment an essay answers automatically by combining support vector machine (SVM) as automatic text classification techniques and LSA as an attempt to deal with synonyms and the polysemy between index terms. Unlike the usual essay scoring system that used index terms features, the feature used for the essay answers assessment process is a generic feature which allows testing of valuation models essays for a variety of different questions. By using these generic features, one does not need to re training if the person will conduct an assessment essay answers to some questions. The features include percentage of keywords, similarity essay answers with the answer reference, percentage of key ideas, percentage of wrong answer, and percentage of keyword synonyms. The test results also show that the proposed method has a higher valuation accuracy rate compared to other methods such as SVM or LSA, use term index as features in machine learning.

scholarly journals Prediksi Pajak Mineral Non Logam dan Batuan dengan Metode Monte Carlo

2019 ◽  
pp. 32-37
Author(s):  
Julius Santony
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regional Government ◽  
Test Results ◽  
Accuracy Rate ◽  
Work Plan ◽  
Tax Revenues ◽  
Average Accuracy ◽  
District Government

Regional government in Indonesia annually sets a target for tax revenues of non-metallic minerals and rocks. Setting targets is very important as a guideline in preparing the current year's budget work plan. So far, the target of non-metal mineral and rock tax revenues has been prepared based on a joint agreement between the regional government and the regional legislature. The prediction of non-metal mineral and rock tax revenues using Monte Carlo simulation can be a solution to predict the next few years. This prediction uses data between 2009 - 2018 taken from the tax and retribution management body one of the districts in Indonesia. Testing the results of predictions is done by comparing the results of predictions with data from 2016 - 2018. The test results show that the average accuracy rate reaches 82.05%. So this study greatly helped the district government in setting the target for the acceptance of non-metal minerals and rock taxes.

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