scholarly journals Identifikasi Kerusakan Mobil Menggunakan Sistem Pakar Berbasis Metode Forward Chaining Pada Global Motor Gorontalo

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Siti Andini Utiarahman
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Programming Language ◽  
Existing Problems ◽  
Is Research ◽  
Flow Diagrams ◽  
Using Data ◽  
Data Design ◽  
Descriptive Method ◽  
Existing Data

Checking and diagnoses of car damage done manually cause the old car working time so that customer satisfaction decreases. To save time from technicians, an application that can help the technician to diagnose damage to his car is required. For that, it can be applied to expert system applications. The expert system as a program enabled to mimic human experts should be able to do things that an expert can do. This app is designed to do diagnose damage to Daihatsu cars. The method used is a descriptive method, which is research that seeks to solve existing problems systematically based on data-existing data, design by using data flow diagrams (DFD), interface form designing Users of the proposed system using the PHP programming language, the database uses Mysql. The result of this research is the expert system used can to provide useful information in assisting in decision making to diagnose car damage.

scholarly journals Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6063
Author(s):  
Francisco Javier Nieto ◽  
Unai Aguilera ◽  
Diego López-de-Ipiña
Keyword(s):  
Decision Making ◽  
Data Cleaning ◽  
Sensor Data ◽  
Statistical Distributions ◽  
Decision Making Processes ◽  
New Type ◽  
Measurement Units ◽  
Using Data ◽  
Different Sources ◽  
Existing Data

Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data). This work analyzes several aspects of the data generated by different sensor types to understand particularities in the data, linking them with existing data mining methodologies. Using data from different sources, this work analyzes how the type of sensor used and its measurement units have an important impact in basic statistics such as variance and mean, because of the statistical distributions of the datasets. The work also analyzes the behavior of outliers, how to detect them, and how they affect the equivalence of sensors, as equivalence is used in many solutions for identifying anomalies. Based on the previous results, the article presents guidance on how to deal with data coming from sensors, in order to understand the characteristics of sensor datasets, and proposes a parallelized implementation. Finally, the article shows that the proposed decision-making processes work well with a new type of sensor and that parallelizing with several cores enables calculations to be executed up to four times faster.

scholarly journals Expert System for Early Diagnosis of Tropical Malaria and Tertiana Malaria using Certainty Factor

2021 ◽  
Vol 2 (4) ◽  
pp. 135-140
Author(s):  
Patmawati Hasan ◽  
Elvis Pawan
Keyword(s):  
Expert System ◽  
Early Diagnosis ◽  
Research Subjects ◽  
Certainty Factor ◽  
Factor Method ◽  
Genus Plasmodium ◽  
Flow Diagrams ◽  
Mode Of Transmission ◽  
Using Data

Twano Health Center is one of the technical implementing units of the Jayapura City Health Office which organizes Health Efforts, but the constraints regarding the facilities and infrastructure of the Puskesmas are not yet adequate in supporting health services. Based on observations, the increase in the level of malaria sufferers in the Jayapura area is caused by parasites (protozoa) of the genus Plasmodium and the mode of transmission is through the bite of a female Anopheles mosquito. There are two types of malaria that are often experienced by Jayapura residents, namely Tropical Malaria (Plasmodium falciparum) and Tertiana (Plasmodium vivax). The purpose of this study is to create an expert system that can diagnose early diseases such as an expert or doctor using the Certainty Factor method which expresses belief in an event (fact or hypothesis) based on evidence or expert judgment in early diagnosis of Tropical Malaria and Tertiana. The research subjects taken were 5 patients who had symptoms of Malaria and 1 doctor to determine the symptoms of the disease) The expert system using the Certainty Factor method was used because this method was suitable in determining the disease, and the result was a percentage which was the level of accuracy in determining the patient's disease. Determination of the percentage is influenced by the MB value (a measure of the increase in confidence) and the MD value (a measure of the increase in distrust) obtained from the assessment of an expert. For data modeling using data flow diagrams (DFD) and website-based. Total accurate patient recapitulation results are 80% of the Expert System for Early Diagnosis of Tropical Malaria and Tertiana using Certainty Factor

scholarly journals Analisis Rekomendasi Calon Debitur Motor pada PT.XYZ menggunakan Algortima C 4.5

2020 ◽  
Vol 7 (4) ◽  
pp. 673
Author(s):  
Lilis Nurellisa ◽  
Devi Fitrianah
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Supervised Learning ◽  
Data Driven ◽  
Classification Method ◽  
Service Company ◽  
C4.5 Algorithm ◽  
Application Data ◽  
Using Data ◽  
Existing Data

<p class="Abstrak">PT.XYZ merupakan perusahaan jasa pembiayaan atau <em>leasing</em> dengan berkonsentrasi kepada pembiayaan sepeda motor. Dalam bisnisnya PT.XYZ sering sekali dihadapkan oleh masalah kredit macet atau bahkan penipuan. Hal ini dikarenakan kesalahan dalam pemberian kredit kepada calon debitur yang tidak potensial. Jika tidak ditangani hal ini tentu saja akan berdampak buruk bagi perusahaan. Perusahaan mengalami penurunan kemampuan dalam membayar angsuran pinjaman ke perbankan bahkan dapat berdampak pada kebangkrutan. Dalam hal ini PT.XYZ perlu melalukan analisis untuk menentukan calon debitur yang potensial dengan menggunakan data driven method atau pendekatan berbasis kepada data. Yaitu pengambilan keputusan dengan melihat data pengajuan kredit yang pernah ada sebelumnya yang disebut juga sebagai <em>supervised learning</em>. Algoritma yang digunakan adalah algoritma C4.5 karena algoritma ini dapat mengklasifikasi data yang sudah ada sebelumnya. Dengan algoritma ini akan dihasilkan sebuah pohon keputusan yang akan membantu PT.XYZ dalam pengambilan keputusan. Dengan pengujian menggunakan 3587 sampel data pengajuan kredit dalam kurun waktu 1 tahun akurasi yang didapatkan ialah 97,96%. Dengan begitu hal ini menunjukkan bahwa metode klasifikasi menggunakan algoritma C4.4 berhasil diimplementasikan dengan baik. Hal ini diharapkan dapat membantu PT.XYZ dalam merekomendasikan calon debitur yang potensial.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p><em>PT. XYZ is a finance or leasing service company by concentrating on motorcycle financing. In its business, PT. XYZ is often faced with problems of bad credit or even fraud. This is due to an error in giving credit to potential debtors. If it is not handled this, of course, will have a bad impact on the company. Companies experiencing a decline in the ability to repay loan installments to banks can even have an impact on bankruptcy. In this case, PT. XYZ needs to do an analysis to determine potential debtors using data-driven methods or data-based approaches. That is decision making by looking at credit application data that has never been before, which is also called supervised learning. The algorithm used is the C4.5 algorithm because this algorithm can classify pre-existing data. With this algorithm, a decision tree will be produced that will help PT. XYZ in decision making. By testing using 3587 samples of credit filing data within a period of 1 year the accuracy obtained was 97.96%. That way this shows that the classification method using the C4.4 algorithm is successfully implemented properly. This is expected to help PT. XYZ in recommending potential debtors.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Diagnosis of Types of Diseases in Cassava Plant by Bayes Method

2020 ◽  
Vol 4 (2) ◽  
pp. 69
Author(s):  
Agung Purnomo Sidik
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Programming Language ◽  
Research Data ◽  
Plant Diseases ◽  
Bayes Method ◽  
Cassava Plant ◽  
Bayes Algorithm ◽  
Existing Data ◽  
The Web

This research was conducted to implement the Bayes algorithm in an expert system to diagnose types of diseases in cassava plants. The research data was taken from the Binjai City Agriculture and Fisheries Office in 2018. The expert system was built based on the web, where the application was built using the PHP programming language and MySQL DBMS. The results showed that the Bayes algorithm can be used in expert system applications to diagnose types of cassava plant diseases. In the Bayes algorithm, the knowledge base is taken from the data of the amount of data from cassava plants that suffer from disease, so the results of diagnosing cassava plants are based on existing data. Therefore, the more patient data that is used as a knowledge base, the better the diagnosis results are given.

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%.

STRATEGI MAKSIMALISASI ANGGARAN BELANJA PADA BALAI PENELITIAN DAN PENGEMBANGAN LINGKUNGAN HIDUP DAN KEHUTANAN MANOKWARI

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Arif Hasan ◽  
Dedi Budiman Hakim ◽  
Irdika Mansur
Keyword(s):  
Decision Making ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Primary Data ◽  
Fiscal Year ◽  
The Third ◽  
Swot Matrix ◽  
Three Stages ◽  
Using Data

This study aims to analyze causes of the low uptake of the budget and formulate a strategy of maximizing the absorption of expenditure on Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari. Respondents involved are 20 people that consist of: treasury officials and holder output of activity. The data used were secondary data in the form of reports on budget realization (LRA) quarter I, II, III and IV of the fiscal year 2011 to 2015, and the primary data were in the form of interviews with the help of a questionnaire. While the analysis of the data used was descriptive analysis using data tabulation, and the analysis of the three stages strategy of the decision making used IFE and EFE matrix, SWOT matrix and QSPM matrix.The results showed that there are 19 factors causing low of budget absorption until the end of the third quarter, and there were 10 drafts of policy as a strategy for maximizing the absorption of the budget on Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari.ABSTRAKPenelitian ini bertujuan untuk menganalisis penyebab rendahnya penyerapan anggaran belanja dan merumuskan strategi maksimalisasi penyerapan anggaran belanja pada Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari. Responden yang terlibat adalah 20 orang yaitu pejabat perbendaharaan dan pemegang output kegiatan. Data yang digunakan adalah data sekunder berupa laporan realisasi anggaran (LRA) triwulan I, II, III dan IV tahun anggaran 2011 sampai 2015, dan data primer berupa wawancara dengan bantuan kuesioner. Sedangkan analisis data yang digunakan adalah analisis deskriptif menggunakan analisis tabulasi, dan analisis analisis strategi tiga tahap pengambilan keputusan menggunakan matriks IFE dan EFE, matriks SWOT dan matriks QSPM. Hasil penelitian menunjukkan bahwa terdapat 19 faktor penyebab rendahnya penyerapan anggaran belanja sampai akhir triwulan III, dan terdapat 10 rancangan kebijakan sebagai strategi maksimalisasi penyerapan anggaran belanja di Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari.

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