Yuhandri
Universitas Putra Indonesia YPTK Padang

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Satisfaction Level of BPJS Kesehatan Participants Using the C4.5 Algorithm Irvan Okta Mazhona; Yuhandri
SYSTEMATICS Vol 2 No 3 (2020): December 2020
Publisher : Universitas Singaperbangsa Karawang

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Abstract

Patient satisfaction is an important thing in a hospital service. The level of patient satisfaction can be a reference for improving service to patients. Satisfaction is the level of feelings that arise as a result of the performance of the service received after comparing it with what is expected. This study aims to measure the level of satisfaction of inpatient BPJS Kesehatan participants with the services provided by the Special Hospital for Mother and Children (RSKIA) Annisa Payakumbuh in terms of five attributes, namely Tangibles (Real Form), Reliability, Assurance, Responsiveness (Responsiveness) and Empathy (Empathy). To measure the level of patient satisfaction at RSKIA Annisa Payakumbuh used data mining method of Classification Algorithm C4.5 which is one of the most effective Decision tree algorithms for classification. The data were obtained from the summary of the results of the BPJS Kesehatan inpatient patient satisfaction survey at RSKIA Annisa Payakumbuh. Furthermore, the data will be processed using the C4.5 algorithm which will produce rules and Decision trees. The results of data processing using the C4.5 Algorithm obtained Responsiveness as the root variable and resulted in 8 rules with 3 satisfied rules and 5 unsatisfied rules. Based on the results of this study, it can be concluded that the use of the C 4.5 Algorithm Decision tree can be used to measure the level of satisfaction of BPJS Kesehatan inpatients at RSKIA Annisa Payakumbuh. The results of this study are expected to help the RSKIA Annisa in making policies to improve services for patients.
Identifikasi Objek pada Citra Thorax X-Ray Pasien COVID-19 dengan Metode Contrast Limited Adaptive Histogram Equalization (CLAHE) Dodi Andre Putra; Jufriadif Na` am; Yuhandri
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 1
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i1.184

Abstract

Chest X-Ray radiography produces digital radiographic images of the chest area such as the lungs, heart, and ribs. This image can visualize the lung condition of COVID-19 patients. Examination of the lung condition of COVID-19 patients with X-Ray is easier, cheaper, and widely available in hospitals than other radiographic techniques. However, the results of the X-Ray radiography digital image have poor quality, so they need to be improved. Low image contrast is a factor in the difficulty of identifying thorax images of COVID-19 patients. Increase the contrast of the low thorax image of COVID-19 patients with Contrast Limited Adaptive Histogram Equalization (CLAHE) so that it is easier to observe concretely and more clearly. The images that were processed in this study were 100 thorax images of COVID-19 patients sourced from the radiology department of Bhayangkara Hospital, Padang Indonesia. Furthermore, the image is processed using digital image processing using Matlab software. The processing stages of the thorax image are converted into grayscale form. The resulting grayscale image is continued with contrast processing using the CLAHE method with Uniform, Rayleigh and Exponential distribution types. The calculation of the Peak Signal to Noise Ratio (PNSR) and Mean Square Error (MSE) values of the image results from the processing of each type of CLAHE was continued. The results of testing all images can be visually improved in contrast quality. The average MSE CLAHE Uniform, Rayleigh and Exponential results were 27.68, 25.86 and 26.33, respectively. The average values of CLAHE Uniform, Rayleigh and Exponential PNSR > 30 dB are 112.32 dB, 171.95 dB and 151.90 dB, which means the CLAHE image is similar to the original image. CLAHE Rayleigh gives the best results in terms of quality and quantity with a total of 85 images or an accuracy value of 85%, while CLAHE Exponential and CLAHE Uniform are 15% and 0%, respectively.
Mining Data In Identification Of Consumer Patterns Of Agricultural Machine Sales Using Fp-Growth Algorithm Eka Sofianti; Sarjo Defit; Yuhandri
SYSTEMATICS Vol 2 No 3 (2020): December 2020
Publisher : Universitas Singaperbangsa Karawang

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Abstract

The sales transaction data for agricultural machinery at the Mandiri Jaya Teknik Solok store is a large data set making it difficult to identify consumer purchasing patterns. Large data sets can be processed into useful information. Sales transaction data available at the Mandiri Jaya Teknik Solok store can be processed into useful information to increase sales. This study aims to identify consumer purchasing patterns in order to know which items are often sold and to find out which items need to be stocked more and to increase sales. The data that is processed in this study uses the sales transaction data obtained from the sales invoice of Toko Mandiri Jaya Teknik Solok. Data is in the form of sales data for 13 weeks of 20 items with a minimum support value of 15% and a confidence value of 60%. The method uses one of the data mining techniques associated with the FP-Growth algorithm, where the Fp-Growth algorithm uses the concept of tree development in searching for the types of items that are often purchased (frequency item sets). The tools used are Rapidminer 9.8 so that the purchase patterns of goods are obtained which are used as information to predict the level of frequently sold items. The result of the sales data processing process is the association rule. Association Rule is obtained in the form of a relationship between goods sold together with other goods in a transaction. From this pattern, it can be recommended to the shop owner as information for preparing stock of goods to increase sales results. This research is very suitable to be applied to determine the patterns of consumer spending such as the relationship of each item purchased by consumers, so this research is appropriate for use by stores.
Sistem Pendukung Keputusan Spesifikasi Biji Jagung Berkualitas Terbaik dengan Metode Multi Attribute Utility Theory Chairul Imam; Julius Santony; Yuhandri
Jurnal KomtekInfo Vol. 5 No. 3 (2018): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1366.315 KB) | DOI: 10.35134/komtekinfo.v5i3.27

Abstract

Farmers sell corn kernels in the company of PT Charoen Pokphan Indonesia Tbk Medan, corn kernels are used to mix feed ingredients to meet the protein values and nutrition of these feeds to be of high quality. The company PT Charoen Pokphan Indonesia Tbk Medan buys corn kernels to farmers by specifying the best quality corn kernels, so they know the total price of corn kernels is in accordance with the quality needed. This research determines the criteria of the best quality types of corn kernels and how to apply the Multi Attribute Utility Theory to decision support systems to determine the quality of corn kernels, to be able to help the company PT Charoen Pokphan Indonesia Tbk Medan in determining the quality of corn kernels. Based on the criteria set out in the company PT Charoen Pokphan Indonesia Tbk Medan to obtain the best quality corn kernels using grade 1 to grade 4 and ranking. The results of testing these methods are produced a decision on an alternative with a total value of 86.7%. So this method is needed to evaluate the determination of the best quality corn kernels to produce the best decisions
Penentuan Materi Layanan Bimbingan TIK Menggunakan Algoritma C4.5 Montesna; Yuhandri; Jufriadif Na’am
Jurnal KomtekInfo Vol. 6 No. 1 (2019): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1186.437 KB) | DOI: 10.35134/komtekinfo.v6i1.38

Abstract

Changes in curriculum from KTSP (Education Unit Level Curriculum) to 2013 Curriculum result in changes in Information and Computer Technology (ICT) subjects to ICT Guidance (BTIK). These subjects are not scheduled in general, so students need to be guided by questionnaires. To find out the right guidance needs data mining is needed. So this research is done in determining the accuracy of the guidance needs in accelerating the process of questionnaire data. The method used is C4.5 Algorithm. The results of the study have an accuracy of 90%, so it can be recommended in determining guidance material for students.
Perbandingan Metode Cropping pada Sebuah Citra untuk Pengambilan Motif Tertentu pada Kain Songket Sumatera Barat Yuhandri
Jurnal KomtekInfo Vol. 6 No. 1 (2019): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (934.023 KB) | DOI: 10.35134/komtekinfo.v6i1.45

Abstract

At the time of image processing where we only need a certain part of an image according to the needs called the Region of Interest (ROI), in order to obtain that, the processing is carried out in a cropping process. Cropping is mostly done by researchers, especially those who research in the field of image processing in order to do data processing on an image, the results of cropping process on an image are usually done to make it easier for researchers to focus on something that is needed only. In this study is to compare existing cropping methods to get a motif found in an image of West Sumatra songket fabric. In this study using the method of cropping rectangle, square, circle, ellipse, polygon and tested using the Matlab programming language. The results of comparison of 5 cropping methods for taking certain motifs on the songket image with 5 different songket image samples, shows that the best results are obtained by using the polygon method. Polygon method can reach certain coordinate points in a songket image, so that the results of cropping are better and other motives that are carried along during the cropping process can be reduced.
Analisis Tingkat Kejahatan Pada Anak Dibawah UmurMenggunakan Metode Fp-Growth Angga Putra Juledi; Sarjon Defit; Yuhandri
Jurnal KomtekInfo Vol. 7 No. 2 (2020): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (839.113 KB) | DOI: 10.35134/komtekinfo.v7i2.71

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Crimes in minors are a series of guardian actions or negligence by parents who endanger or pose a dangerous threat to the child. The purpose of this study is to implement Data Mining, Association rule, andthe FP-Growth Algorithm in cases of crime of minors so that they can extract knowledge and important and interesting information from the database. The data source used is raw data that has not been processed andis a crime data on minors which is summarized in the form of a report from the West Sumatra Regional Police. The results of this study are in the form of software by analyzing data collected using the FP-Growth Algorithm and using the concept of FP-Tree development in searching for Frequent Itemset, for testing the results carried out with applications that have been designed namely the Php programming language. The results of testing are obtained from associations of crime cases that often occur in minors. So it can be seen that data mining using the Fp-Growth Algorithm can be used to analyze cases of crime in minors as a consideration for the police in order to know the ins and outs of crime in children so that it can assist the investigation process.
Optimalisasi Prediksi Biaya Komisi Penjualan Mobil Menggunakan Metode Monte Carlo Zupri Henra Hartomi; Yuhandri; Julius Santony
Jurnal KomtekInfo Vol. 7 No. 2 (2020): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (388.396 KB) | DOI: 10.35134/komtekinfo.v7i2.74

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Sales are the main source of income for every company. Every company in marketing a product, should control the potential market for profit. Predicting the number of sales is important in analyzing sales progress. This study aims to assist companies in predicting car sales and car commission cost budgets based on sales data from the previous year.The data used in the study are car sales data for 2017 and 2018 in the Arengka Automall Pekanbaru Showroom (SAA Pekanbaru).Data processing in research uses the Monte Carlo method.The results of tests that have been carried out state that car sales by Marketing within 1 year resulted in an average accuracy rate of 94% and sales commission fee of Rp 411.000.000.From these results in accordance with calculations performed manually so that with a large accuracy value, the application of the simulation using this Monte Carlo Method feasible to be applied by companies in future decision making to plan the estimated budget for the cost of a car sales commission and as a means to assess Marketing performance at SAA Pekanbaru.
Memprediksi Harga Komoditas Cabe Menggunakan Metode Backpropagation di Wilayah Kota Payakumbuh Allans Prima Aulia; Yuhandri; Fhajri Arye Gemilang
Jurnal KomtekInfo Vol. 8 No. 1 (2021): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (577.926 KB) | DOI: 10.35134/komtekinfo.v8i1.96

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Chili is one of the spices needed by the majority of Indonesian people. These high needs have an impact on the price of this agricultural commodity which has become very fluctuated. This study, uses the backpropagation method to predict chilli prices in Payakumbuh City, with data sourced from the Badan Pusat Statistik Kota Payakumbuh. The data format are weekly chilli price data for the period 2014 to 2019. Data variables are arranged into time series forms with 4 input values from each week per year, and 1 target value. From the test results obtained the MSE value (Mean Squared Error) of 0.00118 with prediction accuracy of 98.56%. The results of this study can prove that Artificial Neural Networks using the backpropagation method can predict commodity prices for chilli in Payakumbuh City with a good level of accuracy, so that it can be used for the following year.
Optimalisasi Pendapatan Integrasi Sawit dengan Sapi Menggunakan Metode Monte Carlo Hermanto; Sarjon Defit; Yuhandri
Jurnal KomtekInfo Vol. 8 No. 4 (2021): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.682 KB) | DOI: 10.35134/komtekinfo.v8i4.183

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Hidup manusia sangat dipengaruhi oleh perkembangan lmu pengetahuan dan teknologi inforrmasi dalam penunjang kehidupan, salah satu nya dalam sektor pertanian. Teknologi informasi dalam sektor pertanian yang tepat waktu dan relevan memberikan informasi yang tepat guna kepada rumah tangga usaha pertanian untuk pengambilan keputusan dalam berusaha tani, sehingga efektif dalam meningkatkan produktivitas,dan pendapatan. Di Kabupaten Sijunjung petani yang merapkan sistem integrasi kelapa sawit dengan sapi hanya beberapa petani yang menerapkan hal itu, di karenakan keterbatasan informasi dan menyebabkan pendapatan dari petani yang menerapkan metode ini tidak menentu. Maka dari itu teknologi di harapkan dapat membantu mengatasi permasalahan tersebut. Metode yang di gunakan dalam penelitian ini adalah metode Monte Carlo dengan mengunakan data pendapatan petani kelapa sawit yang mengunakan metode integrasi kelapa sawit dengan sapi yang berada di kabupaten sijunjung tepatnya di kecamatan Kamang Baru, data di peroleh dengan cara wawancara secara langsung kepada petani di mana mempunyai lahan 1,5 hektar kebun kelapa sawit dengan 8 ekor sapi, dan di peroleh lah data pendapatan petani dari tahun 2018, 2019 dan 2020 dimulai dari bulan januari sampai bulan desember. Variabel yang digunakan dalam penelitian ini adalah jumlah pendapatan petani perbulannya. Data jumlah pendapatan petani tersebut akan di olah menggunakan metode Monte Carlo dibantu dengan Microsoft Excel untuk pencarian manualnya. Data jumlah pendapatan petani tahun 2018 digunakan sebagai data uji coba untuk memprediksi jumlah pendapatan petani pada tahun 2019, data tahun 2019 di gunakan untuk memprediksi jumlah pendapatan petani tahun 2020, dan data tahun 2020 untuk memprediksi pendapatan tahun 2021