43 research outputs found
Implementation of Naive Bayes Classifier Algorithm to Evaluation in Utilizing Online Hotel Tax Reporting Application
Full text linkThe current implementation of tax reporting regional Pasuruan hotels have used online (Web-based), with the aim of reporting systems can run effectively and efficiently in receiving the financial statements especially from taxpayer property. Pasuruan as one small town quite rapidly in East Java, have implemented role models online tax filing system starting in 2015, with the amount of 6 hotels, there are several classes of hotels ranging from the budget class up to class three stars. After the application of the system running for 18 months (2015-2016), from existing data, conducted research on the analysis of the level of compliance of taxpayers reporting incomes in a hotel. On the research was designed and built a system to evaluate the level of compliance with the performance from the taxpayer (WP) in the 2nd year (2016) and are classified in categories (1) the taxpayer (WP) very obedient (ST), (2) the taxpayer (WP) is quite obedient (CT), (3) Taxpayers (WP) less obedient (KT). Input data will be processed using the technique of data mining algorithms Naive Bayes Classifier (NBC) to form the table of probability as a basis for the process of classification levels of taxpayer compliance. Based on the results of the measurement, the test results show with an accuracy of 50% i.e. 3 taxpayers is the very obedient (ST) to pay taxes. Then from the classification, the study could be made of recommendation solutions to guide the taxpayer in reporting revenues well and true
The development of rural area residence based on participatory planning case study: A rural residential area of Pucungrejo village, Magelang through “neighborhood development” program
No full textReduksi Data Latih pada K-Support Vector Nearest Neighbor Menggunakan Entropy
No full textPemilihan sebagian data latih atau reduksi data latih yang mempunyai pengaruh pada garis keputusan klasifikasi penting dilakukan. Tujuannya untuk mengurangi beban sistem pada tahap pelatihan. Sebagai metode reduksi data, K-Support Vector Nearest Neighbour (K-SVNN) mendapatkan hasil berdasarkan ketinggian nilai Significant Degree (SD) masing- masing data. Nilai SD dihitung menggunakan variabel LVRV (Left Value dan Right Value). Sayangnya, LVRV hanya dapat digunakan pada kasus klasifikasi biner. Penelitian ini melakukan uji coba penggunaan Entropy untuk menghitung SD. Secara konseptual, Entropy memberikan nilai kemurnian distribusi kelas data sehingga dimungkinkan penggunaan Entropy untuk menghitung SD pada kasus multi kelas. Pada makalah ini, disajikan analisis perbandingan perilaku nilai SD antara menggunakan LVRV dan Entropy. Hasil reduksi data menggunakan threshold (T) > 0, didapatkan akurasi yang sama pada kedua metode, sedangkan klasifikasi dengan reduksi data latih memberikan nilai akurasi lebih tinggi daripada tanpa reduksi. Hal ini membuktikan bahwa entropy dapat digunakan untuk menggantikan LVRV untuk menghitung SD
Design of Restaurant Billing System (E Bill Resto) by Applying Synchronization of Data Billing in Branch Companies to Main Companies Based on Rest API
No full textONLINE LOAN GROUPING ANALYSIS OF FINANCIAL TECHNOLOGY (FINTECH) PLATFORM-BASED FOR MSMES IN INDUSTRY 4.0 WITH NAÃVE BAYES STATISTICAL METHOD
No full textUMKM (Micro, Small and Medium Enterprises) is one of the Indonesian economy driving forces currently as the foundation of various sectors. Along with the growth of information technology that has increased sharply, many digital applications have been developed that offer convenience to the public, especially in terms of inclusion of funds as working capital, this has indirectly been broadly used by MSME players in seeking working capital in a short way. Peer to Peer Lending / P2PL (Fintech) or commonly referred to as an application online lending-based institutions, currently many have been present in the community either through licensed or unlicensed through the OJK Institution. As of October 14, 2020, OJK has released data on as many as 157 Legal Peer to Peer Lending Companies, while the number of Unlicensed P2PLending Institutions reported to OJK is around 2400, in the research conducted only 108 data were taken. From the data processing using the naïve Bayes method in determining the grouping / classification, it is found that 50% of P2PL companies carried out activities with the Very fraudulently Category, 33% are quite fraudulently and 17% not fraudulently. With the release of research results, it is hoped that MSME players in obtaining loans online can be more vigilant in determining which institutions to appoint in venture capital participation.
Keywords: Peer to Peer Lending, Naïve Bayes, UMK
Mango Leaf Classification with Boundary Moments of Centroid Contour Distances as Shape Features
No full textAverage and Maximum Weights in Weighted Rotation- and Scale-invariant LBP for Classification of Mango Leaves
No full textThe texture features would be important part when we conduct image classification. Local Binary Pattern (LBP) is one of feature extraction method that has most improvements by many researchers. Weighted Rotation- and Scale-invariant LBP (WRSI-LBP) is one of improvement versions. It uses minimum magnitude of local differences as an adaptive weight (WRSI-LBP-min) to adjust the contribution of LBP code in histogram calculation. The motivation is minimum magnitude gives minimum distortion to change LBP code in histogram calculation. In the classification of mango leaves case, the texture characteristic of mango leaves is highly difficult to be differed directly. So, for high accuracy detection, system requires texture feature with strength discrimination character, robust to illumination change, not sensitive to scaling and rotation. To achieve the goal, we propose average and maximum of magnitude of local differences as an adaptive weight of WRSI-LBP (WRSI-LBP-avg and WRSI-LBP-max). This scheme can be used to generate texture features for classification of mango leaves and general classification cases. The motivation of average weight is to cover all local different magnitude, because each LBP code generated would has unique neighbors pattern. The motivation of maximum is it gives maximum distortion to change LBP code, but it gives highest local different magnitude. We use Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) as classification methods. We use 240 images for performance evaluation, contains three varieties: Gadung, Jiwo and Manalagi. The K-Fold Cross Validation and Leave-One-Out are used as validation method. From the experiments show that WRSI-LBP-avg and WRSI-LBP-max achieve the highest accuracy compare to WRSI-LBP-min, LBP, Center Symmetric LBP (CS-LBP) and Dominant Rotated Local Binary Pattern (DRLBP). SVM achieve accuracy 75.21% with 16 bins, while K-NN achieve accuracy 79.17% with 256 bins. For uniform pattern, we apply experiments to WRSI-LBP-min, WRSI-LBP-avg, and WRSI-LBP-max. The highest accuracy is also achieved by WRSI-LBP-avg and WRSI-LBP-max
Pengaruh Karakteristik Individu dan Komitmen Organisasi Terhadap Kinerja Karyawan PT. Sumatran Food Trade Medan
No full textTujuan penelitian ini adalah untuk mengetahui pengaruh Karakteristik Individu secara parsial terhadap kinerja karyawan pada PT. Sumatran Food Trade Medan. Untuk pengaruh Karakteristik Individu dan Komitmen Organisasi secara simultan terhadap kinerja karyawan pada PT. Sumatran Food Trade Medan. Populasi dalam penelitian ini adalah karyawan PT. Sumatran Food Trade Medan sebanyak 102 orang dengan menggunakan sampel karyawan tetap sebanyak 51 orang. Untuk memperoleh data dalam penyusunan skripsi ini, penulis menggunakan instrument, observasi dan wawancara (interview), serta angket (kuesioner). Dalam menganalisis data menggunakan regresi linier berganda, uji t, uji F dan uji koefisien determinasi. Berdasarkan hasil uji parsial (uji t) dapat disimpulkan bahwa Karakteristik Individu mempunyai pengaruh yang signifikan terhadap kinerja karyawan. Komitmen Organisasi mempunyai pengaruh yang signifikan terhadap kinerja karyawan. Berdasarkan hasil uji F dapat disimpulkan bahwa variabel Karakteristik Individu dan Komitmen Organisasi secara bersama-sama berpengaruh secara signifikan terhadap kinerja karyawan. Nilai R-Square yang diperoleh adalah sebesar 0,424 menunjukkan sekitar 42,4% variabel Y (kinerja karyawan) dapat dijelaskan oleh variabel Karakteristik Individu (X1) dan variabel Komitmen Organisasi (X2).The purpose of this study was to study the effect of Individual Characteristics on employee performance at PT. Sumatran Food Trade Medan. To highlight individual characteristics and organizational commitment to the performance of employees at PT. Sumatran Food Trade Medan. The population in this study were employees of PT. Sumatran Food Trade Medan totals 102 people using a sample of permanent employees of 51 people. To receive data in the preparation of this thesis, the author uses the instrument; observation and interviews (interviews), as well as questionnaires (questionnaires). In analyzing the data using multiple linear regression, t test, F test and test coefficient of determination. Based on the results of the partial test (t test) it can be concluded that the Individual Characteristics have a significant influence on employee performance. Organizational Commitment has a significant importance on employee performance. Based on the results of the test it can be concluded that the Individual Characteristics variables and Organizational Commitments together have a significant effect on employee performance. The R-Square value obtained at 0.424 shows that about 42.4% of the Y variable (employee performance) can be assessed by the Individual Characteristics variable (X1) and Organizational Commitment variable (X2)
