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South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts Yoga Religia; Gatot Tri Pranoto; Egar Dika Santosa
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.837

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger Yoga Religia; Amali Amali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing Yoga Religia; Agung Nugroho; Wahyu Hadikristanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The world of banking requires a marketer to be able to reduce the risk of borrowing by keeping his customers from occurring non-performing loans. One way to reduce this risk is by using data mining techniques. Data mining provides a powerful technique for finding meaningful and useful information from large amounts of data by way of classification. The classification algorithm that can be used to handle imbalance problems can use the Random Forest (RF) algorithm. However, several references state that an optimization algorithm is needed to improve the classification results of the RF algorithm. Optimization of the RF algorithm can be done using Bagging and Genetic Algorithm (GA). This study aims to classify Bank Marketing data in the form of loan application receipts, which data is taken from the www.data.world site. Classification is carried out using the RF algorithm to obtain a predictive model for loan application acceptance with optimal accuracy. This study will also compare the use of optimization in the RF algorithm with Bagging and Genetic Algorithms. Based on the tests that have been done, the results show that the most optimal performance of the classification of Bank Marketing data is by using the RF algorithm with an accuracy of 88.30%, AUC (+) of 0.500 and AUC (-) of 0.000. The optimization of Bagging and Genetic Algorithm has not been able to improve the performance of the RF algorithm for classification of Bank Marketing data.

Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging Agung Nugroho; Yoga Religia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The increasing demand for credit applications to banks has motivated the banking world to switch to more sophisticated techniques for analyzing the level of credit risk. One technique for analyzing the level of credit risk is the data mining approach. Data mining provides a technique for finding meaningful information from large amounts of data by way of classification. However, bank marketing data is a type of imbalance data so that if the classification is done the results are less than optimal. The classification algorithm that can be used for imbalance data types can use naïve Bayes. Naïve Bayes performs well in terms of classification. However, optimization is needed in order to obtain more optimal classification results. Optimization techniques in handling imbalance data have been developed with several approaches. Bagging and Genetic Algorithms can be used to overcome imbalance data. This study aims to compare the accuracy level of the naïve Bayes algorithm after optimization using the bagging and genetic algorithm. The results showed that the combination of bagging and a genetic algorithm could improve the performance of Naive Bayes by 4.57%.

Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm Gatot Tri Pranoto; Wahyu Hadikristanto; Yoga Religia
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1097

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.

PENGARUH BRAND IMAGE, ELECTRONIC WORD OF MOUTH DAN CELEBRITY ENDORSER TERHADAP KEPUTUSAN PEMBELIAN KONSUMEN PRODUK DAYPACK EIGER DI KOTA BEKASI Yoga Religia; agus sriyanto; Ravindra Safitra Hidayat; Yugi Setyarko
Jurnal Ekonomika dan Manajemen Vol 11, No 1 (2022)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/jem.v11i1.1745

Tujuan penelitian ini adalah untuk menganalisis pengaruh brand image, electronic word of mouth dan celebrity endorser terhadap keputusan pembelian daypack Eiger. Dalam penelitian ini menggunakan metode survey yang terdiri dari 97 responden dengan teknik non probability khususnya purposive sampling. Pengumpulan data menyebarkan kuesioner dan diolah dengan metode deskriptif menggunakan teknik analisis regresi linear berganda. Alat analisis yang digunakan adalah Statistic Product and Service Solution (SPSS) versi 25. Setiap variabel yang di uji telah valid dan reliabel dan telah layak berdasarkan uji asumsi klasik sehingga penelitian dapat dilakukan. Hasil penelitian menunjukan bahwa seluruh variabel bebas (brand image, electronic word of mouth dan celebrity endorser) secara parsial dinyatakan memiliki suatu hubungan yang positif dan terdapat pengaruh signifikan dengan korelasi yang kuat terhadap keputusan pembelian konsumen Produk daypack Eiger di Kota Bekasi.

Sistem Pendukung Keputusan Karyawan Terbaik Dengan Metode Profile Matching Best Employee Decision Support System With Matching Profile Process Method Yoga Religia
Jurnal SIGMA Vol 11 No 2 (2020): Juni 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Promising human resources can support the advancement of a company. Human resource development must also have clear and well-planned rules. To maintain good resources and produce promising resources, management planning is needed, for example in the form of hiring new employees, conducting training, performance appraisal, etc. The process of selecting the best employees at PT Samudera Ocean Perkasa Indonesia which is still manual and accompanied by decision making takes a long time. So as to simplify the process of selecting the best employee recommendations, it is very necessary to build a decision support system so that it can help to provide the best employee selection recommendations. In this study the problem was formulated about how to implement the profile matching method for the selection of employees in PT Samudra Ocean Perkasa Indonesia. While the aim of this research is to choose the best employees in PT Samudra ocean mighty Indonesia to be more targeted. The result of this application is the system automatically recommends the best employees, and testing this system using a blackbox, and after use the blackbox system is ready to use. Keywords : decision support system, Profile Matching, php.

Analisa Algoritma Binary Search Untuk Mencari Data Mahasiswa Program Studi Manajemen Universitas Pelita Bangsa Dengan Berbasis Android Yoga Religia; Ahmad Nurhakim
Jurnal SIGMA Vol 10 No 3 (2019): September 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pencarian merupakan pekerjaan yang sering kita lakukan dalam kehidupan sehari–hari. Dalam text editor, kita sering melakukan pekerjaan mencari kata, atau mencari kata1 dan menggantikannya dengan kata 2. Tujuan dari penelitian ini untuk mengetahui analisis kecepatan rata rata waktu proses pencarian dan ketepatan algoritma sequential search untuk mencari data mahasiswa teknik informatika pada Universitas Pelita Bangsa. Metode yang digunakan adalah Algoritma Binary Search . Hasil dari penelitian ini dapat diambil kesimpulan yaitu algoritma searching merupakan algoritma yang penting dalam pengelolaan sistem manajemen database. Data yang besar (Big Data) perlu diolah untuk mempermudah dalam pencarian data. Analisis kecepatan rata - rata waktu pencarian dan ketepatan algoritma binary search diketahui dengan perhitungan kompleksitas waktu hasil dari penelitian ini diketahui analisis kecepatan rata- rata waktu 0,025 ms dalam jumlah data mahasiswa untuk membantu proses analisis sebanyak 2000 data berupa nim dan nama mahasiswa. Pengujian dalam penelitian ini menggunakan analisa kompleksitas waktu pencarian, analisa kompleksitas dengan notasi linear O(n), dan black box testing. Black box testing hasil pengujian sesuai dan valid. Kata kunci: Binary Search, Sequential Search, Black Box Testing, Asimptotik, Universitas Pelita Bangsa.

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