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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

Abstract

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.411 KB) | DOI: 10.29207/resti.v5i3.3086

Abstract

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.874 KB) | DOI: 10.29207/resti.v5i1.2813

Abstract

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (270.536 KB) | DOI: 10.29207/resti.v5i3.3067

Abstract

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

Abstract

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

Abstract

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. 
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

Abstract

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.
PERAN PROMOSI, HARGA DAN KUALITAS LAYANAN TERHADAP KEPUTUSAN PEMBELIAN (Studi pada Konsumen Toko Buku Gunung Agung, Jakarta) Yoga Religia; Pebrian Pebrian; Agus Sriyanto; Yugi Setyarko
Jurnal Ekonomika dan Manajemen Vol 12, No 1 (2023)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/jem.v12i1.2231

Abstract

Tujuan penelitian ini adalah untuk mengetahui pengaruh promosi terhadap keputusan pembelian buku di Toko Buku Gunung Agung, untuk mengetahui pengaruh harga terhadap keputusan pembelian buku di Toko Buku Gunung Agung, untuk mengetahui pengaruh kualitas pelayanan terhadap keputusan pembelian buku di Toko Buku Gunung Agung. Populasi dalam penelitian ini adalah Dalam penelitian ini yang menjadi populasi adalah konsumen Toko Buku Gunung Agung yang datang ke toko. Jumlah anggota populasi tidak diketahui. Sampel dalam penelitian ini adalah 100 orang responden. Penelitian ini menggunakan teknik probability sampling karena populasi diketahui jumlah anggotanya, dan dengan Simple Random Sampling sebagai teknik penentuan sampelnya. Hasil dari penelitian ini adalah Promosi berpengaruh terhadap Keputusan Pembelian di Toko Buku Gunung Agung. Harga berpengaruh signifikan terhadap Keputusan Pembelian Keputusan Pembelian di Toko Buku Gunung Agung. Kualitas layanan berpengaruh signifikan terhadap Keputusan Pembelian Keputusan Pembelian di Toko Buku Gunung Agung.
THEORY OF REASONED ACTION DALAM MEMPENGARUHI NIAT ADOPSI TIKTOK DIKALANGAN UMKM Yoga Religia
Journal of Economic, Business and Engineering (JEBE) Vol 4 No 2 (2023): April
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) Universitas Sains Al Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32500/jebe.v4i2.4334

Abstract

Tiktok merupakan salah satu media sosial yang memiliki banyak sekali pengguna dan dapat digunakan untuk pemasaran produk melalui fitur e-commerce yang disediakan. Sayangnya hal tersebut belum dapat dimanfaatkan secara maksimal oleh para pelaku UMKM dalam memasarkan produk mereka. Hal tersebut dapat dilihat dari sedikitnya jumlah UMKM yang telah memanfaatkan platform digital. Penelitian ini bertujuan untuk mengidentifikasi faktor penyebab niat adopsi Tiktok dikalangan UMKM berdasarkan theory of reasoned action. Penelitian ini dilakukan di dengan menguji 56 data responden yang merupakan pemilik UMKM di Daerah Istimewa Yogyakarta. Berdasarkan data yang diperoleh kemudian dianalisis dengan pendekatan SEM-PLS. Setelah dilakukan pengujian diketahui bahwa baik sikap pemilik ataupun norma subjektif ternyata sama-sama memiliki pengaruh posistif signifikan terhadap niat adopsi tiktok dikalangan UMKM. Hasil penelitian ini menegaskan bahwa theory of reasoned action masih sangat relevan dalm memprediksi niat perilaku konsumen. Diharapkan hasil penelitian ini dapat dijadikan acuan bagi seluruh stakeholder yang menangani UMKM dalam penyusunan strategi pemasaran produk mereka melalui media sosial.
TOE Framework for E-Commerce Adoption by MSMEs during The COVID-19 Pandemic: Can Trust Moderate? Yoga Religia; Muhamad Ekhsan; Miftakul Huda; Anton Dwi Fitriyanto
Applied Information System and Management (AISM) Vol 6, No 1 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i1.30954

Abstract

Currently, there are still many MSMEs in the regions that have not been connected to the digital ecosystem. This results in limited market reach, a lack of visibility, a lack of operational efficiency, and difficulty competing in the digital market. The purpose of this study is to review the adoption of e-commerce among MSMEs during the COVID-19 pandemic within the scope of the organization. Integrating the TOE framework (technology, organization, environment) with trust is carried out to explain the key parameters behind the adoption of e-commerce by MSMEs. This study collected samples using a saturated sample technique from 181 people who were members of the population. There were 153 questionnaires that were returned in full for further analysis using SEM-PLS modeling. The test results showed that technology did not have a significant influence on the adoption of e-commerce. Organizations, the environment during the pandemic, and trust have had a significant influence on the adoption of e-commerce. In addition, organizations that are moderated by trust have no significant effect on e-commerce adoption. The role of trust is as a moderation predictor. This research shows that the TOE framework is still strong enough to be used in explaining the adoption of e-commerce by MSMEs. This research also expands the TOE framework, where trust can also influence MSMEs to adopt e-commerce. Researchers and managers can use the set of variables that have been identified to strategize the adoption of e-commerce by MSMEs. This study presents a series of variables that can be used to study the adoption of e-commerce by MSMEs in the future.