Merging Pearson Correlation and TAN-ELR algorithm in recommender system

Kurniawan, Yogiek Indra; Soviana, Elida; Yuliana, Irma

doi:10.1063/1.5042998

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…Penggunaan data masa lampau untuk menganalisis pola yang terjadi pada masa depan Merupakan konsep dari aplikasi prediksi maupun aplikasi rekomendasi [3]. Aplikasi untuk memprediksi suatu kejadian telah banyak diimplementasikan pada banyak kasus dengan menggunakan konsep klasifikasi pada data mining, dengan berbagai macam metode maupun algoritma, seperti algoritma Naïve Bayes [4]- [6], ID3 [7], Rotation Forest [8], Neural Network [9], [10], maupun algoritma yang lain [8], [11], [12].…”

Section: Pendahuluanunclassified

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Kurniawan

Fatikasari

Hidayat

et al. 2021

J. Tek. Inform. (JUTIF)

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BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.

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Section: Pendahuluanunclassified

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Kurniawan

Fatikasari

Hidayat

et al. 2021

J. Tek. Inform. (JUTIF)

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“…Testing is a way to assess quality from an algorithm [25]. This stage is carried out with 2 methods, including algorithm test and precision, recall, and accuracy test.…”

Section: Testingmentioning

confidence: 99%

Naive Bayes modification for intrusion detection system classification with zero probability

et al. 2021

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One of the methods used in detecting the intrusion detection system is by implementing Naïve Bayes algorithm. However, Naïve Bayes has a problem when one of the probabilities is 0, it will cause inaccurate prediction, or even no prediction was found. This paper proposed two modifications for Naïve Bayes algorithm. The first modification eliminated the variable that has 0 probability and the second modification changed the multiplication operations to addition operations. This modification is only applied when the Naïve Bayes algorithm does not find any prediction results caused by zero probabilities. The results of this research show that the value of precision, recall, and accuracy in the modification made tends to increase and better than the original Naïve Bayes algorithm. The highest precision, recall, and accuracy are obtained from modification by changing the multiplication operation to the addition. Increasing precision can reach 4%, increasing recall reaches 2% and increasing accuracy reaches 2%.

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“…Studi literatur digunakan dengan mempelajari dan memahami teori-teori yang digunakan, yakni diantaranya mencari faktor-faktor yang menjadi syarat Sistem Pendukung Keputusan dan Metode Simple Additive Weighting (SAW). Data-data tersebut dicari dengan mengumpulkan literatur, jurnal nasional, browsing internet dan bacaan-bacaan yang ada kaitannya dengan topik baik berupa teks ataupun paper mengenai algoritma Simple Additive Weighting [13]. Rumus algoritma SAW dapat ditunjukkan oleh persamaan (1) [14].…”

Section: Studi Literaturunclassified

Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik Menggunakan Simple Additive Weighting

Ahmad

Kurniawan

2020

Jurnal Teknik Informatika

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Dalam suatu perusahaan yang berkembang, apresiasi pegawai sangat dibutuhkan untuk meningkatkan moral dan kinerja. Penilaian yang akurat dibutuhkan untuk dapat menunjuk pegawai terbaik. Namun, penilaian akan menjadi lebih kompleks seiring dengan bertambahnya pegawai dan beragamnya komponen penilaian. Pada CV. Jenderal Software, penilaian pegawai tersebut masih dilakukan secara manual, sehingga menjadikan proses penilaian kurang efektif dan efisien. Untuk memaksimalkan proses penilaian pegawai terbaik, Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik merupakan suatu sistem yang dapat membantu dan memudahkan perusahaan dalam melakukan proses tersebut. Tujuan dari paper ini adalah untuk membuat sebuah sistem pendukung keputusan menggunakan algoritma Simple Additive Weighting untuk meningkatkan keakuratan, efisiensi, serta efektifitas sistematika penilaian pegawai yang terjadi pada perusahaan CV. Jenderal Software. Pengumpulan data dilakukan dengan observasi langsung serta wawancara kepada pengguna, yaitu pimpinan perusahaan. Data yang didapatkan kemudian diproses dan dianalisis sehingga dapat dijadikan dasar pembuatan sistem. Hasilnya, sistem dapat meningkatkan keakuratan, efisiensi, dan efektifitas proses. Hasil pengujian Blackbox menunjukkan bahwa system telah berjalan sesuai dengan yang diharapkan. Sedangkan dari hasil pengujian User Acceptance Test, didapatkan nilai preferensi 97,5% dengan indikator “Sangat Baik” yang menunjukkan bahwa system tersebut sudah sesuai dan layak dipakai.

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Merging Pearson Correlation and TAN-ELR algorithm in recommender system

Cited by 10 publications

References 2 publications

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Naive Bayes modification for intrusion detection system classification with zero probability

Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik Menggunakan Simple Additive Weighting

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