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All Journal Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika JURNAL PENDIDIKAN TAMBUSAI IJISTECH (International Journal Of Information System & Technology) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) JUKI : Jurnal Komputer dan Informatika Akrab Juara : Jurnal Ilmu-ilmu Sosial IJISTECH Golden Ratio of Data in Summary
Luthfi Indriyani
Universitas Bina Sarana Informatika
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Social Sciences

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Journal : IJISTECH

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Customer Loyalty Classification with Comparison of Naive Bayes, C4.5, and KNN Methods Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i3.361

Abstract

Customer loyalty is indispensable for the survival of a company. Customer loyalty needs to be maintained in order to return to visit and transact with the Company. Customer data consisting of age, annual income, purchase amount, region, purchase frequency, and loyalty score features can produce new information, namely analyzing customers who have high loyalty. Data processing is carried out using three data mining algorithms, namely Naïve Bayes, C4.5 or Decision Tree, and KNN. The stages carried out in data processing consist of data selection, preprocessing, transformation, and modelling. The customer data used amounted to 238. Modelling is carried out using Rapid Miner Software. Customer loyalty classification can be easily done with the three algorithms, namely Naive Bayes, and C4.5 or Decision Tree, and KNN which is validated by the 10-fold cross-validation method so as to produce the highest percentage of accuracy and the similarity of the accuracy value of the Naive Bayes and C4.5 algorithms, which is 96.67%. In the AUC value, it can be seen that the Naive Bayes algorithm is superior to the C4.5 algorithm or Decision Tree and KNN. The result of the highest AUC value is 0.997, the highest precision percentage is 98.92% achieved by the Naive Bayes algorithm. The result of the highest recall percentage is C4.5 of 100%. The results of the AUC value and accuracy percentage on the three algorithms prove that the performance of the three algorithms is very good.
Comparison of Naive Bayes and C4.5 Methods with Particle Swarm Optimization on Customer Loyalty Classification Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.382

Abstract

The Company attaches great importance to customer loyalty for the sustainability of the Company. Loyal customers will buy many times and provide great profits. In this study, the decision tree method or C4.5 and naïve bayes were used with PSO optimization for customer classification which aims to design a strategy in decision-making towards disloyal customers. Some of the stages carried out are data load into MS. Excel, data cleaning from noise, data selection as many as 238 obtained from previous research with several attributes, including, namely age, annual income, purchase amount, region, purchase frequency, and loyalty score, as well as data transformation, namely each attribute is grouped into 2 with their own criteria, data testing by modeling data through Rapidminer, Data evaluation by examining the values of accuracy, precision, recall, and AUC. Both methods have the same accuracy value of 96.67% and the same recall value of 100%. For the precision value, there is a difference of 0.6% and the precision decision tree value is higher than the naïve Bayes which is 96.16%. As for the AUC value, it is higher naïve bayes, which is 0.922 with the difference from the decision tree of 0.059. It can be concluded that the two methods in processing customer loyalty data in this study have the same accuracy, so both methods are equally good.
Co-Authors Ade Setiawan Ahmad Ikhlasul Amal Alda Meilita Aqshal Widianto Saputra Astrid Noviriandini Ayudilla Laili Ramadian Bachtiar, Denny Basuki Basuki Dwiza Riana Embun Fajar Wati Erna Melastuti, Erna Indradewa, Rhian Jordy Lasmana Putra Khairi, Fahmi Mai Hamni Putri Muhaimin Azis Nurmah Nurmah Perangin-Angin, Elvi Sunita Safinatunnaza, Salwa Sandra Dewi Saraswati Septyani, Herlina Syahriani syahriani syahriani Syahriani Syahriani Syahriani Syahriani Syahriani Syahriani, Syahriani Tabina Fasya Benedicta Wahyuni, Asri Weko Susanto weko susanto Yeni Angraini