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All Journal Jurnal Teknologi Informasi MURA Jurnal Teknologi Informasi Mura Jurnal Administrasi Terapan
Join Rachel Luturmas
Politeknik Negeri Ambon
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences Other

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Pengaruh Kualitas Pelayanan Dan Kepuasan Pelanggan Terhadap Loyalitas Pelanggan Pada Usaha Jasa Salon Di Kota Ambon Join Rachel Luturmas
Jurnal Administrasi Terapan Vol. 2 No. 1 (2023): Jurnal Administrasi Terapan
Publisher : P3M Politeknik Negeri Ambon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31959/jat.v2i1.1380

Abstract

This study aims to examine the effect of service quality on customer satisfaction and customer loyalty as well as the effect of customer satisfaction on customer loyalty. The development of the Salon Service Industry in Ambon city has increased competition for salon services. Heterogeneous customers, workers who already have experience and high competence move, usually the salon customers also move. Service providers must recruit new people who are not necessarily quickly accepted by customers. Besides the lack of participation in seminars, courses, and exhibitions related to the salon business in order to follow development trends and increase expertise. Respondents in this study were salon customers at middle to upper-class salons in Ambon City, with a total of 150 respondents. The analysis used in testing data quality is validity and reliability analysis. Hypothesis testing is done using multiple regression analysis The regression test results show that service quality has a positive effect on customer loyalty as well as customer satisfaction has a positive effect on customer loyalty.  Keywords: Service Quality, Customer Satisfaction, Customer Loyalty
Klasifikasi Persiapan Keuangan Mahasiswa Tingkat Akhir dalam Menghadapi Dunia Kerja Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbor Tiska Pattiasina; Stenly Ronaldo Titioka; Frangky Jansens Louth; Grace Fredriksz; Join Rachel Luturmas; Andrie CH Salhuteru; Febiola Matuankotta; Laura S Nunumete
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3154

Abstract

Financial preparation is an important factor for final year students in facing the world of work. This study aims to classify the financial readiness of final year students using the Naive Bayes and K-Nearest Neighbor (K-NN) algorithms based on 15 attributes related to economic conditions, financial behavior, and financial literacy. Data were obtained from 85 final year students of the Business Administration Department of Ambon State Polytechnic and processed using the Synthetic Minority Over-sampling Technique (SMOTE) technique to address class imbalance. Testing was conducted using WEKA software with a 10-fold cross-validation method. The results showed that the Naive Bayes algorithm produced an accuracy of 96.6667%, a precision of 96.7%, a recall of 96.7%, and an ROC Area of ​​0.9988. Meanwhile, the K-Nearest Neighbor (K-NN) algorithm produced an accuracy of 80.0%, a precision of 80.4%, a recall of 80.0%, and an ROC Area of ​​0.8703. These results indicate that Naive Bayes outperforms K-NN in classifying the financial readiness of final-year students. Furthermore, the application of SMOTE has been shown to improve the model's ability to recognize minority classes, resulting in a more balanced and representative classification.
Klasifikasi Persiapan Keuangan Mahasiswa Tingkat Akhir dalam Menghadapi Dunia Kerja Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbor Tiska Pattiasina; Stenly Ronaldo Titioka; Frangky Jansens Louth; Grace Fredriksz; Join Rachel Luturmas; Andrie CH Salhuteru; Febiola Matuankotta; Laura S Nunumete
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3154

Abstract

Financial preparation is an important factor for final year students in facing the world of work. This study aims to classify the financial readiness of final year students using the Naive Bayes and K-Nearest Neighbor (K-NN) algorithms based on 15 attributes related to economic conditions, financial behavior, and financial literacy. Data were obtained from 85 final year students of the Business Administration Department of Ambon State Polytechnic and processed using the Synthetic Minority Over-sampling Technique (SMOTE) technique to address class imbalance. Testing was conducted using WEKA software with a 10-fold cross-validation method. The results showed that the Naive Bayes algorithm produced an accuracy of 96.6667%, a precision of 96.7%, a recall of 96.7%, and an ROC Area of ​​0.9988. Meanwhile, the K-Nearest Neighbor (K-NN) algorithm produced an accuracy of 80.0%, a precision of 80.4%, a recall of 80.0%, and an ROC Area of ​​0.8703. These results indicate that Naive Bayes outperforms K-NN in classifying the financial readiness of final-year students. Furthermore, the application of SMOTE has been shown to improve the model's ability to recognize minority classes, resulting in a more balanced and representative classification.
Co-Authors Andrie CH Salhuteru Febiola Matuankotta Frangky Jansens Louth Grace Fredriksz Laura S Nunumete Stenly Ronaldo Titioka Tiska Pattiasina