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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi INOVTEK Polbeng - Seri Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Sistem Informasi dan Aplikasi JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Scientific Journal of Informatics
Rahmawita, Medyantiwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Analisis Loyalitas Pelanggan Berdasarkan Model LRFM Menggunakan Metode K-Means Putri, Runi Aulia; Jazman, Muhammad; Syaifullah, Syaifullah; Rahmawita, Medyantiwi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6565

Abstract

In the era of intense competition in the beauty industry, it is important for companies to understand customer behavior and identify loyal customer segments. Ths study aims to analyze customer loyalty at the Lanona Skincare Beauty clinic using the LRFM (Length, Recency, Frequency, Monetary) model with the K-Means Clustering method. Beauty clinics have not implemented CRM as part of theur business strategy. There is ineffective marketing strategies. Customer transaction data from April to October 2023 was collected and analyzed to determine customer value based on LRFM parameters. The analysis results show that K-Means is effetive in grouping cutomers until the best three clusters are obtained. Cluster 1 with a results of 0,620 is the most loyal customers, cluster 2 with a results of 0,100 is grouped into new inactive customers and cluster 3 with a results of 0,353 is high frequency customers but low revenue contribution. The proposed marketing strategies for each cluster include rewarding an improving communication to maintain customers loyalty. This research provides valuable insights for Lanona Skincare Beauty Clinic in creating a more focused and succesfull marketing plan to increase customer happiness and loyalty.
Analisis Sentimen Pada Ulasan Aplikasi Bank Syariah Indonesia Mobile Menggunakan Support Vector Machine dan Naïve Bayes Aqilla, Nabila Fadia; Jazman, Muhammad; Syaifullah, Syaifullah; Rahmawita, Medyantiwi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6567

Abstract

The internet plays a crucial role in facilitating various human activities, including in the field of electronic banking services, which encompasses various financial services such as ATMs, internet banking, SMS banking, and mobile banking. All of these aim to enhance service quality with a focus on security, convenience, and effectiveness. BSI is one of the banks offering mobile banking services. Based on user reviews, the BSI Mobile app often experiences technical issues such as bugs and transaction failures. To assess the level of satisfaction with the app, the researcher uses sentiment analysis methods. This method also helps potential customers identify aspects that need improvement or development in the products and services to enhance their quality. The study employs Support Vector Machine (SVM) and Naïve Bayes algorithms. The test results show that the Naïve Bayes algorithm achieves an accuracy of 74.37%, recall of 74.37%, precision of 75.46%, and an F1-score of 74.5%. Meanwhile, the SVM algorithm achieves an accuracy of 77.39%, precision of 77.8%, recall of 77.39%, and an F1-score of 77.38%. These findings indicate that SVM performs better in sentiment classification tasks compared to Naïve Bayes. With its superior performance, SVM is the more suitable algorithm for analyzing user perceptions of the BSI Mobile app. Therefore, the findings of this study can contribute to the development of more innovative digital service strategies and enhance competitiveness in the digital era.
Pengelompokkan Perguruan Tinggi di Indonesia Menggunakan Algoritma BIRCH Husna, Nur Alfa; Mustakim, Mustakim; Afdal, M; Rahmawita, Medyantiwi
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7234

Abstract

Accreditation is currently the main focus for all universities. Each institution strives to get superior accreditation. The evaluation and assessment process carried out by BAN-PT is based on data reported by universities to PDDikti. This research aims to assist universities in achieving superior accreditation, by providing recommendations regarding the most influential attributes and clustering to find patterns or data structures from PDDikti. This research uses two feature selection methods AHP and Chi-Square are used separately to identify the most influential attributes. The results of each method were used as input features for the clustering process using the BIRCH algorithm. The purpose of this approach is to evaluate the effect of feature selection from both methods on the quality of clustering results. The evaluation is done using the Davies-Bouldin Index (DBI) metric. The results showed that the Lecturer attribute has the highest eigenvalue in AHP which is 0.379, indicating its significant role in accreditation assessment. Meanwhile, the Year of Establishment Decree attribute has the highest Chi-Square value of 290.625 which indicates a strong correlation with accreditation results. In addition, based on the cluster DBI value, it shows that AHP is superior to chi-square, so AHP is considered more effective in this context. With the best Davies Bouldin Index (DBI) value of 0.73603 in cluster 7 with a threshold of 0.05 and a branching factor of 50.
Co-Authors Ahsyar, Tengku Khairil Alam, Alji Ridwan Syah Alfarizi, Farrelino Amin, M. Nur Aprinastya, Rachell Aqilla, Nabila Fadia Arif Marsal Arsyad, Ilham Eki Saputra Fahril, Muhammad Febi Nur Salisah Hamzah, M. Luthfi Harmutika, Della Husna, Nur Alfa Jazman, Muhammad M. Afdal M.Afdal, M.Afdal MAULANA, BIMA Megawati - Mona Fronita, Mona Mustakim Mustakim Nurrahma, Intan Oktoriani, Della Putri, Runi Aulia Rianda, Rahmad Didho Rice Novita Rita Novita Rozanda, Nesdi Evrilyan Setiawati, Elsa Siregar, Syafril Sri Anjani Syaifullah Syaifullah Syaifullah Syakirin, Fakhrusy Wulan, Damar Zarnelly Zarnelly Zarry, Cindy Kirana