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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi INOVTEK Polbeng - Seri Informatika
Fitra Kurnia
Universitas Islam Negeri Sultan Syarif Kasim
Computer Science & IT Engineering

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Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour Siti Monalisa; Fitra Kurnia
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.9394

Abstract

The aim of study is to discover outlier of customer data to found customer behaviour. The customer behaviour determined with RFM (Recency, Frequency and Monetary) models with K-Mean and DBSCAN algorithm as clustering customer data. There are six step in this study. The first step is determining the best number of clusters with the dunn index (DN) validation method for each algorithm. Based on the dunn index, the best cluster values were 2 clusters with DN value for DBSCAN 1.19 which were minpts and epsilon value 0.2 and 3 and DN for K-Means was 1.31. The next step was to cluster the dataset with the DBSCAN and K-Means algorithm based on the best cluster that was 2. DBSCAN algorithm had 37 outliers data and K-means algorithm had 63 outliers (cluster 1 are 26 outliers and cluster 2 are 37 outliers). This research shown that outlier in DBSCAN and K-Means in cluster 1 have similarities is 100%. But overal outliers similarities is 67%. Based the outliers shown that the behaviour of customers is a small frequency of spending but high recency and monetary.
Rancang Bangun Sistem Informasi Klinik Umum dan Bersalin Berbasis Customer Relationship Management Siti Monalisa; Samsul Ma’arif; Fitra Kurnia
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 11 No. 1 (2020): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (855.297 KB) | DOI: 10.31849/digitalzone.v11i1.3062

Abstract

Klinik umum dan bersalin Dokter Rini merupakan salah satu klinik yang menyedikan jasa kesehatan. Saat ini, jumlah data pasien klinik yang berkunjung terjadi fluktuatif setiap bulannya dan juga rendahnya pasien yang melakukan konsultasi lanjutan. Hal ini dikarenakan kurang efektifnya pelayanan yang diberikan oleh pihak klinik terhadap pasien dan juga minimnya informasi dalam memberikan pelayanan ke pasien. Oleh karena itu dalam memberikan pelayanan terbaik bagi pasien maka dibangun sistem informasi customer relationship management (CRM) dengan tipe sistem operasional yang dapat membantu mengelola data pasien dengan baik. Selain itu, sistem ini juga digunakan sebagai media promosi dan informasi serta menjalin komunikasi dengan calon pelanggan dan pelanggan tetap dengan menggunakan dua fase CRM yaitu fase akuisisi dan fase retensi. Hasil dari penelitian ini adalah sebuah sistem informasi klinik berbasis CRM dengan fitur reminder system.
Application of ARIMA and ARIMAX Methods to Predict the Number of Visitors to Hotel XYZ Pekanbaru Julio Vernando; Fitri Insani; Okfalisa Okfalisa; Fitra Kurnia
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/enrfna19

Abstract

Predicting the number of visitors to Hotel XYZ is one of the steps that can be taken by the hotel to find out how many visitors will increase in each upcoming holiday season. The purpose of this study is to forecast the number of visitors to Hotel XYZ from June 2023 to July 2024 using the ARIMA and ARIMAX comparison methods. The research methodology encompasses problem identification, data collection, data processing, and ARIMA and ARIMAX analysis, which involves testing the parameters (p, d, q) selected based on the ACF and PACF using the AIC Model. Based on the test results, ARIMAX (5, 0, 3) has the lowest AIC, which is 3495.2, followed by ARIMAX (3, 0, 5), which has a slightly higher AIC. The results showed that the ARIMAX (5, 0, 3) model is the most accurate model for predicting data (eg the number of hotel guests, room demand, or income), with an RMSE value of 15.80% and a MAPE of 18.90%. Therefore, research that applies the ARIMAX model can provide real benefits in supporting operational efficiency, resource management, and hotel business strategy, ultimately increasing the competitiveness and profitability of the hotel.
Co-Authors Fitri Insani Julio Vernando Okfalisa Okfalisa Samsul Ma’arif Siti Monalisa