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All Journal Proceeding Buffalo International Conference JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Inovasi dan Teknologi Pembelajaran (JINOTEP) Kajian dan Riset Dalam Teknologi Pembelajaran JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pendidikan dan Teknologi Indonesia Studium: Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Karya Abdi Masyarakat
M. Afdal
Faculty of Animal Husbandry Jambi University Kampus Mandala Darat Jambi 363611ndonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

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Penerapan Algoritma K-Medoids dan FP-Growth dengan Model RFM untuk Kombinasi Produk Pertiwi, Tata Ayunita; Afdal, M.; Novita, Rice; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5268

Abstract

Competition in the business world has increased, resulting in companies having to optimize sales and retain their customers. Customers are an important company asset that must be well looked after. The aim of customer segmentation is to understand customer purchasing behavior so that companies can implement appropriate marketing strategies. Aurel Mini Mart is a retail business that does not yet consider the recency, frequency and monetary value of customer shopping. So far, promotions have been carried out only based on estimates, without taking into account accurate data and information. This research combines the RFM model with data mining techniques to segment customers. Based on the 5 clusters formed from the clustering process, gold customers are in cluster 1 which has high loyalty with low recency value, high frequency and high monetary value. This shows that customers in this segment often make purchases for quite large amounts of money. Meanwhile, customers in clusters 2, 3, 4, and 5 are dormant customers who rarely make transactions and the amount of money spent is also small. After the customer segmentation process is complete, the next step is to use the FP-Growth Algorithm to associate the products purchased by customers. This aims to obtain a better product combination, so that the sales strategy can be more effective and the company can make a profit.
Implementasi Algoritma Random Forest Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online Digoogle Play Store Wibisono, Yudistira Arya; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5368

Abstract

Online lending programs are examples of financial service platforms offered directly by commercial fintech players. However, there are rampant cases of fraud and unethical actions by some online lenders such as threatening and harassing billing methods due to late payments. This research aims to classify sentiment from user reviews of online loan applications on the Google Play Store into positive, negative, or neutral categories. This research conducts sentiment analysis of user reviews of online loan applications such as AdaKami, AdaModal, Cairin, FinPlus and UangMe using a text mining approach. This approach can perform sentiment classification on user reviews quickly. Data was collected using the scrapping technique on the Google Play Store and obtained a total of 200 data on each online loan application. The modeling used in this research is the division of training data and test data as much as 80:20. The highest accuracy results using the Random Forest algorithm are Cairin and UangMe applications with 85% accuracy. While the application that gets the lowest accuracy result is the AdaModal application with 75% accuracy. A visualization analysis using word clouds was also conducted to understand the context of user reviews of the pinjol apps. The results show that users almost always discuss loan limits in every sentiment across the five apps.
Co-Authors - Mardalena, - AA Sudharmawan, AA Addion Nizori Aini, Delvi Nur Andaranti, Arifah Fadhila Arif Marsal Darlis Darlis Eki Saputra Febi Nur Salisah, Febi Nur FITRY TAFZI Indriyani Indriyani Indriyani Inggih Permana Intan, Sofia Fulvi Jazman, Muhammad Kusuma, Gathot Hanyokro Lisani Mawaddah, Zuriatul Megawati - Mustakim Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Pertiwi, Tata Ayunita Priady, Muhamad Ilham Putra, Moh Azlan Shah Rice Novita Saitul Fakhri Suhessy Syarif, Suhessy Suryadi Suryadi Suryani, Heni Suseno, Rahayu Teja Kaswari Wibisono, Yudistira Arya Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Yuda, Afi Ghufran Yurleni Yurleni Zarnelly Zarnelly