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All Journal Techno LPPM Seminar Nasional Informatika (SEMNASIF) Knowledge Engineering and Data Science Indonesian Journal of Information System BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal of Natural Science and Engineering Progresif: Jurnal Ilmiah Komputer Variance : Journal of Statistics and Its Applications PROGRES PENDIDIKAN
Tahalea, Sylvert Prian
Universitas Pembangunan Nasional Veteran Yogyakarta
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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DYNAMIC ANALYSIS OF SEITR MATHEMATICAL MODEL ON THE SPREAD OF HEPATITIS B DISEASE IN AMBON CITY Larubun, Swine Enggelina; Leleury, Zeth Arthur; Lesnussa, Yopi Andry; Tahalea, Sylvert Prian; Warong, Maria Marlein
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1989-2000

Abstract

Hepatitis B is a disease caused by infection with the HBV (Hepatitis B Virus) virus that commonly infects the liver and can develop into liver cancer. The disease can be transmitted through blood, semen, breast milk, saliva, vaginal fluids, and sperm. One effective way to prevent Hepatitis B disease is by vaccination. This study will construct a mathematical model, such as the SEITR model, to study the spread of Hepatitis B disease in Ambon City. The SEITR epidemic model is a disease spread model that divides the population into five subpopulation classes, namely the susceptible individual subpopulation class, the exposed individual subpopulation class, the infected individual subpopulation class, the treatment individual subpopulation class, and the recovered individual subpopulation class. Based on the dynamic system analysis conducted, two equilibrium points were obtained, namely the disease-free equilibrium point and the endemic equilibrium point. In addition, based on the data and simulation results, it can be concluded that the spread of Hepatitis B in Ambon City depends on the transmission rate from infected individuals to susceptible individuals
CLUSTERING SHRIMP DISTRIBUTION IN INDONESIA USING THE X-MEANS CLUSTERING ALGORITHM Fadhilah, Rahmi; Matdoan, M. Y.; Safira, Dinda Ayu; Tahalea, Sylvert Prian
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page49-54

Abstract

Shrimp is one of the marine biological resources available in almost all Indonesian waters and is one of the mainstay export commodities from the fisheries sub-sector. This is expected to improve the welfare of the community, so it is necessary to cluster the distribution of shrimp in Indonesia. Clustering is a data mining technique used to group data or partition datasets into subsets. One of the best clustering algorithms is X-means. X-means clustering is used to solve one of the main disadvantages of K-means clustering, namely the need for prior knowledge of the number of clusters (K). The purpose of this research is to obtain the results of clustering the distribution of shrimp in Indonesia using the X-means clustering algorithm. The data used in this study comes from the publication of Marine and Coastal Resources Statistics 2022 by the Central Bureau of Statistics of the Republic of Indonesia. This study obtained the results that there are 3 clusters in the clusterization of shrimp distribution in Indonesia. Cluster 0 consists of 1 province, cluster 1 consists of 27 provinces, and cluster 2 consists of 6 provinces.
Optimising the Fashion E-Commerce Journey: A Data-Driven Approach to Customer Retention Fadhila, Hasna Luthfiana; Permadi, Vynska Amalia; Tahalea, Sylvert Prian
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

A fashion e-commerce company offers a wide range of products from domestic and international brands that are popular with young people. However, there has been an increase in non-organically acquired customers, many of whom do not return to make repeat purchases. This has led to a higher customer churn rate, with a significant proportion of non-organically sourced customers failing to become repeat purchasers. Consequently, a churn analysis and prediction model were developed to address this issue. This paper employs the Recency, Frequency, and Monetary (RFM) framework for churn analysis and prediction. The framework is underpinned by three key dimensions: last purchase recency, purchase frequency, and total transaction value. Seven machine learning algorithms were evaluated to identify the optimal approach. Following a comparative analysis of these models, Random Forest emerged as the superior algorithm, demonstrating an accuracy of 0.99, precision of 0.97, recall of 0.99, ROC AUC of 0.98, and F1-score of 0.97. Consequently, this model will be utilized for churn prediction. Based on the analysis and modelling, several recommendations are offered to enhance customer retention for the fashion e-commerce platform. In addition to predicting churn, this paper provides insights into potential refinements to the churn prediction model, such as real-time monitoring, personalized customer experiences, analysis of customer feedback, and lifetime value analysis.
Co-Authors Agusdin, Riza Prapascatama Awang Hendrianto Pratomo Azhari SN Azhari SN Bambang Yuwono Fadhila, Hasna Luthfiana Fadhilah, Rahmi I Nyoman Tri Anindia Putra Ketut Sepdyana Kartini Larubun, Swine Enggelina Matdoan, M. Y. Nur Heri Cahyana Nur Heri Cahyana Putu Wirayudi Aditama Safira, Dinda Ayu Salouw, Elvis Vynska Amalia Permadi Warong, Maria Marlein Wibowo, Astrid Wahyu Adventri Wilis Kaswidjanti Yopi Andry Lesnussa Zeth Arthur Leleury, Zeth Arthur