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All Journal Tensor: Pure and Applied Mathematics Journal
Taihuttu, Pranaya D. M.
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Computer Science & IT Mathematics

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Optimization of LSTM Model for Rainfall Prediction in Ambon City: Comparison of Mean Imputation and Interpolation in Time Series Data Prediction Wattimena, Emanuella M. C.; Taihuttu, Pranaya D. M.; Waas, Devi V.; Palembang, Citra F; Pattiradjawane, Victor E.
Tensor: Pure and Applied Mathematics Journal Vol 6 No 2 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss1pp49-56

Abstract

Rainfall prediction is an essential aspect of meteorology, agriculture, and disaster management, particularly in regions like Ambon, where rainfall patterns significantly impact daily life. However, one of the major challenges in developing an accurate predictive model is handling missing values in the dataset. This study aims to optimize the Long Short-Term Memory (LSTM) model for rainfall prediction in Ambon by comparing two missing value handling techniques: mean imputation and interpolation. The dataset used in this study consists of daily rainfall data from 2021 to 2024, with approximately 26.89% missing values. Two experimental scenarios were conducted: the first using mean imputation to fill in missing values with the average rainfall, and the second using linear interpolation. Both scenarios utilized the same LSTM architecture to evaluate their impact on model performance. The evaluation metrics used in this study include Root Mean Square Error (RMSE) and R-squared (R²). The results show that the interpolation-based model achieved a lower RMSE and a slightly higher R² value than the mean imputation-based model, indicating better predictive performance. However, both models struggled to capture extreme values, necessitating further improvements. To address this limitation, a more complex LSTM architecture was implemented in the subsequent experiments, incorporating additional layers and optimized hyperparameters. The findings suggest that choosing an appropriate missing value handling method significantly influences the predictive accuracy of LSTM models for rainfall forecasting. This research contributes to the development of more reliable weather prediction models, which can aid in agricultural planning, flood risk assessment, and climate change adaptation in Ambon.
The Rainbow Vertex Connection Number of Some Amalgamation of Two Cycles Taihuttu, Pranaya D. M.; Tilukay, Meilin I.; Rumlawang, Francis Y.; Wattimena, E. M. C.
Tensor: Pure and Applied Mathematics Journal Vol 6 No 2 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss1pp57-66

Abstract

This paper focuses on rainbow vertex coloring in a graph G, in which, for every two vertices in G, there exists a rainbow vertex path where all internal vertices have distinct colors. The rainbow vertex connection number of G, denoted by rvc(G), is the minimum number of colors required to make G rainbow-vertex connected. In this paper, we determine the rainbow vertex connection number of some amalgamation of two cycles.
Pemodelan Sistem Antrian Pelayanan BPJS (Badan Penyelenggara Jaminan Sosial) Menggunakan Petri Net dan Aljabar Max-Plus Simbolon, Yohana L.; Rumlawang, Francis Y.; Dahoklory, Novita; Patty, Henry W. M.; Taihuttu, Pranaya D. M.; Wattimena, Abraham Z. Wattimena
Tensor: Pure and Applied Mathematics Journal Vol 6 No 2 (2025): Vol 6 No 2 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss2pp75-86

Abstract

Hospitals are one of the health facilities that serve patients with various types of services, including BPJS patients. Like other hospitals, the queue system is a challenge in service management, especially in outpatient services. The imbalance between the number of patients coming and the service capacity can cause long waiting times. In this study, outpatient queue modeling was carried out at Leimena General Hospital, Ambon, using Petri Net to describe the service flow, and Max-Plus algebraic analysis was applied to estimate patient waiting times more accurately. The simulation results showed that increasing the number of resources, such as adding registration counters and doctors in the laboratory, was able to significantly reduce patient waiting times at various stages of service, especially in the pharmacy. This modeling shows that the Petri Net and Max-Plus approaches are not only effective in mapping the queue system, but can also be used as a basis for decision making in optimizing hospital services. This study is expected to be a reference for hospitals in improving service efficiency and for further researchers to develop more complex models by considering additional relevant variables.
Co-Authors H. W. M. Patty Meilin Imelda Tilukay Novita Dahoklory Palembang, Citra F Pattiradjawane, Victor E. Rumlawang, Francis Y. Simbolon, Yohana L. Waas, Devi V. Wattimena, Abraham Z. Wattimena Wattimena, E. M. C. Wattimena, Emanuella M C