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Harmanus Batkunde
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Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Unversitas Pattimura Jln. Ir. M. Putuhena, Kampus Unpatti, Poka - Ambon 97233, Provinsi Maluku, Indonesia
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Tensor: Pure and Applied Mathematics Journal
Published by Universitas Pattimura
ISSN : 27230325     EISSN : 27230333     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics
Tensor: Pure and Applied Mathematics Journal is an international academic open access journal that gains a foothold in the field of mathematics and its applications which is issued twice a year. The focus is to publish original research and review articles on all aspects of both pure and applied Mathematics. It Publishes original research papers of the highest Algebra Analysis Discrete Mathematics Geometry Number Theory Topology Applied Mathematics Computational Mathematics Probability Theory and Statistics
Arjuna Subject : Matematika - Matematika Terapan
Articles 66 Documents
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Analisis Perbandingan Optimasi Stochastic Gradient Descent dan Adaptive Moment Estimation dalam Klasifikasi Emosi dari Audio Menggunakan Convolutional Neural Network Tutuhatunewa, Aldelia Jocelyn; Rahakbauw, Dorteus Lodewyik; Leleury, Zeth Arthur
Tensor: Pure and Applied Mathematics Journal Vol 6 No 1 (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/tensorvol6iss1pp13-22

Abstract

Emotion plays a fundamental role in human life, influencing behavior, social interaction, anddecision-making. Successful communication and understanding between individuals depend greatly on ourability to recognize and express emotions. In this context, sound or audio plays a key role as a medium thatreflects and conveys human emotional expression. In the era of information technology and artificialintelligence, emotion recognition through sound has become a growing focus of research. Machine learningalgorithms, particularly neural networks, can be trained to understand and classify emotions conveyed invarious forms, including text, images, videos, and audio. Among these algorithms, Convolutional NeuralNetwork (CNN) has shown promising performance in emotion classification tasks. In this study, thecomparison between Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam)optimizers in emotion classification from audio using CNN is investigated. The research aims to determinethe optimal optimizer for emotion classification tasks. The results suggest that SGD optimizer outperformsAdam in terms of overall accuracy, with SGD achieving 53% accuracy compared to Adam's 48% accuracy inThe Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. Therefore, foremotion classification from audio data, Stochastic Gradient Descent (SGD) optimizer is recommended forbetter performance.
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 1 (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 1 (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.
Fungsi Trace dan Fungsi Norm Lapangan Perluasan Atas Q Dahoklory, Novita; Patty, Henry Willyam Michel
Tensor: Pure and Applied Mathematics Journal Vol 6 No 1 (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/tensorvol6iss1pp39-48

Abstract

Suppose L/K is an extension field where K⊆L so that L can be viewed as a vector space over K. Moreover, it is known that for every α∈L, we can construct a linear transformation T_α: L→L where T_α (x)=αx for all x∈L so that we have the representation matrix [T_α] of T_α. In this study, the trace and norm functions are discussed which are defined using the trace and determinant of the matrix [T_α]. Furthermore, this study will also discuss the application of the trace and norm functions in the field of an extension field especially Q(∛2) over Q.
Modeling the Spread of Hepatitis B Disease from the SEIR Model in East Java Using RKF 45 Na'malia, Sakinun; Faisol, Faisol; Yulianto, Tony
Tensor: Pure and Applied Mathematics Journal Vol 6 No 1 (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/tensorvol6iss1pp1-12

Abstract

Hepatitis B is an infectious disease that has a major impact on public health, especially in East Java Province with a high prevalence of cases. This study aims to model the spread of Hepatitis B using the SEIR model (Susceptible, Exposed, Infected, Recovered) and solved numerically with the Runge-Kutta Fehlberg method (RKF45). Simulation results for 10 years showed that the susceptible population decreased from to individuals, while the exposed compartment increased from to . The infected population peaked at around individuals in year 2 and decreased to individuals, while the cured population continued to increase until it reached at the end of the period. The SEIR model with the RKF45 method proved effective in describing the dynamics of the spread of Hepatitis B mathematically and can be utilized as a predictive tool in supporting public health policy.
Kajian Basis dan Dimensi pada Ruang Hipervektor Atas Lapangan Kambu, Loisa Genesis; Patty, Henry Willyam Michel; Bakarbessy, Lusye; Dahoklory, Novita
Tensor: Pure and Applied Mathematics Journal Vol 6 No 1 (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/tensorvol6iss1pp23-38

Abstract

The concept of algebraic hyperstructure is a generalisation of the concept of algebraic structure. The concept of algebraic hyperstructure discussed in this study is hypervector space. The purpose of this paper is to study the basis and dimension of the hypervector space. In hypervector space there is a strong left distributive property, namely (a+b)∘x=a∘x+b∘x, ∀a,b∈K,∀x∈V. In addition, in a hypervector space that has the K-invertible property, the importance of the strong left distribution property and the invertible property in this hypervector space ensures that each linearly independent set has no more than n elements, where n is the dimension of the hypervector space. Furthermore, the addition of vectors from outside the base will result or not linearly independent. Translated with DeepL.com (free version)

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