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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 6 Documents
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Search results for , issue "Vol 4 No 2 (2023): Tensor: Pure and Applied Mathematics Journal" : 6 Documents clear
Modelling Negative Binomial Regression to Resolve Overdispersion (Case Studi: The Number of Families at Risk of Stunting in Maluku Province in 2021) Salenussa, Rosalinda A.; Van Delsen, Marlon Stivo Noya; Haumahu, Gabriella
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): 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/tensorvol4iss2pp63-72

Abstract

Stunting is a condition of stunted growth in children due to some chronic malnutrition and is a serious problem that affects the health and development of children around the world. Maluku Province is one of the regions in Indonesia that also experiences significant stunting problems. Statistical methods that can be used to see the relationship between response variables and predictor variables are Regression analysis, one of which is Poisson regression. However, Poisson regression is not often able to meet the equidispersion assumption, so to overcome this problem, another alternative method is used, namely Negative Binomial regression. The research conducted was to produce the best Negative Binomial Regression model and identify factors that significantly affect stunting families in Maluku Province. This study produced the best Negative Binomial model, namely: with the smallest AIC value of 208.5 and able to correct overdispersion in the data. A significant influential factor in the Negative Binomial model is the age of the wife who is too old ( ) with a significance level of 5%.
Penjadwalan Waktu Proses Produksi Tahu Menggunakan Pendekatan Aljabar Max-Plus (Studi Kasus : Pabrik Sumber Rizki) Nawar, Wa; Rahakbauw, Dorteus L; Patty, Dyana
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): 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/tensorvol4iss2pp73-82

Abstract

Penelitian ini bertujuan untuk memperoleh jadwal yang periodik dari waktu memulai proses produksi hingga selesai proses produksi tahu menggunakan pendekatan Aljabar Max-Plus pada Pabrik Sumber Rizki. Hasil yang diperoleh dalan penelitian diperoleh waktu maksimal proses produksi tahu dalam sehari adalah 7 jam 43 menit (463 menit) dengan waktu kerja dimulai pukul 07.00 – 14.43 WIT. Sedangkan sebelum menggunakan penerapan Aljabar Max-Plus waktu proses produksi selama 9 jam yakni dari pukul 07.00 – 16.00 WIT. Maka Pabrik Sumber Rizki dapat menghemat waktu pengerjaan selama 1 jam 17 menit.
Application of the Spatial Regression Model to Analyze Factors that Influence the Human Development Index (HDI) in West Papua Province Gainau, Intan Friska; Djami, Ronald John; Sinay, Lexy Jansen; Beay, Lazarus Kalvein
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): 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/tensorvol4iss2pp83-92

Abstract

Indeks Pembangunan Manusia merupakan suatu angka yang bertujuan untuk melihat kinerja pembangunan wilayah dengan dimensi yang luas. Provinsi Papua Barat merupakan salah satu provinsi yang IPM terus meningkat setiap tahunnya, meskipun terus mengalami peningkatan, Provinsi Papua Barat tetap menduduki peringkat ke-2 indeks pembangunan manusia terendah di Indonesia. Untuk terus meningkatkan indeks pembangunan manusia di Provinsi Papua Barat perlu diketahui faktor-faktor mengetahui yang mempengaruhinya, salah satu cara yang digunakan untuk menentukannya yaitu dengan pemodelan regresi. Pada penelitian ini dilakukan analisis regresi untuk mendapatkan informasi pengamatan yang dipengaruhi efek lokasi. Hasil penelitian ini menunjukan bahwa pemodelan menggunakan SAR lebih baik dibandingkan menggunkan OLS. Model SAR menghasilkan nilai koefisien determinasi sebesar 0.987126 lebih besar dari model OLS yaitu 0.982664 dan nilai AIC dari model SAR sebesar 40.3641 lebih kecil dibandingkan model OLS yaitu 44.1147.
Solusi Numerik Model Penyebaran Virus Covid-19 Dengan Vaksinasi Menggunakan Metode Runge-Kutta Fehlbrg Orde Lima Pada Provinsi Maluku Rijoly, Monalisa E.; Rumlawang, Francis Y.; Maurits, Stefalya
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): 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/tensorvol4iss2pp93-104

Abstract

COVID-19 is a new type of disease that has never been identified in humans before. The virus that causes COVID-19 is called Servere Acute Respiratory Syndrome Coronavirus-2 (Sars-Cov-2). The purpose of this study is to predict the spread of the COVID-19 virus by vaccination in Maluku Province in the next 20 months. The mathematical model used in this study is SEIRV with five sub-populations. Susceptible sub population (S), patient under surveillance (PDP)/Exposed sub population (E), Infected (I), Recovered (R), and Vaccinated (V) sub population as initial values S0 =190.295, E0=261, R0=172, and V0=7.693. Furthermore, numerical model simulations using the fifth order Runge-Kutta Fehlberg method over the next 20 months are for the susceptible sub population (S) of 693 people, for the Patient Under Monitoring sub population (PDP) (E) of 101 people, for the sub population infected (I) of 301 people, for the rate of recovery population (R) of 704 people and for the vaccinated sub population (V) of 16,951 so that it can be concluded that the sub population (V) has effectiveness because the susceptible sub population (S) decreases so that vaccination can be a solution to prevent the spread of the COVID-19 virus in Maluku Province within the next 20 months.
Perbandingan Model Prediksi Frekuensi Titik Panas di Provinsi Riau dengan menggunakan LSTM Wattimena, Emanuella M C; Tilukay, Meilin Imelda
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): 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/tensorvol4iss2pp53-62

Abstract

The high rate of deforestation in Indonesia due to forest and land fires (karhutla) is still a problem that requires the government's attention because it has become a regional and global disaster. The worst forest fire incident in Indonesia occurred in 2019, where the area of ​​the fire was 1,649,258 ha. Riau Province is one of the provinces in Indonesia that often experiences forest fires. Sipongi noted that an average of 52,986 ha of forest and land burned in Riau Province every year from 2016-2020. Thus, this study builds a predictive model for the emergence of hotspots as one of the forest fires that aims to reduce the rate of forest fires. Prediction model built using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The modeling is carried out using 2 data scenarios, namely multivariate data and univariate data, where multivariate data uses weather variables as predictors of hotspot frequency, and univariate data is hotspot frequency data. The data used is daily data from 2013-2020. Multivariate scenario dataset that produces RMSE of 23,323 and the correlation between actual and predicted data is 0,675554. The RMSE generated by the multivariate dataset is smaller than the RMSE generated by the model with the univariate dataset scenario, which is 25,750. However, datasets with univariate scenarios produce a larger correlation between actual and predicted values ​​when compared to multivariate dataset scenarios. The addition of weather factors as a predictor of hotspot occurrence can improve model performance, where this model is better at predicting values ​​when compared to univariate dataset scenarios even though the running time is longer. Keywords: forest and land fire, hotspots, Long Short-Term Memory, Recurrent Neural Network, prediction, time series
Application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method to Forecast the Number of Vessel Passenger Departures tt Yos Soedarso Ambon Port Butarbutar, Winda; Van Delsen, Marlon Stivo Noya; Djami, Ronald John
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): 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/tensorvol4iss2pp105-118

Abstract

Maluku Province is a fairly large archipelago in Indonesia. The large number of islands which are the administrative areas of Maluku Province, encourages the creation of a supporting transportation system. Yos Soedarso Ambon Port is the largest port in Ambon. Based on the statistics of Indonesia, the number of ship passengers at Yos Soedarso Port in Ambon during the April 2023 period experienced an increase of 44.97 percent, while in the March 2023 period, it only experienced an increase of 42.94 percent. Because the data used is time series data and has a seasonal pattern, the most appropriate method for predicting the number of passengers is the Seasonal Autoregressive Moving Average (SARIMA) method. The SARIMA method is an approach model developed from the Autoregressive Moving Average (ARIMA) model used on time series or data with a seasonal pattern. This research produced the best model for forecasting the number of departures of ship passengers at Yos Soedarso Port, Ambon. With an MSE value of 26.44. with the shape of the model with MAPE 10.23%.

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