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INDONESIA
JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Published by Universitas Hasanuddin
ISSN : 18581382     EISSN : 26148811     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini mempublikasikan paper-paper original hasil-hasil penelitian dibidang Matematika, Statistika dan Komputasi Matematika.
Arjuna Subject : -
Articles 496 Documents
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Analisis Model Diskret Predator-Prey untuk Pengendalian Kepunahan Pesut Mahakam (Orcaella brevirostris) dengan Efek Toksik di Sungai Mahakam Baso Indar; Moh. Nurul Huda; Syaripuddin Syaripuddin
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.26460

Abstract

The Pesut Mahakam (Orcaella brevirostris) is an East Kalimantan animal in critical condition and on the verge of extinction. One of the causes of the extinction of the Mahakam dolphins is the toxic effects of environmental pollution. In this study, an analysis of the discrete predator-prey model using Euler's forward difference between the Pesut Mahakam (Orcaella brevirostris) and its prey was investigated. Dynamic analysis includes determining the equilibrium point, stability analysis of the equilibrium point, and numerical simulation. The value of the Mahakam dolphin growth rate parameter (predator) is obtained by adjusting the curve based on data in the field. Numerical simulations were carried out to describe the results of the analysis. Changes in the value of the toxic effect rate parameter affects the number of Mahakam dolphins and their prey.
Comparison of Transfer Learning Algorithm Performance in Hand Sign Language Digits Image Classification A. Muh. Amil Siddik
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.26503

Abstract

Image hand sign classification has become an interesting topic in image processing and machine learning. However, to achieve optimal performance in hand sign image classification tasks, a large and diverse dataset as well as powerful learning algorithms are required. One popular technique for improving the performance of classification models is transfer learning, which allows the use of knowledge learned from previous models and applies it to new tasks. In this study, the performance of two different transfer learning algorithms, ResNet-50 and VGG-16, was compared on the Sign Language Digits Dataset, which consists of 10 different types of handwriting images. The results of the experiment showed that both tested transfer learning algorithms had good performance. However, VGG-16 provided the best results with an accuracy of 97,29%, precision of 97,38%, recall of 97,45%, and an F1 score of 97,36%, while ResNet-50 achieved an accuracy of 94,57%, precision of 94,75%, recall of 94,96%, and an F1 score of 94,78%. In conclusion, transfer learning algorithms are effective techniques for improving the performance of hand sign image classification models. Choosing the appropriate transfer learning algorithm and dataset can help generate more accurate classification models.
Tensor product for g-fusion frame in hilbert $C^{\ast}-$modules fakhr-dine Nhari; Mohamed Rossafi; Youssef Aribou
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.26675

Abstract

In this paper, we stady the tensor product of $g-$fusion frame in Hilbert $C^{\ast}-$modules and we give the frame operator for a pair of $g-$fusion bessel sequences in tensor product of Hilbert $C^{\ast}-$modules.
Dimensi Partisi Hasil Amalgamasi-Sisi pada Graf Siklus Ananda Dwi Nabila Nanda; Hasmawati Hasmawati; Muh. Nur
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.26808

Abstract

The graph is a pair of sets , where is a finite set whose elements are called vertices, and is the set of pairs of members of . which is called the edge. Let be a simple graph where . The distance between points and is denoted by is the length of the shortest path between and . Given and there is a vertex on the connected graph , then the distance between and is denoted . If is -partition of , then the representation of with respect to is -ordered pairs, . If the -ordered pairs for are all different, then the partition is called a dimension partition. The minimal -number which is the -differentiating partition of is called the partition dimension of and is denoted by . In this study, the partition dimensions of the sided amalgamation result will be determined on an even-order cycle graph. In determining the dimensions of the partition, characterization of the partition dimensions is used in the path graph, the lemma about the distinguishing set and the equivalence point, especially in the even-order cycle graph. The results of this study are pd(Amal(Cn,e,k)) = 3 for n≥4 , pd(Amal(C4,e,k))=4 for k=4 , pd(Amal(C4,e,k))=3+m for k=2m+3 and k=2m+4 where m=1,2,3,...
PENDUGAAN FAKTOR – FAKTOR YANG MEMENGARUHI KASUS STUNTING DI JAWA BARAT TAHUN 2021 MENGGUNAKAN REGRESI SPASIAL BINOMIAL NEGATIF Anik Djuraidah; Mely Amelia; Rahma Anisa
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.26984

Abstract

Stunting is a childhood growth and development disorder characterized by below-normal height.  West Java, with its stunting rate of 24.5 percent, is one of the provinces included in the top 12 priority provinces in implementing the National Action Plan to Accelerate Stunting. Stunting cases are count data and their occurrence is rare. The analysis for the count data is Poisson regression with the assumption that equidispersion must be met. One way to overcome overdispersion is to use negative binomial regression. This study aimed to determine predictors/factors affecting stunting cases in West Java province in 2021 using negative binomial spatial regression. The data in this study comes from the publication of the West Java Health Service and the West Java Central Statistics Agency in 2021 with districts/cities as the object of observation. There is a spatial effect in the stunting data, so the spatial regression model is suitable. The results show that there is an overdispersion in the Poisson regression. The spatial effect test shows that there is a spatial dependence on the response variable and some predictors. The negative spatial autoregressive binomial is the best model with the lowest AIC value. The factors that have a significant effect are the percentage of infants aged less than six months who are breastfed, the percentage of food processing establishments that meet the requirements, and the percentage of infants with low birth weight.
Pemodelan Matematika pada Penyebaran Penyakit Gondongan Berdasarkan Model Siqr Ratna Widayat; Nadzifah Nadzifah; Vika Fatihatul Fiqiyah; Nita Rizkiyani
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27060

Abstract

Mumps is an infectious disease caused by a virus that is quite dangerous because vaccine strategies often fail. In severe cases, mumps can lead to infection of the covering of the brain (15%), pancreatitis (4%), permanent deafness, and painful swelling of the testes which rarely causes infertility. In addition, genetic mutations that are continuously carried out by the virus and the movement of individuals from one area to another can result in the spread of causing an epidemic in an area. This study discusses the spread of mumps by assuming there is a population of individuals who are quarantined. In addition, it is possible to die from mumps in the presence of complications from other diseases. The purpose of this research is to look at the behavior of the system solution analytically depending on the basic reproduction number ( ) accompanied by numerical simulations on initial values with certain parameters. The SIQR model is used by assumes that individuals who have recovered will get permanent immunity against Mumps. The analysis was carried out around the disease-free equilibrium point and the endemic equilibrium point. It was found that the disease-free equilibrium point is locally asymptotically stable with conditions . This means that if the birth rate is less than the natural death rate and the contact rate between individuals infected with mumps and susceptible individuals is less than the sum of the recovery rate, death rate and quarantine rate, then the mumps outbreak will eventually disappear from the population. On the other hand, the local asymptotically stable endemic equilibrium point if . This means that if the mumps disease is about to become epidemic and infected individuals are still in the population. This can be interpreted as if the birth rate is greater than the natural death rate and the contact rate between individuals infected with mumps and susceptible individuals is greater than the sum of the recovery rate, death rate and quarantine rate then the mumps outbreak will still exist in the population. However, if the value of the quarantine rate is increased, the value will decrease and the quarantined individual population approaches the infected individual population. This means that the proposed quarantine scenario with the addition of class Q makes the system solution better. Furthermore, a simulation was carried out with the Maple 18 program.
Penerapan Metode Peramalan Long Short Term Memory (LSTM) pada Faktor-Faktor Penyebab Badai di Indonesia Galuh Oktavia Siswono; Yeni April Lina; Verencia Pricila
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27151

Abstract

Effective disaster mitigation strategies are paramount in the realm of risk management concerning natural calamities, with the primary objective of mitigating potential devastation. A pragmatic and impactful method involves predicting the contributory aspects of such disasters, encompassing variables such as torrential rainfall and formidable wind velocities that tropical cyclones bring. In this study, a comparative analysis of forecasting methodologies is undertaken, precisely the Long Short-Term Memory (LSTM) technique and the Holt Winter approach, both directed toward gauging the impact of tropical cyclones. This investigation focuses on two critical factors: the forecast of precipitation intensity and the estimation of maximum wind speed. The outcomes underscore the superior predictive capabilities of the LSTM method, unequivocally revealing its aptitude for predicting rainfall and wind speed. Impressively, the LSTM method yields remarkable precision levels of 97.433% for rainfall and an even higher accuracy of 99.018% for maximum wind speed forecasting. In essence, this study highlights LSTM's efficacy in disaster prediction with substantial accuracy.
Identification Of Hijaiyah Letters Image Using Extreme Learning Machine Method Luluk Sarifah; Siti Khotijah; Marinatul Khaliqah Khaliqah
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27158

Abstract

The development of digital image processing technology has many benefits, one of which is the identification of an object, such as the identification of the image of the hijaiyah letter. Hijaiyah letters are the letters of the arabic alphabet as the original language of the Qur'an. In essence, humans have the ability to recognize and distinguish hijaiyah letter patterns from one another, but this is not the case with computers, using digital images and machine learning, in this study an identification concept was built by recognizing the image of hijaiyah letters using one of the machine learning methods. namely the extreme learning machine (ELM) method. Extreme learning machine (ELM) is a feedforward neural network with one hidden layer or better known as single hidden layer feedforward neural networks (SLFNs). Therefore, the purpose of this study is that the computer can identify objects as well as human capabilities and see how accurate the results obtained in the ELM method are. The digital image identification process using the extreme learning machine (ELM) method is carried out in two stages, namely training and testing, where previously the preprocessing process was carried out first by changing the color of the RGB image to HSV and processing the color v, then segmentation was carried out with the aim of separating the objects (foreground) with the background, then to make it easier to recognize the pattern, a morphological process is carried out. From the simulation carried out on the test data, the results obtained an average accuracy of 90% with an error of 10%.
Efektivitas Metode Exponential Smoothing untuk Prediksi Data Runtun Waktu Pola Tren dan Non-musiman (Studi Kasus Cakupan Vaksinasi Covid-19): Efektivitas Metode Exponential Smoothing untuk Prediksi Data Runtun Waktu Pola Tren dan Non-musiman (Studi Kasus Cakupan Vaksinasi Covid-19) Wiwik Wiyanti
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27193

Abstract

Purpose of this research is effectiveness the exponential smoothing for predict the time series data which has trend and non-seasonal characteristic. In this research using data case vaccinate of covid-19 1st, 2nd, 3rd and 4th. The advantage of this research is we can choose the best method for this type data. The methodology of this research is quantitative, with analyze data method is exponential smoothing (SES, ARRSES, and HOLT’S linear). The result of data analyze is the predict error for four vaccinate data are very close to zero. SES gets 0% to 0,73%, ARRSES gets 0% to 0,66% and Holt’s linear gets 0% to 0,29%. The result of this research can conclude that exponential smoothing method effective for predict the data with trend and non-seasonal data.  
Analisis Curah Hujan Ekstrem Daerah Provinsi Papua Barat Menggunakan Max Stable Process Model Schlather Alya Azzahra; Pratnya Paramitha Oktaviana; R. Mohamad Atok
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27433

Abstract

Data from Badan Pusat Statistik (BPS) in 2021 notes the province of West Papua as the province with the 5th highest rainfall in Indonesia with a rainfall of 3,811 mm. The province also recorded 268 rainy days, the most amongst all provinces in the country. The excess amount of rain is one of the causes of disasters such as floods. This research uses rainfall data from the Regencies of Manokwari, Fakfak, and Kaimana. The method used is Spatial Extreme Value particularly Schlather's Model of the Max Stable Process. The data used is hourly rainfall for the period of 13 March 2022 to 17 October 2022 with the proportion of training and testing data respectively 85.84% and 14.16%. Extreme data collection was carried out using the Block Maxima method with a fitting to the Generalized Extreme Value (GEV) distribution before being transformed into the Frechet Z margin units. The calculation of the extreme coefficient resulted in a value between 1.4 to 1.85, indicating a relationship between the locations. Next, the best trend surface model was determined, which involves latitude coordinates for the calculation of the location parameter and both longitude and latitude coordinates for the calculation of the scale parameter. The spatial parameter estimation is carried using the powered exponential correlation function. Then, model validation was carried out using MAPE based on a comparison of return levels and testing data. The MAPE values obtained was 22.61% for the BFGS iteration method. The final step is to calculate return levels for periods of 2, 4, 6, 8, and 10 years ahead. All the results were categorized under very heavy rain. These results can be used by related parties to carry out disaster mitigation efforts.

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