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All Journal PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika Sains dan Teknologi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jambura Journal of Mathematics METIK JURNAL Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Griya Journal of Mathematics Education and Application Research in the Mathematical and Natural Sciences Research Review: Jurnal Ilmiah Multidisiplin Prosiding Seminar Nasional Pendidikan Matematika, Universitas Mulawarman Pattimura International Journal of Mathematics (PIJMath) Jurnal Riset Mahasiswa Matematika The Indonesian Journal of Computer Science Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Computational and Applied Mathematics
Isran K Hasan
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Prediksi Wisatawan Mancanegara di Indonesia Menggunakan Metode SARIMAX dengan Efek Variasi Kalender Libur Nasional Pakaya, Desya Neydi Putri; Achmad, Novianita; Hasan, Isran K; Wungguli, Djihad; Abdussamad, Siti Nurmardia
Jurnal Riset Mahasiswa Matematika Vol 4, No 6 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i6.34937

Abstract

Fluctuations in the number of foreign tourist arrivals often produce outlier values that can interfere with the accuracy of the forecasting model. This study uses a boxplot approach to detect outliers, followed by Natural Logarithm (ln) transformation as a treatment step. The Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) method is applied by considering three exogenous variables that show the effect of variations in the National Holiday calendar in the form of Nyepi Day, Idul Fitri Day and year-end holidays. The results of the analysis show that the three variables have a positive effect on the increase in the number of foreign tourist arrivals, where Nyepi Day makes the largest contribution compared to the other two holiday periods. Model 2 (0,1,1)(1,0,1)[12] was selected as the most optimal model based on the evaluation results of several models that have been compared. This model shows excellent performance, indicated by the Mean Absolute Percentage Error (MAPE) value of 3.75\% which indicates that the model has very high prediction accuracy. So that the SARIMAX model is effective in modeling and predicting the number of foreign tourist visits in Indonesia.
RAINBOW CONNECTION NUMBER AND TOTAL RAINBOW CONNECTION NUMBER OF AMALGAMATION RESULTS DIAMOND GRAPH(〖Br〗_4) AND FAN GRAPH(F_3) Ismail, Sumarno; Hasan, Isran K.; Sigar, Tesya; Nasib, Salmun K.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (980.35 KB) | DOI: 10.30598/barekengvol16iss1pp023-030

Abstract

If be a graph and edge coloring of G is a function , rainbow connection number is the minimum-k coloration of the rainbow on the edge of graph G and denoted by rc(G). Rainbow connection numbers can be applied to the result of operations on some special graphs, such as diamond graphs and fan graphs. Graph operation is a method used to obtain a new graph by combining two graphs. This study performed amalgamation operations to obtain rainbow connection numbers and rainbow-total-connection numbers in diamond graphs ( ) and fan graphs ( ) or . Based on the research, it is obtained that the rainbow-connection number theorem on the amalgamation result of the diamond graph ( ) and fan graph ( is with . Furthermore, the theorem related to the total rainbow-connection number on the amalgamation result of the diamond graph( ) and the fan graph ( is obtained, namely with .
Model Matematika Penyebaran Penyakit Demam Berdarah Dengue dengan Faktor Kesadaran Sosial: Analisis dan Simulasi Djuma, Clara Anggriani; Achmad, Novianita; Nuha, Agusyarif Rezka; Hasan, Isran K.; Arsal, Armayani
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33921

Abstract

Dengue haemorrhagic fever (DHF) is a serious health problem in many tropical regions, including Indonesia. The spread of this disease is influenced by various factors, one of which is the level of social awareness in the prevention and control of infection. This study developed a mathematical model of DHF spread by integrating social awareness as an additional compartment. The model was analysed by determining the equilibrium points and the basic reproduction number (R0), as well as stability analysis using the Routh–Hurwitz criterion. The analysis results show the existence of two types of equilibrium points: the disease-free equilibrium point (T1) and the endemic equilibrium point (T2). Point T1 is locally asymptotically stable when R0 1 and unstable when R0 1, while point T2 is locally asymptotically stable when R0 1. Sensitivity analysis shows that the social awareness parameter significantly influences the value of R0. Additionally, numerical simulations indicate that increasing social awareness can effectively reduce disease spread and drive the system toward a disease-free state. These findings underscore the importance of community-based awareness interventions in dengue control strategies.
PENERAPAN MODEL SPASIAL DURBIN DENGAN UJI LANJUTAN LOCAL INDICATOR OF SPATIAL AUTOCORRELATION UNTUK MELIHAT PENYEBARAN STUNTING DI KABUPATEN BONE BOLANGO HASIRU, LISA SYAHRIA; DJAKARIA, ISMAIL; HASAN, ISRAN K
Jambura Journal of Probability and Statistics Vol 3, No 1 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i1.13083

Abstract

One of the spatial regression analysis used is the spatial durbin model (SDM). This model can be applied to obtain the relationship between X and Y variables and their spatial effects. This research was continued by testing the local spatial autocorrelation, namely the local indicator of spatial autocorrelation (LISA) which aims to provide information on the pattern of spatial relationships of each observation area in Bone Bolango regency. Stunting cases in Gorontalo province, especially in Bone Bolango regency, are in a status that needs to be addressed immediately due to the prevalence rate in Bone Bolango regency in 2019 above 20% based on the WHO standard. The results showed that the factors that significantly affected stunting in 2019 in Bone Bolango regency were exclusive breastfeeding, the  proper sanitation and poverty. Meanwhile, based on the spatial effect, the factors that significantly affected stunting in 2019 in Bone Bolango regency were the percentage of exclusive breastfeeding, the percentage of LBW, the number of children with CBI and poverty. Based on result from the LISA, the observation areas of stunting cases showed that the percentage of exclusive breastfeeding, the number of children with CBI and povertu had a spatial autocorrelation or forming a grouping on the distribution of the stunting cases, the number of children with IDL and poverty, there are sub-districts that have spatial autocorrelation.
Implementasi Deep Learning dalam Pengklasifikasian Wajah Menggunakan Library Tensorflow pada Algoritma Convolutional Neural Network (CNN) Usman, Rahmat Setiawan; Hasan, Isran K.; Isa, Dewi Rahmawaty
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v4i2.18264

Abstract

The convolutional neural network is a deep learning method that functions to recognize and classify objects in an image. An example of its application is a facial recognition system which consists of a detection and classification process. Facial recognition by computers can be influenced by many things such as lighting, expressions, and the amount of dataset provided. This study aims to find out how to implement CNN to identify faces using Tensorflow with the Python programming language. The number of datasets used is 120 data and 10 respondents in total with different lighting conditions and shooting angles. Apart from the dataset, this study also uses several different scenarios in the training process, namely the difference in the number of epochs and the difference in the number of learning rates. Based on the results of the discussion, two models were obtained. In the first model, the results obtained an accuracy of 100% in the training process and 65% in the testing process. In the second model, the results obtained are 100% accuracy in the training process and 75% in the testing process. performance of the model made in this study can be said to be optimal in recognizing objects in several lighting conditions and image angles.
Implementation of Fuzzy Time Series Markov Chain Method using Kernel Smoothing in forecasting the Stock Price of PT. Elnusa Tbk. Mokodompit, Marcela; Nasib, Salmun K; Djakaria, Ismail; Yahya, Nisky Imansyah; Hasan, Isran K.
Indonesian Journal of Computational and Applied Mathematics Vol. 1 No. 1: February 2025
Publisher : Gammarise Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64182/indocam.v1i1.9

Abstract

This research aims to apply the Fuzzy Time Series Markov Chain combined with Kernel Smoothing in forecasting stock prices. The Kernel Smoothing technique is used to smooth stock data before the fuzzification process, resulting in more accurate predictions. The research stages include Data Smoothing, Fuzzy interval formation, Fuzzy Logical Relationship and Fuzzy Logical Relationship Group formation, and forecasting using Markov Chain Transition Matrix. Evaluation using MAPE shows a low prediction error rate, with a value of 0.005974257%, so this method is effective for volatile stock data. The implementation of this model is expected to be a reference for investors and analysts in understanding and predicting future stock price movements.
A Mathematical Model of the Mud Crab (Scylla Sp.) Involving Cannibalism and Refuge Abubakar, Agung Sucipto; Panigoro, Hasan S.; Djakaria, Ismail; Nuha, Agusyarif Rezka; Hasan, Isran K.; Asriadi, Asriadi
Indonesian Journal of Computational and Applied Mathematics Vol. 1 No. 1: February 2025
Publisher : Gammarise Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64182/indocam.v1i1.12

Abstract

Cannibalism, a common ecological phenomenon in various species, significantly impacts the population dynamics of mud crabs (Scylla sp.). This study develops a mathematical model to analyze the effects of cannibalism and protective refuges on the population sustainability of juvenile and adult mud crabs. The model identifies two equilibrium points: the extinction equilibrium and the coexistence equilibrium. Stability analysis using the Jacobian matrix reveals that the extinction equilibrium is locally asymptotically stable under specific conditions. In contrast, the coexistence equilibrium depends on the transition rate from juvenile to adult crabs and the effectiveness of protective measures. Numerical simulations demonstrate that increasing the transition rate and implementing higher levels of refuge protection mitigate the adverse effects of cannibalism, enhancing population stability. These findings provide a quantitative foundation for sustainable fisheries management and conservation strategies for mud crab populations in mangrove ecosystems.
Developing a Python-Based Application for a Discrete-Time Population Dynamics Model Nadhilah, Farhah; Panigoro, Hasan S.; Arsal, Armayani; Nurwan, Nurwan; Wungguli, Djihad; Hasan, Isran K.
Indonesian Journal of Computational and Applied Mathematics Vol. 1 No. 2: June 2025
Publisher : Gammarise Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64182/indocam.v1i2.20

Abstract

Difference equation is a type of equation in mathematics that is widely used to describe certain phenomena as time changes, one of which is in the field of population dynamics. In various studies, it is explained that solving complex population dynamics models is by using numerical simulations. Along with the development of technology, computational science is used to help solve mathematical problems that are difficult to solve analytically. One of them is to use a programming language, such as Python, to help present data in a graphical form. This research aims to develop an application that presents a computational solution to a difference equation using Python. The numerical results begin by entering the equation and variable values into the application, which then automatically generates a figure according to the entered equation. The figures generated in the application include one-dimensional and two-dimensional time series, as well as a Bifurcation diagram.
Development of a Hybrid ARIMA–Fourier Series Model for Air Temperature Forecasting at the Gorontalo Climatology Station Suci Tilome; Isran K. Hasan; Agusyarif Rezka Nuha
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcosv9i1.11462

Abstract

Air temperature is a key climatic variable that reflects environmental conditions and influences various human activities. Recent observations indicate a persistent upward trend associated with global warming, leading to greater variability in climate patterns. These changes highlight the importance of forecasting methods that can accurately represent the characteristics of air temperature time series to support planning and decision-making. Reliable prediction is therefore essential for understanding climate dynamics and anticipating potential environmental impacts. This study proposes an air temperature forecasting approach using a hybrid Autoregressive Integrated Moving Average (ARIMA) and Fourier Series Analysis (FSA) model. The ARIMA component is applied to model trend behavior and temporal dependence, while FSA captures the remaining seasonal patterns in the ARIMA residuals. By integrating these two approaches, the hybrid model aims to improve forecasting accuracy in the presence of both stochastic and periodic components. The results show that the hybrid ARIMA–FSA model achieves good forecasting performance, with a Mean Absolute Error (MAE) of 0.56, a Root Mean Square Error (RMSE) of 0.66, and a Mean Absolute Percentage Error (MAPE) of 2.07%. These findings indicate that the proposed model effectively represents air temperature dynamics and can be considered a reliable alternative for climate forecasting applications
Adaptive ANFIS–PSO Model for Forecasting Bird’s Eye Chili Prices in Gorontalo Province Nur Siyam Djibu; Isran K. Hasan; Agusyarif Rezka Nuha
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcosv9i1.11473

Abstract

Bird’s eye chili is one of the strategic food commodities in Indonesia with high price volatility and a significant contribution to food inflation, particularly in Gorontalo Province. The dynamic and nonlinear characteristics of bird’s eye chili prices often hinder accurate forecasting when using conventional methods, thereby requiring an adaptive approach capable of capturing complex data patterns. Therefore, this study applies an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized using Adaptive Particle Swarm Optimization (PSO) to improve the accuracy of bird’s eye chili price forecasting. This study utilizes daily bird’s eye chili price data in Gorontalo Province from 1 January 2019 to 31 October 2025, obtained from the National Strategic Food Price Information Center (PIHPS). The ANFIS model is optimized using adaptive PSO to obtain optimal parameter values that address local convergence problems and parameter sensitivity commonly encountered in conventional ANFIS models. Model performance is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the adaptive ANFIS–PSO model achieves a MAPE value of 17.4487% on the training dataset, which decreases significantly to 5.0741% on the testing dataset. The testing MAPE value below 10% demonstrates that the proposed model has excellent generalization capability in capturing bird’s eye chili price fluctuations. These findings confirm that adaptive PSO-based parameter optimization effectively enhances ANFIS performance in modelling nonlinear and highly volatile time series data. The proposed forecasting model can serve as a reliable analytical tool to support decision-making and regional food price stabilzation policies in Gorontalo Province.
Co-Authors Abubakar, Agung Sucipto Afandi W. Biga Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Alvitha Habibie Armayani Arsal Armayani Arsal Asriadi Asriadi Asriadi Asriadi Asriadi Awalia Emiro Buhungo, Rayhanul Jannah Demas Novaleda Abdul Karim Dewi Rahmawaty Isa Dewinto Burhan Djihad Wungguli Djuma, Clara Anggriani Doholio, Nadya Pratiwi Hanz Franklyn Bachruddin Wewengkang Hasan S. Panigoro HASIRU, LISA SYAHRIA Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Indrawati Lihawa Ismail Djakaria Ismail Saputra R. Harmain Ivan Novri Jefriyanto Ibrahim Karim, Finansiya S. Abd. Kasim, Afrianto Pratama Khairun Nisa Humolungo Kurniawan Kurniawan La Ode Nashar Lailany Yahya Misranti A. Samulu Mokodompit, Marcela Nadhilah, Farhah Nanda Arista Rizki Naufal Daffa Pahrun NISKY IMANSYAH YAHYA Nisky Imansyah Yahya Novianita Achmad Nur Anggraini T. Ali Nur Falaq Nur Miftah Muhammad Nur Siyam Djibu Nurul Afifah Nurwan Nurwan Nurwan, Nurwan Pakaya, Desya Neydi Putri Putri Lapitung Rahmi, Emli Ramdan Hayati Ramla Mohamad Resmawan Resmawan Saiful Pomahiya Salmun K. Nasib Sampe, Nofia Sigar, Tesya Siti Nurmardia Abdussamad Sri Ayu Nurdin Suci Tilome Suharmin, Wandayana Nur'Amanah Sumarno Ismail Suryahaty Aisyah Aulia Kaluku Thesalonica Graina Barung Trieke Nurfadilah Harun Ulopo, Asrul S Usman, Rahmat Setiawan Winni A. Pakaya