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Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango, Gorontalo, Indonesia
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Jambura Journal of Mathematics
Published by Universitas Negeri Gorontalo
ISSN : 26545616     EISSN : 26561344     DOI : https://doi.org/10.34312/jjom
Core Subject : Education,
Mathematics
Jambura Journal of Mathematics (JJoM) is a peer-reviewed journal published by Department of Mathematics, State University of Gorontalo. This journal is available in print and online and highly respects the publication ethic and avoids any type of plagiarism. JJoM is intended as a communication forum for mathematicians and other scientists from many practitioners who use mathematics in research. The scope of the articles published in this journal deal with a broad range of topics, including: Mathematics; Applied Mathematics; Statistics; Applied Statistics.
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Articles 165 Documents
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Kombinasi Metode Bilqis Chastine Erma dan Sumathi Sathiya dengan Metode Stepping Stone untuk Optimasi Masalah Transportasi Mardiansah, Rifki; Tastrawati, Ni Ketut Tari; Sari, Kartika
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23857

Abstract

Transportation problems experienced by UD. Raja Wangi in distributing Maha Dewa fragrant incense. UD. Raja Wangi incurs high transportation costs due to irregular direct distribution patterns and has not paid attention to the route to be passed. One way to solve the problem is to use transportation methods to get the optimal distribution route so that the transportation costs incurred are minimal. This study aims to solve transportation problems using the Bilqis Chastine Erma (BCE) method and the Sumathi Sathiya method with the Stepping Stone method to obtain the optimal solution. The Bilqis Chastine Erma (BCE) and the Sumathi Sathiya methods are indirect methods of solving transportation problems by obtaining an initial solution. After obtaining the initial solution, the Stepping Stone method is used to obtain the optimal solution. The results showed that the optimal solution using the Stepping Stone method based on the initial solution of the Bilqis Chastine Erma (BCE) method obtained a total transportation cost of Rp42,937.00 while the optimal solution using the Stepping Stone method based on the initial solution of the Sumathi Sathiya method obtained a total transportation cost of Rp38,727.00. In addition, the Sumathi Sathiya method gets a difference in total transportation costs of Rp11,790.00 or 23% of the total transportation costs incurred by the UD. Raja Wangi. Therefore, the Sumathi Sathiya method is the best solution for minimizing transportation costs.
Implementasi Metode New Jersey dalam Perhitungan Cadangan Premi dengan Suku Bunga Stokastik dan Konstan Sulistyawati, Yuni; Kartikasari, Mujiati Dwi
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.24668

Abstract

Premium reserve allocation represents an obligation undertaken by insurance companies to set aside funds for future claims payment to policyholders. Some insurance companies have faced operational challenges, leading to their closure, primarily due to inaccurate premium reserve computations. This research aims to calculate premium reserve in lifelong insurance using the New Jersey method, an improvement upon the Illionis method. The New Jersey method initiates the premium reserve at the beginning or end of the first year at zero dollars. The majority of premium reserve calculations still rely on constant interest rates. However, in reality, this approach inadequately reflects future fluctuations in interest rates, which are crucial for long-term life insurance products. Therefore, this study implements a more realistic approach using stochastic elements, using the Vasicek stochastic interest rate model to determine premium reserve values. From this research, it was found that there was quite a significant difference between the New Jersey method premium reserve value and the two interest rates. The calculation graph shows that the premium reserve value using the Vasicek model of stochastic interest rates tends to be lower than when using constant interest rates. This can be caused by the results of non-constan variations in interest rates in the Vasicek model which ultimately results in fluctuations in interest rates which wffect the calculation of premium reserve.
The Comparison A-Optimal and I-Optimal Design in Non-Linear Models to Increase Purity Levels Silicon Dioxide Aliu, Muftih Alwi; Syafitri, Utami Dyah; Fitrianto, Anwar; Irzaman, Irzaman
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26253

Abstract

One of the obstacles that arise in optimal design is the non-linear model. The relationship between temperature factors and the temperature increase rates with the purity of silicon dioxide (SiO2) forms a non-linear pattern. Determining the optimal design for a non-linear model is relatively more complex than a linear model because it requires additional information in its information matrix. Therefore, this issue necessitates further research on optimal design in non-linear models. This study uses the polynomial Taylor approach to approximate the non-linear equation through a linear equation using the appropriate optimal design methods, namely A-Optimal and I-Optimal criterion. The point search algorithm used was variable neighborhood search, this algorithm searches for design points by exploring several different neighborhood structures. These two methods were chosen to compare the characteristics and performance of the designs produced, aiming to obtain an optimal design to improve SiO2 purity (non-linear case) using the same algorithm, VNS. The research results showed that the design pattern produced by the A-Optimal design formed three temperature groups, namely the minimum temperature of 800°C - 820°C, the middle temperature of 850°C, 860°C, and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The design pattern produced by the I-Optimal design formed a full quadratic pattern, namely the minimum temperature of 800°C and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The I-Optimal design demonstrated the best performance (most optimal) in the aspect of prediction variance compared to the A-Optimal design across all alternative points in this study to improve SiO2 purity.
Penerapan Metode I-CHAID Menggunakan SMOTE pada Data Tidak Seimbang untuk Klasifikasi Durasi Studi Mahasiswa Akor, Umar D.; Payu, Muhammad Rezky Fiesta; Nashar, La Ode
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.27978

Abstract

The issue of delayed graduation is often encountered in various universities, including in the Statistics Study Program at Universitas Negeri Gorontalo, for graduates between 2018 and 2023. Among them, 162 students (76.5%) experienced delayed graduation, and 5 students (2.35%) dropped out. This delay in graduation is caused by various factors, necessitating a classification method capable of identifying the most dominant factors. The classification method used in this research is Improved Chi-Square Automatic Interaction Detection (I-CHAID) with the Synthetic Minority Oversampling Technique (SMOTE) approach. SMOTE is employed to address imbalanced data. Based on the I-CHAID classification tree with the SMOTE approach, the significant factors influencing the duration of study completion are the GPA in the fifth semester (67.2%) and the mentoring method (87.5%). As for the classification performance from the 40% testing data, the accuracy achieved was 40.6%, meaning that out of 32 samples, 13 were correctly classified. The sensitivity value was 6.25%, indicating the success rate of classifying data for students who graduated on time. The specificity value was 75%, showing the success rate in classifying data for students who did not graduate on time. The precision value was 20%, reflecting the accuracy of predicting students who actually graduated on time, and the F-measure was 9.52%, indicating the balance between precision and sensitivity.
The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments Prasmono, Amimah Shabrina Putri; Kartikasari, Mujiati Dwi
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23591

Abstract

Childfree is a condition in which a person or couple decides not to have children in marriage. Childfree became popular in Indonesia when YouTuber and influencer Gita Savitri uploaded an Instagram story about it. This brought many pros and cons among the people towards the freedom to have children. Many TV broadcasts and YouTube videos cover this phenomenon. Several YouTube channels that broadcast this phenomenon are Menjadi Manusia and Analisa Channel. We collect YouTube comment data using web scraping techniques. From September 2021 to September 2022, 674 sample data points were obtained from two YouTube videos. Data is labelled (positive, negative, and neutral) using the Indonesian language lexicon approach as well as the Support Vector Machine (SVM) and Random Forest algorithms to determine the best model for classifying YouTube comments. The purpose of this research is to understand the public's perception of childfree and to compare the accuracy and AUC values of the two methods. Based on the results of the analysis, 128 comments are classified as positive, the remaining 39 comments are classified as neutral, and 503 comments are classified as negative. This shows that that the commentators on YouTube do not support or give a negative stigma to people who adhere to childfree. The solution to the balanced data problem for each sentiment class uses the random oversampling (ROS) approach. The RBF kernel SVM classification algorithm is a suitable method for classifying commentary data with an accuracy of 98.01% and an AUC of 98.58%, while the Random Forest algorithm only obtains an accuracy of 94.37% and an AUC of 95.87%.
Perbandingan Propensity Score Stratification dan Propensity Score Matching dengan Pendekatan Multivariate Adaptive Regression Spline Akolo, Ingka Rizkyani; Ningsih, Setia; Dukalang, Hendra
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26137

Abstract

Research on complications of Diabetes Mellitus (DM) is multifactorial, where the risk factors causing DM complications are interrelated, leading to confounding bias, which results in inaccurate research findings. Confounding bias can be reduced using the propensity score method. This study aims to compare the performance of the Propensity Score Stratification (PSS) and Propensity Score Matching (PSM) methods with the Multivariate Adaptive Regression Spline (MARS) approach in estimating treatment effects on DM complication cases. The data used is the medical records of type-2 DM patients at Hospital X. The results showed that the PSS method with the MARS approach is not suitable for small data sets, as it can lead to treatment or control groups lacking members, making it impossible to calculate the p-value in balance testing or the Percent Bias Reduction (PBR). The estimated Average Treatment Effect (ATE) using the PSS method was 0.487 with a PBR of 35.1%, whereas the estimated Average Treatment for Treated (ATT) using the PSM method was 0.531 with a PBR of 99.46%. These PBR values indicate that the best method for estimating treatment effects and the one that can reduce the most bias in this case is the PSM method with MARS. The analysis also showed that serum uric acid levels significantly affect the peripheral diabetic neuropathy (PDN) status of DM patients.
Bilangan Kromatik Permainan Graf Ubur-Ubur, Graf Siput, dan Graf Gurita Abdurahman, M Luthfi; Helmi, Helmi; Fran, Fransiskus
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23958

Abstract

Graph coloring is the process of assigning colors to the vertices or edges of a graph. Specifically, coloring the vertices in graph coloring can be implemented in graph coloring games. This article aims to determine the game chromatic number of the jellyfish graph Jm,n, snail graph SIn, and octopus graph On. For example, give G as a simple, connected, and undirected graph and give two players, namely A as the first player and B as the secondary player. The two players A and B color all the vertices of graph G with available colors. The game's rules are that A must ensure that all vertices of graph G are colored, while B aims to prevent graph G from being uncolored. Players A and B take turns coloring the vertices of graph G, ensuring that the color of neighboring vertices must be different, with A taking the first turn. If all vertices have been colored, A wins the game, but A loses if some vertices remain uncolored despite available colors, A loses. The smallest value of k for which A has a winning strategy in the game with k colors is the game chromatic number, denoted as χg(G). This thesis discusses the graph coloring game of the tadpole graph Tm,n, broom graph Bn,d, jellyfish graph Jm,n, and tribune graph Tn to find the game chromatic number. The results show that player A uses a strategy to color the vertex with the highest degree in the graph, ensuring that player A always wins. Therefore, the game chromatic number of the jellyfish graph, snail graph, and octopus graph is χg (Jm,n) = 3 for m, n ≥ 1, and χg (SIn) = 3 for n ≥ 1, while χg (On) = 3 for n = 2, 3, 4; χg (On) = 4 for n ≥ 5.
Comparison of Fuzzy Grey Markov Model (1,1) and Fuzzy Grey Markov Model (2,1) in Forecasting Gold Prices in Indonesia Soraya, Arthamevia Najwa; Firdaniza, Firdaniza; Parmikanti, Kankan
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26679

Abstract

Currently, gold investment is considered promising despite the ever-changing price of gold. However, obtaining optimal profits is a challenge for investors. Therefore, a proper forecasting method is needed to forecast the gold price so investors can know the best transaction time. This study used two forecasting methods: the Fuzzy Grey Markov Model (1,1) and a new, never-before-used approach, the Fuzzy Grey Markov Model (2,1). The Fuzzy Grey Markov Model (2,1) approach is interesting because it can be considered for forecast data that shows varying increases and decreases, such as the gold price data used in this study. Both methods are combined models that utilize fuzzy logic to handle uncertainty in data; the Grey model forms a forecasting model, and the Markov chain determines the state transition probability matrix. Next, the error rates of the two methods are compared based on the Mean Absolute Percentage Error (MAPE) value to obtain the best forecasting method. As a result of this study, the Fuzzy Grey Markov Model (1,1) was chosen as the best forecasting method with a MAPE value of 0.28%.
Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification Indah, Yunna Mentari; Aristawidya, Rafika; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.28290

Abstract

Machine learning classification is an effective tool for categorizing data based on patterns, which is particularly useful in analyzing the Human Development Index (HDI) in Indonesia. HDI serves as a key indicator of regional development progress, making it crucial to classify HDI categories at the regency/city level to support targeted development planning. This study aims to compare the performance of three ensemble-based classification methods—Random Forest, XGBoost, and LightGBM—in classifying HDI categories in Indonesia. Data from the Central Bureau of Statistics (BPS) in 2023, comprising 514 observations across nine variables, was used for analysis. The study applied these algorithms to analyze the most influential variables affecting HDI. The results show that LightGBM outperformed both Random Forest and XGBoost, achieving an accuracy of 0.937 without outlier handling and 0.944 with outlier handling. Additionally, per capita expenditure was identified as the most influential factor in predicting HDI. These findings contribute to the field of statistical modeling by demonstrating how ensemble methods can improve classification accuracy and provide valuable insights for data-driven policymaking, thus enhancing regional development planning and supporting future HDI-related research.
Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas Putri, Aulia Nabila; Satyahadewi, Neva; Aprizkiyandari, Siti
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.21921

Abstract

Indonesia's export activities are dominated by non-oil and gas exports consisting of four sectors, namely the processing industry, agriculture, mining, and others. The government must pay attention to non-oil and gas exports for each province because exports can play an essential role in a country's economic growth. This study was conducted to cluster provinces in Indonesia using time series clustering in the non-oil and gas export sector based on data patterns concerning Dynamic Time Warping (DTW) distance. The sectors used in this study are the manufacturing industry sector and the agricultural sector in 34 Indonesian provinces in the period 2017 - 2021. Time series clustering analysis uses the average linkage method with DTW distance and the selection of the optimum number of clusters using the silhouette coefficient method. The results of the analysis in the processing industry sector resulted in 3 optimum clusters, namely cluster 1 consisting of 1 province that has high processing industry exports, cluster 2 consisting of 8 provinces that have medium processing industry exports, and cluster 3 consisting of 25 provinces that have low processing industry exports. As for the agricultural sector, it produces 2 optimum clusters, namely cluster 1 consisting of 5 provinces that have high agricultural industry exports, and cluster 2 consisting of 29 provinces that have low agricultural industry exports. The clustering results in the processing industry sector and the agricultural sectors have a silhouette coefficient value of 0.778 and 0.798, so it is said to have a strong cluster structure.

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