cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jose Rizal
Contact Email
jrizal@unib.ac.id
Phone
6281321420921
Journal Mail Official
diophantine@unib.ac.id
Editorial Address
FMIPA Universitas Bengkulu JLWR Supratman Kelurahan Kandang Limun Kecamatan Muara Bangkahulu Kota Bengkulu
Location
Kota bengkulu,
Bengkulu
INDONESIA
Diophantine Journal of Mathematics and Its Applications
Published by Universitas Bengkulu
ISSN : -     EISSN : 2987906X     DOI : https://doi.org/10.33369/diophantine
Core Subject : Economy, Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering
The DJMA is published twice a year in June and December. This journal is managed by the Mathematics Department of Bengkulu University. The scope of this journal includes the fields of: 1. Mathematics 2. Applied Mathematics 3. Statistics 4. Applied Statistics 5. Computer Science.
Arjuna Subject : Matematika - Matematika Terapan
Articles 41 Documents
 1   2   3   4   5 
Pemodelan Jumlah Pengguna Metode Kontrasepsi Jangka Panjang (MKJP) Di Provinsi Bengkulu Menggunakan Metode Arima dan Prophet Dalimunthe, Agus Veriyansah
Diophantine Journal of Mathematics and Its Applications Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v3i2.38539

Abstract

The use of contraceptives, especially Long-Term Contraceptive Methods (MKJP), plays a central role in birth control efforts and family planning. Time series analysis has become a very effective method for identifying and predicting patterns in sequential data such as MKJP usage data. The data used is monthly data on the number of users of MKJP contraceptives (IUD, MOW, MOP and Implant) for the period January 2012 to 2012. December 2023. The aim of this research is to obtain comparative results of the accuracy of forecasting models using the Autoregressive Integrated Moving Average (ARIMA) and Prophet methods and to obtain projected results of MKJP contraceptive users in Bengkulu Province in the coming year. The results obtained overall, the ARIMA model is the best model for forecasting because it has mean absolute percentage error MAPE and root mean square error RMSE values, namely the ARIMA model (0,1,1). The forecast results for the number of MKJP contraceptive users (IUD, MOW, MOP and Implant) in 2024 tend to show a decreasing trend in May 2024 and an increasing trend in March 2024. For IUD contraception, it is known that the number of active family planning (PA) users is the lowest. was May 2024 with a total of 5593 participants, while the highest PA occurred in March 2024, namely 16742 participants. Then for MOW contraception, the lowest number of PAs was in May 2024, amounting to 6028 participants, while the highest PA was in March 2024, amounting to 8417 participants. Furthermore, for MOP contraception, it is known that the lowest number of PAs was in December 2024, amounting to 79 participants, while the peak PA occurred in March 2024, namely 614 participants. And finally, for IMPLANT contraception, it is known that the lowest number of PA was in May 2024, amounting to 26,771 participants, while the highest PA occurred in March 2024, namely 50,957 participants.
Completion of the Diffusion Wave Flood Tracking Model Using the Method of Lines (MOL) Istikomah, Istikomah; Fauzi, Yulian; Nursalim, Rahmat
Diophantine Journal of Mathematics and Its Applications Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v3i2.39007

Abstract

The flood tracking model is a method that can be used to predict when a flood will occur. The flood wave model is developed using a diffusion equation consisting of mass conservation equations and momentum conservation equations. This research was conducted to determine the application of the Method of Lines (MOL) in solving flood tracking models using the diffusion equation. The steps involved are discretizing the flood wave diffusion tracking equation by replacing the spatial derivative using the central difference method, resulting in a system of ordinary differential equations. Then, solving the system of ordinary differential equations using the fourth-order Runge Kutta method. The approach used in this research is quantitative. Simulations are performed by inputting a sample case and entering the data into the MATLAB program. The flow discharge produced increases as the flow velocity increases, and the resulting graph becomes more concave as the velocity increases. Thus, by knowing the changes in flow velocity, flow width, and flow depth in the upstream area of the river, it can be predicted how much the water discharge will change at each observation point downstream of the river.
Analisis Perbandingan Metode Hirearchical, K-Means, dan K-Medoids Clustering dalam Pengelompokan Kasus Penyakit Menular di Bengkulu Tengah Tasti, Desi T.; Gumay, Fridz M.; Aysha, Ulfianida; Agwil, Winalia; Pratami, Wingke Y.
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i1.32048

Abstract

In Indonesia, infectious diseases are still a persistent problem. Experts have accumulated knowledge regarding the emergence of the disease. In the last ten years, Indonesia is still experiencing the problem of triple burden diseases. Where Indonesia is still hit by infectious diseases, non-communicable diseases (NCDs) and diseases that should have been resolved, apart from that, infectious diseases are also still a big problem that must be faced. Researchers are interested in conducting research on infectious diseases in Central Bengkulu Regency. When analyzing infectious diseases, grouping can be done. Cluster analysis is an approach to looking for similarities in data and placing similar data into groups. There are two grouping methods in cluster analysis, hierarchical methods and non-hierarchical methods. One of the cluster analyzes using hierarchical methods is the average linkage method, while non-hierarchical ones are K-Means and K-Medoids. The variables used in this research are TBC and DHF in 2022. The highest rates of TB and DHF occurred in Pondok Kelapa sub-district, namely 29 and 23 cases. Based on the results of the analysis, it consists of 2 clusters, with cluster 1 consisting of 9 sub-districts, while cluster 2 consists of 2 sub-districts. Based on the results of evaluating the best method using the Calinski-Harabasz Index, it was found that the K-medoids method was the best method with a value of 0.
Penerapan Logika Fuzzy Mamdani dalam Menentukan Prioritas Penerima Bantuan Langsung Tunai (BLT) di Desa Sukarami Fikriya, Muhammad; Hersiana, Sivi; Mayasari, Zulfia M.
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i1.39181

Abstract

Penyaluran Bantuan Langsung Tunai (BLT) merupakan salah satu program pemerintah untuk membantu masyarakat dalam menghadapi dampak ekonomi. Namun, dalam pelaksanaannya sering kali terjadi ketidaktepatan sasaran yang menyebabkan BLT tidak tersalurkan ke orang yang benar-benar layak  dan membutuhkan. Untuk mengatasi permasalahan ini, peneliti mengusulkan penggunaan logika fuzzy dengan metode Fuzzy Mamdani sebagai solusi dalam menentukan kelayakan penerima bantuan. Metode Fuzzy Mamdani digunakan untuk mengolah data dengan beberapa kriteria penerima BLT. Melalui proses Fuzzy Mamdani dengan bantuan software Python akan menghasilkan keputusan yang lebih tepat dalam menentukan sasaran penerima BLT. Hasil penelitian dengan metode Fuzzy Mamdani, diperoleh nilai , yang mana dapat disimpulkan calon penerima berada pada tingkat prioritas penerima yang rendah. Hal ini menunjukkan bahwa metode Fuzzy Mamdani terbukti mampu menyaring penerima BLT secara lebih objektif dan sistematis, serta berpotensi besar dalam menekan ketidaktepatan sasaran penerima BLT di Desa Sukarami.
Optimalisasi Jadwal Menggunakan Pewarnaan Vertex Graf Himayati, Ade I. A.; Indriyani, Putri; Bahari, Muhammad F.
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i1.40651

Abstract

The preparation of subject schedules at SMP Negeri 2 Karanganyar Demak is still done manually and there are still errors that occur, such as teachers teaching at the same time but in different classes so the schedule is not optimal. This problem can be solved using scheduling techniques through graph coloring using the dot coloring method by substituting subjects in each class as vertices and the relationship between each subject and the subjects of each other class if the teacher who teaches the same subject is expressed as an edge. The algorithm for graph coloring is to determine the chromatic number using a vertex coloring algorithm, that is, using the minimum color type possible without anyone using the same color on neighboring edges. In this article, scheduling is only carried out for class VII at SMP Negeri 2 Karanganyar Demak. Based on the research results, it was found that the minimum number of colors in subject scheduling at SMP Negeri 2 Karanganyar Demak is that the coloring of the class 7 lesson schedule has 78 vertices resulting in 14 colors. Scheduling using point coloring produces a schedule without any overlapping schedules.
Kinerja Peramalan Autoregressive Integrated Moving Average dan Seasonal Autoregressive Integrated Moving Average dalam Memprediksi Kejadian Gempa Bumi Sumatra Firmansyah, Muhammad Akbar; Oktarina, Cinta R.
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i1.41099

Abstract

Pulau Sumatra merupakan salah satu wilayah di Indonesia dengan tingkat risiko gempa bumi yang tinggi akibat jalur subduksi dan keberadaan sesar aktif. Penelitian ini bertujuan untuk memodelkan dan meramalkan magnitudo tertinggi gempa bumi tahunan di Pulau Sumatra menggunakan metode Autoregressive Integrated Moving Average (ARIMA) dan Seasonal Autoregressive Integrated Moving Average (SARIMA). Data yang digunakan adalah magnitudo tahunan dari tahun 1900 hingga 2023. Sebelum pemodelan, dilakukan uji stasioneritas data melalui transformasi Box-Cox untuk varians dan uji Augmented Dickey-Fuller (ADF) untuk rataan. Setelah proses differencing, data dinyatakan stasioner terhadap rataan. Identifikasi model awal dilakukan dengan analisis plot ACF dan PACF, diikuti dengan seleksi model berdasarkan signifikansi parameter. Evaluasi model menggunakan kriteria AIC, BIC, MAE, RMSE, dan MAPE. Hasil analisis menunjukkan bahwa model SARIMA (1,1,1) (1,1,0)24 memiliki performa terbaik berdasarkan nilai AIC, BIC, MAE, dan MAPE. Namun, dalam tahap peramalan data testing, model ARIMA (2,1,2) menunjukkan hasil prediksi yang lebih mendekati nilai aktual. Penelitian ini menunjukkan bahwa kombinasi penggunaan ARIMA dan SARIMA dapat membantu dalam memodelkan kejadian gempa bumi di Sumatra, dengan pemilihan model terbaik disesuaikan berdasarkan tujuan peramalan.
Forecasting Indonesia's Non-Oil and Gas Exports Using the Exchange Rate as an Exogenous Variable with the ARIMAX Model: Peramalan Ekspor Nonmigas Indonesia Menggunakan Variabel Eksogen Nilai Kurs Dengan Model Arimax Ihwal, Muhammad; Laome, Lilis; Salsabilah, Adelfina; Ningtyas, Rita Ayu
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i1.41348

Abstract

This study aims to develop an ARIMAX model for forecasting Indonesia’s non-oil and gas export values for the period of March to June 2025. The variables used include Indonesia’s non-oil and gas exports (Z) and the exchange rate (X), obtained from the Ministry of Trade and Bank Indonesia. The export data is monthly time series data characterized by autocorrelation. The forecasting method employed is the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX), which extends the ARIMA model by incorporating external predictor variables. The results show that the ARIMAX(0,1,1) model is the most suitable for forecasting, yielding a Mean Absolute Percentage Error (MAPE) of 5.01%. Using the Maximum Likelihood Estimation (MLE) method, the derived model is Ẑₜ = Zₜ₋₁ - 0.4153εₜ₋₁ - 0.00000608Xₜ. The forecast indicates that Indonesia’s non-oil and gas exports will reach USD 23,692.17 million in June, with the lowest projected value in April at USD 23,003.46 million.
Penerapan Metode Logika Fuzzy Sugeno dalam Pengambilan Keputusan Penentuan Jumlah Produksi Sembiring, Destaria Br; Mayasari, Zulfia Memi
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 2 (2025): Vol. 4 No. 2 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i2.39150

Abstract

The rapid development of the industry has led to increasingly fierce competition among companies, driving the need for operational efficiency and maximum profit. One of the main challenges faced by companies is determining the optimal production quantity to meet market demand and manage inventory efficiently. Inaccuracies in production planning, such as excess or insufficient stock, can reduce cost efficiency and customer satisfaction. The production decision-making process is often faced with uncertainty caused by limited information and incomplete data, making traditional approaches such as statistical calculations not always effective. As a solution, the Fuzzy logic method, particularly the Sugeno method, offers a flexible approach to managing uncertainty. This method uses human logic-based rules to model the relationship between demand, inventory, and production quantity adaptively. This research aims to explore the application of the Fuzzy Sugeno method in determining the optimal production quantity based on demand and supply data. Based on the analysis of tests conducted on the production quantity calculation application using the Fuzzy Sugeno method, a truth value of 81.63% was obtained. This high truth level indicates that the implementation of the Fuzzy Sugeno method is effective in determining the production quantity.
Comparison of Robust Regression Methods: Least Trimmed Squares and Maximum Likelihood for Handling Outliers Kurniawan, Andro; Oktarina, Cinta Rizki; Sabarinsyah, Sabarinsyah
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 2 (2025): Vol. 4 No. 2 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i2.46149

Abstract

This study investigates the determinants of per capita expenditure in 154 regencies and cities across Sumatra Island. The use of the Ordinary Least Squares method is deemed inappropriate due to violations of classical assumptions and the presence of outliers within the dataset. To address these issues, robust regression approaches are applied, specifically M-estimation and Least Trimmed Squares (LTS). The dependent variable in the analysis is per capita expenditure, while the explanatory variables include poverty line, human development index, average years of schooling, and expected years of schooling. The estimation procedures are performed using both raw and standardized data. The empirical results demonstrate that each independent variable significantly influences per capita expenditure under both robust estimation techniques. To determine the most reliable method, the residual standard error is used as the evaluation criterion. The outcomes indicate that the LTS estimator applied to standardized data provides the lowest error value, suggesting that it is the most suitable approach for estimating the regression parameters associated with per capita expenditure in Sumatra.
Pananganan Data Hilang pada Data Bangkitan Bivariate Gamma Arib, Muhammad Arib Alwansyah; Khaola, Khaola Rachma Adzima; Rido, Muhammad Rido Wujudi
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 2 (2025): Vol. 4 No. 2 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i2.46691

Abstract

Missing data is a problem in data processing that can reduce the quality of analysis results if not addressed. This study aims to evaluate the performance of two imputation methods, namely Random Forest Imputation (RF) and Classification and Regression Tree (CART), at various levels of missing value proportions, namely 5%, 10%, 15%, and 20%. The data used in this study are Bivariate Gamma data of 200 observations with two variables, which were generated using RStudio software. The evaluation was carried out based on the correlation value between the imputed data and the original data, as well as the error measures Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results showed that at the missing value levels of 5% and 10%, the CART method produced the smallest MAPE and RMSE values, so that the CART method was the best method, although there was no significant difference between the RF method and the 10% missing value data. At 15% and 20% missing values, the RF method demonstrated superior performance with smaller MAPE and RMSE values ​​compared to CART. Overall, the CART method is more suitable for use with a low proportion of missing values, while the RF method provides more stable performance at a high proportion of missing values. The results of this study provide recommendations for selecting a more appropriate imputation method based on the level of missing data.

Page 4 of 5 | Total Record : 41

 1   2   3   4   5 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025): Vol. 4 No. 2 (2025) Vol. 4 No. 1 (2025) Vol. 3 No. 2 (2024) Vol. 3 No. 1 (2024) Vol. 2 No. 2 (2023) Vol. 2 No. 1 (2023) Vol. 1 No. 1 (2022)