cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kab. lamongan,
Jawa timur
INDONESIA
Unisda Journal of Mathematics and Computer Science (UJMC)
Published by Universitas Islam Darul Ulum
ISSN : 24603333     EISSN : 2579907X     DOI : -
Core Subject : Science, Education,
Computer Science & IT Education Mathematics
Unisda Journal of Mathematics and Computational Science (UJMC) is a research journal published by Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan with the scope of pure mathematics, applied science, education, statistics
Arjuna Subject : -
Articles 163 Documents
 13   14   15   16   17 
Penerapan Triple Exponential Smoothing Model Multiplicative dan Additive untuk Memprediksi Harga Saham BRIS.JK Noor Sofiyati
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 11 No 1 (2025): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v11i1.10603

Abstract

When investing in stocks, investors must consider not only potential returns but also the risk of loss. Like conventional stocks, sharia-compliant stocks fluctuate in value, requiring careful analysis to predict future prices. This study aims to forecast the price of sharia-compliant BRIS.JK stocks using both the multiplicative and additive triple exponential smoothing models. These methods were chosen because BRIS.JK stock data exhibits random fluctuations. The analysis uses 270 daily closing prices of BRIS.JK shares from the past five business days. The results indicate that both models predict a continuous rise in stock prices over the next 54 days (June to August 2025). The MAPE values for both models are below 10%, demonstrating excellent predictive accuracy. This insight can assist investors in deciding whether to increase their holdings of BRIS.JK shares or purchase them to maximize future profits.
Peramalan Kemiskinan di Kabupaten Banyumas Menggunakan Regresi Nonparametrik dengan Pendekatan Kernel Nadaraya Watson Adjusted Novita Eka Chandra; Melda Juliza; Muhammad Hafidh Nashrullah
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 11 No 1 (2025): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v11i1.10646

Abstract

Poverty is a serious challenge faced by the Banyumas Regency Government. Although the poverty rate in this region has shown a declining trend for more than a decade, the pattern of decline has not been linear. This study utilizes time series data representing the percentage of the poor population in Banyumas Regency from 2003 to 2024. This research primarily seeks to forecast the poverty rate in 2030 and to differentiate between the performance of two kernel functions, Gaussian and Epanechnikov, which are applied in nonparametric regression using the adjusted Nadaraya-Watson kernel approach. Analysis results suggest that the model performs best when the bandwidth is set at its optimal value of 0,538909 using the Epanechnikov kernel function. Based on the forecast, the poverty rate in 2030 is estimated to be 12,87%. This result indicates the need for well-planned strategies and policies by the Banyumas Regency Government to reduce the poverty rate over the next six years.
Pengendalian Persediaan Bahan Baku Jamur Tiram di UD Khalifa Jamur dengan EOQ Koko Hermanto; Moch lahmudin; Ismi Mashabai; Ulfa Turrahmi
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.11378

Abstract

Khalifa Mushroom is one of the oyster mushroom cultivation places, the company is advised to start implementing the EOQ method consistently in the planning and inventory process of raw materials. This is important so that the amount of production processing and ordering time can be adjusted to actual needs and dynamic market conditions. Second, the company should utilize technology optimally to perform calculations and inventory control simulations to produce fast, accurate, and efficient decisions. This study to anticipate increased production volume, the company is advised to always apply the Economic Order Quantity (EOQ) method to evaluate existing warehouse capacity and consider expanding or rearranging storage space so that raw materials maintain their quality and are not damaged due to accumulation. Finally, the researcher suggests that further research can explore the use of other methods that are integrated with supply risks, price changes, or demand fluctuations to strengthen supply chain resilience and increase business competitiveness in the future
Interpolasi Lagrange dalam Pembuatan Interface Prediksi Jumlah Penduduk NTT Berbasis Python Muhammad Naufal Daru Ciptoning; Nabilah Nesya Adiarni Azzahra; Ermelya Helmi Anggraeni; Arum Nanda Putri Aprilia; Rahmawati Erma Standsyah; Dian Savitri
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.12901

Abstract

This study aims to implement the Lagrange interpolation method in a Python-based Graphical User Interface (GUI) application to predict the population of Nusa Tenggara Timur (NTT) Province. The development of this system was necessary because of the limited number of practical computer tools capable of estimating the population between times for official censuses. The Lagrange interpolation method is applied to construct a polynomial curve based on historical population census data, which is then integrated into a Python-based interactive system. The application interface is designed to allow users to input the prediction year and automatically obtain estimation results along with graphical visualization. The testing results indicate that the system performs interpolation computations accurately, producing results consistent with manual mathematical calculations. Furthermore, the graphical visualization demonstrates that the population estimation curve follows the trend of historical data. Therefore, the developed system simplifies numerical computation while providing a practical digital tool to support regional demographic analysis and planning.
Basis untuk Ruang Pencacah Bobot dari Kode Linier atas GF(4) Intan Putri Wardani; Nur Hamid
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13376

Abstract

This study discusses the basis of a polynomial space generated by the weight enumerators of linear codes over GF(4) for several lengths, namely 2, 4, 6, 8, and 10. The results of the study show that each set of weight counters forms a space with dimensions that depend on the length of the code, namely 1, 1, 2, 4, and 5. This shows that the longer the code, the larger the dimensions of the space obtained.
Evaluasi Metode Inisialisasi pada Model Pemulusan Eksponensial melalui Data IPM Banyumas Raya Melda Juliza; Novita Eka Chandra; Felinda Arumningtyas; Puce Angreni
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13519

Abstract

The forecasting accuracy of exponential smoothing models is significantly influenced by the determination of initial values (initialization). This study aims to evaluate the performance of initialization methods for Brown’s Double Exponential Smoothing model using Human Development Index (HDI) data from the Banyumas Raya region for the period 2010-2025. The research stages included identifying data patterns, constructing models using both simple initialization and optimal initialization with numerical optimization, performing the Ljung-Box test for residual diagnostics, and comparing model accuracy. Evaluation results indicate that the Brown model using the optimal initialization method effectively captures trend patterns. The application of optimal initialization consistently improved model accuracy across all regencies. The highest error improvement was observed in Banyumas Regency (28.65%), followed by Cilacap (28.29%), Banjarnegara (24.77%), and Purbalingga (22.89%). Based on these results, the optimal initialization model was used to project HDI values for the next three periods, revealing a sustained upward trend. In conclusion, determining initial values is a crucial component that alongside smoothing parameter optimization must be seriously considered when developing forecasting models.
Analisis Deret Waktu Peramalan Kecelakaan di Kabupaten Blitar Ewing Rudita arini
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13583

Abstract

The prevalence of traffic accidents in Blitar Regency is driven by multifaceted variables, including poor road infrastructure, mechanical failures, and low compliance among road users. To support preventive measures, this study evaluates time-series forecasting models to project future accident trends. It provides a comparative analysis between Single Exponential Smoothing (SES) and Single Moving Average (SMA) methods, utilizing 52 months of historical data from January 2022 to April 2026. Model performances are validated using MAD, MSE, and MAPE error metrics. The empirical findings indicate that the Single Moving Average configuration with parameter n = 3 outperforms the SES model by delivering the lowest error values. This optimal model projects 42 traffic accident cases for the May 2026 period. The findings of this study are intended to assist relevant stakeholders in formulating data-driven traffic safety policies and mitigation strategies.
Perbandingan Model Klasifikasi Multikelas Tingkat Depresi Mahasiswa dengan Skor PHQ-9 Felinda Arumningtyas; Puce Angreni; Lutfiah Maharani Siniwi
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13601

Abstract

Depression is one of the most common mental health disorders among university students and may adversely affect academic performance and social functioning. The severity of depression can be assessed using the Patient Health Questionnaire-9 (PHQ-9), which classifies individuals into several levels of depression severity. This study aims to compare several machine learning models for multiclass classification of student depression levels based on PHQ-9 scores. The study employed the PHQ-9 Student Depression Dataset consisting of 682 student records. Predictor variables included age, gender, the nine PHQ-9 items, sleep quality, study pressure, and financial pressure. The models evaluated were Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and XGBoost. Model performance was assessed using accuracy, precision, recall, and F1-score metrics. The results indicate that XGBoost achieved the best performance, with an accuracy of 78,10%, macro precision of 0,77, macro recall of 0,77, and macro F1-score of 0,77. These findings demonstrate that XGBoost provides relatively good performance in the multiclass classification of student depression levels. This study suggests that machine learning approaches have the potential to support the identification of depression severity among university students.
Penerapan Model Logistik Fraksional dalam Memproyeksikan Jumlah Penduduk di Kabupaten Indramayu Tahun 2025-2030 Lu’lu Nurzahra; Isnu Aji Saputro Isnu; Noor Sofiyati
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13618

Abstract

The continuous growth in population necessitates accurate projections as a foundation for regional development planning. This study aims to apply a fractional logistic model employing the conformable fractional derivative to project the population of Indramayu Regency for the period 2025–2030. The research was conducted using population data from Indramayu Regency spanning the 2020–2024 period, with two fractional orders, namely α = 0,8 and α = 0,2, each comprising four model variations. The accuracy of the models was evaluated using the Mean Absolute Percentage Error (MAPE). The findings indicate that Model III with a fractional order of α = 0.8 yields the highest accuracy, with a MAPE value of 0.121669%. Based on this model, the population of Indramayu Regency is projected to increase from 1,943,094 inhabitants in 2025 to 1,991,027 inhabitants in 2030. The results demonstrate that the conformable fractional logistic model is capable of providing population projections with excellent accuracy, thereby offering a potential alternative for population-data-based development planning.
Analisis Hubungan Indeks Ketahanan Pangan dan Presentase Kemiskinan di Jawa Tengah dengan Korelasi Kendall Tau Novita Eka Chandra; Melda Juliza; Abdul Aziz; Ari Wardayani
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13623

Abstract

Food security and poverty are two important economic indicators that are interrelated and are a primary focus of the Sustainable Development Goals (SDGs). This study aims to analyze the strength and direction of the relationship between the Food Security Index (IKP) and the percentage of district/city poverty in Central Java Province from 2022 to 2025. The data used do not meet the assumption of a normal distribution and have several identical values (ties), so the analysis method used is the Kendall Tau correlation. The results show a significant negative relationship between the IKP and the percentage of poverty in Central Java. These results indicate that the IKP at the district/city level plays a role in reducing the percentage of poverty.

Page 16 of 17 | Total Record : 163

 13   14   15   16   17 

Filter by Year

2015 2026


Reset
Filter By Issues
All Issue Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science Vol. 11 No. 2 (2025): Unisda Journal of Mathematics and Computer Science Vol 11 No 2 (2025): Unisda Journal of Mathematics and Computer Science Vol 11 No 1 (2025): Unisda Journal of Mathematics and Computer Science Vol 10 No 2 (2024): Unisda Journal of Mathematics and Computer Science Vol 10 No 1 (2024): Unisda Journal of Mathematics and Computer Science Vol 9 No 2 (2023): Unisda Journal of Mathematics and Computer science Vol 9 No 1 (2023): Unisda Journal of Mathematics and Computer science Vol 8 No 2 (2022): Unisda Journal of Mathematics and Computer science Vol 8 No 1 (2022): Unisda Journal of Mathematics and Computer science Vol 7 No 2 (2021): Unisda Journal of Mathematics and Computer science Vol 7 No 1 (2021): Unisda Journal of Mathematics and Computer science Vol 6 No 01 (2020): Unisda Journal of Mathematics and Computer science Vol 6 No 2 (2020): Unisda Journal of Mathematics and Computer science Vol 5 No 01 (2019): Unisda Journal of Mathematics and Computer Science (UJMC) Vol 5 No 2 (2019): Unisda Journal of Mathematics and Computer science Vol 4 No 2 (2018): Unisda Journal of Mathematics and Computer Science Vol 4 No 1 (2018): Unisda Journal of Mathematics and Computer Science Vol 3 No 2 (2017): Unisda Journal of Mathematics and Computer Science Vol 3 No 1 (2017): Unisda Journal of Mathematics and Computer Science Vol 2 No 1 (2016): Unisda Journal of Mathematics and Computer Science Vol 1 No 01 (2015): Unisda Journal of Mathematics and Computer Science More Issue