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Peramalan Inflow dan Outflow Uang Kartal Menggunakan X-13 ARIMA-SEATS Herdiantini , Rizka Fitria; Kartikasari, Mujiati Dwi
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4331

Currency is an important part of Indonesian society's economic transactions. In order to effectively manage the amount of currency in circulation, Bank Indonesia must carefully plan and estimate its currency needs. One way to estimate this need is by looking at Bank Indonesia's inflow and outflow. Therefore, forecasting currency inflow and outflow is crucial for future planning. Inflow and outflow data are included in the time series that is affected by calendar variations. Traditional forecasting methods, such as exponential smoothing and ARIMA, cannot handle these variations. Therefore, this study uses the X-13 ARIMA-SEATS method, which is able to forecast time series data with the effect of calendar variations. Based on monthly data on currency inflow and outflow from January 2015 to December 2022, the results show that the X-13 ARIMA-SEATS method is effective when used with the mean absolute percentage error (MAPE) criteria.

Finding the Factors Influencing the Severity of Traffic Accident Victims in Sleman Regency Using Ordinal Logistic Regression Analysis Cahyani, Amalia Rizqi; Kartikasari, Mujiati Dwi
Jurnal Varian Vol 8 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3769

Special Region of Yogyakarta (Daerah Istimewa Yogyakarta, DIY) is well-known for its tourist, cultural, and educational attractions, but it also has a high accident rate. Sleman Regency is among the DIY regions with the greatest number of traffic accidents. According to Yogyakarta Police records, Sleman Regency had 1,825 traffic incidents in 2022, while 637 accidents occurred there in a short period of time in 2023, specifically from January to April. To stop the rising number of people injured in road accidents, this issue needs to be taken into account. The objective of this study was to examine the profile of traffic accidents that happened in Sleman Regency between January and April of 2023 and use the ordinal logistic regression method to find characteristics that influence the severity of traffic accidents. Sleman Regency traffic accident data was used in this study. The opponent's vehicle factor, with the category of four or more wheeled vehicles and non-motorized vehicles, is one of the elements that influences the severity of traffic accident victims in Sleman Regency, according to the study's findings.

Enhancing the productivity of irrigated rice fields in West Nusa Tenggara through utilizing Multilayer Perceptron (MLP) and Self-Organising Maps (SOM) Chaerunnisa, Azzahra Fajriani; Kartikasari, Mujiati Dwi
Journal of Natural Sciences and Mathematics Research Vol. 10 No. 2 (2024): December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

As Indonesia's population grows, ensuring a stable food supply becomes increasingly important. Recent changes in weather patterns have significantly impacted food production, particularly rice farming. In West Nusa Tenggara (NTB), a key area for rice production, maintaining consistent output is crucial. However, varying responses to unpredictable weather have led to significant differences in productivity across NTB's regencies and cities. This study aims to enhance the productivity of irrigated rice fields in NTB by predicting productivity levels for 2023 to 2024 using the best multilayer perceptron (MLP) model. We will compare 5 MLP model architectures to identify the optimal model for the prediction process. We will use the prediction results to cluster areas regionally through the self-organizing map (SOM) algorithm. We used the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. This research compared DBI values for cluster counts of 2, 3, 4, and 5, determining the optimal cluster number by the smallest DBI value. The lowest DBI is 0.391 observed for 3 clusters. From this clustering, Cluster 1 consists of 7 regencies/cities with the lowest productivity level, Cluster 2 contains 1 regency with a moderate productivity level, and Cluster 3 includes 2 regencies/cities with the highest productivity level. The study concludes that the 7 regencies/cities in Cluster 1, identified as having low productivity require greater focus from local governments to optimize land area and paddy yields to enhance productivity in those areas.

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

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.

Unveiling the Connection: The Impact of Poverty Rate on Human Development Index in the Special Region of Yogyakarta Province Using the Almon Lag Model Vatin, Kayyis; Kartikasari, Mujiati Dwi
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7348

The Human Development Index (HDI) is established as one of the main indicators included in the fundamental framework of regional development. Ideally, HDI, which serves as a benchmark for regional development, has a negative correlation with poverty conditions. Historical poverty can influence the current HDI based on elements of health, education, and a decent standard of living. The Special Region of Yogyakarta is the province with the second-highest HDI in Indonesia but has had the highest poverty rate in Java in recent years. The Almon Lag Model can analyze the impact of poverty on HDI by considering the distributed lag effect. This study aims to analyze the impact of the poverty rate on HDI over the past twenty-seven years. Based on the analysis, the best model utilizes a lag length of three years and a polynomial degree of two. The model has a Mean Absolute Percentage Error (MAPE) of 0.73%, indicating that the applied Almon Lag Model can make accurate predictions.

Analisis Metode Projected Unit Credit dan Entry Age Normal dalam Perhitungan Aktuaria Dana Pensiun dengan Suku Bunga Konstan dan Model Vasicek Mutalip, Nurqalbu Abd.; Kartikasari, Mujiati Dwi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.31077

Retirement planning requires strong financial preparedness, making pension funds a crucial component of long-term financial strategies. One of the key aspects in managing pension funds is the actuarial method used to calculate pension liabilities and contributions. This study aims to compare the normal cost and actuarial liability calculations between two actuarial methods, namely Projected Unit Credit (PUC) and Entry Age Normal (EAN), under two interest rate assumptions: a constant interest rate and a stochastic interest rate modelled using the Vasicek model. The Vasicek model is applied to capture the dynamic fluctuations in interest rates, providing more realistic estimates. Based on parameter estimation, the stochastic interest rate obtained through the Vasicek model is 7.113%. The results show that applying the Vasicek model leads to higher actuarial liabilities compared to the constant interest rate approach. Furthermore, the EAN method results in lower final contributions and higher actuarial liabilities compared to the PUC method, making it more favourable for pension participants. These findings highlight the importance of considering interest rate dynamics in pension valuation to enhance the accuracy and relevance of actuarial calculations in an ever-changing economic environment.

Enhancing the productivity of irrigated rice fields in West Nusa Tenggara through utilizing Multilayer Perceptron (MLP) and Self-Organising Maps (SOM) Chaerunnisa, Azzahra Fajriani; Kartikasari, Mujiati Dwi
Journal of Natural Sciences and Mathematics Research Vol. 10 No. 2 (2024): December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.v10i2.23033

As Indonesia's population grows, ensuring a stable food supply becomes increasingly important. Recent changes in weather patterns have significantly impacted food production, particularly rice farming. In West Nusa Tenggara (NTB), a key area for rice production, maintaining consistent output is crucial. However, varying responses to unpredictable weather have led to significant differences in productivity across NTB's regencies and cities. This study aims to enhance the productivity of irrigated rice fields in NTB by predicting productivity levels for 2023 to 2024 using the best multilayer perceptron (MLP) model. We will compare 5 MLP model architectures to identify the optimal model for the prediction process. We will use the prediction results to cluster areas regionally through the self-organizing map (SOM) algorithm. We used the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. This research compared DBI values for cluster counts of 2, 3, 4, and 5, determining the optimal cluster number by the smallest DBI value. The lowest DBI is 0.391 observed for 3 clusters. From this clustering, Cluster 1 consists of 7 regencies/cities with the lowest productivity level, Cluster 2 contains 1 regency with a moderate productivity level, and Cluster 3 includes 2 regencies/cities with the highest productivity level. The study concludes that the 7 regencies/cities in Cluster 1, identified as having low productivity require greater focus from local governments to optimize land area and paddy yields to enhance productivity in those areas.

Detecting Avocado Freshness In Real-Time: A Yolo-Based Deep Learning Approach Febriani, Atika Dwi; Kartikasari, Mujiati Dwi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4626

The increasing consumption of avocados in Indonesia highlights the need for an effective method to ensure fruit freshness. The main problem lies in the absence of an objective and standardized system for assessing avocado freshness, which may lead to consumer dissatisfaction and food waste. This study aims to address the challenge of identifying avocado freshness to ensure suitability for consumption. Conducted from May 23 to June 5, 2024, the research used butter avocado samples sourced from supermarkets. The method employed is the You Only Look Once version 8 (YOLOv8) deep learning algorithm, known for its real-time object detection capabilities. YOLOv8 offers enhanced performance compared to earlier versions through anchor-free detection, improved speed, and accuracy, making it suitable for fast and reliable freshness detection tasks. Avocados were classified based on estimated spoilage time under room and refrigerator temperatures, ranging from "up to 5 days at room temperature and 14 days in refrigeration" to "not fit for consumption." The model was validated using 120 images categorized into six freshness levels. Evaluation results demonstrated high performance, with 98% accuracy, an F1-Score of 0.978, mAP50 of 0.994, and mAP50-95 of 0.972 after 50 training epochs, confirming the model’s robustness. Real-time tests yielded confidence levels of 96% and 94%, further validating its effectiveness in detecting avocado freshness. To facilitate daily use, a mobile application named Avo Freshify was developed. The app accurately identifies the freshness of avocados and provides valuable information for consumers and sellers. This research contributes to the advancement of artificial intelligence and object detection in food quality control and agricultural technology.

Payment Status Classification Invoice Bank Using Logistic Regression and Random Forest Putri, Farah Anindia; Kartikasari, Mujiati Dwi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5470

Payment management is an essential aspect of a bank’s financial operations, particularly in ensuring the smooth execution of procurement transactions for goods and services. The invoice, as an official document, plays a role in determining whether a transaction can be processed promptly or experiences a delay. Despite its central role, empirical research exploring the factors influencing invoice payment status remains limited, especially within the context of banking institutions. This study aims to analyze the factors that affect invoice payment status based on company type, procurement type, and invoice value. The methods employed include logistic regression and random forest to compare the classification performance of both approaches. The analysis reveals that procurement type and invoice value significantly influence payment status, with invoice value emerging as the most dominant variable based on the smallest p-value. In the random forest model, invoice value also ranks highest in terms of variable importance. In terms of accuracy, the random forest model outperforms logistic regression, achieving an accuracy of 94.47% compared to 59.30%. Although both methods yield similar precision (approximately 97%), random forest demonstrates a substantially higher recall (97.41%) and F1-score, whereas logistic regression records a recall of only 69.19%. These findings suggest that random forest is a more effective method for predicting payment status and holds strong potential for supporting data-driven decision-making in bank payment management systems

FORECASTING OF CURRENCY CIRCULATION IN INDONESIA USING HYBRID EXTREME LEARNING MACHINE Kartikasari, Mujiati Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Forecasting currency circulation, including inflow and outflow, is one of Bank Indonesia's strategies to maintain the Rupiah value's stability. The characteristic of inflow and outflow data is that they have seasonal variations. This study proposes a hybrid model by combining decomposition techniques and Extreme Learning Machine to overcome data that has seasonal variations. The forecasting results of the proposed model are compared with the original Extreme Learning Machine. The comparison results show that the forecasting results with the hybrid model have the smallest errors. Thus, the hybrid model can predict data with seasonal variations better than the original Extreme Learning Machine.

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