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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.

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.

APPLICATION OF SINGULAR SPECTRUM ANALYSIS METHOD IN FORECASTING INDONESIA COMPOSITE DATA Wijayanti, Latifah Nur; Kartikasari, Mujiati Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The wellbeing of the public is a key state objective. To attain this objective, developments are required, including economic development. Economic development can be initiated by enhancing a state's economic growth, as it describes the state's economic conditions. Forecasting future economic conditions is one of the things that may be done to ensure economic stability. Investment business can be utilized as indicators, with the Indonesia Composite Index (ICI) being one of them. Singular Spectrum Analysis (SSA) is one of the available techniques for forecasting. Due to the fact that SSA is non-parametric, no assumptions must be met, resulting in high performance and adaptability. Thus, SSA will be utilized for forecasting ICI. The ICI data utilized is obtained from Yahoo Finance. On the basis of the forecasting result for the closing price of ICI from March 2, 2020 to March 28, 2022 using SSA, which yielded MAPE values of 1.59% for training data and 4.84% for testing data, it can be inferred that this method is accurate. The outcome also revealed that the tendency tends to rise over the next few periods.

COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS Destia, Bella; Kartikasari, Mujiati Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1093-1102

Indonesia is a country that has a population density that is increasing every year, with the increase in population density, the crime rate in Indonesia is increasing. Criminal acts arise because they are supported by factors that cause crime. To improve the security and welfare of the Indonesian people, the authors grouped each province in Indonesia based on the factors that influence crime. This study uses a comparison of the Fuzzy C-Means Clustering (FCM) and Fuzzy Gustafson-Kessel Clustering (FGK) methods by using the validation index for determining the optimal cluster, namely the Davies Bouldin Index The data used is secondary data in the form of variables forming factors that affect the crime rate in Indonesia, where the data obtained comes from the website of the Central Statistics Agency (BPS). The results obtained in this study for the FGK method are better than the FCM method because they have a smaller standard deviation ratio. The results of grouping using the best method, namely FGK, it was found that the optimal number of clusters formed was 5 clusters with the results of grouping cluster 1 consisting of 6 provinces, cluster 2 consisting of 4 provinces, cluster 3 consisting of 11 provinces, cluster 4 consisting of 5 provinces, and cluster 5 consisting of 8 provinces.

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