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All Journal EKSAKTA: Journal of Sciences and Data Analysis Journal of Natural Sciences and Mathematics Research Sistemasi: Jurnal Sistem Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jambura Journal of Mathematics Jurnal Varian Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Journal of Mathematics, Computation and Statistics (JMATHCOS)
Kartikasari, Mujiati Dwi
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.326 KB) | DOI: 10.30598/barekengvol17iss1pp0513-0526

Abstract

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

Abstract

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.
TEXT CLASSIFICATION OF TWITTER OPINION RELATED TO PERMENDIKBUD 30/2021 USING BIDIRECTIONAL LSTM Fitriyah, Zakiyatul; 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/barekengvol17iss2pp1113-1122

Abstract

During the COVID-19 outbreak, sexual violence in Indonesia has risen. Sexual abuse is prevalent even within the realm of education. Many incidents of sexual assault are reported within the higher education sector. The Ministry of Education, Culture, Research, and Technology published Decree of the Minister of Education and Culture Number 30/2021 (Permendikbud 30/2021) on the Prevention and Handling of Sexual Violence in Higher Education in an effort to prevent sexual violence on campus. This regulation's issuance has become a popular topic of discussion on social media. Twitter is one of the social media platforms where opinions are expressed. The publication of Permendikbud 30/2021 elicited a variety of views, from those who supported the rule to those who did not. This study's objective is to categorize tweets about Permendikbud 30/2021. Bidirectional LSTM (BiLSTM) was utilized to classify data in this study. The accuracy values are 87%, the precision values are 82%, and the recall values are 96% based on the findings of the analysis comparing training data of 80% to testing data of 20%.
APPLICATION OF THE NEURAL NETWORK AUTOREGRESSIVE (NNAR) METHOD FOR FORECASTING THE VALUE OF OIL AND GAS EXPORTS IN INDONESIA Junita, Tarisya Permata; Kartikasari, Mujiati Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0341-0348

Abstract

Indonesia is one of the countries with the most diversity and abundant natural resources, consisting of many commodities, and has enormous trade potential with other countries The success of economic activity a country can be measured by the amount of economic growth that occurs in the country. A recession is when a country's economic condition is getting worse. Meanwhile, a recession in Indonesia is expected to occur in 2023. In a 2022 news issue written by the editorial team, tirto.id said that some experts say that if 2023 is a recession, the cause is due to a spike in inflation from the impact of the Russia-Ukraine conflict. It is known that the value of oil and gas exports affects the Indonesian economy. Any increase in the value of oil and gas exports will be followed by an increase in economic growth, and vice versa. However, over time, the value of oil and gas exports has decreased every year. Therefore, forecasting the value of oil and gas exports is needed so that the country's economic sector development strategy can be on target. In addition, oil and gas export forecasting is also needed to determine the distribution of goods exports that must be carried out. In this study, we forecast the value of oil and gas exports using the neural network autoregressive (NNAR) method. The choice of this method is made because there is no assumption of normality of the residuals and white noise like in autoregressive models. From the NNAR method, the best model results are obtained, namely NNAR (2,3) with a MAPE value of 11.75640%, which means that this model has very good forecasting performance.
Efektivitas Metode Hibrida ARIMA-MLP untuk Peramalan Nilai Tukar Petani Mulyawati, Saffanah Nur Elvina; 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.v6i1.23944

Abstract

The agricultural sector remains a crucial pillar of Indonesia's economy, making the most significant contribution. Still, the situation of farmers, primarily the elderly, indicates physical limitations and low income leading to high poverty levels, coupled with fluctuations in the Farmer Exchange Rate (FER) annually tending to decline in D.I. Yogyakarta, indicating losses due to increased production costs. This research aims to assess the effectiveness of the Hybrid Autoregressive Integrated Moving Average (ARIMA) – Multilayer Perceptron (MLP) method in forecasting NTP in D.I. Yogyakarta. This is based on the analysis of comparing the accuracy values of forecasts using Mean Absolute Percentage Error (MAPE) evaluation or through visualizing the forecast graphs generated between the ARIMA and Hybrid ARIMA-MLP methods. The combination (hybrid) of ARIMA and MLP methods addresses the complexity of time series, where ARIMA anticipates NTP changes by handling linear patterns. At the same time, MLP improves forecast accuracy by managing more complex patterns (both linear and nonlinear). Thus, it can provide more accurate information about the welfare development of farmers. The results show that the Hybrid ARIMA-MLP method is significantly better than the individual ARIMA method, with the obtained model being Hybrid ARIMA-MLP (12-5-10-2) and an accuracy of 99.993%.
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%.
Forecasting of Export Value in Indonesia Using Top-Down Hierarchical Time Series Based on Historical Proportion Inas Rafidah; Kartikasari, Mujiati Dwi
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 5, ISSUE 1, April 2024
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol5.iss1.art2

Abstract

Export is a trading activity carried out between countries by bringing or sending goods originating from within the country to foreign countries with the aim of selling or marketing them. Exports as a source of state revenue are needed for the economy because exports can make a major contribution to economic stability and growth. Export values that experience a decrease or increase in the future need to be considered. For this reason, the purpose of this study is to forecast the value of exports in Indonesia for the coming period. Export value data is treated as hierarchical time series data. The top-down method is applied based on historical proportions, so only the total series of export values needs to be modeled. This study implements Autoregressive Integrated Moving Average (ARIMA) to model the total series of export values. The performance of the method is evaluated based on the out-of-sample mean absolute percentage error (MAPE). The results show that the MAPE for out-of-sample is 9.91%. These results indicate that the performance of the method for forecasting export values in Indonesia is highly accurate.
Forecasting COVID-19 Cases in Indonesia Using Hybrid Double Exponential Smoothing Kartikasari, Mujiati Dwi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (291.859 KB) | DOI: 10.20885/enthusiastic.vol1.iss2.art1

Abstract

The COVID-19 epidemic has spread throughout countries around the world. In Indonesia, this case was detected in early March 2020, and until now, there is still an increase in positive cases of COVID-19. The purpose of this paper is to predict COVID-19 cases in Indonesia using a time series approach. The method used is H-WEMA method because this method can capture trend data patterns following the conditions of COVID-19 cases in Indonesia. Based on the analysis results, H-WEMA can predict COVID-19 cases very well. The forecasted results of the COVID-19 cases in Indonesia still have an upward trend, so it needs the cooperation of all elements of community to reduce the spread of COVID-19.
Forecasting International Tourist Arrivals in Indonesia Using SARIMA Model Nurhasanah , Deden; Salsabila , Aurielle Maulidya; Kartikasari, Mujiati Dwi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art3

Abstract

Tourism is an important sector that significantly contributes to the economy, so the tourism sector is a priority development program. International tourist arrivals indirectly contribute to the country's economic growth. The government has an important task to increase the number of foreign tourist visits. One way to encourage an increase in foreign tourist arrivals is by forecasting. In general, the time series data for the arrival of foreign tourists has a seasonal pattern. The forecasting method that can model seasonal data is SARIMA. This study aims to predict the arrival of foreign tourists in Indonesia using the SARIMA model. Forecasting results show that the appearance of foreign tourists to Indonesia has increased every period.
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

Abstract

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.
Co-Authors Cahyani, Amalia Rizqi Chaerunnisa, Azzahra Fajriani Destia, Bella Febriani, Atika Dwi Fitriyah, Zakiyatul Herdiantini , Rizka Fitria Inas Rafidah Junita, Tarisya Permata Mulyawati, Saffanah Nur Elvina Mutalip, Nurqalbu Abd. Nurhasanah , Deden PRASMONO, AMIMAH SHABRINA PUTRI Putri, Farah Anindia Salsabila , Aurielle Maulidya Sulistyawati, Yuni Vatin, Kayyis Wijayanti, Latifah Nur