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All Journal EKSAKTA: Journal of Sciences and Data Analysis Journal of Science and Applicative Technology Jurnal Matematika UNAND Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Science and Technology Journal of Fundamental Mathematics and Applications (JFMA) Smart Society: Community Service and Empowerment Journal Indonesian Journal of Applied Mathematics
Mika Alvionita Sitinjak
Institut Teknologi Sumatera
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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PENENTUAN HARGA OPSI CALL TIPE EROPA MENGGUNAKAN METODE TRINOMIAL Mika Alvionita; Riri Lestari
Jurnal Matematika UNAND Vol 5, No 1 (2016)
Publisher : Jurusan Matematika FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmu.5.1.131-139.2016

Abstract

Abstrak. Dalam makalah ini akan dibahas opsi call tipe Eropa menggunakan metodetrinomial. Metode trinomial memiliki perubahan harga saham yang dipengaruhi olehkoesien naik turun yang relatif sama dengan suku bunga. Permasalahan yang timbuldalam metode ini adalah persamaan linier yang digunakan overdetermined. Persamaanyang overdetermined diselesaikan dengan menggunakan invers semu (pseudoinverse).
Predicting the Number of Train Passengers in Java Island using SARIMA Model Luluk Muthoharoh; Dimas Wahyu Saputro; Dhea Sukma Agustiana; Fadia Dilla Sabine; Lis Nuraini; Rekzi P. Manullang; Taj Shavira; Mika Alvionita
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 2, August 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss2.art5

Abstract

The train at this time has become one of the most popular public transportation for medium and long-distance travel. The number of train passengers is difficult to predict during the holiday season. This study aimed to predict the number of train passengers using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The stages used in this study include (1) dataset preparation, (2) preprocessing data, and (3) experimental testing and methods. The SARIMA model obtained is ARIMA(2,1,0)(0,1,2)[12] with an AIC value of 2379,265. A diagnostic model was carried out, and it was found that the model is quite good. So the SARIMA method used in predicting passengers is accurate.
Indeks Harga Komsumen (IHK) di Lampung Menggunakan Autoregressive Integrated Moving Average (ARIMA) Mika Alvionita Sitinjak; Nuramaliyah ‎
Indonesian Journal of Applied Mathematics Vol 3 No 1 (2023): Indonesian Journal of Applied Mathematics Vol. 3 No. 1 July Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v3i1.1274

Abstract

The Consumer Price Index (CPI) is an indicator that influences economic growth. CPI is an index that calculates the average of price change of a group of goods and services consumed by households in a certain period of time. CPI is also used to measure inflation in a country. Inflation is described by changes in the CPI from time to time. To anticipate and minimize economic risks caused by inflation, forecasting will be carried out on CPI data. In this study, the CPI will be predicted for the next 6 months using the ARIMA (Autoregressive Integrated Moving Average) model. The result of this research shows that the ARIMA models that can be used to predict CPI are ARIMA (0,2,0), ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (1,2,1) . The selection of the best model is carried out based on the model that has the smallest AIC value. Based on this, the best model used to predict CPI is the ARIMA model (0,2,1) with an AIC value of 83.21. In addition, this model fulfills diagnostics with white noise residuals, so that forecasting results using this model will be more accurate.
Recognition of Voronoi Cell Distribution in Earthquake Epicenter Data in the Sunda Strait Region, Indonesia Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot; S, Mika Alvionita
JOSTECH Journal of Science and Technology Vol 4, No 2: September 2024
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v4i2.9830

Abstract

The Sunda Strait is currently one of the busiest transportation hubs. However, this area has a significant history of geological disasters caused by the dynamic tectonic activity of the Eurasian and Indo-Australian tectonic plates. These disasters include the supervolcanic eruption of Krakatoa in 1883, the Sunda Strait tsunami in 2018, and decades of frequent earthquakes. To address these challenges, this study analyzes the frequency and distribution of seismic activity in the Sunda Strait region based on epicenter data recorded in the United States Geological Survey (USGS) Earthquake Catalog. We collected 440 multivariate earthquake data points between 1990 and 2023 (over three decades). The results of this study show that a machine learning approach accurately identified four relevant parameters for k-means clustering, followed by a silhouette value analysis to recognize the distribution of Voronoi cells. Based on earthquake data from the Sunda Strait from 1990 to 2023, the two highest silhouette analysis values, 0.40 and 0.39, are located at k=3 and k=5 in k-means clustering. This approach has recognized and identified the cell area of earthquake activity in the Sunda Strait, particularly around Anak Krakatoa. This study provides new insights into the spatiotemporal characteristics and identifies clusters of earthquake-prone areas. The information generated in this study facilitates the evaluation of future earthquake disaster risks, especially those with epicenters in the Sunda Strait region.
Natural Resources Data Visualization Training Using Google Data Studio in Triharjo Village, Merbau Mataram District, South Lampung Regency Muthoharoh, Luluk; S, Mika Alvionita; Irawati, Febri Dwi; Setiawan, Tirta; Nadeak, Christyan Tamaro
Smart Society Vol 3, No 2 (2023): December 2023
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsociety.v3i2.287

Abstract

Visualization is becoming as the most frequent tool for examining and extracting information from datasets by novice and professional researchers alike. Many data processing applications help to present and report data. One of the digital tools that is quite widely used is Google Data Studio. This Community Service activity aims to provide data visualization training to Triharjo Village, one of the villages that still does not use digitalization to access village data online. Natural Resources Data Visualization Training Using Google Data Studio in Triharjo Village, Merbau Mataram District, South Lampung Regency has been successfully implemented and attended by 10 (ten) participants consisting of village officials. This activity is very necessary to facilitate the monitoring of agricultural products from Triharjo Village on the dashboard via the village website. The target in community service has also been achieved and serves to provide problem solving for problems that occur with partners, namely in the form of: 1. Can introduce the Tiharjo village community to the importance of digitizing performance dashboards. 2. Can teach how to use Google Data Studio tools which can be used to help the process of creating Dashboards. With this training, it is hoped that it can help manage natural resource data which will help village officials and village communities, teachers and students in providing information services related to data visualization.
PERAMALAN NILAI EKSPOR MIGAS DENGAN MENERAPKAN MODEL AUTOREGREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE (ARFIMA) Rahwani, Putri Hazizah; Syaiful, Achmad; Alvionita S, Mika
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.754

Abstract

Indonesia, a nation in Southeast Asia, has a wealth of natural resources that could serve as the basis for future economic growth. Increased exports of natural resources are crucial for market expansion, job creation, foreign exchange gains, and economic progress. Despite the oil and gas industry's significant contribution, Indonesia still has a trade imbalance in these products and has volatility in export values due to changes in international oil prices and the state of the world economy. This study forecasts the value of Indonesia's oil and gas exports using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) approach, which has a long-term time series structure. The goals of this study are to identify the optimal ARFIMA model using MAPE and AIC for data on oil and gas export values, forecast the value of oil and gas exports using the optimal ARFIMA model many months in advance, and assess the ARFIMA model's forecasting accuracy. The best model, according to the results, is ARFIMA (0, [0.32], 2), with a MAPE score of 1.78%, indicating strong predicting accuracy for the upcoming periods. It is anticipated that this model will support Indonesia's economic stability and aid the government in strategic planning.
Recognizing the Spatial Distribution and Voronoi Patterns of the Recorded Earthquake Epicenters in Sunda Strait, Indonesia Muliawati, Triyana; Lestari, Fuji; Alvionita, Mika; Satria, Ardika; Harbowo, DG
Journal of Fundamental Mathematics and Applications (JFMA) Vol 7, No 2 (2024)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v7i2.21931

Abstract

Currently, Sunda Strait is one of the most active transportation hubs. However, this region also bears a notable history of geohazards associated with the dynamics of tectonic activity of the Eurasian and Indo-Australian tectonic plates, such as the super-eruption of Krakatoa volcano in 1883, the Sunda Strait tsunami in 2018, and decades of frequent earthquakes. To address these challenges, this study conducted a statistical analysis of the frequency and distribution of seismic activities in the Sunda Strait region based on recorded epicenter data in the United States Geological Survey's (USGS) Earthquake catalog. We assembled 440 multivariate earthquake data points between 1990 and 2023 (over three decades). The results of this study indicate that the machine learning approach precisely identifies four relevant parameters for -means clustering, followed by an analysis of silhouette values to recognize Voronoi patterns. These statistical patterns also have significant implications for the number of epicenter clusters and recognizing their spatial distribution. It provides a new understanding of the spatial-temporal characteristics and locates the list of frequent earthquake regions. Having all the necessary information would help to comprehensively evaluate geohazard risks in Sunda Strait region.
Prediksi Terkena Diabetes menggunakan Metode K-Nearest Neighbor (KNN) pada Dataset UCI Machine Learning Diabetes S, Mika Alvionita
Indonesian Journal of Applied Mathematics Vol. 3 No. 2 (2023): Indonesian Journal of Applied Mathematics Vol. 3 No. 2 October Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v3i2.1577

Abstract

Penelitian ini menggunakan algoritma K- Nearest Neighbor (KNN) untuk memprediksi resiko seseorang terkena diabetes. Variabel yang digunakan dalam prediksi adalah pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, dan age. Analisis menunjukkan bahwa Glucose, BMI, dan Age memiliki korelasi tinggi dengan diagnosis diabetes, menjadikannya indikator yang kuat untuk prediksi. Melalui metode KNN dengan k=1, dilakukan evaluasi model menggunakan Confusion Matrix. Hasil menunjukkan akurasi sebesar 96%, precision sebesar 91,6%, sensitivitas sebesar 88,7%, dan MSE sebesar 0,1376. Temuan ini menunjukkan bahwa KNN dengan k=1 efektif dalam memprediksi diabetes berdasarkan variabel klinis. Informasi ini dapat memberikan manfaat dalam pencegahan dan pengobatan diabetes secara lebih efektif.
Statistical Pattern Recognition of Lithosphere Anomalous Activity Along the Indonesian Ring of Fire S, Mika Alvionita; Satria, Ardika; Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot
Journal of Science and Applicative Technology Vol. 9 No. 1 (2025): Journal of Science and Applicative Technology June Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v9i1.1850

Abstract

The introduction of statistical pattern recognition becomes highly important for assessing disaster threats such as earthquakes. This approach is significantly more comprehensive and suitable for long-term event forecasting. Therefore, in the future, efforts can be promptly made to reduce the risk of disasters resulting from anomalies in lithospheric activity, especially frequent earthquakes in the Sumatra Island region, Indonesia. Statistical pattern analysis of lithospheric activity anomalies can be categorized through classification. Earthquake classification is performed based on magnitude scale and mathematical calculations of earthquake parameter unit conversion. The classification method employed in this research includes machine learning methods like k-nearest neighbor and support vector machine. The evaluation metrics used for machine learning models are model accuracy and confusion matrix tables.
Co-Authors Danni Gathot Harbowo Dhea Sukma Agustiana Dimas Wahyu Saputro Fadia Dilla Sabine Fuji Lestari Irawati, Febri Dwi Lis Nuraini Luluk Muthoharoh Muliawati Muliawati, Triyana Muthoharoh, Luluk Nadeak, Christyan Tamaro Nuramaliyah, Nuramaliyah Rahwani, Putri Hazizah Rekzi P. Manullang Riri Lestari Satria, Ardika Syaiful, Achmad Taj Shavira Tirta Setiawan, Tirta