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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal Jurnal Abdi: Media Pengabdian Kepada Masyarakat Abimanyu: Journal of Community Engagement MATHunesa: Jurnal Ilmiah Matematika
Maulana, Dimas Avian
Universitas Negeri Surabaya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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LOGISTIC AND PROBIT REGRESSION MODELING TO PREDICT THE OPPORTUNITIES OF DIABETES IN PROSPECTIVE ATHLETES Ariyanto, Danang; Sofro, A'yunin; Hanifah, A’idah Nur; Prihanto, Junaidi Budi; Maulana, Dimas Avian; Romadhonia, Riska Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1391-1402

Abstract

Diabetes is among the most prevalent chronic diseases globally, posing significant health risks to individuals. The identification of individuals at risk of developing these conditions is of paramount importance, particularly in high-stress and physically demanding activities such as athletic training. To find out the chances of a prospective athlete suffering from diabetes or not, models for binary data can be used, including logistic regression and probit models. The data used is primary data from prospective athletes in East Java, including prospective athletes from the State University of Surabaya and East Java Koni Athletes. This study aimed to develop an early prediction model for diabetes in prospective athletic candidates using a bivariate logistic and probit regression approach while considering the influence of socio-demographic and anthropometric factors. To selecting the best model between logistic regression and probit regression using Akaike’s Information Criterion (AIC) value, the smaller the AIC value gets means that the model is closer to the actual value or being the best model. Logistic regression has a smaller AIC value (129,85) than probit regression, this means that the logistic model is the best model. In this paper, an attempt is made to explore the use of logistic and probit regression to determine the factors which significantly influence the diabetes disease and we got that the logistic model as the best model because it has a smaller AIC value than the probit model. Based on the result of analysis and discussion, it can be concluded that there are two factors called mother’s job and finance which are influenced to the response variable, diabetes disease at significance level of 5%.
STOCK PRICE PREDICTION AND SIMULATION USING GEOMETRIC BROWNIAN MOTION-KALMAN FILTER: A COMPARISON BETWEEN KALMAN FILTER ALGORITHMS Maulana, Dimas Avian; Sofro, A'yunin; Ariyanto, Danang; Romadhonia, Riska Wahyu; Oktaviarina, Affiati; Purnama, Mohammad Dian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp97-106

Abstract

Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between GBM-KF, which was manually computed and computed using the Python library. As an example of this research, for BBRI stock, the high GBM MAPE value of 9.02% can be reduced to 3.52% with manually computed GBM-KF and 3.68% with Python library computed GBM-KF. Similarly, INDF and TLKM stocks are showing a significant reduction in MAPE values to deficient levels in some cases. The GBM-KF method employing manual computing may enhance the overall precision of stock price forecasting. Future research may enhance this study by using the GBM-KF model on alternative financial instruments, integrating supplementary market data, or evaluating its efficacy under extreme market conditions.
Penerapan Model Predictive Control (MPC) pada Desain Pengendalian Robot Mobil Beroda Empat Maulana, Dimas Avian
Zeta - Math Journal Vol 3 No 2 (2017): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (899.173 KB) | DOI: 10.31102/zeta.2017.3.2.46-51

Abstract

Robot mobil adalah salah satu contoh dari wahana nir awak (WaNA) yang dapat dikendalikan dari jauh atau memiliki sistem pengendali otomatis untuk bergerak dan berpindah haluan. Robot mobil menjadi salah satu sarana yang digunakan oleh pihak militer untuk untuk melakukan pengintaian, penjelajahan, dan pengawasan ke tempat-tempat yang berbahaya bagi manusia. Pada penerapannya ada beberapa lintasan yang dianggap berbahaya untuk dilalui, didefinisikan suatu lintasan terlebih dahulu agar robot mobil bergerak sesuai lintasan tersebut. Robot mobil tidak bisa mengikuti lintasan dengan baik tanpa diberi perintah terlebih dahulu dan dikendalikan. Untuk itu, diperlukan suatu metode untuk mengendalikan robot mobil agar dapat bergerak mengikuti lintasan dalam misinya untuk melakukan pengintaian, penjelajajahan dan pengawasan. Dari hasil simulasi diperoleh bahwa dengan mengambil horizon prediksi N=3, waktu sampling t=1s dan iterasi sebanyak 10 kali diperoleh nilai x(k) yang mendekati dengan nilai x_r(k)
Co-Authors AFFIATI OKTAVIARINA AGUNG LUKITO Amelia Permata Indah Ardian Nugraha Ramadani Aulia Nurita Sari Auliya, Elok Rizqi Danang Ariyanto Erlix Rakhmad Purnama Hanifah, A'idah Nur Hanifah, A’idah Nur Hunaifi, Muhammad Arif Ika Kurniasari Ika Kurniasari Jihan Fadhilah Khikmah, Khusnia Nurul Mirwa Adiprahara Anggarani Prihanto, Junaidi Budi Purnama, Erlix Rakhmad Purnama, Mohammad Dian Ramadhan, Rafi Rachmad Rana Hisanah Azizah Rasyidah, Salma Azmi Raya Sulistyowati Retno Wulansari Ricky Eka Putra Ricky Eka Putra Romadhonia, Riska Wahyu Salsabila Reza Andriananda Sofro, A'yunin Tri Sudarwanto Yohanes Billy Surya Wijaya Yuliani Puji Astuti Yusuf Fuad YUSUF FUAD