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All Journal Jurnal Matematika: MANTIK Information System for Educators and Professionals : Journal of Information System Edukasia: Jurnal Pendidikan dan Pembelajaran INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System
Sri Harini
UIN Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Identification COVID-19 Cases in Indonesia with The Double Exponential Smoothing Method Sri Harini
Jurnal Matematika MANTIK Vol. 6 No. 1 (2020): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.476 KB) | DOI: 10.15642/mantik.2020.6.1.66-75

Abstract

The time-series approach is a method used to analyze a series of data in a time sequence to estimate the value of a series in the future. This article will identification the COVID-19 case model in Indonesia using the Double Exponential Smoothing Method. The Double Exponential Smoothing method is one method that can be used to optimize the estimation of the ARIMA model with smoothing parameters α. The data used is sourced from the National Disaster Management Agency which was released starting March 2, 2020. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the Covid-19 case in Indonesia following the ARIMA model (0,1,1).
Komparasi Algoritma Naïve Bayes dan k-Nearest Neighbor Pada Klasifikasi Kontribusi Tokoh Politik Moh Ainur Rohman; Sri Harini
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 7 No 1 (2022): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Desember 2022)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (421.192 KB) | DOI: 10.51211/isbi.v7i1.1857

Abstract

Dalam berita politik, banyak sekali informasi tokoh-tokoh politik dalam mendongkrak elektabilitasnya. Berbagai kontribusi mereka lakukan seperti bidang pendidikan, infrastruktur, UMKM, kesehatan, teknologi, dan pelayanan publik. Namun, untuk mengetahui berbagai kontribusi apa saja yang dilakukan mereka, masyarakat masih sulit menilai. Untuk mengatasi masalah tersebut, dibutuhkan sistem yang dapat mengkategorikan kontribusi-kontribusi para tokoh politik. Pada penelitian ini menggunakan dua algoritma untuk mengkomparasi algoritma mana yang terbaik untuk membangun sistem. Penelitian dilakukan menggunakan berbagai variasi jumlah dataset, dan tiga kali pengujian, untuk KNN dilakukan dengan 4 nilai k yaitu k=7, k=9, k=11, dan k=13. Hasilnya, algoritma KNN dengan k=7 yang terbaik dengan nilai precision sebesar 71.5%, nilai recall sebesar 22%, dan nilai f-measure sebesar 19.2%. Abstract: In political news, there is a lot of information about political figures in boosting their electability. They made various contributions such as education, infrastructure, MSMEs, health, technology, and public services. Therefore, it is necessary to classify news into several, in order for the public to know how big the contribution category of political figures is. To overcome this problem, a system is needed that can categorize the contributions of political figures. In this study, two algorithms are used to compare which algorithm is the best to build the system. The study was conducted using various variations in the number of datasets, and three times of testing, for KNN carried out with 4 values of k, namely k=7, k=9, k=11. As a result, the KNN algorithm with k=7 is the best with a precision value of 71.5%, the recall value is 22%, and the f-measure value is 19.2%.
Komparasi Algoritma Naïve Bayes dan k-Nearest Neighbor Pada Klasifikasi Kontribusi Tokoh Politik Moh Ainur Rohman; Sri Harini
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 7 No 1 (2022): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Desember 2022)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v7i1.1857

Abstract

Dalam berita politik, banyak sekali informasi tokoh-tokoh politik dalam mendongkrak elektabilitasnya. Berbagai kontribusi mereka lakukan seperti bidang pendidikan, infrastruktur, UMKM, kesehatan, teknologi, dan pelayanan publik. Namun, untuk mengetahui berbagai kontribusi apa saja yang dilakukan mereka, masyarakat masih sulit menilai. Untuk mengatasi masalah tersebut, dibutuhkan sistem yang dapat mengkategorikan kontribusi-kontribusi para tokoh politik. Pada penelitian ini menggunakan dua algoritma untuk mengkomparasi algoritma mana yang terbaik untuk membangun sistem. Penelitian dilakukan menggunakan berbagai variasi jumlah dataset, dan tiga kali pengujian, untuk KNN dilakukan dengan 4 nilai k yaitu k=7, k=9, k=11, dan k=13. Hasilnya, algoritma KNN dengan k=7 yang terbaik dengan nilai precision sebesar 71.5%, nilai recall sebesar 22%, dan nilai f-measure sebesar 19.2%. Abstract: In political news, there is a lot of information about political figures in boosting their electability. They made various contributions such as education, infrastructure, MSMEs, health, technology, and public services. Therefore, it is necessary to classify news into several, in order for the public to know how big the contribution category of political figures is. To overcome this problem, a system is needed that can categorize the contributions of political figures. In this study, two algorithms are used to compare which algorithm is the best to build the system. The study was conducted using various variations in the number of datasets, and three times of testing, for KNN carried out with 4 values of k, namely k=7, k=9, k=11. As a result, the KNN algorithm with k=7 is the best with a precision value of 71.5%, the recall value is 22%, and the f-measure value is 19.2%.
Learning Quality, Time Management and Social Support on Student Academic Achievement Aliffany Pualam Ariarta; Mohammad Lutfi Alil Mu'in; Muhammad Sabri Latif; Azrina Hilmi Saadah; Sri Harini
EDUKASIA: Jurnal Pendidikan dan Pembelajaran Vol. 5 No. 2 (2024): Edukasia: Jurnal Pendidikan dan Pembelajaran
Publisher : LP. Ma'arif Janggan Magetan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62775/edukasia.v5i2.778

Abstract

This study aims to determine the effect of learning quality, time management and social support on the academic achievement of postgraduate students of UIN Maulana Malik Ibrahim Malang. The method used in analysing research data is descriptive quantitative with a sample of 80 students. The sampling technique used is Non Probability Sampling technique. The results showed the Wald test results with a significance value of learning quality of 0.000 and social support of 0.021 smaller than the value of the error rate of 0.05. While time management with a significance value of 0.348 is greater than the error rate value of 0.05, meaning that time management has no correlation and has no significant effect on student academic achievement. The odds ratio value of learning quality (X1) is 18.283, this indicates that learning quality has an influence of 18.283 times greater on student academic achievement. Then the Odds ratio value of social support (X3) is 3.061, this shows that the effect of social support on student academic achievement is 3.061 times greater than if there is no social support. Meanwhile, variable (X2), namely time management, has no influence on student academic achievement. This is caused by students who do not have self-management skills or the ability to manage time. The implications of learning quality can be seen from students' understanding of the material presented, honing the courage to express opinions, increasing confidence in answering or asking questions and mental readiness and competence. The implication of social support is to reduce student stress and psychological pressure that can affect their academic performance.
Co-Authors Aliffany Pualam Ariarta Azrina Hilmi Saadah Moh Ainur Rohman Mohammad Lutfi Alil Mu'in Muhammad Sabri Latif Nur Azizah Nurwarda Irmadani Sri Murni Indriani