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All Journal Techno.Com: Jurnal Teknologi Informasi JTAM (Jurnal Teori dan Aplikasi Matematika) Asean Journal of Science and Engineering (AJSE) Indonesian Journal of Statistics and Its Applications
Abdullah, Sarini
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Engineering Materials Science & Nanotechnology Mathematics Other

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Jakarta COVID-19 Forecast with Bayesian PIRD Multiwave Model Chandra, Christian Evan; Abdullah, Sarini
ASEAN Journal of Science and Engineering Vol 3, No 3 (2023): AJSE: December 2023
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ajse.v3i3.47248

Abstract

Governments have to consider both socioeconomic and health conditions in handling the COVID-19 pandemic. To help them in understanding possible scenarios behind the numbers and deciding optimum policy, this study proposed a Bayesian protected-infected-recovered-dead (PIRD) multi-wave model. Compounds of the Richards curve are used to understand how many pandemic waves possibly occur, how significant is the occurrence of every single wave, and dynamic in every single wave. The model also estimated the mortality rate due to the COVID-19 pandemic and the duration between infection to death, also infection to recovery. We fitted Jakarta’s COVID-19 data from 3 March 2020 to 25 November 2021 with help of OpenBUGS. We learned that their pandemic should consist of at least two waves, expected to have three waves already. By letting social restriction be looser together with decreasing number of new infection cases, Jakarta could have its fourth and even fifth pandemic wave that starts around mid-May to mid-July 2022 and reach its peak around January to February 2023. Vice versa, they could enter the endemic phase around the end of August 2022 until the beginning of February 2023 and finally have zero COVID-19 new infection around mid-January until mid-June 2023 by having stricter social restrictions.
Analisis Performa Deep Embedded Clustering untuk Pendeteksian Topik Cahyadi, Danu Julian; Murfi, Hendri; Satria, Yudi; Abdullah, Sarini; Widyaningsih, Yekti
Techno.Com Vol. 24 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i1.11841

Abstract

Pendeteksian topik adalah solusi untuk mengungkap struktur laten dalam sebuah dokumen. Kerangka umum pendeteksian topik berbasis clustering terdiri dari dua langkah: pembelajaran representasi dan pendeteksian topik melalui clustering. Dalam penelitian ini, Bidirectional Encoder Representations from Transformers (BERT) digunakan untuk pembelajaran representasi karena BERT mampu menangkap konteks setiap kata berdasarkan kata-kata di sekitarnya. Representasi teks yang diperoleh dari BERT digunakan untuk pendeteksian topik dengan clustering. Deep Embedded Clustering (DEC) dan Improved DEC (IDEC) adalah model clustering berbasis deep learning yang digunakan dalam penelitian ini untuk pendeteksian topik. DEC dan IDEC mampu mengubah data ke dalam ruang dimensi yang lebih rendah serta mengoptimalkan cluster secara simultan. Output dari teknik clustering berupa kata-kata kunci yang menggambarkan setiap topik cluster. Setelah mendapat kata kunci yang mewakili topik, evaluasi model dilakukan dengan melakukan perbandingan nilai topic coherence menggunakan Topic Coherence - Word2Vec (TC-W2V) sebagai analisis kuantitatif. Penelitian ini merupakan perluasan dari penerapan DEC dan IDEC pada pendeteksian topik dengan menambahkan analisis visualisasi dan kata kunci. Simulasi menunjukkan bahwa DEC dan IDEC mengungguli Uniform Manifold  Approximation and Projection (UMAP)-based k-means (UKM) dan Eigenspace-Based Fuzzy C-Means (EFCM) dari segi nilai TC-W2V, hasil visualisasi, dan kata kunci.   Kata kunci: analisis teks, deep clustering, pemrosesan teks
Mathematics in Transition: Assessing the Impact of Curriculum Changes on Student Performance Metrics Siregar, Juni Satria; Abdullah, Sarini
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30514

Abstract

Curriculum changes in higher education, especially in mathematics, are intended to align academic content with scientific advancements and evolving workforce demands; however, such reforms often bring unintended academic challenges for students. In Indonesia, recent changes in the 2016, 2020, and 2024 mathematics curricula introduced shifts in course credit allocations, course learning outcomes (CLOs), material scope, instructional methods, and evaluation systems. This study specifically aims to evaluate the impact of these curriculum changes on student academic performance across five core mathematics courses: Introduction to Data Science, Calculus 1, Calculus 2, Linear Algebra, and Mathematical Statistics. Employing a quantitative, exploratory approach, the research analyses academic records from 586 students using descriptive statistics and visualisation techniques such as boxplots and bar-line charts. The findings reveal fluctuating average grades and a general decline in pass rates, particularly under the 2024 curriculum, which introduced more complex CLOs, deeper content coverage, and application-oriented assessments. These results highlight the urgent need to balance curriculum innovation with student readiness and provide valuable insights for curriculum development and educational policy planning. 
Analysis of Students' Academic Performance in the Department of Mathematics Based on Semester GPA Dynamics: A Case Study of the 2017–2024 Cohorts Rahmat, Shafa Khadijah; Abdullah, Sarini
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30287

Abstract

This quantitative exploratory study investigates changes in students' Semester Grade Point Average (GPA) and their relationship with graduation status and study duration. It uses academic records from the Department of Mathematics at a public university in Indonesia for cohorts from 2017 to 2024. The study addresses concerns raised after the COVID-19 pandemic, which may have disrupted academic progression and altered the predictive power of initial GPA on graduation outcomes a gap not sufficiently explored in existing literature. Data were collected directly from the university's academic database, ensuring accuracy and consistency without relying on self-reported surveys. Descriptive statistical methods and visual analytics (e.g., line charts, boxplots, and scatter plots) were applied to uncover trends and patterns. Results show that earlier cohorts (2017–2020) have high graduation rates (82.7%–94.4%), while the 2019 cohort recorded the highest dropout rate (11.1%). Newer cohorts (2021–2024) predominantly consist of students still enrolled, though some early graduations and dropouts occurred. A positive correlation was found between first-semester GPA and graduation success, yet the pandemic likely introduced new variables that affect academic outcomes. These findings provide actionable insights for academic policy and support the development of early detection systems to identify students at academic risk.
Early Preeclampsia Detection Using XGBoost-Cox Proportional Hazard Model Syahdwinata, Arya Wira; Abdullah, Sarini
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p33-45

Abstract

Various prognostic models based on survival analysis methods have been proposed to predict the risk of preeclampsia (PE). To develop a more accurate yet interpretable prediction approach, we utilized clinical data from pregnant women collected at a hospital in Jakarta and applied the XGBoost-Cox Proportional Hazard Model (XGB-Cox). This model integrates the predictive power of the XGBoost machine learning algorithm with the Cox Proportional Hazard (Cox-PH) model, which estimates the effect of covariates on event time. Our results show that the XGB-Cox model outperforms the traditional Cox-PH model based on four evaluation metrics: log-likelihood, log-rank test, concordance index (C-index), and Brier score. The XGB-Cox model achieved a higher C-index of 0.8908 compared to 0.7548 for Cox-PH, indicating improved risk discrimination. Kaplan-Meier curves suggest that XGB-Cox provides better separation across risk quartiles. While XGB-Cox generally yields lower Brier Scores, its performance declines at later gestational weeks. The Cox-PH model remains superior in interpretability, offering clear hazard ratios, while XGB-Cox enhances model fitness and still provides meaningful insights into feature importance. Additionally, sensitivity analysis underscores the need to carefully determine the proportion of censored data, as excessive censoring affects model stability. These findings suggest that XGB-Cox provides a robust predictive framework for early PE risk assessment, supporting its potential application in clinical decision-making for maternal healthcare.
Co-Authors Cahyadi, Danu Julian Chandra, Christian Evan Hendri Murfi Rahmat, Shafa Khadijah Siregar, Juni Satria Syahdwinata, Arya Wira Yekti Widyaningsih Yudi Satria