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All Journal MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Riset Pembelajaran Matematika Sekolah Jurnal Statistika dan Aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi International Journal of Progressive Mathematics Education MATHunesa: Jurnal Ilmiah Matematika International Journal of Applied Science and Sustainable Development (IJASSD) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Pattimura International Journal of Mathematics (PIJMath) JMT (Jurnal Matematika dan Terapan) Parameter: Jurnal Matematika, Statistika dan Terapannya BAKTI : Jurnal Pengabdian Kepada Masyarakat

Vera Maya Santi

Unknown Affiliation

Author-ID : 2706808

Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Efektifitas Pelatihan Materi Statistika Dasar bagi Guru SMA untuk Meningkatkan Kompetensi Peserta Didik Santi, Vera Maya; Muhammad Rafli; Zahrah Hashifah; Widyanti Rahayu; Dian Handayani; Ria Arafiyah; Suyono; Bagus Sumargo; Tri Murdiyanto; Siti Rohmah Rohimah; Dania Siregar; Faroh Ladayya
Bakti : Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2025): Bakti: Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Layanan Pendidikan Tinggi (LLDIKTI) Wilayah XII Ambon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51135/bakti.5.1.70-77

Kurikulum Merdeka yang diterapkan di Indonesia menekankan pada pembelajaran yang adaptif, kreatif, dan inovatif, sehingga mengharuskan guru untuk memiliki kompetensi yang lebih tinggi dalam penguasaan mata pelajaran, termasuk statistika. Namun, banyak guru yang kesulitan dalam mengimplementasikan kurikulum ini dan menyiapkan bahan ajar karena kurangnya referensi. Pengabdian masyarakat ini bertujuan untuk meningkatkan kompetensi guru melalui pelatihan yang dirancang untuk memperdalam pengetahuan, meningkatkan keterampilan, dan memberikan referensi tambahan untuk membuat bahan ajar statistika. Pelatihan ini melibatkan 24 guru SMA dari kelompok MGMP Matematika Sukabumi. Berdasarkan analisis uji-t berpasangan terhadap hasil pre-test dan post-test, nilai p-value (0,00) lebih kecil dari α (0,05), yang mengindikasikan bahwa pelatihan ini secara signifikan dan efektif, mampu meningkatkan pengetahuan, keterampilan, dan kemampuan guru dalam rangka meningkatkan kompetensi peserta didik.

ANALYSIS OF INDONESIAN STUDENTS' READING LITERACY USING THE SMOOTHLY CLIPPED ABSOLUTE DEVIATION (SCAD) PENALTY Santi, Vera Maya; Riyantobi, Ariq Muammar; Widyanti Rahayu
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09111

Reading literacy significantly impacts a country's educational level, making it crucial to further investigate into this issue. Identifying factors that influence students' reading literacy, particularly in Indonesia, is a key area of exploration. PISA survey data, conducted every three years, is relevant for researching student proficiency. Each survey period focuses on one of three main topics: science literacy, mathematics literacy, and reading literacy. The 2018 PISA survey data is suitable for studying students’ reading literacy, as the main topic that year was reading literacy. However, PISA survey data includes many strongly correlated independent variables, potentially violating the multicollinearity assumption. To address this, regression analysis with a penalty function is used for variable selection. The SCAD (Smoothly Clipped Absolute Deviation) penalty function has proven effective in previous studies on PISA data. The model using the SCAD penalty function yielded excellent results, indicated by an Adjusted R2 value of 0.967. Based on this model, three main factors influence students' reading literacy in Indonesia: learning facilities, general knowledge, and students' self-confidence.

ITEM RESPONSE MODEL FOR ANALYZING ITEM RESPONSES IN THE INSTRUMENT OF CHANGE MANAGEMENT AND ORGANIZATIONAL CULTURE Dian Handayani; Muhammad Alief Ghifari; Vera Maya Santi; Rahfa Qur’aniyatin Dhuha
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09104

Item Response Theory (IRT) is an approach that can be used to analyze the responses/answers given by respondents to a measurement instrument. Unlike the classical test theory (CTT) approach that measures the latent traits of respondents based on the total score, IRT measures latent traits based on the responses given by respondents to each item. Another difference between CTT and IRT is that the CTT approach is theory-based while IRT is model-based. The purpose of this study is to apply Item Response Theory (IRT) to analyze the item responses of the employees of the Kementerian Desa, Pembangunan Daerah Tertinggal, dan Transmigrasi/KDPDTTT (Ministry of Village, Development of Disadvantaged Regions and Transmigration) on the items in the instrument/questionnaire which was administered to the employees, in order to understand their attitudes towards the changes management and organizational culture in the KDPDTT. We applied item response theory to analyze the answers provided by the respondents to the items. These responses were modelled based on the dichotomous IRT models, namely the 1PL, 2PL, and 3PL models. The IRT modeling in this study is based on the results of a survey conducted by KDPDTTT in 2020. Among the three models, the 2PL model is the most suitable for our item responses data because it has the smallest AIC, BIC, and G2. Based on the 2PL model, the probability for endorsing the items related to the change management ranges from 0.68 to 0.95. Meanwhile, the probability for endorsing items related to organizational culture ranges from 0.87 to 0.98. Although each item in the instrument has three response options, namely "disagree", "undecided (neutral)", and "agree", we will treat them as dichotomous. We classify the "undecided" answer as the "disagree" category. The reason is that many Indonesian people usually find it hard to say "disagree" for a question related to the evaluation of a policy. They tend to feel safer by choosing “undecided”. Therefore, the item responses that have been analyzed in our study are dichotomous, that is, "agree" or "disagree". The novelty of this research is utilizing a non-classical approach, namely IRT, which has several advantages over Classical Test Theory (CTT), including that item characteristics do not depend on respondent characteristics, and vice versa.

RESTRICTED MAXIMUM LIKELIHOOD ESTIMATION FOR MULTIVARIATE LINEAR MIXED MODEL IN ANALYZING PISA DATA FOR INDONESIAN STUDENTS Santi, Vera Maya; Notodiputro, Khairil Anwar; Indahwati, Indahwati; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The Program for International Student Assessment (PISA), becomes one of the references or indicators used to assess the development of students' knowledge and skills in each member country of the Organization for Economic Cooperation and Development (OECD). The results of the PISA survey in 2018 placed Indonesia in the bottom 10, indicating that the implementation of the national education system has not been successful. This underlies the need for a more in-depth study of the factors that influence PISA data scores not only statistically qualitatively but also quantitatively which is still very rarely done. The data structure of the PISA survey results is complex, which involves multicollinearity, multivariate response variables, and random effects. Thus, it requires an appropriate statistical analysis method such as the multivariate mixed linear regression (MLMM) model. In this study, secondary data from the results of the 2018 PISA survey with Indonesian students as the smallest unit of observation were used as sample. School is used as an intercept random effect which is assumed to be normally distributed. Multicollinearity is overcome by selecting independent variables based on AIC and BIC values. Estimation of variance and random effect parameters was performed using the restricted maximum likelihood (REML) method. Based on the estimator of the variance of random effects for the response variables of mathematics, science, and reading literacy, it was obtained 1548.12, 1359.39, and 1082.48, respectively, which explains the significant effect of each school as a random effect on the three response variables.

MULTILEVEL REGRESSION WITH MAXIMUM LIKELIHOOD AND RESTRICTED MAXIMUM LIKELIHOOD METHOD IN ANALYZING INDONESIAN READING LITERACY SCORES Santi, Vera Maya; Kamilia, Rifa; Ladayya, Faroh
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The multilevel regression model is a development of the linear regression model that can be used to analyze data that has a hierarchical structure. The problem with this data structure is that individuals in the same group tend to have the same characteristics, so the observations at lower levels are not independent. Education research often produces a hierarchical structure, one of which is PISA data, where students as level-1 nested within schools as level-2. In the PISA 2018 survey, reading literacy is the main focus. The data are sourced from the Organisation for Economic Co-operation and Development (OECD). The survey results show that the reading literacy scores of Indonesian students have decreased, thus placing Indonesia at 74th out of 79 countries. However, it is still very rare to research the reading literacy of Indonesian students' using a multilevel regression model. This study aims to apply a multilevel regression model to determine the factors influencing Indonesian reading literacy scores in PISA 2018 survey data. The results of this study indicate that the factors that influence response variable are gender, grade level, mother's education, facilities at home, age at school entry, student discipline behavior at school, and failing grade, while at the school level are the type of school and school location. The magnitude variance of student reading literacy scores can be explained by the explanatory variables the student level is 11,42% and the school level is 60,66%, while the rest is explained by another factor outside the study.

FORECASTING THE VALUE OF INDONESIA'S OIL AND GAS IMPORTS USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL Santi, Vera Maya; Wahyu, Rahadian; Hadi, Ibnu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2047-2058

The value of Indonesia's oil and gas imports is a combination of the value of crude oil (petroleum), oil and natural gas products. Throughout 2021, the value of Indonesia's oil and gas imports reach US$ 25.53 billion or the equivalent of 382.95 trillion rupiah (estimated at US$ 1 = Rp. 15,000.00). The high demand for petroleum in Indonesia is due to the fact that petroleum is the main source of energy for daily life needs, especially for industrial, transportation and household needs. The requirment for oil imports is expected to increase along with the growth in Indonesia's population. Therefore, a step is needed to prevent an increase in the value of oil and gas imports in the coming period. One method of analysis that can be used is forecasting using the time series method with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The SARIMA model is a time series method with data that has a seasonal pattern and the forecasting results will get a pattern similar to the previous data. The data used is data on the monthly value of oil and gas imports from January 2005 to December 2022 with totaling 216 data. This research aims to find the best model and predict the value of Indonesia's oil and gas imports in the next 12 periods with data test in 4 periods (Januari to April 2023). The best model for the results of this research is (2, 1, 0)(0, 1, 1)43 with a MAPE value of 13.90%. Based on the accuracy of the MAPE value, this percentage has good quality forecasting results.

- ANALISIS FAKTOR-FAKTOR YANG MEMENGARUHI DIABETES MELITUS DI JAWA BARAT MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS): - Santi, Vera Maya
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Diabetes mellitus is a metabolic disorder characterized by chronically elevated blood sugar levels due to impaired insulin secretion or action. West Java ranks second among Indonesian provinces with the highest number of diabetes mellitus cases based on medical diagnoses. In 2022, the number of diabetes mellitus cases in this province reached its lowest point in the past five years, suggesting the influence of significant factors contributing to this decline. Therefore, identifying the factors affecting diabetes mellitus prevalence in West Java in 2022 is essential. However, the data exhibit no clear pattern, necessitating a nonparametric regression approach for modeling these factors. This study employs Multivariate Adaptive Regression Splines (MARS), a flexible method that partitions data into segments and applies linear regression within each segment. Model selection criteria include Generalized Cross Validation (GCV) and Akaike Information Criterion (AIC). Based on GCV, seven significant variables were identified, whereas AIC indicated eight significant variables influencing diabetes mellitus prevalence in West Java. Structural differences between models are also observed in the number of basis functions: the GCV model utilizes 13 basis functions, while the AIC model employs 14. In terms of model performance, the GCV model achieves an R² value of 0.994, whereas the AIC model attains an R² value of 0.995.

MODELING POVERTY IN WEST JAVA PROVINCE USING NEGATIVE BINOMIAL REGRESSION WITH PENALIZED SMOOTHLY CLIPPED ABSOLUTE DEVIATION Santi, Vera Maya; Baihaqi, Aulia; Siregar, Dania
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2557-2570

The number of poor people is an example of discrete or count data. One commonly used regression model for count responses is the Negative Binomial regression. Regression modeling with many predictor variables results in the problem of multicollinearity. This condition causes the parameter estimator to become unstable. One method to overcome this problem is to use the penalty function to optimize the selection of predictor variables. This study aims to analyze the factors influencing the number of poor people in West Java Province using Negative Binomial regression with the Smoothly Clipped Absolute Deviation (SCAD) penalty function. The research data was sourced from the Central Bureau of Statistics in 2022, covering 27 districts/cities in West Java Province with 21 predictor variables. The method applied selects variables and estimates parameters simultaneously in the Negative Binomial regression model. Based on the AIC value, it was found that the Negative Binomial penalized SCAD model (AIC = 628.12) had better performance than the Negative Binomial regression model (AIC = 634.34). The Negative Binomial penalized SCAD regression model yielded five significant predictor variables with value of 92.8%. This model is simpler than the Negative Binomial regression model with six predictor variables. The regional minimum wage, number of cooperatives, percentage of the population who have health insurance, the pure college enrollment rate, and non-food expenditure are important variables as factors affecting the number of poor people in West Java Province.

Indonesian Students Reading Literacy Score in Framework Hierarchical Data Structure Using Multilevel Regression Maya Santi, Vera; Rahayuningsih, Yuliana; Sumargo, Bagus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i2pp353-368

Education is essential for improving the quality of Indonesian society. Indonesia participated in the Programme International Students Assessment (PISA) survey to improve the quality of education. Based on the 2018 PISA survey data, Indonesia's reading literacy score has a hierarchical data structure, which means students at level 1 are nested by schools at level 2. The multilevel model is an appropriate approach to analyze such hierarchical structures. However, quantitative analysis of PISA data is still rarely carried out. This study aims to analyze the explanatory variables that significantly affect Indonesian students' reading literacy from the PISA survey using multilevel regression. This study examined student-level and school-level explanatory variables obtained from the Organization for Economic Cooperation and Development (OECD). Significant parameter tests revealed that, at the student level, factors such as socioeconomic status, teacher support in language learning, teacher-directed instruction, enjoyment of reading, perceived difficulty, competitiveness, mastery goal orientation, disciplined classroom climate in reading, general fear of failure, attitudes toward school, and perceived feedback significantly influence reading literacy. At the school level, school size was found to be a significant factor affecting reading literacy scores. Furthermore, the Intraclass Correlation Coefficient (ICC) indicated that schools accounted for 49% of the total variance.

Zero Inflated Poisson Regression Analysis in Maternal Death Cases on Java Island Santi, Vera Maya; Ambarwati, Defina; Sumargo, Bagus
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

The basic regression model used to analyze the count data is the Poisson regression.. However, applying the Poisson regression model is unsuitable for excess zero data because it can cause overdispersion where the variance data is greater than its mean. One of the developments of the Poisson regression model can overcome this condition, Zero Inflated Poisson Regression (ZIP). In the health sector, the death of pregnant women on the Java island is an event that still rarely occurs and forms an excess zero data structure. However, the analysis of cases of maternal mortality using ZIP regression has never been studied in more depth. In this article, the maternal mortality cases in Java were modelled using ZIP regression to specify the variables that had a significant effect. The initial analysis results indicated the occurrence of overdispersion due to excess zero where there are 52% zero values in the data. The ZIP regression applied in this research provides enhancements to the Poisson regression based on the Vuong test. The results showed that the variables that had a significant effect on the maternal death cases in Java in the count model are the percentage of maternal health service coverage and the percentage of coverage of postpartum visit coverage, while in the zero-inflation model, the percentage of deliveries in health facilities and the percentage of obstetric complications treatment

Co-Authors Abi Wiyono Afifah Nur Mutia Ambarwati, Defina Auria Yusrin Fathya Bagus Sartono Bagus Sumargo Bagus Sumargo Bagus Sumargo, Bagus Baihaqi, Aulia Binoto, Rustham Michael Contillo, Gerry Dania Siregar Dania Siregar Dian Handayani Dwi Antari Wijayanti Ellis Salsabila, Ellis Fanya Izmi Hawa Faoza Saaroh Fariani Hermin Faroh Ladayya Gatri Eka Kusumawardhani Gusnia, Farida Herlina Nofita Ibnu Hadi Indahwati Indiyah, Fariani Hermin Jadid Irtakhoiri Jaisy Aulia Kamil, Adine Ihsan Kamilia, Rifa Khoirunnisa Koeshella, Ajeng Ladayya, Faroh Lina Nafisah Lukman El Hakim Mahatma, Yudi Maulida Audia Firdaus Meidianingsih, Qorry Meila Nadya Muhammad Alief Ghifari Muhammad Rafli Muzakki Tamami Nia Rahayu Ningsih Nilam Novita Sari Novia Sucy Aristawidya Pinta Deniyanti Sampoerno Pinta Deniyanti Sampoerno Prima Riyani Rahayuningsih, Yuliana Rahfa Qur’aniyatin Dhuha Ria Arafiyah Riam Nurussilmah Rianiati Monica Ridana, Farah Fadhilah Rifqy Marwah Akhsanti Riska Agustin Riyantobi, Ariq Muammar Safira Datu Sinta Rahmadani Siregar, Dania Siti Rohmah Rohimah Sudarwanto Sudarwanto, Sudarwanto SUYONO Suyono Suyono Suyono Suyono Syarifah Ayu Angela Syifa Azzahra Tamami, Muzakki Tian Abdul Aziz Tri Murdiyanto Wahyu, Rahadian Wardani Rahayu Widyanti Rahayu Widyanti Rahayu Widyanti Rahayu Widyanti Rahayu Widyanti Rahayu Wilsen Wilsen Zahrah Hashifah

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