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All Journal Pedagogi: Jurnal Ilmu Pendidikan Kontinu: Jurnal Penelitian Didaktik Matematika MUST: Journal of Mathematics Education, Science and Technology Jurnal Administrasi dan Manajemen Pendidikan Jurnal SOLMA 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 JAMP : Jurnal Administrasi dan Manajemen Pendidikan Jurnal Riset Pembelajaran Matematika Sekolah Jurnal Statistika dan Aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Griya Journal of Mathematics Education and Application 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) Parameter: Jurnal Matematika, Statistika dan Terapannya BAKTI : Jurnal Pengabdian Kepada Masyarakat E-Jurnal Matematika

Vera Maya Santi

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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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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.

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.

PENCAPAIAN SDGs: LITERASI DATA STATISTIK POTENSI DESA DI KELURAHAN KAMPUNG RAWA, JAKARTA PUSAT Bagus Sumargo; Suyono; Dian Handayani; Ria Arafiyah; Nilam Novita Sari; Vera Maya Santi
Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT - SNPPM2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Negeri Jakarta

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

Abstrak Program Desa Cantik (Cinta Statistik) bertujuan untuk meningkatkan literasi data serta kemampuan aparat kelurahan, dan masyarakat dalam mengelola serta memanfaatkan data statistik secara mandiri dan sistematis. Sehubungan dengan hal ini, kami hadir di Kelurahan Kampung Rawa, Jakarta pusat dalam progam pengabdian kepada masyarakat. Kami sebagai civitas akademika terpanggil untuk melaksanakan Tri Dharma Perguruan Tinggi yaitu sesuai tujuan Sustainable Development Goals SDGs Nomor 4 yaitu pendidikan berkualitas dan Nomor 17 yaitu Kemitraan untuk mencapai tujuan. Pendidikan berkualitas dalam rangka memberikan literasi tentang data statistik – khususnya data PODES potensi Desa. Abstract The Beautiful Village (Love Statistics) program aims to improve data literacy and the ability of village officials and communities to manage and utilize statistical data independently and systematically. In this regard, we are present in Kampung Rawa Village, Central Jakarta, as part of a community service program. As academics, we are called to implement the Tri Dharma of Higher Education, in accordance with Sustainable Development Goals (TPB) Number 4, namely quality education, and Number 17, Partnership to Achieve Goals. Quality education aims to provide statistical data literacy—specifically PODES data regarding village potential. The statistical data literacy activity was held on July 15, 2025, with 19 participants: 2 village officials, 12 Regional Community members, and 5 Dasawisma cadres (Village Community Empowerment). The effectiveness of the training was evaluated through analysis of pre- and post-test results, which consisted of 10 statements with "Yes" and "No" answer options. The analysis was conducted using the McNemar test. The results of the pre- and post-test evaluations showed a significant increase in participant understanding, as confirmed by analysis using the McNemar test. Most participants also stated that the material presented was easy to understand, applicable, and useful for supporting data management at the sub-district level. Keywords: Beautiful Village, Village Potential, Statistical Data Literacy; McNemar; Improvements

PELATIHAN ANALISIS STATISTIK MENGGUNAKAN WEBSITE INTERAKTIF UNTUK MENDUKUNG PENGAMBILAN KEPUTUSAN BERBASIS DATA PENDIDIKAN BAGI GURU SMA MATEMATIKA DI KABUPATEN SUKABUMI Siregar, Dania; Suyono; Vera Maya Santi; Auria Yusrin Fathya; Sinta Rahmadani; Jaisy Aulia; Maulida Audia Firdaus
Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT - SNPPM2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Negeri Jakarta

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

Tantangan utama dalam pengambilan keputusan pendidikan adalah keterbatasan literasi statistik dan keterampilan guru dalam mengolah data, terutama melalui teknologi interaktif. Hal ini terlihat dari kuesioner pra-pelatihan, di mana sebagian besar guru menyatakan keraguan atau ketidaksetujuan terhadap pengetahuan mereka, dan mayoritas belum pernah menggunakan situs web interaktif untuk analisis statistik. Program layanan masyarakat ini bertujuan untuk meningkatkan literasi statistik guru melalui pelatihan analisis data menggunakan situs web interaktif berbasis R-Shiny. Pelatihan dilaksanakan pada 13 Agustus 2025, dengan peserta terdiri dari guru matematika SMA di Kabupaten Sukabumi, bekerja sama dengan MGMP Matematika SMA Sukabumi sebagai mitra layanan masyarakat. Materi pelatihan mencakup statistik deskriptif, analisis inferensial, pengujian hipotesis, dan regresi. Evaluasi pasca-pelatihan menunjukkan peningkatan yang signifikan: lebih dari 80% peserta setuju atau sangat setuju bahwa materi pelatihan sistematis, mudah dipahami, dan relevan, serta aplikasi tersebut mudah diakses dan ramah pengguna. Selain itu, 75% peserta sangat setuju bahwa mereka memperoleh pengetahuan baru yang berguna untuk pengambilan keputusan berbasis data dalam pendidikan. Kesimpulannya, pelatihan berbasis teknologi interaktif secara efektif meningkatkan kompetensi guru, memperkuat motivasi mereka, dan menumbuhkan budaya pengambilan keputusan berbasis data di sekolah. Translated with DeepL.com (free version) Abstract The main challenge in educational decision-making is the limited statistical literacy and skills among teachers in processing data, particularly through interactive technology. This was evident from the pre-training questionnaire, in which most teachers expressed doubt or disagreement about their knowledge, and the majority had never used an interactive website for statistical analysis. This community service program aimed to enhance teachers’ statistical literacy through training in data analysis using an R-Shiny-based interactive website. The training was conducted on August 13, 2025, with participants consisting of senior high school mathematics teachers in Sukabumi Regency, in collaboration with the Sukabumi Senior High School Mathematics MGMP as the community service partner. The training materials covered descriptive statistics, inferential analysis, hypothesis testing, and regression. Post-training evaluation showed a significant improvement: more than 80% of participants agreed or strongly agreed that the materials were systematic, easy to understand, and relevant, and that the application was accessible and user-friendly. Furthermore, 75% of participants strongly agreed that they gained new knowledge useful for data-driven decision-making in education. In conclusion, interactive technology-based training effectively improved teachers’ competence, strengthened their motivation, and fostered a data-driven decision-making culture in schools.

PCR DAN PLSR ALGORITMA NIPALS DALAM MENANGANI MULTIKOLINIERITAS PADA PREVALENSI STUNTING DI NUSA TENGGARA TIMUR NATALIE EFRATA SUSANTI; VERA MAYA SANTI; DEVI EKA WARDANI
E-Jurnal Matematika Vol. 14 No. 4 (2025)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2025.v14.i04.p491

Nutritional problems contribute to 50% of deaths among children under five, particularly in low- and middle-income countries. One of the most common issues in Indonesia is stunting, a condition where a child's height falls below the standard for their age. In 2022, East Nusa Tenggara (NTT) recorded the highest stunting prevalence in Indonesia at 35.3%. However, quantitative statistical analyses of its contributing factors in NTT remain limited. This study aims to compare partial least squares regression (PLSR) using the NIPALS algorithm with principal component regression (PCR) in addressing multicollinearity. The secondary data were obtained from the 2022 Indonesian Nutrition Status Survey (SSGI), published by the Ministry of Health and BPS NTT, consisting of one response variable and ten predictor variables. Results showed that the PLSR model outperforms PCR, with an adjusted R² of 0.741 compared to 0.322. The superiority of PLSR is also evident from its lower RMSE and MAE values (2.783 and 1.910) compared to PCR (4.742 and 3.346). PLSR identified five significant predictors: average daily protein consumption per capita, number of children receiving DPT and HB immunizations, Human Development Index, percentage of households with access to safe drinking water, and number of people living in poverty.

Peningkatan Kompetensi Guru melalui Pelatihan Pembuatan Infografis sebagai Media Pembelajaran Digital Interaktif Sari, Nilam Novita; Sumargo, Bagus; Santi, Vera Maya; Rahayu, Widyanti; Adzima, Khaola Rachma; Khotimah, Tiara Husnul; Sahila, Sahiba; Mahardika, Baihaqy; Fadya, Khansa Salsabil
Jurnal SOLMA Vol. 15 No. 1 (2026)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v15i1.21350

Background: Kualitas pendidikan yang unggul menuntut guru untuk memiliki kompetensi literasi digital, kemampuan berpikir kritis, dan keterampilan kolaboratif. Namun, kesenjangan kompetensi digital guru di Indonesia masih cukup besar, khususnya dalam kemampuan mendesain media pembelajaran visual seperti infografis. Penelitian ini bertujuan untuk menganalisis pengaruh pelatihan pembuatan infografis terhadap peningkatan kompetensi guru dalam mendesain media pembelajaran digital interaktif. Metode penelitian menggunakan desain eksperimen semu (quasi-experimental) dengan model one-group pre-test and post-test. Sampel penelitian berjumlah 15 guru SMP di Kabupaten Sukabumi yang dipilih secara purposif. Data dikumpulkan melalui kuesioner pre-test dan post-test untuk mengukur pengetahuan konseptual dan persepsi guru terhadap infografis. Analisis data dilakukan dengan uji Wilcoxon Signed-Rank dan perhitungan effect size. Hasil penelitian menunjukkan adanya peningkatan signifikan pada kompetensi guru setelah pelatihan (p = 0,000714 < 0,05) dengan nilai effect size sebesar 0,881 yang termasuk kategori pengaruh sangat besar. Hal ini menunjukkan bahwa pelatihan pembuatan infografis efektif dalam meningkatkan keterampilan guru baik secara teknis maupun pedagogis. Dengan demikian, pelatihan ini berkontribusi dalam memperkuat literasi digital dan kemampuan komunikasi visual guru di era pembelajaran digital.

Co-Authors Abi Wiyono Adzima, Khaola Rachma Afifah Nur Mutia Alifia Taufika Rahma Ambarwati, Lukita Auria Yusrin Fathya Bagus Sartono Bagus Sumargo Bagus Sumargo Bagus Sumargo Bagus Sumargo, Bagus Baihaqi, Aulia Contillo, Gerry Dania Siregar Dania Siregar Defina Ambarwati Devi Eka Wardani Dhioatmaja Megafajari Dian Handayani Dian Handayani Dwi Antari Wijayanti Ellis Salsabila, Ellis Erin Naudy Kemalasari Fadya, Khansa Salsabil 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 Janna Sri Bina Br Barus Kamil, Adine Ihsan Kamilia, Rifa Khairunnisa Putri Alif Khoirunnisa Koeshella, Ajeng Ladayya, Faroh Lina Nafisah Lukman El Hakim Mahardika, Baihaqy Mahatma, Yudi Makmuri Marweli Yusuf Maulida Audia Firdaus Meidianingsih, Qorry Meila Nadya MUHAMAD RIFAN Muhammad Alief Ghifari Muhammad Rafli Muzakki Tamami NATALIE EFRATA SUSANTI Nia Rahayu Ningsih Nilam Novita Sari 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 Rifqy Marwah Akhsanti Riska Agustin Riyantobi, Ariq Muammar Rustham Michael Binoto Safira Datu Sahila, Sahiba Sari Febrianti Sinta Rahmadani Siregar, Dania Siti Rohmah Rohimah Siti Rohmah Rohimah Sudarwanto Sudarwanto Sudarwanto SUYONO Suyono Suyono Suyono Suyono Syarifah Ayu Angela Syifa Azzahra Tamami, Muzakki Tian Abdul Aziz Tiara Husnul Khotimah Tri Murdiyanto Vinsensius Crispinus Lemba Wahyu, Rahadian Wardani Rahayu Widyanti Rahayu Widyanti Rahayu Widyanti Rahayu Widyanti Rahayu Widyanti Rahayu Wilsen Wilsen Yuliana Rahayuningsih Zahra Ayu Rahmadani Zahrah Hashifah

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