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All Journal PROSIDING SEMINAR NASIONAL Media Statistika Jurnal Ilmu Dasar Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Variance : Journal of Statistics and Its Applications LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Litbang Edusaintech Journal of Data Insights Prosiding Seminar Nasional Unimus Jurnal Meteorologi dan Geofisika
Tiani Wahyu Utami
Program Studi Statistika, Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Peramalan Uang Kartal Provinsi Jawa Barat Menggunakan Hybrid ARIMAX-QR DAN QRNN Biru, Pelangi Langit; Utami, Tiani Wahyu; Wasono, Rochdi
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Penelitian ini membahas peramalan aliran uang kartal (inflow dan outflow) di Provinsi Jawa Baratmenggunakan dua model hybrid, yaitu ARIMAX-QR dan Quantile Regression Neural Network (QRNN).Aliran uang kartal yang diatur oleh Bank Indonesia memiliki peran penting dalam perekonomian, sehinggadiperlukan model yang akurat untuk memprediksi pergerakannya. Penelitian ini menggunakan data inflowdan outflow uang kartal dari Kantor Perwakilan Bank Indonesia Provinsi Jawa Barat untuk periode Januari2011 hingga Desember 2023. Model hybrid ARIMAX-QR digunakan untuk memprediksi komponen linear, sedangkan QRNN digunakanuntuk menangkap pola non-linear dalam data. Selain itu, penelitian ini juga membandingkan akurasi darikedua model hybrid tersebut dalam memprediksi aliran uang kartal. Hasil penelitian menunjukkan bahwamodel hybrid memberikan akurasi yang lebih tinggi dibandingkan model individu, dengan QRNNmenunjukkan performa terbaik dalam memodelkan fluktuasi non-linear. Penelitian ini memberikankontribusi dalam pengembangan model peramalan yang lebih presisi dan akurat, yang dapat membantuBank Indonesia dalam merencanakan dan membuat kebijakan terkait pengelolaan uang kartal. Manfaat dari penelitian ini tidak hanya untuk memberikan metode peramalan yang lebih akurat tetapi jugasebagai rujukan kebijakan bagi Bank Indonesia dalam pengelolaan aliran uang kartal di Jawa Barat. Modelyang dikembangkan juga dapat diterapkan pada berbagai konteks peramalan keuangan lainnya yangmembutuhkan akurasi tinggi dalam kondisi yang dinamis dan tidak pasti. Kata Kunci : Uang Kartal, Hibrid, QRNN, ARIMAX-QR, Jawa Barat
Estimasi Value at Risk Saham Sub-Sektor Perbankan dengan Pendekatan Copula Ali-Mikhail-Haq Wulandari, Ayu; Haris, M. Al; Utami, Tiani Wahyu
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Di era modern, investasi telah menjadi instrumen penting dalam kehidupan masyarakat. Saham di subsektorperbankan menjadi pilihan populer karena pertumbuhannya yang pesat dan kapitalisasi pasar yang besar.Namun, risiko tetap menjadi faktor utama yang harus dipertimbangkan, karena fluktuasi pasar dan kondisiekonomi dapat berdampak negatif pada investasi. Value at Risk (VaR) adalah metode umum untukmengukur risiko investasi, tetapi VaR memiliki keterbatasan karena hanya berlaku jika data berdistribusinormal. Untuk mengatasi keterbatasan ini, copula hadir sebagai alternatif yang tidak mengasumsikannormalitas data dan mampu menangani distribusi gabungan variabel dengan lebih fleksibel. Ada beberapajenis copula yang sering digunakan, termasuk copula dari keluarga Archimedean seperti Copula Frank,Copula Gumbel, dan Copula Ali-Mikhail-Haq. Di antara jenis-jenis copula tersebut, Copula Ali-MikhailHaqadalah yang paling unggul karena kemampuannya dalam menggambarkan dependensi negatif danpositif antar variabel serta menangani data dengan struktur dependensi yang kompleks dalam analisis risikoinvestasi. Penelitian ini berfokus pada penerapan Copula Ali-Mikhail-Haq pada data harga penutupansaham PT Bank Central Asia Tbk (BBCA) dan PT Bank Negara Indonesia Tbk (BBNI) untuk periode 25Januari 2022 hingga 10 Mei 2023. Copula Ali-Mikhail-Haq dimodelkan menggunakan korelasi RhoSpearman, dengan nilai korelasi ? =  0,4532 dan parameter ? =  0,499991, yang kemudian digunakandalam estimasi VaR. Hasil estimasi VaR menunjukkan nilai risiko pada tingkat kepercayaan 90%, 95%,dan 99% selama periode 80 hari masing-masing sebesar -0,028, -0,038, dan -0,057. Hasil ini menunjukkankerugian yang mungkin dialami investor jika menginvestasikan dana di BBCA dan BBNI. Kata Kunci: Copula Ali-Mikhail-Haq, Investasi, Risiko, Sektor Perbankan, Value at Risk (VaR) Abstract
Meningkatkan Kompetensi Teknologi Informasi Siswa Melalui Pelatihan Tiga Microsoft di SMA 1 Kembang Jepara Fathoni Amri, Ihsan; Wahyu Utami, Tiani; Hikmah Nur Rohim, Febrian; Aura Hisani, Zahra; Rahma Dhani, Oktaviana; Suherdi, Andri
LOSARI: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2024): Desember 2024
Publisher : LOSARI DIGITAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53860/losari.v6i2.238

Abstract

Artikel ini membahas pelaksanaan program pengabdian kepada masyarakat yang bertujuan untuk meningkatkan keterampilan digital siswa kelas 11 di SMA 1 Kembang Jepara. Di era digital saat ini, kemampuan dalam menggunakan aplikasi Microsoft Office seperti Word, Excel, dan PowerPoint menjadi penting untuk keberhasilan akademis dan profesional. Namun, banyak siswa yang belum mendapatkan pelatihan yang memadai dalam penggunaan alat-alat ini, sehingga menghambat kemampuan mereka untuk memenuhi tuntutan dunia teknologi yang terus berkembang. Program ini bertujuan untuk mengatasi kekurangan tersebut dengan memberikan pelatihan intensif Microsoft Office yang disesuaikan dengan kebutuhan siswa. Pelatihan ini tidak hanya meningkatkan keterampilan teknis siswa tetapi juga meningkatkan kepercayaan diri mereka dalam menerapkan keterampilan ini dalam situasi nyata. Hasil survei kepuasan menunjukkan bahwa 45% siswa merasa sangat puas, 30% puas, 20% cukup puas, dan 5% kurang puas dengan pelatihan yang diterima. Hal ini mencerminkan keberhasilan program dalam memenuhi kebutuhan dan harapan mereka. Inisiatif ini menjadi model untuk mengintegrasikan pelatihan keterampilan IT praktis ke dalam kurikulum pendidikan.
FUZZY GEOGRAPHICALLY WEIGHTED CLUSTERING WITH OPTIMIZATION ALGORITHMS FOR SOCIAL VULNERABILITY ANALYSIS IN JAVA ISLAND Fadlurohman, Alwan; Utami, Tiani Wahyu; Amrullah, Setiawan; Roosyidah, Nila Ayu Nur; Dhani, Oktaviana Rahma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1841-1852

Abstract

The Social Vulnerability Index (SoVI) measurement assesses social vulnerability. However, the measurement of SoVI can only describe the general conditions without being able to show which factors dominate. Therefore, a clustering approach has been proposed to characterise the dominant social vulnerability factors. Fuzzy Geographically Weighted Clustering (FGWC) is a method that works for this purpose. FGWC is an extension of the Fuzzy C-Means algorithm, which involves geographical influences in calculating membership values. However, the FGWC method is sensitive because the initial initialisation to determine the centroid is randomised, and it will affect the cluster quality. This research uses a metaheuristic approach to overcome the weakness of FGWC by using Particle Swarm Optimisation (PSO) and Artificial Bee Colony (ABC). This study aims to cluster districts/cities in Java Island using the PSO-FGWC and ABC-FGWC methods based on social vulnerability variables and determine the dominant factors of social vulnerability in each region. Optimum cluster selection uses the index of the largest Partition Coefficient (PC) and the smallest Classification Entropy (CE). Clustering social vulnerability in Java Island resulted in the best clustering using the ABC-FGWC method with 5 optimum clusters based on the PC and CE index values of 0.343 and 1.298, respectively. This research found that social vulnerability exists in each region of Java Island. Cluster 1, consisting of 19 districts/cities, is characterized by vulnerabilities in demography and education. Cluster 2, consisting of 33 districts/cities, is characterized by demographic and health vulnerabilities. Cluster 3, which consists of 24 districts/cities, is dominated by education and economic vulnerability factors. Cluster 4, consisting of 14 districts/cities, has the highest social vulnerability characteristics on the unemployment rate and the proportion of house rent. The last one, cluster 5, consists of 29 districts/cities and has a vulnerability problem in the population growth variable.
STATISTICAL DOWNSCALING USING REGRESSION NONPARAMETRIC OF FOURIER SERIES-POLYNOMIAL LOCAL OF CLIMATE CHANGE Utami, Tiani Wahyu; Fauzi, Fatkhurokhman; Yuliyanto, Eko
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1411-1418

Abstract

Indonesia is a tropical country that is vulnerable to the impacts of climate change. Climate change causes an effect on the level of comfort (heat stress) that can affect the level of human immunity, one of the indices to calculate the level of human comfort (heat stress) is the Thermal Humidity Index (THI). Climate change scenarios modeled in Earth System Models (ESMs). ESM has a coarse resolution and is subject to considerable bias. This research is using secondary data. The data source used in this study comes from the Coupled Model Intercomparison Project (CMIP5). This research will focus on projected heat stress which is calculated based on THI with the temperature and humidity variables. Therefore, in this research to reduce the bias correction method used Statistical Downscaling (SD) and nonparametric regression. The results of the bias correction using the Statistical Downscaling (SD) method and Nonparametric Regression Fourier-Polynomial Local Series in this study the R-square value for Relative Humidity yields 95% and for Temperature yields 94%. The projection of climate change based on the value of the Temperature Humidity Index (THI) in Indonesia in the category of 50% of the population of Indonesians feeling comfortable conditions occurred in 2006-2059. Then the population of citizens in Indonesia felt uncomfortable conditions occurred in 2060 to 2100 with a THI value of 27.0730°C - 27.7800°C.
COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE Fauzi, Fatkhurokhman; Setiayani, Wiwik; Utami, Tiani Wahyu; Yuliyanto, Eko; Harmoko, Iis Widya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1439-1448

Abstract

The last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source of data for understanding public opinion and the perceived risk of an issue. In recent decades, when discussing climate change, there are those who agree and those who oppose it. Sentiment analysis is a branch of learning in the realm of text mining that is used as a solution to see opinions on a problem, one of which is climate change. In this study, we will try to analyze opinions on climate change issues using the Random Forest and Naïve Bayes classifier methods. Data were obtained from Twitter for the period January 2022-June 2022. The training data used in this research is 80%:20%. There are slightly more positive statements than negative ones. The results obtained with the Naïve Bayes classifier method are an accuracy of 76.25%, an F-1 score of 78%, and a recall of 80%. While the results of the random forest method are 70.6% accuracy, 69% F-1 score, and 63% recall. The Nive Bayes method is better than the Random Forest method for classifying climate change opinions with an accuracy of 76.25%.
MODELING DHF IN CENTRAL JAVA USING HYBRID NONPARAMETRIC SPLINE TRUNCATED-FOURIER SERIES APPROACH Utami, Tiani Wahyu; Maharani, Endang Tri Wahyuni; Fadlurohman, Alwan
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/barekengvol18iss3pp1459-1470

Abstract

Regression analysis aims to determine the relationship and influence of predictor variables on response variables through regression curve. The problem with nonparametric regression research so far is that it only uses one approach, causing the estimation results to be biased, even though each data sub-pattern has its own suitability depending on the approach method used. Therefore, the hybrid method emerged as a development of nonparametric regression. Hybrid models are models that combine approach methods, with the hope of increasing accuracy in modeling analysis. This research was carried out using two non-parametric approaches, namely Spline Truncated and Fourier Series. Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus. DHF is endemic and occurs throughout the year, especially during the rainy season because mosquitoes reproduce optimally. The aim of this research is to estimate the Hybrid Nonparametric Spline Truncated -Fourier Series model and apply the estimation results to data on DHF cases in Central Java. The data used to apply the hybrid nonparametric Spline Truncated-Fourier series regression model is DHF in the city/districts of Central Java. Estimation smoothing parameters uses the GCV (Generalized Cross Validation) method. The best model is selected based on largest R-Square and the smallest MSE. Modeling the disease of DHF cases in Central Java using the Spline Truncated-Fourier Series hybrid estimator produced the best model from the Spline Truncated model with two knot points for each predictor and the Fourier Series model with value of 9. Based on the results obtained, it can be compared that the Truncated Spline-Fourier Series hybrid model is better than the Spline Truncated model, this can be seen from the largest R-square, namely 99.94% and the smallest MSE.
Platelet Modeling in DHF Patients Using Local Polynomial Semiparametric Regression on Longitudinal Data Utami, Tiani Wahyu; Chamidah, Nur; Saifudin, Toha
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.17427

Abstract

Regression analysis is one of the statistical methods used to model the relationship between response variables and predictor variables. Semiparametric regression is a combination of parametric and nonparametric regression. The estimator used in estimating the semiparametric regression model in this research is the Local Polynomial. Longitudinal data can be found in the health sector, including dengue hemorrhagic fever (DHF) data. The laboratory criteria for indication of DHF is thrombocytopenia. This research aims to obtain platelets model for DHF patients that can be used for forecasting so that it is hoped that it can provide information to the medical team in treating DHF patients. The estimated model used is Local Polynomial semiparametric regression on longitudinal data. The response variables in this research were platelets of DHF patients, which were influenced by hemoglobin as parametric predictor variable and examination time while hospitalized as nonparametric predictor variable. In the local polynomial regression model, it is necessary to select the optimal bandwidth and polynomial order method, GCV. The optimum bandwidth selection based on the GCV method obtained is 1.5 and polynomial order of 2, then applied to DHF patient platelet data, producing an estimated local polynomial semiparametric regression model that follows the actual data pattern. Modeling the platelets of DHF patients obtained using a local polynomial estimator resulted in an R2 value of 84.25% and MAPE of 4.5%, indicating highly accurate forecasting, so it can be concluded that the resulting model is better at predicting.
PREDIKSI RESIKO GEMPA MENGGUNAKAN MODEL SPATIAL POINT PROCESS Rahman, Budiono; Utami, Tiani Wahyu; Fauzi, Fatkhurokhman
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page39-38

Abstract

Pertemuan lempeng Eurasia, lempeng Indo-Australia, dan lempeng Pasifik terjadi di sekitar Pulau Sulawesi dan Maluku. Pertemuan ketiga lempeng ini menyebabkan sering terjadi gempa bumi. Faktor-faktor yang mempengaruhi gempa bumi secara geologis adalah jarak terhadap zona subduksi, gunung berapi, dan sesar aktif. Untuk keperluan mitigasi risiko bencana gempa bumi, perlu dilakukan prediksi risiko terjadinya gempa bumi. Pada penelitian ini menggunakan model inhomogeneous thomas cluster process yang digunakan untuk memprediksi risiko kejadian gempa bumi. Variabel yang digunakan dalam penelitian ini adalah koordinat zona subduksi, sesar aktif, dan gunung api di pulau Sulawesi dan Maluku. Hasil yang diperoleh berdasarkan fungsi K gempa bumi di pulau Sulawesi dan Maluku membentuk cluster. Uji chi-square menyimpulkan kejadian gempa bumi di Pulau Sulawesi dan Maluku bersifat inhomogen (tidak homogen). Berdasarkan model estimasi inhomogeneous thomas cluster process, variabel zona subduksi, sesar aktif, dan gunung berapi mempunyai pengaruh yang signifikan terhadap terjadinya gempa bumi. Dari ketiga variabel yang mempengaruhi terjadinya gempa terbesar (1,8 kali) adalah variabel letak gunung api. Berdasarkan hasil prediksi, Provinsi Gorontalo, Sulawesi Utara, Sulawesi Tengah, Maluku, dan Maluku Utara memiliki risiko gempa bumi yang tinggi.
Clustering Untuk Menentukan Indeks Kesejahteraan Rakyat di Provinsi Jawa Tengah 2022 Menggunakan Metode Fuzzy C-Means: Clustering to Determine the People's Welfare Index in Central Java Province 2022 Using the Fuzzy C-Means Method Indra Firmansyah; Salmaa Fauziah; Hanif Nur Ibrahim; Fatkhurokhman Fauzi; Tiani Wahyu Utami
Journal of Data Insights Vol 1 No 2 (2023): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v1i2.149

Abstract

Kesejahtraan rakyat merupakan salah satu tujuan negara yang tercantum pada Undang-undang Dasar 1945. Dalam meningkatakan kesejahtraan rakyat, tentunya perlu adanya pembangunan yang merata. Untuk menjalankan program pembangunan yang merata, harus dilakukan identifikasi berdsarkan karaktaeristik tingkat kesejahtraan rakyat berdasarkan variabel-variabel yang telah ditentukan agar dalam membuat strategi dan mengambil kebijakan untuk meningkatkan kesejahtraan rakyat dapat tepat sasaran dan optimal. Tujuan dari penelitian ini adalah untuk mengetahui pengelompokkan 35 Kabupatan/Kota di Provinsi Jawa Tengah dan k arakteristik dari setiap kelompok berdasarkan indeks kesejahtraan rakyat. Berdasarkan hasil analisis yang dilakukan, bahwa terdapat 35 Kabupaten/Kota di Provinsi Jawa Tengah dapat membentuk 4 kelompok (cluster), dimana pada cluster 0 beranggotakan 8 Kabupaten/Kota dengan karakteristik Jumlah peduduk miskin tinggi, Daya beli cenderung rendah, rata-rata lama sekolah rendah, angka harapan hidup sangat rendah. Pada cluster 1 terdapat 12 Kabupaten/Kota dengan karakteristik Nilai PDRB sangat tinggi, angka pengangguran relatif rendah, angka lama sekolah relatif tinggi. Pada cluster 2 terdapat 5 Kabupaten/Kota dengan karakteristik Nilai PDRB sangat rendah, jumlah penduduk miskin sangat rendah, daya beli sangat tinggi, kepemilikan rumah rendah, kepadatan penduduk tinggi, daya beli tinggi, angka melek huruf tinggi, rata-rata lama sekolah tinggi, dan yang terakhir cluster 3 terdapat 10 Kabupaten/Kota dengan karakteristik PDRB rendah, kepadatan penduduk sangat rendah, kepemilikan rumah sangat tinggi, daya beli cenderung rendah.
Co-Authors Abdul Rohman Agus Rusgiyono Aisyah Lahdji, Aisyah Alan Prahutama Alan Prahutama Alwan Fadlurohman Amrullah, Setiawan Anissatush Sholiha Arianti, Irma Arini Rizky Wahyuningtyas Aulia, Syifa Aura Hisani, Zahra Ayu Wulandari Azqia Fajriyani Biru, Pelangi Langit Dannu Purwanto Devi Nurlita Dewi Ratnasari Wijaya Dhani, Oktaviana Rahma Dheanyta Alif Shafira Diana Wahyu Safitri Dwi Ispriyanti Eko Yuliyanto, Eko Elvia Nanda Sofiyanti Endah Suryaningsih Endang Tri Wahyuni Maharani Fathikatul Arnanda Fatimahthus Zahra, Diandra Fatkhurokhman Fauzi Fatkhurokhman Fauzi Fatmawati Nurjanah Fauzi, Fatkhurokhman Hanif Nur Ibrahim Hasbi Yasin Hikmah Nur Rohim, Febrian Iffah Norma Hidayati Ihsan Fathoni Amri Iis Widya Harmoko Iis Widya Harmoko, Iis Widya Imaroh Izzatun Indah Manfaati Nur Indah Sulistiya Indra Firmansyah Iqbal Kharisudin Ismawati - Juwita Rahayu Laila Khoirun Nisa Lia Miftakhul Janah M. Al Haris M. Saifudin Nur Martyana Prihaswati Maulana Afham Mifta Luthfin Alfiani Moh Yamin Darsyah Moh Yamin Darsyah Moh. Yamin Darsyah Nila Amelinda Putri Nur Chamidah Nursamsiah Nursamsiah Pranandira Rilvandri, Quinsy Prizka Rismawati Arum Rahma Dhani, Oktaviana Rahman, Budiono Rahmi, Mulya Asy-syifa Rizma Novinda Puteri Rochdi Wasono Rochdi Wasono Roosyidah, Nila Ayu Nur Salmaa Fauziah Septi Winda Utami Setiayani, Wiwik Silvia Tri Wahyuni Sri Kustiara Sudarno Sudarno Sugito Sugito Suherdi, Andri Suparti Suparti Suparti Suparti Suparti, S. Syaifullah, Ahmad Reyhan Toha Saifudin Ujang Maulana Ujiati Suci Rahayu Ulinuha, Samikoh Vega Zayu Varima Velia Arni Widyasari Wahyu Putri Pratiwii Wisudawati, Dinda Tri Yulianita, Tanti Yuliardi, Fahrul Raditiar Yusnia Kriswanto