Article Per Year (5 Year)
p-Index From 2020 - 2025
15.661
P-Index
This Author published in this journals
All Journal Jurnal Matematika BIMASTER Epsilon: Jurnal Matematika Murni dan Terapan CESS (Journal of Computer Engineering, System and Science) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) KABILAH : Journal of Social Community Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora (KAGANGA) BULETIN AL-RIBAATH Jambura Journal of Mathematics GERVASI: Jurnal Pengabdian kepada Masyarakat Sarwahita : Jurnal Pengabdian Kepada Masyarakat M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Transformasi : Jurnal Pendidikan Matematika dan Matematika Variance : Journal of Statistics and Its Applications International Journal of Economics, Business and Accounting Research (IJEBAR) Insan Cita Jurnal Pengabdian Kepada Masyarakat Jurnal Pendidikan Dasar Perkhasa Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Mathematics Education and Science AR-RIAYAH : Jurnal Pendidikan Dasar Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi CENDEKIA : Jurnal Ilmu Pengetahuan Journal of Community Development Empiricism Journal Jurnal Eurekamatika Jurnal Forum Analisis Statistik Pattimura International Journal of Mathematics (PIJMath) STATISTIKA Bina Bahari : Jurnal Pengabdian Kepada Masyarakat Equiva
Neva Satyahadewi
Program Studi Statistika Fakultas MIPA Universitas Tanjungpura, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

Published : 172 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 172 Documents
Search

 9   10   11   12   13 
Penentuan Jumlah Cluster Optimum Menggunakan Davies Bouldin Index dalam Pengelompokan Wilayah Kemiskinan di Indonesia Nanda Shalsadilla; Shantika Martha; Hendra Perdana; Neva Satyahadewi; Evy Sulistianingsih
Statistika Vol. 23 No. 1 (2023): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v23i1.1743

Abstract

Abstrak Kemiskinan merupakan suatu permasalahan yang sampai saat ini masih menjadi fokus pemerintah terutama pasca pandemi Covid-19. Permasalahan terkait kemiskinan dapat diatasi apabila pemerintah mengusung program pengentasan kemiskinan yang direalisasikan secara efektif dan efisien. Tujuan penelitian ini adalah mengelompokkan provinsi di Indonesia berdasarkan indikator kemiskinan serta menentukan jumlah cluster optimum yang terbentuk. Analisis cluster merupakan teknik multivariat yang dapat digunakan untuk tujuan pengelompokan. Dengan analisis cluster seluruh provinsi yang ada di Indonesia dapat dikelompokkan berdasarkan kesamaan karakteristik yang dimiliki sehingga kedaruratan dan kebutuhan tiap cluster dapat diketahui. Ward merupakan salah satu metode dalam analisis cluster yang mengelompokkan objek dengan meminimalisir variasi antar objek dalam satu cluster. Selanjutnya, penentuan jumlah cluster optimum penting dilakukan agar seluruh provinsi di Indonesia dapat dikelompokkan dengan tepat. Davies Bouldin Index (DBI) merupakan suatu metode yang menentukan banyaknya cluster optimum berdasarkan kedekatan objek terhadap centroidnya dalam satu cluster dan jarak antar centroid cluster. Data yang digunakan merupakan data 10 indikator kemiskinan untuk setiap provinsi di Indonesia tahun 2021. Berdasarkan penelitian yang telah dilakukan banyaknya cluster optimum yang terbentuk untuk mengelompokkan provinsi di Indonesia berdasarkan indikator kemiskinan adalah berjumlah 5 cluster dengan nilai validitas DBI yang diperoleh sebesar 1,1420 yang merupakan nilai validitas terkecil dari jumlah cluster lainnya. Cluster 1 dengan tingkat kemiskinan tertinggi beranggotakan 3 provinsi yaitu Nusa Tenggara Timur, Papua Barat, dan Papua, cluster 2 beranggotakan 10 provinsi, cluster 3 beranggotakan 11 provinsi, cluster 4 beranggotakan 9 provinsi, dan cluster 5 dengan tingkat kemiskinan terendah beranggotakan 1 provinsi yaitu DKI Jakarta. Abstract Poverty has been an issue that received significant government attention, particularly in response to the Covid-19 pandemic. The study aimed to classify Indonesian provinces based on poverty indicators and determine the optimal number of clusters. Cluster analysis, a multivariate technique, was employed to group provinces based on their similaritycharacteristics, facilitating the identification of specific needs and emergencies within each cluster. The Ward method, a clustering technique, minimized variations between objects within a cluster during the grouping process. Determining the correct number of clusters was crucial to ensure accurate provincial classification. The Davies Bouldin Index (DBI) was used to determine the optimum number of clusters by assessing the proximity of objects to their centroids and the inter-centroid distances. The dataset consisted of 10 poverty indicators for each province in Indonesia in 2021. The research findings revealed that the optimum number of clusters for classifying provinces based on poverty indicators was five, with a DBI value of 1.1420, the lowest among other cluster configurations. Cluster 1, characterized by the highest poverty rate, comprised three provinces: East Nusa Tenggara, Papua, and West Papua. Cluster 2encompassed ten provinces, while cluster 3 consisted of eleven provinces. Cluster 4 comprised nine provinces, and cluster 5, characterized by the lowest poverty rate, consisted of a single province: DKI Jakarta.
Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM Rahadi Ramlan; Neva Satyahadewi; Wirda Andani
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20860

Abstract

Twitter is one of the social media with the most active users, which is 24 million active users. Information published on twitter contains comments from users on an object. Sentiment analysis is used to determine whether the data includes negative comments or positive comments because the comments taken on twitter are textual data. The method used in this sentiment analysis is Support Vector Machine (SVM) about public comments on fuel price increases on twitter. The comment data used was 258 data on September 4, 2022 because on that date it was exactly the day after the fuel price increase. First, preprocessing is done to remove unnecessary words or information. Then the data is divided into training data by 80% and testing data by 20%. The accuracy rate is 82.69%, sensitivity is 100%, and specificity is 79.07%. Then from the results of testing 52 data obtained the results of 43 negative comments and 9 positive comments so that it can be concluded that more people disagree with the increase in fuel prices.
Pelatihan Analisis Data Menggunakan Software Minitab untuk Mahasiswa Tingkat Akhir Setyo Wira Rizki; Naomi Nessyana Debataraja; Shantika Martha; Dadan Kusnandar; Ray Tamtama; Neva Satyahadewi; Nurfitri Imro'ah; Hendra Perdana
GERVASI: Jurnal Pengabdian kepada Masyarakat Vol. 7 No. 3 (2023): GERVASI: Jurnal Pengabdian kepada Masyarakat
Publisher : LPPM IKIP PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/gervasi.v7i3.5425

Abstract

Pelatihan analisis data pada Sekolah Tinggi Keguruan dan Ilmu Pendidikan (STKIP) Pamane Talino bertujuan meningkatkan kemampuan analisis data tugas akhir mahasiswa di STKIP Pamane Talino sehingga diharapkan dapat mempersingkat waktu penyelesaian tugas akhir. Pelatihan dilaksanakan secara daring pada 27 Oktober 2021 dan diikuti 140 mahasiswa. Kegiatan diawali dengan persiapan, pengabdi menggali informasi mengenai software dan metode statistik yang biasa digunakan. Setelah itu diberikan pre-test untuk mengukur kemampuan sebelum penyampaian materi, dilanjutkan dengan penyampaian materi analisis data serta demonstrasi software Minitab untuk uji validitas dan reliabilitas, uji hipotesis, dan uji-t oleh narasumber didampingi tujuh dosen Program Studi Statistika Universitas Tanjungpura lainnya. Setelah penyampaian materi, diberikan posttest untuk mengukur kembali kemampuan analisis data mahasiswa. Dilakukan pengujian hipotesis hasil pre-test dan posttest menggunakan uji-t guna menarik kesimpulan. Pengujian hipotesis menunjukkan p-value 0,094 lebih kecil dari taraf signifikansi (α) 10%, sehingga disimpulkan hasil dari pengabdian ini adalah meningkatnya kemampuan analisis data tugas akhir mahasiswa.
Regresi Data Panel dalam Analisis Faktor-Faktor yang Mempengaruhi IPM di Kalimantan Barat Neva Satyahadewi; Siti Aprizkiyandari; Risky Oprasianti
Statistika Vol. 23 No. 2 (2023): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v23i2.2201

Abstract

ABSTRAK Analisis regresi data panel digunakan untuk meneliti data di Kalimantan Barat meliputi kabupaten/kotanya dari tahun 2017 sampai tahun 2021 yaitu Indeks Pembangunan Manusia (IPM) dan faktor pengaruhnya. Tingkat penduduk miskin (), kepadatan penduduk (), tingkat partisipasi angkatan kerja (), angka harapan hidup (), rata-rata lama sekolah () dan IPM () merupakan variabel yang digunakan. Analisis regresi yang menggabungkan data silang dan deret waktu merupakan regresi panel. Dalam pengestimasiannya ada tiga pendekatan yakni Common Effect Model (CEM), Fixed Effect Model (FEM), dan Random Effect Model (REM). Uji Chow, Uji Hausman, dan Uji LM dilakukan untuk mendapatkan model terbaik. Estimasi pendekatan model regresi panel terbaik adalah model FEM atau Fixed Effect Model. Variabel angka harapan hidup () dan rata-rata lama sekolah () secara signifikan berpengaruh terhadap IPM (). ABSTRACT Panel data regression analysis is used to see the Human Development Index (HDI) data and what factors affect it in West Kalimantan from 2017 to 2021.The level of poverty (), population density (), labor force participation rate (), life expectancy (), average years of schooling () and HDI () are the variables used. Regression analysis that combines cross-sectional and time series data is panel regression. In its estimation, there are three approaches, namely the Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). Chow test, Hausman test, and Lagrange Multiplier (LM) test were carried out to the best. This study found that the best panel regression model approach estimation is the Fixed Effect Model (FEM). Life expectancy variable () and average years of schooling () significantly affect HDI ().
IMPLEMENTATION OF AHP-TOPSIS AS A SUPPORT FOR MAKING DECISIONS ON MICRO BUSINESS FUNDING IN SAMBAS REGENCY Noerul Hanin; Della Zaria; David Jordy Dhandio; Dadan Kusnandar
International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 7, No 1 (2023): IJEBAR, VOL. 07 ISSUE 01, MARCH 2023
Publisher : LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijebar.v7i1.8705

Abstract

The economy is one of the fields affected by the Covid-19 pandemic in the world, including Indonesia. One of the drivers of the economy in Indonesia is Micro, Small and Medium Enterprises (MSMEs). Among the three types of businesses, micro-enterprises contribute the most to Indonesia's Gross Domestic Product. Therefore, an analysis of the decision support system for financing micro-enterprises in Indonesia is carried out. This study aims to determine the application of decision support methods in selecting micro-enterprises that are entitled to receive grants and appreciation for developing their business. The choice of Sambas Regency as the case study location was due to the good development of micro-enterprises and one of the front lines in exporting products to other countries. The method used is a collaboration between the Analytical Hierarchy Process (AHP) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Through the process of weighing the criteria with AHP and several stages with TOPSIS, the results are obtained in the form of preference values and sequences of micro businesses that are eligible to receive funds. For the category of grants, it was found that the most feasible business to be financed was Roemah Jamoer Ikram, while for the category of appreciation funds, the most feasible business to be financed was Amplang 9. Thus, the analysis of supporting micro business financing using the AHP-TOPSIS method was successfully carried out with the output in the form of micro business rating results.
Model Spasial Data Panel dalam Menganalisis Faktor-Faktor yang Mempengaruhi Kemiskinan di Provinsi Kalimantan Barat: Spatial Data Panel Model in Analyzing Factors Affecting Poverty in Kalimantan Barat Province Hairil Al-Ham; Neva Satyahadewi; Nur Asih Kurniawati
Jurnal Forum Analisis Statistik Vol. 4 No. 1 (2024): Jurnal Forum Analisis Statistik (FORMASI)
Publisher : Badan Pusat Statistik Provinsi Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57059/formasi.v4i1.71

Abstract

Poverty is a complex and multidimensional problem in many countries, including Indonesia, which includes a lack of access to economic resources, education and adequate health services. In 2023, the percentage of the poor people in West Kalimantan Province has decreased to 7.03%, while the target of the RPJMD for West Kalimantan Province is 6.92%. One of the efforts that can be made to overcome this problem is by determining the factors that influence poverty. This research focuses on modeling with a spatial panel econometric approach on the percentage of the poor population. With this modeling, time period effects and spatial effects can be obtained on the percentage of poor people in districts/cities in West Kalimantan Province. The factors analyzed consisted of four sectors, namely education, social, health and employment. The panel data regression model obtained from this study is random effect. Then, in testing spatial effects, the results obtained showed that there was spatial autocorrelation and spatial dependence on error. So the analysis was continued using the spatial error model-random effect (SEM-random effect). The influence between locations or in this case districts/cities is measured using the queen contiguity spatial weighting matrix. From the model formed, it was found that districts/cities that are close to each other have an influence on reducing the percentage of the poor population in West Kalimantan Province. There are two variables that have a significantly influence the percentage of poor people in West Kalimantan Province. The school participation rate has a positive effect, while the percentage of the working population in the labor force has a negative effect on the percentage of the poor.
Estimasi Pengunjung Pontianak Interactive Center dengan Menggunakan Metode Double Exponential Smoothing Satyahadewi, Neva; Aprizkiyandari, Siti; Rivaldo, Rendi
Empiricism Journal Vol. 4 No. 2: December 2023
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/ej.v4i2.1517

Abstract

Pontianak Interactive Center (Pontive Center) adalah salah satu layanan publik yang disediakan pemerintah melalu Dinas Komunikasi dan Informatika Kota Pontianak. Pontive Center menyediakan layanan publik seperti pusat kendali CCTV dan berbagai sensor pengamatan, pengelolaan informasi, sistem TIK, dan sebagainya. Jumlah kunjungan setiap bulannya di Pontive Center cukup beragam sehingga diperlukan estimasi jumlah kunjungan agar Pontive Center dapat mempersiapkan segala alternatif yang dapat digunakan jika terjadi lonjakan jumlah kunjungan. Metode yang digunakan pada penelitian ini yaitu metode Double Exponential Smoothing (DES) dalam mengestimasikan jumlah kunjungan di Pontive Center. Data yang digunakan yaitu data jumlah kunjungan di Pontive Center dalam bulanan dari tahun 2019-2022. Data ini merupakan jumlah kunjungan di Pontive Center seperti kunjungan instansi, rapat kerja, seminar, sosialisasi dan sebagainya. Penelitian ini mengestimasikan jumlah kunjungan selama 12 bulan ke depan dan didapat rata-rata jumlah kunjungannya yaitu 11-12 kunjungan perbulannya. Perhitungan nilai Mean Absolut Percentage Error (MAPE) yang didapat yaitu sebesar 42%, sehingga model estimasi dengan Double Exponential Smoothing pada penelitian ini cukup layak digunakan. Estimated Visitors to the Pontianak Interactive Center Using the Double Exponential Smoothing Method Abstract Pontianak Interactive Center (Pontive Center) is one of the public services provided by the government through the Pontianak City Communication and Information Service. Pontive Center provides public services such as CCTV control centers and various observation sensors, information management, ICT systems, and so on. The number of visits each month at the Pontive Center is quite varied, so an estimate of the number of visits is needed so that the Pontive Center can prepare all alternatives that can be used if there is a spike in the number of visits. The method used in this research is the Double Exponential Smoothing (DES) method in estimating the number of visits to the Pontive Center. The data used is data on the number of visits to the Pontive Center monthly from 2019-2022. This data represents the number of visits to the Pontive Center such as agency visits, work meetings, seminars, socialization and so on. This study estimated the number of visits over the next 12 months and found that the average number of visits was 11-12 visits per month. The calculation of the Mean Absolute Percentage Error (MAPE) value obtained is 42%, so the estimation model with Double Exponential Smoothing in this research is quite suitable for use.
Pelatihan Aplikasi Mendeley Sebagai Manajemen Referensi bagi Mahasiswa Peserta Magang dan Studi Independen Bersertifikat Satyahadewi, Neva; Warsidah, Warsidah; Nabil, Ilhan Nail
Journal of Community Development Vol. 4 No. 3 (2024): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v4i3.183

Abstract

Compiling a list of references is often an obstacle in completing a scientific work, which then presents applications that can help with this problem. Mendeley software is one of the many applications that is very helpful in compiling a list of citations in a scientific work. It is a very popular application or software with millions of users in academic circles. The large number of students who do not know and understand this application has encouraged Mendeley application training activities for MSIB student participants at the Papua Central Statistics Agency. This activity aims to improve the ability of MSIB participating students in compiling a list of references or references in writing written work. The activity was attended by 28 students from various study programs from various universities throughout Indonesia. This training activity was carried out over 2 days and used lecture, discussion and practice methods. From the results of the activity evaluation through reviewing written work using the Mendeley application, it shows that all participants were able to write written works using the Mendeley application as a reference manager. On the first day the percentage of participants' ability when operating the application was only 36%, then after training on the second day the percentage increased to 100% or all participants were able to understand the use of the Mendeley application due to direct practice in using the application, thereby providing participants with an understanding of both theory, practice and discussions held during the training.
Proyeksi Peningkatan Perekonomian melalui Pemanfaatan Bonus Demografi 2040 Satyahadewi, Neva; Amir, Amriani; Hendrianto, El
Kaganga:Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora Vol 6 No 2 (2023): Kaganga:Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/kaganga.v6i2.7943

Abstract

Tujuan penelitian ini utnuk mengetahui proyeksi peningkatan perekonomian melalui pemanfaatan bonus demografi 2040. Penelitian ini menggunakan metode analisis deskriptif kualitatif. Hasil Penelitian bahwa bonus demografi menawarkan peluang emas, di mana kebanyakan penduduk berada dalam usia produktif, memungkinkan pertumbuhan ekonomi yang luar biasa. Namun, kemajuan teknologi, terutama dalam bidang kecerdasan buatan, membawa dampak besar terhadap struktur pasar tenaga kerja. kemudahan akses pendidikan dan peningkatan keterampilan bagi angkatan kerja harus dimulai sedini mungkin serta kolaborasi antara pemerintah dan swasta sangat penting untuk memastikan angkatan kerja Indonesia dapat terserap oleh industry. Simpulan penelitian ini menunjukakan dengan adanya visi jelas, strategi yang tepat, dan kolaborasi yang kokoh antara pemerintah, industri, dan masyarakat, Indonesia dapat memanfaatkan bonus demografi ini untuk menciptakan masa depan yang sejahtera dan berkelanjutan. Kata Kunci: Bonus Demografi, Produktif, Revolusi Industri 4.0
PEMODELAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION DALAM PENENTUAN STATUS GIZI BURUK BERDASARKAN PROVINSI DI INDONESIA Arsyi, Fritzgerald Muhammad; Satyahadewi, Neva; Perdana, Hendra
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 13, No 4 (2024): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v13i4.78049

Abstract

Gizi merupakan suatu proses penggunaan makanan yang dikonsumsi yang dibutuhkan oleh anak khususnya balita dalam jumlah banyak. Gizi buruk merupakan tingkat keparahan terjadinya kekurangan gizi yang terjadi setiap tahun. Upaya penanganan gizi buruk tidak dapat dilakukan di setiap provinsi karena banyak faktor yang berpengaruh, salah satunya faktor geografis. Penelitian ini bertujuan untuk menentukan model terbaik dalam penentuan status gizi buruk berdasarkan provinsi di Indonesia dengan menggunakan GWLR. Variabel yang digunakan dalam penelitian ini terbagi menjadi dua, yaitu variabel dependen (Y) dan variabel independen (X). Variabel independen yang digunakan adalah Cakupan Imunisasi, Bayi mendapat ASI, Pemberian Vitamin A, Sanitasi yang layak, Penduduk Miskin, Air Minum Layak, Balita dipantau pertumbuhan dan Perkembangan, dan Kunjungan Neonatal. Variabel dependen yang dianalisis adalah status gizi balita. Hasil Penelitian yaitu analisis global dengan menggunakan regresi logistik menunjukkan bahwa terdapat 3 variabel yang berpengaruh signifikan terhadap gizi buruk pada balita di Indonesia. Variabel-variabel tersebut adalah cakupan bayi yang mendapatkan ASI, balita yang dipantau tumbuh kembangnya, dan cakupan kunjungan neonatal lengkap. Sedangkan secara lokal menggunakan GWLR dengan pembobot Adaptive Tricube Kernel, terdapat 34 model yang terbagi lima kelompok berdasarkan variabel yang berpengaruh. Model GWLR dengan pembobot Adaptive Tricube Kernel memiliki ketepatan klasifikasi sebesar 94,1% adalah model terbaik dibandingkan dengan model regresi logistik yang memiliki ketepatan klasifikasi sebesar 82,4%.Kata Kunci:   GWLR, Fungsi Kernel, Malnutrisi
Co-Authors . Apriansyah Afghani Jayuska Afghany Jayuska Al-Ham, Hairil Amriani Amir Amriani Amir Amriani Amir Andani, Wirda Antoni, Frans Xavier Natalius Apriliyanti, Rita Aprizkiyandari, Siti Ardhitha, Tiffany Ari Hepi Yanti Arsyi, Fritzgerald Muhammad Arti, Reyana Hilda Ashari, Asri Mulya Asri Mulya Ashari Asty Fistia Ningrum Atikasari, Awang Aulia Puteri Amari Bambang Kurniadi Banu, Syarifah Syahr ciptadi, wahyudin Cornellia, Amanda Crismayella, Yuveinsiana Dadan Kusnandar Dadan Kusnandar Dadan Kusnandar David Jordy Dhandio Debataraja, Naomi Nessyana Della Zaria Desriani Lestari Desriani Lestari Desriani Lestari Dhandio, David Jordy Dinda Lestari Dwi Nining Indrasari Dwinanda, Maria Welita Eka Febrianti, Eka Esta Br Tarigan Evy Sulistianingsih Ewaldus Okta Ferdina Ferdina Feriliani Maria Nani Fitriawan, Della Fransisca Febrianti Sundari Fransiska Fransiska Grikus Romi Gusti Eva Tavita Gusti Eva Tavita Hairil Al-Ham Halim, Alvin Octavianus Hamzah, Erwin Rizal Handayani, Aditya Hanin, Noerul Harimurti, Puspito Harnanta, Nabila Izza Helena, Shifa Hendra Perdana Hendrianto, El Herina Marlisa Huda, Nur'ainul Miftahul Huriyah, Syifa Khansa Ibnur Rusi Ikha Safitri Imro'ah, Nurfitri IMRO’AH, NURFITRI Imtiyaz, Widad Isra’ Sagita Jawani Jawani Karlina, Sela Kusnandar, Dadan Tonny Lucky Hartanti Lucky Hartanti Lucky Hartanti M. Deny Hafizzul Muttaqin Maga, Fahmi Giovani Margareta, Tiara Margaretha, Ledy Claudia Marlisa, Herina Marola, Geby Martha, Shantika Mega Sari Juane Sofiana Mega Sari Juane Sofiana Mega Tri Junika Millennia Taraly Misrawi Misrawi Muhammad Ahyar Muhammad fauzan Muhammad Radhi Muhammad Rizki Muliadi Muliadi Muslimah (F54210032) Nabil, Ilhan Nail Nanda Shalsadilla Naomi Nessyana Debataraja Naomi Nessyana Debataraja Noerul Hanin Nona Lusia Nugrahaeni, Indah Nur Asih Kurniawati Nur Asiska Nurfadilah, Kori’ah Nurfitri Imro'ah Nurfitri Imro’ah Nurhalita Nurhalita Nurmaulia Ningsih Oktaviani, Indah Ovi Indah Afriani Paisal Paisal Pertiwi, Retno Pratama, Aditya Nugraha Preatin, Preatin Putri Putri Putri, Aulia Nabila Qalbi Aliklas R Puspito Harimurti Radhi, Muhammad Radinasari, Nur Ismi Rafdinal Rafdinal Rahadi Ramlan Rahmadanti, Putri Rahmanita Febrianti Rusmaningtyas Rahmawati, Fenti Nurdiana Ramadhan, Nanda Ramadhania, Wahida Reni Unaeni Retnani, Hani Dwi Ria Andini Ria Fuji Astuti Rina Rina Risky Oprasianti Rita Kurnia Apindiati Rivaldo, Rendi Riza Linda Rizki Nur Rahmalita Rizki, Setyo Wira Rosi Kismonika Roslina Rosi Tamara Rovi Christova Safira, Shafa Alya Salsabilla, Arla Santika Santika Sary, Rifkah Alfiyyah Seftiani, Seftiani Selvy Putri Agustianto Setyo Wir Rizki Setyo Wira Rizki Setyo Wira Rizki Setyo Wira Rizki Shantika Martha Shantika Martha Sinaga, Steven Jansen Sintia Margun Sista, Sekar Aulia Siti Aprizkiyandari Siti Aprizkiyandari, Nurul Qomariyah, Shantika Martha, Siti Hardianti Suci Angriani Sukal Minsas Sukal Minsas syuradi, Syuradi Tamtama, Ray Taraly, Inggriani Millennia Tiara, Dinda Trifaiza, Fadhela Wahyu Diyan Ramadana Wahyudin Ciptadi Warsidah Warsidah Warsidah, Warsidah Wilda Ariani Wirda Andani Yopi Saputra Yudhi Yuliono, Agus Yumna Siska Fitriyani Yundari, Yundari Yuveinsiana Crismayella Zakiah, Ainun