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All Journal FORUM STATISTIKA DAN KOMPUTASI Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma Journal of Mathematical and Fundamental Sciences JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Socioeconomics and Development Zero : Jurnal Sains, Matematika, dan Terapan Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan Jurnal Jamu Indonesia Jurnal Penelitian Kebijakan Pendidikan Indonesian Journal of Educational Assesment Jurnal Pendidikan dan Kebudayaan Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Bulletin of Computer Science Research Jurnal Penelitian Kebijakan Pendidikan Aceh International Journal of Science and Technology Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Xplore: Journal of Statistics Indonesian Journal of Jamu Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications Integra: Journal of Integrated Mathematics and Computer Science Limits: Journal of Mathematics and Its Applications Indonesian Journal on Learning and Advanced Education (IJOLAE)
Budi Susetyo
Departemen Statistika, IPB University
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Penentuan Gugus Kombinasi Taraf-taraf Faktor Optimal pada Percobaan Respon Ganda Berkorelasi Aceng Komarudin Mutaqin; Aunuddin Aunuddin; Budi Susetyo
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 5, No 1 (2005)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v5i1.911

Abstract

Makalah ini membahas metode penentuan gugus kombinasi taraf-taraf faktor optimal padapercobaan respon ganda berkorelasi. Kombinasi taraf-taraf faktor optimalnya diperoleh denganmenggunakan kriteria yang memaksimumkan proporsi sesuai. Gugus kombinasi taraf-taraf faktoroptimalnya diperoleh melalui pendugaan selang proporsi sesuai untuk setiap kombinasi taraf-taraffaktor dalam daerah percobaan dengan menggunakan metode persentil bootstrap parametrik.Sebagai bahan untuk mengilustrasikan metode tersebut adalah data penelitian hasil percobaanpembuatan briket batubara yang dilakukan di Lab. Batubara Puslitbang TekMIRA Bandung, JawaBarat.
OPTIMUM DESIGN PADA PERANCANGAN PERCOBAAN MIXTURE Bagus Sartono; Aunuddin Aunuddin; Budi Susetyo
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.519

Abstract

Salah satu hal penting pada perancangan percobaan adalah melakukan perancangan level secara optimum terhadapkomponen penyusun perlakuan sehingga ragam penduga akan minimum. Penduga yang dimaksud adalah penduga koefisienmodel dan penduga respon. Semakin banyak kendala-kendala pada besarnya komponan campuran dipercobaan mixturemenyebabkan titik rancangan optimum yang harus dicoba juga sebaik banyak. Algoritma XVERTI merupakan algoritma untukmenemukan titik-titik tersebut. Namun, ketersediaan bahan dan biaya percobaan menjadi kendala sendiri. Untuk itulah perludipilih sebagian titik saja, sehingga target biaya bisa dipenuhi namun tidak paling optimum dalam hal ragam penduga. Kekurangsepakatan antar kriteria adalah permasalahan tersendiri pada proses pemilihan titik. Pada makalah ini dibahas beberapa hasilsimulasi algoritma pemilihan titik antara lain greedy search algorithm (dengan kriteria D-optimality, dan A-optimality), Naesalgorithm, dan uniform mapping algorithm
PENDEKATAN EXTREME VALUE THEORY (EVT) UNTUK PENENTUAN UKURAN RESIKO (NILAI VAR) Zainal A Koemadji; Budi Susetyo; Bambang Juanda
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.907

Abstract

Extreme ValueTheory (EVT) adalah suatu metode untuk menentukan nilai VAR (Value at Risk) dengan mencobamenentukan sebaran dari nilai-nilai atau kejadian-kejadian ekstrim. Metode EVT terdiri atas dua metode yangmenggunakan sebaran yang berbeda, yaitu metode Generalized Extreme Value Theory (GEVD) dan Generalized ParetoDistribution (GPD). Nilai VAR sendiri adalah nilai harapan rugi maksimum (maximum expected loss) dari nilai aset atausaham pada suatu periode tertentu dan pada tingkat kepercayaan tertentu. Hasil penelitian dengan menggunakan dataperdagangan saham pada Bursa Efek Jakarta (BEJ) menunjukkan bahwa metode GEVD dapat menduga nilai VAR denganlebih baik dibanding metode GPD.
Historical Fire Detection of Tropical Forest from NDVI Time-series Data: Case Study on Jambi, Indonesia Dyah R. Panuju; Bambang H. Trisasongko; Budi Susetyo; Mahmud A. Raimadoya; Brian G. Lees
Journal of Mathematical and Fundamental Sciences Vol. 42 No. 1 (2010)
Publisher : Institute for Research and Community Services (LPPM) ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.sci.2010.42.1.5

Abstract

In addition to forest encroachment, forest fire is a serious problem in Indonesia. Attempts at managing its widespread and frequent occurrence has led to intensive use of remote sensing data. Coarse resolution images have been employed to derive hot spots as an indicator of forest fire. However, most efforts to verify the hot spot data and to verify fire accidents have been restricted to the use of medium or high resolution data. At present, it is difficult to verify solely upon those data due to severe cloud cover and low revisit time. In this paper, we present a method to validate forest fire using NDVI time series data. With the freely available NDVI data from SPOT VEGETATION, we successfully detected changes in time series data which were associated with fire accidents.
Development of direct marketing strategy for banking industry: The use of a Chi-squared Automatic Interaction Detector (CHAID) in deposit subscription classification Anwar Fitrianto; Wan Zuki Azman Wan Muhamad; Budi Susetyo
JOURNAL OF SOCIOECONOMICS AND DEVELOPMENT Vol 5, No 1 (2022): April
Publisher : Publisher of Widyagama University of Malang (UWG Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jsed.v5i1.3420

Abstract

A comparison between Chi-squared Automatic Interaction Detector (CHAID) and logistic regression analysis was performed for classification problems on bank direct marketing data. CHAID Performance Comparison and comparison with Logistic Regression (LR) performance were also conducted. Priority performance with two statistical measures was evaluated: classification accuracy and sensitivity in the presence of data containing categorical imbalances. Random over sampling (ROS) was then applied to deal with class balance problems to get better performance of CHAID analysis. Segmentation analysis was also performed using the CHAID approach to improve the performance of the analysis results. CHAID outperforms LR because of its advantages that it can be used to perform segmentation modeling. Direct marketers should pay attention to traits are Duration, Month, Contact, and Housing. To get a higher subscription, the bank must extend the call duration. Based on these results, the banking industry needs to prepare regulations related to human resources, infrastructure, costs, and government support to achieve higher subscriptions.JEL Classification  A10; C10; G21
Peta Mutu Pendidikan Madrasah Berdasarkan Akreditasi Budi Susetyo; Cut N. Ummu Athiyah
Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan Vol 9 No 1 (2021): Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan
Publisher : Pusdiklat Tenaga Teknis Pendidikan dan Keagamaan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36052/andragogi.v9i1.223

Abstract

[QUALITY OF MADRASAH EDUCATION BASED ON ACCREDITATION]. The purpose of this article is to analyze a map of the quality of madrasah throughout Indonesia based on the results of accreditation in 2018 and 2019. The results of the analysis show that nationally the quality of madrasas has increased. However, the quality of madrasah varies greatly between provinces, levels, types of madrasah, and madrasah status. The accreditation results show that more than 80% of MI accredited at least B in 2018 and 2019, MTs in 2019 have reached 79.5%, while MA has only reached 73.3%. Provinces that have good quality at all levels of madrasah: DI Yogyakarta, West Java, and DKI Jakarta. In contrast, there are still 10 provinces that have a low quality, where madrasah accredited at least B is below 50%. Another result shows that the average fulfillment of the 8 quality standards for new madrasah is lower than that of re-accredited madrasah, the quality of private madrasah is lower than public madrasah. In general, the standards of education that are still low are the standards for teachers and education personnel and the standards for facilities and infrastructure.
PENERAPAN METODE KLASIFIKASI RANDOM FOREST DALAM MENGIDENTIFIKASI FAKTOR PENTING PENILAIAN MUTU PENDIDIKAN Aditya Ramadhan; Budi Susetyo; Indahwati
Jurnal Pendidikan dan Kebudayaan Vol. 4 No. 2 (2019)
Publisher : Badan Standar, Kurikulum, dan Asesmen Pendidikan, Kemendikbudristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24832/jpnk.v4i2.1327

Abstract

National Education Standards serves as the basis of education development strategy based on the result of evaluation the implementation of education. The evaluation is implemented through accreditation and national exam. The objective of this study is to analyze the score of computer-based national exam based on accreditation scores per items of instrument by applying multiclass random forest classification modeling. The research used Computer-Based National Exam data in 2018 and accreditation data from the year of 2017 and 2018. This study employed random forest for multiclass classification. The results showed that, based on the evaluation model, classification accuration value in multiclass random forest was 83.49%. In addition, this model produces important variables in classifying the average value of computer-based national examination, i.e., items laboratory conditions (x71, x68, x69, x67), electrical installation (x62), infrastructure (x64), canteen (x83), laboratory (x55), special service officers (x56), certified teachers (x39), library staff (x54), head of administration (x51), literacy activities for students (x33), use of textbooks (x14), and community/partner collaboration in education management (x96). Based on the indicators of important variables, National Education Standards that have important role are facility and infrastructure standards, educator and educational staff standards, and graduate competence standards. Therefore, improving the quality of education can be done by improving school facilities, the competency of teacher and education staff, and graduate competency. Abstak Standar Nasional Pendidikan (SNP) berfungsi sebagai dasar strategi pengembangan pendidikan berdasarkan hasil evaluasi pelaksanaan pendidikan. Evaluasi pelaksanaan pendidikan dilaksanakan melalui akreditasi dan ujian nasional (UN). Tujuan penelitian ini untuk menganalisis nilai ujian nasional berbasis komputer (UNBK) berdasarkan skor akreditasi per butir instrumen dengan menerapkan pemodelan klasifikasi random forest multikelas. Penelitian ini menggunakan data UNBK tahun 2018 dan data hasil akreditasi tahun 2017 dan 2018. Metode penelitian yang digunakan adalah pemodelan klasifikasi random forest multikelas. Hasil penelitian menunjukkan bahwa, pertama, berdasarkan evaluasi model, nilai akurasi klasifikasi dalam pemodelan klasifikasi random forest multikelas sebesar 83.49%. Kedua, model ini menghasilkan tingkat kepentingan variabel prediktor (butir-butir instrumen akreditasi) dalam mengklasifikasikan nilai rataan UNBK yakni kondisi laboratorium (x71, x68, x69, x67), instalansi listrik (x62), prasarana (x64), kantin (x83), kondisi laboran (x55), petugas layanan khusus (x56), guru tersertifikat (x39), tenaga perpustakaan (x54), kepala administrasi (x51), kegiatan literasi S/M bagi peserta didik (x33), penggunaan buku teks (x14), dan kerja sama masyarakat/mitra dalam pengelolaan pendidikan (x96). Berdasarkan indikator variabel penting tersebut, SNP yang memiliki peran penting adalah Standar Sarana dan Prasarana, Standar Pendidik dan Tenaga Kependidikan, dan Standar Kompetensi Lulusan. Oleh karena itu, peningkatan mutu pendidikan dapat dilakukan dengan meningkatkan sarana dan prasarana, kompetensi pendidik dan tenaga kependidikan, serta kompetensi lulusan.Â
PETA MUTU SATUAN PENDIDIKAN DI INDONESIA (Studi Pilotting Project akreditasi 2020) H Karwono; Budi Susetyo
Jurnal Penelitian Kebijakan Pendidikan Vol. 14 No. 1 (2021)
Publisher : Balitbang Kemendikbud

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24832/jpkp.v14i1.434

Abstract

The 2020 school accreditation instrument (IASP2020) has changed the paradigm from accreditation as simply fulfilling administrative requirements to performance-based evaluation. IASP2020 focuses on measuring the quality of graduates, the learning process, quality of teachers, and school management. This research aims to examine the roadmap of education quality by type, level and area, and performance quality based on the accreditation components in IASP 2020 and the challenges to attain higher education quality. The data analyzed were the results of the piloting accreditation conducted by Board of National Accreditation for Schools/Madrasas (BAN-S/M) in 2020 on 4817 schools and madrasas. Sample selection was done through quota sampling. The results concluded that the majority of schools were accredited B. Senior High Schools (SMA) had better accreditation rating compared to other levels. In contrast, Elementary Schools/Madrasas (SD/MI) had the fewest A accreditation rating. Quality between provinces vary widely. DKI had the highest percentage of A rating while the lowest is NTT. The teacher quality component had the lowest score compared to the other three components. The factors causing the low quality at SD/MI were the students' low ability to communicate effectively, think critically in problem-solving and the lack of teachers’ initiative to carry out sustainable professional development. The low quality of SMK was due to the lack of graduates who were able to obtain competency certificates from the Professional Certification Institute, the poor management of the production unit/business center/techno park, and the lack of teachers who apply the results of training in the learning process. Instrumen Akreditasi Satuan Pendidikan tahun 2020 (IASP2020) mengubah paradigma penilaian akreditasi dari berbasis pemenuhan administratif menjadi berbasis kinerja. IASP2020 fokus mengukur komponen mutu lulusan, proses pembelajaran, mutu guru, dan manajemen sekolah. Tujuan dari penelitian ini adalah mengkaji peta mutu pendidikan berdasarkan jenis, jenjang dan wilayah serta kinerja komponen mutu dan faktor kendala pencapaian mutu berdasarkan hasil IASP2020. Data yang dianalisis berasal dari hasil piloting yang dilakukan oleh BAN-S/M tahun 2020 terhadap 4817 sekolah dan madrasah. Sekolah sampel pada piloting ini dipilih melalui sampling kuota, yang terwakili di seluruh provinsi, jenjang, dan jenis satuan pendidikan. Hasil analisis menyimpulkan bahwa mayoritas sekolah/ madrasah terakreditasi B. SMA memiliki peringkat akreditasi lebih baik dibandingkan dengan jenjang lainnya, sebaliknya SD/MI memiliki jumlah peringkat akreditasi A terkecil. Mutu antar provinsi sangat bervariasi. Provinsi DKI memiliki jumlah peringkat A terbanyak sedangkan terendah adalah NTT. Komponen mutu guru memiliki skor paling rendah dibandingkan tiga komponen lainnya. Faktor penyebab rendahnya mutu jenjang SD/MI adalah masih rendahnya kemampuan siswa dalam berkomunikasi secara efektif, berpikir kritis dalam pemecahan masalah, dan inisiatif guru melakukan pengembangan profesi berkelanjutan. Rendahnya mutu SMK terletak pada kurangnya lulusan yang memperoleh sertifikat kompetensi dari Lembaga Sertifikat Profesi, pengelolaan unit produksi/business center/technopark belum baik, dan rendahnya guru yang menerapkan hasil pelatihan dalam proses pembelajaran.
Efek Sinergis Bahan Aktif Tanaman Obat Berbasiskan Jejaring Dengan Protein Target Nur Hilal A. Syahrir; Farit Mochamad Afendi; Budi Susetyo
Jurnal Jamu Indonesia Vol. 1 No. 1 (2016): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1489.882 KB) | DOI: 10.29244/jji.v1i1.6

Abstract

Medicinal plants contain inherently active ingredients. Such ingredients are beneficial to prevent and cure diseases, as well as to perform specific biological functions. In contrast to synthetic drugs, which is based on one single chemicals, medicinal plants exert their beneficial effects through the additive or synergistic action of several chemical compounds. Those chemical compound act on single or multiple targets (multicomponent therapeutic) associated with a physiological process. Active ingredients combinations show a synergistic effect. This means that the combinational effect of several active ingredients is greater than that of individual one acting separately. A network target can be used to identify synergistic effects of plants active ingredients. The method of NIMS (Network target-based Identification of Multicomponent Synergy) is a computational approach to identify the potential synergistics effect of active ingredients. It also assessess synergistic strength of any active ingradients at the molecular level by synergy scores. We investigate these synergistic on a Jamu formula for diabetes mellitus type 2. The Jamu formula is composed of four medicinal plants, namely Tinospora crispa , Zingiber officinale, Momordica charantia, and Blumea balsamivera. Our work succesfully demonstrates that the highest synergy scores on medicinal plants synergy can be seen in pairs of several active ingredients in Zingiber officinale. On the other hand, the synergy of pairs of active ingredients in Momordica charantia and Zingiber officinale posseses a relatively high score. The same occurs in Tinospora crispa and Zingiber officinale.
Analisis Gerombol Simultan dan Jejaring Farmakologi antara Senyawa dengan Protein Target pada Penentuan Senyawa Aktif Jamu Anti Diabetes Tipe 2 Nurul Qomariasih; Budi Susetyo; Farit Mochamad Afendi
Jurnal Jamu Indonesia Vol. 1 No. 2 (2016): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1519.787 KB) | DOI: 10.29244/jji.v1i2.16

Abstract

Selama ini pembuatan obat untuk menyembuhkan suatu penyakit masih menargetkan hanya satu protein khusus yang menjadi penyebab penyakit tersebut, yang tentu hanya menggunakan satu senyawa aktif. Padahal selain menimbulkan efek samping, penanganan suatu penyakit perlu menyasar banyak protein sekaligus. Sehingga, baru-baru ini terjadi perubahan paradigma dari “one drug, one target” menjadi “multi-components, network target”. Paradigma baru ini telah melahirkan beberapa penelitian untuk menghasilkan formulasi jamu, hal ini dikarenakan konsep formulasi jamu memerlukan beberapa senyawa aktif yang terlibat. Formula jamu yang diteliti sebagai upaya menyembuhkan penyakit Diabetes Melitus (DM) tipe 2 terdiri dari 4 tanaman yaitu Pare (Momordica charantia), Sembung (Blumea balsamifera), bratawali (Tinospora crispa), dan jahe (Zingiber officinale) berdasarkan hasil penelitian Nurishmaya tahun 2014 serta berdasarkan ramuan jamu yang sedang dikembangkan di Pusat Studi Biofarmaka, Bogor. Evaluasi senyawa yang berkaitan dengan DM tipe 2 dilakukan dengan terlebih dahulu menambahkan 19 obat sintetis yang ditujukan untuk DM tipe 2 dari basis data Drug Bank. Sehingga terdapat total sebanyak 74 senyawa aktif yang terdiri dari 55 senyawa alami dari tanaman dan 19 senyawa sintetis obat. Sebanyak 100 protein yang berkaitan erat dengan masing-masing senyawa diperoleh melalui hasil skor konkordan DrugCHIPER. Skor konkordan tersebut kemudian digunakan dalam analisis gerombol simultan antara senyawa dan protein target. Plot komponen utama dan submatrix penggerombolan simultan menunjukkan 2 dari 3 senyawa dari bratawali sangat dekat dengan kelompok sintetis. Selain itu, ada 11 dari 44 senyawa dari Jahe terkumpul bersama dengan senyawa sintetis tetapi dalam jarak yang jauh. Sedangkan berdasarkan jejaring kemiripan, lebih spesifik lagi terdapat 17 dari 19 senyawa obat sintetis yang memiliki kemiripan berdasarkan protein target dengan 2 senyawa tanaman Bratawali dan 5 senyawa tanaman Jahe.
Co-Authors Aam Alamudi Aceng Komarudin Mutaqin Aditya Ramadhan adwendi, satria june Agus Mohamad Soleh Ahmad Ansori Mattjik Aji Hamim Wigena Akbar Rizki Amir, Sulfikar Anak Agung Istri Sri Wiadnyani Anang Kurnia Andina Fahriya Anis Sulistiyowati Anisa, Rahma ASEP SAEFUDDIN Asyifah Qalbi Aulia Dwi Oktavia Aunuddin Aunuddin Bagus Sartono Bambang H. Trisasongko Bambang Juanda Brian G. Lees Cici Suhaeni Cut N. Ummu Athiyah DAMAYANTI BUCHORI Darfiana Nur Dewi Jasmina Dewi Jasmina, Dewi Dhea Dewanti Dian Kurniasari Dito, Gerry Alfa Dyah R. Panuju Endah Febrianti Erfiani Erfiani Erfiani Fadjrian Imran Fahriya, Andina Faisal Arkan Farit Mochamad Afendi Fitrianto, Anwar H Karwono Hafidz Muksin Hamid, Assyifa Lala Pratiwi Hari Wijayanto Herlina Herlina Hermawati, Neni Hiola, Yani Prihantini I Made Sumertajaya Inayatul Izzati Diana Yusuf Indahwati Indahwati Indahwati Indahwati, NFN Intan Juliana Panjaitan Iswan Achlan Setiawan Izzati Rahmi HG Jap Ee Jia Jia, Jap Ee Karwono, H Kesuma Millati Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kristuisno Martsuyanto Kapiluka Kriswan, Suliana Kusman Sadik Kusni Rohani Rumahorbo La Ode Abdul Rahman La Ode Abdul Rahman La Ode Abdul Rahman M Nur Aidi M Nur Aidi, M Nur Mahmud A. Raimadoya Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Amirullah Yusuf Albasia Muhammad Nur Aidi Muhammad Sayuti Mustofa Usman Nurfadilah, Khalilah Nurfajrin, Tria Ermina Nurul Qomariasih Pannu, Abdullah Pika Silvianti Pika Silvianti Putri, Mega Ramatika Putri, Rizki Alifah Qalbi, Asyifah Qomariasih, Nurul Rachman, Nurul Aulia Rahma Anisa Rahmawat, NFN Rahmawati, nFN Rais Ratnasari, Andika Putri Rifannisa Bahar Rifki Hamdani Rizki, Akbar Robert, Zahira Rahvenia Safitri, Wa Ode Rahmalia Sanusi, Ratna Nur Mustika Satriyo Wibowo Sembiring, Febryna Sri Ningsih Desi Afriany Sulandra, Ardelia Maharani Sulfikar Amir Suliana Kriswan Supriatin, Febriyani Eka Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Sylvia P. Soetantyo Syukri, Nabila Tina Aris Perhati Tiya Wulandari Ulfa Afilia Shofa Utami Dyah Syafitri Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Warsono Wulan Andriyani Pangestu Yasmin Erika Faridhan Zahira Rahvenia Robert Zainal A Koemadji