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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Manajemen dan Organisasi Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Agrikultura Jurnal Teknik ITS IPTEK Journal of Proceedings Series TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Seminar Nasional Informatika (SEMNASIF) Dental Journal (Majalah Kedokteran Gigi) Jurnal Ilmiah Pertanian Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Makara Journal of Technology Sewagati

Anny Yuniarti

Institut Teknologi Sepuluh Nopember

Author-ID : 425621

Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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PEMISAHAN GIGI PADA DENTAL PANORAMIC RADIOGRAPH DENGAN MENGGUNAKAN INTEGRAL PROJECTION YANG DIMODIFIKASI Bilqis Amaliah; Anny Yuniarti; Anindita Sigit Nugroho; Agus Zainal Arifin
Jurnal Ilmiah Kursor Vol 6 No 2 (2011)
Publisher : Universitas Trunojoyo Madura

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Tidak mudah untuk mengetahui identitas seorang korban, jika sebagian besar tubuhnya sudah tak berbentuk lagi. Terdapat banyak cara untuk mengidentifikasi korban yang meninggal dunia, antara lain dengan DNA, sidik jari dan citra gigi. Gigi merupakan bagian dari tubuh yang biasanya masih utuh, karena struktur gigi yang padat. Sehingga peneliti mengajukan penelitian tentang identifikasi korban dengan menggunakan citra gigi. Terdapat beberapa tahap untuk identifikasi korban menggunakan citra gigi. Tahapan awal dan sangat menentukan adalah tahap pemisahan citra gigi. Dengan semakin akuratnya hasil dari pemisahan citra gigi, maka akan semakin akurat pula hasil identifikasi korban menggunakan citra gigi. Pemisahan citra gigi yang dilakukan adalah menggunakan metode Integral Projection yang dimodifikasi. Metode Integral Projection yang dimodifikasi ini digunakan untuk memberi garis pemisah antara satu gigi dengan gigi lainnya. Citra gigi yang digunakan adalah dental panoramic radiograph. Keberhasilan Integral Projection biasa dalam memisahkan antara gigi adalah 88,23 %, sedangkan dengan menggunakan Integral Projection yang dimodifikasi meningkat menjadi 93,47 %. Kata Kunci: Dental Panoramic Radiograph, Segmentasi, Integral Projection. Abstract It’s not easy to find out the identity of a victim, if most of his body was not shaped anymore. There are some ways to identify a victims, for example are using DNA matching, fingerprints and dental image. Teeth are part of the body that usually remains intact, because the solid tooth structure. Because of that, identify victim using dental image are purposed. There are several stages for victim identification using dental images. The first stage and the important one is teeth separation. The more accurate the results of the teeth separation, the more accurate the identification victim using dental images. Teeth separation is using modified integral projection method. The modified integral projection method is to make a line between the teeth so that the result is more accurate than the ordinary integral projection. In this research, dental panoramic radiographs are used. Accuration of ordinary integral projection is 88,23 %, and modified projection integral is 93,47 %.

ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS I Made Widiartha; Agus Zainal Arifin; Anny Yuniarti
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

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ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS a I Made Widiartha, b Agus Zainal Arifin, c Anny Yuniarti a Jurusan Ilmu Komputer, FMIPA, Universitas Udayana Kampus Bukit, Gedung BJ Lt.I, Jimbaran Bali, b,c Informatics Department, Faculty of Information Technology Institute of Technology Sepuluh Nopember E-Mail: a imdewidiartha@cs.unud.ac.id Abstrak Berbagai metode telah dibuat untuk dapat melakukan klasterisasi data. Salah satu metode tersebut adalah K-Harmonic Means Clustering (KHM). KHM merupakan metode klasterisasi data yang menyempurnakan K-Means Clustering (KM). Metode KHM telah mampu mengurangi permasalahan KM dalam hal sensitifitas pada inisialisasi titik pusat awal, meskipun demikian dalam KHM masih terdapat kemungkinan solusi yang dihasilkan merupakan suatu lokal optimal. Permasalahan lokal optimal ini dapat diatasi dengan memanfaatkan suatu metode yang memiliki karakteristik pencarian solusi global ke dalam metode KHM. Artificial Bee Colony (ABC) merupakan suatu metode swarm yang berbasis pada perilaku mencari makan dari koloni lebah madu yang memiliki karakteristik untuk menghindari kemungkinan konvergensi terhadap lokal optimal. Dalam penelitian ini diusulkan sebuah metode baru untuk klasterisasi data yang berbasis pada metode ABC dan KHM (ABC-KHM). Kinerja metode ABC-KHM ini telah dibandingkan dengan metode KHM dan ABC dengan memanfaatkan lima dataset. Dari hasil penelitian didapatkan hasil dimana metode ABC-KHM ini telah berhasil mengoptimalkan posisi titik pusat klaster KHM yang mengarahkan hasil klaster menuju suatu solusi global. Kata kunci: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM. Abstract Various methods have been made to cluster the data. One such method is K-Harmonic Means Clustering (KHM). KHM is a clustering method that improves K-Means Clustering (KM). KHM method was able to reduce the problem of KM in terms of sensitivity to the initialization of the initial center point nevertheless there is still a possibility that the result of KHM is a local optimum. The local optimal problem can be solved by utilizing a method that has characteristic of a global search into KHM method. Artificial Bee Colony (ABC) is a swarm method based on foraging behavior of honey bee colony that has characteristics to avoid the possibility of local optimum convergence. In this research, a new method for data clustering based on ABC and KHM (ABC-KHM) is proposed. The performance ABC-KHM method has been compared with ABC and KHM by using five datasets. The results show that ABCKHM method is able to optimize the position of the cluster center and directs the center to a global solution. Key words: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM.

THE OVERTAKING CAR SIMULATION USING THE TECHNOLOGY OF VIRTUAL REALITY Darlis Heru Mukti; Ridho Rahman Hariadi; Anny Yuniarti; Imam Kuswardayan; Wijayanti Nurul Khotimah
Jurnal Ilmiah Kursor Vol 9 No 3 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i3.116

Currently, motor vehicles are very widely used in everyday life. In Indonesia the growth of motor vehicle is very rapid, even reaching 10 percent per year. With the high value of the development of this motor vehicle, the number of accidents also increases. The cause of the accident is not only from the error engine but also it can be caused by the driver fault. The driver should be given more attention and information about the rule and how to ride the motor vehicles well. Sometimes the experience is needed to learn how to act in the different condition when the driver drives the motor vehicle. This paper implements the technology of Virtual Reality for the simulation of overtaking. There are two additional devices used in this research. There are the Steering Wheel and the Oculus Rift. This research aims to explore the VR technology and explore the implementation of the Steering Wheel and the Oculus Rift in overtaking simulation game.

IMPRESSION DETERMINATION OF BATIK IMAGE CLOTH BY MULTILABEL ENSEMBLE CLASSIFICATION USING COLOR DIFFERENCE HISTOGRAM FEATURE EXTRACTION Hani Ramadhan; Isye Arieshanti; Anny Yuniarti; Nanik Suciati
Jurnal Ilmiah Kursor Vol 7 No 4 (2014)
Publisher : Universitas Trunojoyo Madura

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IMPRESSION DETERMINATION OF BATIK IMAGE CLOTH BY MULTILABEL ENSEMBLE CLASSIFICATION USING COLOR DIFFERENCE HISTOGRAM FEATURE EXTRACTION aHani Ramadhan, b Isye Arieshanti, cAnny Yuniarti, d Nanik Suciati a,b,c,d Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS) E-Mail: hani.its.042@gmail.com Abstrak Hampir setiap orang akan memperhatikan impresi busana yang dipakai, termasuk busana dengan motif batik. Namun, perpaduan berbagai motif dan warna batik memberikan impresi yang beragam. Sehingga, penentuan impresi dari satu kain batik menjadi sulit. Untuk membantu seseorang dalam menentukan impresi dari busana batik yang dipilih, dibutuhkan sistem yang mampu mengklasifikasikan impresi citra kain batik secara otomatis. Akan tetapi, pembuatan sistem klasifikasi label jamak merupakan memiliki tantangan tersendiri. Penelitian sebelumnya membuktikan bahwa metode klasifikasi ansambel label jamak dengan pencarian threshold mampu menjawab tantangan tersebut dengan kehandalannya dalam menangani himpunan data label jamak. Studi ini bertujuan untuk mengembangkan sistem yang menerapkan metode klasifikasi ansambel label jamak untuk menentukan impresi citra kain batik. Sistem ini memanfaatkan fitur tekstur dan warna yang dihasilkan dari Histogram Perbedaan Warna. Hasil uji coba metode ini memberikan performa yang baik dalam evaluasi label jamak. Nilai evaluasi tersebut antara lain Hamming Loss sebesar 0,173 dan Average Precision 0,866. Kata kunci: Histogram Perbedaan Warna, Impresi Citra Kain Batik, Klasifikasi Label Jamak Abstract Many people will consider the fashion products’ impression that will be worn, including the one with batik motif. Unfortunately, diverse impressions could be produced from combinations of the motif and color from a single batik cloth. Therefore, impression determination becomes a difficult case. To overcome this difficulty, an automatic batik cloth multi-impression classification system should be necessary to aid in choosing certain batik cloth. Nevertheless, this system implementation has its own intriguing challenge. Previous researches implied that multilabel ensemble classification method could deal with the problem against the highly imbalanced dataset. Thus, the aim of this study is to develop the multilabel classification system, which features come from the color and texture feature by Color Difference Histogram. From the test, this method demonstrated good performance by several multilabel evaluations, which are 0.173 by Hamming Loss and 0.866 by Average Precision. Keywords: Color Difference Histogram, Batik Cloth Image Impression, Multi-Label Classification.

Butterfly Image Classification Using Color Quantization Method on HSV Color Space and Local Binary Pattern Dhian Satria Yudha Kartika; Darlis Herumurti; Anny Yuniarti
IPTEK Journal of Proceedings Series No 1 (2018): 3rd International Seminar on Science and Technology (ISST) 2017
Publisher : Institut Teknologi Sepuluh Nopember

A lot of methods are used to develop on image research. Image detection to relay back new information, widely used in various research field, such as health, agriculture or other field research. Various methods are used and developed to get better results. A combination of several methods is performed for testing as part of the research contribution. In this study will perform the combination results of the process color feature extraction with texture features. In color feature extraction using HSV color space method that gets 72 feature extraction and on texture feature extraction using local binary pattern that gets 256 feature extraction. The process of merging the two extracted results gets 328 new feature extractions. The result of combining color feature extraction and texture feature extraction is further classified. Results from image classification of butterflies get an accuracy score of 72%. The results obtained will be tested performance. The results obtained from performance testing get precision value, recall and f-measure respectively 76%, 72% and 74%

Computer-aided diagnosis for osteoporosis based on trabecular bone analysis using panoramic radiographs Agus Zainal Arifin; Anny Yuniarti; Lutfiani Ratna Dewi; Akira Asano; Akira Taguchi; Takashi Nakamoto; Arifzan Razak; Hudan Studiawan
Dental Journal (Majalah Kedokteran Gigi) Vol. 43 No. 3 (2010): September 2010
Publisher : Faculty of Dental Medicine, Universitas Airlangga https://fkg.unair.ac.id/en

Background: Mandibular bone on panoramic radiographs has been proven to be useful for identifying postmenopausal women with low skeletal bone mineral density. One of the important parts of mandibular bone is trabecular bone. Trabecular bone architecture is one of the factors that governs bone strength and may be categorized as a contributor to bone quality. Purpose: The purposes of this study were to develop a computer-aided system for measuring trabecular bone line strength on panoramic radiographs in identifying postmenopausal women with osteoporosis and to clarify the diagnostic efficacy of the system. Methods: Reduction and expansion of trabecular bone sample images using a two level Gaussian pyramid for removing noises and small segments were first introduced. Then, line strength at each pixel was calculated based on its existence on the trabecular bone with emphasizes line segment which has similar orientation with the root of tooth. The density was measured with respect to line strength of segment structure which has similar orientation with the root of tooth, either on the left and the right in the mandibular bone. Number of pixels in the line segment area was compared with a threshold value to determine whether normal or osteoporosis. Results: From experiment on 100 data, the accuracy of 88%, sensitivity of 92%, and specificity of 86.7% were achieved. Conclusion: The computer-aided system of trabecular bone analysis may be useful for detecting osteoporosis using panoramic radiographs.Latar belakang: Tulang mandibula pada panoramik radiografi telah banyak diteliti dan terbukti mampu digunakan untuk mengidentifikasi wanita pasca menopause dengan menggunakan bone mineral density rendah. Salah satu bagian tulang mandibula yang penting adalah tulang trabekula. Arsitektur tulang trabekula merupakan salah satu dari faktor-faktor yang mempengaruhi kekuatan tulang dan dapat digolongkan sebagai kontributor bagi kualitas tulang. Tujuan: Penelitian ini bertujuan untuk membangun sebuah sistem dengan bantuan komputer untuk mengukur kekuatan garis pada tulang trabekula dan menggunakannya untuk mendeteksi osteoporosis pada wanita postmenopause. Metode: Dilakukan sampling pada sebagian tulang mandibular yang menghasilkan sebuah sampel citra. Sampel citra ini selanjutnya diperbaiki dari derau (noise) dengan menggunakan piramida Gaussian dua level. Kekuatan garis pada tiap piksel dihitung berdasarkan orientasi segmen garis tulang trabekula yang sejajar dengan akar gigi. Setelah dilakukan binerisasi, luasan segmen yang dihasilkan dihitung dan dibandingkan dengan sebuah nilai ambang. Bila luasan melebihi nilai threshold maka dikategorikan sebagai normal. Sebaliknya bila luasan dibawah nilai threshold, dikategorikan sebagai osteoporosis. Hasil: Berdasarkan eksperimen terhadap 100 data, sistem mampu mencapai akurasi identifikasi sebesar 88%, sensitivitas 92%, dan spesifisitas 86,7%. Kesimpulan: Sistem analisa trabecular bone dengan bantuan komputer ini dapat digunakan oleh para dokter gigi untuk mendeteksi osteoporosis menggunakan panoramik radiografi.

Effect of exopolysaccharide-producing Azotobacter and cow manure on nutrient uptake and root-to-shoot ratio of sorghum Reginawanti Hindersah; Anny Yuniarti; Hidiyah Ayu Ratna Ma’rufah
Jurnal Ilmiah Pertanian Vol. 17 No. 2 (2021): Jurnal Ilmiah Pertanian
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/jip.v17i2.5205

Nitrogen-fixing Azotobacter synthesizes exopolysaccharide, which is important among other to improve aggregate stability and hence nutrients uptake. A pot experiment has been conducted to determine the effect of exopolysaccharide-producing Azotobacter and organic matter on nitrogen, phosphor, and potassium uptake by the shoot of sorghum (Sorghum bicolor (L.) Moench), and plant growth. The pot experiment was setup in randomized block design which test eight combination treatments of Azotobacter isolates (AS5, AS6, and AS5 + AS6) and organic matter application (with and without 20 t ha-1 of cow manure). The result showed dual inoculation of Azotobacter AS5 and AS6 inoculation combined with cow manure application increased N and P uptake. The dual inoculation treatment did not affect root length; but increased the shoot height and dry weight when accompanied by the application of cow manure. The ratio of root and shoot dry weight was not influenced by single or dual Azotobacter inoculation with or without organic matter.

Pengenalan Wajah Menggunakan Two Dimensional Linear Discriminant Analysis Berbasis Optimasi Feature Fusion Strategy Sahmanbanta Sinulingga; Chastine Fatichah; Anny Yuniarti
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 3 No 1 (2016): JATISI SEPTEMBER 2016
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

The era of technology today,, research on biometric image is not common to do. One well researched biometric image is a face recognition (face recognition). Problems on the human face recognition is a diversity of features or shape between one another face to face. Therefore, the need for facial feature extraction and classification using a particular method so that the classification can be recognized correctly.In this study proposed feature extraction method that can overcome the problems of non-linear automatic data contained in the face image, called the Two Dimensional Linear Discriminant Analysis based on Feature Fusion Strategy (TDLDA-FFS). Not stopping on feature extraction, classification methods proposed also faces that can overcome the problems of the adaptive matrix which aims to study the benefit of weight on each - each input with the method Relevanced Generalized Learning Vector quantization (GRLVQ).This research integrates methods TDLDA-FFS and GRLVQ for face recognition. With the combination of both methods are proven to provide optimal results with a level of recognition accuracy ranged between 77.78% to 82.22% with a pilot using a databaseof facial images from the Institute of Business and Information Stikom Surabaya. While the test uses a database derived from YaleB Database achieve accuracy levels ranging from 88.89% to 94.44%.

Ensemble Method for Indonesian Twitter Hate Speech Detection M. Ali Fauzi; Anny Yuniarti
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp294-299

Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.

Knowledge Dictionary for Information Extraction on the Arabic Text Data Saputra, Wahyu Syaifullah Jauharis; Arifin, Agus Zainal; Yuniarti, Anny
Makara Journal of Technology Vol. 16, No. 2
Publisher : UI Scholars Hub

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Information extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention in many studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledge dictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%.

Co-Authors Achmad Chabiburrohman Achmad Fahriza Agus Arifin Agus Arifin, Agus Agus Z. Arifin, Agus Z. Agus Zainal Arifin Agus Zainal Arifin Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ahmad Raihan Muzakki Akira Asano Akira Taguchi Al-Haddad, Abdullah Alifiansyah Arrizqy Hidayat Amrullah, Muhammad Syiarul Andi Baso Kaswar Andi Baso Kaswar Anindhita Sigit Nugroho Anindita Sigit Nugroho Anita Hakim Nasution Arif Fathur Mahmuda Arifiani, Siska Arifzan Razak Aris Fanani Aris Tjahyanto Arya Yudhi Wijaya Berlian Rahmy Lidiawaty Betty Natalie Fitriatin Bilqis Amaliah Budi Nugroho Budi Nugroho Chastine Fatichah Chilyatun Nisa' Christy Atika Sari Darlis Heru Mukti Darlis Herumurti Devira Wiena Pramintya Dhian Satria Yudha Kartika Diana Suteja Dini Adni Navastara, Dini Adni Eva Yulia Puspaningrum Fawwaz Abdulloh Al-Jawi Feni Siti Fauziah2 Fetty Tri A. Fiandra Fatharany Gulpi Qorik Oktagalu Pratamasunu Hadziq Fabroyir Handayani Tjandrasa Hani Ramadhan Hidiyah Ayu Ratna Ma’rufah Hisyam Syarif, Hisyam Hudan Studiawan I Made Satria Bimantara I Made Widiartha I Putu Gede Hendra Suputra Imam Kuswardayan Imam Kuswardayan Ishardan Ishardan Isye Arieshanti Kelly Rossa Sungkono Khairun Nisa Kostidjan, Okky Darmawan Lutfiani Ratna Dewi M. Ali Fauzi M. Ali Fauzi Maulana, Hendra MIFTAHOL ARIFIN, MIFTAHOL Mohamad Dion Tiara Muhammad I. Rosadi, Muhammad I. Muhammad Meftah Mafazy Muhammad Rayyaan Fatikhahur Rakhim Muhammad Riduwan Nadya Anisa Syafa Nafiiyah, Nur Nanik Suciati Nanik Suciati Oviyanti Mulyani Pasnur Pasnur Purwanto, Yudhi Puspitasari, Leny Ratri Enggar Pawening Reginawanti Hindersah Ridho Rahman Hariadi Riduwan, Muhammad Rindah Febriana Suryawati Rizky Damara Ardy Sahmanbanta Sinulingga Saiful Bahri Musa Saprina Mamase Saputra, Wahyu Syaifullah Jauharis Siska Arifiani Soegeng Soetedjo Sofyan Sauri, Sofyan Takashi Nakamoto Thoha Haq Wahyu Syaifullah Jauharis Saputra Wibowo, Della Aulia Wijayanti Nurul K Wijayanti Nurul Khotimah Zeng, Xinyou

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