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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science JAST : Jurnal Aplikasi Sains dan Teknologi

Imam Cholissodin

Universitas Brawijaya

Author-ID : 437952

Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Other

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Prediksi Tinggi Muka Air (TMA) Untuk Deteksi Dini Bencana Banjir Menggunakan SVR-TVIWPSO Soebroto, Arief Andy; Cholissodin, Imam; Wihandika, Randy Cahya; Frestantiya, Maria Tenika; Arief, Ziya El
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2, No 2 (2015)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1302.981 KB)

Abstrak Banjir merupakan salah satu jenis bencana alam yang tidak dapat diprediksi kedatangannya, salah satu penyebabnya adalah adanya hujan yang terus â menerus(dari peristiwa alam). Faktor penyebab banjir dari segi meteorologi yaitu curah hujan yang tinggi dan air laut yang sedang pasang sehingga mengakibatkan tinggi permukaan air meningkat. Analisis terhadap data curah hujan serta tinggi permukaan air setiap periodenya dirasa masih belum dapat menyelesaikan permasalahan yang ada. Oleh karena itu, pada penelitian ini diusulkan teknik integrasi metode Time Variant Inertia Weight Particle Swarm Optimization(TVIWPSO) dan Support Vector Regression(SVR). Implementasi memadukan metode Regresi yaitu SVR untuk forecasting TMA, sedangkan TVIWPSO digunakan untuk mengoptimalisasi parameter â parameter yang digunakan di dalam SVR untuk memperoleh kinerja yang maksimal dan hasil yang akurat. Harapannya sistem ini akan dapat membantu mengatasi permasalahan untuk pendeteksian dini bencana banjir karena faktor cuaca yang tidak menentu. Hasil pengujian yang didapat dari 10 data bulanan yang berbeda menunjukkan bahwa didapatkan nilai error terkecil sebesar 0.00755 dengan menggunakan Mean Absolute Error untuk data Juni 2007 dengan menggunakan integrasi metode SVR-TVIWPSO. Kata Kunci : Support Vector Regression, Tinggi Muka Air, Time Variant Inertia Weight Particle Swarm Optimization. Abstract Flood is one type of natural disaster that can not be predicted its arrival, one reason is the rain that constantly occurs (from natural events). Factors that cause flooding in terms of meteorology are high rainfall and sea water was high, resulting in high water level increases. Analysis of rainfall data and water level in each period it is still not able to solve existing problems. Therefore, in this study the method proposed integration techniques Time Variant Inertia Weight Particle Swarm Optimization (TVIWPSO) and Support Vector Regression (SVR). Implementation combines regression method for forecasting TMA is SVR, while TVIWPSO used to optimize parameters that used in the SVR to obtain maximum performance and accurate results. Hope this system will be able to help solve the problems for the early detection of floods due to erratic weather. The result of forecasting experiment in water level forecasting from 10 monthly different data show that the smallest error rate is amount to 0.00755 using Mean Absolute Error for June 2007 with the integration method SVR-TVIWPSO. Keywords: Support Vector Regression, water level, Time Variant Inertia Weight Particle Swarm Optimization.

Implementasi Teknik Watershed Dan Morfologi Pada Citra Satelit Untuk Segmentasi Area Universitas Brawijaya ., Sutrisno; Supianto, Ahmad Afif; Cholissodin, Imam
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 1, No 1 (2014)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2057.225 KB)

AbstrakPenelitian di bidang segmentasi citra telah banyak dilakukan, terutama di bidang citra satelit. Proses segmentasi ini dilakukan untuk melakukan deteksi terhadap objek-objek yang terdapat di dalam citra. Pada penelitian ini, diimplementasikan sebuah metode segmentasi citra dengan menggunakan teknik watershed dan morfologi. Pertama, citra diubah ke dalam format citra grayscale.Kemudian, citra grayscale tersebut diolah dengan metode watershed untuk mendapatkan segmentasi awal. Selanjutnya, citra segmentasi tersebut diperbaiki menggunakan metode morfologi untuk mengurangi segmentasi berlebih yang dihasilkan oleh proses sebelumnya. Uji coba dilakukan terhadap 5dataset citra satelitarea Universitas Brawijaya dengan tingkat skala yang berbeda-beda. Skala yang digunakan dalam penelitian ini meliputi 20m, 50m, 100m, 200m, dan 500m. Uji coba menunjukkan bahwa metode yang diusulkan berhasil melakukan segmentasi citra dengan skala kurang dari 100 meter.Semakin rendah nilai skala yang digunakan sebagai uji coba, segmentasi yang dihasilkan semakin baik.Kata kunci: Watershed, Morfologi Citra, Citra SatelitAbstractResearch in the field of image segmentation has been widely applied , especially in the field of satellite imagery. The segmentation process is performed to detect the objects present in the image. In this study, implemented a method of image segmentation using watershed and morphological techniques. First, the image is converted into grayscale format. Then the grayscale image is processed by the watershed method to get initial segmentation. Furthermore, the improved image segmentation using morphological methods to reduce the excessive segmentation generated by the previous process. Tests performed on 5 satellite imagery dataset UB area with levels varying scales. The scale used in this study include the 20 meters , 50 meters, 100 meters, 200 meters, and 500 meters. The trials showed that the proposed method successfully to segment the image with the scale of less than 100 meters . The lower the scale value is used as a test , the better the resulting segmentation .Keywords: Watershed, Morphological Image, Satellite Imagery

Segmentasi Kendaraan Menggunakan Improve Blob Analysis (BA) Pada Video Lalu Lintas ., Sutrisno; Cholissodin, Imam; Christanti, Rina; Dewi, Candra; Hidayat, Nurul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2, No 1 (2015)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (996.895 KB)

AbstrakPenggunaan citra digital untuk keperluan penelitian sudah banyak dilakukan, salah satunya yaitu segmentasi. Segmentasi berfungsi untuk mendeteksi objek - objek yang terdapat pada citra, sehingga hasil segmentasi sangat penting untuk proses selanjutnya. Pada penelitian ini diusulkan teknik optimasi hasil background subtraction menggunakan kombinasi frame difference (FD) atau difference image dengan filter SDGD dan running average (RA) atau background updating dengan filter SDGD untuk diterapkan pada blob analysis. Alasan utama menggunakan penggabungan kedua metode tersebut adalah karena seringnya terdapat piksel objek yang tidak mampu dideteksi sehingga akan mengurangi tingkat optimasi pengenalan objek. Hasil pengujian akurasi dari 10 data uji yang masing â masing terdiri dari 30 frame menunjukkan bahwa aplikasi ini memiliki nilai akurasi tertinggi yakni 90% untuk pengujian threshold dan 100% untuk pengujian ukuran structure element. Sehingga dapat disimpulkan bahwa aplikasi ini mampu melakukan segmentasi kendaraan dengan baik.Kata kunci: filter SDGD, blob analysis, video lalu lintas, background subtraction.AbstractThe use of digital images for the purposes of research has been often applied, one of them is segmentation. Segmentation is used to detect objects contained in the image, so the segmentation result is very important for further processing. In this study, the results of the optimization technique proposed background subtraction using a combination of frame difference (FD) or a difference image with filter SDGD and running average (RA) or background updating with SDGD filter to be applied blob analysis. The main reason to use the merger of these two methods is that often there are pixels that are not able to detect objects that will reduce the level of optimization object recognition. The results of accuracy testing using 10 data testing for each data consisting of 30 frames shows that the system proposed in this paper has best accuracy of 90% for testing the threshold and 100% for testing the size of structure element. So it can be concluded that this system capable to segmentation the vehicle properly.Keywords: filter SDGD, blob analysis, traffic video, background subtraction

Pelatihan Budidaya Multi-Culture Farming Berbasis Teknologi Sistem Pakar serta Optimasi untuk Kemandirian Ekonomi dan Ketahanan Pangan Masyarakat Indonesia Sutrisno, Sutrisno; Cholissodin, Imam; Soebroto, Arief Andy; Muflikhah, Lailil
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 5, No 2 (2021): EDISI DESEMBER 2021
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v5i2.2878

Poverty alleviation efforts in Indonesia are still challenging due to the lack of job opportunities. Therefore, creative efforts are needed that are easy to do at home, especially during this Covid-19 pandemic. In this service activity, the creative business was introduced, which was packaged in multi-culture farming cultivation training. The community service activities were preceded by conducting a field survey on three partners, i.e., the farmer groups in Sunge Geneng Village, Sekaran District, Lamongan Regency, RT 3 / RW III Kauman Village Malang City, and Poncokusumo District Malang Regency. Since the survey plan was carried out during the Covid-19 Pandemic and the frequent Public Activity Restrictions (PPKM), the team could only survey the initial two partners. However, during the implementation process, the second partner was the only one to reach the implementation stage of Multi-Culture Farming with non-AI technology and introduce the use of AI Technology. Therefore, the second partner, RT 03 / RW 03 Kauman Village, Klojen District, Malang City, also became the leading partner. The main results of activities were ten budikdamber tools, catfish feed, and training modules about optimizing plant fertilizer nutrition. The other results were land-use using AI Engine and educational videos that received an excellent response from the partners and non-partners being sustainable to create a kind of fostered village, especially in entrepreneurship that utilizes digital technology.ABSTRAKUpaya pengentasan kemiskinan di Indonesia masih sulit dilakukan, karena minimnya lapangan pekerjaan. Oleh karena itu dibutuhkan usaha kreatif yang mudah dilakukan di rumah, terutama dimasa pandemi Covid-19 ini. Dalam kegiatan pengabdian ini, dikenalkan usaha kreatif tersebut yang dikemas dalam bentuk pelatihan budidaya multi-culture farming. Rangkaian kegiatan pengabdian masyarakat tersebut didahului dengan melakukan survey lapangan terhadap tiga Mitra, yaitu di kelompok Tani desa Sunge Geneng Kecamatan Sekaran Kabupaten Lamongan, Kampung Kauman RW/RT III/03 dan Poncokusumo Malang. Pada rencana survey tersebut dikarenakan saat itu masih di tengah Pandemi Covid-19 dan juga sering ada PPKM. Maka tim hanya mampu melakukan survey terhadap 2 Mitra awal, dan ketika proses pelaksanaan hanya Mitra yang ke-2 yang sampai pada tahapan implementasi Multi-Culture Farming dengan teknologi non-AI, tetapi sudah dikenalkan juga pada saat pelatihan menggunakan Teknologi AI, di mana Mitra ke-2 tersebut tepatnya pada RT 03, RW-03 kelurahan Kauman kecamatan Klojen kota Malang yang sekaligus menjadi Mitra utama. Hasil utama kegiatan berupa pemberian bantuan 10 alat budikdamber, pakan lele serta modul pelatihan optimasi pupuk tanaman, penggunaan lahan dan video edukasi yang telah mendapatkan respon sangat baik dari tanggapan Mitra sekaligus juga dari Non Mitra untuk terus dapat berkelanjutan sampai membuat semacam kampung binaan terutama dalam hal wirausaha yang memanfaatkan Teknologi digital.

Pelatihan Smart Multi-Culture Farming Berbasis Teknologi Cloud-AI untuk Pemantauan Objek Budidaya dengan Tenaga Surya sebagai Eco-Green Energy Masyarakat Indonesia Santoso, Nurudin; Cholissodin, Imam; Soebroto, Arief Andy; Hidayat, Nurul; Sutrisno, Sutrisno; Pratiwi, Destyana Ellingga; Fathuroya, Vivien
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 6, No 2 (2022): EDISI DESEMBER 2022
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v6i2.4015

Working in multicultural agriculture is exhausting and has several bad risks for farmers in rural and urban areas. The risks start from the considerable time required in cultivation, especially when maintaining the growth and development of plants and other cultivation objects, the large number of costs required in the use of irrigation for fuel purchases, and the risk of carrying out specific processes using high voltage electricity which is very dangerous for farmers. Based on these problems, an automated technology approach that can work to help farmers is necessitated. In this community service, two partners are involved, i.e., a group of farmers who are also workers in Kampung Kauman RW/RT III/03 as the primary partner and a group of farmers who are also workers at the plantation in Poncokusumo Malang as the supporting partner. Both partners used solar electricity for irrigation and other uses through the Cloud-AI approach obtained from the results of multi-disciplinary research several years earlier at the Filkom UB Intelligent Computing Laboratory. Cloud-AI can work adaptively according to weather conditions from a Web App from application programming interface (API) data to provide recommendations for predicting the length of time for irrigation in observing cultivation objects which later can be modified for other particular purposes. The activity's primary results are providing training and assistance with intelligent multi-culture farming installation tools for hydroponics, solar panels, and pumps for irrigation: cloud-AI-based agricultural training modules and educational videos with excellent responses from the partners.ABSTRAKProses pengerjaan bidang pertanian multi-culture sangat menguras banyak tenaga dan memiliki beberapa resiko kurang baik bagi petani, baik di pedesaan maupun perkotaan. Mulai dari waktu yang cukup banyak dibutuhkan dalam pembudidayaan terutama saat pemeliharaan tumbuh kembangnya tanaman maupun objek budidaya lainnya, lalu banyaknya biaya yang dibutuhkan dalam penggunaan irigasi untuk pembelian bahan bakar serta resiko ketika melakukan proses tertentu menggunakan listrik tegangan tinggi yang sangat membahayakan petani. Berdasarkan permasalahan tersebut dibutuhkan pendekatan teknologi otomasi yang dapat bekerja membantu petani. Dalam pengabdian ini melibatkan Dua Mitra, yaitu di kelompok petani yang sekaligus pekerja Kampung Kauman RW/RT III/03 dan pada Perkebunan di Poncokusumo Malang yang memanfaatkan listrik tenaga surya untuk irigasi dan kegunaan lainnya serta pendekatan Cloud-AI yang dapat bekerja secara adaptif baik luring maupun daring untuk mengendalikan kelistrikan, prediksi untuk pengambilan keputusan dalam pengamatan objek budidaya dan lainnya. Hasil utama kegiatan berupa pemberian pelatihan, lalu bantuan paket alat instalasi smart multi-culture farming untuk hidroponik, panel surya dan pompa untuk irigasi serta modul pelatihan pertanian berbasis Cloud-AI dan video edukasi dengan respon yang sangat baik dari Mitra.

Klasifikasi Aktivitas Manusia Menggunakan Metode Long Short-Term Memory Afida, Latansa Nurry Izza; Bachtiar, Fitra Abdurrachman; Cholissodin, Imam
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127060

Klasifikasi aktivitas manusia merupakan salah satu topik penelitian yang penting karena dapat diterapkan pada berbagai bidang. Penelitian mengenai klasifikasi aktivitas manusia sebelumnya telah banyak dikembangkan dengan menerapkan dataset public HAR Repository yang telah tersedia. Namun dataset tersebut memiliki hasil dari ekstraksi fitur keluaran nilai sensor memiliki dimensi yang tinggi. Tingginya dimensi fitur dapat menyebabkan penurunan akurasi, untuk itu pada penelitian ini diusulkan penerapan dataset primer tanpa ekstraksi fitur. Selain tingginya dimensi, pada penelitian sebelumnya, banyaknya jumlah label dengan menerapkan machine learning tradisional tidak mampu melebihi akurasi 88%. Sehingga pada penelitian ini menerapkan dataset primer dengan menggunakan label kelas sebanyak 16 sehingga diusulkan metode deep learning Long Short Term Memory (LSTM). Proses penelitian dimulai dari pengambilan data, preprocessing data, modelling dan perbandingan algoritma deep learning LSTM dan machine learning KNN. Berdasarkan hasil pengujian perbandingan kedua algoritma tersebut dengan implementasi dataset yang sama, algoritma terbaik yaitu LSTM dengan nilai akurasi sebesar 0.94 dan KNN dengan nilai akurasi sebesar 0.71.

Selection and Recommendation Scholarships Using AHP-SVM-TOPSIS Putra, M Gilvy Langgawan; Ariyanti, Whenty; Cholissodin, Imam
Journal of Information Technology and Computer Science Vol. 1 No. 1: June 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Gerakan Nasional Orang Tua Asuh Scholarship offers a number of scholarship packages. As there are a number of applicants, a system for selection and recommendation is required. we used 3 methods to solve the problem, the methods are AHP for feature selection, SVM for classification from 3 classes to 2 classes, and then TOPSIS give a rank recommendation who is entitled to receive a scholarship from 2 classes. In testing threshold for AHP method the best accuracy 0.01, AHP selected 33 from 50 subcriteria. SVM has highest accuracy in this research is 89.94% with Sequential Training parameter are Î» =0.5, constant of Î³ =0.01 , Îµ = 0.0001, and C = 1. Keywords: Selection, Recommendation, Scholarships, AHP-SVM-TOPSIS

Review: A State-of-the-Art of Time Complexity (Non-Recursive and Recursive Fibonacci Algorithm) Cholissodin, Imam; Riyandani, Efi
Journal of Information Technology and Computer Science Vol. 1 No. 1: June 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Solving strategies in the computation the time complexity of an algorithm is very essentials. Some existing methods have inoptimal in the explanations of solutions, because it takes a long step and for the final result is not exact, or only limited utilize in solving by the approach. Actually there have been several studies that develop the final model equation Fibonacci time complexity of recursive algorithms, but the steps are still needed a complex operation. In this research has been done several major studies related to recursive algorithms Fibonacci analysis, which involves the general formula series, begin with determining the next term directly with the equation and find the sum of series also with an equation too. The method used in this study utilizing decomposition technique with backward substitution based on a single side outlining. The final results show of the single side outlining was found that this technique is able to produce exact solutions, efficient, easy to operate and more understand steps. Keywords: Time Complexity, Non-Recursive, Recursive, Fibonacci Algorithm

Optimizing SVR using Local Best PSO for Software Effort Estimation Novitasari, Dinda; Cholissodin, Imam; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 1 No. 1: June 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. In the software industry world, itâ€™s known to fulfill the tremendous demand. Therefore, estimating effort is needed to optimize the accuracy of the results, because it has the weakness in the personal analysis of experts who tend to be less objective. SVR is one of clever algorithm as machine learning methods that can be used. There are two problems when applying it; select features and find optimal parameter value. This paper proposed local best PSO-SVR to solve the problem. The result of experiment showed that the proposed model outperforms PSO-SVR and T-SVR in accuracy. Keywords: Optimization, SVR, Optimal Parameter, Feature Selection, Local Best PSO, Software Effort Estimation

Optimization of Healthy Diet Menu Variation using PSO-SA Cholissodin, Imam; Dewi, Ratih Kartika
Journal of Information Technology and Computer Science Vol. 2 No. 1: June 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Optimal healthy diet in accordance with the allocation of cost needed so that the level of nutritional adequacy of the family is maintained. The problem of optimal healthy diet (based on family budget) can be solved with genetic algorithm. The algorithm particle swarm optimization (PSO) has the same effectiveness with genetic algorithm but PSO is superior in terms of efficiency, PSO algorithm has a lower complexity than genetic algorithm. However, genetic algorithms and PSO have a problem of local optimum because these algorithm associated with random numbers. To overcome this problem, PSO algorithm will be improved by combining it with simulated annealing algorithm (SA). Simulated annealing algorithm is a numerical optimization algorithms that can avoid local optimal. From our results, optimal parameter for PSO-SA are popsize 280, crossover rate 0.6, mutation rate 0.4, first temperature 1, last temperature 0.2, alpha 0.9, and generation size 100.Keywords: PSO, SA, optimization, variation, healthy diet menu.

Co-Authors ., Maryamah A. N., Aditya Yudha Achmad Jafar Al Kadafi, Achmad Jafar Afida, Latansa Nurry Izza Agnes Rossi Trisna Lestari, Agnes Rossi Ahmad Afif Supianto AJI, IBRAHIM Alaydrus, Zaien Bin Umar Alexander, Mikhael Anang Hanafi, Anang Ardisa Tamara Putri, Ardisa Tamara Arief Andy Soebroto Arniantya, Raissa Asikin, Moh. Fadel Bayu Rahayudi Brigitta Ayu Kusuma Wardhany, Brigitta Ayu Kusuma Budi Darma Setiawan Caesar, Canny Amerilyse Candra Dewi Dahnial Syauqy Daisy Kurniawaty, Daisy Daneswara Jauhari, Daneswara Destyana Ellingga Pratiwi Dharmawan, Muhammad Robby Dinda Novitasari, Dinda Dyan Putri Mahardika, Dyan Putri Efi Riyandani, Efi Evanita, Felicia Marvela Fauzi, Handika Agus Fauziyah, Aprilia Nur Firmanda, Dwi Ady Firmansyah, Ilham Fitra Abdurrachman Bachtiar Ghofur, Afwan H, Luqman Hakim Hanafi, Audi Nuermey Harahap, Syazwandy Hasan, Muhammad Ismail Heru Nurwarsito Hidayatullah, Adam Syarif Husin Muhamad, Husin Idham Triatmaja, Idham Irawan, Fathony Teguh Irma Lailatul Khoiriyah, Irma Lailatul Istiana Rachmi, Istiana Jonemaro, Eriq Muhammad Adams Jupiyandi, Sisco Kartikasari, Oktavianis Ksatria, Willyan Eka Kurnianingtyas, Diva Lailil Muflikhah Latifah Hanum Listiya Surtiningsih, Listiya Luqyana, Wanda Athira M Gilvy Langgawan Putra, M Gilvy Langgawan Maâ€™rufi, Muhammad Rizal Mahardika, Guedho Augnifico Maria Tenika Frestantiya, Maria Tenika Mayangsari, Lintang Resita Muhammad Fhadli, Muhammad MUHAMMAD SYAFIQ Muzayyani, Muhammad Farid Najib, Mochammad Ainun Nanda Agung Putra, Nanda Agung Nur Firra Hasjidla, Nur Firra Nurul Hidayat Pardede, Andreas Prabowo, Dhimas Anjar Pramana, Firadi Surya Prasojo, Cahyo Adi Pratama, Andhica R, Bariq Najmi Rahardian, Brillian Aristyo Rahman, Edy Randy Cahya Wihandika Ratih Kartika Dewi Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rina Christanti, Rina Rizaldi, Hilmi Ilyas Robbana, Siti Saâ€™rony, Akhmad Salsabila, Rona Saniputra, Fadhil Rizqullah Santoso, Nurudin Sari, Selly Kurnia Satria, Arrofi Reza Seisarrina, Maulidya Larasaty Septino, Fernando Seruni, Diajeng Sekar Sugianto, Nur Afifah Sukmawati, Annisa Sunaryo, Aryeswara Sutrisno . Sutrisno, Sutrisno Tedjasulaksana, Jeffrey Junior Tusty Nadia Maghfira, Tusty Nadia Umi Rofiqoh, Umi Uswatun Hasanah Vivien Fathuroya, Vivien Wahyuditomo, Kukuh Wicaksono Wayan Firdaus Mahmudy Whenty Ariyanti Winda Cahyaningrum, Winda Winda Estu Nurjanah, Winda Estu Wulandari, Ulfa Lina Yoga Pratama Yuniarsa, M Fahrul Alam Yusuf Priyo Anggodo, Yusuf Priyo Zakiyyah, Rizka Husnun Ziya El Arief, Ziya El Zulianur Khaqiqiyah, Zulianur Zulqornain, Junda Alfiah

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