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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Knowledge Engineering and Data Science IT JOURNAL RESEARCH AND DEVELOPMENT JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi MIND (Multimedia Artificial Intelligent Networking Database) Journal Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) EXPLORE TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal SITECH : Sistem Informasi dan Teknologi Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Journal of Innovation Information Technology and Application (JINITA) Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Vivabio : Jurnal Pengabdian Multidisiplin Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) JP2KG AUD (Jurnal Pendidikan, Pengasuhan, Kesehatan dan Gizi Anak Usia Dini) JUSTIN (Jurnal Sistem dan Teknologi Informasi) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer JAIA - Journal of Artificial Intelligence and Applications EXPLORE Jurnal Bina Komputer JAPI: Jurnal Akses Pengabdian Indonesia Data Sciences Indonesia (DSI) Jurnal Masyarakat Madani Indonesia Journal Of Artificial Intelligence And Software Engineering The Indonesian Journal of Computer Science Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi
Puspitasari, Novianti
Program Studi Informatika, Fakultas Teknik, Universitas Mulawarman
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Other

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Implementasi Metode Naïve Bayes dan Forward Chaining Untuk Diagnosis Penyakit Gangguan Bipolar Puspitasari, Novianti; Agus, Fahrul; Zali, Wahyu Noor
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 8, No 2 (2023): IJCIT November 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcit.v8i2.17015

Abstract

Bipolar disorder merupakan salah satu gangguan suasana perasaan (mood afektif) yang menunjukan suasana perasaan pasien, dimana tingkat aktivitasnya jelas terganggu. Gangguan ini pada waktu tertentu terdiri dari peninggian maupun penurunan suasana perasaan serta peningkatan maupun pengurangan energi dan aktivitas. Kurangnya pengetahuan masyarakat mengenai pemahaman tentang gangguan kesehatan mental bipolar disorder yang gejalanya hampir sama dengan perubahan mood pada manusia normal, menyebabkan telatnya penanganan terhadap pasien. Penelitian ini bertujuan untuk membuat suatu sistem pakar untuk mendiagnosa penyakit bipolar disorder. Sistem pakar yang dibuat menerapkan metode forward chaining dan naïve bayes dalam mendiagnosa jenis penyakit bipolar disorder. Penelitian ini menggunakan 26 gejala dan tiga jenis penyakit yaitu bipolar disorder episode mania, bipolar disorder episode depresi dan bipolar disorder episode campuran. Penelitian ini menghasilkan sistem pakar yang dapat membantu masyarakat umum dalam mendiagnosa jenis penyakit bipolar disorder yang diderita serta solusi dan penanganannya. Sehingga masyarakat dapat mengetahui apakah terkena penyakit bipolar disorder tersebut atau tidak. Berdasarkan hasil pengujian Black Box diperoleh tingkat akurasi 100% dan sistem pakar yang dibuat dapat berjalan dengan baik. Bipolar disorder is a mood disorder (affective mood) that shows the patient's mood, in which the level of activity is clearly disturbed. These disturbances at any given time consist of either elevated or decreased mood and increased or decreased energy and activity. The lack of public knowledge regarding the understanding of mental health disorder bipolar disorder, whose symptoms are almost the same as changes in mood in normal humans, causes delays in treating patients. This research aims to create an expert system to diagnose bipolar disorder. The expert system created applies forward chaining and naïve Bayes methods in diagnosing bipolar disorder. This study used 26 symptoms and three types of illness, namely bipolar disorder manic episode, bipolar disorder depressive episode, and mixed episode bipolar disorder. This research produces an expert system that can help the general public in diagnosing the type of bipolar disorder they are suffering from as well as solutions and treatment. So that people can find out whether they have bipolar disorder or not. Based on the results of Black Box testing, an accuracy rate of 100% was obtained and the expert system created could run well.
Pendampingan Pengelolaan Bank Sampah di Kelurahan Karang Asam Ulu Kota Samarinda : Optimalisasi Manajemen Keuangan dan Pengolahan Sampah Puspitasari, Novianti; Suryadi, Ahmad; Sandyanegara, Aryasena Bela; Nursari, Ayla; Pusparini, Faradilla; Rezky, Muhammad; Kalista, Nazwa Nur Maulida Qintani; Rahayu, Rizqi Widya; Gunawan, Santika; Wijaya, Zhienka Putri
Jurnal Pengabdian UNDIKMA Vol. 5 No. 2 (2024): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v5i2.5990

Abstract

This service activity aims to optimize the management of financial transactions at waste banks by increasing the knowledge of waste bank officers. Apart from that, this activity also seeks to help the community in handling and processing waste into products that are valuable and beneficial for the environment. The method for implementing this activity uses persuasive mentoring and socialization with stages of preparation, socialization, implementation, and evaluation. This activity was attended by 30 participants consisting of family representatives and women who manage the waste bank in Karang Asam Ulu Village. The evaluation instrument for this service activity is in the form of distributing questionnaires and analyzing them descriptively. The results of this service show that the knowledge of the women managing the Ramli "Sylva Lestari" waste bank regarding financial transaction management has increased and they can record transactions correctly. This activity has also succeeded in getting the community to produce bokashi soap and fertilizer from recycled household waste, so it is hoped that it will improve the community's economy.
COMBINATION OF WP AND TOPSIS METHODS IN A DECISION SUPPORT SYSTEM FOR WATERMELON SEED RECOMMENDATION Tejawati, Andi; Puspitasari, Novianti; Pasorong, Hillary Bella; Masa, Amin Padmo Azam
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2317

Abstract

Watermelon is a horticultural plant that can be cultivated by the wider community with adequate profits. In Indonesia, watermelon production is still unable to meet the huge market demand and has not been able to be met by local watermelon-producing areas. One of the reasons why watermelon production is insufficient is because the fruit is easily damaged due to inappropriate watermelon seeds. The right watermelon seeds can be selected using a Decision Support System. This study uses two combination methods, namely Weighted Product (WP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to be applied in the Decision Support System for watermelon seed recommendations. The WP method is used to determine the weight of the criteria, while TOPSIS is used to determine the order of watermelon seed recommendations. The data used in this study were twenty alternative watermelon seeds with five criteria, namely land recommendations, yield potential, fruit weight, harvest age, and disease resistance. Of the five criteria determined by the WP method, the largest criterion value is in the land recommendation. The results of the implementation with both methods produced recommendations for watermelon seeds, with the first ranking result being the F1 Series (3n) watermelon seeds with a preference value of 0.85442, and black box testing showed that this system was able to provide recommendations for quality watermelon seeds according to their functionality based on the application of the WP and TOPSIS methods.
Case Base Reasoning for Diagnosing the Level of Hyperemesis Gravidarum in Pregnant Women using K-Nearest Neighbor Puspitasari, Novianti; Rahayu, Ervina; Pakpahan, Herman Santoso; Taruk, Medi; Haviluddin, Haviluddin
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.2584

Abstract

Hyperemesis gravidarum is a disease that causes excessive nausea and vomiting in pregnant women. Due to dehydration, this disease can interfere with daily work and get worse. Medical personnel generally recognize hyperemesis gravidarum as one type of disease. In fact, hyperemesis gravidarum is divided into 3 levels, namely grade I or general hyperemesis gravidarum, grade II hyperemesis gravidarum and grade III. This shows that information about hyperemesis gravidarum has yet to be widely known by some medical personnel. If this is left untreated, these two conditions can cause deep vein thrombosis in pregnant women. This study aims to apply the Case-Based Reasoning and K-Nearest Neighbor (KNN) methods to produce accurate information on the diagnosis of hyperemesis gravidarum levels in pregnant women based on symptom management in cases of an old diagnosis. The study used medical record data for hyperemesis gravidarum sufferers in 2018-2019, totalling 228 data. The calculation results of the Case-Based Reasoning method with the K-Nearest Neighbor using the confusion matrix produce an accuracy value of 74%, a precision value of 55% and a recall value of 57%, which indicates that this method is good enough to diagnose levels in patients with hyperemesis gravidarum.
Pembuatan Poster Infografis Data Kependudukan Kelurahan Jahab dengan Microsoft Excel Puspitasari, Novianti; Hemelia, Junita
Inovasi Teknologi Masyarakat (INTEKMAS) Vol. 1 No. 2 (2023): December 2023
Publisher : Wadah Inovasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53622/intekmas.v1i2.230

Abstract

Salah satu permasalahan yang ada di masyarakat kelurahan Jahab, Kecamatan Tenggarong, Kabupaten Kutai Kartanegara adalah kurangnya pemanfaatan teknologi serta kebaruan zaman oleh perangkat kelurahan dalam hal mengetahui informasi data kependudukan. Salah satu upaya yang dapat dilakukan untuk mengatasi permasalahan tersebut adalah dengan membuat sebuah poster infografis tentang data kependudukan menggunakan Canva dan Microsoft Excel. Tujuan diadakannya program poster infografis ini adalah selain dapat memanfaatkan teknologi dan mengasah kreatifitas, perangkat kelurahan dapat menjadi lebih termotivasi untuk menggunakan teknologi sehingga memudahkan pekerjaan. Selain itu program poster infografis ini juga dapat membantu perangkat kelurahan dalam membaca statistik tingkat naik atau turunnya data kependudukan tahun terbaru.
Fuzzy C-Means untuk Klasterisasi Perkiraan Kerugian Bencana Kebakaran Puspitasari, Novianti; Pebianoor, Pebianoor; Rosmasari, Rosmasari; Wati, Masna; Septiarini, Anindita; Mewengkang, Alfrina
SISFOTENIKA Vol 13, No 1 (2023): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v13i1.1384

Abstract

Bencana kebakaran merupakan bencana yang sering terjadi dan mendapatkan perhatian serius dari pemerintah Kota Samarinda. Namun, informasi tentang perkiraan jumlah kerugian yang dialami oleh korban kebakaran masih kurang memadai dan bahkan tidk diketahui. Informasi tentang perkiraan kerugian bencana kebakaran sangat diperlukan oleh pemerintah untuk memberikan penanganan yang tepat sasaran terhadap korban bencana kebakaran. Fuzzy C-Means merupakan metode yang dapat digunakan untuk memberikan informasi tentang perkiraan kerugian bencana kebakaran melalui klusterisasi. Hal ini dikarenakan Fuzzy C-Means mampu mengelompokkan data ke dalam kategori berdasarkan fungsi obyektif yang dihasilkan. Data yang digunakan merupakan data bencana kebakaran di Kota Samarinda sebanyak 306 data. Jumlah perkiraan kerugian bencana kebakaran dikelompokkan ke dalam tiga cluster yaitu sedikit, sedang dan banyak. Dari hasil perhitungan menggunakan Fuzzy C-Means, jumlah perkiraan kerugian bencana kebakaran dengan kategori sedikit (C3) sebanyak 180 data, kategori sedang (C2) sebanyak 83 data dan kategori banyak (C1) sebesar 43 data. Hasil validasi cluster menggunakan Partition Coefficient menunjukkan bahwa tiga cluster adalah cluster yang optimal dengan nilai partisi fuzzy sebesar 0.230. Nilai ini lebih besar dari dua cluster maupun empat cluster sehingga model pembentukan tiga cluster sangat tepat digunakan untuk mengelompokkan perkiraan kerugian bencana kebakaran.okjktroyal88tt789Ladang78Ladang78Jawa88Jawa88Jawa88Royal88ladang78toto slotladang78jejuslotPULSASLOT Platform DEPOSIT PULSA
Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based Anindita Septiarini; Rita Diana; Rahmat Kamara; Novianti Puspitasari; Anton Prafanto
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2676

Abstract

Indonesia has many species of mangrove plants scattered throughout the coast to the river's edge. Species of mangrove plants can be distinguished based on root type, stem size, leaf shape, flower color, and fruit. Although each type of mangrove plant has different characteristics, several types look similar, especially on the leaves. Therefore, a model was needed to classify mangrove plant species by applying current technology to make it easier to recognize the type of mangrove plant. This research aims to implement the Convolutional Neural Network (CNN) method in classifying mangrove plant species. The algorithm used is the 5th version of You Only Look Once (YOLO) with 3 different variants (YOLOv5s, YOLOv5m, and YOLOv5l). The three variants have various processing times and numbers of layers. This study uses mangrove leaf images with a total image dataset of 400 images consisting of 4 types of mangrove plants: Avicennia alba, Bruguiera gymnorhiza, Rhizopora apiculata, and Sonneratia alba. The model performance achieved 82.50%, 88.75%, and 93.75% accuracy using YOLOv5s, YOLOv5m, and YOLOv5l, respectively.
Metode Fuzzy Multiple Attribute Decision Making (FMADM) dengan Weighted Product (WP) dalam Menentukan Varietas Bawang Merah Rantetana, Stevie Falentino; Puspitasari, Novianti; Tejawati, Andi
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 3 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i3.21948

Abstract

Bawang merah atau Allium cepa.L dalam bahasa latin merupakan tanaman rempah yang menjadi salah satu komoditas pertanian di Indonesia. Rempah ini banyak digunakan sebagai bahan masakan yang menyebabkan kebutuhan bawang merah di masyarakat sangat besar. Salah satu cara untuk memenuhi kebutuhan bawang merah dapat dilakukan dengan membudidayakan bawang merah secara mandiri. Namun, banyaknya varietas bawang merah menjadikan masyarakat bingung untuk memilih varietas yang sesuai. Penelitian ini mengembangkan metode Fuzzy Multiple Attribute Decision Making (FMADM) dengan pendekatan Weighted Product (WP) untuk membantu dalam proses pengambilan keputusan pemilihan varietas bawang merah yang paling sesuai berdasarkan beberapa kriteria. Kriteria yang digunakan meliputi susut bobot, umur panen, daya simpan, jumlah umbi, dan potensi hasil. Hasil penerapan metode FMADM WP menunjukkan bahwa dari sebelas varietas yang ada, varietas TSS Agrihort 1 sebagai varietas terbaik dengan nilai preferensi  0,1556. Dari hasil tersebut terlihat bahwa metode ini dapat menjadi alat bantu yang efektif dalam mendukung pengambilan keputusan varietas bawang merah yang optimal.
Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet Simanungkalit, Julius Rinaldi; Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Puspitasari, Novianti; Wati, Masna
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.521.32-38

Abstract

Rubber plantation sector is one of the leading commodities in East Kalimantan Province contributing greatly to non-oil and gas exports. Currently, the price of rubber in the world is increasingly competitive. The aim of this research is to predict the rubber prices as a reference for the government and companies in making policies and preparing work plans. Data of 60 months during the period of 2014-2018 taken from Plantation office of East Kalimantan Province has been analyzed using Backpropagation Neural Network (BPNN) algorithm in predicting rubber prices. Based on the testing results, parameters of the BPNN algorithm with ratio of 4: 1, architectural models 5-10-10-10-1, trainlm learning function, learning rate of 0.5, error tolerance of 0.01, and epoch of 1000 have gained good accuracy with a mean square error (MSE) of 0.00015464. The results showed that the BPNN algorithm can be used as an alternative method in forecasting.
Pengelompokan Provinsi Penghasil Padi Di Indonesia Menggunakan Hierarchical Agglomerative Clustering Ali, Vigo Santri; Septiariani, Anindita; Puspitasari, Novianti
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 1 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i1.26235

Abstract

Produksi padi merupakan salah satu indikator penting dalam ketahanan pangan di Indonesia dan memiliki karakteristik yang berbeda antar provinsi. Perbedaan tersebut perlu dianalisis untuk memperoleh pola pengelompokan provinsi yang memiliki karakteristik serupa. Penelitian ini bertujuan untuk mengelompokkan provinsi di Indonesia berdasarkan luas panen, produksi, dan produktivitas padi menggunakan metode Hierarchical Agglomerative Clustering (HAC) dengan pendekatan Average Linkage. Data yang digunakan merupakan data sekunder yang diperoleh dari instansi resmi dan dianalisis menggunakan beberapa ukuran jarak, yaitu Manhattan, Euclidean, dan Minkowski. Untuk menentukan kualitas dan jumlah cluster optimal digunakan metode Silhouette Coefficient. Hasil penelitian menunjukkan bahwa pengelompokan terbaik diperoleh pada tiga cluster, di mana setiap cluster memiliki karakteristik produksi padi yang berbeda. Cluster yang terbentuk mampu menggambarkan perbedaan tingkat produksi dan produktivitas padi antar provinsi secara jelas. Hasil pengelompokan ini diharapkan dapat menjadi bahan pertimbangan bagi pengambil kebijakan dalam perencanaan dan evaluasi sektor pertanian, khususnya komoditas padi.
Co-Authors Abu Bakar Adelowys Sinaga Adhistya Erna Permanasari Adnan, Fahrizal Afifah, Dinda Nur AHMAD ANSYORI Ahmad Suryadi, Ahmad Ahmad Wahbi Fadillah Ajay, Muhammad Aji Ayu Muvita Putri Alameka, Faza Alameka, Faza Alfajriani Alfajriani Alfredo Sinaga Ali Sholihin Ali, Vigo Santri Alif Rifa’i Almasari Aksenta Alvito Gabbriel Saputra Ambari, Nasser Andre Ardin Maulana Anindita Septiarini, Anindita Anton Prafanto Arinda Mulawardani Kustiawan Asdar Zulkiawan Awang Harsa Kridalaksana Brins Leonard Pailan Budiman, Edy Didit Suprihanto, Didit Ery Burhandenny, Aji Fahrul Agus Fairil Anwar Fajar Fatimah Farisha Rizky Amalia Fathia Nuq Qamarina Fauzan, Ahmad Nur Fayza Virdana Addiza Faza Alameka Faza Alameka Fazma Urmila Jannah Helmi Puadi Firdaus, Muhammad Firdaus, Muhammad Bambang Fornia, Daviana Dwitasari Enka Frans Karta Sayoga Sitohang Fuad, Natalie Gerda, Misselina Madya Gubtha Mahendra Putra Gunawan, Ayu Lestari Gunawan, Santika Haeruddin, Haeruddin Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hanung Adi Nugroho Hasnida, Rima Yustika Hatta, Heliza Rahmania Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Hemelia, Junita Henderi . Heni Sulastri Herman Santoso Pakpahan Hidayat, Ahmad Nur Hijratul Aini Iin Nurkarima Indah Wulan Lestari Irfan, Aliya Islamiyah Islamiyah Joan Angelina Widians, Joan Angelina Kalista, Nazwa Nur Maulida Qintani Kamila, Vina Zahrotun Kurniati, Wendy Kurniawan, Tri Basuki Lalu Delsi Samsumar, M.Eng. Laraswati, Sherina Latifa Gorriana Gusmaningrum Lempas, Gidion M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Maya Agustina Mega Yoalifa Mewengkang, Alfrina Muhammad Abdillah Muhammad Dzacky Muhammad Firdaus Mulia, Amalia Budiana Nataniel Dengen Noval Bayu Setiawan Nur Hasanah Nurhidayat, Rifki Nurkarima, Iin Nursari, Ayla Nurul Kusuma Dewi, Nurul Kusuma Olivia Octavia Paripurna, Rian Bintang Pasorong, Hillary Bella Patricia Chandra Pebianoor, Pebianoor Prafanto, Anton Pramudya, Pranata Eka Pratama, Fhanji Wilis Pusparini, Faradilla Putra, Gubtha Mahendra Rahayu, Ervina Rahayu, Rizqi Widya Rahmat Kamara Raihanfitri Adi Kalipaksi Ramadhaniaty, Dinda Rantetana, Stevie Falentino Raudhya Azzahra Rayner Alfred Rayner Alfred Reza Nur Muhammad Rezky, Muhammad Rima Yustika Hasnida Rita Diana Rizky Pratama Putra Rondongalo Rismawati Rosita, Aliffia Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari, Rosmasari Rosmasari, Rosmasari Saipul, Saipul Sandyanegara, Aryasena Bela Sarira, Brayen Tisra Septiani, Reni D. Septiariani, Anindita Septirini, Anindita Setyadi, Hario Jati Sihombing, Yobel Fernanda Simanungkalit, Julius Rinaldi Stefanie Stefanie Sugandi Sugandi Sulastri, Heni Sumaini Sumaini Surya Eka Priyatna Suryani Junita Patandianan Syachmiral, Zidane Althaariq Syarah, May Siti Taruk, Medi Tejawati, Andi Tjikoa, Ade Fiqri Ummul Hairah Vebi C.G, Sindi Vicky Pranandika Wijaksana Wahyudi, Moh Ikhwan Waksito, Alan Zulfikar Wati, Masna Wibisono, Bramantyo Ardi Harimurti Widians, Joan Angelina Wijaya, Zhienka Putri Willyardo Tampubolon Wintin, Chintia Liu Yasmin, Annisa Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin Zali, Wahyu Noor