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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 Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics MUSTEK ANIM HA Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Wikrama Parahita : Jurnal Pengabdian Masyarakat IT JOURNAL RESEARCH AND DEVELOPMENT JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi MIND (Multimedia Artificial Intelligent Networking Database) Journal KOMPUTIKA - Jurnal Sistem Komputer TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Elektronik Scientific Journal of Informatics Journal of Innovation Information Technology and Application (JINITA) Indonesian Journal of Data and Science Infotek : Jurnal Informatika dan Teknologi Jurnal Teknologi Informatika dan Komputer SKANIKA: Sistem Komputer dan Teknik Informatika Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Sains Teknologi dan Sistem Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Transformasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer JOMPA ABDI: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Data Sciences Indonesia (DSI) Jurnal Masyarakat Madani Indonesia Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Journal of Computer Science Contributions (Jucosco) Journal of Computer Science and Information Technology Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi International Journal of Applied Mathematics and Computing.
Anindita Septiarini, Anindita
Mulawarman University
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Pelatihan Mikrokontroler Arduino dengan Simulasi Berbasis WebsiteWokwi pada Siswa SMAN 6 Samarinda Saragih, Muhammad Nabil; Sadewa, Bintang Putra; Setiawan, Maulana Agus; Nupa, Joy Disanto; Hutagalung, Wilson Boyaron; Wati, Masna; Septiarini, Anindita
Inovasi Teknologi Masyarakat (INTEKMAS) Vol. 2 No. 2 (2024): December 2024
Publisher : Wadah Inovasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53622/intekmas.v2i2.265

Abstract

Perkembangan teknologi yang pesat pada era Revolusi Industri Keempat membawa banyak teknologi baru seperti Internet of Things (IoT) yang mulai diadopsi oleh berbagai sektor. Namun, implementasi teknologi ini di masyarakat sering kali menghadapi berbagai rintangan, terutama dari keterbatasan industri dan penelitian. Oleh karena itu, pengenalan teknologi sedini mungkin menjadi penting. Program pengabdian kepada masyarakat ini bertujuan untuk mengenalkan dan melatih penggunaan mikrokontroler Arduino dengan simulasi berbasis website Wokwi kepada siswa SMA Negeri 6 Samarinda. Mikrokontroler adalah komponen vital dalam IoT, dengan contoh yang umum digunakan adalah Arduino, sebuah hardware open-source yang mudah diprogram. Metode yang digunakan meliputi pelatihan langsung dan simulasi untuk meningkatkan pemahaman teknikal siswa terhadap mikrokontroler. Berdasarkan kuesioner yang diberikan pada akhir pelatihan, Hasil pelaksanaan program menunjukkan rata-rata sebesar 82,58% dalam pemahaman dan keterampilan siswa dalam menggunakan mikrokontroler Arduino.­ Serta indeks kepuasan maksimum sebesar 60% terhadap keseluruhan isi pelatihan.
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
IMPLEMENTASI LOGIKA FUZZY MAMDANI DALAM SISTEM PENILAIAN KESEHATAN MAKANAN KEMASAN BERDASARKAN LABEL NUTRITION FACTS Ahmad Nur Fauzan; Muhammad Abdillah; Reviansa Fakhruddin Aththar; Anindita Septiarini; Masna Wati
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The growth of the packaged food industry has increased the need for an easy-to-understand health assessment system for consumers, especially those with limited nutrition literacy. This study develops a Mamdani fuzzy logic-based decision support system to evaluate the healthiness of packaged foods using Nutrition Facts labels. The system processes nutritional parameters such as fat, sugar, salt, fiber, protein, fruit/vegetable/nut content, and calorie content, converting them into linguistic categories like "low," "moderate," and "high" for easier interpretation by lay users. It effectively handles uncertainties and ambiguities in nutrition data, providing classifications like "Unhealthy," "Healthy," or "Very Healthy." Implemented through a web platform using Python and Flask, the system was tested with five food samples, achieving an 80% agreement with the official NutriScore classification. This indicates the potential of the system as a reliable, practical tool to help consumers make quicker and more accurate dietary decisions and improve nutrition awareness.
KOMBINASI METODE SAW DAN TOPSIS UNTUK MENDUKUNG KEPUTUSAN KESESUAIAN LAHAN PADI Syaffira Rizky Amalia; Hamdani Hamdani; Anindita Septiarini
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 12 No. 3 (2025): Prosisko Vol. 12 No. 3 November 2025
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v12i3.10142

Abstract

Tanaman padi (Oryza Sativa L.) merupakan komoditas tanaman pangan utama di Indonesia, karena sebagian besar penduduk Indonesia makanan pokoknya adalah beras. Salah satu penyebab rendahnya produksi padi di Indonesia, karena umumnya petani masih membudidayakan padi secara tidak tepat, seperti pengolahan tanah atau pemilihan lahan. Kesesuaian lahan dalam pertanian tanaman sangat berpengaruh terhadap produktivitas tanaman. Proses yang dapat dilakukan untuk mendukung keputusan kesesuaian lahan padi adalah membangun sebuahwebsite Sistem Pendukung Keputusan (SPK) dengan menggunakan kombinasi metode Simple Additive Weighting (SAW) dan Technique for Order Performance of Similarity to Ideal Solution (TOPSIS). Kombinasi ini dilakukan dengan cara mengambil rata-rata (µ) dari hasil akhir metode SAW dan TOPSIS. Skor akhir dari masing-masing metode dihitung terpisah, lalu dilakukan rata-rata (µ) dari kedua hasil tersebut untuk mendapatkan peringkat akhir alternatif. Data yang digunakan dalam menentukan kesesuaian lahan padi menggunakan data sebanyak 5 kriteria, yaitu jenis tanah, pH tanah, curah hujan, suhu, irigasi dan perairan. Data alternatif yang digunakan dalam penelitian ada 6 alternatif, yaitu Sungai Kunjang, Sambutan, Samarinda Utara, Palaran, Loa Janan Ilir dan Samarinda Seberang. Tujuan dari penelitian ini adalah untuk membantu memberikan informasi solusi alternatif kepada petani atau kelompok tani dalam menentukan kesesuaian lahan padi. Hasil kombinasi dari metode SAW dan TOPSIS menunjukkan bahwa alternatif dengan nilai akhir tertinggi adalah Samarinda Utara (A3), dengan nilai akhir sebesar 0,7337. Sedangkan, alternatif dengan nilai akhir terendah adalah Sambutan (A2), dengan nilai akhir sebesar 0,4402.
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.
Klasifikasi Citra Emosi Wajah Menggunakan Convolutional Neural Network Untuk Penderita Depresi Hariyanto; Novianti Puspitasari; Anindita Septiarini
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Facial analysis is widely used as information to determine a person's psychological condition, such as depression. Someone suffering from depression tends to have a face that looks sad, empty, or unhappy. The appearance of a depressed person's face is almost similar to that of someone experiencing sadness. However, facial appearance is not always perceived as depressed, so facial emotion recognition is needed for depression treatment. A Convolutional Neural Network (CNN) is often used in image processing to identify key features and patterns in images, particularly for facial emotion recognition. CNN can be used to learn the relationship between facial shape and related emotions. This study employs the CNN method to classify facial emotions from facial expression images collected from a dataset of 30,724 images. The training process uses seven classes: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The accuracy results obtained a value of 67% with a training dataset of 21,507 images, a validation dataset of 6,143 images, and a testing dataset of 3,080 images.
Identification of Housing Eligibility Status Using Family Data in Samarinda City Antonieta Aryuka Paskalia Nggotu; Hamdani, Hamdani; Anindita Septiarini
International Journal of Applied Mathematics and Computing Vol. 3 No. 2 (2026): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i2.294

Abstract

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.
Combination Of SAW And TOPSIS Methods for Support Decisions on Rice Land Suitability Amalia, Syaffira Rizky; Hamdani, Hamdani; Septiarini, Anindita
International Journal of Applied Mathematics and Computing Vol. 3 No. 2 (2026): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i2.310

Abstract

Rice plants (Oryza Sativa L.) are the main staple food commodity in Indonesia, as most of the Indonesian population relies on rice as their primary food. One of the causes of low rice production in Indonesia is that farmers generally cultivate rice improperly, such as in land preparation or land selection. Land suitability in rice cultivation greatly affects crop productivity. A process that can support decisions regarding rice land suitability is the development of a Decision Support System (DSS) website using a combination of the Simple Additive Weighting (SAW) method and the Technique for Order Performance of Similarity to Ideal Solution (TOPSIS). This combination is performed by taking the average (µ) of the final results from the SAW and TOPSIS methods. The final scores of each method are calculated separately, and then the average (µ) of these two results is taken to obtain the final ranking of the alternatives. The data used to determine the suitability of rice land is based on five criteria: soil type, soil pH, rainfall, temperature, irrigation and water supply. The alternative data used in the study includes six alternatives: Sungai Kunjang, Sambutan, Samarinda Utara, Palaran, Loa Janan Ilir, and Samarinda Seberang. The aim of this research is to provide information on alternative solutions to farmers or farmer groups in determining rice land suitability. The results of the combination of the SAW and TOPSIS methods show that the alternative with the highest final score is Samarinda Utara (A3), with a final score of 0.7337. Meanwhile, the alternative with the lowest final score is Sambutan (A2), with a final score of 0.4402.
Multiclass SVM with Kernel Optimization for Schizophrenia Subtype Classification Using Clinical Symptom Records Rohman, Reisa Maulidya; Septiarini, Anindita; Tejawati, Andi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15926

Abstract

Schizophrenia is a mental disorder that affects about 0.3% of the world population. It is characterized by a wide range of symptoms that form several subtypes. Overlapping symptoms and subjective clinical assessments may reduce consistency and make subtype classification challenging. Machine learning algorithms that use patients’ medical records offer a potentially objective approach for subtype classification. This study aims to classify four schizophrenia subtypes: paranoid, catatonic, undifferentiated, and residual, based on subtype labels recorded in the hospital using a multiclass SVM approach with kernel optimization. The dataset consists of 218 medical records of schizophrenia patients with 25 binary symptom variables used as input features. SVM was trained using two multiclass approaches, namely OAO and OAA. Evaluation was performed using five-fold stratified cross-validation. Performance was calculated using accuracy, macro-precision, macro-recall, and macro F1-score. Optimal performance was achieved using the OAA approach with an RBF kernel at C = 10 and gamma = 0.1. This configuration achieved an accuracy, macro-precision, macro-recall, and macro F1-score of 0.89, 0.90, 0.86, and 0.87, respectively. These results show that the multiclass approach, kernel functions, and parameter configuration influence classification performance. The proposed model may serve as a screening or decision-support tool to assist subtype identification based on clinical symptom records.  
Automatic identification of herbal medicines using deep learning on leaf images Anita Ahmad Kasim; Lukman Nadjamudiin; Muhammad Bakri; Chairunnisa Ardiansyah Lamasitudju; Harry Tanni Pagiu; Puguh Budi Prakoso; Anindita Septiarini; Bima Prihasto
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.2024

Abstract

Indonesia has a high diversity of medicinal plants that are widely used in traditional healthcare practices. Identification of medicinal plants is commonly based on leaf morphology; however, similarities in leaf shape, texture, and color often cause misidentification, particularly among non-experts. This limitation highlights the need for an automated and reliable identification approach. The primary objective of this study is to develop and evaluate a deep learning–based system for the automatic identification of medicinal plants using leaf images, with a specific focus on comparing the performance and efficiency of MobileNetV2 and ResNet50V2 architectures. The research design adopts an experimental approach using an internally collected dataset of medicinal plant leaf images representing multiple plant classes. The dataset is divided into training and testing sets to evaluate model generalization. The methodology involves image preprocessing steps, including resizing, normalization, and data augmentation, followed by the application of transfer learning using MobileNetV2 and ResNet50V2 as feature extractors. Both models are trained under the same experimental settings and evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The main outcomes and results indicate that both deep learning models achieve high classification performance. MobileNetV2 achieves an accuracy of 98.77%, precision of 98.84%, recall of 98.77%, and F1-score of 98.77%, while ResNet50V2 achieves an accuracy of 97.53%, precision of 97.87%, recall of 97.53%, and F1-score of 97.58%. The results demonstrate that MobileNetV2 provides slightly superior performance with lower computational complexity. In conclusion, lightweight deep learning architectures such as MobileNetV2 are effective and efficient for medicinal plant leaf identification and are suitable for implementation in mobile or resource-constrained environments.
Co-Authors Abdul Razak Aliudin Achmad Solichan Adi Muhammad Syifai Adnan, Fahrizal Afifah, Dinda Nur Agus Qomaruddin Munir AHMAD ANSYORI Ahmad Nur Fauzan Ajay, Muhammad Akhmad Masyudi Akhmad Syaifudin, Encik Alameka, Faza Aldi Daffa Arisyi Alif Rifa’i Alvito Gabbriel Saputra Alyani Noor Septalia Amalia, Syaffira Rizky Ambari, Nasser Ambon, Matelda Yunanta Andi Tejawati Andri Syafrianto Anita Ahmad Kasim Annisa Putri Novalianti Anton Prafanto Antonieta Aryuka Paskalia Nggotu Ardi Setyiawan Arif Hidayat Arindra Nurshadrina Ramadini Arini Wijayanti Asmita, Rizka Aulia Rahman Awang Harsa Kridalaksana Awang Zheri Rhesvianur Az Zahrah, Rezha Nur Bandhaso, Victor Bima Prihasto Briyan Efflin Syahputra Budi Rahmani Budiman, Edy Cakra Dewandaru Chairunnisa Ardiansyah Lamasitudju Christy Maulidiah Daffa Putra Mahardika Didit Suprihanto, Didit Dwi Prasetio Dyna Marisa Khairina Edy Winarno Enny Itje Sela Ery Burhandenny, Aji Ery Burhandeny, Aji Evi Wildana Fahrozi, Muhammad Naufal Fairil Anwar Fajri, Muhamad Mushfa Hikmatal Fandi Alief Al Akbar Farida Djumiati Sitania Fathia Nuq Qamarina Fauzan, Ahmad Nur Fayza Virdana Addiza Firyal, Tasya Nadina Fornia, Daviana Dwitasari Enka Fuad, Natalie Gempar Panggih Dwi Gideon Simalango, Yanuar Gunawan, Ayu Lestari Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hariyanto Harry Tanni Pagiu Hatta, Heliza Rahmania Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Henderi . Heni Sulastri Herlawati Herlawati Heru Ismanto Hidayat, Ahmad Nur Hutagalung, Wilson Boyaron Hutapea, Vedra Dian Sierrafina Ibnu Amri Thaher Ifnu Umar Indah Fitri Astuti Indah Wulan Lestari Irfan, Aliya Irsyad, Akhmad Kalingga Dwindra Putraka Kamila, Vina Zahrotun Kiki Purwanti Laraswati, Sherina Lempas, Gidion Lili, Juniver Veronika Lukman Nadjamuddin M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Maya Agustina Mewengkang, Alfrina Muhamad Azhari Muhammad Abdillah Muhammad Abdillah Muhammad Aidil Saputra Muhammad Andas Lesmana Muhammad Bakri Muhammad Dzacky Muhammad Ifandi Muhammad Nur Ramadhan Muhammad Rafif Hanif Muhammad Sofian Sauri Mu’nisah Assisi Najwa Felira Zetti Nanda Arianto Nathaniela Aptanta Parama Nggotu, Antonieta Aryuka Paskalia Novi Puspitasari Novianti Puspitasari Nupa, Joy Disanto Nur Madia Nurcahyono, Damar Nurhidayat, Rifki Nurmadewi, Dita Olivia Octavia Padmo Azam Masa, Amin Patricia Chandra Pebianoor, Pebianoor Prafanto, Anton Pramudya, Pranata Eka Pratiwi, Sinthya Ayu Puguh Budi Prakoso Puspitasari, Novianti Puspitasari, Novitanti Putra Ramdani, Aditya Putri, Septi Aulia Rafi Ichsanul Iqbal Rahmadya Trias Handayanto Rahmat Kamara Raihanfitri Adi Kalipaksi Rajiansyah, Rajiansyah Rakhmat Purnomo Ramadhaniaty, Dinda Raudhya Azzahra Reski Harisma Dewi Barkah Reviansa Fakhruddin Aththar Ricky Anggari Risky Kurniawan Riswandi Syam Rita Diana Riyayatsyah, Riyayatsyah Rizqi Saputra Rohman, Reisa Maulidya Rondongalo Rismawati Rosmasari, Rosmasari Sadewa, Bintang Putra Saipul, Saipul Sakti, Dwi Nika Salsabila, Nur Maya Saragih, Muhammad Nabil Sarira, Brayen Tisra Satria Bagus Eka Chandra Saucha Diwandari Setiawan, Maulana Agus Sihombing, Yobel Fernanda Siti Retno Wulandari Sophy Awaliah Sugandi Sugandi Sumaini Sumaini Supriyono Supriyono Supriyono Supriyono Surya Eka Priyatna Syaffira Rizky Amalia Syifani, Sarah Taruk, Medi Tejawati, Andi Theresia Amelia Pawitra Tulili, Hadie Pratama Ummul Hairah Ummul Hairah Vicky Pranandika Wijaksana Viny Christanti M Wahyudi, Moh Ikhwan Wati, Masna Wibisono, Bramantyo Ardi Harimurti Widians, Joan Angelina Wintin, Chintia Liu Wiwien Hadikurniawati Yanuar Satria Gotama Yasmin, Annisa Yudi Sukmono Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin Zulfariansyah, Muhammad