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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics CESS (Journal of Computer Engineering, System and Science) Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) International Journal of Computing and Informatics (IJCANDI) JURNAL REKAYASA TEKNOLOGI INFORMASI ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation International Journal of Advanced Science and Computer Applications Adopsi Teknologi dan Sistem Informasi Information Technology Education Journal Bulletin of Social Informatics Theory and Application Periodicals of Occupational Safety and Health Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi The Indonesian Journal of Computer Science
Haviluddin Haviluddin
(SCOPUS ID: 56596793000, Universitas Mulawarman)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Lestari, Widya; Saputra, Irzan Tri; Izdihar, Zahra Nabila; Pujianto, Utomo; Haviluddin, Haviluddin; Nafalski, Andrew
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.97

Abstract

Sessions or unique visitors is the number of visitors from one IP who accessed a journal portal for the first time in a certain period of time. The large number of unique daily average subscriber visits to electronic journal pages indicates that this scientific periodical is in high demand. Hence, the number of unique visitors is an important indicator of the accomplishment of an electronic journal as a measure of the dissemination in accelerating the journal accreditation system. Numerous methods can be used for forecasting, one of which is the backpropagation neural network (BPNN). Data quality is very important in building a good BPNN model, because the success of modeling at BPNN is very dependent on input data. One way that can be carried out to improve data quality is by smoothing the data. In this study, the forecasting method for predicting time series data for unique visitors to electronic journals employed three models, respectively BPNN, BPNN with mean smoothing, and BPNN with median smoothing. Based on the findings, the results of the smallest error were obtained by the BPNN model with a mean smoothing with MSE 0.00129 and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture which can be used as a forecast for unique visitors of electronic journals.
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Work accident reporting in coal mining, Indonesia: A systematic literature review Sultan, Muhammad; Setyadi, Djoko; Ramdan, Iwan Muhamad; Haviluddin, Haviluddin; Hidayati, Tetra
Periodicals of Occupational Safety and Health Vol. 2 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/posh.v2i1.7761

Abstract

Background: Work accidents and work-related diseases are still considered as nightmares in Indonesia, especially in the coal mining sector. Badan Penyelenggara Jaminan Sosial (BPJS) Ketenagakerjaan (Social Security Agency for Employment) reported that there were as many as 234,370 cases of work accidents and work-related diseases in 2021, where the mining sector contributed as many as 6,565 cases. This study aims to present the analysis and synthesis of various research to provide solution recommendations in the management of accident reporting which are suitable to the characteristics of coal mining in Indonesia. Method: This study is a Systematic literature review of a number of studies sourced from Elsevier, Science Direct, Google Scholar, Pubmed, Proquest, DOAJ, Perpusnas RI, Garuda, and other sources. Results: Based on the literature analysis, it is found out that reporting management based on digitalization either in the form of website portal or application is a solution to optimize the effort to control work accidents and work-related disease in coal mining. Conclusion: This reporting system can be applied in coal mining in Indonesia.
Ensemble semi-supervised learning in facial expression recognition Purnawansyah, Purnawansyah; Adnan, Adam; Darwis, Herdianti; Wibawa, Aji Prasetya; Widyaningtyas, Triyanna; Haviluddin, Haviluddin
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1880

Abstract

Facial Expression Recognition (FER) plays a crucial role in human-computer interaction, yet improving its accuracy remains a significant challenge. This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. The research implements data augmentation and feature extraction techniques, utilizing advanced architectures such as VGG19, ResNet50, and InceptionV3 to improve the quality and representation of facial expression data. Evaluations were conducted across three dataset scenarios: original, feature-extracted, and augmented, using various label-to-unlabeled ratios. The results indicate that the ensemble model achieved a notable accuracy improvement of 87% on the augmented dataset compared to individual classifiers and other ensemble methods, demonstrating superior performance in handling occlusions and diverse data conditions. However, several limitations exist. The study’s reliance on the JAFFE dataset may restrict its generalizability, as it may not cover the full range of facial expressions encountered in real-world scenarios. Additionally, the effect of label-to-unlabeled ratios on the model's performance requires further exploration. Computational efficiency and training time were also not evaluated, which are critical considerations for practical implementation. For future research, it is recommended to employ cross-validation methods for more robust performance evaluation, explore additional data augmentation techniques, optimize ensemble configurations, and address the computational efficiency of the model to better advance FER technologies.
Penerapan Algoritma K-Means untuk Pengelompokan Negara di Dunia Berdasarkan Indikator Ekonomi Anggari, Ricky; Ifandi, Muhammad; Firdaus, Ardhifa; Wati, Masna; Haviluddin, Haviluddin
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 8, No 2 (2024): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v8i2.19745

Abstract

Perkembangan ekonomi global menuntut pemahaman mendalam tentang karakteristik ekonomi negara-negara di dunia. Penelitian ini bertujuan menerapkan algoritma K-Means Clustering untuk mengelompokkan negara berdasarkan indikator ekonomi seperti Gross Domestic Product (GDP), ekspor, impor, inflasi, dan tingkat pengangguran. Metode clustering digunakan untuk mengidentifikasi pola dan struktur ekonomi negara-negara dengan menganalisis data sekunder dari World Bank tahun 2022. Proses preprocessing meliputi pembersihan data, normalisasi menggunakan Min-Max Scaling, dan seleksi variabel ekonomi kunci. Algoritma K-Means diterapkan dengan jumlah klaster optimal sebanyak 3, yang diperoleh melalui metode Elbow. Hasil clustering menunjukkan tiga kelompok negara: negara dengan ekonomi kecil, negara berkembang, dan negara dengan ekonomi raksasa. Klaster 0 terdiri dari 52 negara yang cenderung memiliki ekonomi kecil, klaster 1 mencakup 165 negara berkembang dengan karakteristik ekonomi menengah, sedangkan klaster 2 hanya terdiri dari 2 negara yang memiliki ekonomi sangat besar. Evaluasi menggunakan Silhouette Score (0,52), Davies-Bouldin Index (0,71), dan Calinski-Harabasz Index (145,73) mengindikasikan kualitas clustering yang baik. Penelitian ini memberikan wawasan tentang klasifikasi negara berdasarkan indikator ekonomi dan dapat menjadi referensi bagi pembuat kebijakan dalam merancang strategi ekonomi yang lebih efektif.
Desiminasi Merawat Uang Rupiah & Pengenalan Profil Pahlawan di Uang Kertas Emisi 2022 Menggunakan Augmented Reality Muhammad Bambang; Haviluddin; Arifin, Zainal; Ibrahim, M. Rivani; Yahya, Fiqri Khaidar; Allo, Adriati Manuk
Pengabdian kepada Masyarakat Bidang Teknologi dan Sistem Informasi (PETISI) Vol. 2 No. 2 (2024): Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/petisi.v2i2.1865

Abstract

Kegiatan pengabdian masyarakat dengan judul "Diseminasi Merawat Uang Rupiah & Pengenalan Profil Pahlawan di Uang Kertas Emisi 2022 Menggunakan Augmented Reality" bertujuan untuk meningkatkan kesadaran dan pengetahuan masyarakat mengenai pentingnya merawat uang rupiah serta mengenal pahlawan nasional yang diabadikan pada uang kertas emisi terbaru. Teknologi Augmented Reality (AR) dipilih sebagai media inovatif untuk mencapai tujuan tersebut, karena kemampuannya dalam menyajikan informasi secara interaktif dan menarik. Program ini dirancang untuk berbagai kelompok masyarakat, termasuk pelajar, mahasiswa, dan masyarakat umum. Kegiatan utama meliputi seminar edukatif tentang cara merawat uang kertas agar tahan lama, serta sesi interaktif menggunakan aplikasi AR yang menampilkan profil pahlawan secara visual dan informatif ketika kamera ponsel diarahkan pada uang kertas. Diharapkan, melalui pendekatan ini, masyarakat tidak hanya mendapatkan pengetahuan yang mendalam tentang sejarah dan jasa pahlawan nasional, tetapi juga termotivasi untuk merawat uang rupiah dengan lebih baik. Hasil dari kegiatan ini diharapkan dapat meningkatkan literasi finansial dan sejarah, serta menciptakan rasa bangga dan penghargaan terhadap simbol-simbol nasional yang terdapat pada uang kertas. Kegiatan ini juga diharapkan menjadi model bagi program edukasi lainnya di masa depan.
Pengelompokan Harga Cabai Rawit Berdasarkan Provinsi Menggunakan Principal Component Analysis dan K-Means Kesuma, Muhammad Afrizal; Nugraha, Cellia Auzia; Cahyani, Oktari Indi; Wati, Masna; Haviluddin, Haviluddin
JUKI : Jurnal Komputer dan Informatika Vol. 7 No. 1 (2025): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2025
Publisher : Yayasan Kita Menulis

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

Abstract

Cabai rawit (Capsicum frutescens L.) merupakan komoditas penting di Indonesia dengan permintaan yang tinggi. Namun, harga cabai rawit sering mengalami fluktuasi yang signifikan akibat ketergantungan pada musim, cuaca, serta kendala distribusi. Penelitian ini bertujuan untuk mengelompokkan harga cabai rawit berdasarkan provinsi di Indonesia menggunakan algoritma Principal Component Analysis (PCA) dan K-Means. Data yang digunakan berupa harga cabai rawit dari 34 provinsi di Indonesia pada periode Januari 2018 hingga Desember 2024. Analisis pengelompokan dilakukan dengan 3 variasi jumlah klaster, yaitu 2, 3, dan 4 klaster. Pengujian akurasi klaster menggunakan metode Silhouette Coefficient menunjukkan bahwa jumlah klaster paling optimal adalah 4 dengan nilai sebesar 0,511. Hasil penelitian ini menunjukkan bahwa pengelompokan harga cabai rawit dengan metode PCA dan K-Means dapat membantu dalam memahami pola harga di berbagai provinsi. Selain itu, hasil pengelompokan ini diharapkan dapat menjadi dasar bagi perencanaan distribusi dan pengendalian harga yang lebih efektif.
Pengelompokan Minat Akademik Siswa SMA Negeri 1 Loa Janan Menggunakan Metode Clustering K-means Fauzan, Ammar Nabil; Wandi, Faizul Anwar; Aiman, Ahmad Zuhair Nur; Wati, Masna; Haviluddin, Haviluddin
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 2 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i2.19673

Abstract

Penentuan minat akademik masih menjadi tantangan dalam proses mempersiapkan diri sebelum memilih jurusan di perguruan tinggi, terutama jika siswa sendiri masih belum sepenuhnya mengetahui kemampuan dan minat belajarnya. Penelitian ini dilakukan dengan tujuan untuk membentuk kelompok-kelompok siswa kelas XI 3 di SMA Negeri 1 Loa Janan berdasarkan minat akademik mereka dengan menggunakan pendekatan data mining. K-means merupakan algoritma yang dipilih dari metode clustering, dengan menggunakan Knowledge Discovery in Database (KDD) yang dimulai dari seleksi data, kemudian tahap preprocessing data melalui normalisasi. Evaluasi menggunakan Silhouette Score dan Davies Bouldin Index. Hasil menunjukkan bahwa 2 merupakan jumlah cluster yang tepat dengan nilai Silhouette Score 0.74, nilai Davies Bouldin Index sebesar 0.34 dan visualisasi Scatter Plot yang menunjukkan pemisahan cluster yang cukup jelas. Hasil clustering ini bisa menjadi referensi bagi tenaga pengajar seperti guru untuk memudahkan proses penentuan jurusan sebelum masuk perguruan tinggi.
Klasterisasi Wilayah Penghasil Tanaman Lada Menggunakan Algoritma K-Means Puspitasari, Novianti; Haviluddin, Haviluddin; Helmi Puadi, Fazma Urmila Jannah
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3104

Abstract

Wilayah potensial untuk menanam lada semakin berkurang, sehingga jumlah produksi lada menjadi semakin menurun. Hal ini tentunya perlu menjadi perhatian mengingat lada merupakan salah satu komoditas unggulan yang sangat penting untuk menunjang perekonomian. Informasi tentang daerah yang berpotensi sebagai daerah penghasil tanaman lada perlu dilakukan. Penelitian ini bertujuan untuk mendata dan menganalisa wilayah potensial untuk tanaman lada menggunakan pendekatan algoritma cerdas yaitu K-Means. Data penelitian berasal dari Dinas Perkebunan Provinsi Kalimantan Timur sebanyak 1200 data dalam rentang waktu tahun 1990 sampai 2019 telah digunakan untuk dianalisis. Lebih lanjut, ketiga metode jarak yaitu Euclidean Distance, Manhattan Distance dan Minkowski Distance digunakan dalam penelitian ini. Dari ketiga metode tersebut dicari nilai akurasi yang tertinggi menggunakan metode Silhouette Coefficient (SC). Metode Sum Square Error (SSE) dan R-squared (R2) juga digunakan untuk mengukur cluster optimal. Hasil percobaan memperlihatkan bahwa metode jarak Manhattan Distance memiliki nilai akurasi terbaik. Sedangkan, cluster optimal untuk klusterisasi wilayah diperoleh tiga cluster yang merupakan cluster ideal untuk mengelompokkan wilayah penanam lada dengan SSE sebesar 238.7377116 dan nilai R2 adalah 0.459398609. Berdasarkan hasil tersebut, diperoleh informasi tentang wilayah yang berpotensi untuk produksi lada menggunakan tiga kategori yaitu kurang berpotensi, cukup berpotensi dan berpotensi baik dengan algoritma K-Means dan metode jarak Manhattan Distance.
An extraction of shapes and support vector machine methods for identification of decorative wall “Lamin” motifs of the Dayak Kenyah Pampang tribe Haviluddin, Haviluddin; Wati, Masna; Alfred, Rayner; Burhandenny, Aji Ery; Pratama, Arief Ardi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.475

Abstract

One of the Dayak cultures of Kalimantan Island, Indonesia is a traditional house called Lamin where each wall is decorated according to tribal characteristics. This study aims to identify the image on the Lamin wall using the Support Vector Machine (SVM) method based on the eccentricity and metric parameter values. The data of this study consisted of 50 types of images of the Lamin wall motifs of the Dayak Kenyah tribe consisting of tebengaang, dragon, crocodile, tiger, and arch which were taken from the tourist village, Pampang, Samarinda, East Kalimantan. Based on the experiment, the shape feature extraction method has produced the highest value of the eccentricity parameter which is 0.6979 and the metric parameter is 0.9953 on the image of the arch. Motif identification using the SVM method using linear, Gaussian/RBF, and polynomial kernel parameters has resulted in the highest accuracy with 80% image composition of kernel polynomial at 85%, Gaussian/RBF at 80%, and linear at 78%.
Co-Authors Achmad Fanany Onnilita Gaffar Achmad Fanany Onnilita Gaffar Adnan, Adam Afdal Jamil Tanjung Agus Soepriyadi Ahmad Hijazi, Mohd Hanafi Ahmad Jawahir Ahmad Jawahir Aiman, Ahmad Zuhair Nur Aina Musdholifah Aini, Hijratul Aji Prasetya Wibawa Akhmad Masyudi Albertus Juvensius Pontus Aldi Bastiatul Fawait Fawait Alfiansyah, M Nur Ali Sholihin Allo, Adriati Manuk Anam, M Khairul Anggari, Ricky Anindita Septiarini, Anindita Anton Prafanto Arda Yunianta Arda Yunianta Arif Bramantoro Arif Harjanto Arinda Mulawardani Kustiawan Astuti, Wistiani Aulia Rahman Awang Harsa Kridalaksana Bambang Nur Basuki Bangkit Bekti Nurdianto Basuki, Nur Bambang Brins Leonard Pailan Budiman, Edy Burhandenny, Aji Ery Cahyani, Oktari Indi Cahyani, Oktaria Indi Cellia Auzia Nugraha Chrisman Bonor Sinaga Davina Putri Ananta Dedy Cahyadi Dedy Mirwansyah Delvina Dwiani Samjar Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Didit Suprihanto, Didit Dinda Izmya Nurpadillah Djoko Setyadi Dwiyanto, Felix Andika Efrizoni, Lusiana Emmilya Umma Azizah Gaffar Fahrul Agus Faizul Anwar Wandi Fatkhul Hani Rumawan Fauzan, Ammar Nabil Faza Alameka Fazma Urmila Jannah Helmi Puadi Firdaus, Ardhifa Firdaus, Muhammad Bambang Fui Fui, Ching Fui, Ching Fui Gaffar, Achmad Fanany Onnlita Gubtha Mahendra Putra Gubtha Mahendra Putra Gultom, Tiopan Hendry Manto Hairah, Ummul Hamdani Hamdani Hasihi, Cholisah Erman Hasnida, Rima Yustika Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Herlina Jayadiyanti Herman Santoso Pakpahan Hersa Safitri Hery Widijanto Hijazi, Mohd Hanafi Ahmad Hijratul Aini Hijratul Aini Huzain Azis Ibrahim, Muhammad Rivani Ifandi, Muhammad Imam Tahyudin Imam Tahyudin Irwan Gani Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah, Islamiyah Iwan Muhamad Ramdan Izdihar, Zahra Nabila Jainuddin Jainuddin Jayadiyanti, Herlina Kesuma, Muhammad Afrizal Kim On, Chin Leong, Jing Mei Lilik Hendrajaya Malani, Rheo Maratus Soleha Medi Taruk Mega Yoalifa Milkhatun, Milkhatun Ming Foey Teng, Ming Foey Moham, Ni’mah Mohd Shahizan Othman Mohd Shahizan Othman Mualin Renaldy Setiabudi Muhammad Bambang Muhammad Rafif Hanif Muhammad Soleh Muhammad Sultan, Muhammad Muhammad Syarif Abdillah Nafalski, Andrew Nataniel Dengen Ngurah Satria Darmawangsa Ni’mah Moham Norazah Yusof Novianti Puspitasari Nugraha, Cellia Auzia Nugroho, Basuki Rahmat Nur Fadhilah Nurfaizi Amin Nurpadillah, Dinda Izmya Olivia Angelica Murtioso Omar Mohammed Barukab Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Paroliyan, Abraham Pradinata, Muhammad Aji Prafanto, Anton Pratama, Arief Ardi Prawira, Muhammad Nanda Purnawansyah Purnawansyah Puspitasari, Novianti Putra, Gubtha Mahendra Putut Pamilih Widagdo, Putut Pamilih Qonita, Adiba Rahayu, Ervina Raihanfitri Adi Kalipaksi Raja, Roesman Ridwan Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rendy Ramadhan Revia Oktaviani Rima Yustika Hasnida Salim, Yulita Saputra, Irzan Tri Sarjon Defit Saudi, Azali Setyadi, Hario Jati Simanungkalit, Julius Rinaldi Sitompul, Tua Delima Soepriyadi, Agus Suryani Junita Patandianan Sutikno Sutikno Suwardi Gunawan Tindik, Emmanuel Steward Tommy Trides Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Utama, Agung Bella Putra Utomo Pujianto Vina Zahrotun Kamila Wandi, Faizul Anwar Wati, Masna Wei, Toh Yin Widians, Joan Angelina Wong, Kelvin Yahya, Fiqri Khaidar Yazeed Al Moaiad Yudhi Saputra Yudi Sukmono Yulita Salim Yunianta, Arda Yusof, Omar Obarukab Norazah Zainal Arifin Zainal Arifin Zakaria Ahmad Dahlan