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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic CommIT (Communication & Information Technology) Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi Creative Information Technology Journal SISFOTENIKA Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics Jurnal Komputer Terapan JTERA (Jurnal Teknologi Rekayasa) Jurnal Ilmiah Matrik Conference SENATIK STT Adisutjipto Yogyakarta ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Informasi dan Komputer Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Indonesian Journal of Business Intelligence (IJUBI) Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Respati ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics IJIIS: International Journal of Informatics and Information Systems Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Kajian Ilmiah JINAV: Journal of Information and Visualization Journal of Applied Data Sciences International Journal for Applied Information Management Jurnal Ekonomi dan Teknik Informatika Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Universal Raharja Community (URNITY Journal) IJOEM: Indonesian Journal of Elearning and Multimedia Blockchain Frontier Technology (BFRONT) Journal of Digital Market and Digital Currency
Henderi .
Raharja University
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture 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 Neuroscience Social Sciences Transportation Other

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Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice's Coefficient wahyuningsih, Tri; Henderi, Henderi; Winarno, Winarno
Journal of Applied Data Sciences Vol 2, No 2: MAY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i2.31

Abstract

This study aims to find correlation assessment of Automatic Short Answer Grading (ASAG) by comparing three methods of Cosine Similarity, Jaccard Similarity and Dice Coefficient by providing one reference answer. From the results of computing using Python programming language and data processing using spreadsheets, it was obtained that the Dice Coefficient method had the highest correlation average value of 0.76, followed by Cosine Similarity with an average correlation value of 0.76, and the lowest correlation average value was the Jaccard method with a value of 0.69. The contribution to this study is the use of three methods in one data, whereas the previous research only used 1 method for 1 data or 2 methods for 1 data. So, the value in this study resulted in a more complete comparison and accuracy of data.
An Extensive Exploration into the Multifaceted Sentiments Expressed by Users of the myIM3 Mobile Application, Unveiling Complex Emotional Landscapes and Insights Hayadi, B Herawan; Henderi, Henderi; Budiarto, Mukti; Sofiana, Sofa; Padeli, Padeli; Setiyadi, Didik; Swastika, Rulin; Arifin, Rita Wahyu
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.187

Abstract

This study investigates user sentiment towards the myIM3 application, an application used for telecommunication service management in Indonesia. Using text analysis and machine learning methods, we analyzed user reviews to identify dominant sentiment patterns and evaluate different classification models. Word cloud analysis, sentiment distribution, and donut plots were utilized to gain deeper insights into user preferences and issues. Results indicate that the majority of user reviews are neutral (52.2%), with 37% positive reviews and 33.4% negative reviews. Users consistently pay attention to aspects such as internet connection (Neutral: 92%, Positive: 95%, Negative: 87%) and pricing (Neutral: 92%, Positive: 92%, Negative: 93%) in their reviews. Evaluation of classification models like Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest shows that the SVM model performs the best with an accuracy of 93%, high precision (Negative: 93%, Neutral: 92%, Positive: 95%), recall (Negative: 93%, Neutral: 95%, Positive: 91%), and F1-score (Negative: 93%, Neutral: 94%, Positive: 93%). These findings can serve as a basis for service improvement and better product development in the future, while also affirming the capability of text analysis and machine learning techniques in providing valuable insights for telecommunication service providers.
Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA Henderi, Henderi; Hayadi, B Herawan; Sofiana, Sofa; Padeli, Padeli; Setiyadi, Didik
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.v4i3.102

Abstract

This research compares unsupervised learning methods in topic extraction and modeling in large-scale text corpora. The methods used are Singular Value Decomposition (SVD) and Latent Dirichlet Allocation (LDA). SVD is used to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. The research involves data collection, data exploratory analysis (EDA), topic extraction using SVD, data preprocessing, and topic extraction using LDA. The data used were large-scale text corpora. Data explorative analysis was conducted to understand the characteristics and structure of text corpora before topic extraction was performed. SVD and LDA were used to identify the main topics in the text corpora. The results showed that SVD and LDA were successful in topic extraction and modeling of large-scale text corpora. SVD reveals cohesive patterns and thematically related topics. LDA identifies hidden topics based on the probability distribution of words. These findings have important implications in text processing and analysis. The resulting topic representations can be used for information mining, document categorization, and more in-depth text analysis. The use of SVD and LDA in topic extraction and modeling of large-scale text corpora provides valuable insights in text analysis. However, this research has limitations. The success of the methods depends on the quality and representativeness of the text corpora. Topic interpretation still requires further understanding and analysis. Future research can develop methods and techniques to improve the accuracy and efficiency of topic extraction and text corpora modeling.
PERANCANGAN SISTEM E-TICKET PELAPORAN INCIDENT BERBASIS WEB PADA PT. AEROFOOD INDONESIA Muntasir, Ibnu; Pramono, Galih; Nurninawati, Euis; Santoso, Sugeng; Henderi, Henderi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 2 (2023): JATI Vol. 7 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i2.7216

Abstract

PT Aerowisata merupakan anak perusahaan dari Garuda Indonesia yang mengawasi beberapa unit perusahaan yang beroperasi di berbagai bidang, termasuk catering, tour, travel, dan transportasi. Salah satu unit usahanya adalah PT Aerofood Indonesia, yang berfokus pada bidang catering. Saat ini, proses pengolahan laporan incident di PT Aerofood Indonesia masih dilakukan secara manual dengan menggunakan pendekatan konvensional. Pelaporan incident masih bergantung pada pencatatan sederhana melalui media cetak dan bahkan tulisan tangan, serta menggunakan tiket berbentuk dokumen kertas. Dalam perancangan sistem, pendekatan yang digunakan adalah analisis berorientasi objek dengan bantuan Unified Modeling Language (UML). Proses analisis sistem mengadopsi metode Waterfall. Diharapkan bahwa rancangan yang diajukan dalam penelitian ini dapat diterima dan berkontribusi dalam mengatasi permasalahan yang ada. Selain itu, diharapkan bahwa dengan implementasi website, sistem pengolahan laporan incident yang saat ini berjalan akan menjadi lebih efektif dan efisien.
Pengembangan Model Aplikasi E-Learning Menggunakan Metode Rapid Application Development Jamaludin, Dieng Asep; Henderi, Henderi; Ladjamudin, Al-Bahra Bin
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 5 (2024): Oktober 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i5.8011

Abstract

Abstrak - Sistem evaluasi proses belajar mengajar yang saat ini diterapkan di Jurusan Multimedia SMK Pasundan 1 Kota Serang masih bersifat manual, baik secara teknis maupun administratif dalam pengelolaan proses belajar mengajar.Penelitian ini menggunakan model pengembangan Rapid Application Development (RAD), yang merupakan model siklus pengembangan perangkat lunak (SDLC) yang merupakan bagian dari metodologi penelitian RND.Berdasarkan hasil penelitian, evaluasi validitas oleh 4 validator mendapat nilai rata-rata 86,6% dengan penilaian "Sangat Valid", menunjukkan bahwa e-Learning layak digunakan sebagai sistem pengelolaan kelas. Selain itu, evaluasi efektivitas oleh 35 siswa menunjukkan nilai rata-rata 93,2% dengan penilaian "Sangat Baik", mendapat bahwa e-Learning efektif dalam pengelolaan kelas. Penggunaan e-Learning dalam operasional kelas harus disesuaikan dengan kebutuhan dan keadaan lingkungan sekolah. Harus ada infrastruktur yang memadai agar setiap siswa yang menggunakan e-Learning memiliki smartphone atau laptop dengan akses internet sehingga tidak mengganggu waktu belajar mereka..Kata kunci: e-Learning, Website, Pengelolaan Kelas, RAD Abstract - The teaching and learning process evaluation system currently implemented in the Multimedia Department of SMK Pasundan 1 Serang City is still manual, both technically and administratively in the management of the teaching and learning process.This research uses the Rapid Application Development (RAD) development model, which is a software development cycle (SDLC) model that is part of the RND research methodology.Based on the results of the study, the validity evaluation by 4 validators received an average score of 86.6% with an assessment of “Very Valid”, indicating that e-Learning is feasible to use as a class management system. In addition, the effectiveness evaluation by 35 students showed an average score of 93.2% with a rating of “Very Good”, finding that e-Learning is effective in classroom management. The use of e-Learning in classroom operations must be adjusted to the needs and circumstances of the school environment. There should be adequate infrastructure so that every student using e-Learning has a smartphone or laptop with internet access so that it does not interfere with their learning time..Keywords: e-Learning, Website, Classroom Management, RAD
Optimalisasi Database 3.0 untuk Verifikasi Data Pelatihan Pelaut Nugraha, Rizal Fitrah; Henderi, Henderi; Sudaryono, Sudaryono
JTERA (Jurnal Teknologi Rekayasa) Vol 9, No 2: December 2024
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v9.i2.2024.101-112

Abstract

This study explores the optimization of Database 3.0 to enhance the registration of training participants and data verification in seafarer training programs. The increasing complexity of managing and verifying vast training data demands advanced database technologies. Database 3.0, with its capabilities for real-time updates, automated data entry, and system integration, presents a solution to these challenges. The research employs SmartPLS to model the relationships between Database Optimization, Data Accuracy, Verification Efficiency, and User Satisfaction, aiming to assess how optimization impacts the overall effectiveness of training data management. The study fills a gap in the literature by focusing on Database 3.0 optimization within the maritime training context, an underexplored area. The results indicate that optimized databases significantly improve data accuracy and verification efficiency, leading to higher user satisfaction among administrators and trainers. The findings suggest that integrating Database 3.0 into seafarer training programs can streamline data verification processes, ultimately enhancing certification reliability and operational efficiency in maritime education. These insights offer a novel perspective on utilizing advanced database technologies in specialized sectors like maritime training.
Utilization of Testimonials Menu as Submission Media Information on Buyer Satisfaction on the Website E-Commerce Raharja Internet Café Henderi, Henderi; Zcull, Harph; Putri, Cheetah Savana
Aptisi Transactions On Technopreneurship (ATT) Vol 1 No 1 (2019): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v1i1.12

Abstract

Raharja Internet Cafe is a facility at Raharja College, which provides various needs for Raharja's private lecture activities. Raharja Internet Cafe is used to help lecture activities by facilitating students such as computers, printers and scanners. Also helps students to install or service iPad. However, sometimes the facilities available at Raharja Internet Cafe are still experiencing problems so students are less interested in visiting Raharja Internet Cafe. This study uses 2 (two) methods, namely the literature review method and questionnaire. As a result, to make it easier for sellers and buyers to know the quality of services provided by Raharja Internet Cafe to Pribadi Raharja, Raharja Internet Cafe's website is used by adding testimonials menus. In the testimonials menu, there are many testimonials that have been given by Raharja Internet Cafe users. And the results obtained from the research conducted are that Raharja Internet Cafe is very helpful in lecturing activities
Scalable Machine Learning Approaches for Real-Time Anomaly and Outlier Detection in Streaming Environments Dewi, Deshinta Arrova; Singh, Harprith Kaur Rajinder; Periasamy, Jeyarani; Kurniawan, Tri Basuki; Henderi, Henderi; Hasibuan, M. Said
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.v5i4.444

Abstract

The prevalence of streaming data across various sectors poses significant challenges for real-time anomaly detection due to its volume, velocity, and variability. Traditional data processing methods often need to be improved for such dynamic environments, necessitating robust, scalable, and efficient real-time analysis systems. This study compares two advanced machine learning approaches—LSTM autoencoders and Matrix Profile algorithms—to identify the most effective method for anomaly detection in streaming environments using the NYC taxi dataset. Existing literature on anomaly detection in streaming data highlights various methodologies, including statistical tests, window-based techniques, and machine learning models. Traditional methods like the Generalized ESD test have been adapted for streaming data but often require a full historical dataset to function effectively. In contrast, machine learning approaches, particularly those using LSTM networks, are noted for their ability to learn complex patterns and dependencies, offering promising results in real-time applications. In a comparative analysis, LSTM autoencoders significantly outperformed other methods, achieving an F1-score of 0.22 for anomaly detection, notably higher than other techniques. This model demonstrated superior capability in capturing temporal dependencies and complex data patterns, making it highly effective for the dynamic and varied data in the NYC taxi dataset. The LSTM autoencoder's advanced pattern recognition and anomaly detection capabilities confirm its suitability for complex, high-velocity streaming data environments. Future research should explore the integration of LSTM autoencoders with other machine-learning techniques to enhance further the accuracy, scalability, and efficiency of anomaly detection systems. This study advances our understanding of scalable machine-learning approaches and underscores the critical importance of selecting appropriate models based on the specific characteristics and challenges of the data involved.
Utilizing Sentiment Analysis for Reflect and Improve Education in Indonesia Henderi, Henderi; Asro, Asro; Sulaiman, Agus; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; AlQudah, Mashal
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.527

Abstract

This study explores the potential of sentiment analysis in providing valuable insights into education in Indonesia based on comments from the YouTube platform. Utilizing the Naive Bayes Classifier method, this research analyzed 13,386 processed comments out of 17,920 original comments. The results show that 53.8% of comments were negative, while 28.5% were positive, and 17.7% were neutral, reflecting diverse perspectives on existing educational issues. The Accuracy of this model reached up to 72.51% with testing on various sample sizes (10%-30%), indicating the model's effectiveness in identifying sentiments. Although the model tends to classify comments as unfavorable, this opens opportunities for introspection and improvement within the educational system. Further analysis with a word cloud revealed dominant keywords, indicating areas that require more attention in public discussions about education. By leveraging this sentiment analysis, the study offers practical and valuable guidance for policymakers to reflect on and enhance educational strategies and policies in Indonesia. This research measures public reactions and aims to foster more constructive and inclusive discussions about the sustainable development of education in Indonesia.
Model Sistem Pendukung Keputusan Dosen Berprestasi di Bidang Tri Dharma Menggunakan Metode Simple Attribute Rating Technique Nugraha, Rizal Fitrah; Henderi, Henderi; Sudaryono, Sudaryono
ICIT Journal Vol 11 No 1 (2025): Februari 2025
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/icit.v11i1.3593

Abstract

Penelitian ini menyoroti pentingnya pemilihan dosen berprestasi sebagai langkah strategis untuk meningkatkan motivasi dan kualitas akademik di perguruan tinggi. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) berbasis metode Simple Multi Attribute Rating Technique (SMART) untuk menentukan dosen berprestasi berdasarkan kinerja penelitian dan pengabdian masyarakat. Metode SMART digunakan karena kemampuannya dalam mengolah data multi-atribut melalui pembobotan kriteria dan normalisasi. Penelitian ini mengatasi gap yang ada pada sistem evaluasi yang saat ini dengan menawarkan solusi yang objektif dan transparan dalam memilih dosen yang memiliki kinerja terbaik. Novelty dari penelitian ini terletak pada penerapan metode SMART dalam konteks akademik, dengan memanfaatkan berbagai kriteria kinerja seperti hibah penelitian, publikasi jurnal, hak kekayaan intelektual, dan kegiatan seminar, untuk memberikan rekomendasi yang akurat dan efektif mengenai dosen berprestasi. Kesimpulan dari penelitian ini menunjukkan bahwa sistem yang dihasilkan dapat meningkatkan proses pengambilan keputusan, memberikan kemudahan bagi LPPM dan kepala program studi dalam evaluasi dosen, serta berkontribusi dalam meningkatkan motivasi dosen untuk menghasilkan karya akademik berkualitas. Oleh karena itu, penelitian ini diharapkan dapat menjadi referensi untuk pengembangan sistem pendukung keputusan berbasis SMART di institusi pendidikan lainnya.
Co-Authors Abas Sunarya Abas, Ashardi bin Achmad Badrianto Achmad Udin Zailani Adi Setiawan Aditya Prihantara Agung Yudo Ardianto Ahmad Sidik Ainiyatul Maghfiroh Al- Bahra Aldi Destaryana Alfiah, Fifit Ali Djamhuri Alwan Hibatullah Andang Wijanarko Andrian Saputra Andrie Prajanueri Kristianto Anggrahini, Yunia Riska Anindita Septiarini, Anindita Ar Ridho Gusti Ari Ari Suhartanto Ari Suhartanto Arie Afriyoga Arief Setyanto Arif, Achmad Yusron Arifin, Rita Wahyu Aris Martono Ary Budi Warsito Asep Saefullah Asro, Asro Auliasari, Siti Risma B. Herawan Hayadi Badrianto, Achmad Bambang Soedijono W.A Bambang Soedijono, Bambang Bambang Soedjiono W.A Bangun Mukti Prasetyo Bin Ladjamudin, Al Bahra Bramantyo Yudi Wardhana Budiarto, Mukti Destyanto, Febrian Devi Rositawati Dewi, Deshinta Arrova Didi Rahmat Didik Setiyadi Dwinda Etika Profesi Efana Rahwanto Efana Rahwanto Ema Utami Euis Nurninawati Euis Siti Nur Aisyah Fahmie Al Khudhorie Fata Nidaul Khasanah FAUZAN, AKMAL Fazlul Rahman Fitria Dewi, Alda Galuh Fitria Nursetianingsih Frama Yenti Giandari Maulani, Giandari Gugun Gunawan Gunawan, Deddy Gutama, Deden Hardan Hady, Hamdy Haekal Simangunsong, Fikri Muhammad Hamdani Hamdani Hamdani Hamdani Hari Agustiyo Hatta, Heliza Rahmania Husein Muhammad Fahrezy Husni Teja Sukmana I Ketut Gunawan Ignatius Agus Supriyono Ilham Hizbuloh Ina Sholihah Widiati, Ina Sholihah Indri Handayani Indri Handayani Ira Tyas Ningrum Irwan Sembiring Ismail, Abdul Azim Bin Iwan Setyawan Jahiri, Muhamad Jahri, Muhamad Jamaludin, Dieng Asep Julia Kurniasih Junaidi Junaidi Junaidi Junaidi Kartawinata, Dea Karunia Suci Lestari Kasim, Shahreen Binti Khairunnisak Nur Isnaini Khurotul Aeni, Khurotul Kurniawan, Tri Basuki Kusrini - Kusrini . Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Ladjamudin, Al-Bahra bin Ladjamudin, AlBahra Bin M Rizeki Yuda Saputra M Said Hasibuan M. Rizeki Yuda Saputra M. Suyanto, M. Maimunah Maimunah Maimunah, Maimunah Mashal Alqudah Maulidina, Muhammad Muflih Meta Amalia Dewi Misinem, Misinem Moenawar Kholil Moh Muhtarom Mohammad Hairidzulhi Mohammad Santosa Mulyo Diningrat Muhamad Hendri Muhamad Yusuf Muhamad Yusup Mujianto, Ahmad Heru Mulyana, Muhamad Mulyati Mulyati Mulyati Mulyati Muntasir, Ibnu Nathan, Yogeswaran Neno, Friden Elefri Nia Kusniawati Novi Cholisoh Nugraha, Rizal Fitrah Nur Aisyah, Euis Siti Nur Azizah Padeli Padeli Periasamy, Jeyarani Pipin Romansyah Po Abas Sunarya Prabowo Pudjo Widodo Pradana, Restu Adi Praditya Aliftiar Pramono, Galih Prih Diantono Abda`u Puspitasari, Novianti Putri, Cheetah Savana Putri, Dian Mustika Qory Oktisa Aulia Rafika, Ageng Setiani Rahma Farah Ningrum Rahmat, Didi Rahwanto, Efana Raja, Berisno Hendro Pardamean Manik Randy Andrian Rani Putri Merliasari Rano Kurniawan Riki Mardiana Rita Wahyuni Arifin Ruli Supriati, Ruli Safar Dwi Kurniawan Saputra, M Rizeki Yuda Saputra, M. Rizeki Yuda Setianto, Yuni Ambar Shofiyul millah Singh, Harprith Kaur Rajinder Siti Khodijah Siti Ria Zuliana, Siti Ria Sofiana, Sofa Sri Rahayu Sudaryono Sudaryono Sudaryono Sudaryono Sugeng Santoso Suharto - Sulaiman, Agus Sutami, Sutami Suyatno Suyatno Swastika, Rulin Syahrial Shaddiq Taufik Hidayat Theopillus J. H. Wellem Toga Parlindungan Silaen Tri Wahyuningsih Tri Wahyuningsih Tri Wahyuningsih Tuah, Nooralisa Mohd Tubagus Ahmad Harja Kusuma Umdatur Rosyidah Uning Lestari Untung Rahardja Viola Tashya Devana W, Bambang Soedijono Winarno Winarno Winarno Winarno Wing Wahyu Winarno Yeni Nuraeni Yulika Ayu Rantama Yuni Ambar S Yunia Riska Anggrahini Yusuf, Inayatul Izzati Diana Zakaria, Mohd Zaki Zcull, Harph