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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Pertanian Indonesia ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika International Journal of Advances in Intelligent Informatics POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic International Journal of Advances in Data and Information Systems Madaniya Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Makara Journal of Science Journal of Data Science and Software Engineering Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Integra: Journal of Integrated Mathematics and Computer Science Jurnal Komputasi
Muhammad Reza Faisal, Muhammad Reza
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences

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Gender Classification of Twitter Users Using Convolutional Neural Network Fitra Ahya Mubarok; Mohammad Reza Faisal; Dwi Kartini; Dodon Turianto Nugrahadi; Triando Hamonangan Saragih
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3318

Abstract

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and
Analysis of Static and Contextual Word Embeddings in Capsule Network for Sentiment Analysis of The Free Nutritious Meal Program on Twitter Raditya, Virgi Atha; Saragih, Triando Hamonangan; Faisal, Mohammad Reza; Abadi, Friska; Muliadi, Muliadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5424

Abstract

Public discourse surrounding Indonesia’s Makan Bergizi Gratis (MBG) program reflects diverse opinions that have not yet been systematically examined using computational methods. This study addresses that gap by evaluating the effectiveness of static and contextual word embeddings within a Capsule Network (CapsNet) framework for sentiment analysis of MBG-related tweets on Twitter. A total of 7,133 Indonesian-language tweets were collected through web crawling, preprocessed, and manually labeled into positive, neutral, and negative categories. Four embedding techniques—Word2Vec, FastText, ELMo, and IndoBERT—were tested under two preprocessing settings, raw and stemming. The experimental results show that Word2Vec on raw text achieved the highest accuracy of 96.17%, while FastText obtained the best performance on stemmed data with 94.10%. These findings indicate that morphological normalization benefits static and subword-based embeddings, whereas contextual models maintain stable performance without extensive fine-tuning. Overall, this study demonstrates the potential of combining CapsNet with appropriate embedding strategies for Indonesian-language sentiment analysis and provides evidence that natural language processing can support data-driven evaluation of public programs such as MBG.
The Effect of Smote-Tomek on the Classification of Chronic Diseases Based on Health and Lifestyle Data Muhammad Adika Riswanda; Friska Abadi; Muhammad Itqan Mazdadi; Mohammad Reza Faisal; Rudy Herteno
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.324

Abstract

Machine learning models for chronic disease prediction are often trained on imbalanced healthcare datasets, where non-disease cases dominate. This condition can lead to misleadingly high accuracy while failing to identify patients with chronic diseases, limiting clinical usefulness. This study aims to analyze the impact of class imbalance on model performance and to evaluate the effectiveness of the SMOTE–Tomek resampling technique in improving chronic disease prediction. This research provides empirical evidence that accuracy alone is insufficient for evaluating healthcare models and demonstrates that imbalance-aware preprocessing is essential for valid and reliable chronic disease detection. Five classification models, such as Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost, were evaluated on a lifestyle-based chronic disease dataset under two conditions: without resampling and with SMOTE–Tomek. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC. Without SMOTE–Tomek, all models failed to detect chronic disease cases, producing near-zero recall and F1-scores despite accuracy exceeding 80%. After applying SMOTE–Tomek, substantial improvements were observed across all models, particularly in recall and AUC. Support Vector Machine achieved the best overall performance, with an accuracy of 92.9%, a precision of 92%, a recall of 93.9%, an F1-score of 0.93, and an AUC of 0.98. The findings confirm that handling class imbalance is a prerequisite for meaningful chronic disease prediction. The consistent increase in recall and AUC across all evaluated models confirms that the improvement stems from enhanced class separability rather than metric inflation. The proposed approach supports more reliable early screening and decision-support systems in preventive healthcare
Dampak dari Parameter Variasi Koneksi, Node dan Kecepatan Node Terhadap Delay pada Routing Protocol AODV dan BATMAN Jaringan MANET Dodon Turianto Nugrahadi; M Reza Faisal; Liling Triyasmono; Muhammad Janawi
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2675

Abstract

Mobile ad-hoc Network (MANET) is a multihop wireless network that a many collection of mobile nodes that are dynamic. MANET each node on the network have the same position, so it needs the appropriate routing protocol, to support the exchange of data to be optimal. In this study, the routing protocol to be tested is AODV and BATMAN based scenario increasing the number of connections, nodes and speed. Simulation parameter scenarios is number connection 1 UDP, 2 UDP, 3 UDP, and number of node 25 node, 50 node, 100 node, and then number node speed 20 m/s, 50 m/s. in this AODV routing protocol will establish a rute from the source node to the destination only if there is a request from the source node. BATMAN routing protocols, all decisions and information disseminated throughout the node and will regularly update on each node. The performance parameters to be measured such as delay by using OMNET ++ 4.6. Output of simulation will analysis with two way anova and multivariate to know correlation between variation scenario impact to delay. The results obtained in this study AODV and BATMAN have their respective advantages, analisys with two-way anova show that both AODV and BATMAN get the impact of the scenario from incrising the number of connections, the number of nodes and the number of nodes speed with a p-value of 0.012212 (<0.05) with two-way anova. From all scenarios, the number of UDP connections has the greatest impact, from UDP 1, UDP 2 and UDP 3. Followed by the number of speed 50 and node 100. So it can be concluded that the connection has an effect on increasing delay. The increasing number of speed and nodes can contribute to an increase in delay if number of nodes above 100 and speed above 50. With multivariate analysis, the BATMAN protocol had the most impact on the delay under the scenario then AODV.
Metrics Based Feature Selection for Software Defect Prediction Radityo Adi Nugroho; Friska Abadi; M. Reza Faisal; Rudy Herteno; Rahmat Ramadhani
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2670

Abstract

Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.
Analisis Komparasi Implementasi Steganografi White-Space dan White-Space Modified pada Artikel Terenkripsi AES dalam HTML5 Rudy Herteno; Dodon Turianto Nugrahadi; Muhammad Sholih Afif; M Reza Faisal; Friska Abadi
Jurnal Komputasi Vol. 8 No. 1 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i1.2525

Abstract

The level of internet usage continues to increase until now.  information exchange requires security that cannot be predicted by others.  one technique for securing information is steganography.  Steganography techniques are the science and art of hiding information.  This technique can hide the content of information in media that cannot be guessed by ordinary people, so as not to arouse suspicion of the people who see it.  One of the media that can implement the white-space modified steganography method is HTML pages.  in addition, AES (Advanced Encryption Standard) is a lighter encryption security algorithm compared to other algorithms. In this study, plain text that has been encrypted into cipher text is then inserted with white-space and white-space modification steganography techniques. Data changes have occurred but only less than 1 percent.  In experiments that have been implemented on Google Chrome and Mozilla Firefox are the same except in Internet Explorer, which changes the data slightly larger.The implementation of AES encryption and stegano white-space original, has 100% success but the 80% decryption process is successful, but the decryption results contain additional binaries. This happen because the use of tabulation (tabs) instead of spaces in HTML5 articles, and this is often found in HTML articles. while the implementation of AES encryption and stegano whitespace modified, has a success of 100% and the decryption process of 90% succeeded without any changes. 1 article failed because the number of articles is too small compared to the amount of space provided. The conclusion that implementation of AES encryption and white-space modified is more appropriate to be implemented in HTML5 articles, and than the use of tabulation and the number of characters also consequences on the implementation.Keywords: Information, Steganography, White-space modified, Security, AES, Web Browser 
Studi Ekstraksi Fitur Berbasis Vektor Word2Vec pada Pembentukan Fitur Berdimensi Rendah irwan budiman; M Reza Faisal; Dodon Turianto Nugrahadi
Jurnal Komputasi Vol. 8 No. 1 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i1.2517

Abstract

Klasifikasi teks adalah salah satu metode untuk mengelola dan mencari informasi penting yang terdapat pada format tekstual yang tidak terstruktur. Ekstraksi fitur merupakan proses penting pada klasifikasi teks untuk mengubah format tekstual yang tidak terstruktur menjadi terstruktur sehingga dapat diproses oleh algoritma machine learning untuk mengklasifikasikan ke class yang telah ditentukan. Salah satu teknik ekstraksi fitur yang umum digunakan adalah vector space representation. Teknik ini mudah digunakan tetapi berpotensi menghasilkan data dengan dimensi banyak yang berakibat kepada peningkatan waktu komputasi bahkan tidak dapat diproses karena limitasi perangkat keras. Pada riset ini kami melakukan studi terhadap teknik ekstraksi fitur yang mampu menghasilkan data berdimensi sedikit. Ekstraksi fitur yang digunakan memanfaatkan vektor word2vec untuk mengontrol jumlah fitur yang dihasilkan. Pada riset ini kami membandingkan beberapa model yang dihasilkan sendiri dengan jumlah fitur yang bervariasi dan model yang telah disedikan oleh Google. Hal ini dilakukan untuk mengetahui jumlah fitur yang dapat menghasilkan kinerja klasifikasi terbaik. Hasilnya didapat nilai kinerja tertinggi akurasi yaitu 0.877 dengan jumlah fitur adalah 300 dari model yang dihasilkan sendiri.
AdaBoost Classifier untuk Klasifikasi Tanaman Jarak Pagar Triando Hamonangan Saragih; Muliadi Muliadi; Mohammad Reza Faisal; Muhammad Al Ichsan Nur Rizqi Said
Jurnal Komputasi Vol. 9 No. 2 (2021)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i2.2865

Abstract

Tanaman Jarak Pagar merupakan tanaman multi fungsi yang memiliki banyak kegunaan di kehidupan sehari-hari, baik itu untuk pengobatan, kecantikan hingga pengganti bahan bakar biodiesel. Penyakit yang menyerang tanaman jarak pagar dapat menurunkan kualitas yang dihasilkan jarak pagar. Minimnya pengetahuan petani dan sedikitnya jumlah pakar yang memahami tentang jarak pagar menjadi masalah yang harus diselesaikan. Pengguanaan sistem pakar menjadi solusi yang bisa ditawarkan. AdaBoost Classifier pada sistem pakar dapat digunakan sebagai mengklasifikasikan penyakit tanaman jarak pagar. Hasil yang diperoleh dari penelitian ini yaitu didapat akurasi rata-rata sebesar 50% dan maksimal terbaik sebesar 53,01% pada jumlah fold sebanyak 2. Hasil pada penelitian ini lebih baik dibanding penelitian sebelumnya, tetapi tidak bisa memberikan hasil yang maksimal. Jumlah data tiap kelas menjadi perrmalasahan mengapa hasil pada AdaBoost kurang maksimal dan harus diselesaikan pada penelitian selanjutnya.
Implementasi Reduksi Fitur t-SNE Pada Clustering Gambar Head shape Nematoda Muhammad Rizky Adriansyah; Mohammad Reza Faisal; Abdul Gafur; Radityo Adi Nugroho; Irwan Budiman; Muliadi Muliadi
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2963

Abstract

Pada penelitan ini dilakukan clustering terhadap gambar head shape nematoda, dalam melakukan pengolahan gambar diperlukan metode ekstraksi fitur untuk menemukan informasi penting dari gambar yang akan diolah, salah satu esktraksi fitur yang bisa digunakan adalah wavelet. Setelah gambar melewati ekstraksi fitur dihasilkan sebanyak 5624 fitur, dengan fitur sebanyak ini dapat mengakibatkan waktu komputasi yang lama. Oleh sebab itu perlu dilakukan reduksi fitur untuk mengurangi jumlah fitur yang awalnya 5624 fitur menjadi 2 atau 3 fitur saja, salah satu metode reduksi fitur terbaru yang bisa digunakan adalah t-SNE. Pada penelitian ini dilakukan perbandingan hasil kualitas cluster antara yang menggunakan reduksi fitur dengan yang tidak. Hasil Silhouette Index   yang didapatkan tanpa reduksi fitur adalah 0.046 dan setelah menggunakan reduksi fitur t-SNE terjadi peningkatan yang cukup signifikan menjadi 0.418.
Perbandingan Ekstraksi Fitur dengan Pembobotan Supervised dan Unsupervised pada Algoritma Random Forest untuk Pemantauan Laporan Penderita COVID-19 di Twitter Sulastri Norindah Sari; Mohammad Reza Faisal; Dwi Kartini; Irwan Budiman; Triando Hamonangan Saragih; Muliadi Muliadi
Jurnal Komputasi Vol. 11 No. 1 (2023)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v11i1.6650

Abstract

Dimasa sekarang masyarakat sudah berani melaporkan dirinya terpapar COVID-19 melalui unggahan di media sosial seperti Twitter. Hal ini dapat dimanfaatkan oleh masyarakat sekitar atau lembaga kesehatan untuk memberikan bantuan terhadap pelapor. Pemantauan laporan penderita COVID-19 di Twitter dapat dilakukan secara otomatis dengan algoritma machine learning untuk klasifikasi teks. Pada kasus klasifikasi teks, algoritma machine learning menerima input berupa data terstruktur hasil ekstraksi fitur dengan teknik unigram dengan pembobotan. Metode pembobotan kata unsupervised merupakan pembobotan yang tidak memperhatikan letak term di kelas positif atau negatif. Kemudian metode pembobotan ini dikembangkan menjadi pembobotan supervised, karena dalam proses pembobotannya metode ini membobotkan term dengan memperhatikan letak term di kelas positif atau negatif. Pada riset ini dilakukan perbandingan kedua jenis pembobotan pada klasifikasi data tweet gejala covid dengan algoritma machine learning yaitu Random Forest. Dari hasil penelitian didapat hasil kinerja klasifikasi dengan pembobotan supervised Delta TF-IDF terbukti lebih bagus dengan akurasi sebesar 88,5% sedangkan dengan pembobotan unsupervised TF-IDF diperoleh hasil akurasi 87,9%
Co-Authors Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Adi, Puput Dani Prasetyo Adini, Muhammad Hifdzi Admi Syarif Aflaha, Rahmina Ulfah Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Amalia, Raisa Andi Farmadi Andi Farmadi Angga Maulana Akbar Anggi Mardiyono Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Bedy Purnama budiman, irwan Dewi Sri Susanti Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emma Andini Erick Kurniawan Fadhillah, Muhammad Alif Fatma Indriani Fatma Indriani Fatma Indriani Fitra Ahya Mubarok Fitriani, Karlina Elreine Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Fuad Muhajirin Farid Ghinaya, Helma Halim, Kevin Yudhaprawira Hana, Elvina Nur Hanif Rahardian Herteno, Rudi Herteno, Rudy Hertono, Rudy Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kamil, Hawariul Kartika, Najla Putri Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Lumbanraja, Favorisen R Mafazy, Muhammad Meftah Mahmud Mahmud Maisarah Maisarah, Maisarah Maulana, Muhammad Khalid Mauldy Laya Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Adika Riswanda Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Mada Muhammad Mursyidan Amini Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Sholih Afif Muhammad Zaien MUJIZAT KAWAROE Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Mustofa, Fahmi Charish Nafiz, Muhammad Fauzan Ngo, Luu Duc Noordyanti, Erna Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Pratama, Muhammad Yoga Adha Priyatama, Muhammad Abdhi Purnajaya, Akhmad Rezki Putri Nabella Raditya, Virgi Atha Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Ramadhan, As’ary Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reisa Siva Nandika Reza Rendian Septiawan Riadi, Agus Teguh Riadi, Putri Agustina Rinaldi Riza Susanto Banner Riza, Yusi Rizal, Muhammad Nur Rizian, Rizailo Akfa Rizki, M. Alfi Rizky Ananda, Muhammad Rizky, Muhammad Hevny Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi Said, Naufal SALLY LUTFIANI Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Sari, Risna Satou, Kenji Sa’diah, Halimatus Septyan Eka Prastya Setyo Wahyu Saputro Siti Aisyah Solechah Solly Aryza Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Syamsiar, Syamsiar Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Umiatin, Umiatin Utami, Juliyatin Putri Uthami, Mariza Vina Maulida, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Warsuta, Bambang Wildan Panji Tresna Winda Agustina Yabani, Midfai Yeni Rahkmawati Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi