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All Journal Jurnal Algoritma Jurnal Wawasan Ilmiah Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Jurnal Ilmu Sosial dan Ilmu Politik JOIN (Jurnal Online Informatika) Edu Komputika Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Elektronika dan Telekomunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Sistem Cerdas Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal PkM MIFTEK Jurnal Algoritma Engineering Science Letter IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Integrated : Journal Of Information Technology and Vocational Education JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Intelligent Systems Technology and Informatics International Journal of Applied Information Systems and Informatics
Dede Kurniadi
Institut Teknologi Garut
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Other

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Analisis Sentimen Pengguna Twitter dalam Pemilihan Presiden (PILPRES) 2024 dengan Menggunakan Algoritma K-Means Amin, Abdusy Syakur; Kurniadi, Dede; Nurzaman, Muhammad Zein; Nurfadillah, Rifa Sri; Khoerunisa, Sarah; Khaerunisa, Nisrina; Ajiz, Rafi Nurkholiq; Jembar, Tegar Hanafi; Faisal, Ridwan Nur
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1596

Abstract

One form of upholding democracy carried out by the Unitary State of the Republic of Indonesia is through holding presidential elections or often known as presidential elections. which is held every five years to elect the next President. Apart from that, in this digital era, people are increasingly actively using social media to convey their views, opinions and sentiments regarding the presidential election. Ahead of the 2024 presidential election, many groups such as political parties, success teams, buzzers and supporters are using social media as a campaign medium to increase the popularity and electability of their prospective candidates. One of the social media that is widely used in political party promotion media is Twitter. Which is used by people to post various comments that can be positive or negative regarding the election. Sometimes, people also express hoax opinions before or during the election. Considering that comments on Twitter are currently difficult to categorize as positive or negative, sentiment analysis is needed to understand public attitudes towards the presidential election. This research aims to evaluate text documents and determine whether the documents have a positive or negative sentiment orientation. Apart from that, the method used is K-Means to cluster the data. The results of this weighting are in the form of positive and negative sentiment. Data taken from Twitter regarding the 2024 presidential election (pilpres) totaling 1015 tweet data.
THE ROLE OF FEATURE SELECTION IN ENHANCING THE ACCURACY OF AI ASSISTANT AUTO-LABELING Julianto, Indri Tri; Kurniadi, Dede; B. Balilo Jr, Benedicto; Rohman, Fauza
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 1 (2024): Desember 2024
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i1.3364

Abstract

Abstract: The development of AI assistants such as Gemini and ChatGPT can significantly assist in daily human tasks. In the field of Sentiment Analysis, AI assistants can be utilized as an automated labeling alternative to provide positive, negative, or neutral sentiments within a dataset. This research aims to enhance the performance of AI assistants in automated labeling processes by employing the Feature Selection algorithm, specifically Forward Selection. The methodology involves utilizing the Naïve Bayes and K-NN algorithms, and subsequently improving accuracy through the Feature Selection algorithm. The evaluation is conducted using K-Fold Cross Validation. Research findings indicate an improvement in the accuracy of the best model, which is ChatGPT, when using the Naïve Bayes algorithm and Shuffled Sampling technique. The initial accuracy of 79.09% increased to 87.18% after Feature Selection was applied. This demonstrates the effectiveness of Feature Selection, particularly Forward Selection, in enhancing the accuracy performance of the model.            Keywords: ai; assistant; chat gpt; feature selection; gemini.  Abstrak: Pekembangan Asisten AI seperti Gemini dan Chat GPT dapat membantu pekerjaan manusia sehari-hari. Dalam bidang Analisis Sentimen, Asisten AI dapat digunakan sebagai alternatif pelabelan otomatis untuk memberikan sentimen positif, negatif atau netral dalam suatu dataset. Penlitian ini bertujuan untuk meningkatkan performa yang dihasilkan oleh Asisten AI dalam proses pelabelan otomatis menggunakan Algortima Feature Selection yaitu Forward Selection. Metode yang digunakan adalah dengan menggunakan Algoritma Naïve Bayes dan K-NN kemudian hasil akurasi akan ditingkatkan menggunkan Algoritma Feature Selection. Evaluasi yang digunakan adalah K-Fold Cross Validation. Hasil penelitian menunjukkan peningkatan akurasi model terbaik berada pada Chat GPT dengan menggunakan Algoritma Naïve Bayes dan Teknik Shuffled Sampling, dari nilai akurasi awal sebesar 79.09%, setelah ditingkatkan menggunakan Feature Selection, maka nilai akurasinya meningkat menjadi 87.18%. Hal ini membuktikan peran Feature Selection, dimana yang digunakan adalah Forward Selection dalam meningkatkan akurasi ternyata memang efektif dalam meningkatkan performa akurasi model. Kata kunci: ai; assisten; chat gpt; feature selection; gemini 
Klasifikasi Perputaran Karyawan Perusahaan Menggunakan Algoritma Random Forest dan Random Over-sampling Kurniadi, Dede; Nuraeni, Fitri; Faturrohman, Nadhif; Mulyani, Asri
Edu Komputika Journal Vol 10 No 2 (2023): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v10i2.73782

Abstract

Pergantian karyawan merupakan permasalahan yang berat dalam suatu perusahaan, karena pergantian karyawan dapat menyebabkan kinerja perusahaan menurun akibat kekurangan karyawan. Penelitian ini bertujuan untuk membangun model untuk mengklasifikasikan apakah karyawan akan meninggalkan perusahaan atau tidak untuk mencegah pergantian karyawan. Metode yang digunakan dalam penelitian ini adalah Machine Learning Life Cycle (MLLC). Model dibangun menggunakan algoritma Random Forest dan Random Over-sampling untuk mengatasi data yang tidak seimbang dengan rasio pembagian data untuk data pelatihan sebesar 90% dan data pengujian sebesar 10%. Selain itu untuk mengetahui kinerja model yang dibangun dilakukan evaluasi dengan menggunakan Confusion Matrix dan kurva AUC-ROC. Hasil penelitian ini menunjukkan bahwa model yang dibangun berdasarkan hasil evaluasi mempunyai kinerja yang sangat baik dan hampir sempurna, dengan nilai akurasi sebesar 99,8%, recall sebesar 100%, dan presisi sebesar 99,6%. Hanya terdapat 4 dari 2000 data pengujian yang tidak diklasifikasikan dengan benar, dengan nilai AUC yang dihasilkan sebesar 99,8%, sehingga model termasuk dalam kategori Excellent berdasarkan nilai AUC.
Perbandingan Kinerja Algoritma KNN dan SVM Menggunakan SMOTE untuk Klasifikasi Penyakit Diabetes Asri Mulyani; Sarah Khoerunisa; Dede Kurniadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i1.15198

Abstract

Diabetes frequently goes undetected or is diagnosed too late. Consequently, it may lead to a range of serious complications, such as organ damage, stroke, and heart disease. The International Diabetes Federation (IDF) reports that 10.5% of the adult population aged 20 to 79 are diagnosed with diabetes, and almost half are unaware of the condition. Hence, the number of people with diabetes has increased by fourfold compared to the prior period. One essential step for preventing complications in patients with diabetes is early detection, one of which is by utilizing artificial intelligence (AI) technology, namely data mining. Therefore, knowledge about effective algorithms used to detect diabetes is needed. This study aimed to compare two algorithms, namely k-nearest neighbor (KNN) and support vector machine (SVM), for diabetes classification using the synthetic minority oversampling technique (SMOTE). In this study, both algorithm performance was measured using the machine learning life cycle method. The results showed they had good performance in detecting diabetes; yet, there were significant performance differences between the two. The SVM algorithm with radial basis function (RBF) kernel achieved 81.67% accuracy, 85.91% precision, 79.01% recall, and 82.32% F1 score. Meanwhile, the KNN algorithm with k = 3 found through cross-validation achieved 83.33% accuracy, 85.00% precision, 83.95% recall, and 84.47% F1 score. Based on confusion matrix evaluation, KNN showed superior performance compared to SVM in terms of accuracy and other evaluation metrics. These results indicate that KNN is more effective in detecting diabetes in the dataset used in this study.
Sentiment Analysis Using Grok AI as an Auto-Labeling Tool in The Text Processing Stage Agustin, Yoga Handoko; Kurniadi, Dede; Julianto, Indri Tri; B. Balilo Jr , Benedicto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14632

Abstract

A critical aspect of Natural Language Processing (NLP) is text processing, where text labeling represents the most significant challenge due to its resource-intensive nature when conducted manually. At this stage, automatic labeling emerges as a more practical solution, particularly with the advent of Artificial Intelligence (AI), which offers tools to address this obstacle. Grok AI, equipped with a new feature operable on Platform X, provides a promising approach. This study aims to leverage the Grok AI feature on Platform X for automatic text labeling. The research methodology involves labeling text data obtained from a public dataset. To assess the quality of the labeling results, an evaluation method employing Naive Bayes classification modeling is applied. The findings reveal that Grok AI's performance closely approximates that of human labeling. The highest accuracy achieved by Grok AI is 51.71% using the k-Nearest Neighbors (k-NN) algorithm, approaching the human labeling accuracy of 60.52% with k-NN. Furthermore, Grok AI surpasses the performance of VADER labeling, which achieves an accuracy of only 49.49% with Naive Bayes. Consequently, the Grok AI feature on Platform X presents a viable alternative for the automatic labeling of text data.
Aplikasi Voice Assistant Pada Smartwach Menggunakan Open Artificial Intellegence Kusmayadi, Kusmayadi; Kurniadi, Dede; Setiawan, Ridwan
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.1499

Abstract

Voice assistant digunakan sebagai teknologi untuk memproses bahasa alami dari pengguna dan memberikan respon perintah dari pengguna. Voice assistant digunakan sebagai assistant digital mempermudah pekerjaan pengguna dengan memberi perintah suara, voice assistant akan memberikan respon pencarian pada device yang terkoneksi dengan adanya fitur yang memberikan kontrol pada device, voice assistant pada saat ini banyak di terapkan pada device smartphone, untuk melakukan perintah  pada voice assistant tidak bisa menanggapi perintah untuk tanya jawab dari pengguna secara langsung, voice assistant pada smartphone tidak memberi tingkat kepercayaan bahwa penggunaan sebuah system akan meningkatkan kinerja (perceived usefulness). Tujuan dari penelitian ini mendapatkan respon tanya jawab pengguna dengan system dari proses open artificial intellegance, kemudian memberikan kinerja kerja bagi pengguna dimana voice assistant diterapkan pada smartwatch, untuk melengkapi fitur yang ada pada penelitian sebelumnya dan menambahkan bahasa dukungan text to speech. Penelitian ini menggunakan metode Extreme Programming (XP) dengan tahapan yang digunakan yaitu tahapan planning, design, coding, dan testing dengan pemodelan Unifed Modelling Language (UML). Hasil dari penelitian ini adalah dibangunnya aplikasi voice assistant pada smartwatch yang dapat berfungsi dengan baik, yang divalidasi menggunakan pengujian usability testing, kemudian dapat melakukan tanya jawab perintah suara dari pengguna berupa informasi yang di tanyakan secara random dan memberikan perceived usefulness bagi pengguna.
Implementation of Machine Learning Model to Detect Sign Language Movement in SIBI Learning Media Fitriani, Leni; Kurniadi, Dede; Rajab, Ilham Syahidatul
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1159

Abstract

This research focuses on the development of a web-based Indonesian Sign Language System (SIBI) learning application with motion detection to improve the precision of sign language practice. Despite the government's introduction of SIBI as an official system, existing platforms lack tools to validate the accuracy of hand movements. Using the Design Sprint methodology—comprising Understand, Define, Sketch, Decide, Prototype, and Validate phases—this study employs Microsoft Azure Machine Learning to create a motion detection model capable of recognizing SIBI gestures. The application offers an interactive learning experience, allowing users to practice and receive real-time feedback on their accuracy. Initial trials demonstrated high prediction accuracy, achieving 99.82% on public datasets and 96.4% on private datasets. Beta testing revealed an 86% satisfaction rate among users, indicating the application’s effectiveness in enhancing the learning process. By providing accessibility through standard web browsers and incorporating advanced motion detection, this application contributes to inclusivity, facilitating broader public understanding and interest in learning sign language.
Perbandingan Penggunaan Optimizer dalam Klasifikasi Sel Darah Putih Menggunakan Convolutional Neural Network Dede Kurniadi; Rifky Muhammad Shidiq; Asri Mulyani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i1.17162

Abstract

White blood cells are crucial components of the immune system responsible for combating infections and diseases. The classification and counting of white blood cells are typically performed manually by experienced operators or via automated cell analysis systems. The manual method is inefficient, time-consuming, and labor-intensive, while automated analysis machines are often expensive and require stringent sample preparation. This study aimed to compare the performance of three optimizers—root mean square propagation (RMSProp), stochastic gradient descent (SGD), and adaptive moment estimation (Adam)—in a white blood cell classification model using a convolutional neural network (CNN) algorithm. The dataset consisted of 12,392 images spanning four white blood cell classes: eosinophils, neutrophils, lymphocytes, and monocytes. The results indicate that the Adam optimizer achieved the best performance, with a training accuracy of 98.65% and an evaluation accuracy of 97.73%. Adam also outperformed the other optimizers in key metrics, including recall (97.43%), precision (97.42%), F1-score (97.42%), and specificity (99.11%). The AUC values for all classes exceeded 90%, demonstrating the model’s exceptional ability to distinguish between different cell types. The RMSProp optimizer yielded a training accuracy of 98.63%, whereas SGD achieved a lower training accuracy of 83.46%. This study highlights the significant impact of optimizer selection on CNN performance in white blood cell image classification, providing a foundational step toward the development of more accurate medical classification systems.
Aplikasi Validasi e-KTP Berbasis Mobile Dengan Menerapkan Teknologi Optical Character Recognition Sermana, Elsa Maharani; Kurniadi, Dede
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.1761

Abstract

The Electronic Identity Card (e-KTP) is an electronic identification document issued by the Indonesian government. The Department of Population and Civil Registration (Disdukcapil) is the government agency responsible for administering civil registration. Currently, a major issue in Garut Regency is the falsification of e-KTPs. The validation process is still performed manually, which is time-consuming and prone to errors. This study aims to design a mobile application that can validate the authenticity of e-KTPs using Optical Character Recognition (OCR) technology. The methodology used is the Rational Unified Process (RUP), which consists of four phases: Inception, Elaboration, Construction, and Transition. The application is built using the open-source OCR library Tesseract to recognize and extract text from e-KTP images. The results of this study show that the application can validate e-KTPs in real-time, although the accuracy is affected by lighting conditions during scanning. The implementation of OCR in this application successfully reduces manual errors and speeds up the e-KTP validation process. The contribution of this research lies in reducing the risk of identity fraud and improving the efficiency of administrative processes at Disdukcapil. The implication of this study is the potential for broader application of OCR in validating other types of identity documents.
Perancangan Aplikasi Rekomendasi Calon Penerima Beasiswa Menggunakan Metode Simple Additive Weighting dan Rapid Throwaway Prototyping Model Maulina, Wina Senja; Kurniadi, Dede
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.1778

Abstract

Economic factors often prevent outstanding students from continuing their education in Indonesia. SMP Plus Madaarikul Ulum offers scholarships as a solution, but the selection process which is still done manually often makes it difficult to determine the right criteria based on student data. This research aims to overcome the problem by designing a scholarship recommendation application using the Simple Additive Weighting (SAW) method and the Rapid Throwaway Prototyping Model. The SAW method is used to calculate student scores based on predetermined criteria, while the Rapid Throwaway Prototyping Model is applied to design the application in stages by involving user input. The results showed that the developed application was able to improve the efficiency of scholarship selection with 80 percent accuracy compared to manual calculations, and passed the blackbox test with a score of 82.8 percent, which indicated the performance and effectiveness of the application in the scholarship selection process.
Co-Authors Abania, Nia Abdulah, Farhan Naufal Abdurrahman, Fauzan Abdussalam, Iqbal Abdussalam Abdusy Syakur Amin Ade Sutedi Ade Sutedi Ade Sutedi, Ade Adiwangsa, Alfian Akmal Agus Hermawan Agus Nugraha Agustiansyah, Yoga Ahmad Habib Lutfi Aisyah Fitri Islami Ajif, Arvin Muhammad Ajiz, Rafi Nurkholiq Akbar, Gugun Geusan Alamsyah, Renaldy Aldy Rialdy Atmadja Ali Djamhuri Alisha Fauzia, Fathia Alkamal, Chaerulsyah Alvin Zainal Musthafa Alwan Nul Hakim Amrulloh, Muhammad Fawaz Andri Saepuloh Aneu Suci Nurjanah Asri Indah Pertiwi Asri Mulyani Asri Rahayu Ningsih Ayu Suryani B. Balilo Jr , Benedicto B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Barlinti Maryam Budik Burhanuddin, Ridwan Cahya Mutiara Dede Sopiah Della Adelia Anugrah Detila Rostilawati Dewi Tresnawati Dhea Arynie Noor Annisa Diar Nur Rizky Diaz Radhian Salam Diazki, Moch Haiqal Diki Jaelani Dini Destiani Siti Fatimah Diva Nuratnika Rahayu Dudy Mohammad Arifin Dyka Afan Afthori Dzikri Nursyaban Efi Sofiah Elsen, Rickard Eri Satria Erick Fernando B311087192 Erwan Yani Erwan Yani, Erwan Erwin Gunadhi Rahayu, Raden Erwin Widianto Fadillah, Hadi Bagus Faisal, Ridwan Nur Fajar Rahman Faturrohman, Nadhif Fauziah, Fathia Alisha Fauziyah, Asyifa Fikri Zakaria Rahman Firmansyah, Marshal Fitri Nuraeni Fitriani, Ranti Fitriyani Gelar Panca Ginanjar Ghilman Hasbi Basith Gisna Fauzian Dermawan H. Bunyamin Hadi Wijaya, Tryana Haekal, Mohamad Fikri Hamzah Nurrifqi Fakhri Fikrillah Hari Ilham Nur Akbar Hasfi Syahrul Ramadhan Hazar, Aura Fitria Helmalia P, Nabilla Febriani Hendri Aji Pangestu Heri Johari Heri Suhendar Heri Suhendar Hilmi Aulawi Ida Farida Ikbal Lukmanul Hakim Ikhrom, Taufik Darul Ikmal Muhammad Fadhil Ilham Muhamad Ramdan Imas Dewi Ariyanti Inda Muliana Indra Trisna Raharja Indri Tri Julianto Indri Tri Julianto Intan Sri Fatmalasari Irawan, Muhammad Randy Irfan Qusaeri Irfanov, Muhammad Irsyad Ahmad Iskandar, Joko Jajang Jaenudin Jajang Romansyah Jembar, Tegar Hanafi Khaerunisa, Nisrina Khoerunisa, Sarah Kusmayadi, Kusmayadi Latif, A. Abdul Latifah, Ayu Leni Fitriani Leni Fitriani, Leni Lia Amelia Lindayani, Lindayani M. Mesa Fauzi Mahendra Akbar Musadad Maulana , Muhammad Arief Maulana, Ahmad Rakha Maulana, Ilham Ahmad Maulana, Yusep Maulina, Wina Senja Meta Regita Mochamad Deni Ramdani Muhamad Solihin Muhammad Abdul Yusup Hanifah Muhammad Affan Al Sidqi Muhammad Rikza Nashrulloh Muhammad Saleh Muhammad Sanusi Muhammad Wildan Muliana, Inda Muttaqin, Moch Riefky Chaerul Nita Nurliawati Nugraha, M Aldi Nugraha, Nikolas Pranata Nurfadillah, Rifa Sri Nurhaliza, Nabila Putri Nurlisina, Elisa Nurpatmah, Lisna Nursa'diah, Rifania Sapta Nursyaban, Dzikri Nurul Fauziah Nurul Khumaida Nurzaman, Muhammad Zein Omar Komarudin Pratama, Reifalga Gais Prayoga, Moch. Gumelar Putri, Mita Hidayani Raharja, Indra Trisna Rahayu, Diva Nuratnika Rahayu, Raden Erwin Gunadhi Rahmat, Agil Rahmi, Murni Lestari Rajab, Ilham Syahidatul Ramdhan, Dekha Ramdhani Hidayat Randy Wardan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Rifky Muhammad Shidiq Rinda Cahyana Rinda Cahyana Risfiyanisa Fasha Rizki Fauziah Roeri Fajri Firdaus Rohman, Fauza Rohmanto, Ricky Rostina Sundayana Rubi Setiawan Rudi Sutrio Safei P, M Iqbal Ismail Sarah Khoerunisa Sermana, Elsa Maharani Sheny Puspita Indriyani Siti Rima Fauziyah Sofwan Hamdan Fikri Sopiah, Dede Sri Intan Multajam Sri Mulyani Lestari Sri Rahayu SRI RAHAYU Sri Rahayu Syahrul Sidiq Syaiffani, Moch Assami Tina Maryana Undang Indrajaya W, Faksi Ahmad Wahidah, Tania Agusviani Wiwit Septiani Yanti Sofiyanti Yayat Supriatna Yoga Handoko Agustin Yosep Septiana Yosep Septiana Yuni Yuliani Yusfar Ilhaqul Choer Yusuf Mauluddin Zaqiah, Neng Nufus Zulkarnaen, Ade Iskandar