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All Journal Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan Jurnas Nasional Teknologi dan Sistem Informasi ANDHARUPA RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal Pilar Nusa Mandiri CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JMM (Jurnal Masyarakat Mandiri) JTAM (Jurnal Teori dan Aplikasi Matematika) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Building of Informatics, Technology and Science Infotekmesin Jurnal Teknologi Informasi dan Multimedia Journal of Information Systems and Informatics Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Scientific Journal of Informatics JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) IJIIS: International Journal of Informatics and Information Systems Indonesian Journal of Data and Science JPMB: Jurnal Pemberdayaan Masyarakat Berkarakter Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Teknika Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Technology and Informatics (JoTI) TIERS Information Technology Journal Indonesian Journal of Innovation Studies Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Minfo Polgan (JMP) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Pengabdian Mitra Masyarakat (JPMM) JOMPA ABDI: Jurnal Pengabdian Masyarakat Digital Transformation Technology (Digitech) Journal of Multimedia Trend and Technology Journal of Artificial Intelligence and Digital Business Jurnal Krisnadana Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Kepada Masyarakat Ceria Jurnal Medika: Medika Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) TECHNOVATE Edu Komputika Journal Jurnal Informatika Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer ABDINE :Jurnal Pengabdian Masyarakat
Pungkas Subarkah
Universitas Amikom Purwokerto
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Public Sentiment Classification of Danantara in Social Media X Using Support Vector Machine and Random Forest Primandani Arsi; Pungkas Subarkah; Ranggi Praharaningtyas Aji
Edu Komputika Journal Vol. 12 No. 2 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i2.38254

Abstract

The increasing use of social media as a platform for public discourse provides valuable data for understanding societal responses to national strategic policies. One prominent example is the establishment of Danantara (Daya Anagata Nusantara), a sovereign wealth fund launched by the Indonesian government in February 2025. This study aims to analyze public sentiment toward Danantara using Indonesian-language posts collected from social media platform X and to comparatively evaluate the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms. A dataset of 1,434 public tweets was collected through web scraping and processed using text preprocessing techniques, including cleaning, tokenization, stopword removal, stemming, and TF-IDF feature extraction. Sentiment labels were generated using an Indonesian RoBERTa model and validated by a linguistic expert. Class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE). Model performance was evaluated using 5-fold stratified cross-validation with accuracy, precision, recall, and F1-score metrics. Experimental results show that Random Forest achieved slightly superior performance, reaching an average accuracy of 91.47%, compared to 91.06% obtained by SVM. Confusion matrix analysis indicates that RF better distinguishes neutral sentiment, while SVM performs competitively in identifying strong sentiment polarity. This study contributes by providing the first empirical comparison of classical machine learning approaches for analyzing public sentiment toward Indonesia’s sovereign wealth fund discourse, offering methodological insights and practical implications for data-driven policy evaluation using social media analytics.
Sentiment Perspective of Government's Free Nutritious Meal Policy on Social Media X using Indo-BERT and Bi-LTSM Pungkas Subarkah; Ali Nur Ikhsan; Epri Anggraeni; Arbangi Puput Sabaniyah
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1065

Abstract

This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, especially in the context of government policies regarding the Free Meal Program that will be implemented throughout Indonesia. This research was conducted using Indo-BERT and Bi-LSTM algorithms. These approaches were used to categorize emotions into three groups: neutral, negative, and positive. Data is obtained from posts on social media X, then after processing the data, it will be applied to both algorithms, namely Indo-BERT and Bi-LSTM. The research findings show that the model's performance in determining the public sentiment of government policies. Validation and valuation were conducted using the f1 score, recall, and precision metrics. The evaluation findings show that the Indo-BERT algorithm is better than the Bi-LSTM algorithm with an accuracy value of 80% for Indo-BERT and 78% for the accuracy value of the Bi-LSTM algorithm, and the Indo-BERT accuracy value is included in the good classification accuracy value. The sentiment analysis results are also represented by word clouds for each positive, negative and neutral class, providing an intuitive picture of the words frequently used in public discourse on free nutritious meals.
Comparative Sentiment Analysis of Indonesian Social Media Opinions on Fuel Subsidy Policy Using IndoBERT and NusaBERT Dini Ambarwati; Pungkas Subarkah; Septi Nurhayati
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12964

Abstract

Rising geopolitical tensions in the Middle East have triggered public concerns in Indonesia regarding fuel subsidy policies and fuel availability. This study aims to compare the performance of IndoBERT and NusaBERT in classifying Indonesian public sentiment on social media related to fuel subsidy policies. Data were collected from X (Twitter) and Instagram comments between October and November 2025 using keywords such as “BBM”, “Pertalite”, “fuel subsidy”, and “Middle East conflict”. After filtering duplicate, spam, and irrelevant content, a total of 1,500 opinion texts were manually annotated into positive, neutral, and negative sentiment classes and divided using an 80:20 train-test split configuration. The preprocessing stage included case folding, text cleansing, slang word normalization, emoji removal, duplicate filtering, and tokenization. Both Transformer models were fine-tuned using the AdamW optimizer with a learning rate of 2e-5, batch size of 16, and 3 training epochs. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that NusaBERT achieved better performance than IndoBERT, obtaining an accuracy of 96.3% and weighted F1-score of 96.3%, while IndoBERT achieved an accuracy of 92.5%. Additional evaluation through confusion matrix and error analysis indicates that both models still face challenges in handling sarcasm, ambiguous expressions, and mixed-context sentences commonly found in informal Indonesian social media text. The findings suggest that NusaBERT is more effective for Indonesian social media sentiment classification due to its stronger adaptation to informal language patterns.
Hybrid CNN for Sleep Stage Classification Based on EEG Maria Angelina Cahyani Candrakasih; Bagus Adhi Kusuma; Pungkas Subarkah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12999

Abstract

Sleep stage classification is essential for diagnosing sleep disorders such as insomnia and sleep apnea. However, manual scoring of polysomnography (PSG) is time consuming and subjective. Automatic systems based on single channel EEG are promising for home based monitoring, but they face challenges due to class imbalance and inter subject variability. This study proposes a hybrid model that combines 15 handcrafted features (statistical and spectral) with 128 dimensional features extracted by a one dimensional Convolutional Neural Network (1D CNN), followed by a stacking ensemble (Random Forest and Support Vector Machine as base learners, Logistic Regression as meta learner). Using 40 subjects from the Sleep EDF Expanded dataset, a strict subject independent split (80% train / 20% test) was applied to avoid data leakage. The dataset contained 107,258 epochs with extreme imbalance (Wake 67.6%, N1 2.95%). After SMOTE oversampling on the training set, the model achieved an accuracy of 67.5%, macro F1 score of 31.4%, and Cohen’s Kappa of 0.34. An ablation study showed that CNN features alone (72.2% accuracy) outperformed handcrafted features (70.4%) and hybrid features (67.5%). The confusion matrix revealed that minority stages (especially N1, N3, REM) were poorly recognized. These results highlight that cross subject generalization remains a major challenge in EEG based sleep staging, and proper subject independent validation is critical to avoid overoptimistic claims.
Pendampingan Teknologi Informasi E- Smart Care sebagai Upaya Pencegahan Stunting secara Dini pada Remaja melalui Sekolah Siaga Kependukan (SSK) Subarkah, Pungkas; Hermanto, Nandang; Sari, Rida Purnama; Kholifah Dwi Prasetyo Kartika, Nur; Nasar Ghanim, Nadif; Arsi, Primandani
ABDINE: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2024): ABDINE : Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Riau Pesisir

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/abdine.v4i2.984

Abstract

Sekolah Siaga Kependudukan (SSK) di SMA Negeri 1 Wangon, merupakan SSK rintisan sekolah yang mengintegrasikan pndidikan kependudukan dan keluarga berencana, ke dalam beberapa mata pelajaran sebagai pengayaan materi pembelajaran, dimana di dalamnya terdapat pojok kependudukan sebagai salah satu sumber belajar peserta didik. Permasalahan yang dihadapi oleh mitra yaitu belum adanya teknologi informasi yang menunjang untuk pencegahan stunting secara dini di SSK. Metode pelaksanaan pengabdian masyarakat ini dilakukan dengan tahapan pra-pelaksanaan, tahap pelaksanaan dan tahap evaluasi. Dari hasil pelaksanaan kegiatan yang sudah dilakukan maka didapatkan  para peserta kegiatan mengikuti pelatihan dengan baik,  dengan menguasai materi selama pelatihan berlanggsung dan peningkatan kemampuan peserta dalam menggunakan teknologi infomasi E-Smart Care berbasis android dan website. Dengan terlaksana program pendampingan ini dari Tim Program Kemitraan Masyarakat (PKM) 2024, bahwa mitra mendapatkan peningkatan pengetahuan yaitu penggunaan aplikasi E-smart Care berbasis android serta para peserta mendapatkan peningkatan keterampilan cara mengoperasikan aplikasi secara benar. Hasil respon terhadap pelatihan ini yaitu rata-rata memberikan predikat “Sangat Baik”.
Sentiment Analysis of Skincare Product Reviews: A Comparison of Naïve Bayes and Support Vector Machine Algorithms Refida Septiana Putri; Reykha Putri Randika; Febi Dwi Sasmita; Pungkas Subarkah
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13227

Abstract

The rapid growth of the skincare industry has generated a massive volume of consumer reviews on e-commerce platforms, making manual sentiment analysis increasingly impractical. This study compares the performance of Multinomial Naïve Bayes and Support Vector Machine (SVM) for sentiment classification of skincare product reviews using the Sephora Products and Skincare Reviews dataset from Kaggle, consisting of 602,130 reviews. Unlike previous studies that often employ different datasets and experimental settings, this research evaluates both algorithms using a uniform pipeline, including automatic sentiment labeling based on rating values, text preprocessing, Bag of Words feature representation, and identical evaluation procedures. Model performance was assessed using Area Under Curve (AUC), Accuracy, Precision, Recall, F1-Score, and Matthews Correlation Coefficient (MCC). The results indicate that positive sentiment dominates the dataset (82.37%), resulting in a highly imbalanced class distribution. Multinomial Naïve Bayes achieved better performance than SVM on most evaluation metrics, with an AUC of 0.784, F1-Score of 0.764, Precision of 0.749, and MCC of 0.153, whereas SVM obtained an AUC of 0.497, F1-Score of 0.743, Precision of 0.695, and MCC of 0.000. The near-zero MCC and low AUC of SVM suggest that the model struggled to distinguish minority classes under extreme class imbalance despite achieving high accuracy. These findings highlight the importance of employing multiple evaluation metrics beyond accuracy when assessing classification performance on imbalanced datasets. Furthermore, the results indicate that Multinomial Naïve Bayes provided better performance than SVM under the dataset characteristics and experimental configuration used in this study.
SINKRONISASI DATA SISTEM MONITORING SKOLIOSIS IOT BERBASIS RESTFUL API LARAVEL MENGGUNAKAN METODE HTTP POLLING Dominic Dinand; Bagus Adi Kusuma; Pungkas Subarkah
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.9859

Abstract

Skoliosis merupakan kelainan tulang belakang yang memerlukan deteksi dini secara akurat. Pengukuran manual menggunakan scoliometer konvensional sering kali terkendala dalam dokumentasi data yang terfragmentasi. Penelitian ini bertujuan untuk membangun infrastruktur sistem monitoring skoliosis yang mengintegrasikan perangkat keras IoT dengan platform berbasis web untuk sinkronisasi data hasil pengukuran secara otomatis. Metode: Sistem dikembangkan menggunakan arsitektur RESTful API dengan framework Laravel dan Inertia.js. Proses sinkronisasi data dilakukan melalui metode HTTP Polling dengan interval tiga detik untuk memastikan data dari sensor digital scoliometer terkirim ke database MySQL dan tersaji secara real-time pada antarmuka pengguna tanpa membebani performa server. Hasil pengujian menunjukkan bahwa infrastruktur sistem mampu melakukan sinkronisasi data dengan tingkat keberhasilan 100% dan rata-rata waktu latensi di bawah 1 detik pada jaringan lokal. Fitur automatic polling berhasil memperbarui informasi klinis pasien dan riwayat pengukuran sudut Atr Angle (ATR) secara konsisten. Kesimpulan: Integrasi teknologi IoT dan arsitektur web yang dibangun memberikan solusi efektif dalam digitalisasi data pemeriksaan skoliosis. Sistem ini mempermudah tenaga medis dalam memantau perkembangan pasien melalui manajemen data yang terpusat dan sinkron.
Analisis Perspektif Mengenai Kebijakan Pemerintah Bergabung Dengan Board Of Peace Menggunakan IndoBERT dan NusaBERT Yanuar Wardanu; Pungkas Subarkah; Agus Pramono
Jurnal Minfo Polgan Vol. 15 No. 3 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i3.16145

Abstract

The Board of Peace is an international body initiated by U.S. President Donald Trump. The government’s decision to join this body has naturally sparked debate among the public, particularly on the social media platform X. The social media platform X (Twitter) is one of the best platforms for monitoring public opinion on various topics, including government policies—such as those implemented by President Prabowo Subianto. Twitter provides users with a space to follow the latest developments, participate in conversations, and express their opinions on public policy and leadership. In Indonesia, Twitter is frequently used for planning social events, sharing information, and voicing criticism or expectations toward national leaders. This study aims to analyze public sentiment regarding Indonesia’s joining the Board of Peace using the IndoBERT and NusaBERT algorithms. The dataset used, consisting of 290 data points, was obtained through data crawling techniques from the social media platform X. The research stages included Data Collection, Data Pre-Processing, Data Balancing, Fine-Tuning of the Transformer Model, and Model Evaluation (accuracy, precision, recall, and F1-score). The results of this study show that the IndoBERT algorithm achieved an accuracy of 90%, while NusaBERT achieved an accuracy of 97%. This indicates that the NusaBERT algorithm has a 7% advantage in accuracy. This study underscores the importance of selecting the appropriate algorithm to enhance the performance of sentiment analysis systems on public policy topics.
Multimodal Emotion Classification of Indonesian Memes on Platform X Using IndoBERT and YOLOv11 Pungkas Subarkah; Esti Widianti; Agus Pramono
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1742

Abstract

The widespread use of social media, particularly Platform X, has increased the popularity of memes as a multimodal communication medium that combines textual and visual elements to express emotions, opinions, and social reactions. This study proposes a multimodal emotion classification approach for Indonesian-language memes by integrating IndoBERT for textual analysis and YOLOv11 operating in image classification mode for visual analysis through a weighted late fusion strategy. An initial dataset of 2,810 Indonesian-language memes was collected through web scraping. After removing corrupted or unreadable images, 2,547 valid samples remained. Each meme was manually annotated into one of six emotion categories—Happiness, Disgust, Anger, Sadness, Fear, and Surprise—based on the annotators' judgment of the dominant emotion conveyed by the combination of text and image, following Ekman's Basic Emotion framework. The dataset was divided using a stratified 80:10:10 split into 2,037 training, 255 validation, and 255 testing samples. The validation set was used to determine the optimal fusion weight, while the held-out test set was reserved exclusively for final evaluation. The best-performing weighted late fusion model (α = 0.6) achieved 72.55% accuracy, 73.03% macro precision, 72.76% macro recall, and 72.66% macro F1-score on the test set. Within the constructed dataset, the proposed multimodal approach outperformed the evaluated IndoBERT-only and YOLOv11-only baselines, indicating that combining textual and visual information can improve emotion classification performance for Indonesian-language meme content.
Co-Authors A. Kholil Hidayat Abdallah, Muhammad Marshal Abdul Azis Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adhimah, Laily Farkhah Aditya Permana, Reza Afifah, Erika Luthfi Agus Pramono Akhmad Mustolih Ali Nur Ikhsan Alif Nur Fadilah Alifah Dafa Iftinani Alifian , Raditya Sani Alya Khansa Dzakkiyah Amin, M. Syaiful Amira Aida Rashifa Anggi Tri Dewi Septiani Anggraeni, Eling Sekar Anggraini, Nova Anshari, Muhammad Rifqi Anunggilarso, Luky Rafi Arbangi Puput Sabaniyah Argiyan Dwi Pritama Arsi, Primandani Astrida, Deuis Nur Augst Nurandini Aulia Dian Agustina Aulia Shafira Tri Damayanti Aunillah, Puteri Johar Awal Rozaq, Hasri Akbar Awali, Uston Azhar Andika Putra Azhari Shouni Barkah Azizan Nurhakim Azmi, Mohd Sanusi Azzahra, Delia Oktaviana Baehaqi Wahyu Kurniawan Bagus Adhi Kusuma Bagus Adhi Kusuma Bagus Adi Kusuma Baihaqi, Wiga Maulana Banu Dwi Putranto Bibit Raikhan Azzaki Bryan Jerremia Katiandhago Budi Utami, Dias Ayu Busyro, Muhammad Chendri Irawan Satrio Nugroho Chyntia Raras Ajeng Widiawati Cindy Magnolia Damayanti, Wenti Risma Darmo, Cahyo Pambudi Dava Patria Utama Dermawan, Riky Dimas Desi Riyanti Dewi Fortuna Dhanar Intan Surya Saputra Dias Ayu Budi Utami Dias Ayu Budi Utami, Dias Ayu Budi Didit Suhartono Dinar Mustofa Dini Ambarwati Dini Riandini Dominic Dinand Dwi Krisbiantoro, Dwi Dwi Putra, Ruly Niko Eka Nada Rinjani Elistiana, Khoerotul Melina Enggar Pri Pambudi Epri Anggraeni Esti Widianti Fadilah, Alif Nur Fandy Setyo Utomo Faridatun Nida Farizi, Amar Al Febi Dwi Sasmita Fiby Nur Afiana Fiby Nur Afiana Firmanda, Reza Arief Fitriya Maharani, Lulu Amnah Gina Cahya Utami Harun Alrasyid Hellik Hermawan Hendra Marcos Hendra Marcos, Hendra Hidayah, Debby Ummul hidayatulloh, hanif Husna Maulida Ika Romadoni Yunita Ika Romadoni Yunita Ikhsan, Ali Nur Ilham, Fatah Iphang Prayoga Irfan Santiko Irma Darmayanti Isnaini, Khairunnisak Nur Isnaini, Khairunnisak Nur Jali Suhaman Katiandhago, Bryan Jerremia Khoerida, Nur Isnaeni Kholifah Dwi Prasetyo Kartika, Nur Kisma, Atmaja Jalu Narendra Kusuma, Bagus Adhi Kusuma, Velizha Sandy Latifah Adi Triana Lestari, Tri Endah Widi Lestari, Vika Febri Luki Rafi Anuggilarso Maharani Kusuma Dewi Maria Angelina Cahyani Candrakasih Marlita, Reva Ma’ruf, Muhammad Merliani, Nanda Nurisya Mohammad Imron Muflikhatun, Siti Muhammad Marshal Abdallah Muhammad Rifqi Anshari Mustolih, Akhmad Nanda Nurisya Merliani Nandang Hermanto Nandang Hermanto Nasar Ghanim, Nadif Neta Tri Widiawati Nida, Faridatun Nikmah Trinarsih Nur Hidayah, Septi Oktaviani Nur Isnaeni Khoerida Nuraini , Rema Sekar Nurul Hidayati Permana, Reza Aditya Pramudya, Reyvaldo Shiva Prasetya, Eko Budi Prasetyo Kartika, Nur Kholifah Dwi Prastyadi Wibawa Rahayu Prayoga, Iphang Primandani Arsi Primandani Arsi Purba, Mariana Purwadi Purwadi Ragil Wilujeng Ramadani, Nevita Cahaya Ranggi Praharaningtyas Aji Ratih Anggraeni Ratih Anggraeni Rayinda Maya Anjani Refida Septiana Putri Reykha Putri Randika Reza Aditya Permana Reza Arief Firmanda Riandini, Dini Riyanto Riyanto Riyanto Riyanto Riyanto Riyanto Riyanto Rizki Sadewo Rizki Wahyudi Rofiqoh, Dayana Rohman, M. Abdul Romadoni, Nova Salma Rosana Fadilla Sari Rujianto Eko Saputro Sabaniyah, Arbangi Puput Sadewo, Rizki Salma Ngarifatul Khofiyah Salsabiela, Ayuni Saputra, Dhanar Sari, Rida Purnama Sarmini Sarmini Satrio Nugroho, Chendri Irawan Sekhudin, Sekhudin Septi Nurhayati Septi Oktaviani Nur Hidayah Septi Oktaviani Nur Hidayah Sholikhatin, Siti Alvi SITI ALVI SHOLIKHATIN Siti Alvi Solikhatin Siti Alvi Solikhatin Siti Rahayu Selamat Sugiarti Sugiarti Suhaman, Jali Susanto, Wachyu Dwi Syabani, Amin Syamsiar, Syamsiar Tarwoto, Tarwoto Tri Astuti Trian Damai Triana, Latifah Adi Tripustikasari, Eka Tripustikasari Triyo Ginanjar Pamungkas Umma, Rofiqul Utami, Melida Ratna Utomo, Anwar Tri V, Jay Velizha Sandy Kusuma Wachyu Dwi Susanto Wahyu, Herta Tri Wanda Fitrianingsih Wenti Risma Damayanti Wenti Risma Damayanti Widiawati, Neta Tri Wiga Maulana Baihaqi Yanuar Wardanu Yofi Yulianto Yuli Purwati Yunita, Ika Romadhoni Zahra Revadinika Apriliani