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All Journal Teknika Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal EMT KITA Pendas : Jurnah Ilmiah Pendidikan Dasar JTAM (Jurnal Teori dan Aplikasi Matematika) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan INTECOMS: Journal of Information Technology and Computer Science Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) Jurnal Informatika dan Rekayasa Perangkat Lunak Journal of Applied Engineering and Technological Science (JAETS) Jurnal Teknik Elektro dan Komputasi (ELKOM) JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Teknologi Informasi dan Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Pengabdian Nasional (JPN) Indonesia Journal of Applied Data Sciences Jurnal Cahaya Mandalika International Journal for Applied Information Management JUTECH : Journal Education and Technology COMMUNITY : Jurnal Pengabdian Kepada Masyarakat AJAD : Jurnal Pengabdian kepada Masyarakat International Journal Software Engineering and Computer Science (IJSECS) Aptekmas : Jurnal Pengabdian Kepada Masyarakat KNOWLEDGE: Jurnal Inovasi Hasil Penelitian dan Pengembangan Journal of Artificial Intelligence and Digital Business CKI On Spot JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI SmartComp Journal of Computer Science Application and Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Nasional (JPN) Indonesia kawanad Journal Innovations Computer Science International Journal of Applied Mathematics and Computing. International Journal of Computer Technology and Science International Journal of Information Engineering and Science Informasi interaktif : jurnal informatika dan teknologi informasi Computer Journal Journal of Engineering, Electrical and Informatics Jurnal Sinergi Digital dan Pemberdayaan Masyarakat
Betty Yel, Mesra
ST Ilmu Komputer Cipta Karya Informatika
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Sistem Inventory Barang Gudang Berbasis Web Studi Kasus Yayasan Indonesia Care Adzani, Adinda Mutiara; Mulya, Citra Pricylia Ananda; Ahluna, Faza; Febrianti, Syafira; Akbar, Yuma; Yel, Mesra Betty
AJAD : Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Divisi Riset, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/ajad.v4i1.284

Abstract

Along with the increasingly rapid development of technology, this can be characterized by increasingly sophisticated technology. Using computers as a tool in carrying out work in information technology is becoming increasingly common and developing in all fields. This research discusses implementing an equipment inventory information system at the Indonesia Care Foundation to increase the efficiency of managing equipment availability. This study includes requirements analysis, system design, and implementation of information technology-based solutions to monitor and manage equipment more effectively. Data collection methods involve interviews, observation, and documentation analysis. The research results show that implementing an inventory information system provides significant benefits, including increased recording accuracy, more efficient stock management, and better monitoring of equipment use and maintenance. This implementation's success positively contributes to operational efficiency and improves equipment management performance at the Foundation Indonesia Care.
Analisis Sentimen Tanggapan Publik Mengenai E-Tilang Melalui Twitter Menggunakan Algoritma K-Nearest Neighbor Septiansyah, Ade; Yel, Mesra Betty; Akbar, Yuma
TEKNIKA Vol. 18 No. 2 (2024): Teknika Juli - Desember 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.12773163

Abstract

Suatu kota harus memiliki sistem transportasi yang baik. Sistem tilang elektronik pemerintah untuk mengawasi lalu lintas disebut e-tilang. Banyak pelanggaran lalu lintas yang terjadi di jalan raya yang cenderung menyebabkan kecelakaan dan menambah kemacetan. Beberapa faktor penyebab pelanggaran lalu lintas termasuk ketidakpatuhan individu terhadap peraturan lalu lintas, misalnya larangan berhenti dan parkir di mana pun. Tujuan dari penelitian adalah untuk mengidentifikasi bagaimana pandangan masyarakat Indonesia tentang tilang elektronik dalam peneletian ini digambarkan melalui analisis sentimen tentang tilang elektronik dan mengelompokkan berbagai jenis komentar. Twitter adalah platform media sosial yang ideal untuk menyampaikan opini karena mudah digunakan, topik terkini, dan memungkinkan komentar. Setelah data dikumpulkan dari Twitter, preprocessing dilakukan untuk membersihkan data, tokenizing, case normalization, stopword, dan stemming. Selain itu, seleksi mengurangi noise dari label yang tidak relavan. Dataset terdiri dari 1.762 baris record tweets diklasifikasi menghasilkan dua kelas data kelas data positif dan kelas data negatif dengan total 1.342 data. Dalam studi ini, algoritma mesin pengklasifikasi K-Nearest Neighbor digunakan data yang sudah ada dari kelas data ini digunakan sebagai data pelatihan untuk mesin pengklasifikasi. Hasil evaluasi menunjukkan akurasi rata-rata 75,41%.
Analisa Sentimen Menggunakan Algoritma C4.5 Dan Naïve Bayes Dengan Topik Artificial Intelligence Pada Media Sosial Twitter (X) Prasetyo, Aji Dwi; Yel, Mesra Betty
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 5 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i5.11729

Abstract

Artificial intelligence (AI) atau yang biasa disebut dengan kecerdasan buatan manusia merupakan hal yang cukup ramai diperbincangkan dalam masyarakat saat ini dengan dampak yang positif yang signifikan dapat membantu meringankan pekerjaaan manusia. Namun disisi lain artificial intelligence juga memberikan dampak yang negatif yaitu dapat dapat mengambil alih pekerjaan manusia dan berpotensi menimbulkan kekacauan ekosistem informasi akibat dari propaganda yang berisi berita bohong dan disinformasi. Tujuan dari penelitian ini untuk mengetahui persepsi masyarakat terhadap dampak perkembangan artificial intelligence apakah positif atau negatif serta mencari nilai akurasi dari kinerja metode yang digunakan. Hasil dari penelitian ini dapat dijadikan referensi bagi pemerintah untuk mendukung pengambilan keputusan mengenai langkah yang dapat diambil dalam menghadapi dampak perkembangan artificial intelligence berdasarkan sudut pandang pada masyarakat luas. Algoritma yang digunakan adalah C4.5 dan Naïve bayes. Dari kedua metode ini digunakan sebagai hasil akhir akurasi dari penelitian ini dan menjadikan perbandingan nya antara satu sama lain.
Klasifikasi Jajanan Tradisional Jawa Tengah Dengan Metode Transfer Learning Dan MobileNetV2: Metode Transfer Learning Dan MobileNetV2 Aloisius Awang Hariman; Dadang Iskandar Mulyana; Mesra Betty Yel
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 8 No 1 (2023): JII Volume 8, Number 1, Januari 2023
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37159/jii.v8i1.15

Abstract

Perkembangan zaman di Indonesia telah membawa perubahan sejarah budaya, salah satunya adalah makanan atau jajanan tradisional. Jajanan tradisional merupakan makanan khas dari nenek moyang dan biasanya digunakan untuk acara atau sebuah tradisi, Masyarakat di Indonesia sudah cukup mengenal berbagai jenis jajanan tradisional dari daerah masing-masing namun untuk mengenal jajanan tradisional dari daerah lain dapat dibilang kurang memahami. Banyaknya jajanan tradisonal yang ada di Indonesia khususnya di pulau Jawa membuat peneliti tertarik untuk membuat program pengenalan jenis jajanan tradisional yang ada di Jawa Tengah berdasarkan dataset foto dengan 6 jenis jajanan tradisional yaitu Grontol, Lanting, Lumpia, Putu Ayu, Serabi Solo dan Wajik menggunakan metode Transfer Learning. Implementasi pengenalan citra ini dilakukan dengan memanfaatkan Pre-Trained model MobileNetV2 yang diambil fitur ektrasinya kemudian training CNN berjalan pada aplikasi Google Collaboratory dan Tensorflow. Dataset yang digunakan dalam pengujian sebanyak 1800 data training atau sebesar 80% dan 450 data testing atau sebesar 20% dengan melakukan pengujian sebanyak 50 kali dan batch size sebesar 32, maka diperoleh hasil akurasi sebesar 99,11% nilai loss sebesar 0.06. Keywords : jajanan tradisional, CNN, MobileNetV2, transfer learning, tensorflow
Optimasi Penerapan Metode Text Recognition Dalam Fitur Catatan Otomatis Berbasis Mobile Mulyana, Dadang Iskandar; Yel, Mesra Betty; Rahmanto, Muhammad Dzaky
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.634

Abstract

This study discusses the optimization of Machine Learning Kit Text Recognition in creating an automatic note feature based on mobile devices. Machine Learning Kit Text Recognition is used to recognize text captured from photos taken by users. In this study, a mobile application was developed to allow users to create automatic notes by taking pictures of documents or texts, which are then recognized and transformed into editable digital text. This feature can help users to create notes more quickly and easily, as well as avoid errors in manually typing the text. In addition, the test results show that Machine Learning Kit Text Recognition is capable of recognizing text with fairly high accuracy, effectively recognizing text from various texts and fonts, supporting uncommon languages, preserving user privacy, and being able to perform text recognition processes offline or with more efficient resources. Therefore, the automatic note feature can run well on mobile applications built using Machine Learning Kit Text Recognition technology.
Sentiment Analysis of Doctor’s Responses to Patient Inquiries in a Medical Chatbot: A Logistic Regression Approach Yel, Mesra Betty; Rodhiyah
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.80

Abstract

This study addresses the challenge of improving doctor-patient communication in medical chatbot systems by integrating sentiment analysis to classify doctor responses as positive or negative. The primary objective was to develop a model that enhances the emotional intelligence and appropriateness of chatbot interactions using Logistic Regression. The model achieved 98.63% accuracy, 99.68% precision, 95.90% recall, and 97.75% F1-score, demonstrating its high reliability in classifying sentiments with minimal misclassifications. While the model performs well, further improvements could focus on reducing false negatives to increase recall. The implications of this research are significant for digital healthcare, as the model enables chatbots to provide more empathetic, context-aware responses, improving patient engagement and overall communication. The novelty of this study lies in applying sentiment analysis within medical chatbot systems, contributing to the growing field of emotional intelligence in digital healthcare. The findings highlight the potential of sentiment analysis to enhance patient interactions, making medical chatbots more effective and human-like. This study provides a solid foundation for further advancements in healthcare chatbots, demonstrating the potential of machine learning to improve the quality of doctor-patient communication in a digital context.
Forecasting Beef Production with Comparison of Linear Regression and DMA Methods Based on n-th Ordo 3 Tundo, Tundo; Yel, Mesra Betty; Nugroho, Agung Yuliyanto
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24706

Abstract

Beef is considered a high-value commodity because it is an important food source of protein. Interest in beef is increasing along with increasing people's incomes and awareness of the importance of fulfilling nutrition. Demand for beef is expected to continue to increase. According to the Central Statistics Agency (CSA), beef production in Jakarta shows an increasing trend every year. In the last 10 years, beef production has increased significantly, but in 2020 there was a decrease in production of 7,240.68 tons due to the lockdown due to the corona virus outbreak. After that, in 2021, production reached 16,381.81 tons and will continue to increase in 2022 and 2023. Based on the above phenomenon, the aim of this research is to support the success and sustainability of the beef industry by ensuring that supply matches demand, resources are used optimally, and risks can be managed well. To predict beef production, an accurate method, model or approach is needed. One way to predict beef production in Jakarta is to use the Linear Regression and Double Moving Average (DMA) methodsThe way the Linear Regression and DMA methods work is to forecast based on concepts and properties. The concepts and properties of Linear Regression are models, functions, estimates and forecasting results, while DMA performs time series analysis based on moving averages. After analysis using MAPE, it was found that the algorithm that had the smallest error value was the linear regression algorithm with a percentage for the monthly period of 15% while for the year period it was 17% compared to DMA. So in this case it would be very appropriate to use the Linear Regression method from the error values obtained.
ANALISIS SENTIMEN PDI PERJUANGAN PASCA PILPRES 2024 DI JAKARTA TIMUR DENGAN NAÏVE BAYES Marsan, Alvin Cahya Pratama; Yel, Mesra Betty
KNOWLEDGE: Jurnal Inovasi Hasil Penelitian dan Pengembangan Vol. 5 No. 3 (2025)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/knowledge.v5i3.6782

Abstract

This study aims to determine the level of trust of the people of East Jakarta towards PDI Perjuangan after the 2024 Presidential General Election (Pilpres) and identify the tendency of sentiment formed, both positive, negative, and neutral. The background of this research is based on the complex dynamics of national politics, including the controversy over the candidacy of Gibran Rakabuming Raka as vice president, which caused pros and cons among the public and had the potential to influence public perception of PDI Perjuangan as the main supporting party. This study uses a quantitative approach with data collection techniques through a Likert scale questionnaire survey and essay questions distributed to respondents in the East Jakarta area. The data obtained is then processed through the text preprocessing stage, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and analyzed using the Naïve Bayes algorithm with the help of the RapidMiner application. The results of the study show that the majority of public sentiment tends to be positive, followed by neutral and negative sentiments. This shows that PDI Perjuangan still has a strong support base in East Jakarta despite the controversial national political dynamics. The findings of this study not only provide a comprehensive picture of local political perceptions, but can also be used as a strategic reference in the preparation of political communication patterns, strengthening the party's image, and planning the legislative campaign of PDI Perjuangan in the 2029 elections. ABSTRAK Penelitian ini bertujuan untuk mengetahui tingkat kepercayaan masyarakat Jakarta Timur terhadap PDI Perjuangan pasca Pemilihan Umum Presiden (Pilpres) 2024 serta mengidentifikasi kecenderungan sentimen yang terbentuk, baik positif, negatif, maupun netral. Latar belakang penelitian ini didasari oleh dinamika politik nasional yang cukup kompleks, termasuk kontroversi pencalonan Gibran Rakabuming Raka sebagai wakil presiden, yang menimbulkan pro dan kontra di kalangan publik serta berpotensi memengaruhi persepsi masyarakat terhadap PDI Perjuangan sebagai partai pengusung utama. Penelitian ini menggunakan pendekatan kuantitatif dengan teknik pengumpulan data melalui survei kuesioner skala Likert dan pertanyaan esai yang disebarkan kepada responden di wilayah Jakarta Timur. Data yang diperoleh kemudian diproses melalui tahapan preprocessing teks, ekstraksi fitur dengan metode Term Frequency–Inverse Document Frequency (TF-IDF), serta dianalisis menggunakan algoritma Naïve Bayes dengan bantuan aplikasi RapidMiner. Hasil penelitian memperlihatkan bahwa mayoritas sentimen masyarakat cenderung positif, diikuti dengan sentimen netral dan negatif. Hal ini menunjukkan bahwa PDI Perjuangan masih memiliki basis dukungan yang cukup kuat di Jakarta Timur meskipun terdapat dinamika politik nasional yang kontroversial. Temuan penelitian ini tidak hanya memberikan gambaran komprehensif mengenai persepsi politik lokal, tetapi juga dapat dijadikan sebagai acuan strategis dalam penyusunan pola komunikasi politik, penguatan citra partai, serta perencanaan kampanye legislatif PDI Perjuangan pada Pemilu 2029.
Forecasting Roof Tiles Production with Comparison of SMA and DMA Methods Based on n-th Ordo 2 and 4 Yel, Mesra Betty; Tundo, Tundo; Arinal, Veri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22225

Abstract

This research aims to predict roof tile production trends at one of the roof tile companies in Kebumen to assist company management in determining and providing management recommendations for the tile production that occurs. A comparison of Single Moving Average (SMA) and Double Moving Average (DMA) Forecasting methods was used to better accommodate trends in roof tile production data optimally. Where the forecast is presented for several steps ahead, and is equipped with a value measuring the accuracy of the forecast using Mean Absolute Percentage Error (MAPE), on roof tile production transaction data over 60 months, namely January-December 2019 to January-December 2023 to produce a monthly forecast for predicting roof tile production with n-th ordo 2 and 4. The total sample of training data processed was 1,415,987 records which were roof tile production transaction data, as well as data in January 2024 as test data (to test the accuracy of the forecast). The results of testing the forecast results produced a MAPE calculation of 6.6% for SMA with n-th ordo 2, while for n-th ordo 4 it was 7.2%. The MAPE value for DMA is 6.3% for n-th ordo 2, while for n-th ordo 4 it is 8.2%, which means the accuracy level is very good, namely above 90%. Based on the MAPE results obtained, the DMA method with n-th ordo 2 is a suitable method for carrying out periodic forecasting for roof tile companies in carrying out the production process to maintain stability and avoid unexpected events.
Enhancing Student Learning Engagement Through Game-Based Learning Implementation Using Naive Bayes Algorithm at BQ Boarding School Junior High Yusuf, Musalim; Yel, Mesra Betty
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.323

Abstract

This study investigates the impact of Game-Based Learning (GBL) on students’ learning interest and examines the effectiveness of the Naive Bayes algorithm in predicting engagement levels among junior high school students. Using a quasi-experimental quantitative design, data were collected from fifty seventh-grade students at SMP BQ Boarding School through pre-test and post-test questionnaires administered before and after a four-week GBL intervention. Statistical analysis revealed a significant increase in learning interest, with mean scores rising from 2.85 to 4.10 (t(49) = –10.24, p < 0.001), confirming the positive influence of GBL in promoting motivation and participation. The Naive Bayes classification model achieved an accuracy rate of 90%, with precision and recall values of 0.92 and 0.95 for the high-interest category, respectively. These results demonstrate that GBL effectively transforms classroom dynamics into interactive learning experiences while the Naive Bayes model reliably identifies students’ motivational levels. The combination of pedagogical innovation and predictive analytics presents a practical framework for educators to design adaptive interventions and data-informed teaching strategies. This study underscores the importance of integrating artificial intelligence and game-based methods in education to enhance engagement, motivation, and learning outcomes in the digital era.
Co-Authors ., Novriyenni A&#039;allya Nabilla Abdulloh Abror Khanif Adicandra, M. Dicky Adityo Purwo Nugroho Adjie Wongso Moerdyanto Adnan Wahab Adzani, Adinda Mutiara Agus Tanti Rahayu Ahluna, Faza Ahmad Bustomi Zuhari Ahmad Bustomi Zuhari Ahmad Saepudin Ahmad Syahran Zidane Akbar, Yuma Alaqsha Gilang Imantara Aldi Sitohang Almazar, Nufaisa Aloisius Awang Hariman Alwanto, Hilmi Amat Solihin Amelia, Ika Angel, Gadies Apriyanus Laia Ari Ramadhan Ari Sandi Fadhilah Arinal, Veri Aris Sufriman Aryanti, Putri Gea Aulia Maulida Axel Rajaliu Chaniago Azzizah, Putri Salfa Dhiyaa A’allya Nabilla B, Muhamad Hasbi Toharudin Bachrul Ulum Fajar Arrafi Bobby Arvian James Buaton, Relita Candra Milad Ridha Eislam Dadang Iskandar Mulyana` Desilia Silvida Devara Isbani Yusuf Dewi Riyanti Wibowo Dewi Sartika Br Ginting Dwi Swasono Rachmad Eddy Purwandono Edhy Poerwandono Eka Maheswara Elmira Rafa Putri Elviwani Elviwani Eri Novitasari Eva Widiyanti F, Joe Renaldy Fadhil Khanifan Achmad Fadiliah, M. Ilyan Fadlurohman Zaki Rifa’i Fallah, Ahlan Nur Fauzan Azima Febrianti, Syafira Fernanda Adhipramana Ferry Fajar Pratama Franido, Richard Frencis Matheos Serimole Fuzy Manik Gabriel Alezhandro Pakpahan Hayu Fadilatin Heny Nengsih Herdiyansyah herdi Hartanto Herlina Ade Saputri I Komang Dewa Ananda Putra Ibnazia Darnov Ikhwanul Kurnia Rahman Indah Wahyuni Irma Nur Najwa Jodi Juliansah Joey Abdiner Parlindungan Hutabarat Kastum Kastum Kiki Setiawan Kiki Setiawan Lailany, Afyra Ar’bah Mahyuddin K. M Nasution Mainia Mayasari Marsa Rifqa Marsan, Alvin Cahya Pratama Masaranto Laia Maulida, Aulia Miftahul Huda Mohammad Farhan Muhamad Fawaz Kamali Muhamad Ikbal Muhammad Dzaky Rahmanto Muhammad Fadlan Muhammad Faizal Lazuardi Muhammad Ikhsan Hakim Muhammad Rafli Fadillah Muhammad Syahrul Fattah Ramadhan Muhammad Umar Hafidz Mulya, Citra Pricylia Ananda Mutia Ramadhan Nandang Sutisna Nandang Sutisna Nandini Ladivina Sari Nesti Lutfianti Nova Dahliyanti Nova Dahliyanti Nova Mahendra Nugroho, Agung Yuliyanto Nur Aswan Multazam Nurfaishal, Muhammad Dzaky Nurhidayanti, Dian Nurjanah Nurjanah Nurul Khoiriyah Oka Prasetiyo Okta Saputra Oky Tria Saputra7 Opi Irawansah Prasetyo, Aji Dwi Putra, Adaffi Aditya Putri S, Dhiyah Labibah Nauli Rafi Bowo Raharjo Rafi Bowo Raharjo Rahmanto, Muhammad Dzaky Rasiban Rasiban Rasiban Regita, Anggit Nur Hannaa Rekardius Tafonao Richard Franido Ridho Arif Hidayat Ridho Arif Hidayat Rifai, Hanna Sabilla Rizkia Rachma Rodhiyah Romi Naufal Salam Ropindo Pelix Pane Rosiana, Grace Lolita Ryan Rivaldo Sahrul Hidayat Saidah, Andi Saifullah, Shoffan Salsabila Listania Iqhlima Salsabila Salsabila Sari Agustia Ningtyas Sarimole, Frencis Matheos Satria Wira Yudha Septiansyah, Ade Setiawan, Kiki Sfenrianto Sfenrianto Sfenrianto, Sfenrianto Shofy Kamila El Fajriah Siahaan, Rizkyrino Ronaldhino Simanungkalit, Giraldi Freddy Sinaga, Putri Cahyani SOPAN ADRIANTO Sopan Adrianto Sri Lestari Sri Lestari Sri Lestari Sugiono Sugiono Sugiyono Sugiyono Sugiyono Sugiyono Sutisna Sutisna Sutisna Sutisna T.Henny Febriana Harumy Tri Wahyudi Tri Wahyudi Tundo Tundo Tundo, Tundo Untung Surapati Untung Wahyudi Wahyu Hidayat Wewisman Tafonao Wijayanto, Willy Willy Wijayanto Wuri Nur Aini Yusi Gunawan Yusuf, Musalim Zaeny Miftah Ziyad Fernanda Syams Zulfikar Yusya Mubarak