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All Journal Prosiding SNATIF Infotech Journal PROCEEDING IC-ITECHS 2014 JITEK (Jurnal Ilmiah Teknosains) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTECOMS: Journal of Information Technology and Computer Science IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Applied Informatics Jurnal Teras Kesehatan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu Journal of Information System, Applied, Management, Accounting and Research Jurnal ICT : Information Communication & Technology Jurnal Manajemen Informatika dan Sistem Informasi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Bumigora Information Technology (BITe) Journal of Advanced in Information and Industrial Technology (JAIIT) Infotek : Jurnal Informatika dan Teknologi Madaniya Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) IJISTECH Infotech: Jurnal Informatika & Teknologi Jurnal Pendidikan dan Teknologi Indonesia DEVICE International Journal Software Engineering and Computer Science (IJSECS) Jurnal Dinamika Informatika (JDI) Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Asian Journal of Community Services (AJCS) Mafaza : Jurnal Pengabdian Masyarakat STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Journal of Software Engineering and Information System (SEIS) Journal Of Artificial Intelligence And Software Engineering SmartComp RESLAJ: Religion Education Social Laa Roiba Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) International Journal of Community Service Implementation
Maulindar, Joni
Universitas Duta Bangsa Duta Surakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Veterinary Other

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PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

Abstract

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.
Challenges in The Academic Promotion Process: Perspectives From Faculty Members Maulindar, Joni; Awang Long, Zalizah; Che Mustapha, Jawahir; Purnomo, Singgih
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4127

Abstract

The academic promotion process faces various challenges that cause delays for faculty members in reaching higher ranks. This research aims to identify and analyze the factors contributing to delays in the academic promotion process. The research method used is a quantitative approach, with data collection techniques involving the distribution of questionnaires to faculty members who are currently undergoing or about to undergo the academic promotion process. The research results indicate that the lack of transparency in rules, policy changes, evaluation complexity, communication limitations, and institutional support all have a significant and equal impact on the challenges of academic promotion, with each factor having a coefficient of 0.2000. The R-squared and Adjusted R-squared values of 1.000 indicate that this model can explain the entire variation in academic promotion challenges. The high statistical significance of all coefficients suggests that these results are almost certainly not due to chance. Data analysis also shows that there is little autocorrelation in the model's residuals, and the residual distribution is nearly normal. These findings highlight the importance of transparency, policy consistency, effective communication, and institutional support in the academic promotion process. Improvements in these areas are expected to reduce the challenges faced by faculty members during the promotion process
Intelligent Traffic Sign Detection Using Yolov9 Pradana, Afu Ichsan; Harsanto, Harsanto; Maulindar, Joni
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4205

Abstract

This research examines the automatic detection and classification of traffic signs using artificial intelligence (AI) and computer vision technologies. As urban traffic increases, quickly and accurately recognizing traffic signs becomes a challenge, especially under adverse conditions such as bad weather and limited visibility. Conventional technologies that rely on human vision are prone to errors, so an automated solution is needed. This research uses the YOLOv9 algorithm for real-time traffic sign detection, utilizing the Generalized ELAN (GELAN) architecture that combines the advantages of CSPNet and ELAN for efficiency and accuracy. The dataset used consists of 1924 images processed through various stages, including data augmentation and normalization. The model was trained for 15 epochs with fairly high accuracy results in the prohibitory, danger, and mandatory sign categories. However, there were still some misclassifications, especially in the prohibitory category which was sometimes mistakenly detected as another category or background. Overall, the model performed well in detecting traffic signs in various environmental conditions, but still needs improvement to increase accuracy in certain cases.
Development of a Control and Monitoring System for an IoT Rover Based on ESP32 and LoRa in Hazardous Areas Ananda, Naufal Choirul; Maulindar, Joni; Ardiyanto, Marta
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6962

Abstract

This study developed a control and monitoring system for an IoT-based rover using ESP32 and LoRa, designed for hazardous area exploration. The system integrates two wireless communication methods: LoRa for long-range sensor data transmission and NRF24L01 for real-time control. The MQ-6 sensor detects LPG gas, while ultrasonic sensors function as an automatic safety system. A web-based interface built with Next.js and Supabase displays real-time sensor data. The system was developed using a prototyping method that includes requirement analysis, system design, hardware and software development, and testing. Test results show that LoRa transmits data reliably up to 15 meters without obstructions, and NRF24L01 supports stable control up to 100 meters. The MQ-6 sensor accurately detects gas presence, and ultrasonic sensors consistently stop the rover when obstacles are detected within 30 cm. The monitoring website successfully presents real-time data for operator decision-making. Overall, the system is effective and responsive for remote operation in high-risk environments, with strong potential for deployment in scenarios such as gas leaks, disaster zones, or other dangerous areas.
Application of Item-Based Collaborative Filtering Method for Skincare Recommendation System Hidayat, Almaranda Aisyanissa; Joni Maulindar; Indah, Ratna Puspita
Journal of Advances in Information and Industrial Technology Vol. 7 No. 1 (2025): May
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i1.588

Abstract

Skincare refers to skin care products. These products have different purposes depending on the user's skin type. Over time, public awareness and interest in skincare will increase, leading to a rapid growth of skincare products. Therefore, an Item-Based Collaborative Filtering (CF) method is used to develop a skincare recommendation system. This method will provide personalised recommendations by leveraging the behaviour data of other users with similar preferences and characteristics. This study uses user ratings and preferences for skincare products as data. This data is then used to build a CF model, which will be analysed to calculate user similarity patterns using the cosine similarity matrix. The application of the CF method demonstrates its effectiveness in matching user preferences, resulting in the most relevant product recommendations. This system not only increases the accuracy of recommendations but also helps users find products that meet their skin care needs, as the error rate in the system is 0.245.
Sistem Informasi Penjualan Roti Homemade Berbasis Web Pada Amaracake Solo Warihaji, Wijasena; Joni Maulindar; Bondan Wahyu Pamekas
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 3 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i3.937

Abstract

Amaracake merupakan bisnis yang bergerak dibidang kuliner dengan layanan pembuatan roti untuk acara. Dalam operasionalnya Amaracake mengalami masalah yang selalu dihadapi. Masalah tersebut diantaranya pemesanan kurang efektif karena kita belum tau jenis roti yang tersedia apa saja dan waktu pembuatan berapa lama sehingga perlu solusi untuk memecahkan masalah tersebut. Perancangan sistem informasi Amaracake ini bertujuan untuk membuat rancangan sistem yang dapat mengatasi masalah tersebut, dengan beberapa fitur yang dibutuhkan yaitu kelola data pemilik, memilih daftar jenis roti, melakukan pemesanan, laporan transaksi, pembayaran, integrasi data antar pengguna secara real time.  Perancangan sistem menggunakan metode waterfall dan analisa kelemahan sistem menggunakan  metode PIECES. Metode perancangan menggunakan UML (Unifield Modeling Language) dengan use case diagram, activity diagram dan class diagram. Perancangan sistem ini akan membantu dalam pengelolaan penjualan pada amaracake .
Pemodelan Sistem Informasi Rental Playstation Berbasis Web Pada Sanjaya Playstation Menggunakan Metode Waterfall Pramoedya Ananta Dzikri; Vihi Atina; Joni Maulindar
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 3 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i3.939

Abstract

Seperti usaha rental Sanjaya Playstation adalah satu – satunya  dari usaha  rental  yang menyewakan mainan Playstation (PS) yang berlokasi di Jalan Dalangan-Tawangsari Karangasem Kateguhan Tawangsari, Kec. Tawangsari, Kabupaten Sukoharjo. Permasalahan utama dalam usaha rental PlayStation adalah ketidakefisienan data yang hanya ditulis di kertas, menyebabkan risiko kehilangan, kerusakan, kesulitan pencarian, dan kurangnya informasi ketersediaan serta hambatan pemasaran. Solusi yang diajukan adalah sistem rental PlayStation berbasis web yang menyajikan informasi jenis, ketersediaan, dan harga sewa PlayStation di "Sanjaya Playstation" secara online. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi berbasis web guna meningkatkan efisiensi dan efektivitas operasional di "Sanjaya Playstation" Tawangsari, Sukoharjo. Metode penelitian meliputi observasi, wawancara, dan studi literatur, menggunakan metode pengembangan sistem waterfall. Hasil menunjukkan sistem mampu menampilkan jenis PlayStation, menghasilkan kwitansi, memproses penyewaan oleh admin, dan membantu dalam pengelolaan data pelanggan. Sistem ini diharapkan memperluas jangkauan pemasaran dan meningkatkan omset usaha.
Sistem Rekomendasi Kuliner Ikonik Kota Solo Menggunakan Metode Content Based Filtering Warta, Danu; Pramono, Pramono; Maulindar, Joni
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i1.1870

Abstract

Content-Based Filtering is a user-independent method, meaning it does not rely on whether an item is new or previously selected by other users. This study aims to design and develop a recommendation system for iconic culinary places in Solo City using the Content-Based Filtering method. The system helps tourists find culinary options based on individual preferences such as food name, rating, and price. Culinary data was collected through web scraping from Google Maps using the Instant Data Scraper extension. The data is processed using the TF-IDF algorithm and cosine similarity to calculate the similarity between content features. The system development follows the Rational Unified Process (RUP) with four phases: inception, elaboration, construction, and transition. It is built using PHP with the Laravel framework and MySQL database. The system provides a list of culinary recommendations complete with images, names, ratings, addresses, and prices. Black-box testing on six main scenarios showed 100% success, proving the system meets functional requirements. The final recommendation results show a similarity score above 70%, indicating accurate and relevant suggestions. This system helps users discover Solo's iconic culinary spots more efficiently and according to their preferences.
IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI PEMINATAN SISWA SMA NEGERI 1 BANYUDONO Nurfadilah, Nurfadilah; Susanto, Rudi; Maulindar, Joni
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 3 (2025): EDISI 25
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i3.5857

Abstract

Penentuan peminatan siswa di tingkat SMA merupakan aspek penting dalam menunjang proses pembelajaran yang sesuai dengan potensi dan minat siswa. Penelitian ini bertujuan mengimplementasikan algoritma Naive Bayes tipe CategoricalNB untuk memprediksi peminatan siswa di SMA Negeri 1 Banyudono berdasarkan nilai beberapa mata pelajaran. Tiga paket peminatan yang digunakan yaitu: Paket 1 (Matematika Lanjut, Fisika, Kimia, Biologi, PKWU), Paket 2 (Geografi, Ekonomi, Kimia, Informatika, PKWU), dan Paket 3 (Geografi, Ekonomi, Sosiologi, PKWU). Data nilai dikonversi menjadi kategori A (?90), B (80–89), C (70–79), dan D (<70), lalu diproses melalui encoding dan dibagi menjadi data latih dan uji dengan skema 70:30 dan 80:20. Pengembangan sistem menggunakan model SDLC Waterfall. Hasil pengujian menunjukkan akurasi sebesar 84% pada skema 70:30 dan 82% pada skema 80:20, dengan precision, recall, dan F1-score yang cukup stabil. Sistem ini diimplementasikan dalam dashboard berbasis Streamlit untuk memudahkan siswa melakukan prediksi dan pihak sekolah melakukan pemantauan. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes cukup efektif dalam mengklasifikasikan peminatan siswa jika data telah diproses dengan tepat dan sistem didukung antarmuka yang ramah pengguna.
Implementasi Algoritma Random Forest untuk Prediksi Permintaan dan Optimasi Stok pada Sistem Manajemen Inventori Layanan Pengiriman Makanan Ichwani, Achmad; Irawan, Egie; Wahyu Aji Saputro, Lintang; Putra Pradana, Gibrand; Maulindar, Joni
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2025
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/e99md674

Abstract

Industri pengiriman makanan menghadapi tantangan besar dalam pengelolaan inventori akibat sifat produk yang mudah rusak dan fluktuasi permintaan yang tinggi. Penelitian ini bertujuan mengembangkan sistem prediksi permintaan dan optimasi stok berbasis algoritma Random Forest, menggunakan metode CRISP-DM dengan data historis dari Kaggle dan implementasi sistem web berbasis Python Flask. Hasil penelitian menunjukkan Random Forest memiliki kinerja terbaik dibandingkan Decision Tree dan Linear Regression, dengan skor MSE 109.570,40, RMSE 331,01, dan R² sebesar 0,26. Skor R² ini secara spesifik mengindikasikan bahwa model hanya mampu menjelaskan 26% dari variabilitas data permintaan. Performa yang terbatas ini utamanya disebabkan oleh ketiadaan variabel eksternal yang krusial seperti data cuaca atau hari libur nasional dalam dataset yang digunakan, yang diketahui sangat mempengaruhi dinamika pasar.Meskipun demikian, model ini berhasil membuktikan keunggulan Random Forest dalam menangani hubungan non-linier dan dapat mendukung estimasi kebutuhan stok mingguan melalui sistem manajemen inventori yang fungsional. Visualisasi seperti grafik feature importance dan heatmap korelasi juga diimplementasikan untuk membantu pemahaman pola data serta pengambilan keputusan. Penelitian ini menegaskan potensi besar machine learning sebagai fondasi sistem prediksi di rantai pasok makanan, dengan rekomendasi utama untuk pengembangan selanjutnya adalah memperkaya dataset dengan variabel eksternal guna meningkatkan akurasi model secara signifikan.
Co-Authors Abdullah Abdullah Syaifudin Aditya Rachman Putra Afu Ichsan Pradana Agil Husnul Khotimah Agustina Srirahayu Ahmad Qashid H Ahmad Setiawan Aji, Sindhu Purnomo Akbar Galih Saputra Akbar Akbar, Akbar Galih Saputra Akbar, Reza Maulana Aldin Fathiray Ananda, Naufal Choirul Andreas Abi Permana Andrianto, Albertus Ari Anggita, Febri Arif Anisatul Farida Annas Setiawan Prabowo Anugrah Putra, Muhammad Ardani, Hasby Arif Ardhianto, Aan Ardiyanto, Kevin Ardiyanto, Marta Arif Eko Fitrianto Arif Wicaksono Septyanto Arnan Dwi Arsandy, Noelino Grevansha Atina, Vihi Avianto Adi Pratama Awang Long, Zalizah Azza Al Abbas, Abdullah A’an Jati Susilo Badrudin, Muhamad Bagas Mutaqqi Bagaskara, Ikrar Bagos Erwanto Bagus Prakoso, Ahmad Bahrul Aziz Rifai BAHTIAR, YUSUF Bayu Tri Pramono Berlian Agustina, Anggun Binuko, Raafika Studiviani Dwi brigitta harlim Che Mustapha, Jawahir Christopher Jody Widiyono Da Costa, Alexandre Dewi, Nandita Dewi, Nandita Sekar Sukma Dhimas Arya Rakadipa Didik Kurniawan Difan Agra Susilo Dika Adi Pratama Dimas Abimanyu Sutrisno Putro Dimas Cahyo utomo Dison Librado Dita Putra Pratama Divangga Revansa Arya Pradhana Dwi Hartanti Dwi Hartanti Dwi Hartanti Dwi Kurniawan Saputro Dwiirawan, Ridwan Dyah Aprimavista Cahyani Edy Kurniawan Eko Purwanto Em Sutrisna Enggar Wijaya Putra Erlinawati, Mira Ery Permana Yudha Ester Anugrayningtyas Fachruddin Edi Fandi Aziz Pratama Fathur Iqbal Hilmi Ulhadi Faulinda Ely Nastiti FAULINDA ELY NASTITI Firdaus, Azkha Brilliant Fitria Eko Nurjanah Fitroh Ahmad Abdul Aziz Frisca Tri Arumsari Guterres, Juvinal Ximenes Hafid Affan Wahid Hamna Zakiya , Nasywa Hani Rifdah Azizah Hanif Hilmi, Muhammad Hanif Nur Ahmad Hari Windiyastuti Harsanto, Harsanto Hartanti, Dwi Hartanto, Didik Mayur Hasanah, Herliyani Hidayat, Almaranda Aisyanissa Hiyarunnisa Kahes Waypi Ichsan Pradana, Afu Ichwani, Achmad immaculata yolia dewi Widayanti Indah, Ratna Puspita Indrastata, Ilham Buyung Indriyas Kukuh Wijayanti Intan Oktaviani Iqbal Hanan Junaidi Irawan, Egie Irawan, Ridwan Dwi Istiana Hanifah Istiqomah, Yasinta Jawahir Che Mustapha Yusuf Jawahir Che Mustapha Yusuf Jofan Fathurahman Juvinal Ximenes Guterres Karlina Kusuma Ningrum Kevin Yoga Ananta Kurniawan, Daniel Ade Leny Monica Lidia Earlene Rendhiva Lola Sekar Arum Lufti Puspitasari Margaretha Evi Yuliana Margaretha Evi Yuliana Mashkul Ryan Ibrahim Matin Muhith Meraldy Fiko Rastio Ajie Mink Poo Lexy Utomo Mink Poo Lexy Utomo Moh Muhtarom Moh. Muhtarom Muhammad Daivany Nur Auliya Saleh Muhammad Nur Ikhsanudin Mulyanto, Hari Munawaroh, Maysani Mustapha, Jawahir Che Nadia Hepyntha Nailurrizqi, Adistya Nanda Ramadhani, Fikko Nandita Sekar Sukma Dewi Nibras Faiq Muhammad Ningrum, Karlina Kusuma Nugroh, Heri Nur Auliya Saleh, Muhammad Daivany Nur Cahyo Nugroho Nurchim Nurchim Nurfadilah Nurfadilah Nurlaili, Dewi Nurohman Nurohman, Nurohman Nurrohman Oktaviyana Dwi Hendra Jati Pamekas, Bondan Wahyu Permatasari, Hanifah Praba M.A.R.K Pradana, Afu Ichsan Pradana, Gibran Arya Prajadi Cipto Utomo, Bangun Pramoedya Ananta Dzikri Pramono Pramono prastiwi, yuyun Prastya, Alvian Bagus Prastyo, Okik Dwi Pratiwi, Dinita Christy Pujiati Edy Santoso, Elysa Mei Puput Dwi Mandiri Puput Dwi Mandiri Purwanto, Eko Putra Pradana, Gibrand Raafika Studiviani Dwi Binuko Raditya Koesyan Dipo P Rais Suryo Wahono Rendi Enggar Bintang Pratama Restu Gilang Wijanarko Reza Mar Hendra Putra Rifa'i, Rifan Rifan Amirul Hafizh, Muhammad Rifqi Firdausi Arafad Riska Rosita Rizki Hendra Rizqy Mahendra Abdul Rahman Romy Rajawali Nusantara Saifullah Rudi Susanto Saifudin Umar Sandy Yustisio O Saputra, Muchammad Yoga Setiawan, Gilang Setya Pradhana, Wahyudi Shelvi Azizah Sindhu Purnomo Aji Singgih Purnomo SRI SUMARLINDA Studiviani Dwi Binuko, Raafika Suci Bunga Pritalina Sudarminingsih Sudarminingsih Sulistyo Adi Prasetyo Syahrul Agung Fathoni Tasya Mutiara Diva Tasya Mutiara Diva Tesalonika, Angel Titi Jayanti Tiur Bunga Gadissa Tory, Alfa Rado Andre Yusa Saka Vita Sofia Prihatini Wahyu Adi Pratama Wahyu Aji Saputro, Lintang Wahyu Kuncoro Wahyu Pamekas, Bondan Warihaji, Wijasena Warta, Danu Widayanti, immaculata yolia dewi Wihan Perkasa Nugraha Putra Wijayanti, Indriyas Kukuh Wijayanti, Sefi Ayuk WIJI LESTARI Wiji Lestari Wijiyanto Yafa Arsyida Aulia Rakhma Yasinta istiqomah Yeyen Santi Putri Yoma Patria Risky, Satya Yusuf Bahtiar Zaenuar Erfandi Zalizah Binti Awang Long