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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi Jurnal Informatika Upgris Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Sistem Informasi dan Aplikasi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Indonesian Journal of Applied Informatics Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Informatika Global IJEEIT : International Journal of Electrical Engineering and Information Technology Journal of Information Systems and Informatics bit-Tech Jurnal Informatika dan Rekayasa Perangkat Lunak JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi Infotech: Journal of Technology Information Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Best : Journal of Applied Electrical, Science and Technology Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) JUTECH : Journal Education and Technology Journal Computer Science and Informatic Systems : J-Cosys Jurnal Mandiri IT International Journal Software Engineering and Computer Science (IJSECS) International Journal of Management Science and Information Technology (IJMSIT) Jurnal Teknik Informatika Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Journal of Scientech Research and Development Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Journal Of Artificial Intelligence And Software Engineering SmartComp CSRID Jurnal PETISI (Pendidikan Teknologi Informasi) Edu Komputika Journal Smatika Jurnal : STIKI Informatika Jurnal
Permatasari, Hanifah
Universitas Duta Bangsa Surakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Konsep Desain Sistem Informasi Manajemen Berkas Terpusat di Lembaga Amil Zakat Menggunakan Perspektif Nirlaba Hanifah Permatasari; Indah Nofikasari
JUSIFO : Jurnal Sistem Informasi Vol 7 No 2 (2021): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v7i2.9390

Abstract

The Amil Zakat Institution (LAZ) is a private non-profit organization that assists the Government of Indonesia in managing zakat. LAZ will produce performance reports and financial reports every year to ensure the accountability of the institution. The report is crucial, because it is the result of the performance of all LAZ partners and branch offices, so it must be stored and controlled properly. This performance reporting will be done centrally, so that in the process of making it, some authentic evidence that can be used as proof that it has been accepted and happened, or as a reference and complement, must be managed and distributed efficiently. The file management information system is a solution that can be applied for the management of report materials and the storage of valid reports. This research has resulted in business process flow design, system activity design, and file management information system interface design on LAZ. The theory of accountability of non-profit organizations is used as an analytical study along with the PIECES parameters, to ensure that the resulting design is in accordance with the supposed accountability. The design in this study has been presented systematically, so that it can be understood by LAZ managers or executives who are not from the information technology field.
Performance Evaluation of Naive Bayes and SVM in Classifying Public Opinion toward Game-Based Learning Policy Septi Dwi Supriati; Hanifah Permatasari; Vihi Atina
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2319

Abstract

This study aims to analyze public sentiment toward the EDUBLOX program on Instagram comments using the Naive Bayes and Support Vector Machine (SVM) algorithms. The research process consisted of data collection, text preprocessing, manual sentiment labeling, feature extraction using TF-IDF, model training, and performance evaluation. The labeling process was conducted manually by two independent annotators based on predefined sentiment guidelines, and annotation reliability was evaluated using Cohen’s Kappa coefficient. The obtained Cohen’s Kappa value was (\kappa = 0.7907), indicating substantial agreement and good consistency between annotators. The dataset was divided into training and testing data using an 80:20 ratio. The evaluation process used confusion matrix metrics, including accuracy, precision, recall, and F1-score, while the McNemar test was applied to determine whether the performance difference between the two models was statistically significant. The results showed that the SVM model achieved a testing accuracy of 76.67%, marginally outperforming the Naive Bayes model with a testing accuracy of 75.29%. In addition, SVM demonstrated slightly better precision, recall, and F1-score values compared to Naive Bayes. However, the McNemar test produced a p-value of 0.1366, indicating that the performance difference between the two algorithms was not statistically significant. Therefore, both models can be considered to have relatively comparable classification capabilities, although SVM showed a slight numerical advantage in sentiment classification performance on Instagram comments related to the EDUBLOX program.
Comparison of Machine Learning and Deep Learning Algorithms for Daily Retail Sales Forecasting Eko Purwanto; Bangun Prajadi Cipto Utomo; Hanifah Permatasari; Farahwahida Mohd
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.v12i2.32773

Abstract

This study presents a comparative analysis of four machine learning (ML) and deep learning (DL) algorithms: Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for predicting daily retail sales time series. The models were evaluated using key metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). Results show that RF and SVM outperformed both CNN and LSTM in terms of MAE (3500.28 and 3325.11, respectively) and RMSE (4660.60 and 4293.42, respectively). However, all models had negative R² values, indicating none could explain the variation in the data. LSTM, in particular, was the least efficient model, with an MAE of 54087.25, RMSE of 54257.51, and R² of -158.59. The poor performance of LSTM can be attributed to overfitting, improper model configuration, and misalignment with the nature of the data. The dataset used includes over 1,000 daily retail sales transaction records collected over one year, with key attributes like CustomerID, ProductID, Quantity, Price, TransactionDate, PaymentMethod, StoreLocation, ProductCategory, DiscountApplied, and TotalAmount. While the dataset is representative, its size and complexity may not have been sufficient for deep learning models like LSTM and CNN, which generally require larger datasets for optimal performance. This study highlights the challenges of using deep learning for retail forecasting and suggests future research should focus on refining models and incorporating external datasets to improve prediction accuracy.
Sistem Pendukung Keputusan Pemilihan Calon Penerima Beasiswa dengan Multi Objective Optimization on The Basis of Ratio Analysis Dewi Kuncorowati; Eko Purwanto; Hanifah Permatasari
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6579

Abstract

The selection of scholarship candidates has so far been conducted manually and is not well-documented. The selection process is based on the opinions or personal preferences of the selection team, which can lead to unfairness. There is no consistent standard for evaluating the criteria of scholarship candidates.This study develops a Decision Support System (DSS) for selecting school scholarship candidates using the Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method. This method is chosen for its ability to handle various qualitative and quantitative evaluation criteria, such as academic achievement, economic conditions, and extracurricular participation. The system is designed to produce candidate rankings objectively and transparently, facilitating fair and accurate decision-making by the school. Testing results indicate that the MOORA-based DSS can provide accurate and consistent recommendations, enhancing the efficiency of the selection process and stakeholder satisfaction. This research also opens opportunities for further development by integrating technologies such as machine learning to enhance system capabilities. The results of this study can assist in determining acceptance of the scholarship
Comparative Analysis of Classification Models for Sales Prediction in E-commerce: Decision Tree, Random Forest, SVM, Naive Bayes, and KNN Purwanto, Eko; Cipto Utomo, Bangun Prajadi; Permatasari, Hanifah; Mohd, Farahwahida
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5224

Abstract

The swift expansion of e-commerce has markedly heightened the necessity for precise sales forecasting, essential for efficient marketing tactics and inventory control. This research evaluates five classification models—Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN)—to predict sales outcomes using e-commerce transaction data. The models were assessed utilizing criteria including accuracy, precision, recall, F1-score, AUC, and Log Loss. The findings indicate that Random Forest exceeds the performance of the other models, with an accuracy of 97.5% and an AUC of 0.991, markedly outperforming the alternatives. This study presents a unique contribution by contrasting these classification models in the realm of e-commerce in Indonesia, yielding significant insights for the advancement of more effective predictive algorithms in informatics. The results not only enhance the optimization of marketing strategies but also enrich the comprehension of machine learning applications in sales forecasting. This study underscores the necessity of choosing the appropriate model for enhanced sales forecasting, with considerable ramifications for data-driven decision-making in the e-commerce sector.
Implementasi Sistem Rekomendasi Pengajuan Usulan Plt dan Plh Pejabat Struktural PNS Menggunakan Metode SAW di Pemerintah Kabupaten Sragen Muhamad Ilhamsyah Amara Ramadana; Afu Ichsan Pradana; Hanifah Permatasari
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.98559

Abstract

Abstrak : Pelaksana Tugas (Plt) dan Pelaksana Harian (Plh) adalah jabatan penugasan yang sangat penting jika jabatan struktural pada suatu perangkat daerah mengalami kekosongan. Karena jika suatu jabatan struktural kosong maka penanggung jawab, penentu kebijakan dan yang memiliki wewenang penunjukan tugas Aparatur Sipil Negara (ASN) baik Pegawai Negeri Sipil (PNS) ataupun Pegawai Pemerintah dengan Perjanjian Kerja (PPPK) dibawahnya menjadi tidak ada. Hal tersebut tentunya akan mengganggu jalannya fungsi pemerintahan, pengadministrasian , dan pelayanan publik suatu Perangkat Daerah. Proses pemilihan Plt dan Plh yang tidak transparan dan objektif sering menjadi kendala, sehingga diperlukan solusi berupa sistem rekomendasi berbasis teknologi. Penelitian ini mengembangkan sistem rekomendasi menggunakan metode Simple Additive Weighting (SAW) dengan mempertimbangkan empat kriteria utama: pangkat, masa kerja, kompetensi, dan potensi. Sistem ini terbukti mampu meningkatkan efisiensi rekomendasi hingga 30% dan mencapai tingkat akurasi sebesar 95%. Hasil penelitian ini berkontribusi pada pengelolaan sumber daya manusia di pemerintah daerah dengan menyediakan proses penilaian yang transparan, akuntabel, dan berbasis data untuk mendukung kebijakan pengisian jabatan struktural secara objektif.===================================================Abstract : Pelaksana Tugas (Plt) and Pelaksana Harian (Plh) are critical assignment positions when structural positions in a regional government agency are vacant. The absence of a structural position results in the lack of a responsible party, policy maker, and authority to delegate tasks to Aparatur Sipil Negara (ASN), whether they are Pegawai Negeri Sipil (PNS) or Pegawai Pemerintah dengan Perjanjian Kerja (PPPK). This situation can disrupt the functioning of governance, administration, and public services in a regional agency. The selection process which is often non-transparent and subjective, poses significant challenges. Therefore, a technology-based recommendation system is required. This study develops a recommendation system using the Simple Additive Weighting (SAW) method, considering four key criteria: rank, years of service, competence, and potential. The system has proven to improve decision-making efficiency by up to 30% and achieve an accuracy rate of 95%. The findings contribute to human resource management in local governments by providing a transparent, accountable, and data-driven assessment process to support objective policies in filling structural vacancies.
Klasifikasi Ancaman Keamanan Siber Menggunakan Algoritma Naive Bayes Irwan Budianto; Nurchim Nurchim; Hanifah Permatasari
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.104668

Abstract

Abstrak : Saat ini keamanan siber menjadi permasalahan utama didalam tata kelola keamanan informasi Pemerintah Daerah. Untuk mencegah terjadinya kerugian akibat serangan siber maka perlu dilakukan identifikasi dan klasifikasi terhadap ancaman siber secara cepat dan akurat. Sehingga diperlukan sebuah system untuk mengklasifikasikan ancaman siber yang terjadi. Penelitian ini adalah membangun sistem klasifikasi ancaman keamanan siber menggunakan algoritma Naive Bayes sehingga dapat dilakukan analisis data ancaman secara efektif dan mengklasifikasikan jenis ancaman dengan akurasi yang tinggi. Metode yang digunakan adalah pengumpulan dataset terkait log aktifitas serangan yang terekam di aplikasi Wazuh. Selanjutnya dilakukan preprocessing data untuk mendapatkan atribut yang sesuai dengan kebutuhan sistem. Penerapan algoritma Naive Bayes digunakan sebagai metode klasifikasi berdasarkan probabilitas atribut terhadap kategori ancaman. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu mengklasifikasikan ancaman keamanan siber dengan akurasi yang baik, sehingga dari system yang dibangun dapat ditentukan bahwa serangan yang terjadi pada area sistem operasi server atau aplikasi web serta mampu memberikan dukungan pengambilan keputusan yang lebih cepat dalam mitigasi serangan. Hasil pengujian menunjukkan performa yang sangat baik dari model Naive Bayes pada kedua kelas yaitu presisi=0.98, recall=1, f1-score=0.99, support=57.===================================================Abstract :Currently, cybersecurity is a major problem in the governance of regional government information security. To prevent losses due to cyber attacks, it is necessary to identify and classify cyber threats quickly and accurately. So a system is needed to classify cyber threats that occur. This study is to build a cybersecurity threat classification system using the Naive Bayes algorithm so that threat data analysis can be carried out effectively and classify types of threats with a high level of accuracy. The method used is collecting datasets related to attack activity logs recorded in the Wazuh application. Furthermore, data preprocessing is carried out to obtain attributes that match system needs. The Naive Bayes algorithm is implemented as a classification technique that evaluates the probability of attributes relative to threat categories. The findings indicate that this algorithm effectively categorizes cybersecurity threats with high accuracy. Consequently, the developed system can identify whether an attack targets the server operating system or the web application, while also enabling faster decision-making to support attack mitigation. The Naive Bayes model performs exceptionally well in both classes according to the test results, with precision=0.98, recall=1, f1-score=0.99, and support=57.
Zakat, Trust, and the Digital Window: A Comparative Analysis of Web Disclosure Practices Among Indonesia's National Zakat Institutions Hanifah Permatasari; Liana Trihardianingsih; Eko Purwanto
International Journal of Management Science and Information Technology Vol. 6 No. 2 (2026): July - December 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i2.7646

Abstract

The National Zakat Amil Institutions (LAZNAS) play a vital role in Indonesia's zakat ecosystem as independent non-governmental organizations connecting muzakki with beneficiaries nationwide, and official websites have become a primary medium through which they communicate identity, programs, and fund management accountability to the public. This study examines digital disclosure patterns across all 15 nationally licensed LAZNAS websites through qualitative content analysis conducted in June 2026, using a five-dimensional rubric adapted from global NPO web disclosure frameworks and enriched with Islamic accountability principles. Findings reveal considerable variation across dimensions and websites, with program reporting the most consistent and financial transparency the most varied. A fund efficiency summary is not yet explicitly available on any website in the sample, reflecting the absence of sectoral norms or regulatory provisions encouraging this type of public digital disclosure, and more than half of the websites face technical barriers that limit public accessibility. The study proposes technical website accessibility as a distinct analytical dimension in Islamic philanthropy web disclosure research and identifies innovative practices with potential for broader sectoral adoption.
Sistem Informasi Point of Sale Dengan Pencatatan Utang Berjalan Dan Rekomendasi Produk Muhammad Abdullah; Hanifah Permatasari; Agustina Srirahayu
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 11, No 2 (2026): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v11i2.961

Abstract

Toko Sembako Pak Sabar in Sukoharjo still records sales transactions, customer debts, and stock manually, leading to recording errors, data loss, stock inaccuracies, and operational losses. This study aims to design and build an integrated web and mobile-based Point of Sale (POS) information system with a running-debt recording module and a hybrid product recommendation system combining 30-day internal transaction data and 30-day Google Trends external data. The system was developed using the Waterfall SDLC model. The web admin application uses Laravel, the cashier mobile application uses Flutter, and MySQL as the database. Inter-platform communication uses REST API with token-based authentication via Laravel Sanctum. Testing used Black Box Testing across twelve functional scenarios and API security testing via Postman. Results show the system successfully records transactions, manages stock in real-time, handles complete running-debt mechanisms, generates automatic financial reports, and provides hybrid product recommendations with 80% Precision, 75% Recall, and 77.4% F1-score—higher than the internal-only approach (68% Precision, 62% Recall). All functions performed as required with an average response time below 2 seconds.
Sistem Informasi Manajemen Persediaan Bahan Baku Menggunakan Metode FIFO Berbasis Web Dzakaria Azizi; Wijiyanto -; Hanifah Permatasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Manajemen persediaan bahan baku di CV. Bengawan Jaya Abadi saat ini masih berjalan manual menggunakan buku fisik dan Microsoft Excel. Hal ini menyulitkan pelacakan stok secara real-time, memicu kesalahan pencatatan, dan meningkatkan risiko bahan baku kedaluwarsa akibat penumpukan stok lama. Penelitian ini bertujuan membangun sistem informasi manajemen persediaan bahan baku berbasis web dengan metode First In First Out (FIFO). Kehadiran sistem ini dirancang untuk mengotomatisasi pengelolaan data master, transaksi logistik, validasi Quality Control (QC), dan pengeluaran barang. Sistem dikembangkan menggunakan metode Waterfall dan pemodelan UML, dengan dukungan teknologi PHP, MySQL, serta framework Laravel dan Filament. Hasil pengujian melalui Black Box Testing dan User Acceptance Testing (UAT) membuktikan bahwa sistem berfungsi valid dan meraih predikat sangat layak. Implementasi sistem ini terbukti efektif dalam mengoptimalkan efisiensi pengelolaan persediaan bahan baku di CV. Bengawan Jaya Abadi.
Co-Authors Abdul Rohim Bayu Aji Prayogo Abdullah Sajad Aditya Gema Pratama Aditya Rachman Putra Afu Ichsan Pradana Agustina Niken Laraswati Agustina Purwatiningsih Agustina Srirahayu Ahmad Khairul Adi Aldova Herbryan Putra Alfisal Punjung Kurniawan Amad Tri Yanto Andi Saputro Andika Pratama Aprilisa Arum Sari Areta Reza Pradana Arif Wicaksono Septyanto Arif Wicaksono Septyanto Armeta Eka Putri Wibowo Artdelia Pingkan Salsabilla Artdelia Pingkan Salsabilla Atmojo, Fernando Winantya Bagas Setiadi Bahrul Aziz Rifai Bangun Prajadi Cipto Utomo Berlian Agustina, Anggun Brian Bagus Apriansah Cikal Fauziah Fatin Sawitri Claudia Swastikawati Dear Whizkid Aziiz Deleviar, Angky Fay Dewangga Ranggi Wiku Dewi Kuncorowati Didik Kurniawan Dinda Amelia Galuh Puspita Sari Dinda Rizky Asmara Dwi Hartanti Dwi Hartanti Dwi Hartanti Dwi Prasetiyo Dwi Septieni DWI WAHYUNINGTYAS Dwika Nur Arifin Dzakaria Azizi Eka Arya Saputra Eka Wijanarka Satata Putra Eko Purwanto Erlang Bagus Sadewa Fahriza Wahyu Akbar Faiq Muhammad, Nibras Fajar Saputra Fany Kusuma Dewi Farahwahida Mohd Farida Surya Jati Fatimah Naim Azahara FAULINDA ELY NASTITI Febrianti, Krisna Fitriana Sekar Kinasih Ghani Ardiesta, Alif Gian, Habib Nur Habib Nur Gian Hanif Nur Ahmad Hanif Sangga Paramanandi Hany Arya Wardhany Hasanah, Herliyani Henokh Lugo Hariyanto Hidayatullah, Muhamad Ichsan Ika Putri Pujianti Indah Nofikasari Inta Oktaviani Intan Oktaviani Irawan, Ridwan Dwi Irwan Budianto Janah, Selvi Miftakhul Joel Adikurnia Purnama Kukuh Supriyanto Liana Trihardianingsih Liana Trihardianingsih Linda Kusuma Dewi Ma'ruf Nur Muhammad Maharani, Tiara Putri Maulindar, Joni Mochamad Saefudin Moh. Muhtarom Mohd, Farahwahida Muhamad Ilhamsyah Amara Ramadana Muhammad Abdullah Muhammad Alwan Nurdin Muhammad Exsa Nugroho Muhammad Hashfi Rafid Muttaqin Muhammad Khovivul Anam Muhtarom, Moh Nafaria Rohmandani Ni’mal ‘Abdu, Aghni Rizqi Nibras Faiq Muhammad Novita Widyasari Nur Indahsari, Reggie Nurchim Nurlita, Catarina Ivanda Nurmalitasari Nurmalitasari Okta Ramma Saputri Otami Amalina Pipin Widyaningsih Pramono Pramono Putri Wibowo, Armeta Eka Rahadian, Dwiki Rasya Raihan Abdurrahim Al Ayyubi Ramadhan, Chandra Randis Wahyuni Rani Elsa Putri Ricky Eko Novianto Rico Yoga Pradana Ridho, Taufik Rini, Selfia Yustika Rivaldi Faqih Rahmawan Riyan Hidayah Robi Wariyanto Abdullah Rohmad Rifa Ardianto Rudi Susanto Sabar Sularno Salsabila Nurul Afifah Saputro, Khoirul Adi Saputro Sebastian, Angga Sejati, Ariya Putra Septi Dwi Supriati Setiadi, Bagas Setiawan, Stevania Shandra Isti Kharisma Auliya Alamsyah Sigit Gunawan Siti Fatimah Siti Munawaroh Sofa Marwati Sri Mahardhika Pratiwi SRI SUMARLINDA Stevania Frederica F.S Stevania Setiawan Sukma, Cahyaning Arum Surya Jati, Farida Syawaludin, M Ainur Telaga Nabila Putri Riyanto Titin Listiani Toni Iksanudin Tri Wulandari, Rahmawati Desi Triyono Triyono Triyono Triyono Vanya Tabitha Vihi Atina Wahyu Cahya Adi Putra Wahyudi Wahyudi Wanda Fadillah Wayan Hany Robbayani Wibowo, Anita Carolina Wijiyanto Wijiyanto - Yogi Setyawan Putra Pratama Yuda Abi Bagaskara Zeno Candragufa Muria