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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika ELKHA : Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System JADECS (Journal of Art, Design, Art Education and Culture Studies) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi dan Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JE (Journal of Empowerment) Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)

Ramadhan Rakhmat Sani

Universitas Dian Nuswantoro

Author-ID : 587814

Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Mechanical Engineering Public Health Social Sciences Other

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Deep Learning Factor Investing in the Indonesian Stock Market Atha Rohmatullah, Fawwaz; Alzami, Farrikh; Rakhmat Sani, Ramadhan; Novita Dewi, Ika; Winarno, Sri; Sulistyono, Teguh
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112549

Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe > 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -> β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.

Mitigating Class Imbalance in DDoS Detection: The Impact of Random Over Sampling on Machine Learning Performance Ghozi, Wildanil; Hussein, Jasim Nadheer; Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Paramita, Cinantya; Supriyanto, Catur
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.95037

Distributed Denial of Service (DDoS) attacks are a major cybersecurity threat, involving malicious traffic generated from numerous compromised sources to overwhelm and disable targeted services. Although machine learning (ML) has shown promise in detecting DDoS attacks through network traffic analysis, a key challenge remains: the class imbalance in datasets such as UNSW-NB15, where normal traffic significantly outweighs attack instances. This imbalance leads to biased predictions and degraded detection performance for minority attack classes. To address this issue, our study investigates the impact of Random Over Sampling (ROS), a simple yet effective balancing technique on improving detection accuracy in multi-class DDoS classification tasks. While prior works have primarily focused on ensemble algorithms or feature selection, our approach is distinct in emphasizing the effect of data balancing on macro evaluation metrics such as macro precision, macro recall, and macro F1-score. ROS was selected over more complex alternatives, such as SMOTE or ADASYN, due to its computational efficiency and ability to establish a performance baseline without introducing synthetic noise. We evaluate four machine learning algorithms: Decision Tree, NaÃ¯ve Bayes, Random Forest, and XGBoost, using the UNSW-NB15 dataset. The results show that Decision Tree combined with ROS yields the highest improvement in macro F1-score, increasing by 36%. However, this improvement is accompanied by a moderate reduction in accuracy for certain algorithms. These findings highlight the critical role of class balancing in enhancing the reliability of DDoS detection models, especially in imbalanced multi-class scenarios.

Rancang Bangun Sistem Try Out Berbasis Paperless untuk Evaluasi Hasil Belajar Siswa dengan MVC Sani, Ramadhan Rakhmat; Kurniawan, Defri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 3: Juni 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Sistem pelaksanaan Ujian Nasional di Indonesia saat ini mulai beralih menggunakan komputer sebagai media dalam pelaksanaannya menggantikan sistem yang lama. Sebagai bentuk dukungan dalam mengurangi penggunaan kertas dalam pembelajaran dan evaluasi baik lembar soal maupun materi di sekolah. Tujuan dari penelitian ini adalah menghasilkan purwarupa model sistem tryout berbasis paperless untuk evaluasi hasil belajar siswa untuk menggantikan pemakaian kertas dengan konsep yang diberikan oleh media komputer. Sehingga memberikan keuntungan dalam efisiensi waktu dan biaya, mengurangi kecurangan serta mempercepat dalam proses evaluasi. Metode yang digunakan dalam pengembangan aplikasi ini menggunakan Rapid Application Development (RAD) yang meliputi tahapan analisa kebutuhan perangkat lunak, perancangan perangkat lunak, implementasi perangkat lunak dan pengujian perangkat lunak dengan penerapan konsep Sistem Development Life Cycle (SDLC) sehingga cepat untuk dievaluasi oleh pengguna. Untuk bahasa pemodelan sistem menggunakan UML (Unified Modeling Language) yang terdiri use case diagram, sequential diagram dan pemodelan database. Penggunaan framework Codeigniter memberikan kemudahan dalam konsep Object Oriented Programing (OOP) dengan menggunakan arsitektur web MVC (Model, View, Controller). Pemisahan logika pembuatan kode pada tampilan website menjadikan lebih terstruktur, sederhana dan aman sehingga memberikan kemudahan bagi developer maupun programmer dalam pengembangannya tanpa harus dimulai dari awal. Hasil pengujian sistem menggunakan blackbox testing menunjukan hasil yang baik dan sudah mencapai 90% dari prinsip usability yang telah diimplementasikan.AbstractThe current National Examination System in Indonesia has begun to switch to using computers as a medium in its implementation to replace the old system. As a form of support in reducing paper use in learning and evaluating both question sheets and material at school. The purpose of this study was to produce a prototype paperless based tryout system model for evaluating student learning outcomes to replace paper use with the concepts provided by computer media. So as to provide benefits in time and cost efficiency, reduce fraud and accelerate the evaluation process. The method used in the development of this application uses Rapid Application Development (RAD) which includes the stages of software requirements analysis, software design, software implementation and software testing with the application of the concept of System Development Life Cycle (SDLC) so that it is quickly evaluated by users. For system modeling languages use UML (Unified Modeling Language) which consists of use case diagrams, sequential diagrams and database modeling. The use of CodeIgniter framework provides convenience in Object Oriented Programing (OOP) concepts using the MVC web architecture (Model, View, Controller). Separation of logic in making code on the website display makes it more structured, simpler and safer so that it makes it easy for developers and programmers to develop without having to start from scratch. The results of testing the system using blackbox testing showed good results and has reached 90% of the usability principle that has been implemented.

Securing Medical Images Using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for Image Steganography Pramudya, Elkaf Rahmawan; Handoko, L. Budi; Harjo, Budi; Sani, Ramadhan Rakhmat; Sari, Christy Atika; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Sarker, Md. Kamruzzaman
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4426

Steganography is a technique for embedding secret information into digital media, such as medical images, without significantly affecting their visual quality. The primary challenge in medical image steganography is preserving the quality of the cover image while ensuring robustness against distortions such as compression or data manipulation attacks, which may impact diagnostic accuracy. This study proposes an enhanced steganographic method based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to improve the security and robustness of medical image embedding. DWT decomposes the medical image into four frequency sub-bands (LL, LH, HL, HH), while SVD is applied to embed the secret image while maintaining essential medical features. Experimental results show that the proposed method achieves a PSNR value of up to 78 dB and an SSIM value approaching 1, indicating that the stego image quality is nearly identical to the original cover image. Compared to previous DCT-SVD and IWT-SVD-based approaches, the DWT-SVD method offers superior robustness and imperceptibility, particularly in preserving image quality in complex-textured medical images. This method contributes to enhancing data security in telemedicine and AI-based medical imaging applications by ensuring that sensitive medical data remains protected while preserving image integrity for diagnostic use.

Fuzzy tahani Implementation for Food Nutritional Status in Achieving Balanced Nutritional Dietary Dewi, Ika Novita; Priyo Utomo, Rino Agung; Sani, Ramadhan Rakhmat
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3644

Fulfilling nutritional needs with the Recommended Dietary Allowances (RDA) shows the average value of the number of vitamins, protein and other nutrients the body needs to function properly. Each person's nutritional needs are different, so the RDA can be calculated based on different age groups and gender. In general, there are still many people who do not know the nutritional value of food and consume food without considering whether it meets the body's needs. This usually happens because calculating the RDA value is not yet familiar to do. Efforts are needed to increase awareness about the importance of a balanced diet through RDA calculations. Calculation and determination of nutritional status using the RDA number serves as a measuring tool to monitor whether a person's nutritional intake is in accordance with daily needs. This research developed a web-based application to calculate RDA numbers and group RDA numbers into nutritional status of less, enough, or more. Determination of nutritional status is carried out using the fuzzy tahani method, by displaying the results in percentage form, so that users can easily see the proportion or percentage of nutritional status obtained. This application not only calculates nutritional values, but provides a food record feature to help users manage healthy eating patterns.

Implementing BOOM in Designing a Knowledge Management System for the Information Systems Study Program at Dian Nuswantoro University Sani, Ramadhan Rakhmat; Sukamto, Titien S.; Rohmani, Asih; Maszuda, Akbar Alvian
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.3677

Knowledge management within the Information Systems Study Program at Dian Nuswantoro University is currently still at an ineffective stage. Although the program focuses on the development and distribution of information systems, the lack of an implemented Knowledge Management System (KMS) has hindered the effective organization and utilization of knowledge within the department. By implementing a KMS, the program could more easily manage, store, and collaborate on knowledge assets. To address this issue, a web-based KMS design is proposed using the Business Object Oriented Modelling (BOOM) method. This method involves several stages: SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, discovery, construction, and final verification and validation—each aimed at optimizing knowledge management within the academic scope of the study program. The design process was supported by data collected through questionnaires distributed to both faculty members and students. The final output of this study includes a test scenario and user interface design for the Information Systems Program's Knowledge Management System website. This proposed design is expected to enhance and streamline knowledge management within the department.

PENYULUHAN GAME & PSIKOLOGI 2.0 KEPADA PESERTA DIDIK DAN GURU Budi, Setyo; Gamayanto, Indra; Novianto, Sendi; Haryanto, Hanny; Wibowo, Sasono; Sani, Ramadhan Rakhmat; Sukamto, Titien Suhartini
JE (Journal of Empowerment) Vol 4, No 1 (2023): JUNI
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/je.v4i1.2851

ABSTRAK Kata perubahan merupakan kata penting karena perubahan membutuhkan tingkat kreativitas tinggi serta implementasi bertahap. Dalam dunia game, perubahan terjadi sangat signifikan. Game yang ada sekarang dapat memberikan dampak besar terhadap pemainnya yaitu dampak positif dan negatif. Peserta Didik/Murid dan Guru harus mengetahui Â dampak yang diakibatkan dari permainan game, Â kemudian mengetahui solusi atau alternatif apa yang terbaik untuk mengatasi dampak tersebut. Agar pemahaman dasar tentang game ini dimiliki oleh Murid dan Guru SMA Negeri 3 Semarang, maka diperlukan penyuluhan terkait permaian game dan psikologi. Metode yang digunakan pada Pengabdian kepada Masyarakat (PkM) ini adalah model penyuluhan. Untuk mengetahui indikator keberhasilan pada penyuluhan ini yaitu dengan membagikan kuisioner terkait dengan materi yang disampaikan. Hasil dari penyuluhan ini adalah Murid dan Guru dapat memahami dampak yang lebih jelas mengenai game, serta solusi apa saja yang perlu dipertimbangkan untuk mendapatkan hal-hal yang positif dari permainan game.Â ABSTRACTThe word change is an important word because change requires a high level of creativity and gradual implementation. In the world of games, changes are very significant. Games that exist now can have a big impact on players, namely positive and negative impacts. Learners/Students and Teachers must know the impact caused by playing games, then know what solutions or alternatives are best to overcome these impacts. So that the students and teachers of SMA Negeri 3 Semarang have a basic understanding of this game, counseling is needed regarding game play and psychology. The method used in Community Service (PkM) is an extension model. To find out the indicators of success in this extension, namely by distributing questionnaires related to the material presented. The result of this counseling is that students and teachers can understand a clearer impact on games, as well as what solutions need to be considered to get positive things from playing games.

Medical Named Entity Recognition from Indonesian Health-News using BiLSTM-CRF with Static and Contextual Embeddings Ignasius, Darnell; Novita Dewi , Ika; Bernadette Chayeenee Norman , Maria; Rakhmat Sani, Ramadhan
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11574

Named Entity Recognition (NER) is vital for structuring medical texts by identifying entities such as diseases, symptoms, and drugs. However, research on Indonesian medical NER remain limited due to the lack of annotated corpora and linguistic resources. This scarcity often leads to difficulties in learning meaningful word representations, which are crucial for accurate entity identification. This research aims to compare the effectiveness of static and contextual embeddings in enhancing entity recognition on Indonesian biomedical text. The experimental setup involved utilizing both static (Word2Vec) and contextual (IndoBERT) embeddings in conjunction with neural architectures (BiLSTM) along with Conditional Random Fields (CRF). The BiLSTM architecture was selected for its ability to capture bidirectional dependencies in language sequences. Specifically, four models: Word2Vec-BiLSTM, Word2Vec-BiLSTM-CRF, IndoBERT-BiLSTM, and IndoBERT-BiLSTM-CRF were evaluated to assess the impact of contextual representations and structured decoding. The models were trained on a manually annotated DetikHealth corpus, where specific medical entities such as diseases, symptoms, and drugs were labeled with the BIO-tagging scheme. Performance was subsequently evaluated based on standard metrics: precision, recall, and F1-score. Results indicate that IndoBERT’s contextual embeddings significantly outperform static Word2Vec features. The IndoBERT-BiLSTM-CRF model achieved the highest performance micro-F1 0.4330, macro-F1 0.3297, with the Disease entity reaching an F1-score of 0.5882. Combining contextual embeddings with CRF-based decoding enhances semantic understanding and boundary consistency, demonstrating superior performance for Indonesian biomedical NER. Future work should explore domain-adaptive pretraining and larger biomedical corpora to further improve contextual accuracy.

Enhancing Feature-Efficient Network Intrusion Detection Using Gradient Boosting and Chi-Square Selection on NSL-KDD Soares, Gilardinho Javiere Oscoraldo Pedrosa; Fauzi Adi Rafrastara; Ramadhan Rakhmat Sani
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15650

This study examines the growing complexity of cyber threats that increasingly challenge the effectiveness of traditional Network Intrusion Detection Systems (NIDS). Modern attacks, particularly zero-day intrusions, require detection approaches capable of handling high-dimensional network traffic data. However, existing studies rarely examine the trade-off between feature efficiency and generalization performance in boosting-based NIDS under controlled feature-reduction strategies. Moreover, the role of statistical feature selection in mitigating overfitting in classical boosting models remains underexplored. This study evaluates the performance of NIDS by combining boosting ensemble algorithms, namely AdaBoost, Gradient Boosting, and XGBoost, with filter-based feature selection methods, including Information Gain, Chi-Square, and ReliefF. The NSL-KDD dataset is used as the primary benchmark, with Min–Max normalization applied during preprocessing to ensure numerical feature consistency. Model development is conducted using Orange Data Mining, and performance is assessed through 10-fold cross-validation. Experimental results show that Gradient Boosting achieves the highest baseline accuracy among the evaluated models. Further performance improvements are obtained through feature selection, with the Chi-Square method yielding the best result at 81.2% accuracy using 19 selected features. Information Gain also enhances performance, achieving 80.8% accuracy with 13 features, while ReliefF provides comparatively lower gains. These findings demonstrate that effective feature reduction improves generalization performance, reduces computational complexity, and mitigates overfitting. Overall, the proposed combination of Gradient Boosting and statistical feature selection provides a feature-efficient, generalizable intrusion detection strategy for modern network environments.

Identifikasi Faktor Risiko Serangan Jantung di Indonesia Menggunakan Model Prediktif LightGBM Fahmi, Amiq; Muhammad Fais Ramadhani; Ramadhan Rakhmat Sani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9065

Peningkatan prevalensi serangan jantung di Indonesia telah menjadi isu kesehatan publik yang signifikan karena penyakit ini konsisten termasuk penyebab kematian tertinggi secara nasional. Meskipun berbagai studi epidemiologis telah mengidentifikasi faktor risiko klinis maupun perilaku, pendekatan berbasis data untuk prediksi individual masih relatif terbatas, terutama pada konteks populasi Indonesia dengan karakteristik heterogen. Untuk menjawab kesenjangan tersebut, penelitian ini mengembangkan model prediktif serangan jantung menggunakan algoritma Light Gradient Boosting Machine (LightGBM) yang dikenal efisien pada data berukuran besar. Dataset terdiri dari 158.355 observasi dan 28 fitur demografis, gaya hidup, dan indikator medis. Prosedur prapemrosesan mencakup imputasi nilai hilang, pengkodean variabel kategorikal, seleksi fitur menggunakan Principal Component Analysis (PCA), serta penyeimbangan distribusi kelas melalui Synthetic Minority Over-Sampling Technique (SMOTE). Kinerja prediksi dievaluasi menggunakan metrik klasifikasi standar, di mana LightGBM mencapai akurasi 83,39% (train) dan 77,92% (test); presisi 85,67% dan 79,38%; recall 80,19% dan 75,44%; F1-score 82,84% dan 77,36%; serta AUC-ROC 91,84% dan 87,37%. Analisis komponen utama menunjukkan kontribusi varians yang tinggi pada fitur terkait pola konsumsi, penggunaan obat, stres, dan hipertensi. Hasil ini mengindikasikan bahwa LightGBM merupakan pendekatan yang menjanjikan untuk mendukung deteksi risiko serangan jantung secara lebih awal dan berpotensi meningkatkan strategi mitigasi penyakit kardiovaskular di Indonesia.

Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abdussalam Abdussalam, Abdussalam Abu Salam Ade Nurul Aisyah Agung Priyo Utomo, Rino Ahmad Khotibul Umam, Ahmad Khotibul Al zami, Farrikh Alzami, Farrikh Ardytha Luthfiarta ARIYANTO, MUHAMMAD Arta Moro Sundjaja, Arta Moro Arunia, Aurelya Prameswari Asih Rohmani Asih Rohmani Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Bernadette Chayeenee Norman , Maria Budi Harjo Budi, Setyo Candra Irawan Catur Supriyanto Christy Atika Sari Darnell Ignasius Defri Kurniawan Defri Kurniawan Diana Aqmala Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erika Devi Udayanti Fahmi Amiq Farah Syadza Mufidah Farrikh Al Zami Farrikh Al Zami Fauzi Adi Rafrastara Fauzi Adi Rafrastara Florentina Esti Nilasari Florentina Esti Nilawati Guruh Fajar Shidik Hanny Haryanto Harun Al Azies Hercio Venceslau Silla Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ignasius, Darnell Ika Novita Dewi Ikhwansyah Kurniawan Indra Gamayanto Iswahyudi ISWAHYUDI ISWAHYUDI Ivan Bayu Fachreza Junta Zeniarja Karin, Tan Regina Kiki Widia Kurniawan, Defri L. Budi Handoko Lekso Budi Handoko Maszuda, Akbar Alvian Megantara, Rama Aria Melati Anggreni Sitorus Muhammad Fais Ramadhani Muhammad Nabhan Rifa’i Muhammad Naufal MY. Teguh Sulistyono Nadya Azizah Nida Aulia Karima Novita Dewi , Ika Nugraha, Purwa Esti Pangesti, Galih Mentari Paramita, Cinantya Pergiwati, Dewi Priyo Utomo, Rino Agung Pulung Nurtantio Andono Purwanto Purwanto Ramadhani, Dwi Arya Resha Meiranadi Caturkusuma Rhyan David Levandra Ricardus Anggi Pramunendar Richard Emmerig S. Sukamto, Titien Salsabilla, Annisa Ratna Sarker, Md. Kamruzzaman Sasono Wibowo Sendi Novianto Sendi Novianto Sendi Novianto Setyo Budi Setyo Budi Sirait, Tamsir Hasudungan Soares, Gilardinho Javiere Oscoraldo Pedrosa Sri Winarno Sri Winarno Suharnawi Suharnawi Suharnawi Suharnawi Suharnawi Sukamto, Titien S. Sukamto, Titien Suhartini Sulistyono, Teguh Syahrizal, Muhammad Iqbal Titien Suhartini Sukamto Titien Suhartini Sukamto Utomo, Danang Wahyu Wibowo, Isro' Rizky Wildanil Ghozi Wulan Puspita Loka Yani Parti Astuti Yanuaresta, Dianna Yunita Ayu Pratiwi Yupie Kusumawati Zahro, Azzula Cerliana Zami, Farrikh Al

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