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All Journal Jurnal Sains Materi Indonesia JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Jurnal Informatika Universitas Pamulang INTECOMS: Journal of Information Technology and Computer Science JSiI (Jurnal Sistem Informasi) Indonesian Physical Review Building of Informatics, Technology and Science Infotekmesin Piston: Journal of Technical Engineering Journal of Innovation and Future Technology (IFTECH) Jurnal Sistem Komputer & Kecerdasan Buatan Buletin LOUPE (Laporan Umum Penelitian) Journal of Applied Sciences and Advanced Technology Indonesian Journal of Computing, Engineering, and Design Jurnal Pengendalian Pencemaran Lingkungan (JPPL) Jurnal Teknologi Informatika dan Komputer Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Mesin Nusantara Jurnal Pengabdian kepada Masyarakat Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia International Journal of Engagement and Empowerment (IJE2) JURNAL ILMIAH GLOBAL EDUCATION Jurnal Penelitian Inovatif Journal Of Sustainable Research In Management Of Agroindustry (SURIMI) ACROPORA: Jurnal Ilmu Kelautan dan Perikanan Papua JAMAIKA: Jurnal Abdi Masyarakat Abdi Jurnal Publikasi Jurnal Teknologi Pangan dan Industri Perkebunan Metalurgi OKTAL : Jurnal Ilmu Komputer dan Sains Hikmayo: Jurnal Pengabdian Masyarakat Jurnal Ilmiah Multidisiplin Indonesia MULTIPLE: Journal of Global and Multidisciplinary JARI Acta Solum Food and Agro-industry (FAGI) Journal Jurnal Ilmu Komputer
Murni Handayani
Universitas Pamulang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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ANALISIS PREDIKTIF UNTUK MENINGKATKAN RETENSI MAHASISWA MENGGUNAKAN METODE RECURRENT NEURAL NETWORK DAN SUPPORT VECTOR MACHINE Siti Cici Carliah; Tukiyat Tukiyat; Murni Handayani
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4382

Abstract

Student retention is a critical indicator in evaluating the quality of higher education institutions. High dropout rates pose significant challenges, including at Al-Khairiyah University in Cilegon, Banten. This study develops a predictive model for student retention using two machine learning approaches: Recurrent Neural Network (RNN) and Support Vector Machine (SVM), while identifying the most influential factors. The dataset comprises 3371 records from 2021-2024, including academic variables (GPA, semester grades 1-8, attendance) and non-academic variables (organizational activity, competition achievements, parental income, admission pathway, and study system). Data was split into 80% training and 20% testing sets. Results show that the RNN model demonstrates superior performance with 93.5% accuracy, 99.7% precision, 89.3% recall, 94.2% F1-score, and 0.967 AUC, while SVM achieved 85.5% accuracy, 89.8% precision, 85.3% recall, 87.5% F1-score, and 0.912 AUC. Feature importance analysis reveals that Total GPA and first-semester grades (IPS.1) are the dominant factors influencing student retention, while non-academic factors have relatively small contributions. This research provides practical contributions through an Early Warning System framework that can be implemented by universities to detect at-risk students early, enabling proactive academic interventions.
Chatbot dengan RAG untuk Sistem FAQ Pondok Pesantren menggunakan Model Retriever dan Generator: Chatbot dengan RAG untuk Sistem FAQ Pondok Pesantren menggunakan Model Retriever dan Generator Surya, Surya Ariwibowo; Heryandi Suradiradja, Kahfi; Handayani, Murni
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 9 No. 2 (2026): Volume IX - Nomor 2 - Februari 2026
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v9i2.890

Abstract

Pesatnya perkembangan teknologi informasi mendorong lembaga pendidikan, termasuk pondok pesantren, untuk menyediakan akses informasi yang lebih efisien. Saat ini, penyampaian informasi di pondok pesantren masih dilakukan secara manual, sehingga memakan waktu dan tenaga karena harus menjawab pertanyaan yang berulang dari wali santri. Penelitian ini mengembangkan chatbot FAQ berbahasa Indonesia menggunakan pendekatan Retrieval-Augmented Generation (RAG) yang menggabungkan model retriever dan generator untuk menghasilkan jawaban yang relevan dan alami. Model retriever yang digunakan meliputi SBERT, DPR, dan E5, sedangkan model generator terdiri dari IndoBART, IDT5, dan IndoGPT. Data dikumpulkan melalui observasi, wawancara, dan studi literatur. Sistem dirancang menggunakan pencocokan semantik berbasis vektor dan decoding autoregresif untuk menghasilkan jawaban. Evaluasi performa dilakukan dengan metrik Hit@1, BLEU, dan BERTScore. Berdasarkan Hasil penelitian menunjukkan bahwa kombinasi SBERT + IndoBART mencapai akurasi tertinggi sebesar 96%, diikuti SBERT + IdT5 sebesar 95%. Model E5 mencatat skor CSAT tertinggi sebesar 70% dan respon tercepat 0.066 detik, sementara RoBERTa memperoleh skor CSAT terendah 39%, sehingga kombinasi SBERT + IndoBART cocok diterapkan dalam chatbot FAQ PPS. Imam Syafi’i.
SOSIALISASI TEKNOLOGI MODIFIKASI CUACA UNTUK PENINGKATAN LITERASI SAINS ATMOSFER BAGI SISWA SMA IT BAITUL ILMI BEKASI Tukiyat; Murni Handayani; Uliyatunisa
JAMAIKA: JURNAL ABDI MASYARAKAT Vol 6 No 3 (2025): OKTOBER
Publisher : Universitas Pamulang

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

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) Universitas Pamulang dilaksanakan di SMA IT Baitul Ilmi Bekasi dengan tujuan meningkatkan literasi sains atmosfer melalui sosialisasi teknologi modifikasi cuaca (TMC). Literasi sains atmosfer di kalangan pelajar masih tergolong rendah, padahal pemahaman terhadap dinamika atmosfer penting dalam menghadapi perubahan iklim dan bencana hidrometeorologi. Kegiatan dilaksanakan dalam tiga sesi, yaitu pengenalan konsep atmosfer, pemutaran video edukatif dan diskusi kelompok, serta evaluasi melalui pre-test dan post-test. Hasil kegiatan menunjukkan peningkatan signifikan pada pemahaman siswa, dengan tingkat kepuasan rata-rata mencapai 83,7% dan 98% peserta menyatakan kegiatan bermanfaat. Penilaian kualitas materi, metode, dan penyampaian narasumber berada pada kategori sangat baik, meskipun aspek interaktivitas dan antusiasme masih perlu ditingkatkan. Secara umum, kegiatan ini efektif, edukatif, dan kontekstual, serta berhasil meningkatkan motivasi dan kesadaran lingkungan siswa. Kegiatan ini berkontribusi terhadap pelaksanaan Tridharma Perguruan Tinggi melalui luaran berupa laporan PKM dan publikasi artikel ilmiah. Kata kunci: literasi sains atmosfer; sosialisasi; modifikasi cuaca; pengabdian masyarakat; siswa SMA
PENGOLAHAN BUAH PEDADA MENJADI SIRUP DAN PUPUK ORGANIK DI KAWASAN WISATA CIGIMBAL PARK KABUPATEN CILACAP Nurlinda Ayu Triwuri; Theresia Evila Purwanti Sri Rahayu; Oto Prasadi; Annisa Yuliana Angeline; Nur Indah Wardani; Murni Handayani; Ilma Fadlilah
HIKMAYO: JURNAL PENGABDIAN MASYARAKAT AMAYO Vol. 5 No. 1 (2026): HIKMAYO : Jurnal Pengabdian Masyarakat
Publisher : LPPM Akademi Manajemen Administrasi Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56606/hikmayo.v5i1.379

Abstract

The mangrove forest in Cilacap Regency, Central Java, is located in the Cigimbal Park area of ??Tritih Kulon Village. Mangroves grow in river mouths or estuaries, where organic sediment and mud are collected. One type of mangrove, namely pedada fruit (Sonneratia alba), contains flavonoids, terpenoids, tannins, phenolics, steroids, and other bioactive compounds that function as antioxidants. The implementation method of community service activities consists of socialization and training stages. Based on the content of pedada fruit, pedada fruit processed into syrup also has nutritional content such as 1.20% protein, 0.20% fat, 3.50% carbohydrates, and 55.30% vitamin C. The storage time of pedada syrup at room temperature lasts for 7 days, if at a cold temperature of 4oC lasts for 30 days. In addition, the residue from pedada syrup production in the form of pedada skin, petals and dregs can be processed into organic fertilizer. The decomposition period of organic material is added with effective microorganisms 4 for 28 days, until it is black and odorless.
Perbandingan Proses Mineralisasi Karbon dan Nitrogen serta Humifikasi Pada Sistem Pertanian yang Berbeda di Tanah Andisol: Comparison of Carbon and Nitrogen Mineralization Processes and Humification in Different Agricultural Systems in Andisol Soil Fajar Hidayanto; Retno Tri Purnamasari; Sari Widya Utami; Murni Handayani
Acta Solum Vol. 3 No. 3 (2025): Juli 2025
Publisher : Department of Soil, Faculty of Agriculture, Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/actasolum.v3i3.3128

Abstract

The organic farming system has become the choice of most farmers in Indonesia because it has a major impact on agricultural quality and soil fertility. Vegetable farming in Magelang and Semarang Regencies is cultivated on andisol soil which has a relatively high soil fertility level, but farmers still use organic fertilizers to increase the organic matter content of the soil. The organic farming system applies 10 tons ha-1 of cow manure, 20 liters ha-1 of liquid fertilizer, and the return of plant residues at each planting season. The conventional farming system with high organic matter applies 7 tons ha-1 of cow manure, 50 kg ha-1 of urea, 50 kg ha-1 of NPK fertilizer, 15 liters ha-1 of liquid fertilizer, while the conventional farming system with low organic matter applies 3 tons ha-1 of chicken manure, 50 kg ha-1 of ZA fertilizer, 50 kg ha-1 of KCl fertilizer, and 50 kg ha-1 of NPK fertilizer. Observation variables include pH NaF, C-organic, N-total, C/N ratio, humic acid, fulvic acid and humification rate. Data were analyzed for variety and if different, the method of the smallest significant difference test was continued. The results of the study showed that the organic farming system was more effective in increasing carbon and nitrogen in the soil and accelerating humification, so that nutrients were available more quickly. However, the deeper the soil layer, the mineralization and humification processes will decrease because they are greatly influenced by the availability of organic matter.
Comparative Evaluation of Local Llama and Mistral Models in RAG for Helpdesk Documentation Septian Pratama; Sajarwo Anggai; Murni Handayani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.8895

Abstract

The increasing volume of technical documentation and repetitive support requests at PT XYZ has made it difficult for helpdesk personnel to retrieve accurate information efficiently and consistently. The 114 internal applications that PT XYZ oversees have different documentation, troubleshooting protocols, and frequently asked questions, which leads to dispersed knowledge sources and drawn-out problem solving. This study uses locally deployed Large Language Models (LLMs), specifically Llama and Mistral, to design and assess a Retrieval-Augmented Generation (RAG) based chatbot for internal helpdesk knowledge retrieval in order to solve this issue. The suggested solution combines generative language models with semantic document retrieval in PostgreSQL using PGVector. The study compares the effectiveness of local LLMs in RAG and non-RAG configurations, employing 1,068 internal question-answer pairs for knowledge retrieval and 214 evaluation questions for performance assessment. ROUGE, BLEU, and Cosine Similarity measures are used for evaluation. According to experimental findings, RAG greatly enhances both models' performance. While Mistral improved from 0.0984 to 0.8983, Llama ROUGE-1 score rose from 0.1710 to 0.7345. With a ROUGE-1 score of 0.8983, a BLEU-1 score of 0.8161, and a Cosine Similarity score of 0.8916, Mistral with RAG performed the best of all setups. These results show that integrating RAG with locally installed LLMs greatly improves contextual correctness and response relevance, enabling safe and scalable implementation for enterprise helpdesk knowledge management.
Nickel Electroplating on 3D Printed Polylactic Acid (PLA) for Hardness Enhancement Ramses Maur Ragaventrand; Kushendarsyah Saptaji; Iwan Setiawan; Murni Handayani; Nikolas Krisma Hadi Fernandez
Journal of Applied Sciences and Advanced Technology Vol. 8 No. 2 (2025): Journal of Applied Science and Advanced Technology
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jasat.8.2.27-34

Abstract

Electroplating is a process that uses electric current to deposit a layer of metal onto the surface of a conductive material, enhancing its properties such as corrosion resistance, electrical conductivity, and mechanical strength. This study investigates the process of nickel electroplating on 3D-printed polylactic acid (PLA) substrates, focusing on the efficiency and quality of the nickel coatings achieved through electroplating techniques. The methodology encompasses several stages, starting with the design and 3D printing of PLA specimens. Following this, the preparation of the electroplating setup is meticulously carried out, ensuring optimal conditions for the electroplating process. The quality of the nickel coating is then evaluated through a series of tests to assess its mechanical and electrical properties. The key findings from this research indicate that the electroplating process effectively deposits nickel onto the PLA substrates. This deposition significantly enhances the mechanical strength and electrical conductivity of the PLA specimens. The study's results suggest that nickel electroplating on PLA can be a viable method for improving the material properties of 3D-printed parts. This advancement not only contributes to the development of cost-effective and sustainable metal coating techniques for polymer-based materials but also has the potential to broaden the application scope of 3D-printed parts in various fields of engineering and technology. Such improvements could be particularly beneficial in industries requiring enhanced material performance, such as electronics, automotive, and aerospace sectors.
ANALISIS SISTEM DETEKSI DINI FRAUD PADA TRANSAKSI PERBANKAN MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM) DAN TRANSFORMER Arken Abdullah; Arya Adhyaksa Waskita; Murni Handayani
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 1 (2026): 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: 10.31539/83yrz474

Abstract

The development of digital banking services in electronic payment channels has led to a significant increase in transaction volumes, accompanied by higher fraud risk. Fraud patterns are dynamic and temporal, making detection based solely on individual transactions ineffective. This study aims to develop an early fraud detection system using a cluster-aware sequential deep learning approach. Transaction data are processed through data cleansing, behavioral feature extraction, and customer clustering based on transaction characteristics. Long Short-Term Memory (LSTM) is employed to learn temporal transaction patterns, while Transformer is used to capture global context and nominal transaction deviations. Both models are integrated through a dynamic ensemble approach with adaptive thresholds for each cluster. Model evaluation is conducted in a supervised manner using PR-AUC as the primary metric, supported by ROC-AUC, Precision, Recall, and F1-Score. The results demonstrate that the cluster-based ensemble approach improves detection stability, reduces false positives, and adapts effectively to differences in customer behavior. Experimental results show that models trained without oversampling provide more stable precision–recall performance on datasets where fraud manifests as extreme behavioral outliers, while SMOTE is used as a comparative scenario.  Keywords: Fraud Detection, Deep Learning, LSTM, Transformer, Bank
A Comparative Study of DenseNet-201 and Swin Transformer for Malignant and Benign Skin Lesion Classification Dahlan Hidayat; Ahmad Musyafa; Murni Handayani
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3265

Abstract

Skin cancer has a high global prevalence, underscoring the need for accurate and efficient early detection systems to support screening. This study presents a comparative analysis of DenseNet-201 and Swin Transformer for binary classification of malignant and benign skin lesions using the BCN20000 dataset, which contains 12,413 dermoscopic images. The proposed workflow includes image preprocessing and augmentation, transfer learning-based model training, and evaluation under a 5-fold stratified cross-validation protocol. Performance is assessed using Accuracy, Precision, Sensitivity (Recall), F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). In addition, computational efficiency is examined in terms of parameter count, model size, and training time. Across five folds, DenseNet-201 achieved 88.05% Accuracy, 88.90% Precision, 89.48% Sensitivity, 89.17% F1-score, and 94.73% AUC, whereas Swin Transformer achieved 87.42% Accuracy, 89.77% Precision, 87.06% Sensitivity, 88.39% F1-score, and 94.33% AUC. A paired t-test at α = 0.05 indicated no statistically significant performance difference between the two models. Model interpretability was investigated using Grad-CAM for DenseNet-201 and EigenCAM for Swin Transformer to verify that predictions were driven by lesion-relevant regions. Overall, the results suggest that both architectures are suitable candidates for dermoscopic image-based skin lesion screening support systems, including teledermatology applications.
Optimization of RNN and Tree-Based Models with Imbalance Handling for Fraud Detection in Digital Banking Transactions Rizki Ahmad Darmawan; Ahmad Musyafa; Murni Handayani
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

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

Abstract

This study focuses on addressing the growing challenge of fraud detection in digital banking transactions, which has intensified alongside the rapid expansion of digital financial services. Fraud detection is particularly complex due to the highly imbalanced nature of transaction data, large data volumes, and intricate transaction patterns that make fraudulent activities difficult to identify accurately. Although previous research has applied a wide range of methods, from conventional machine learning techniques to advanced deep learning models, many approaches still face limitations in balancing high detection accuracy with computational efficiency. The main objective of this research is to compare the performance of Recurrent Neural Network (RNN)–based models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), with tree-based ensemble models such as XGBoost and LightGBM in detecting fraudulent banking transactions. To enhance model effectiveness, the study implements a comprehensive data preprocessing framework that includes data cleaning, feature engineering, and techniques for handling class imbalance, particularly the use of Synthetic Minority Over-sampling Technique (SMOTE). Furthermore, model performance is optimized through systematic hyperparameter tuning using Optuna, Hyperopt, and Keras Tuner. Evaluation is conducted using metrics suitable for imbalanced datasets, such as precision, recall, F1-score, and AUC-ROC. The expected outcome is the identification of a robust and efficient fraud detection model that improves detection accuracy and sensitivity, while offering valuable insights for both academic research and practical banking applications.
Co-Authors Abu Khalid Rivai Ahmad Gunawan Wibisono Ahmad Musyafa Ahmad Musyafa Ahmad Randy Akhmad Rinaldy Kurnia Al Habib, Irsyad Allam Ramzy Allam Ramzy Amartya Natayu Anggai, Sajarwo Annisa Yuliana Angeline Any Kurniawati, Any Ari Kristiningsih Arken Abdullah Arrozi , Ubed Sonai Fahruddin Arsita Nur Rizkia Putri Arya Adhyaksa Waskita Astuti, Wijayanti Dwi Aza Fauziana, Noer Bambang Wisnu Widagdo Budi Hadisujoto Charlene Tiara Rehueliah Pingak Choirunnisa Firdaus Ivana Da, Oh Wen Dahlan Hidayat Dawam Agung Pribadi Destiny, Keysi Devain Dewanto, Hizkia Alpha Dewi Sondari Djukardi Kurniawan Dr TUKIYAT, M.Si Dwi, Sadina Sahitya Dwityaningsih, Rosita Eko Sulistiyono Erland Sumantri Evi Triwulandari Evila Purwanti Sri Rahayu, Theresia Fadillah Fajar Hidayanto, Fajar Felix Firdiyono Feni Aryanti Fitri Khoerunnisa Giyanto Gunawarman Hartanto Heryandi Suradiradja, Kahfi Hesti Rahayuningsih Hizam, Fadli Iasya, Yurin Karunia Apsha Albaina Ilma Fadlilah Ipung Saputra Irnanda, Istifhamy Isykariman Biridlwanillah Iwan Dwi Antoro Iwan Setiawan Iwan Setiawan Jatmoko Awali Joko Setia Pribadi Jon Affi, Jon Kanim Kristiningsih, Ari Kushendarsyah Saptaji Latifatul Khusna Lutfi Syafirullah Mahendra, Brillian Ardy Makhsun Makhsun Makhsun Mardiyana Mardiyana Mardiyana Mardiyana Maulana, Romdon Mila Prametha, Novika Mochamad Adhari Adiguna Moh. Triyana Abbas Mohammad Nurhilal Mohammad Rayhan Afdillah Muhamad Sibli Muhammad Rizky Fadillah Muji Mulyani Mustofa, Rizki Nabila, Najwa Natasya Arifah Natasya Arifah Nikolas Krisma Hadi Fernandez Nur Fuat, Muhamad Nur Indah Wardani Nur Rohmat Nuraini Sitepu Nuraini Sitepu Nurhayati, Mita Nurlinda Ayu Triwuri Oto Prasadi putri, raekhanrahmah Putri, Restiani Alia Raekhan Rahmah Putri Rafli Rafli Rafli Rafli Ramses Maur Ragaventrand Reno Saeprani Retno Tri Purnamasari Rifqi Fakhri Yogi Syafruddin Rizki Agustian Rizki Ahmad Darmawan Rosita Dwityaningsih Rosita, Rosita Dwityaningsih Saefudin Saefudin Saefuloh, Iman Sajarwo Anggai Sajarwo Anggai Salma Maulikhatun Zulfa Santika, Ariel Sari Widya Utami Sari Widya Utami Satrio Herbirowo, Satrio Septian Pratama Siti Cici Carliah Siti Khoirunnisa Slamet Raharjo Slamet Raharjo Sudarno Sudarno Sudarno Wiharjo Sumardiono, Arif Surya, Surya Ariwibowo Syadilla Ega Maharani Taswanda Taryo Taswanda Taryo Tukiyat Tukiyat Tukiyat Tukiyat Tukiyat, Tukiyat Ulikaryani Uliyatunisa Uliyatunisa Uliyatunisa, Uliyatunisa Waskita, Arya Adhyaksa Widuri Indana Saleha Widuri Indana Saleha Witriansyah, Khoeruddin Wittriansyah, Khoeruddin Yessi Permana Yuli Yetri Yunita Triana, Yunita Yunus Yunus Yuswono Yuswono Zain, Rafi Mahmud Zeni Ulma Zikrullah, Mochamad Fachrudin