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All Journal Dinamik Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Innovation Information Technology and Application (JINITA) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Journal of Information Technology (JIfoTech) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal of Information System Exploration and Research Jurnal Kecerdasan Buatan dan Teknologi Informasi IPSIKOM Manexia Jurnal Nasional Komputasi dan Teknologi Informasi
NUK GHURROH SETYONINGRUM
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Neuroscience Public Health Social Sciences Transportation Other

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PENERAPAN ALGORITMA NAÏVE BAYES CLASSIFIER UNTUK KLASIFIKASI PENJUALAN OBAT DI APOTEK NUGRAHA Rifa Maulida; Nuk Ghurroh Setyoningrum; Alam
IPSIKOM Vol. 14 No. 1 (2026): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v14i1.446

Abstract

This study discusses the application of the Naive Bayes Classifier algorithm in analyzing drug sales transaction data at Apotek Nugraha. The purpose is to assist stock management and decision-making related to drug availability based on consumer purchasing patterns. The data used consists of sales transaction records, which are then processed and classified to predict drug demand trends. The Naive Bayes Classifier method was chosen for its ability to perform classification quickly, simply, and with relatively good accuracy even when using limited datasets. The results indicate that this algorithm can identify frequently purchased drug categories and provide predictions that support efficient inventory management. Thus, the implementation of data mining using Naive Bayes Classifier can improve the effectiveness of pharmacy services while minimizing the risks of both stock shortages and overstocking.
Prediksi Kelulusan Mahasiswa dengan Metode K-Nearest Neighbor Kholiq Mochamad Ramdan; Nuk Ghurroh Setyoningrum
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/pkhr1k63

Abstract

Abstrak - Prediksi kelulusan mahasiswa menjadi salah satu aspek penting dalam pengambilan keputusan akademik karena dapat membantu perguruan tinggi dalam mengidentifikasi potensi kelulusan mahasiswa secara lebih dini. Permasalahan yang dihadapi adalah belum optimalnya pemanfaatan data akademik dalam memprediksi kelulusan mahasiswa di Fakultas Sains dan Teknologi Universitas Cipasung Tasikmalaya. Penelitian ini bertujuan untuk mengimplementasikan metode K-Nearest Neighbor (KNN) dalam memprediksi kelulusan mahasiswa serta mengukur tingkat akurasi model yang dihasilkan. Metode penelitian yang digunakan adalah pendekatan kuantitatif berbasis data mining dengan algoritma KNN. Data penelitian diperoleh melalui observasi, wawancara, dan dokumentasi berupa data akademik mahasiswa yang meliputi nilai, jumlah SKS, dan variabel pendukung lainnya. Proses analisis dilakukan melalui tahapan preprocessing data, penentuan parameter K, pembagian data menjadi training data dan testing data, perhitungan jarak menggunakan Euclidean Distance, penentuan tetangga terdekat, hingga evaluasi model menggunakan metrik akurasi. Hasil dari penelitian ini adalah metode K-Nearest Neighbor (KNN) bisa digunakan dalam mengolah data akademik mahasiswa untuk memprediksi kelulusan dengan tingkat akurasi sebesar 87,27%. Penelitian ini dapat menghasilkan model prediksi kelulusan mahasiswa yang akurat, objektif, dan berbasis data sehingga dapat digunakan sebagai alat bantu dalam pengambilan keputusan akademik di lingkungan perguruan tinggi. Kata kunci : Data Mining; K-Nearest Neighbor; Prediksi Kelulusan; Mahasiswa; Akurasi;   Abstract - Student graduation prediction is an important aspect of academic decision-making because it helps universities identify students’ graduation potential at an early stage. The main problem addressed in this study is the suboptimal utilization of academic data in predicting student graduation at the Faculty of Science and Technology, Universitas Cipasung Tasikmalaya. This study aims to implement the K-Nearest Neighbor (KNN) method to predict student graduation and measure the accuracy level of the resulting model. The research employed a quantitative approach based on data mining using the KNN algorithm. Research data were collected through observation, interviews, and documentation in the form of students’ academic records, including grades, completed credit units, and other supporting variables. Data analysis was conducted through several stages, namely data preprocessing, determination of the K parameter, division into training and testing datasets, distance calculation using Euclidean Distance, nearest-neighbor determination, and model evaluation using accuracy metrics. The results of this study are that the K-Nearest Neighbor (KNN) method can be used to process student academic data to predict graduation with an accuracy level of 87.27%. The expected result of this study is an accurate, objective, and data-driven student graduation prediction model that can be utilized as a supporting tool for academic decision-making in higher education institutions. Keywords: K-Nearest Neighbor; data mining; graduation prediction; students; accuracy;
Analisis Pola Pembelian Produk Menggunakan Algoritma Apriori untuk Mendukung Strategi Penjualan Ansor, Muhamad; Setyoningrum, Nuk Ghurroh
Bahasa Indonesia Vol 13 No 1 (2026): Bina Insani ICT Journal (Juni) 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/biict.v13i1.3953

Abstract

Dokumentasi riwayat transaksi pada sektor distributor umumnya baru dimanfaatkan sebatas pengarsipan administratif, belum dieksplorasi mendalam untuk membaca kecenderungan perilaku konsumen. Riset ini bertujuan memetakan interkoneksi antarproduk yang dibeli pelanggan di PT Tridaya Sinergi Indonesia melalui teknik Market Basket Analysis berbasis model Apriori. Alur pengerjaan mencakup pembersihan data (preprocessing), restrukturisasi format berkas, penentuan kombinasi produk populer (frequent itemset), hingga ekstraksi aturan asosiasi bersandar pada indikator support, confidence, dan lift ratio. Komputasi data dioperasikan lewat ekosistem Google Colaboratory menggunakan bahasa pemrograman Python terhadap 253 rekaman transaksi yang telah tervalidasi bersih dari galat maupun duplikasi. Proses ini menghasilkan 274 frequent itemset serta 1.367 formulasi aturan asosiasi produk. Pengujian model pada aturan paling berpengaruh menunjukkan tingkat performa yang kuat dengan skor akurasi 0,874, presisi 0,935, recall 0,927, dan F1-score 0,931. Riset ini membuktikan keandalan sistem Apriori dalam mengonversi data transaksional menjadi rekomendasi taktis untuk manajemen stok dan perencanaan program penjualan korporasi.
SOSIALISASI APLIKASI WEB REPOSITORY STEAM EDUCATION BAGI KELOMPOK KERJA GURU WIRADADAHA I N Nelis Febriani SM; Nuk Ghurroh Setyoningrum; Anisa Solehah Nurwendah; Miftahudin; Windriyana; Pepi Alpiani; Putri Khozizah
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 4 No. 3 (2026): Juni
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v4i3.3964

Abstract

Perkembangan teknologi digital menuntut guru mampu memanfaatkan platform pembelajaran yang mendukung implementasi pembelajaran abad ke-21. Salah satu pendekatan yang relevan adalah STEAM (Science, Technology, Engineering, Arts, and Mathematics), yang mengintegrasikan berbagai disiplin ilmu untuk mengembangkan kreativitas, kolaborasi, komunikasi, dan kemampuan pemecahan masalah peserta didik. Namun, implementasi pembelajaran STEAM di sekolah dasar masih menghadapi kendala, antara lain keterbatasan media pembelajaran digital, minimnya referensi proyek STEAM, serta belum tersedianya wadah kolaborasi untuk berbagi praktik baik antar guru. Kegiatan pengabdian kepada masyarakat ini bertujuan mensosialisasikan aplikasi Web Repository STEAM Education kepada guru-guru Sekolah Dasar yang tergabung dalam Kelompok Kerja Guru (KKG) Wiradadaha I sebagai media pendukung pembelajaran berbasis STEAM. Metode pelaksanaan meliputi analisis kebutuhan, sosialisasi, demonstrasi aplikasi, praktik penggunaan, pendampingan, dan evaluasi. Aplikasi Web Repository STEAM Education menyediakan fitur pengelolaan kelas, publikasi proyek STEAM, unggah materi pembelajaran, dokumentasi praktik baik, serta berbagi media ajar antar guru. Hasil kegiatan menunjukkan bahwa peserta mampu memahami konsep pembelajaran STEAM dan mengoperasikan fitur utama aplikasi. Berdasarkan hasil angket evaluasi, sebesar 82,1% peserta menyatakan sangat puas dan 84,3% menyatakan puas terhadap pelaksanaan kegiatan, dengan rata-rata tingkat kepuasan sebesar 83,2% (kategori sangat baik). Selain meningkatkan kompetensi digital guru, aplikasi ini juga mendukung kolaborasi dalam pengembangan media pembelajaran, publikasi karya, dokumentasi praktik baik, serta penguatan komunitas belajar profesional secara berkelanjutan
Peningkatan Kompetensi Guru KKG Wiradadaha 1 dalam Pengembangan Media Ajar Digital Berbasis STEAM melalui Pelatihan dan Pendampingan Terstruktur Nuk Ghurroh Setyoningrum; N. Nelis Febriani SM; Anisa Solehah Nurwendah; Pepi Alpiani; Kholiq Mochamad Ramdan; Putri Khozizah
JURPIKAT Vol 7 No 1 (2026): 7.1 2026
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v7i1.2882

Abstract

The community service program was initiated due to the limited educational technology skills and lack of variation in digital learning media used by teachers in the Wiradadaha 1 Teacher Working Group, Tasikmalaya. These conditions hinder the optimal implementation of project-based learning aligned with 21st-century competencies. This program aims to enhance teachers’ abilities in developing digital learning media based on Science, Technology, Engineering, Arts, and Mathematics (STEAM) through structured training and mentoring. The methods included material presentation, demonstration, guided practice, and technical assistance in designing storyboards and producing digital media. The results indicate a significant improvement in participants’ competencies in creating creative, interactive, and STEAM-oriented learning media. This program has contributed positively to teachers’ readiness to integrate educational technology and is recommended for continuation through sustained mentoring initiatives.
Analysis and Visualization of Purchasing Pattern in Retail Product Transaction using Apriori Algorithm N. Nelis Febriani SM; Nuk Ghurroh Setyoningrum; Mae Lodana; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
Journal of Information System Exploration and Research Vol. 4 No. 1 (2026): January 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i1.650

Abstract

The rapid growth of the retail industry generates large volumes of transaction data that can be analyzed to support data-driven business decision making. This study aims to analyze and visualize purchasing patterns in retail product transactions by applying data mining techniques using the Apriori algorithm and business intelligence visualization through Microsoft Power BI. The dataset consists of 1 million retail transactions collected from an open retail transaction repository. The research stages include data collection, transaction data preprocessing, implementation of the Apriori algorithm with a minimum support threshold of 0.002 and a minimum confidence of 0.5, and visualization of the analysis results through interactive dashboards using Power BI and a Python-based application developed with the Streamlit framework. The results indicate that the Apriori algorithm successfully identifies frequent product associations and generates 12 association rules that meet the criteria of strong association rules. Power BI visualizations provide comprehensive insights into transaction trends based on customer categories, store types, payment methods, seasons, and transaction regions. These findings are expected to assist retail companies in formulating marketing strategies, developing product recommendations, and optimizing inventory management in a more effective and data-driven manner. This study contributes by integrating large-scale association rule mining with interactive business intelligence visualization for retail decision support.
Klasifikasi Ketepatan Waktu Keberangkatan Jamaah Haji pada Kemenag Kabupaten Tasikmalaya Menggunakan Algoritma Decision Tree Nenden Nurkhopipah; Nuk Ghurroh Setyoningrum
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 4 (2026): Agustus, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/j4w34388

Abstract

Abstrak - Masa tunggu antrean pendaftaran haji yang sangat panjang memerlukan sistem pemetaan yang objektif untuk mengidentifikasi karakteristik jamaah rentan. Penelitian ini bertujuan untuk membangun model klasifikasi ketepatan waktu keberangkatan jamaah haji pada Kantor Kementerian Agama Kabupaten Tasikmalaya dengan menerapkan algoritma Decision Tree C4.5. Data historis sebanyak 1.407 baris diolah berdasarkan tiga atribut utama: Usia, Nomor Porsi, dan Kuota Daerah. Kebaruan penelitian ini terletak pada integrasi variabel kuota proporsional berbasis regulasi (1% dari populasi penduduk Muslim daerah, yaitu 7.659 kuota) langsung ke dalam logika pelabelan target porsi ("Tepat Waktu" dan "Terlambat"), serta penerapan teknik Pre-Pruning untuk mengatasi masalah overfitting yang umum terjadi pada pohon keputusan murni. Eksperimen dilakukan melalui pemodelan interaktif dengan mengunci kedalaman maksimum pohon (max depth) pada level 6 berdasarkan evaluasi validation curve. Hasil pengujian menunjukkan model mampu menghasilkan performa klasifikasi yang sangat baik dengan tingkat akurasi mencapai 83.93%, presisi sebesar 83.25%, dan F1-Score sebesar 88.25%, serta sensitivitas deteksi (Recall) sebesar 93.89%. Aturan keputusan (IF-THEN Rules) yang diekstrak secara otomatis dari model ini sukses membentuk basis pengetahuan (knowledge base) terstruktur yang bernilai aplikatif bagi pemangku kebijakan untuk optimalisasi manajemen antrean haji di daerah. Penelitian ini masih terbatas pada studi kasus satu wilayah kabupaten dan belum dibandingkan dengan algoritma klasifikasi lain sebagai pembanding kinerja. Kata kunci: Klasifikasi; Jamaah Haji; Decision Tree C4.5; Pre-Pruning; Abstract - The very long waiting times for hajj registration requires an objective mapping system to identify the characteristics of vulnerable pilgrims. This study aims to build a classification model for the timeliness of hajj pilgrims departure at the Office of the Ministry of Religious Affairs in Tasikmalaya Regency by applying the Decision Tree C4.5 algorithm. Historical data consisting of 1,407 rows were processed based on three main attributes: Age, Portion Number, and Regional Quota. The novelty of this research lies in the integration of a regulation-based proportional quota variable (1% of the local Muslim population, equivalent to 7,659 quotas) directly into the target portion labeling logic ("On Time" and "Delayed"), alongside the implementation of Pre-Pruning techniques to address the overfitting issues common in pure decision trees. Experiments were conducted through interactive modeling by locking the maximum tree depth at level 6 based on validation curve evaluation. The test results show that the model is capable of generating excellent classification performance, achieving an accuracy rate of 83.93%, a precision of 83.25%, and an F1-Score of 88.25%, along with a detection sensitivity (Recall) of 93.89%. The decision rules (IF-THEN Rules) extracted automatically from this model successfully formed a structured knowledge base that is practically valuable for policymakers in optimizing regional hajj queue management. This study is still limited to a single-regency case and has not been benchmarked against other classification algorithms. Keywords: Classification; Hajj Pilgrims; Decision Tree C4.5; Pre-Pruning;
Implementasi Algoritma C4.5 dalam Klasifikasi Jenis Konsumen LPG 3 Kg Menggunakan Data Transaksi Pelanggan Syifa Aenun Naja; Nuk Ghurroh Setyoningrum
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 4 (2026): Agustus, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/r1vbsf74

Abstract

Abstrak - LPG 3 Kg merupakan salah satu kebutuhan energi bersubsidi yang banyak digunakan oleh masyarakat, khususnya rumah tangga dan usaha mikro. Tingginya jumlah transaksi yang terjadi pada pangkalan LPG menghasilkan data yang berpotensi dimanfaatkan untuk mendukung pengambilan keputusan dalam pengelolaan distribusi. Namun, data transaksi tersebut umumnya hanya digunakan sebagai arsip sehingga informasi mengenai karakteristik konsumen belum dapat dimanfaatkan secara optimal. Penelitian ini bertujuan untuk mengimplementasikan algoritma C4.5 dalam mengklasifikasikan jenis konsumen LPG 3 Kg pada PT Hasan Basri Putra Jaya berdasarkan pola transaksi pelanggan. Data yang digunakan sebanyak 1.159 data konsumen yang terdiri atas 893 konsumen rumah tangga dan 266 konsumen usaha mikro. Atribut yang digunakan meliputi kategori frekuensi transaksi, kategori jumlah pembelian, dan waktu transaksi. Tahapan penelitian meliputi pengumpulan data, preprocessing data, transformasi atribut, pembentukan model C4.5, serta evaluasi model menggunakan confusion matrix, accuracy, precision, recall, dan F1-score. Data dibagi menjadi data latih sebesar 80% dan data uji sebesar 20%. Hasil penelitian menunjukkan bahwa algoritma C4.5 mampu menghasilkan akurasi sebesar 85,34%, precision sebesar 84,60%, recall sebesar 85,34%, dan F1-score sebesar 84,20%. Selain itu, atribut kategori jumlah pembelian memiliki pengaruh terbesar terhadap proses klasifikasi dengan nilai importance sebesar 52,15%, diikuti kategori frekuensi transaksi sebesar 36,25%, dan waktu transaksi sebesar 11,61%. Hasil penelitian menunjukkan bahwa algoritma C4.5 mampu mengklasifikasikan jenis konsumen LPG dengan baik sehingga dapat digunakan sebagai pendukung pengambilan keputusan dalam pengelolaan distribusi LPG. Kata Kunci: C4.5; Data Mining; Klasifikasi; Konsumen LPG; Decision tree; Abstract - LPG 3 Kg is one of the subsidized energy sources widely used by households and micro-enterprises. The large number of transactions generated at LPG distribution points produces valuable data that can be utilized to support decision-making in distribution management. However, transaction data are generally used only as archives, preventing the optimal utilization of consumer behavior information. This study aims to implement the C4.5 algorithm to classify LPG 3 Kg consumers at PT Hasan Basri Putra Jaya based on transaction patterns. The dataset consisted of 1,159 consumer records, including 893 household consumers and 266 micro-enterprise consumers. The attributes used were transaction frequency category, purchase quantity category, and transaction time. The research stages included data collection, preprocessing, attribute transformation, C4.5 model construction, and model evaluation using confusion matrix, accuracy, precision, recall, and F1-score metrics. The dataset was divided into 80% training data and 20% testing data. The results showed that the C4.5 algorithm achieved an accuracy of 85.34%, precision of 84.60%, recall of 85.34%, and F1-score of 84.20%. Furthermore, the purchase quantity category was identified as the most influential attribute with an importance value of 52.15%, followed by transaction frequency category at 36.25% and transaction time at 11.61%. These findings indicate that the C4.5 algorithm is capable of classifying LPG consumers effectively and can support decision-making processes in LPG distribution management. Keywords: C4.5; Data Mining; Classification; LPG Consumer; Decision tree;
Data Augmentation Strategies on Spectrogram Features for Infant Cry Classification Using Convolutional Neural Networks Alam Alam; Nuk Ghurroh Setyoningrum; Robby Maududy; Dea Dewi Damayanti; Hilmi Rahmawati
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16823

Abstract

Infant cry classification is an important task to support parents and healthcare professionals in understanding infants needs, yet the challenge of limited and imbalanced datasets often reduces model accuracy and generalization. This study proposes the application of diverse audio data augmentation strategies including time stretching, time shifting, pitch scaling, and polarity inversion combined with spectrogram representation to enhance Convolutional Neural Network (CNN) performance in classifying infant cries. The dataset from the Donate-a-Cry Corpus was expanded from 457 to 6,855 samples through augmentation, improving class balance and variability. Experimental results show that CNN accuracy increased from 85% before augmentation to 99.85% after augmentation, with precision, recall, and F1-score reaching near-perfect values across all categories. The confusion matrix further confirms robust classification with minimal misclassifications. These findings demonstrate that data augmentation is crucial to overcoming dataset limitations, enriching acoustic feature diversity, and reducing model bias, while offering practical implications for the development of accurate, reliable, and real-world applicable infant cry detection systems.
Optimization of the Decision Tree Algorithm Using SMOTE for the Classification of Diabetes Mellitus Types Based on Clinical Medical Record Data Ia Riham Nurrahma; Nuk Ghurroh Setyoningrum
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.531

Abstract

Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood glucose levels. Accurate early classification of diabetes types is crucial for determining appropriate clinical interventions. However, clinical datasets often suffer from severe class imbalance, leading to model bias toward the majority class. This study proposes the optimization of the Decision Tree classification algorithm by integrating the Synthetic Minority Over-sampling Technique (SMOTE) using real-world clinical medical record data from the Singaparna Community Health Center. The dataset comprises 225 patient records with 38 health attributes. Preprocessing and attribute transformation techniques were applied, followed by stratified random sampling (80:20 split). To resolve the extreme class imbalance (1 Type 1 DM vs 224 Type 2 DM samples), SMOTE was applied exclusively to the training set, balancing the dataset to 358 instances (179 samples per class). The Decision Tree model was trained and evaluated using validation curves to prevent overfitting. The experimental results demonstrate that the SMOTE-optimized Decision Tree model with an optimal max depth of 3 achieved robust classification performance, yielding an accuracy of 86.67%, precision of 88.54%, recall of 84.12%, F1-score of 85.20%, and an ROC-AUC score of 0.9333. Furthermore, the decision tree visualization revealed transparent, age-based rule splits matching clinical domain knowledge. This study confirms that SMOTE effectively mitigates class imbalance in clinical decision-tree modeling, providing a reliable and interpretable decision-support tool for diabetes screening in primary healthcare settings.
Co-Authors Agung Prihandono Ahmad Mundzir Aini Syifa, Raisa Hilia ALAM Anisa Solehah Nurwendah Ansor, Muhamad Ardhiansyah Dea Dewi Damayanti Drajat, Hilmi Maulana Dwika Ananda Agustina Pertiwi Ema Utami Fauzan Natsir Ferry Wahyu Wibowo Ferry Wahyu Wibowo Fithri Sri Mulyani Hanina Sapira Hari Murti Hilmi Rahmawati Ia Riham Nurrahma KAPI, CECEP M. Kholiq Mochamad Ramdan Khozizah, Putri Kusrini Lodana, Mae B Mae Lodana mar'atusholihat, amelia Miftahudin Miftahudin Much Aziz Muslim Mustofa, Pramesti Melyna N. Nelis Febriani SM Nenden Nurkhopipah Nurdin, Arif Muhamad Nursamsi , Dede Rizal Nursamsi, Dede Rizal Pepi Alpiani Praseptiawan, Mugi Priyadi, Dimas Putri Khozizah Rifa Maulida Rizal Nursamsi, Dede Robby Maududy Sri Sudiarti Sudiarjo, Aso Syifa Aenun Naja Wildan Hannan, Muhammad Windriyana Yustiana Yustiana