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All Journal International Journal of Evaluation and Research in Education (IJERE) ComEngApp : Computer Engineering and Applications Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Jurnal Simetris Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Intiqad: Jurnal Agama dan Pendidikan Islam Telematika : Jurnal Informatika dan Teknologi Informasi Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Fourier InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal JPSE (Journal of Physical Science and Engineering) Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika INTEGER: Journal of Information Technology Jurnal Matematika: MANTIK JURNAL MEDIA INFORMATIKA BUDIDARMA Desimal: Jurnal Matematika JTERA (Jurnal Teknologi Rekayasa) BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Informatika Universitas Pamulang METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Telematika Mathvision : Jurnal Matematika Building of Informatics, Technology and Science Transformasi : Jurnal Pendidikan Matematika dan Matematika Specta Journal of Technology Jurnal Mnemonic Majalah Ilmiah Matematika dan Statistika (MIMS) Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Insyst : Journal of Intelligent System and Computation JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Sains dan Teknologi INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Papanda Journal of Mathematics and Sciences Research Jurnal Informatika: Jurnal Pengembangan IT Sains Data Jurnal Studi Matematika dan Teknologi Journal Serambi Engineering (JSE) Indonesian Journal of Mathematics and Natural Sciences
Dian Candra Rini Novitasari
Universitas Islam Negeri Sunan Ampel
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Pemodelan banjir pluvial perkotaan berbasis SWMM untuk strategi mitigasi di kawasan Ciledug seskoal, Jakarta Selatan Chandra dewi; Dian Krisnawati; Airu Nahdloh; Dian Candra Rini Novitasari
Papanda Journal of Mathematics and Science Research Vol. 5 No. 1 (2026): Volume 5 Nomor 1 Maret 2026
Publisher : Papanda Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56916/pjmsr.v5i1.3064

Abstract

Urban pluvial flooding is one of the most frequent hydrometeorological disasters in densely populated areas and is a serious problem for many cities around the world. This flooding is triggered by high rainfall intensity, represented by the Intensity Duration Frequency (IDF) curve, as well as limited drainage network capacity due to inadequate channel size, hydraulic roughness, sedimentation, and waste accumulation. To address these issues, this study applies an integrated modeling method between SWMM and a 2D hydrodynamic model using rainfall data derived from Intensity Duration Frequency (IDF) analysis based on historical rainfall records from BMKG during the period 2014-2024, DEM/topography, land use, drainage channel characteristics, and actual inundation observations in the Ciledug Seskoal area, South Jakarta. This study aims to analyze the characteristics of pluvial flooding in the study area and evaluate the performance of the SWMM–2D model in simulating surface runoff and flooding processes. The calibration results show that the model is able to capture inflow patterns and hydrographs that are consistent with the observation data. Model validation demonstrated moderate agreement between simulated and observed inundation depths, with a correlation coefficient of r = 0.79 (R² = 0.63). The distribution of flooding depth indicated that most of the affected areas experienced flooding of more than 60 cm, signifying a significant level of risk. The novelty of this study lies in the integration of SWMM and a 2D hydrodynamic model validated with actual inundation data in a dense urban setting, which improves the accuracy of pluvial flood simulation compared to conventional methods. Overall, the integrated SWMM-2D model proved effective in predicting urban pluvial flood characteristics and can be used as a scientific basis for formulating flood mitigation strategies in densely populated residential areas
Perbandingan Ekstraksi Fitur pada Klasifikas Kanker Payudara Implan Menggunakan ELM Zumrotul Muallifah; Dian Candra Rini Novitasari
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 17 No. 1 (2026): JURNAL SIMETRIS VOLUME 17 NO 1 TAHUN 2026
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v17i1.14951

Abstract

Kanker payudara merupakan penyakit tidak menular dengan 18 juta kasus baru dilaporkan pada tahun 2018. Di Indonesia, kanker payudara lebih banyak ditemukan pada perempuan dan menjadi penyebab kematian utama. Kurangnya program skrining dan rendahnya inisiatif masyarakat untuk deteksi dini berkontribusi terhadap tingginya angka kematian akibat kanker di negara berkembang. Salah satu metode deteksi dini adalah mamografi. Namun, citra mammogram yang dihasilkan oleh mesin mamografi memiliki keterbatasan pada situasi tertentu, seperti mendeteksi kanker payudara pada pasien dengan implan payudara. Oleh karena itu, diperlukannya kecerdasan buatan sebagai alat pendukung keputusan untuk deteksi dini kanker payudara. Proses pengolahan data dengan kecerdasan buatan tersebut dapat meliputi beberapa langkah yaitu, augmentasi data, CLAHE, median filtering, ekstraksi fitur menggunakan transformasi Wavelet, dan klasifikasi menggunakan Extreme Learning Machine (ELM). Penelitian ini melibatkan empat kelas kanker yaitu, kanker positif, kanker negatif, kanker implan negatif, dan kanker implan positif. Tujuan dari penelitian ini adalah untuk memperoleh model optimal dalam mengidentifikasi kanker pada implan payudara dengan kecerdasan buatan. Model optimal yang dicapai dalam penelitian ini pada DB 2 Level 1 dengan K-Fold 10 dan 50 hidden node yang menghasilkan akurasi sebesar 80%, sensitivitas 80%, dan spesifisitas 93,33%.
Landslide Modeling with the Savage-Hutter Approach Using the Finite Volume Method Brilian Prilindaputra; Syifa Nasiratun Toyibah; Dinda Rima Rachcita Putri; Dian Candra Rini Novitasari
International Journal of Mechanical Computational and Manufacturing Research Vol. 14 No. 4 (2026): February: Mechanical Computational And Manufacturing Research
Publisher : Trigin Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/computational.v14i4.285

Abstract

Landslides are one of the most frequent disasters in Indonesia and have a major impact on the environment and society. This study focuses on modeling the dynamics of landslides in Peniraman Hill, West Kalimantan, using the Savage-Hutter (SH) model solved through the finite volume method (FVM) and the Harten-Lax-van Leer flux scheme. (HLL), supported by the Courant–Friedrichs–Lewy (CFL) method to maintain stable conditions. This study aims to apply the model to real conditions and assess the effectiveness of the numerical approach in describing the movement of land masses. Simulations were conducted on Slopes 1 and 3 which are at risk of landslides due to their soil stability, with three variations of the soil friction angle  to see how changes in these parameters affect the flow mechanism and sliding distance. The results show that the soil friction angle  is a factor that influences landslide behavior. Decreasing the value  makes the landslide move faster and cover a wider area in all parts of the topography. The initial maximum velocity of Slope 1 ranges from ~12–17 m/s with a range of around ~18 meters, while on Slope 3 it reaches ~20–27 m/s with a range of up to ~23.5 meters. Slope 3 consistently produces faster movement and longer sliding distance. Overall, the combination of the SH model with the FVM method and the HLL scheme controlled by CFL conditions has proven to be effective, stable, and capable of representing landslide dynamics. The research results can be an important basis for risk analysis and disaster mitigation strategy planning in the environment around Peniraman Hill to establish exclusion zones and design high load-bearing structures in the potential landslide reach area of ~23.5 meters
Prediksi Potensi Daya Listrik PLTGL-OWC di Selat Makassar Menggunakan Metode Gradient Boosted Tree-Regression (GBT) Adellia Juni Astine; Dian Candra Rini Novitasari; Ratna Cintya Dewi
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 2 (2026): April 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i2.7917

Abstract

The increasing demand for electricity continuously emphasizes the importance of developing renewable energy sources. Indonesia, as a country consisting of many islands, has significant potential to utilize energy from ocean waves, one of which is by using Oscillating Water Column (OWC) technology that can convert wave energy into electrical energy. The purpose of this study is to estimate the electrical energy generated by the OWC type Ocean Wave Power Plant (PLTGL) in the Makassar Strait using the Gradient Boosted Tree Regression (GBT) method. Two approach schemes are used in this study, but the focus of the discussion is on the first scheme, namely the indirect approach. In this approach, predictions are first made on four main variables, namely significant wave variables (Hsig), maximum wave height (Hmax), wave period (wave period), and wind speed (wind speed). The results of the main variable predictions are then used to calculate the electrical power generated by the PLTGL-OWC. The maximum power is estimated to be 23,926 Watts on January 3, 2025 at 12:00, and the minimum power is 3,256 Watts on January 7, 2025 at 19:00. This approach is effective for predicting the power output of ocean wave generators in the Makassar Strait.
Identifikasi Kualitas Pelayanan Kesehatan di Jawa Timur Menggunakan Metode Fuzzy C-Means Nabila Rahma Cahyani; Dian Candra Rini Novitasari; Muhammad Azhar
JTERA (Jurnal Teknologi Rekayasa) Vol 10 No 2: December 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.217-226

Abstract

Ketimpangan distribusi pelayanan kesehatan di Jawa Timur menjadi salah satu tantangan dalam upaya pemerataan akses dan kualitas layanan kesehatan. Penelitian ini bertujuan menggunakan pendekatan Fuzzy C-Means (FCM) untuk menentukan kualitas pelayanan kesehatan di 38 kabupaten dan kota di Jawa Timur. Variabel data yang digunakan meliputi kepadatan penduduk, tenaga kesehatan, dan fasilitas kesehatan di tahun 2024. Sebelum proses klasterisasi, dilakukan analisis korelasi untuk menyederhanakan variabel melalui penggabungan kelompok variabel yang saling berkorelasi tinggi. Hasil klasterisasi FCM menunjukkan bahwa wilayah di Jawa Timur terbagi menjadi tiga klaster, yaitu klaster dengan kualitas pelayanan sangat memadai, cukup memadai, dan kurang memadai. Nilai 0,725 diperoleh melalui evaluasi menggunakan Silhouette Coefficient, yang menunjukkan bahwa struktur klaster yang dihasilkan memiliki kualitas yang tergolong baik. Hasil pemetaan menunjukkan bahwa sebagian besar wilayah berada dalam kategori cukup memadai, namun masih terdapat beberapa wilayah yang memerlukan perhatian lebih dalam pemerataan tenaga dan fasilitas kesehatan. Penelitian ini diharapkan menjadi dasar pertimbangan kebijakan pemerataan pelayanan kesehatan di Jawa Timur melalui program Jatim Sehat.
ANALISIS SENTIMEN FENOMENA PENGIBARAN BENDERA STRAW HAT PIRATES DI MOMEN HUT RI KE-80 DENGAN MENGGUNAKAN SUPPORT VECTOR MACHINE DAN EKSTRAKSI FITUR GLOVE: SENTIMENT ANALYSIS OF THE STRAW HAT PIRATE FLAG HOISTING PHENOMENON DURING THE 80TH ANNIVERSARY OF INDONESIAN INDEPENDENCE DAY USING SUPPORT VECTOR MACHINE Syukron Abdul Aziz; Dian Candra Rini Novitasari; Maunah Setyawati; Jiphie Gilia Indrayani
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7621

Abstract

The phenomenon of hoisting the Straw Hat Pirate flag during the commemoration of the 80th Anniversary of Indonesian Independence Day has sparked diverse public reactions on social media platform X. Some members of society interpret it as a symbol of freedom of expression and social criticism, while others consider the action inappropriate as it is perceived to reduce the meaning of national symbols. This study aims to analyze public sentiment toward this phenomenon by classifying public opinion into positive and negative sentiments and to determine the performance of the classification method used. The method employed is Support Vector Machine (SVM) with text feature extraction based on Global Vectors for Word Representation (GloVe). The research data consists of 2,660 tweets collected during 1 August 2025 into 31 August 2025 through a crawling process using keywords related to the flag-hoisting phenomenon. The research stages include text pre-processing, word vector formation using GloVe, and sentiment classification using SVM. Model validation was conducted using the K-Fold Cross Validation method and evaluated using Confusion Matrix based on accuracy, precision, recall, and F1-score metrics. The research results demonstrate that the SVM model with GloVe features is capable of classifying public sentiment, achieving an accuracy value of 0.9708, precision of 0.9733, recall of 0.9698, and F1-score of 0.97154, while providing a mapping where positive sentiment regards the phenomenon as a form of freedom of expression and negative sentiment reflects views that consider the action inappropriate.
Detecting Lung Disease Based on Chest X-ray Images Using a Hybrid CNN-KELM Approach Dian Candra Rini Novitasari; Musfiroh Musfiroh; Dina Zatusiva Haq
Intelligent System and Computation Vol 8 No 1 (2026): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v8i1.479

Abstract

Tuberculosis (TB) is a disease caused by the Mycobacterium tuberculosis (M.tb) bacterium. TB ranks among the top 10 deadliest diseases worldwide and is the second most contagious disease after COVID-19. The World Health Organization (WHO) recommends using Chest X-ray (CXR) imaging techniques, given their high sensitivity and cost-effectiveness. This study proposes a hybrid CNN-KELM (CKELM) method for the classification of four lung disease categories based on chest X-ray (CXR) images: tuberculosis, pneumonia, COVID-19, and normal, all within a short computational time. This study experimented with several types of CNN architectures implemented for feature extraction, while KELM for classification used hyperparameters that tested various kernel types and regularization coefficients. The experimental results indicate that the best performance is achieved using the DenseNet201 architecture with a polynomial kernel and a regularization coefficient of 0.1. The polynomial kernel demonstrates superior performance across all CNN architectures. Furthermore, a regularization coefficient of 0.1 exhibits the highest accuracy in the kernel and CNN architecture experiments. The DenseNet201-KELM model attains an accuracy, sensitivity, specificity, precision, and F1-score of 99.57%, 99.57%, 99.86%, 99.57%, and 99.57%, which is 7% better than without under sampling and detection using the DenseNet201-KELM method requires a computational time of 309.19 seconds. The proposed method achieved good performance in multi-class classification, especially for balanced data, with fast computational time.
THE GENERALIZED SPACE-TIME ARIMA (GSTARIMA) MODEL FOR PREDICTING NITROGEN MONOXIDE TO MITIGATE EID AL- FITR AIR POLLUTION IN SURABAYA Hani Khaulasari; Dian Candra Rini Novitasari; Maunah Setyawati; Jeneiro Maulana; Shukor Sanim Mohd Fauzi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0069-0086

Abstract

Air quality is a crucial factor due to its significant impact on environmental sustainability and public health. One of the major pollutants affecting air quality is Nitrogen Monoxide (NO), especially during periods of increased human mobility such as Eid al-Fitr. Monitoring and predicting NO levels are essential for early mitigation efforts. This study aims to evaluate the performance of the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model with three types of spatial weighting schemes and compare it with other forecasting methods, namely ARIMA, VARIMA, and Support Vector Regression (SVR), in predicting NO concentrations in Surabaya for April 2024. The data used in this study consist of daily NO concentration measurements obtained from the Surabaya City Environment Agency’s monitoring stations located at SPKU Tandes, SPKU Wonorejo, and SPKU Kebonsari, covering the period from January 2023 to March 2024. The GSTARIMA model was selected for its capability to capture both spatial and temporal dependencies across monitoring locations. As an extension of the ARIMA model, GSTARIMA incorporates spatial weight matrices to model spatial heterogeneity. Parameter estimation was conducted using the Ordinary Least Squares (OLS) method. The results indicate that the GSTARIMA model with Inverse Distance Weighting (IDW) and order (3,1,0)₁ in the first spatial order yields the most accurate predictions, outperforming ARIMA, VARIMA, and SVR models. The model produced the lowest Symmetric Mean Absolute Percentage Error (sMAPE) of 0.93% and Root Mean Square Error (RMSE) of 5.32. A notable spike in NO concentrations was observed between April 23 and 25, 2024, coinciding with the post-Eid al-Fitr return flow, indicating a surge in population mobility.
OPTIMIZATION OF ARIMA RESIDUALS USING LSTM IN STOCK PRICE PREDICTION OF PT MEDCO ENERGI INTERNASIONAL TBK Achmad Fachril Yusuf Ababil; Abdulloh Hamid; Hani Khaulasari; Dian Candra Rini Novitasari; Wika Dianita Utami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1405-1420

Abstract

The capital market plays an important role in the economy by providing a means for companies to obtain capital and as a place to invest. Stocks are one of the popular investment instruments because their potential profits are attractive to investors. The stocks used in this study are PT Medco Energi Internasional Tbk (MEDC) shares. The purpose of this study is to obtain the optimal ARIMA-LSTM residual optimization model, how much the accuracy, and to predict Medco stock prices for the next 8-month period. The data used starts from January 4, 2021, to October 31, 2024, was obtained from the yahoofinance.com website. The ARIMA model, which is known to be effective in handling linear data, will be combined with LSTM. The use of residuals in the LSTM model can help LSTM capture patterns in the entire stock data so as to increase prediction accuracy. The research results obtained are the optimal ARIMA-LSTM optimization model, namely, ARIMA ([5,9],1,[5,9,11]) and LSTM with the best hyperparameter, namely, hidden layer 64, batch size 16, and learning rate 0.01. The accuracy of the ARIMA-LSTM optimization model is classified as very accurate, with a MAPE value of 0.3%. Medco Energi’s stock price for the next 8-month period is predicted to increase from IDR1312 to IDR1430 or an increase of 9%.
Analisis Sentimen Pengguna Aplikasi X Terhadap Pelaksanaan Makan Bergizi Gratis Menggunakan Metode Adaptive Neuro-Fuzzy Inference System Wahyu Ningtiyas Mergianti; Nasroh Khudin; Achsan Afandi; Novia Adibatus Shofah; Dian Candra Rini Novitasari
Sains Data Jurnal Studi Matematika dan Teknologi Vol 4, No 1: January - June 2026
Publisher : Institut Nurul Islam Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52620/sainsdata.v4i1.339

Abstract

Program Makan Bergizi Gratis (MBG) menimbulkan beragam sentimen positif dan negatif di media sosial yang mencerminkan perbedaan persepsi publik terhadap pelaksanaannya. Penelitian ini bertujuan menganalisis sentimen masyarakat menggunakan metode Adaptive Neuro-Fuzzy Inference System (ANFIS) agar perbedaan sentimen positif dan negatif dapat diidentifikasi secara lebih jelas dan terukur. Data penelitian berupa 3520 tweet hasil crawling dengan beberapa kata kunci. Dataset melalui empat tahap persiapan dan dilakukan pembobotan menggunakan TF-IDF. Penelitian ini menguji kinerja ANFIS menggunakan validasi silang K-Fold dengan variasi learning rate (0.001, 0.01, 0.1, 0.2) dan 4 optimasi yakni sgd, adam, RMSProp, Adagrad. Hasil pengujian menunjukkan model uji terbaik menghasilkan akurasi sebesar 56.55%, sensitivitas 19.73%, presisi 38.06%, dan f1-score 25.98% dengan kombinasi parameter learning rate sebesar 0.01 dan optimizer Adam. Hasil evaluasi menggunakan confussion matrix menunjukkan bahwa sentimen positif terhadap pelaksanaan program MBG masih rendah, sehingga diperlukan evaluasi dan perbaikan pada aspek implementasian program.
Co-Authors Abdulloh Hamid Abdulloh Hamid Abdulloh Hamid Achmad Fachril Yusuf Ababil Achmad Teguh Wibowo Achsan Afandi Adam Fahmi Khariri Adelia Rova Chumairo Adellia Juni Astine Adyanti, Deasy Ahmad Hanif Asyhar Ahmad Hidayatullah Ahmad Yusuf Ahmad Zoebad Foeady Ahmad Zoebad Foeady Airu Nahdloh Aisyah, Nora Aliya Octavia Ramadhani Alvin Nuralif Ramadanti Amin, Faris Mushlihul Ananda Nur Izza Arifin, Ahmad Zaenal Aris Fanani Ariyanto Wijaya, Indra Ariyanto, Dimas Azmi, Tasya Auliya Ulul Brilian Prilindaputra Cahyani, Nabila Rahma Chalawatul Ais Chandra dewi Damayanti, Adelia Deasy Adyanti Desy Nur Fitriani Dewi Sukmawati, Chandra Dewi, Ratna Cintya Dian Krisnawati Dianita Utami, Wika Dilla Dwi Kartika Dinda Rima Rachcita Putri Diva Ayu Safitri Nur Maghfiroh Elen Riswana Safila Putri Fahriza Novianti Fajar Setiawan Fajar Setiawan Fajar Setiawan Fajar Setiawan FAJAR SETIAWAN Fanny Maulidya Faris Mushlihul Amin Farmita, Mayandah Febriana Eka Adkhaniyah Ferryan, Dhandy Ahmad Firmansjah, Muhammad Fitria, Nur Annisa Foeady, Ahmad Zoebad Galuh Andriani Ganeshar B.D. Prasanda Gita Purnamasari R Hani Khaulasari Hani Khaulasari Hani Khaulasari Hanimatim Mu'jizah Haq, Dina Zatusiva Ifadah, Corii Indriyani, Jiphie Gilia Irkhana Indaka Zulfa Ivone Lovenia Youvita Izza Dhinillah Jauharotul Inayah Jeneiro Maulana Jiphie Gilia Indrayani Kurniawan, Mohammad Lail Kusaeri Lubab, Ahmad Luluk Mahfiroh Lutfi Hakim Lutfi Hakim Lutfi Hakim Lutfi Hakim Luthfi Hakim Luthfi Hakim M. Hasan Bisri Maliki, Naufal Ridho Mardiyah, Ilmiatul Masruroh Kusman, Umi Maulana, Aashif Amiruddin Maulana, Achmad Resnu Maunah Setyawati Maunah Setyawati Moh. Hafiyusholeh Mohammad Rizal Abidin Monika Refiana Nurfadila Muhammad Azhar Muhammad Azhar MUHAMMAD FAHRUR ROZI Muhammad Fahrur Rozi Muhammad Syaifulloh Fattah Muhammad Thohir Musfiroh Musfiroh Musfiroh Musfiroh, Musfiroh Nabila Rahma Cahyani Nanang Widodo Nanang Widodo Nanang Widodo Nanang Widodo Nasroh Khudin Naufal Ridho Maliki Nimas Nabila Anggraeni Nisa Trianifa Novia Adibatus Shofah Noviati Maharani Sunariadi Noviati Maharani Sunariadi Nur Afifah Nur Hidayah Nurissaidah Ulinnuha Nurul Istiqomah Pramesti, Diah Devi Pramono, Wahyu Joko Puspitasari, Wahyu Tri Putri Oktavia, Nabiilah Putri Wulandari Putri, Evi Septya Putroue Keumala Intan Rachma Raudhatul Jannah Rafika Veriani Ramadanti, Alvin Nuralif Ratna Cintya Dewi Ratnasari, Cristanti Dwi Rifa Atul Hasanah RIFA ATUL HASANAH Rozi, Muhammad Fahrur Rozzy, Fahrul Safira, Icha Dwi Sani, Puteri Permata Sari, Firda Yunita Sari, Ghaluh Indah Permata Setiawan, Fajar Shukor Sanim Mohd Fauzi Siti Nur Aisah Siti Nur Fadilah Siti Nur Fadilah Siti Ria Riqmawatin Sukarni, Adinda Ika Sulistiya Nengse Sulistiyawati, Dewi Suwanto Suwanto Suwanto Suwanto Swindiarto, Victory T. Pambudi Syamil Waris Dien Muhammad Syifa Nasiratun Toyibah Syukron Abdul Aziz Tasya Auliya Ulul Azmi Thalia Anindya Ardine Unix Izyah Arfianti USWATUN KHASANAH Utami, Tri Mar'ati Nur Utami, Wika Dianita Utami Dianita Veriani, Rafika Vina Fitriyana Wahyu Ningtiyas Mergianti Wanda N.P. Sunaryo Wijaya, Indra Ariyanto Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wisnawa, Gede Gangga Yana Vita Sari Yasirah Rezqita Aisyah Yasmin Yuliati, Dian Yuliawanti, Felia Dria Yuni Hariningsih Yuniar Farida Yuyun Monita Yuyun Monita Zahroh, Khofifah Auliyatuz Zulfa, Elok Indana Zumrotul Muallifah