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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jiko (Jurnal Informatika dan komputer) JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Pendidikan dan Konseling bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Advances in Data and Information Systems Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Studies in Learning and Teaching Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Bisnis Indonesia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Kolaboratif Sains Al Khidma: Jurnal Pengabdian Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial STATISTIKA Kohesi: Jurnal Sains dan Teknologi Information Technology International Journal (ITIJ) Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia RAGAM: Journal of Statistics and Its Application Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)

Trimono, Trimono

Universitas Pembangunan Nasional Veteran Jawa Timur

Author-ID : 1140628

Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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ANALISIS FAKTOR EKSTERNAL YANG MEMPENGARUHI FREKUENSI PEMBELIAN PADA APLIKASI SHOPEE MENGGUNAKAN REGRESI DUMMY Rhomaningtias, Lina; Khairunisa, Adenda; Trimono, Trimono
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.12735

Penelitian ini bertujuan untuk menganalisis pengaruh faktor-faktor eksternal seperti diskon, harga, kemudahan akses, iklan, dan rating terhadap frekuensi pembelian pada aplikasi e-commerce Shopee. Faktor-faktor ini dipilih karena merupakan aspek yang sering dipertimbangkan oleh konsumen dalam membuat keputusan pembelian di platform e-commerce. Penelitian ini menggunakan desain kuantitatif dengan pendekatan regresi dummy pada data yang dikumpulkan melalui kuesioner daring dari 63 responden. Pendekatan regresi dummy dipilih karena memungkinkan peneliti untuk menganalisis pengaruh variabel kategorik terhadap variabel dependen numerik. Hasil analisis menunjukkan bahwa diskon, kemudahan akses, iklan, dan rating memiliki pengaruh positif dan signifikan terhadap frekuensi pembelian, sementara harga memiliki pengaruh negatif yang signifikan. Model regresi yang digunakan mampu menjelaskan 76,8% variasi dalam frekuensi pembelian, sementara 23,2% sisanya dapat dijelaskan oleh faktor lain yang tidak dianalisis dalam penelitian ini. Penelitian ini memberikan kontribusi penting bagi pemilik platform e-commerce dalam merancang strategi pemasaran yang lebih efektif, serta memberikan wawasan bagi konsumen dalam memanfaatkan promosi dan memilih produk dengan harga kompetitif.

Prediction of Purchase Volume Coffee Shops in Surabaya Using Catboost with Leave-One-Out Cross Validation Nariyana, Calvien Danny; Idhom, Mohammad; Trimono, Trimono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30610

Indonesia's coffee consumption grew from 265,000 tons in 2015 to 294,000 tons in 2020. Averaging 2% annual growth with a projected 368,000 tons by 2024. One of the coffee businesses is coffee shops, Coffee shop businesses often struggle to attract customers quickly, risking low purchase volume within their first five years. In their first year, challenges include management, company size, service quality, and customer preferences. This study adopts a quantitative approach and new solutions to develop a purchase prediction application based on machine learning and strategy to enhance purchase volumes for three coffee shops in Surabaya. It utilizes CatBoost, with LightGBM as a comparison, across multiple coffee shop locations. LOOCV (Leave-One-Out Cross-Validation) is used in this model to address research limitations, such as data overfitting and biases, while enhancing evaluation accuracy. As a result, the study established CatBoost as the superior model for purchase prediction, providing insights and practical applications in business forecasting. The Catboost model achieved an MAE of 0.91 and MAPE of 15%, outperforming LightGBM’s MAE of 1.13 and MAPE of 18%. These results confirmed CatBoost’s effectiveness for the coffee shop industry with good accuracy. This research also contributes to helping coffee shop owners in Surabaya understand market characteristics, such as the most profitable coffee types and high-customer-density locations. Additionally, it aids in optimizing purchase volume to leverage profit by developing new strategies based on prediction result. In conclusion, CatBoost accurately predicts purchase volume, helping coffee shops identify target markets and refine strategies based on customer preferences.

PREDIKSI PERMINTAAN DARAH DI UTD KOTA SURABAYA MENGGUNAKAN METODE HYBRID ARIMA-ANFIS Oktaviani, Sheny Eka; Trimono, Trimono; Damaliana, Aviolla Terza
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.938

Blood supply is a crucial aspect for UTD which must meet the demand for blood for those who need it. UTD Surabaya City faces challenges in meeting blood needs caused by the uncertainty of blood demand which varies and is individualized according to the recipient's clinical condition which has an impact on the quality of UTD Surabaya City services, thus creating challenges in meeting blood needs optimally. Therefore, it is necessary to predict blood demand to assist UTD Surabaya City in ensuring adequate blood stock, planning the blood stock needs that will be requested, and avoiding stock overstocks and stock shortages. To overcome this, blood demand is predicted using the Autogressive Moving Average (ARIMA) and Adaptive Neuro Fuzzy Inference System (ANFIS) approaches. This combination of the ARIMA-ANFIS method combines the advantages of ARIMA in capturing linear patterns and ANFIS in handling non-linear patterns from ARIMA residuals. The prediction results from the ANFIS model will be added to the prediction results from the ARIMA model to obtain a hybrid ARIMA-ANFIS model. The ARIMA-ANFIS model is used to predict the number of blood requests by combining ARIMA predictions and residuals modeled using ANFIS. This process includes stationarity analysis, selecting the best ARIMA model, residual modeling with ANFIS, as well as performance evaluation using MAPE to ensure prediction accuracy. The best ARIMA (6,1,0) model was obtained with the lowest AIC value of -153.838, then from the ARIMA modeling results the residuals were obtained as input for ANFIS modeling. Analysis shows that the ARIMA-ANFIS hybrid model has better performance, with a MAPE value of 5.28%, compared to the ARIMA model which only achieved a MAPE of 6.21%.

MODEL VECTOR AUTOREGRESSIVE (VAR) UNTUK PREDIKSI INDEKS HARGA KONSUMEN, HARGA BERAS, DAN INFLASI KOTA SURABAYA Suprapto, Rheinka Elyana; Trimono, Trimono; Aviolla Terza Damaliana
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.965

The current global economic conditions face increasingly complex challenges, with projections of economic weakness continuing until 2025. Globalization has reduced the role of domestic factors and strengthened the impact of the global economy on the formation of inflation. From a macroeconomic perspective, the level of economic growth is often used as a leading indicator of a country's success, reflecting continuous changes in the economy with the aim of achieving better conditions over a certain period of time. Historically, the inflation rate in Indonesia tends to be higher compared to other developing countries. Data shows that during the 2010–2020 period, Indonesia's quarterly inflation was consistently higher than other developing countries. This study uses time series data analysis with a multivariate approach that includes three main variables: inflation, rice prices, and the consumer price index (CPI). The method used is Vector Autoregressive (VAR), which is an analysis technique for data with more than one related variable. The results of the analysis show that the VAR method produces a Mean Absolute Percentage Error (MAPE) value of 32.73% for inflation, 6.24% for CPI, and 5.78% for rice prices. These findings indicate that the VAR model has varying levels of accuracy for each variable, with more accurate predictions for CPI and rice prices compared to inflation.

Customer Transaction Clustering with K-Prototype Algorithm Using Euclidean-Hamming Distance and Elbow Method Kuswardana, Dendy Arizki; Prasetya, Dwi Arman; Trimono, Trimono; Diyasa, I Gede Susrama Mas; Awang, Wan Suryani Wan
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1381

This study aims to cluster customer transactions in a Japanese food stall using the K-Prototype Algorithm with a combination of Euclidean-Hamming Distance and the Elbow method. Facing intense industry competition, this study seeks to understand customer purchasing behavior to increase loyalty and sales. From 9.721 initial entries, 9.705 cleaned and transformed records were analyzed. K-Prototype was chosen because of its ability to handle numeric features (Total Sales, Product Quantity) and categorical features (Payment Method, Order Type, Day Category and Time Category). The combination of Euclidean-Hamming distances was used for distance measurement. The optimal number of clusters was determined using the Elbow method, with the results recommending three clusters as the most optimal number. A Silhouette score of 0.6191 indicates a Good Structure clustering result, effectively identifying three distinct customer grouping: "Loyal Regulars" (49.5%), "Casual Shoppers" (42.3%), and "Premium Shoppers" (8.2%). Statistical validity was also tested using ANOVA and Chi-Square, the results showed significant differences between the clusters in numerical and categorical variables with a p-value <0.0001. The clusters are statistically valid in both numerical and categorical aspects. These insights provide an understanding of customer characteristics and reveal a strategically valuable cluster for targeted marketing.

Prediction Of Loss Risk Investment On The Idx Indonesia: Quantitative Approach With Var And Adj-Es Trimono, Trimono; Fahrudin, Tresna Maulana; Ardiani, Ardia Eva
JURNAL STUDI MANAJEMEN ORGANISASI Vol 22, No 1 (2025)
Publisher : Faculty of Economics and Business | Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsmo.v22i1.73062

Loss is the primary risk associated with any investment. In stock investments, the risk of loss can occur at any time and its magnitude cannot be precisely determined. Improper risk management can negatively impact the investment activities carried out by investors. One way to manage risk effectively and prevent bankruptcy is by estimating the potential future risk. This study aims to predict the risk of loss using the quantitative Value-at-Risk (VaR) model, particularly for stocks listed on IDX Indonesia. VaR has the main advantage of being a simple model that can be applied to various types of financial assets. However, VaR also has a drawback it does not satisfy the subadditivity principle. Therefore, this study also employs the Adjusted-Expected-Shortfall (Adj-ES) model as an improvement to VaR. The VaR and Adj-ES models will be implemented on the stocks AMRT.JK and BBCA.JK. These two stocks are part of the IDX Indonesia 2024 blue chip stocks, with a significant increase in market capitalization. The results show that VaR provides prediction results for the risk of loss in the range of 1.2% - 3.4 for AMRT.JK data, and 1.1 - 3.2% for BBCA.JK data. Referring to the Violation Ratio value, it is known that both VaR and Adj-ES have VR values <1 so it is concluded that the prediction accuracy is very good

ANALISIS FAKTOR YANG MEMPENGARUHI KEPUASAN PESERTA BPJS KESEHATAN PADA RUMAH SAKIT WILAYAH SURABAYA DENGAN PENDEKATAN ANALISIS SENTIMEN Amanillah, Rahmatul; Trimono, Trimono; Terza Damaliana, Aviolla
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13393

Program Jaminan Kesehatan Nasional (JKN) yang dikelola oleh BPJS Kesehatan bertujuan untuk memberikan akses layanan kesehatan yang merata di Indonesia. Namun, tingkat kepuasan peserta masih menjadi perhatian utama, sehingga perlu dilakukan identifikasi aspek layanan yang perlu diperbaiki. Penelitian ini menganalisis faktor-faktor yang memengaruhi kepuasan peserta BPJS Kesehatan di rumah sakit wilayah Surabaya dengan pendekatan analisis sentimen. Data dikumpulkan melalui kuesioner online dan offline, kemudian dianalisis menggunakan algoritma Support Vector Machine (SVM) dengan pelabelan sentimen berbasis Lexicon. Teknik ekstraksi fitur Term Frequency-Inverse Document Frequency (TF-IDF) digunakan untuk meningkatkan performa model, serta dibandingkan efektivitas metode Synthetic Minority Over-sampling Technique (SMOTE) dalam menangani ketidakseimbangan data. Hasil klasifikasi menunjukkan performa yang cukup baik, terutama pada aspek asuurance dengan akurasi 0.94, presisi 0.96, dan recall 0.88 setelah penerapan SMOTE. Model mampu mengklasifikasikan sentimen positif dengan sangat baik, namun masih menghadapi tantangan dalam mengenali sentimen negatif. Secara keseluruhan, sentimen positif lebih dominan, namun beberapa aspek perlu diperbaiki, seperti antrean yang panjang, keterbatasan fasilitas medis, ketidakefisienan administrasi, serta inkonsistensi layanan tenaga medis.

PREDIKSI HARGA SAHAM SEKTOR ENERGI MENGGUNAKAN METODE SPATIAL TEMPORAL ATTENTION-BASED CONVOLUTIONAL NETWORK BERDASARKAN DATA TEKS DAN NUMERIK Anggraini, Novita; Arman Prasetya, Dwi; Trimono, Trimono
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13443

Perubahan harga saham dipengaruhi oleh berbagai faktor, termasuk data historis harga saham dan sentimen yang terkandung dalam berita keuangan. Penelitian ini bertujuan untuk mengembangkan model prediksi harga saham yang lebih akurat dengan memanfaatkan Spatial-Temporal Attention-Based Convolutional Network (STACN). Model ini dirancang untuk menggali hubungan kompleks antara data historis harga saham dan informasi dari berita finansial. Metode yang digunakan melibatkan integrasi Convolutional Neural Network (CNN) untuk mengekstraksi fitur dari thought vectors berita, Long Short-Term Memory (LSTM) untuk menangkap pola temporal dari data harga saham, dan Spatial-Temporal Attention Network (STAN) untuk memberikan perhatian pada fitur-fitur yang relevan. Studi kasus dilakukan pada saham sektor energi yang terdaftar di Bursa Efek Indonesia, dengan menggunakan data historis harga saham dan berita dari portal bisnis Indonesia. Hasil eksperimen menunjukkan bahwa model STACN Bi-LSTM menghasilkan akurasi yang lebih tinggi dibandingkan dengan model-model lain seperti LSTM dan Bi-LSTM konvensional, dengan nilai MAE sebesar 24.2776, RMSE 32.9127, dan R² 0.9365. Temuan ini membuktikan bahwa integrasi analisis spasial-temporal dan mekanisme perhatian efektif dalam meningkatkan akurasi prediksi harga saham.

ANALISIS PERBEDAAN POLUSI UDARA ANTAR KOTA DI BANGLADESH DENGAN UJI MANOVA Hadiyan Pradipta, Alvino; Rafli Feandika Nugroho, Muhammad; Fairuz Luthfia Winoto Putri, Maretta; Nasrudin, Muhammad; Trimono, Trimono
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13940

Polusi udara menjadi masalah lingkungan yang serius di Bangladesh dengan konsentrasi polutan seperti PM2.5, PM10, SO2 dan NO2 yang terus meningkat dan berdampak pada kesehatan. Permasalahan ini mendorong perlunya analisis untuk memahami perbedaan tingkat polusi udara di berbagai kota di Bangladesh. Penelitian ini bertujuan untuk mengidentifikasi perbedaan signifikan konsentrasi polutan antar kota menggunakan metode Multivariate Analysis of Variance (MANOVA). Data yang digunakan diperoleh dari World Health Organization (WHO) yang mencakup pengukuran polusi udara dari tahun 2010 hingga 2019 di kota seperti Dhaka, Chittagong, Khulna, Rajshahi, dan Barisal. Uji asumsi yang dilakukan seperti Uji Mardia, Uji Bartlett, dan Uji Box’s M. Hasil penelitian menunjukkan semua uji asumsi yang dilakukan sudah terpenuhi yaitu data mengikuti distribusi normal multivariat dan matriks kovarians antar kelompok tidak independen serta dianggap homogen. Selain itu, didapatkan pula hasil pengujian MANOVA dengan empat pengujian yaitu Uji Wilk’s Lambda yang menunjukkan bahwa terdapat perbedaan yang signifikan antara kelompok dalam variabel dependen secara simultan, Uji Pillai’s Trace yang menunjukkan bahwa terdapat perbedaan antar kelompok signifikan, Uji Hotteling’s Trace yang menunjukkan bahwa terdapat perbedaan signifikan antar kelompok dalam model yang diuji, dan Uji Roy’s Largest Root yang menunjukkan bahwa variabel bebas yang digunakan dalam analisis memiliki pengaruh yang signifikan terhadap variabel dependen secara bersama-sama. Seluruh hasil analisis ini menunjukkan bahwa terdapat perbedaan yang signifikan dalam konsentrasi polutan di berbagai kota yang dapat menjadi dasar untuk pengembangan strategi pengendalian polusi udara yang lebih efektif.

Prediction of Rice Harvesting During the Rainy Season in Kabupaten Lamongan Using Stochastic Frontier Analysis Ningrum, Imelda Widya; Prasetya, Dwi Arman; Trimono, Trimono; Kassim, Anuar bin Mohamed
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1393

The agricultural sector plays a critical role in ensuring national food security, yet it faces challenges in achieving technical efficiency due to limited land and input resources. This study aims to model and predict the technical efficiency of rice production in Lamongan Regency during the rainy season using a data science-driven Stochastic Frontier Analysis (SFA) approach. The dataset includes key inputs such as land area, labor, fertilizer, and environmental variables. The methodology involved data preprocessing, feature selection based on Pearson correlation and VIF thresholds, and model validation using metrics like R-squared, MAPE, and log-likelihood. The SFA model demonstrated high predictive capability, with R² values exceeding 0.91 in cross-validation and MAPE under 15%. The low gamma value (? = 0.0100) indicates minimal yet consistent inefficiency. The results suggest that integrating SFA with data science techniques provides an effective framework for identifying inefficiencies and can serve as a decision-support system for evidence-based agricultural policy.

Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Adelia Adelia, Adelia Adiwidyatma, Afdhal Reshanda Afidria, Zulfa Febi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Arrum Marwani Aurelia, Cenditya Ayu Aviolla Terza Damaliana Aviolla Terza Damaliana Aviolla Terza Damaliana Awang, Wan Suryani Wan Azni Aisyah Azzahra, Adelia Ramadhina Bagus Widduro Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Cindi Adam Damaliana, Aviolla Terza Desy Miftachul Ilmi Arifin Putri Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Dinda Putri Arnindi Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Eny Widayawati Erna Novita Anggie Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Hendri Prabowo Herlina Herlina Hervrizal, Hervrizal I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Gusti Putu Asto Buditjahjanto idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Ilil Musyarof Asfiani Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Jacinda Ardina Gestyaki Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela M Zufar Irhab S Putra Maharani Ikaningtyas Maruddani, Di Asih Mas'ad Mas'ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Milla Akbarany Baktiar Putri Mochammad Abudrrochman Faiz Mohammad Idhom Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Muhammad Nasrudin Muhammad Nasrudin Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Nevia Desinta Putri Ningrum, Imelda Widya Nova Auliyatul Maulidiyyah Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putri, Irma Amanda Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Sekar Arum Melati Selly Rizkiyah Shindi Shella May Wara Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Tiara Audrey Anugerah Hadin Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wahyu Syaifullah Jauharis Saputra Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widison, Daffin Tanjiro Yuciana Wilandari Zalfa Assyadida, Azizah

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