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All Journal Jurnal Teknologi Industri Pertanian Jurnal Masyarakat Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) Abdimas Pedagogi: Jurnal Ilmiah Pengabdian kepada Masyarakat JOIV : International Journal on Informatics Visualization Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ekonomi, Manajemen Akuntansi dan Perpajakan (Jemap) J I M P - Jurnal Informatika Merdeka Pasuruan Applied Information System and Management Jurnal Teknoinfo Jurnal Nasional Komputasi dan Teknologi Informasi Energi & Kelistrikan Indonesian Journal of Applied Informatics Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmu Komputer dan Bisnis Aisyah Journal of Informatics and Electrical Engineering Jurnal Sistem Informasi dan Informatika (SIMIKA) Journal of Innovation and Future Technology (IFTECH) TIN: TERAPAN INFORMATIKA NUSANTARA JURNAL AKTUAL AKUNTANSI KEUANGAN BISNIS TERAPAN (AKUNBISNIS) Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Sistem Informasi Journal of Industrial and Engineering System Jurnal Sains Indonesia Bulletin of Computer Science Research Journal of Students‘ Research in Computer Science (JSRCS) Journal Software, Hardware and Information Technology Jurnal Media Informatika Jurnal Mandiri IT J-Intech (Journal of Information and Technology) Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Sains dan Teknologi Jurnal Sains Informatika Terapan (JSIT) Paradigma Indonesian Journal Computer Science (ijcs) Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research Jurnal Komputer dan Teknologi (JUKOMTEK) Jurnal Ilmiah Sistem Informasi Bulletin of Artificial Intelligence Riau Jurnal Teknik Informatika Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Journal of Information Technology Jurnal Teknoinfo Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Jurnal Teknik Informatika dan Teknologi Informasi
Sumanto, Sumanto
Universitas Bina Sarana Informatika
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Reinforcement learning for bitcoin trading: A comparative study of PPO and DQN Prasetyo, Romadhan Edy; Sumanto, Sumanto; Chaidir, Indra; Supriyatna, Adi
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.455

Abstract

Bitcoin’s high volatility demands automated strategies that adapt to changing market regimes while managing risk. This study compares Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) for Bitcoin trading using hourly BTC/USDT data from 2019 to early 2025. The models are trained to generate buy and sell signals from technical indicators including the Relative Strength Index (RSI), MA20, volatility, Moving Average Convergence Divergence (MACD), volume trend, SMA200, and a weekly trend filter. All features are computed on hourly bars. The evaluation shows that PPO tends to trade more aggressively and delivers higher performance during bullish phases, though with greater risk in unstable markets. By contrast, DQN trades more selectively and maintains better stability in sideways or choppy conditions. These findings support the effectiveness of reinforcement learning for adaptive cryptocurrency trading and highlight complementary strengths between PPO and DQN across market regimes.
Komparasi Naive Bayes dan SVM untuk Analisis Sentimen Pada E-Commerce Seller Center Yanuar Laik, Abraham Adrian; Nabilla, Adinda; Diah, Andi; Sumanto; Indra, Ahmad; Arya, Yudi
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3211

Abstract

The development of e-commerce drives the need to understand customer opinions through sentiment analysis to improveservice quality. Tokopedia and TikTok Shop as popular e-commerce platforms provide a review feature that can be asource of data to analyze consumer perceptions. This study aims to compare the performance of two text classificationalgorithms, namely Naive Bayes and Support Vector Machine (SVM), in analyzing the sentiment of customer reviews takenfrom the TikTok Tokopedia Seller Center dataset. The research method used is a computational experiment with aquantitative approach. The dataset used is sourced from the Kaggle site and is available in clean and labeled conditions(positive and negative). Model evaluation is done by measuring accuracy, precision, recall and F1-score. The results showthat Naive Bayes is superior with 97.50% accuracy and 84.00% F1-score, compared to SVM which obtained 94.90%accuracy and 76.80% F1-score. Thus, Naive Bayes is considered more effective for sentiment analysis of e-commercecustomer reviews
Optimizing printer usage through data analytics for enhanced institutional efficiency Kadir, Fauwas Abdul; Sumanto, Sumanto
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.453

Abstract

The advancement of information technology had simplified various workplace processes, including document processing and printing. In an institution, the use of printers played a crucial role in daily operations. However, without proper management, printer usage often became inefficient, leading to increased operational costs and unnecessary waste of resources. Therefore, an analytical system was needed to monitor and optimize printer usage. Such a system provided valuable insights by analyzing data generated from printing activities. This data analysis revealed patterns in work habits and allowed institutions to make informed decisions. As a result, institutions were able to improve operational efficiency, reduce costs, and minimize environmental impact. Paper and ink waste were significantly reduced by implementing data-driven policies. Overall, the integration of data analytics into printer management contributed to sustainable practices and better resource allocation in institutional environments.
Combination of Objective Weighting Method using MEREC and A New Additive Ratio Assessment in Coffee Barista Admissions Arshad, Muhammad Waqas; Suryono, Ryan Randy; Rahmanto, Yuri; Sumanto, Sumanto; Sintaro, Sanriomi; Setiawansyah, Setiawansyah
TIN: Terapan Informatika Nusantara Vol 5 No 3 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i3.5771

Abstract

A coffee barista is a professional who is skilled in the art of brewing and serving coffee in an attractive and high-quality way. The role of a barista is not only limited to operating an espresso machine and grinding coffee beans, but also includes in-depth knowledge of different types of coffee beans, manufacturing techniques, and the resulting flavors. The main problem in the acceptance of coffee baristas often has to do with the gap between industry expectations and the skills possessed by prospective workers. Many candidates may lack formal training or practical experience in brewing coffee, so they do not meet the standards expected by cafes or restaurants. The purpose of the research on the Combination of Objective Weighting Methods using MEREC and ARAS in Coffee Barista Admission is to develop and apply a more systematic and objective approach in the selection process of prospective baristas. The combination of objective weighting methods and the new additive ratio assessment (ARAS) approach offers a sophisticated framework for evaluating candidates in coffee barista admissions. The objective weighting method ensures that evaluation criteria are prioritized based on their intrinsic importance, thereby minimizing subjective preference. When combined with the ARAS method, which ranks alternatives based on their performance ratio to the ideal solution, this approach provides a balanced and comprehensive assessment for each candidate. Based on the results of the evaluation of the barista admission selection, Clara Dewi ranked first with the highest final score of 0.98553, followed by Hanafi Lestari with a score of 0.95921 and Erika Santosa with a score of 0.95726 who ranked second and third.
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Buono, Agus; Priandana, Karlisa; Paruhum Silalahi, Bib; Sri Hendrastuti, Elisabeth
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Prediksi Harga Emas di Indonesia menggunakan Metode Linear Regression Berbasis Data Historis Antam Cahya, Titus Dwi; Sumanto, Sumanto; Chaidir, Indra
Innovative: Journal Of Social Science Research Vol. 5 No. 4 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i4.21047

Abstract

This study uses a simple linear regression method to predict gold prices in Indonesia using historical Antam gold data. Linear regression is applied to model the linear relationship between the 2024 daily gold price (variable Y) and the date (variable X). Model performance is evaluated using Mean Squared Error (MSE) and R-squared (R²) to ensure more stable and accurate results. The evaluation results show that the linear regression model used has an MSE of 1403425123.8609 and an R² of 0.93, indicating good performance in predicting gold prices. This study concludes that the simple linear regression method can be used to predict gold prices throughout the year (long-term), but cannot accurately predict daily prices.
Comparison of Supervised Learning Classification Methods on Accreditation Data of Private Higher Education Institutions Noviyanto; Wahyudi, Mochamad; Sumanto, Sumanto
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3306

Abstract

This research aims to analyze and compare supervised learning classification methods using a case study of accreditation data for private higher education institutions within the LLDikti Region III contained in BAN-PT. In addition, this research also uses Weka machine learning software in its calculations. The initial step taken is to prepare the software used for supervised learning analysis, then pre-processing the data, namely labeling data that has a categorical data type, after that determining data for testing data. The next step is to test each classification method. The methods used for comparison are logistic regression, K-nearest neighbor, naive bayes, super vector machine, and random forest. Based on the calculation results, the Kappa Statistic and Root mean squared error values obtained are 1 and 0 for the logistic regression method, 0.979 and 0.0061 for the K-nearest neighbor method, 1 and 0.2222 for the super vector machine method, 0.969 and 0.0341 for the naive bayes method, 1 and 0 for the decision tree method, and 0.5776 and 0.1949 for the random forest method, respectively. The logistic regression and decision tree methods in this study get Kappa Statistic and Root mean squared error values of 1 and 0 respectively so that they are said to be good and acceptable, thus the two classification methods are the most appropriate methods and are considered to have the highest accuracy.
Pengembangan Sistem Deteksi Objek Botol Real-Time dengan YOLOv8 untuk Aplikasi Vision Triyanto, Dedi; Zidan, Muhammad; Wahyudi, Mochamad; Pujiastuti, Lise; Sumanto, Sumanto
Indonesian Journal Computer Science Vol. 3 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v3i1.6070

Abstract

Plastik daur ulang berperan penting dalam menanggulangi masalah limbah lingkungan sekaligus mendukung praktik keberlanjutan. Penelitian ini bertujuan mengembangkan sistem deteksi botol plastik dan kaleng daur ulang secara real-time menggunakan algoritma YOLOv8 yang terkenal akan kecepatan dan akurasinya. Dengan memanfaatkan dataset yang terdiri dari 2.900 gambar dan melatih model melalui Google Colab selama 25 epoch, penelitian ini berhasil menunjukkan performa luar biasa dari YOLOv8, dengan hasil mAP sebesar 99,5%, precision 99,7%, dan recall 99,5%. Model ini terbukti sangat efektif dalam mendeteksi objek daur ulang, memberikan prediksi yang tepat tanpa kesalahan negatif pada confusion matrix. Untuk penelitian lanjutan, disarankan menambah variasi kelas objek seperti botol kaca dan karet serta memperluas dataset guna meningkatkan generalisasi model. Selain itu, pengujian dalam kondisi nyata sangat diperlukan untuk memastikan kinerja optimal dalam lingkungan yang lebih kompleks. Pendekatan serupa dalam penelitian sebelumnya juga telah membuktikan kinerja unggul dalam deteksi real-time, menjadikan metode ini salah satu yang terdepan dalam pengembangan teknologi berbasis YOLO.
Deteksi dan Prediksi Cerdas Penyakit Paru-Paru dengan Algoritma Random Fores Kurniawan, Deny; Wahyudi, Mochamad; Pujiastuti, Lise; Sumanto, Sumanto
Indonesian Journal Computer Science Vol. 3 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v3i1.6071

Abstract

Penyakit paru-paru, seperti COPD, kanker paru-paru, dan asma, adalah masalah kesehatan global yang menyebabkan lebih dari tujuh juta kematian setiap tahun. Teknologi canggih, termasuk model deep learning dan algoritma Random Forest, telah digunakan secara efektif untuk mendeteksi dan mengklasifikasikan penyakit paru-paru dari data pencitraan dengan akurasi tinggi. Penelitian ini bertujuan menunjukkan efektivitas algoritma Random Forest dalam memprediksi penyakit paru-paru. Dataset yang digunakan terdiri dari 30.000 data dengan 11 atribut, diperoleh dari Kaggle dan diproses menggunakan perangkat lunak Orange versi 3.36.2. Algoritma Random Forest diimplementasikan dengan 10 pohon keputusan dan enam atribut yang dipertimbangkan pada setiap pembagian data. Model ini diuji menggunakan validasi silang dengan 10 lipatan, dan hasil pengujian menunjukkan nilai AUC sebesar 0,993, yang mengindikasikan tingkat akurasi yang sangat tinggi. Matriks kebingungan digunakan untuk mengevaluasi kinerja model, dengan mengukur akurasi, presisi, recall, F1-Score, dan AUC. Model ini menunjukkan akurasi yang tinggi, dengan nilai ROC AUC 0,453 untuk prediksi adanya penyakit paru-paru dan 0,547 untuk prediksi ketiadaan penyakit paru-paru. Hasil ini menunjukkan bahwa algoritma Random Forest dapat menjadi alat yang efektif dalam mengidentifikasi penyakit paru-paru. Penelitian ini berkontribusi pada pengembangan teknik diagnostik yang lebih akurat dan efisien, yang dapat membantu tenaga medis dalam mendiagnosis penyakit paru-paru pada pasien. Dengan pemahaman yang lebih baik tentang penerapan algoritma ini dalam dunia kesehatan, diharapkan dapat meningkatkan kualitas diagnosis dan perawatan pasien secara signifikan.
Klasterisasi Jumlah Tindak Pidana Kepolisian Daerah Pada Algoritma K-Means Klustering Pangestu, Adi; Umam, Hairul; Wattilah, Florentina; Ramadhan, Muhammad Gilang; Sumanto, Sumanto; Kuswanto, Andi Diah
Jurnal Ilmu Komputer dan Bisnis Vol. 16 No. 2a (2025): Vol. 16 No. 2a Special Issue (2025)
Publisher : STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/jikb.v16i2a.1152

Abstract

Penelitian ini bertujuan untuk menganalisis jumlah tindak pidana di Indonesia berdasarkan wilayah Kepolisian Daerah (Polda) dengan menerapkan algoritma K-Means Clustering. Data yang digunakan meliputi berbagai fitur numerik terkait tindak pidana dari sejumlah Polda di Indonesia. Proses analisis dilakukan dengan teknik normalisasi data serta pengelompokan menggunakan tiga klaster utama (C1, C2, C3), yang menunjukkan tingkat keparahan atau intensitas tindak pidana di masing-masing wilayah. Validitas hasil klaster diukur menggunakan nilai Silhouette, yang menunjukkan rata-rata sebesar 0,63, menunjukkan bahwa kualifikasi antar klaster cukup baik dan representatif. Hasil clustering menampilkan bahwa Polda-Polda yang tergolong dalam klaster C1 cenderung memiliki tingkat tindak pidana yang lebih rendah, yang ditunjukkan oleh nilai fitur yang relatif kecil. Sebaliknya, klaster C3 berisi wilayah dengan tingkat tindak pidana tertinggi, seperti Sumatera Utara dan beberapa wilayah padat penduduk lainnya. Klaster C2 berada di antara kedua kategori tersebut. Pengelompokan ini dapat membantu pihak berwenang dalam menyusun strategi penanggulangan kejahatan yang lebih tepat sasaran, dengan fokus pada klaster yang menunjukkan tingkat kejahatan tinggi. Penelitian ini menegaskan bahwa pendekatan data mining seperti K-Means Clustering efektif digunakan dalam menganalisis data kriminalitas dan memberikan wawasan geografis yang berguna untuk pengambilan keputusan. Ke depan, penelitian serupa dapat ditingkatkan dengan menambahkan variabel sosio-ekonomi atau demografi untuk memperkaya pencetakan dan interpretasi hasil.
Co-Authors Abdurrachman, Qais Achmad Rivai Syahputra Ade Budiman, Ade Ade Christian Ade Christian Ade Christian Ade Christian, Ade Adi Pangestu Adi Supriyatna Aditia Yudhistira Agung Wibowo Agus Buono Ahmad Habibullah Ahmad Yani ahmad yani Ahmad Yani , Ahmad Yani Alamsyah, Muhammad Arkan Alghifar Firgiawan Alghiffary, Muhammad Adya Ali, Muhamad Hafis Ali, Satrio Nur Alwan Kapi Muntaha Alya Avisa Andi Diah Kuswanto Andika Amansyah Andri Amico Anggreani, Namira Anita Adelia Syahfitri Antony Pangaribuan, Rizky Daud Apip Supiandi Aprilyanto, Ryan Dwi Ardiyansyah, Rizqi Ari Sulistiyawati Ari Sulistiyawati Arnata Nur Rasyid Arshad, Muhammad Waqas Arya, Yudi Asmawati Asmawati Asy'ari, Muhammad Rifqi Audy Aulia Azzahra Aulia Rachmat, Daffa Azkia, Farah Diba Bib Paruhum Silalahi Bismo Raharjo, Yohanes Aryo Budhi Adhiani Budhi Adhiani Christina Budi Santoso Budiman, Ade Surya Cahya, Titus Dwi Cahyani Ayu Sulistyawati Christian , Ade Damayanti Damayanti Dedi Darwis Dedi Triyanto Dedi Triyanto DENY KURNIAWAN Deny Kurniawan Desiana Nuranudin Putri Dewi, Revinta Arrova Diah, Andi Dinda Aprillia Dyah Ayu Megawaty Dyani Kalyana Mitta Eka Dyah Setyaningsih Eka Putri Alvi Syahrina Elisabeth Sri Hendrastuti Erlangga Rizki Ekaptra Faatin, Safinah Fahrian Fahroni, Aldiwa Alfa Thira Nur Faiz Djarot, Raihan Jamal Fajar Akbar Fajar Yoga Adiansyah Fajrian, Ihsan Fardha Hasykir Farhan Fadhilah Faris Syahrendra Faruk Ulum Fathur Rismansyah Fauzan Nawwir Andriansyah Fauzan, Muhammad Indra Ganda Wijaya Ganda Wijaya, Ganda Ghofar Taufiq, Ghofar Ginting Wibi Prasetyo Gustian, Riansyah Hafis Nurdin Harianto Harianto Hariyanto HARIYANTO HARIYANTO Hartanti Hartanti Hernawan, Muhammad Hendra Hidayat, Manarul Hilmy Ibrahim, Farras Imam Budiawan Imam Budiawan Imam Wahyudi Indah Purwandani Indra Chaidir, Indra Indra, Ahmad Indriani , Karlena Indriyanti, Zahra Kiky Dwi Insani Abdi Bangsa Iqro Mukti Arto Jefina Tri Kumalasari Joseph Melchior Nababan Jumadi, Yakobus Linus Jumaryadi, Yuwan Junhai Wang Junhai Wang Kadir, Fauwas Abdul Kaisar Ages Querio Karlena Indriani Karlisa Priandana Karo-Karo, Julkarnaen Kevin Dwi Satria Kotjek, Rafie Kumalasari Kumalasari Kuswanto, Andi Diah Laksono, Andriansyah Tri Laura Gabriel da Silva Lia Mazia, Lia Lise Pujiastuti Lise Pujiastuti Lita Sari Marita Maharani Rona Makom Mantriwira, Daniel Mardinawat Mardinawat Mardinawati Mardinawati Marundrury, Aberahamo Onoma Megawaty, Dyah Ayu Mochamad Wahyudi Muhammad Furqon Prasetyo Muhammad Raviansyah Musfiroh Musfiroh, Musfiroh Nabilla, Adinda Naufal Hermawan, Rezan Nindya Dwi Lestari Nirwana Hendrastuty Noviyanto Nur Rachmat Nugraha Nurfia Oktaviani Syamsiah Nurrahman, Alvin Oprasto, Raditya Rimbawan Paduloh Paduloh Pakpahan, Roida Pasaribu, A. Ferico Octaviansyah Paulus Paulus Permata, Permata Prasetyo Adi Suwignyo Prasetyo, Romadhan Edy Pribadi, Denny Pricillia Primadana, Raihan Pujiastuti, Lise Putra Satria Putra, Imam Hanif Rachmat Adi Purnama Rafi Kurniawan Raihan Naufal Ramadhan Raihan Raihan, Raihan Ramadani, Achmes Dade Ramadhan, Muhammad Gilang Ramadhani, Dwiki Gilang Ramadhani, Varla Octavia Rani, Maulidina Cahaya Rasendriya, Rafi Ratiyah* Ratiyah Respati Putra, Micho Reynaldi , Reynaldi Rian Hidayat Rifda Ilahy Rosihan Rifki Nur Hidayat Putra Riska Aryanti Riska Aryanti Rivaldi, Muhammad Rizal Maulana Rizqi Ramadhani, Muhammad Rofiqi, Ainur Roida Pakpahan Roida Pakpahan Roni Saputra Pratama Ruhul Amin Ruli , Ahmad Rais Rumidjan Rumidjan, Rumidjan Rusda Wajhillah Ryan Randy Suryono Ryehan Alfiansyah Sanriomi Sintaro Saputra, Sabita Abigail Saputra, Yusup Sefriani, Shintia Putriayu Sentanu, Quinn Abrar Athallah Setiawan, Dandi Setiawansyah Setiawansyah Siregar, Denny Solihin Solihin Souisa, Juanny Cheristy Sri Hendrastuti, Elisabeth Sri Sugiharti Suci, Bintang Dyas SUKAMTI . Sulaiman Sulaiman Sumarna Sumarna Sumarna Sumarna Syakir, Adryan Raihan Tarmidzi Ibrahim Taufig, Ghofar Teguh Budhi Santosa Teguh Budi Santosa Temi Ardiansah Teuku Vaickal Rizki irdian Tito, Herdinan Tri Widian Ratnasari Ulum, Faruk Umam, Hairul Umar, Muhammad Hussein Ummu Radiyah, Ummu Vemi Januar Pratama Vera Agustina Yanti Virgiawan, Gilang Wahyudi, Agung Deni Wang, Junhai Wardani, Maidy Tri Wattilah, Florentina Widya Viona Septi Tanjung Wijaya, Filzah Wina Ningsih Yamani, Teuku Arrasy Yanuar Laik, Abraham Adrian Yunardus, Yunardus Yundari, Yundari Yuri Rahmanto Zahwa Asfa Rabbani Zalmi, Indah Oktavia Zidan, Muhammad `Diah Kuswanto, Andi