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Sumanto, Sumanto
Universitas Bina Sarana Informatika
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Klasterisasi Data Produksi Daging Sapi Menggunakan Algoritma K-Means Orange Data Mining Ramadani, Achmes Dade; Hilmy Ibrahim, Farras; Hidayat, Manarul; Habibullah, Ahmad; Sumanto, Sumanto; Kuswanto, Andi Diah
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1013

Abstract

     Produksi daging sapi merupakan salah satu komponen penting dalam sektor peternakan yang mendukung ketahanan pangan nasional. Mengingat fluktuasi produksi dari tahun ke tahun dan perbedaan karakteristik antar wilayah, diperlukan metode analisis yang tepat untuk mengolah data secara efektif. Penelitian ini bertujuan untuk mengelompokkan data produksi daging sapi di Indonesia selama periode 2021 hingga 2024 menggunakan algoritma K-Means Orange Data Mining. Proses analisis mengikuti tahapan CRISP-DM, mulai dari pemahaman bisnis hingga deployment. Data yang digunakan diperoleh dari Badan Pusat Statistik dan diproses untuk menghasilkan tiga klaster utama: wilayah dengan produksi daging sapi tinggi, rendah, dan sedang. Hasil penelitian menunjukkan bahwa algoritma K-Means Orange Data Mining mampu mengelompokkan data produksi daging sapi secara efektif ke dalam beberapa klaster yang berbeda. Orange Data Mining turut membantu proses analisis data dengan tampilan antarmuka visual yang inovatif dan hasil yang mudah diinterpretasikan. Temuan ini diharapkan menjadi acuan dalam perumusan kebijakan strategis peternakan dan perencanaan distribusi produksi berbasis data. Hasil klasterisasi ini memberikan gambaran kepada pemerintah mengenai tingkat produksi daging sapi di setiap wilayah, sehingga memungkinkan pengambilan kebijakan atau langkah-langkah strategis yang lebih tepat dan sesuai dengan kondisi masing-masing wilayah berdasarkan hasil klasterisasi.
An Entropy-Assisted COBRA Framework to Support Complex Bounded Rationality in Employee Recruitment Oprasto, Raditya Rimbawan; Wang, Junhai; Pasaribu, A Ferico Octaviansyah; Setiawansyah, Setiawansyah; Aryanti, Riska; Sumanto
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.505

Abstract

In the employee recruitment process, decision-making often involves many criteria and relies on the subjective judgment of the decision-maker. The main problem lies in how to develop a decision support system that can overcome this complexity while maintaining rationality and objectivity. This study aims to apply a hybrid framework based on the entropy and COBRA methods to support objective decision-making in the employee recruitment process, and to overcome the limitations of subjectivity and bounded rationality in candidate selection with a structured data-driven approach. The entropy method is used to objectively determine the weight of criteria based on data variations, thereby helping to reduce subjectivity in decision-making and increase the rationality of COBRA analysis results. The results of the final calculation using the Entropy-COBRA method, were ranked nine candidates based on their final scores which reflected relative proximity to the ideal solution in the recruitment process. The candidate with the lowest score is considered to be the closest to the ideal solution and has the best overall performance. Raka employees ranked first with a final score of -0.0618, followed by Andra in second place with a score of -0.0597, and Fajar in third place with -0.0357. The results of the final score in the COBRA method with a lower score indicate that an alternative shows superior performance over the other. This framework makes a real contribution to data-driven decision-making for human resource management, particularly in the context of recruitment involving multiple criteria and alternatives.
Analisa Pola Penyebaran Pengguna Layanan Transjakarta dengan Metode K-Means Clustering Reynaldi , Reynaldi; Faiz Djarot, Raihan Jamal; Wahyudi, Mochamad; Sumanto , Sumanto; Budiman, Ade Surya
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.205

Abstract

This study analyzes the spatial distribution patterns of Transjakarta service users in Jakarta using the K-Means Clustering algorithm. The dataset, obtained from the Kaggle platform, consists of 189,501 passenger transaction records, including tap-in and tap-out locations, travel times, and user-related information. The research process involves data collection, preprocessing to remove missing values, application of the K-Means Clustering algorithm, and determination of the optimal number of clusters using the elbow method. Based on the analysis, the optimal number of clusters is identified as four (K=4). A scatter plot visualization presents user distribution patterns based on geographic coordinates and service usage times. Each cluster represents a group of users with similar travel characteristics. This analysis results in a segmentation that reflects variations in Transjakarta passenger mobility patterns and illustrates how travel activity is distributed across spatial and temporal dimensions within the urban area of Jakarta.
Pengelompokkan Provinsi Berdasarkan Kelayakan Ruang Kelas dan Tenaga Kependidikan Sekolah Dasar Menggunakan Algoritma K-Means: Analisi Data Periode 2023-2024 Wardani, Maidy Tri; Ramadhani, Varla Octavia; Anggreani, Namira; Sumanto; Kuswanto, Andi Diah
Riau Jurnal Teknik Informatika Vol. 4 No. 2 (2025): Juli 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i2.3389

Abstract

Abstract This study analyzes the disparity in primary education quality across Indonesian regions using K-Means clustering methodology to group 38 provinces based on classroom adequacy indicators and teaching staff availability for the 2023-2024 period. Adopting the CRISP-DM methodology and utilizing datasets from the Ministry of Education, Culture, Research, and Technology, this research reveals significant educational gaps across Indonesian regions. The analysis results show three clusters reflecting educational conditions: Cluster 1 (low category) encompasses 23 provinces (67.6%) dominated by Eastern Indonesia regions such as Papua, Maluku, and other remote areas; Cluster 2 (medium category) consists of 13 provinces (38.2%) including South Sumatra, Riau, and DKI Jakarta; and Cluster 3 (high category) contains only 3 provinces: East Java, West Java, and Central Java. Clustering validity is confirmed through silhouette coefficient with the highest value of 0.795 for Cluster 2. These findings identify structural inequality between Java and outer Java regions, providing empirical foundation for the government to design more targeted educational equity policies, with priority on infrastructure rehabilitation and teaching staff augmentation in disadvantaged areas to achieve national educational justice. The research demonstrates that educational quality distribution in Indonesia remains heavily concentrated in Java, while remote and eastern regions face significant challenges in both physical infrastructure and human resources, requiring comprehensive government intervention strategies for sustainable educational development. Keywords: K-Means clustering, primary education, regional disparity, educational infrastructure, teaching personnel.
Perbandingan Algoritma Random Forest, Naive Bayes, Dan Neural Network Dalam Klasifikasi Penyakit Jantung Rani, Maulidina Cahaya; Dewi, Revinta Arrova; Azkia, Farah Diba; Wahyudi, Mochamad; Sumanto; Budiman, Ade Surya
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.609

Abstract

Penyakit jantung merupakan salah satu masalah kesehatan paling mematikan di dunia, dengan jumlah kematian yang terus meningkat setiap tahunnya. Penyakit kardiovaskular adalah penyebab utama kematian di seluruh dunia, dengan lebih dari 17 juta kematian setiap tahun, menurut data WHO. Gangguan fungsi jantung ini dapat dipicu oleh berbagai faktor risiko seperti pola makan tidak sehat, obesitas, kurang aktivitas fisik, kebiasaan merokok, dan riwayat penyakit dalam keluarga. Oleh karena itu, deteksi dini sangat penting untuk mencegah dan mengurangi risiko kematian akibat penyakit jantung. Penelitian ini bertujuan untuk membandingkan performa tiga metode klasifikasi, yaitu Random Forest, Neural Network, dan Naive Bayes dalam mengklasifikasi risiko penyakit jantung. Pengujian model dilakukan menggunakan metode Random Sampling dengan skema repeat train/test sebanyak 10 kali, di mana setiap iterasi menggunakan 80% data sebagai training set. Berdasarkan hasil evaluasi, model Random Forest menghasilkan nilai AUC sebesar 0,996, model Naive Bayes sebesar 0,980, dan model Neural Network sebesar 0,957. Selain itu, analisis dilakukan untuk menilai keunggulan dan kelemahan masing-masing metode dalam menangani data dengan fitur yang kompleks dan saling berkorelasi. Hasil penelitian ini diharapkan dapat memberikan rekomendasi metode klasifikasi yang paling efektif dan efisien untuk diterapkan dalam sistem pendukung keputusan medis guna deteksi dini penyakit jantung.
Klasifikasi Nilai Ujian Siswa Berdasarkan Kebiasaan Belajar Menggunakan K-Nearest Neighbor dan Support Vector Machine Kotjek, Rafie; Pricillia; Wijaya, Filzah; Wahyudi, Mochamad; Sumanto; Budiman, Ade
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.612

Abstract

Kinerja akademik siswa merupakan indikator penting keberhasilan belajar, namun penilaian konvensional sering kali belum optimal dalam memanfaatkan data kebiasaan dan gaya hidup siswa. Penelitian ini bertujuan untuk mengklasifikasikan nilai ujian siswa (Rendah, Sedang, Tinggi) berdasarkan kebiasaan belajar menggunakan algoritma K-Nearest Neighbor (kNN) dan Support Vector Machine (SVM), serta membandingkan performa keduanya. Data sebanyak 1000 entri siswa dari Kaggle.com diolah melalui tahap pra-pemrosesan yang meliputi diskritisasi nilai ujian menjadi kategori dan pemilihan fitur yang relevan, seperti jam belajar, persentase kehadiran, waktu tidur, dan peringkat kesehatan mental. Pembagian data dilakukan dengan random sampling (90% training dan 10% testing) yang diulang 10 kali. Hasil evaluasi menunjukkan kNN dengan N=10 mencapai akurasi tertinggi 0.982. Sementara itu, SVM dengan kernel Linear memperoleh akurasi 0.974 , diikuti RBF dengan 0.939 , dan Polynomial dengan 0.946 , sedangkan kernel Sigmoid hanya 0.712. Performa terbaik kNN (N=10) lebih lanjut dikonfirmasi melalui confusion matrix, menunjukkan tingkat kesalahan klasifikasi yang sangat rendah dan prediksi yang konsisten. Penelitian ini menyimpulkan bahwa algoritma k-NN, khususnya dengan N=10, adalah pendekatan yang paling akurat dan efektif untuk klasifikasi nilai ujian berdasarkan kebiasaan siswa, mendukung pihak sekolah dalam prediksi dan perencanaan pendidikan yang lebih baik.
Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection Ulum, Faruk; Wang, Junhai; Megawaty, Dyah Ayu; Sulistiyawati, Ari; Aryanti, Riska; Sumanto, Sumanto; Setiawansyah, Setiawansyah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1810

Abstract

Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
Decision support for trucking vendor selection at PT. Ricakusuma Lestari Abadi Based on the SAW method Indriyanti, Zahra Kiky Dwi; Sumanto, Sumanto
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.420

Abstract

PT. Ricakusuma Lestari Abadi is a company engaged in freight forwarding services, distributing goods both domestically and internationally. In the shipping process, the company heavily relies on third-party trucking services. However, the selection process for trucking vendors has so far been conducted manually, without standardized evaluation criteria, which risks leading to subjective and inefficient decisions. Therefore, this study aims to develop a decision support system to select the best trucking vendor using the Simple Additive Weighting (SAW) method. The SAW method is used because it provides objective evaluation results based on the weighting of five main criteria: service quality (40%), cost (25%), vehicle condition (15%), vendor location (10%), and fleet availability (10%) (Alamsyah et al., 2021; Gunawan et al., 2023; Wibowo & Azizah, 2022). This research adopts a quantitative approach through observation, interviews, and literature study. The collected data were used to calculate the scores of seven trucking vendor alternatives. The results show that Johan Putra Perkasa scored the highest with a value of 0.80 and is recommended as the best vendor. Kumala ranked second with a score of 0.75, followed by Global Sukses Transportama with a score of 0.72. The developed system was implemented as a web-based application using PHP and MySQL to facilitate a more efficient, faster, and standardized vendor selection process (Lim & Silalahi, 2023).
Analisis Klaster Pasien Diabetes Menggunakan Algoritma K-Means Berdasarkan Usia, Kadar Glukosa, dan Tekanan Darah Rizqi Ramadhani, Muhammad; Naufal Hermawan, Rezan; Fajrian, Ihsan; Aulia Rachmat, Daffa; Sumanto; `Diah Kuswanto, Andi
Riau Jurnal Teknik Informatika Vol. 4 No. 2 (2025): Juli 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i2.3435

Abstract

Diabetes melitus adalah penyakit kronis yang terjadi akibat gangguan produksi atau pemanfaatan insulin, menyebabkan kadar gula darah tinggi. Penyakit ini dapat memicu komplikasi serius seperti jantung, ginjal, dan kerusakan saraf. Jumlah penderita diabetes terus meningkat, termasuk di Indonesia, yang dipengaruhi oleh faktor seperti genetik, gaya hidup tidak sehat, dan pola makan buruk. Untuk mendeteksi risiko diabetes lebih dini, teknologi data mining dapat dimanfaatkan. Penelitian ini menggunakan algoritma K-Means Clustering untuk menganalisis data kesehatan seperti kadar glukosa darah, tekanan darah, dan usia. Algoritma ini mengelompokkan individu ke dalam beberapa klaster berdasarkan kesamaan karakteristik kesehatan, guna mengidentifikasi kelompok risiko diabetes. Hasil analisis ini diharapkan dapat membantu tenaga medis dalam merancang intervensi dan rekomendasi pencegahan yang lebih tepat sasaran. Pendekatan ini memberikan solusi efisien dalam pengelolaan data besar di bidang kesehatan dan mendukung upaya penanggulangan diabetes secara lebih sistematis di Indonesia
KOMPARASI DECISION TREE, RANDOM FOREST, DAN K-NN MEMPREDIKSI KELULUSAN SISWA MENGGUNAKAN ORANGE Rasendriya, Rafi; Fahrian; Marundrury, Aberahamo Onoma; Jumadi, Yakobus Linus; Sumanto; Kuswanto, Andi Diah
Jurnal Komputer dan Teknologi Vol 4 No 2 (2025): JUKOMTEK JULI 2025
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v4i2.414

Abstract

Predicting student graduation is one of the challenges in the field of education that requires a data-driven approach. Not only final grades play a role, but also other factors such as attendance rate, weekly study hours, previous exam scores, and extracurricular activities. This study compares the performance of three classification algorithms—Decision Tree, Random Forest, and k-Nearest Neighbor (k-NN)—in predicting student graduation status based on the Student Performance dataset from Kaggle, which contains 708 student records. The modeling process was conducted using Orange Data Mining through a visual workflow approach. The models were evaluated using 20-fold cross-validation and assessed with performance metrics including AUC, accuracy, precision, recall, F1-score, and MCC. The results show that the Random Forest algorithm achieved the best performance, with an AUC of 97.1%, accuracy of 94.1%, F1-score of 94.2%, precision of 94.2%, recall of 94.1%, and MCC of 79.7%. While Decision Tree and k-NN also performed well, their results were still below those of Random Forest. These findings indicate that Random Forest is the most accurate and stable model for classifying student graduation and demonstrate that Orange Data Mining is an effective tool for applying data mining techniques in the educational field.
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