Article Per Year (5 Year)
p-Index From 2021 - 2026
13.096
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) TEKNIK INFORMATIKA Techno.Com: Jurnal Teknologi Informasi Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Scientific Journal of Informatics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Fountain of Informatics Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal JTERA (Jurnal Teknologi Rekayasa) Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL REKAYASA TEKNOLOGI INFORMASI JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo ILKOM Jurnal Ilmiah Voice Of Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JURTEKSI ComTech: Computer, Mathematics and Engineering Applications CSRID (Computer Science Research and Its Development Journal) JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika METIK JURNAL Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Sistem informasi dan informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Sains, Aplikasi, Komputasi dan Teknologi Informasi Grouper: Jurnal Ilmiah Perikanan JISA (Jurnal Informatika dan Sains) Aiti: Jurnal Teknologi Informasi Jurnal Sistem Informasi dan Sistem Komputer Journal of Applied Data Sciences Jurnal J-PEMAS DECODE: Jurnal Pendidikan Teknologi Informasi Ikhtisar: Jurnal Pengetahuan Islam Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Formosa Journal of Science and Technology (FJST) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi Bulletin of Social Informatics Theory and Application Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA) The Indonesian Journal of Computer Science Indonesian Journal of Health Research Innovation
Anam, M Khairul
Universitas Samudra
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

Published : 120 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 120 Documents
Search

 8   9   10   11   12 
Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions M. Khairul Anam; Lestari, Tri Putri; Efrizoni, Lusiana; Handayani, Nadya Satya; Andhika, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6708

Abstract

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.
Analisis Pilkada Medan pada Sosial Media Menggunakan Analisis Sentimen dan Social Network Analyisis Anam, M. Khairul; Firdaus, Muhammad Bambang; Fitri, Triyani Arita; Lusiana; Agustin, Wirta; Agustin
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i1.3027

Abstract

The simultaneous regional head elections were over, but during the campaign until it was decided to become regional head there were many comments, both pro and contra. The city of Medan is one of the regions that will hold the 2020 ELECTION during the pandemic. The Medan City Election has decided that the pair Bobby Nasution and Aulia Rachman have won. This victory certainly gets a variety of comments on social media, especially Twitter. This study conducts sentiment analysis to see the sentiment that occurs, namely seeing negative, positive, or neutral comments. This sentiment analysis uses two methods to see the resulting accuracy, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). This study also looks at the interactions that occur using Social Network Analysis (SNA). In addition to sentiment analysis and SNA, this study also looks at the existence of BOT accounts used in the #PilkadaMedan. The results obtained from the sentiment analysis show that NBC has the highest accuracy, which is 81, 72% with a data proportion of 90:10. Then on SNA, the @YanHarahap account got the highest nodes, namely 911 nodes. Then from 10326 tweets, 11% were suspected of being BOT by the DroneEmprit Academic system.
OPTIMALISASI KINERJA KLASIFIKASI TEKS BERDASARKAN ANALISIS BERBASIS ASPEK DAN MODEL HYBRID DEEP LEARING Salsabila Rabbani; Agustin; Susandri; Rahmiati; M. Khairul Anam
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4034

Abstract

The conflict between Palestine and Israel has generated strong debates and reactions on social media, including in Indonesia. Public perception of various aspects is certainly important to identify issues in the Palestinian-Israeli conflict. However, the process of manually classifying aspects of the Palestinian-Israeli conflict requires human resources and considerable time. This research aims to explore the views of Indonesians on the Palestinian-Israeli conflict through sentiment analysis based on aspects of Territory, Religion, Politics, and History. Using deep learning technology, specifically a combination model of Convolutional Neural Networks with Long Short-Term Memory (CNN-LSTM), this research analyzes opinion and views data collected from X social media platform (Twitter). This research shows the results of the dataset obtained that the Political aspect dominates more than other aspects. The model evaluation results obtained an accuracy value of 96%, which indicates that the model's ability to classify X users' sentiments towards the Palestinian-Israeli conflict achieved a high level of success.
Klasifikasi Emosi Terhadap Konflik Israel-Palestina Menggunakan Algoritma Gated Recurrent Unit Saputra, Eko Ikhwan; Fatdha, T.Sy. Eiva; Agustin; Junadhi; M. Khairul Anam
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4106

Abstract

The Israel-Palestine conflict intensified following the October 7, 2023, attack by Hamas on Israel, triggering various emotional reactions on social media. Emotion classification is crucial for understanding public sentiment related to this conflict. This study utilizes 9,917 tweets from platform X (Twitter) to classify emotions such as joy, sadness, anger, fear, disgust, and surprise. The deep learning algorithm used is Gated Recurrent Unit (GRU), developed with three different training and testing data splits: 70:30, 80:20, and 90:10. For text representation, Global Vector (GloVe) word embedding is employed. Given the imbalanced dataset, this study applies the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The research results indicate that the GRU model with a 90:10 data split without using SMOTE achieves the highest accuracy of 75%, followed by the models with 70:30 and 80:20 splits, which each have an accuracy of 73%.
OPTIMASI TEKNIK VOTING PADA SENTIMEN ANALISIS PEMILIHAN PRESIDEN 2024 MENGGUNAKAN MACHINE LEARNING Kharisma Rahayu; M. Khairul Anam; Lusiana Efrizoni; Nurjayadi; Triyani Arita Fitri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4119

Abstract

The presidential election is an important event in the democratic system of the Unitary State of the Republic of Indonesia or NKRI held every five years. There are many pros and cons of the presidential election, especially on social media Twitter or X. X is one of the media platforms where people leave positive, neutral, and even negative comments. Therefore, this research aims to build a sentiment analysis model to classify the sentiment of the 2024 presidential election. This research uses the Support Vector machine, Naïve Bayes and Decision Tree algorithms in performing classification with the addition of the Syntethic Minority Over-Sampling and Ensemble Voting methods. The test results show that public sentiment towards the presidential election dominates negative sentiment of 5008 positive 3582 and neutral 1411 sentiments. Then the results of data processing SVM, NB and DT algorithms plus SMOTE and ensemble voting optimization, provide 92.8% accuracy, 93% precision, 93% recall and 93% F1-Score. This research can make a significant contribution by classifying public sentiment towards the 2024 presidential election data.
IMPLEMENTASI SISTEM INFORMASI AKADEMIK DI PKBM AR ROYYAN UNTUK MENINGKATKAN EFISIENSI ADMINISTRASI DAN MONITORING Yuda Irawan; Refni Wahyuni; Abdi Muhaimin; M. Khairul Anam
Jurnal Masyarakat Berdikari dan Berkarya (Mardika) Vol 3 No 2 (2025): Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA)
Publisher : Fakultas Teknik, Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/mardika.v3i2.12965

Abstract

PKBM Ar Royyan menghadapi tantangan dalam pengelolaan data akademik seiring dengan peningkatan jumlah siswa dan kompleksitas administrasi. Pengelolaan data yang masih dilakukan secara manual menyebabkan inefisiensi, risiko kehilangan data, serta keterbatasan sumber daya manusia. Oleh karena itu, adopsi sistem informasi akademik digital menjadi sangat penting untuk mengatasi masalah ini. Tujuan dari kegiatan pengabdian masyarakat ini adalah meningkatkan efisiensi pengelolaan data akademik dan administrasi di PKBM Ar Royyan melalui implementasi Sistem Informasi Akademik (SIAKAD). Selain itu, kegiatan ini bertujuan menjamin keamanan dan keberlanjutan data, serta meningkatkan kapasitas teknologi informasi di sekolah melalui pelatihan penggunaan sistem digital. Hasil kegiatan menunjukkan bahwa penerapan SIAKAD secara signifikan berhasil meningkatkan efisiensi pengelolaan data dan mempercepat proses administratif. Meskipun terdapat tantangan infrastruktur dan keterampilan teknologi, masalah tersebut berhasil diatasi melalui pelatihan dan dukungan teknis. Implementasi ini juga mendorong transparansi dan memberikan contoh bagi sekolah lain di wilayah tersebut dalam memanfaatkan teknologi untuk meningkatkan kualitas pendidikan. Secara keseluruhan, kegiatan ini membangun fondasi bagi pengembangan teknologi pendidikan yang berkelanjutan di masa depan.
ANALISIS KESIAPAN SEKOLAH MENENGAH DALAM MENERAPKAN E-VOTING MENGGUNAKAN MODEL TECHNOLOGY READINESS INDEX Hazira, Nadila; Anam, M. Khairul; Agustin, Wirta; Fitri, Triyani Arita; Zamsuri, Ahmad; Syam, Salmaini Safitri
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 2 (2024): Publikasi Artikel ZONAsi: Periode Mei 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i2.18400

Abstract

Voting can be interpreted as a way of making decisions based on the largest number of votes. So far, voting is carried out by ticking or voting on a ballot paper as an option in holding the election for OSIS chairman at SMAN 15 Pekanbaru. This method is considered still very conventional amidst advances in technology and information which has weaknesses in terms of efficiency and effectiveness. The weaknesses of conventional voting are: the decision is not the result of consensus, some participants are forced to accept the decision that has been taken, some participants often do not accept the decision, the aspirations of the participants are not fully channeled. To reduce problems arising from manual voting, it is necessary to analyze the readiness of secondary schools in implementing e-voting using the Technology Readiness Index model. The method that can be used to measure the level of user readiness in using technology is the Technology Readiness Index (TRI). In order to find out the results of the analysis and test the readiness of secondary schools in implementing the new system, the author will conduct a survey by distributing a Google Form link containing a list of statements regarding the readiness of secondary school residents, especially at SMAN 15 Pekanbaru, in using the web-based E-Voting system for the election of chairman. Student Council. The survey results will be analyzed using the SPSS 25.0 application and also calculated using the Technology Readiness Index Method
Optimization of Machine Learning Models in Student Graduation Prediction Systems Using Ensemble Learning with PSO and SMOTE Hamdani, Hamdani; Susanti, Susanti; Lathifah, Lathifah; Anam, M. Khairul; Pradipta, Rahman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15335

Abstract

The timely graduation of students is a key metric in evaluating the academic effectiveness of higher education institutions. However, accurately identifying students at risk of delayed graduation remains challenging due to imbalanced data distributions and the instability of single-model prediction approaches. This study proposes an optimized ensemble-based machine learning system for predicting on-time graduation among university students. The model integrates C4.5, K-Nearest Neighbor (KNN), and Random Forest algorithms through a hard voting classifier, which is further optimized using Particle Swarm Optimization (PSO) to determine the most effective weighting configuration. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is implemented, ensuring balanced representation between timely and delayed graduates. A dataset of 809 student academic records from Universitas Sains dan Teknologi Indonesia (USTI) was used, and performance was evaluated using 5-fold cross-validation. The proposed ensemble model achieved an average accuracy of 93.70%, a precision of 0.94, a recall of 0.93, and an F1-score of 0.94, outperforming each individual classifier. These results confirm that the combination of ensemble learning, PSO-based optimization, and data balancing effectively improves both accuracy and model stability. The findings highlight the system’s potential as a reliable decision-support tool for educational institutions to anticipate delayed graduations and improve academic supervision strategies.
Penerapan Model Technology Readiness Index untuk Mengukur Tingkat Kesiapan Mahasiswa dalam Penerimaan Sistem E-Polvot Anam, M Khairul; Zoromi, Fransiskus; Soni; Nasution, Torkis; Andesa, Khusaeri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107368

Abstract

BEM (Badan Eksekutif Mahasiswa) merupakan ujung tombak dalam menjalankan tata pemerintahan di kalangan mahasiswa dan media untuk menyampaikan aspirasi baik berupa kesejahteraan, keamanan baik secara lisan maupun dalam tulisan kepada perguruan tinggi. Pemilihan BEM di perguruan tinggi rutin dilaksanakan setiap setahun. Namun dalam pemilihan BEM, beberapa mahasiswa tidak dapat menggunakan hak memilih karena keterbatasan waktu yang disediakan oleh panitia pemilihan. Dalam pelaksanaan pemilihan, disediakan 3 jenis waktu perkuliahan, yaitu regular siang jam 08.00 – 17.00, malam jam 17.45 – 09.30, dan non-reguler diadakan perkuliahan jarak jauh atau online setiap akhir pekan. Untuk pemilihan biasanya mahasiswa reguler malam dan non reguler tidak melakukan voting atau pemilihan dikarenakan waktunya diadakan siang hari. Untuk mengatasi permasalahan tersebut perlunya sebuah sistem bisa digunakan dimana saja tanpa harus datang ke kampus. Salah satu sistem yang dapat digunakan adalah e-polvot atau elektronik polling dan voting. Namun untuk menghadirkan sistem tersebut perlu kesiapan baik dari infrastruktur maupun pengguna. Penelitian ini melakukan analisis terhadap kesiapan mahasiswa STMIK Amik Riau dalam penerimaan sistem e-polvot. Tujuan penelitian adalah menganalisis kesiapan mahasiswa menggunakan sistem e-polvot. Analisis kesiapan mahasiswa menggunakan model Technology Readiness Index (TRI). Model ini memiliki 4 variabel yaitu Optimism, Innovativeness, Discomfort dan Insecurity. Populasi yang digunakan dalam penelitian ini adalah seluruh mahasiswa STMIK Amik Riau dengan teknik total sampling. Hasil yang didapatkan pada penelitian ini yaitu mahasiswa STMIK Amik Riau siap untuk menerima sistem e-polvot. Hal ini dilihat dari nilai yang didapatkan dari pengukuran ini adalah 3,93 yang dikategorikan HIGH.   Abstract The Student Executive Board (BEM) plays a pivotal role in governing students and serves as a platform to express aspirations, both in terms of welfare and security, through both oral and written means to the university. BEM elections at the university are regularly conducted annually. However, in the BEM elections, some students are unable to exercise their voting rights due to time constraints set by the election committee. The election process offers three types of lecture schedules: regular daytime from 08:00 to 17:00, evening lectures from 17:45 to 09:30, and non-regular lectures held during weekends for distance or online learning. Consequently, regular evening and non-regular students often abstain from voting or participating in the election due to the daytime scheduling. To address this issue, a system is needed that can be accessed from anywhere without physically coming to the campus. One such system that can be used is the e-polvot or electronic polling and voting system. However, implementing such a system requires readiness in terms of infrastructure and user acceptance. This research aims to analyze the readiness of STMIK Amik Riau students in accepting the e-polvot system. The research objective is to assess the readiness of students in using the e-polvot system. The analysis of students' readiness utilizes the Technology Readiness Index (TRI) model, which consists of four variables: Optimism, Innovativeness, Discomfort, and Insecurity. The population used for this study comprises all students of STMIK Amik Riau, and the total sampling technique is employed. The findings of this research indicate that the students of STMIK Amik Riau are ready to accept the e-polvot system, as evidenced by a TRI score of 3.93, which falls into the "HIGH" category.
Comparison of Support Vector Machine and Random Forest Algorithms for Analyzing Online Loans on Twitter social media Hamdani; N.A, Randi; M. Khairul Anam
JAIA - Journal of Artificial Intelligence and Applications Vol. 4 No. 1 (2024): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v4i1.1087

Abstract

Online loans represent a form of financial service wherein borrowers can apply for loans through digital platforms without the need to visit physical offices. The application, approval, and disbursement processes are conducted online, leveraging technology to facilitate financial access and transactions. However, some online lending services impose high-interest rates, resulting in a significant financial burden for borrowers. Moreover, there are instances of inappropriate debt collection practices, such as contacting the borrower's friends or family, leading to discussions and comments on social media platforms like Twitter. This research aims to analyze the patterns of comments in Indonesian society regarding online lending. The study utilizes sentiment analysis and compares machine learning algorithms to assess their accuracy. The algorithms employed in this study are Support Vector Machine (SVM) and Random Forest. The results indicate that the SVM algorithm achieves an accuracy of 93.85%, while Random Forest achieves an accuracy of 91.62%.
Co-Authors -, Tashid Abrar Hadi Ade Riyanda Putra Agus Tri Nurhuda Agustin Agustin Agustin Agustin Agusviyanda Agusviyanda Ahmad Ihsan Ahmad Zamsuri Ahmad Zamsuri, Ahmad Aisum Aliyah Sari Al Amin Fadillah Sani Alkadri Masnur Ambiyar, Ambiyar Andesa, Khusaeri Andhika, Imam Andi Supriadi Chan, Andi Supriadi Anwar, Reksi Aprillian Kartino Arba, Muhammad Hendra Arda Yunianta Arda Yunianta Arief Hidayat Arita Fitri, Triyani Arsyah, Ulya Ilhami Atalya Kurnia Sari Bambang Kurniawan Br.Situmorang, Elisabet Sinta Romaito Budiman, Edy Budiman, Edy Bunga Nanti Pikir Bunga Nanti Pikir Chatarina Umbul Wahyuni Damar Sanggara Habibie Daryanto, Diki Dea Safitri Dedy Irfan Devi Yuliana Dewi Sari Wahyuni Dewi, Nina Nurmalia Didik Sudyana Didik Sudyana Diki Daryanto Diky Daryanto Dona Wahyuning Laily Eddy Kurniawan Pradana Efrizoni, Lusiana Elangga Sony Widiharsono Elva, Yesri Emerlada, Esi Tri Erlin Erlin Erlinda, Susi Ersan Fadrial, Yogi Esi Tri Emerlada Fadli Suandi Fahrul Yamani Faisol Mas’ud Fajar Arifandi Fajrizal Fatdha, T.Sy. Eiva Faza Alameka Fernando Elda Pati Fika Felanda Ardelia Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Fryonanda, Harfebi Fuquh Rahmat Shaleh Gendhy Dwi Harlyan Gubtha Mahendra Putra Gunadi Gunanti Mahasri Gunawan, Chichi Rizka Habibi Ulayya Hadi Asnal, Hadi Hairah, Ummul Hamdani Hamdani - Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Handayani, Nadya Satya Hanif Aulia Happy Yugo Prasetiya Hartomi, Zupri Henra Hasan J. Alyamani Haviluddin Haviluddin Hazira, Nadila Helda Yeni Helda Yenni, Helda Hendra Saputra Hendrawan, Riki hendri, nofri Herianto Herianto Herwin Herwin Ika Purnamasari Ike Yunia Pasa Ikhsan Ikhsan Indah Mukhlis Tamara Indra Prayogo Indra Prayogo Indri Febrianti Irfan Putra Pratama Irfansyah Irfansyah Irfansyah Irfansyah Irsyad, Akhmad Irwanda Syahputra Irwanda Syahputra Istianah Istianah Jamaris, Muhamad Jamaris, Muhammad Jasmarizal Junadhi Junadhi Junadhi Junadhi Kadek Mirnawati Karfindo, Karfindo Kartina Diah K. W. Kharisma Rahayu Khusaeri Andesa Khusaeri Andesa Kresnapati, I Nyoman Bagus Aji Kudadiri, Parlindungan Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah, Lathifah Latifah Lia Oktavia Ika Putri Lilis Cahaya Septiana Liza Fitria Lucky Lhaura Van FC Lucky Lhaura Van FC, Lucky Lhaura Lusiana Lusiana Efrizoni Lusiana Lusiana M Syauqi Hafizh Machdalena Mahamad, Abd Kadir Mahendra, Muhammad Ihza Mahessya, Raja Ayu Mardainis Mardainis Mardainis Martilinda Panjaitan Mega Susanti Mega Susanti Melda Royani Michal Dennis Mi`rajul Rifqi Muhaimin, Abdi Muhamad Jamaris Muhamad Sadar Muhamad Sadar, Muhamad Muhammad Bambang F Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Budi Saputra muhammad Fuad Muhammad Nur Ihwan Muhammad Wisdan Pratama Putra Munawir Munawir Munawir N.A, Randi Nadila Rahmadhani Nadya Alinda Rahmi Nariza Wanti Wulan Sari Nasrul Sani Neci Nirwanda Nisa, Aida Nora Lizarti Novi Yona Sidratul Munti Nu'man Nu'man Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurkholifah Dwi Rahayu Nurul fadillah, Nurul Nurul Indriani Nurwijayanti Pandu Pratama Putra, Pandu Pratama Paradila, Dinda Pradipta , Rahman Pranata, Angga Pratiwi, Mutiana Purwanto Putra, Ryanda Satria Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rahmiati Rebecca La Volla Nyoto Refni Wahyuni Reksi Anwar Rini Yanti Rini Yanti Rini Yanti Rinno Hendika Putra Rio Andika Malik Rivaldi Dwi Andhika Rohana Yola Parastika Hutasoit Rohmat Romadhoni Rometdo Muzawi, Rometdo Saiful Bukhori Salman Aldo Alfaresi Salsabila Rabbani Salsabila Rabbani Saon, Sharifah Saputra, Eko Ikhwan Sari Irma Yani Sitorus Sari, Atalya Kurnia Sarjon Defit Silvyana Dwi Putri Sofiansyah Fadli Sofiansyah Fadli Soni suaidah suaidah Sumijan Sumijan Susandri, Susandri Susanti Susanti Susanti Susanti Susanti Susanti Susanti, Mega Susanti, Susanti SUSI ERLINDA Susi Erlinda Susi Erlinda Syam, Salmaini Safitri Syamsiar, Syamsiar T. Sy. Eiva Fatdha Taruk, Medi Tashid Tashid Tashid Tatang Hidayat Tejawati, Andi Tengku Alvin Firdaus Teri Ade Putra Torkis Nasution Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari, Tri Putri Triyani Arita Fitri Ulfah, Aniq Noviciate Wahyudianto, Mochamad Rizky Waksito, Alan Zulfikar Wifra, Rizki Wirta Agustin Wirta Agustin Woro Hastuti Setyantini Yaakub, Saleh Yansyah Saputra Wijaya Yesaya Twin Situmorang Yogi Ersan Fadrial Yogi Yunefri, Yogi Yoyon Efendi Yuda Irawan Yumami, Eva Zeki Kurniadi zeki Kurniadi