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All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta CogITo Smart Journal JTERA (Jurnal Teknologi Rekayasa) Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat JSiI (Jurnal Sistem Informasi) JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal Jurnal Pendidikan dan Konseling Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Investasi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madaniya Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Sekretari & Administrasi (Serasi) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Decode: Jurnal Pendidikan Teknologi Informasi Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Sisfotek Global Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Malcom: Indonesian Journal of Machine Learning and Computer Science Telematika MKOM AMMA : Jurnal Pengabdian Masyarakat Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Tekno INOVTEK Polbeng - Seri Informatika Jurnal Pintar Pengabdian dan Pemberdayaan Masyarakat
Triyono, Gandung
Universitas Budi Luhur
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PREDICTION MODEL FOR STUDENTS' ON-TIME GRADUATION USING ALGORITHM SUPPORT VECTOR MACHINE (SVM) BASED  PARTICLE SWARM OPTIMIZATION (PSO) Hidayatulloh, Syarif; Gandung Triyono; Kiki Ari Suwandi kosasih
Jurnal Sistem Informasi Vol. 12 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i1.9964

Abstract

One of the indicators in assessing the tridharma of higher education outcomes and achievements is students' timely graduation and one indicator of the success of the higher education system is timely graduation. However, some students cannot complete their studies on time. In the process of completing the study, several problems emerged, one of which was in completing studies on time, there are problems that arise, such as students who are still repeating because there are grades that have not passed in the course, the Grade Point Average (GPA) is still lacking, the Semester Achievement Index (SAI) is still below the minimum, the total number of semester credit units (total credits) which still have not reached the minimum limit, then the number of active lecture statuses still exceeds 8 semesters, so this problem will have an impact on the accuracy of student study graduate data, where the target performance indicator for graduates is on time The student's target of graduating on time has not been achieved. The factors that cause the students not to graduate on time are not known. In identifying the problem, it was found that the Study Program does not have adequate information regarding the potential for students to graduate on time and the limitations of the study program in assisting students in completing on-time graduation The method used to solve this problem is by creating a prediction model for students' on-time graduation, so that students can receive adequate information regarding the potential for graduating on time. This research aims to create a prediction model for graduating on time using the Support Vector Machine (SVM) method based on Particle Swarm Optimization (PSO) with feature selection. information gain, so that the attributes selected and used are Semester Achievement Index 1, Semester Achievement Index 2, Semester Achievement Index 3, Semester Achievement Index 4, Grade Point Average (GPA) 1, Grade Point Average (GPA) 2, Grade Point Average (GPA) 3, Grade Point Average (GPA) 4, Semester Credit Units 1, and Semester Credit Units 4. The results in this study obtained accuracy values ​​of 0.799, precision 0.851, recall 0.605 and AUC 0.86
Klasterisasi Provinsi di Indonesia Berdasarkan Angka Harapan Hidup Menggunakan K-Means dengan Evaluasi Elbow Method Asep Lukman Arip Hidayat; Helmi Zulqan; Gandung Triyono
Jurnal Sistem Informasi Vol. 12 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i1.10116

Abstract

Angka Harapan Hidup (AHH) adalah indikator penting untuk mengukur kesejahteraan masyarakat dan tingkat kesehatan di suatu daerah. AHH Indonesia sangat berbeda antar provinsi, yang menunjukkan perbedaan dalam infrastruktur dan akses layanan kesehatan. Tujuan dari penelitian ini adalah untuk menggunakan algoritma K-Means untuk meklasterisasi provinsi-provinsi di Indonesia berdasarkan AHH, dan untuk mengevaluasi hasil klasterisasi dengan metode Elbow untuk menentukan jumlah klaster yang ideal. Data yang digunakan mencakup AHH dari seluruh provinsi Indonesia dalam beberapa tahun terakhir. Untuk mempermudah identifikasi daerah yang memerlukan perhatian khusus dalam perencanaan kebijakan kesehatan, provinsi diklasifikasikan ke dalam klaster berdasarkan karakteristik AHH yang sebanding. Studi ini fokus pada optimasi Algoritma K-Means dengan metode Elbow. Percobaan iterasi dilakukan sepuluh kali dan menemukan nilai K ideal, yaitu K=3. Hasil klasterisasi menunjukkan bahwa Cluster 0 memiliki 103 anggota, Cluster 1 memiliki 181 anggota, dan Cluster 2 memiliki 260 anggota. Hasil penelitian menunjukkan bahwa algoritma K-Means berhasil mengelompokkan provinsi-provinsi berdasarkan AHH dengan tingkat variasi yang rendah di setiap klaster. Metode Elbow membantu menentukan jumlah klaster yang ideal.
Analisis Sentimen Kebijakan Pembatasan Subsidi Bahan Bakar Minyak di Indonesia Tahun 2024 Menggunakan Algoritma Klasifikasi Chaerul, Muh; Septiadi, Septiadi; Triyono, Gandung
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 5 (2025): JPTI - Mei 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.825

Abstract

Kebijakan pembatasan subsidi Bahan Bakar Minyak (BBM) yang diterapkan pemerintah Indonesia pada tahun 2024 memicu beragam tanggapan masyarakat di media sosial. Penelitian ini bertujuan untuk menganalisis sentimen pengguna X (Twitter) terhadap kebijakan pemerintah tersebut. Dataset yang digunakan terdiri dari 2.011 tweet yang dikumpulkan melalui teknik scraping pada tweet periode 1 September 2024 hingga 31 Desember 2024. Data tersebut kemudian melalui tahap cleansing, preprocessing dan pelabelan sentimen menggunakan lexicon-based approach untuk mengkategorikan tweet ke dalam tiga kelas sentimen yaitu positif, negatif, dan netral. Selanjutnya, dilakukan ekstraksi fitur menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer dan penyeimbangan data dengan teknik Synthetic Minority Oversampling Technique (SMOTE). Pemodelan dilakukan menggunakan dua algoritma klasifikasi yaitu Support Vector Classifier (SVC) dan Random Forest Classifier (RFC). Penalaan parameter dilakukan dengan memanfaatkan Stratified K-Fold Cross-Validation untuk menjaga keseimbangan distribusi kelas selama proses validasi model. Berdasarkan hasil analisis dataset, mayoritas sentimen yang ditemukan adalah negatif sebesar 59%, diikuti oleh sentimen positif sebesar 31%, dan netral sebesar 10%. Selain itu, penelitian juga menghasilkan temuan bahwa model SVC mencapai akurasi lebih baik dibandingkan model RFC yaitu sebesar 77%, sedangkan RFC memiliki nilai akurasi sebesar 73%. Temuan ini mengindikasikan bahwa SVC lebih efektif dalam mengklasifikasikan sentimen terkait kebijakan pembatasan subsidi BBM. Penelitian ini memberikan juga insight mengenai persepsi masyarakat terhadap kebijakan tersebut, yang dapat menjadi pertimbangan bagi pemerintah dalam merumuskan strategi komunikasi dan implementasi kebijakan yang lebih baik di masa depan.
Penentuan Jumlah Project Implementation Staff dengan Metode Workload Analysis Full Time Equivalent (FTE) dan Analytical Hierarchy Process (AHP) : (Studi Kasus: PT Indodev Niaga Internet) Hakim, Sulaiman; Subekti, Yogi Agung; Triyono, Gandung
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2235

Abstract

Dalam suatu proyek yang dikerjakan oleh Tim Implementasi pada PT. Indodev Niaga Internet terkadang Implementation Staff yang ada pada Tim Implementasi sering kali mengalami beban kerja yang berlebih ataupun sebaliknya beban kerja terlalu rendah dengan tidak adanya kesesuaian jumlah Implementation Staff yang terlibat dalam suatu proyek yang dapat mengakibatkan terjadinya ketidakefisienan dalam pengerjaan proyek. Oleh karena itu diperlukan suatu cara untuk mengoptimalkan jumlah Implementation Staff yang ada pada Tim Implementasi agar masalah beban kerja yang berlebih ataupun terlalu rendah dapat teratasi. Cara yang digunakan untuk menyelesaikan masalah tersebut ialah dengan menggunakan Analisis Beban Kerja (Workload Analysis). Analisis Beban Kerja akan mencari kesesuaian jumlah tugas dan beban kerja yang ada dalam suatu organisasi yang akan diekuivalenkan dengan satuan waktu dengan jumlah pegawai yang dimiliki oleh suatu perusahaan dan dikombinasikan dengan Analytical Hierarchy Process (AHP) yang akan menentukan siapa saja Implementation Staff yang tepat dan ideal guna untuk mengisi kekurangan Implementation Staff. 7 kriteria yang telah didapatkan dari hasil kuesioner dan sudah dilakukan pengujian menggunakan Chocran Q-Test ketujuh kriteria yang didapat ialah Tanggung Jawab, Kerjasama Tim, Kemampuan Analisis, Programming Skills, Orientasi terhadap Pencapaian Target, Product Knowledge, dan Kemampuan Berkomunikasi. Dengan menggunakan kedua model tersebut dapat membantu efisiensi Sumber Daya Manusia (Implementation Staff) pada proyek teknologi informasi berbasis layanan.
Fake News Detection using the Random Forest Algorithm Setyadin, Rahmat Dipo; Winasis, Reza Handaru; Triyono, Gandung
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.4995

Abstract

Detecting fake news has become increasingly important in the digital era, where false information can spread rapidly and significantly influence public opinion. The dissemination of fake news can lead to public distrust in the media, economic losses, and even social conflict. This study aims to develop an effective fake news detection system using the Random Forest algorithm approach. The dataset used in this research was collected from the official Kominfo website and includes attributes such as title, description, author, date, category, page, news URL, and image URL. The text preprocessing process involves tokenization, stop word removal, text normalization, and feature extraction using Term Frequency and Inverse Document Frequency (TF-IDF) to generate numerical representations of the textual data. The Random Forest model was evaluated using accuracy, precision, recall, and F1-score metrics to assess its effectiveness in detecting fake news. The results show that the model performed exceptionally well, with k-fold cross-validation (k=5) yielding high average accuracy—Random Forest achieved an accuracy of 0.9890.
Design and Construction of Employee Recruitment System Application using Profile Matching Method Mahendra, M. Azmi; Firmansyah, Maulana; Triyono, Gandung
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/xvzgjq78

Abstract

The employee recruitment process is one of the important aspects of human resource management, which ensures a match between company needs and available candidates. This research aims to design an E-Recruitment system based on Profile Matching to improve efficiency and accuracy in the employee selection process at PT XYZ. The research methods used include literature study, observation, and interviews with the HRD team. System development follows the SDLC waterfall model, which includes planning, analysis, design, implementation, and testing stages. In this case study, we use data samples from 5 applicants. For the criteria used, there are 3 criteria, namely administration, interview results, and skills/expertise. The results showed that the developed system was able to automate the selection process, reduce administrative burden, and increase objectivity in candidate selection. In conclusion, the implementation of Profile Matching-based E-Recruitment can optimise the recruitment process, but still needs to be combined with other selection methods to get a more comprehensive picture of candidates.
Peningkatan Literasi Digital dan Keamanan Data Pribadi pada Siswa SMK Triguna 1956 Pebry, Fachry Ajiyanda; Sakti, Dolly Virgian Shaka Yudha; Santika, Reva Ragam; Permana, Iman; Triyono, Gandung
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 5 No 1 (2024): Juni 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v5i1.5892

Abstract

Penggunaan layanan keuangan berbasis teknologi seperti PayLater semakin populer di kalangan masyarakat Indonesia, terutama di kalangan generasi muda. Meskipun menawarkan kemudahan dalam berbelanja, layanan ini membawa resiko terkait privasi dan keamanan data pribadi. Di era digital, literasi digital dan kesadaran akan pentingnya menjaga data pribadi menjadi sangat penting untuk menghindari penyalahgunaan data dan potensi kejahatan finansial. Untuk mengatasi masalah ini, seminar "Dampak PayLater Sebagai Gaya Hidup Instan: Nggak Bahaya Tah?" diadakan di SMK Triguna 1956 dengan tujuan meningkatkan literasi digital dan kesadaran akan privasi dan keamanan data pribadi di kalangan siswa-siswi. Seminar ini dihadiri oleh 20 peserta dari kelas XII dan beberapa guru, serta dihadiri oleh kepala sekolah yang memberikan sambutan. Metode pengabdian masyarakat yang digunakan mencakup analisa kondisi objek mitra, persiapan konsep dan administrasi kerjasama, survey kebutuhan materi pelatihan, pembuatan proposal PKM, pembuatan materi seminar, pelaksanaan kegiatan, evaluasi kegiatan, serta penyusunan laporan dan publikasi kegiatan. Hasil dari seminar menunjukkan bahwa 86% peserta pernah menggunakan layanan PayLater, dan 85% dari mereka menyatakan bahwa materi yang dibahas merupakan pengetahuan baru. Penilaian terhadap kualitas penyampaian materi oleh narasumber juga menunjukkan hasil yang positif, dengan rata-rata skor di atas 4 dari skala 5. Sebagian besar peserta merasa bahwa seminar ini bermanfaat dan ingin diadakan seminar serupa dengan topik yang berbeda di masa mendatang. Kritik dan saran yang diterima akan menjadi masukan berharga untuk perbaikan kegiatan berikutnya.
Selection of Recipients of Excellent Scholarship Educational Assistance using Simple Addictive Weighting Method Rudi Hidayat; Ryan Prasetya; Gandung Triyono
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2360

Abstract

The selection of educational assistance recipients is an important process that determines the effectiveness of aid distribution. However, inconsistencies in assessment criteria and less systematic data management often become obstacles in determining the right recipient candidates. This problem results in subjectivity and lack of transparency in the selection process. This study proposes a solution in the form of implementing the Simple Additive Weighting (SAW) method as a multi-criteria-based decision support system. This method is used to process data on prospective recipients with criteria including economic conditions, number of family dependents, written test results, and interviews. The approach used is quantitative descriptive with stages of data collection, criteria weighting, SAW score calculation, and evaluation of results. The results of the study show that the SAW method is able to provide objective and consistent rankings of prospective recipients. Evaluation of real data on scholarship recipients shows an accuracy level of 84.62%, indicating the effectiveness of this method in the selection process. These results indicate that the SAW method can be an effective solution to increase transparency, consistency, and fairness in the educational assistance selection process.
Application of Data Mining on Player Statistics for Scouting in Football Triyono, Gandung; Wisanto, Aditya Agus; Fachrurozy, Achmad
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3886

Abstract

The sophistication of today's technology makes the use of data increasingly massive. All digital aspects must have data that is ready to be processed, including in the football industry. The use of data in the football industry is one of them used to record all activities carried out by players to see their performance in the match. Dewa United has a scouting division that is tasked with finding talented players according to the wishes of the head coach. In its search, the scouting division observes the players on the field and also uses raw statistical data to see the player's performance. However, the implementation of these activities still has obstacles as evidenced by the difference between the results of observations and the performance of players when joining the team. To solve this problem, the use of data mining can provide scouting recommendations according to player statistics, making the scouting process effective and efficient. The purpose of this study is to make it easier for the team to search for players according to what is desired, which is obtained is a web-based application that has a scouting recommendation feature based on attributes or players according to choice and detailed descriptions of the selected players..
INTEGRATED AHP-TOPSIS DECISION SYSTEM FOR FAIR STUDENT PERFORMANCE EVALUATION Hafiz, Rahmad; Triyono, Gandung; Assegaf , Noval; Yasmin , Nadia; Effendi , Muhtar
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4064

Abstract

Giving awards is essential to motivate students; however, selecting outstanding students at the junior high school level is often conducted manually and subjectively, which can lead to unfairness and prolonged processing time. This study develops a Decision Support System (DSS) that integrates the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support objective and transparent student selection. A quantitative descriptive approach was employed, with data collected through questionnaires, interviews, and documentation at two state junior high schools in Banjarmasin City. Seven assessment criteria were applied: attendance, behavior, uniform neatness, extracurricular participation, academic grades, competition achievements, and disciplinary records. AHP was used to determine the weight of each criterion, while TOPSIS ranked students based on these weights. The web-based system was developed using PHP and MySQL and evaluated using the Technology Acceptance Model (TAM). Results show that academic grades had the highest weight (28.5%), followed by attendance (22.3%) and competition performance (15.2%). The TAM evaluation yielded average scores of 4.32 for Perceived Ease of Use, 4.40 for Perceived Usefulness, 4.15 for Attitudes Towards Use, and 4.28 for Behavioral Intention to Use. The DSS produces accurate rankings, is well-received by users, and offers an efficient, fair, and replicable solution for data-driven educational governance in the digital era.
Co-Authors - Sumardianto Abdul Hamid Abdurrahman, Faris Nur Achmad Ardiansyah Achmad Solichin Achmad Syarif Adhi, Ajar Parama Aditya Ikhbal Maulana Agus Umar Hamdani Aji Guntoro Ajinarasena Hermanu Al Ghozali, Isnen Hadi Al-akbari, Munawir Fikri Amirudin Amirudin Ananda Dian Nugraha Angga Prasetyo Angga Prasetyo Anggita Pamukti Anggraini Ujianti Annisa Hanifa Annisa Putri Gita Cahyani Anwarsyah, Anwarsyah Arif Pirman Aris Subagyo, Wismoyo Asep Lukman Arip Hidayat Assegaf , Noval Azizi, Hibatul Azrul Azmani Chaerul, Muh Coudry Bernadeth Dana Indra Sensuse Daniel Iskandar Dara Putri Oktiara Dede Wahyu Saputra Dermawan Ginting Devy Fatmawati Dini Astuti Dini Handayani Djafar, Muhammad Agung A. Djati Kusdiarto Dolly Virgian Shaka Yudha Sakti Dwi Kristanto Dyah Puji Utami Dzakiyyah, Syifa Ghina Effendi , Muhtar Eliyani, Eliyani Ery Rinaldi Eva Yulyanti Fachrurozy, Achmad Fadel, Muhamad Fahlevi, Noval Fajriah, Riri Febri Maulana Febrianti, Rizkia Saski Feby Lukito Wibowo Firmansyah, Maulana Gilang Ramadhan Hadi rahadian Hafiz, Rahmad Hakim, Sulaiman Hardjianto, Mardi Helmi Zulqan Henny Hikmah, Maulida Idam Risnaputra Idmi, Idmi Iman Permana, Iman Indra Indra Jotri Firdani Maharaja Juhari Juhari, Juhari Jumaryadi, Yuwan Kanasfi, Kanasfi Kiki Ari Suwandi kosasih Kristiyantho, Yutdhi Lestari, Triardani Lis Suryadi Lis Suryadi, Lis Lutfan Lazuardi Luthfi Mawardi M. Azmi Mahendra Mahendra, M. Azmi Malik Aziz Habibie Marentek, Billy Maruanaya, Greghar Juan Tjether Maskur A, Moch Riyadi Masnuryatie, Masnuryatie Maulana Firmansyah Maya Asmita Maya Gian Sister Megananda Hervita P. Melyana, Melyana Mepa Kurniasih MHD. Reza M.I. Pulungan Moch. Rezaf Ivanka Haris Mohammad Aldinugroho Abdullah Mohammad Syafrullah Muhamad Dikhi Rohman Muhamad Rizky Syawalludi Muhammad Dzakky Ikhwani Imaduddin Munandar, Muhamad Arief Naurah Huwaida Ningrum, Sekar Ayu Novandy, Axel Nurhikmah, Suci Nurjanah, Septiana Ono Taryono Pebry, Fachry Ajiyanda Pipit Ari Mufidah Prasetia, Andika Rohman Prasetyo, Angger Totik Putri Hayati putri yani, putri Rahmat Hidayat Ramadani, Romi Reza Ariftiarno Ridho Firmansyah Ridho Putra Kusmanda Riki Ramdani Saputra Rima Tamara Aldisa Rinto Prasetyo Adi Riski Amalia Rita Fransina Maruanaya Rizka Pitriyani Rizky Adhi Saputra Rizky Fernanda Aprianto Rizky Tahara Shita Rojakul, Rojakul Rudi Hidayat Ryan Prasetya Safrina Amini Samuel Samuel Septiadi, Septiadi Setyadin, Rahmat Dipo Shindy Yuliyatini Sigit Ari Prasetyo Siswahyudianto Sittah Ifadah Sri Hartati Sri Melati Subekti, Yogi Agung Sudiyatno Yudi Nugroho Sufyan Asaury, Akhmad Suriah Setiana Widiastuti SURYANI Syarif Hidayatulloh Tansya Ingmukti Tunggal Saputra, Tri Aji Tutik Lestari Umar Alfaruq Utomo Budiyanto Vasthu Imaniar Ivanoti Wahyu Adi Setyo Wibowo Wahyu Cesar Wahyuningram, Nugroho Warih Dwi Cahyo Wawan Gunawan Widyanto, Tetrian Wilsen Grivin Mokodaser Winasis, Reza Handaru Wisanto, Aditya Agus Wisnu Cahyadi Wulan Trisnawati Yasmin , Nadia Yeros Fathullah Achmad Yulia Nita Zaenul Muttaqin Zainal Arifin