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All Journal Dinamik SEGMEN Jurnal Manajemen dan Bisnis GEMA TEKNOLOGI Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Syntax Jurnal Informatika Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Mahasiswa FEB Prosiding SNATIF Jurnal Ketahanan Nasional Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Berkala Epidemiologi Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri InComTech: Jurnal Telekomunikasi dan Komputer Prosiding Seminar Nasional Teknoka JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL EDUCATION AND DEVELOPMENT Jiko (Jurnal Informatika dan komputer) JSiI (Jurnal Sistem Informasi) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JURIKOM (Jurnal Riset Komputer) Jurnal Telematika STRING (Satuan Tulisan Riset dan Inovasi Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research METIK JURNAL Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Ilmu Komputer dan Bisnis Syntax Idea Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Mnemonic Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi International Journal of Advances in Data and Information Systems Journal of Computer Science and Engineering (JCSE) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pewarta Indonesia Djtechno: Jurnal Teknologi Informasi JURNAL KOMUNIKASI DAN BISNIS Ascarya: Journal of Islamic Science, Culture and Social Studies Jurnal PkM (Pengabdian kepada Masyarakat) Humantech : Jurnal Ilmiah Multidisiplin Indonesia Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Journal Of Human And Education (JAHE) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Economic Reviews Journal Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Berita Kedokteran Masyarakat Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Systems Engineering and Information Technology J-Icon : Jurnal Komputer dan Informatika Jurnal Teknik Indonesia Research Horizon IIJSE Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian Masyarakat Nasional Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) EDUCATIONAL: Jurnal Inovasi Pendidikan dan Pengajaran Prosiding SeNTIK STI&K Journal of Medical and Health Science Jurnal Ilmu Kesehatan Immanuel Jurnal Ekonomi, Manajemen, Akuntansi dan Keuangan
Arief Wibowo
Universitas Budi Luhur
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Analisis Data PMB di STITEK Bontang dengan FP Growth dan Apriori Untuk Mendukung Strategi Promosi di Masa Pandemi Wibowo, Arief; Megawati, Rina; Henry
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.v11i1.3041

Abstract

Data on new student admissions is always available and owned by all universities, both private and public. One of the private universities that has this data is STITEK Bontang (Bontang College of Technology). This data can be used as a reference or database owned by STITEK Bontang to be used optimally. This utilization is used as strategic information for new student admissions during the pandemic. This study aims to group new student data using data mining techniques. The data mining technique used is the FP Growth algorithm and the Apriori Algorithm. For the research steps using the CRISP-DM methodThis research is used to determine the right Promotion Strategy. Determining the right promotional strategy will be able to reduce promotional costs and achieve the right promotion goals. 1) Fp-Growth and Apriori methods in building a knowledge base from a collection of student databases accepted at STITEK Bontang by showing the relationship between student identity and the study program that the student chooses. 2) Obtained the most entrances with a lift ratio of 2.
Implementasi Algoritma K-Means untuk Pengelompokan Pengguna QRIS Tap pada Moda Transportasi Umum di Wilayah Jabodetabek Chintya Paramitha; Arief Wibowo
Jurnal Ekonomi, Manajemen, Akuntansi dan Keuangan Vol. 7 No. 1 (2026): January
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/emak.v7i1.3685

Abstract

This study aims to segment QRIS Tap users in public transportation payments through the Bayarind e-wallet application. The K-Means clustering method is employed using factual, non-perceptual data, including users’ age, duration of application usage, frequency of QRIS Tap usage, and average transaction value. The dataset consists of 63 QRIS Tap users in the Jabodetabek area, which has been transformed into numerical form. Cluster evaluation is conducted using the Davies–Bouldin Index (DBI) and Within-Cluster Sum of Squares (WCSS) to determine the optimal number of clusters. The scientific contribution of this study lies in the application of user segmentation based on actual behavioral data (non-perceptual) within the specific context of QRIS Tap usage for public transportation payments, a topic that remains limited in prior studies. Furthermore, this study integrates DBI and WCSS as complementary evaluation metrics to ensure a more objective and robust cluster configuration. The results indicate that a four-cluster configuration (K = 4) provides the most informative segmentation. These clusters represent new users with low activity, loyal users with moderate transaction levels, experienced users with diverse transaction patterns, and premium users with high transaction frequency and value. This segmentation offers empirical insights into QRIS Tap user characteristics and serves as a strategic foundation for decision-making in the development of digital payment systems and public transportation services.
ANALYSIS OF THE ACCURACY LEVEL OF FINANCIAL DISTRESS PREDICTION MODELS USING THE NAÏVE BAYES METHOD Ridho Dwi Maulida; Arief Wibowo; Selamet Riyadi
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/pz5ckv70

Abstract

The ability to accurately predict financial distress is crucial for State-Owned Enterprises (SOEs), given their strategic role in maintaining national economic stability. However, existing studies predominantly examine financial distress models in isolation and rely mainly on financial ratios, with limited attention to comparative evaluation under a unified machine learning framework and alternative input structures. This gap limits the understanding of how model performance may vary across different data representations. This study aims to evaluate and compare the predictive performance of four financial distress models Altman Z-Score, Springate S-Score, Zmijewski X-Score, and Grover G-Score by integrating them within a Naïve Bayes classification approach. Using a dataset of 20 Indonesian SOEs listed on the Indonesia Stock Exchange over the 2020–2023 period, this study applies a quantitative comparative method with two types of input variables, namely financial ratios and financial statement account balances. The results show that the Springate S-Score model demonstrates the highest predictive accuracy, achieving 95% when using financial ratios and 82.5% when using account balances. Overall, models based on financial ratios outperform those utilizing raw financial statement data, indicating that structured financial indicators provide more effective signals for classification. The main contribution of this study lies in providing a comprehensive and consistent comparison of multiple financial distress prediction models within a single probabilistic machine learning framework, while also highlighting the impact of different input variable structures on model performance. This study extends the financial distress literature by bridging traditional financial analysis and data mining approaches, and offers practical implications for developing more reliable early warning systems for financial distress in SOEs.   Keywords : Financial Distress Prediction, Naïve Bayes, Machine Learning
Perbandingan K-Means, DBSCAN, dan Louvain pada Peserta Pendidikan Kesetaraan di Kabupaten Balangan Ida Ariyani Hasanah; Riama Simanjuntak; Arief Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3350

Abstract

This study aims to analyze the clustering patterns of equivalency education participants through a comparative clustering approach using three algorithms: K-Means (centroid-based), DBSCAN (density-based), and Louvain (graph-based). The dataset consists of 1,057 participants with numerical and categorical attributes representing heterogeneous characteristics. The research stages include data preprocessing and the implementation of clustering algorithms on the same dataset to maintain comparison consistency. Evaluation was conducted using the Silhouette Score and Davies-Bouldin Index (DBI) as internal validation metrics, as well as external validation through expert confirmation to ensure the contextual relevance of the results. The findings indicate that K-Means and DBSCAN produced a Silhouette Score of 0.040, reflecting poor cluster separation quality and the dominance of one large cluster. DBSCAN demonstrated an advantage in detecting noise; however, it was unable to significantly improve cluster separation quality in data with a high level of homogeneity. In contrast, the Louvain algorithm generated a more balanced community structure with a low imbalance ratio, making it more capable of representing relational connections among data points that are not fully captured by distance-based approaches. This study contributes through a comparative analysis across clustering approaches in the context of equivalency education, as well as through the integration of quantitative and contextual validation. The findings confirm that graph-based approaches are more adaptive for data with high homogeneity and have the potential to serve as a basis for participant segmentation to support more effective data-driven decision-making in the education sector.
Model Rekomendasi Karier Lulusan Sekolah Menengah Kejuruan Berdasarkan Kompetensi dan Bakat Menggunakan Perbandingan Algoritma Apriori dan FP-Growth Tarwan; Eko Aji Putra; Arief Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3410

Abstract

The increasingly dynamic development of the job market requires Vocational High School (SMK) graduates to possess adaptive abilities, not only in mastering vocational competencies but also in determining appropriate career paths. However, in reality, many SMK graduates still experience difficulties in choosing careers that align with their competencies and talents. This condition highlights the need for a systematic approach capable of providing data-driven career recommendations. This study aims to develop a data-based career recommendation system using the Apriori and FP-Growth algorithms to identify relationship patterns among vocational competencies, students’ academic talents, and alumni tracer study data. The study offers a new approach to career recommendation systems for Vocational High Schools by integrating students’ academic data and alumni post-graduation histories (tracer studies) within a single pattern analysis framework. In addition to generating association rules that can easily be used as a basis for decision-making, the system also incorporates validation from guidance and counseling teachers (BK teachers) to strengthen data-driven career decisions. Talents are classified into two categories, namely exact/science-oriented and non-exact/non-science-oriented, based on comparisons between average Mathematics grades and non-science subject grades from semesters 1 to 6. Alumni tracer data include post-graduation status (employment, higher education, or others), job relevance, competency certificates, and the positions or work sectors pursued. Subsequently, each student and alumni entity was transformed into transactional data analyzed using the Apriori and FP-Growth algorithms to discover association rules between student profiles and career recommendations. The analysis results indicate strong relationships between combinations of talents and vocational competencies with specific career choices. The inclusion of data from guidance and counseling teachers serves as qualitative input that strengthens the validity of the system’s results. This system can be utilized by schools, guidance counselors, and students as a decision-support tool for making more objective and data-driven career decisions. Therefore, the system supports a vocational education direction that is more integrated with labor market needs.
Perbandingan Apriori dan FP-Growth dalam Association Rule Pola Pembelian Sparepart Preventive Maintenance Anita; Arief Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3427

Abstract

Spare part inventory management is an important aspect of preventive maintenance activities. This study aims to analyze the performance comparison between the Apriori and FP-Growth algorithms in identifying spare part purchasing patterns for preventive maintenance activities. The main problem in spare part management is the lack of optimal inventory planning, which can lead to overstock or stock shortages. The method used in this study is Association Rule Mining with two algorithms, namely Apriori and FP-Growth, applied to spare part purchasing transaction data. The analysis process was conducted through data preprocessing, frequent itemset generation, and association rule formation using minimum support and confidence parameters. The results indicate that the FP-Growth algorithm performs more efficiently than Apriori in terms of computation time and the ability to handle large datasets. Meanwhile, the Apriori algorithm is easier to implement and understand. The resulting association patterns can be used as a basis for decision-making in more effective and efficient spare part inventory management. Therefore, this study is expected to contribute to improving data-driven preventive maintenance strategies.
K-Means dan Random Forest Faktor Pemicu Perceraian di Seluruh Provinsi Wisnu Supri Harmito; Arief Wibowo
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/f1npya68

Abstract

The integration of the K-Means Clustering algorithm and the Random Forest Regressor aims to map divorce patterns in Indonesia for the 2018–2024 period. This study introduces a paradigm for categorizing variables into axis factors—such as economic issues, disputes, and abandonment—and triggering factors, which can be referred to as behavioral factors, such as apostasy, gambling, domestic violence, and so on. The clustering results show a Silhouette Score of 0.5912, with Java Island identified as the region with the highest risk and a well-defined risk zoning. The Random Forest model achieved an R-squared accuracy of 0.9563, revealing that trigger factors—specifically gambling, incarceration, apostasy, and domestic violence—possess a higher deterministic weight in precipitating divorce compared to other factors. This key finding indicates that the impact/triggering factors do not always correlate directly with the number of cases. Therefore, it recommends policy interventions focused on breaking the chain of behavioral triggers to strengthen family resilience nationwide.
Analisis Keamanan Sistem Informasi menggunakan Metode Penetration Testing berbasis Metasploit Framework Purwadi Purwadi; Herriyawan Herriyawan; Arief Wibowo
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10491

Abstract

Abstract Information system security has become a crucial aspect as organizations' reliance on digital technology increases. Increasingly complex cyber threats demand systematic and ongoing security evaluations. This study aims to analyze the security level of information systems by applying a penetration testing method based on the Metasploit Framework. The research methodology refers to the Penetration Testing Execution Standard (PTES), which includes pre-engagement, intelligence gathering, vulnerability analysis, exploitation, post-exploitation, and reporting stages. The test results showed that of the 10 vulnerabilities identified, four were categorized as high risk, three as medium risk, and three as low risk. The exploitation phase demonstrated a 70% success rate, allowing researchers to gain initial access to the target system. In the post-exploitation phase, the access gained allowed attackers to access system files, read configurations, and escalate limited privileges. These findings confirm that implementing regular penetration testing can help organizations improve their information system security posture and minimize the risk of data leaks and service disruptions.
Analisis Perilaku Pelanggan dan Segmentasi Pasar pada Driving Range menggunakan Teknik Data Mining Purwadi Purwadi; Herriyawan Herriyawan; Arief Wibowo
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10513

Abstract

Penelitian ini bertujuan untuk menganalisis penerapan teknik data mining dalam mengidentifikasi pola perilaku pelanggan dan melakukan segmentasi pasar pada fasilitas driving range. Seiring meningkatnya persaingan di industri olahraga rekreasi, khususnya golf, pemahaman berbasis data terhadap perilaku pelanggan menjadi krusial untuk mendukung strategi pemasaran dan peningkatan layanan. Metode yang digunakan dalam penelitian ini meliputi eksplorasi data menggunakan Kernel Density Estimation (KDE) untuk menganalisis distribusi usia pelanggan, serta K-Means Clustering untuk melakukan segmentasi pelanggan berdasarkan usia, lama bermain golf, dan frekuensi kunjungan per minggu. Penentuan jumlah klaster optimal dilakukan menggunakan Elbow Method, yang menunjukkan nilai K optimal sebesar tiga klaster. Data penelitian diperoleh dari transaksi harian pelanggan, data keanggotaan, dan survei kepuasan pelanggan. Hasil analisis menunjukkan bahwa mayoritas pelanggan berada pada rentang usia 20–40 tahun dengan puncak distribusi pada usia sekitar 30–32 tahun. Hasil clustering mengidentifikasi tiga segmen utama, yaitu pelanggan berpengalaman dengan usia rata-rata 43 tahun dan lama bermain sekitar 116 jam, pelanggan senior atau profesional dengan usia rata-rata 53 tahun dan pengalaman bermain tertinggi sekitar 310 jam, serta pelanggan pemula dengan usia rata-rata 32 tahun dan pengalaman bermain sekitar 17 jam namun memiliki frekuensi kunjungan relatif tinggi, yaitu sekitar 1,9 kali per minggu. Temuan ini menunjukkan bahwa penerapan data mining efektif dalam mendukung segmentasi pelanggan dan pengambilan keputusan strategis dalam pengelolaan driving range.
Analisis Email-Borne Malware: Teknik Infeksi melalui Lampiran Email dan Dampaknya terhadap Keamanan Siber Purwadi Purwadi; Herriyawan Herriyawan; Arief Wibowo
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10516

Abstract

Email masih menjadi salah satu vektor serangan siber paling dominan dalam penyebaran malware, baik terhadap organisasi maupun end-user. Berbagai laporan keamanan siber menunjukkan bahwa lebih dari 90% serangan malware diawali melalui email, dengan sekitar 30–40% email yang diterima organisasi dikategorikan sebagai spam atau berpotensi berbahaya, serta sebagian besar memanfaatkan lampiran berbahaya yang disamarkan sebagai dokumen sah. Hampir 95% organisasi dilaporkan pernah mengalami serangan phishing berbasis email setidaknya satu kali dalam satu tahun, dan lebih dari 50% insiden ransomware pada lingkungan korporasi bermula dari email yang mengandung lampiran malware. Pada tingkat end-user, ratusan juta email phishing dan malware dikirimkan setiap hari secara global, dengan sebagian berhasil mencapai kotak masuk pengguna dan menyebabkan pencurian data serta kerugian finansial. Penelitian ini bertujuan untuk menganalisis teknik infeksi email-borne malware melalui lampiran email serta dampaknya terhadap keamanan siber pada lingkungan korporasi dan end-user. Metode penelitian yang digunakan adalah analisis literatur dan studi kasus terhadap laporan insiden keamanan siber serta publikasi ilmiah terkini. Hasil analisis menunjukkan bahwa jenis malware yang paling sering disebarkan melalui lampiran email meliputi trojan, ransomware, dan spyware, dengan rekayasa sosial sebagai faktor utama keberhasilan serangan. Dampak yang ditimbulkan mencakup pencurian data sensitif, gangguan operasional sistem, kerugian finansial, serta penurunan tingkat kepercayaan pengguna dan reputasi organisasi. Penelitian ini menegaskan bahwa tingginya intensitas serangan email-borne malware menuntut penerapan strategi mitigasi yang komprehensif, seperti peningkatan kesadaran keamanan siber, pemanfaatan sistem penyaring email, dan penggunaan perangkat lunak keamanan yang andal untuk meminimalkan risiko infeksi malware melalui email.
Co-Authors - Arientawati - Sumardianto Abdul Haris Achadi Achadi, Abdul Haris Adita, Ita Afifah Khaerani Afifatussalamah, Rizka Ahmad Sururi Ahmad Sururi Akbar, Ahmad Aldizar Al Fatach, M Khabib Anggraini, Julaiha Probo Anita Anita Diana Antika Zahrotul Kamalia Anugrah Sandy Yudhasti Anuqman Fitriadi Apriati Suryani Ardhianto, Angga Ardianah, Eva Ari Wibowo Arief Umarjati Asep Permana Atik Ariesta Bayu Sadewo Bayu Satria Pratama Binarto, Antonius Jonet Bintang, Bagus Boerhan Hidayat, Boerhan Chairul Rizal Chintya Paramitha Danar Wido Seno Danniswara, Ahmad Deni Mahdiana Diah Indriani Didik Hariyadi Raharjo Didin Muhidin Dwi Kristanto Dwi Yulianti Dyah Retno Utari Dyah Retno Utari, Dyah Retno Ebine, Masato Eko Aji Putra Eko Aprianto Endah Sarah Wanty Fajar Siddik Chaniago Farah Chikita Venna Farid Setiawan Farid Setiawan, Farid Fathin Aulia Rahman Febrilliani, Jihan Sastri Fenny Irawati Fernando, Donny Firman Noor Hasan Firmanty Mustofa, Vina Fitri Nur Masruriyah, Anis Fitriadi, Rifqi Fitriani, Netty Fransiska Vina Sari Frenda Farahdinna Fried Sinlae Ganteng Hasanudin Ghapur, Abdul Gurdani Yogisutanti Hadidtyo Wisnu Wardani Hananto, Agustia Handoko, Andy Rio Hanindita, Meta Herdiana Hari Basuki Notobroto Haris Achadi, Abdul HARIYANTO HARIYANTO Harun Nasrullah Hassan, Shiza Hayatul Khairul Rahmat Henry Henry Herriyawan Herriyawan Herriyawan, Herriyawan Hidayat, Manarul Hidayat, Sarifudlin Huda, Ratu Najmil Ida Ariyani Hasanah Indah Rizky Mahartika Indra Indra Inge Virdyna Irfan Hadi Irfan Nurdiansyah Irman Efendi Istiqoomatun Nisaa Joko Sutrisno Jovansgha Avegad Jumaryadi, Yuwan Kanasfi, Kanasfi Karyaningsih, Dentik Kresno Yulianto KRESNO YULIANTO KUNTORO Kurnia Setiawan Kutanto, Haronas Larasati, Pamela Linda Lingga Desyanita Luthfi Akbar Ramadhan Mahmudah Mahmudah Mailana, Agus Maria Adiningsih Marlina, Hesti Martens, Brigitta Griselda Maskur A, Moch Riyadi Megananda Hervita Permata Sari Megawati, Rina Miechael Miechael Miftahul Arifin Miftahul Arifin Mochammad Rizky Royani Moh Makruf Monica, Silvi Muhamad Fadel Muhammad Bagus Bintang Timur Muhammad Bagus Bintang Timur, Muhammad Bagus Bintang Muhammad Febrian Rachmadhan Amri Muhammad Noor Hasan Siregar Muhammad Risky Mulyati Mulyati Nazihah, Fasya Nendi, Nendi Ningrum, Yogi Ajeng Nugroho, Angelika Pratiwi Widya Nur Aisiyah Widjaja, Nur Aisiyah Nur Anisah Rahmawati Nur Rohman Nurcahya, Gelar Nurfadhiilah, Annisa Nurfidaus, Yasmine Nursyi, Muhamad Pattipeilohy, William Frado Pattipeilohy, William Frado Pebriaini, Prisma Andita Poppy Ruliana Pradiptha, Anindya Putri Prastiyo, Krisna Probo Anggraini, Julaiha Purwadi Purwadi Purwadi Purwadi Putra, Andi Agung Putra, Rinaldi Febryatna Duriat Qamarullah Popalia Rachmah Indawati Rahman, Fathin Aulia Rakhman, Abdulah Rakhmat Rakhmat Rakhmat Rakhmat RAMAYU, I Made Satrya Rangkuti, Muhammad Yusuf Rizqon Ratna Ayu Sekarwati Ratna Ayu Sekarwati Relawanto, Bowo Ria Puspitasari Riama Simanjuntak Ridho Dwi Maulida Rika Nurhayati Riki Ramdani Saputra Rina Megawati Risaychi, Diva Ajeng Brillian Ristiana, Ina Rizkiyanto, Muhamad Ardiansyah Rizky Tarmudzi Roedi Irawan Rojakul, Rojakul Rosita Dewi, Erni Ruliana, Poppy Rusdah Ruwirohi, Jan Everhard Ryo Tanaka Sabirin, Sahril Sadewo, Bayu Santoso, Febrina Mustika Saptari Wijaya Mulia Sari Anggar Kusuma Melati Sari, Fransiska Vina Sasongko, Raden Satiri Satiri, Satiri Selamet Riyadi Selly Rahmawati Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Setyowati, Erlin Sevtian Ferdian Shofinurdin Shofinurdin Siddik Chaniago, Fajar Sigit Ari Saputro Sigit Budi Nugroho Siregar, Sutan Syahdinullah SITI NURUL HIDAYATI Sitti Aliyah Azzahra Soenarnatalina Melaniani Sudewo, Andika Hasbigumdi Sugiyarta, Ahmad Sujiharno Sujiharno Sumarna, Presma Dana Scendi Suntoro, Dimas Fahmi Supiyandi Supiyandi Tarwan Tiaharyadini, Rizka Triantoro, Ery TRISNAWATI, WULAN Tulus Yuniasih Umam, Mohamad Hafidhul Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyu Setiawan Wahyudi, Widi Wahyuni, Chatarina Unggul Wangsajaya, Yosia Heartha Dhalasta Wibiyanto, Alif Dewan Daru Widiyaningrum, Diyah Kiki Widyanto, Tetrian Windhu Purnomo Wisnu Supri Harmito Wulan Novita Sari Yahya Darmawan Yudanto, Satyo Zakaria Anshori Zaqi Kurniawan