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All Journal TEKNIK INFORMATIKA 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 JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research 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 JURNAL KOMUNIKASI DAN BISNIS Ascarya: Journal of Islamic Science, Culture and Social Studies Jurnal PkM (Pengabdian kepada Masyarakat) Humantech : Jurnal Ilmiah Multidisiplin Indonesia Media Publikasi Promosi Kesehatan Indonesia (MPPKI) 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 Health Dynamics Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) 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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Pengaruh Kualitas Sistem dan Kualitas Pelayanan Terhadap Loyalitas Pengguna dengan Kepuasan Pengguna Sebagai Variabel Intervening Aplikasi Digital Korlantas pada Kantor Polisi Sektor Ciledug Kota Tangerang Tarmudzi, Rizky; Wibowo, Arief
Economic Reviews Journal Vol. 5 No. 1 (2026): Economic Reviews Journal
Publisher : Masyarakat Ekonomi Syariah Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/mrj.v5i1.973

Abstract

The digital transformation of public services requires government institutions, including the Indonesian National Police (Polri), to provide efficient and responsive technology-based services. The Digital Korlantas application is one of Polri’s initiatives aimed at facilitating online traffic-related services. However, low user satisfaction and loyalty—particularly in local sectors such as the Ciledug Police Sector, Tangerang City—indicate challenges in its implementation. This study aims to analyze the influence of system quality and service quality on user loyalty, with user satisfaction as a mediating variable. A quantitative approach was used with Partial Least Square Structural Equation Modeling (PLS-SEM) as the analytical technique. The sample consisted of 100 respondents who were users of the Digital Korlantas application within the jurisdiction of the Ciledug Police Sector. The results show that both system quality and service quality have a positive and significant impact on user satisfaction. Furthermore, user satisfaction significantly mediates the relationship between system and service quality and user loyalty. These findings highlight the critical role of improving both technical and service aspects of the application to enhance user satisfaction and loyalty in the context of digital public services.
Purchasing Behavior Based Consumer Segmentation on TikTok Shop in Indonesia Using K-Means Sari, Wulan Novita; Wibowo, Arief
SEGMEN: Jurnal Manajemen dan Bisnis Vol 22, No 1 (2026): SEGMEN Jurnal Manajemen dan Bisnis
Publisher : FE Program Studi Manajemen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/sjmb.v22i1.9251

Abstract

This study aims to analyze consumer segmentation on TikTok Shop in Indonesia based on purchasing behavior. The rapid growth of social commerce, particularly through TikTok Shop, has changed consumer shopping patterns, creating challenges for businesses in understanding diverse consumer characteristics to design effective marketing strategies.This study uses a quantitative approach with primary data collected through questionnaires distributed to TikTok Shop users in Indonesia. The variables used include Age (X1), Purchase Frequency (X2), and Type of Product Purchased (X3). The data was analyzed using the K-Means clustering method to classify consumers into homogeneous segments. The results of the study show that TikTok Shop consumers can be grouped into several different segments with different purchasing patterns, spending levels, and product preferences. These findings have practical implications for developing targeted and personalized marketing strategies
The Influence of Service Features, User Interface, and Security on User Interest in Wondr Mobile Banking by BNI with Digital Trust as an Intervening Variable (Case Study of the Wondr BNI Application) Prastiyo, Krisna; Wibowo, Arief
Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE) Vol 9 No 1 (2026): Sharia Economics
Publisher : Universitas KH. Abdul Chalim Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/iijse.v9i1.8985

Abstract

This study aims to analyse the influence of service features, interface appearance, and security on user interest in the Wondr by BNI mobile banking application, with digital trust as an intervening variable. This study uses a quantitative approach with a survey method, involving 115 respondents selected through purposive sampling. Data were collected through a Likert scale-based questionnaire and analysed using the Partial Least Squares-Structural Equation Modelling (PLS-SEM) method. The results indicate that service features and security significantly influence digital trust but do not significantly influence user interest. The interface does not significantly influence digital trust but does influence user interest. The role of digital trust in mediating the influence between service features, interface design, security, and user interest is not significantly influential. The research model shows moderate predictive relevance, with significant influence on the structural model. This study provides important insights into service features, interface design, security, and digital trust that influence user interest in mobile banking applications, particularly in the Wondr by BNI application.
Penerapan Data Mining Menggunakan Teknik Classification Untuk Melihat Potensi Kepatuhan Wajib Pajak Badan Anuqman Fitriadi; Popalia, Qamarullah; Wibowo, Arief
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9354

Abstract

The application of data mining using classification techniques has significant potential to assist tax authorities in identifying and mapping the compliance levels of corporate taxpayers. This study aims to develop a corporate taxpayer compliance classification model using the Naive Bayes algorithm based on the ratio of Annual Tax Return (SPT) filing and the ratio of tax payments. The data used consist of aggregated data from Tax Service Offices (Kantor Pelayanan Pajak/KPP) for the 2022–2024 period obtained from the Directorate General of Taxes. The research stages follow the Knowledge Discovery in Databases (KDD) methodology, which includes data selection, preprocessing, transformation, modeling, and evaluation. The experimental results indicate that the Naive Bayes model is able to classify compliance levels with an accuracy of 100%, precision of 1.00, recall of 1.00, and an F1-score of 1.00. These findings suggest that the SPT filing ratio is the dominant factor in determining corporate taxpayer compliance. The proposed model can be utilized as a decision support system to assist tax authorities in determining supervision and guidance priorities for corporate taxpayers
Hybrid Relevance and Sentiment Classification of Indonesian Gold Tweets Using Machine Learning for Market Risk Signal Extraction Kamalia, Antika Zahrotul; Indra, Indra; Wibowo, Arief; Riwurohi, Jan Everhard; Hassan, Shiza
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1517

Abstract

This study proposes a hybrid relevance–sentiment classification framework to analyze public opinion on physical Antam gold from Indonesian Twitter data and to support exploratory market-risk signal extraction. Tweets were collected during February–November 2025, after preprocessing and text-normalized deduplication, 1,271 unique tweets were retained. The approach combines weak supervision (rule-/lexicon-based silver labels) with TF-IDF-based machine learning in two stages: (1) relevance classification to separate tweets genuinely discussing physical Antam gold from non-relevant contexts (e.g., ANTM stock/capital-market discussions), and (2) two-class sentiment classification (positive vs negative) applied to relevance-filtered tweets. Random Forest achieved the strongest relevance performance (Accuracy = 0.984; macro-F1 = 0.943; 5-fold CV macro-F1 = 0.928 ± 0.033). For sentiment classification, performance was moderate and close across models; the most stable model under cross-validation (Logistic Regression/Naive Bayes) was used for downstream aggregation. Sentiment outputs were aggregated into a monthly sentiment index for descriptive comparison with gold prices; the observed association was weak, indicating that the index is better interpreted as a risk-perception proxy rather than a direct price predictor.
Analisis Sentimen Opini Masyarakat Terhadap Keefektifan Pembelajaran Daring Selama Pandemi COVID-19 Menggunakan Naïve Bayes Classifier Ari Wibowo; Firman Noor Hasan; Luthfi Akbar Ramadhan; Rika Nurhayati; Arief Wibowo
Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Volume 4 Nomor 2 Tahun 2022
Publisher : Fakultas Teknik Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/asiimetrik.v4i1.3577

Abstract

Since Indonesia was affected by the Covid-19 pandemic, one of the sectors affected was Education. The government makes an online learning system policy where the system is run with an online process. Not a few of them complained about the limitations of activities issued by the government. Twitter social media is often used to express opinions about concerns about programs issued by the government. The Twitter data crawling process was carried out using the hashtag "learning from home" to get as many as 1,000 datasets, followed by the process of removing duplicates which left 524 datasets and then carrying out the implementation stage of the Naïve Bayes Classifier Algorithm. The purpose of this study was to determine the number of positive and negative sentiments from the dataset labeling classification and to determine the accuracy results of using the Naïve Bayes Classifier method as well as the results of evaluation tests on positive and negative sentiment datasets. Based on the experiment, positive sentiment was obtained as many as 480 and negative sentiment as many as 44 out of 524 datasets. The accuracy results in the evaluation test process get results of 88.5% where negative sentiments get a precision value of 12%, recall 17%, and f1-score 14%, while positive sentiments get a precesion value of 95%, recall 93%, and f1 -score 94%.
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.
ANALISIS PREDIKSI PENYAKIT DIABETES MENGGUNAKAN METODE DECISION TREE, NAÏVE BAYES, K-NEAREST NEIGHBOR DAN RANDOM FOREST Manurung, Ridho Parmonangan; Syahirah, Afifah; Wibowo, Arief
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7887

Abstract

Diabetes merupakan penyakit kronis yang ditandai dengan mening-katnya kadar gula darah yang tidak normal. Kadar gula darah yang tinggi, usia, hipertensi, sariawan terus menerus, gatal-gatal, penglihatan yang buram, berat badan berlebihan, penurunan pen-dengaran, kesemutan, dan faktor lainnya adalah beberapa penyebab potensial diabetes. Jika tidak ditangani dengan cepat dan tepat, dia-betes dapat mengakibatkan penyakit komplikasi seperti jantung, stroke, gagal ginjal, kebutaan hingga amputasi. Data training dan data testing diabetes yang digunakan dalam penelitian ini adalah data yang berasal dari database aplikasi rekam medis dan akan dio-lah menggunakan empat model algoritma. Metode klasifikasi ialah bagian dari Teknik data mining untuk melakukan prediksi. Terdapat 2460 data training dan 308 data testing. Penelitian ini bertujuan un-tuk mengetahui model algoritma yang paling tepat untuk mempred-iksi diabetes. Untuk menentukan model algoritma yang paling tepat, dapat dilihat dari tingkat akurasi, presisi dan recall dari model algo-ritma. Hasil penelitian menunjukkan bahwa model algoritma K-Nearest Neighbor terbukti dapat melakukan prediksi penyakit dia-betes dengan baik. Diperoleh nilai akurasi model algoritma K-Nearest Neighbor sebesar 83.78%, presisi sebesar 83.35% dan recall sebesar 73.02% yang diukur dengan Confusion Matrix menggunakan Rapidminer.
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
Co-Authors - Arientawati - Sumardianto Abdul Rachman 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 Efendi, Irman Eko Aji Putra Endah Sarah Wanty Fajar Siddik Chaniago Farah Chikita Venna Farid Setiawan Farid Setiawan, Farid Febrilliani, Jihan Sastri Fenny Irawati Ferdian, Sevtian Fernando, Donny Firman Noor Hasan Firmanty Mustofa, Vina Fitri Nur Masruriyah, Anis Fitri Rachmilah Fadmi Fitriadi, Rifqi Fitriani, Netty Fransiska Vina Sari Frenda Farahdinna Fried Sinlae 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 Hidayat, Manarul Hidayat, Sarifudlin Huda, Ratu Najmil Ida Ariyani Hasanah Indah Rizky Mahartika Indra Indra Inge Virdyna Irfan Hadi Irfan Nurdiansyah Istiqoomatun Nisaa Jasmine, Meuthia Joko Sutrisno Jovansgha Avegad Jumaryadi, Yuwan Kanasfi, Kanasfi Karyaningsih, Dentik Kresno Yulianto KRESNO YULIANTO KUNTORO Kuntoro Kuntoro Kurnia Setiawan Kutanto, Haronas Larasati, Pamela Linda Lingga Desyanita Luthfi Akbar Ramadhan Mahmudah Mahmudah Mailana, Agus Manurung, Ridho Parmonangan 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 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 Rohman Nurcahya, Gelar Nurfadhiilah, Annisa Nurfidaus, Yasmine Nursyi, Muhamad Pattipeilohy, William Frado Pattipeilohy, William Frado Pebriaini, Prisma Andita Popalia, Qamarullah Poppy Ruliana Pradiptha, Anindya Putri Prastiyo, Krisna Probo Anggraini, Julaiha Purwadi Purwadi Putra, Andi Agung Putra, Rinaldi Febryatna Duriat Rachmah Indawati Rahman, Fathin Aulia Rahmawati, Nur Anisah 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 Ririh Yudhastuti Risaychi, Diva Ajeng Brillian Ristiana, Ina Riza, Yeni Rizkiyanto, Muhamad Ardiansyah 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 Sari, Wulan Novita Sasongko, Raden Satiri Satiri, Satiri Selamet Riyadi Selly Rahmawati Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Setya Haksama Setyowati, Erlin 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 Syahirah, Afifah Tarmudzi, Rizky Tarwan Tiaharyadini, Rizka Triantoro, Ery TRISNAWATI, WULAN Tulus Yuniasih Umam, Mohamad Hafidhul Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyudi, Widi Wahyuni, Chatarina Unggul Wangsajaya, Yosia Heartha Dhalasta Wasis Budiarto Wibiyanto, Alif Dewan Daru Widiyaningrum, Diyah Kiki Widyanto, Tetrian Windhu Purnomo Yahya Darmawan Yudanto, Satyo Zakaria Anshori Zaqi Kurniawan