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Enhancing Face Authentication for Online Examination Systems Using Median Filtering and MobileNetV2 Dadang Sudrajat; Dian Ade Kurnia; Kurniawan, Rudi; Othman bin Mohd; Maulana Sujarwadi; Salman Alfarizi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7185

Abstract

Digital transformation in higher education is driving the uptake of online tests, which require academic integrity, security, and robust user experience. In the context of authentication of users, deep learning based face recognition, in particular the Convolutional Neural Network (CNN) architectures, such as MobileNetV2, combined with intermediate filter, promises to deliver a consistent performance across a wide range of devices and imaging environments. However, there are limited comprehensive studies evaluating the final integration of the median filter and MobileNetV2 in high-value test scenarios. This study contributes by proposing an effective end-to-end Face Authentication Pipeline, assessing the median impact of filtering on MobileNetV2 performance, and validating it with a prototype application. The authentic face dataset was collected using the Teachable Machine, preprocessed with cropping, resizing, and median filtering, and then augmented through rotation, shift, shear, zoom, reversal, and brightness adjustment. The MobileNetV2 model was trained with Adam in a stepwise manner, starting with 0.001 and then 0.0001 for 20 epochs in a batch size of 32, and was evaluated for accuracy, precision, recall, and F1 score. Results show that the accuracy curve has remained stable at almost 95 percent during the 20th epoch; most grades achieved 1.00 in both precis, recall and F1, with some classings showing a limited decrease due to facial similarity or expression differences. These findings confirm that MobileNetV2 median filtering can be the basis for an effective, accurate and ready to integrate face recognition in online testing applications on a wide range of devices.
A MODEL HIBRID RESNET-SVM UNTUK KLASIFIKASI PENYAKIT TANAMAN JAGUNG BERBASIS CITRA DAUN Tri Susilo, Andri Anto; Basri, Hasan; Kurniawan, Rudi
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2744

Abstract

Abstrak Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) memberikan dampak signifikan dalam bidang pertanian, khususnya pada deteksi dan klasifikasi penyakit tanaman. Penelitian ini mengusulkan model hibrid yang mengintegrasikan Residual Network (ResNet) sebagai ekstraktor fitur dengan Support Vector Machine (SVM) sebagai classifier utama untuk mengklasifikasikan penyakit pada tanaman jagung berbasis citra daun. Dataset yang digunakan mencakup empat kelas, yaitu Blight, Common Rust, Gray Leaf Spot, serta daun jagung Healthy atau sehat. Hasil pengujian menunjukkan bahwa model hibrid ResNet-SVM mampu mencapai akurasi akhir sebesar 94,61%. Berdasarkan laporan klasifikasi, performa terbaik ditunjukkan pada kelas Healthy dengan nilai precision, recall, dan f1-score mencapai 1,00. Kelas Common Rust juga memperoleh hasil tinggi dengan f1-score 0,96, sedangkan kelas Blight mencapai f1-score 0,92. Namun, kelas Gray Leaf Spot masih menjadi tantangan dengan f1-score 0,62 akibat jumlah data yang relatif lebih sedikit. Secara keseluruhan, nilai macro average f1-score tercatat sebesar 0,88, sementara weighted average f1-score mencapai 0,94. Temuan ini menunjukkan bahwa kombinasi ResNet dan SVM efektif dalam meningkatkan akurasi klasifikasi penyakit jagung, sekaligus memperkuat potensi penerapan metode hibrid deep learning dan machine learning dalam sistem deteksi penyakit tanaman berbasis citra digital. Kata kunci: Resnet, SVM, Model Hibrid, Klasifikasi, Penyakit Jagung Abstract The advancement of Artificial Intelligence (AI) has significantly impacted agriculture, particularly in plant disease detection and classification. This study proposes a hybrid model that integrates Residual Network (ResNet) as a feature extractor with Support Vector Machine (SVM) as the main classifier for classifying corn leaf diseases based on image data. The dataset consists of four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy leaves. Experimental results show that the hybrid ResNet-SVM model achieved a final accuracy of 94.61%. The best performance was obtained in the Healthy class with precision, recall, and f1-score of 1.00. The Common Rust class also achieved a high f1-score of 0.96, while the Blight class reached 0.92. However, the Gray Leaf Spot class remained more challenging, with an f1-score of 0.62 due to the relatively smaller number of samples. Overall, the model achieved a macro average f1-score of 0.88 and a weighted average f1-score of 0.94. These findings demonstrate that the combination of ResNet and SVM is effective in enhancing classification accuracy compared to single methods, highlighting its potential application in developing automated corn disease detection systems based on digital leaf images. Keywords: ResNet, SVM, hybrid model, classification, corn disease
A MODEL HIBRID RESNET-SVM UNTUK KLASIFIKASI PENYAKIT TANAMAN JAGUNG BERBASIS CITRA DAUN Tri Susilo, Andri Anto; Basri, Hasan; Kurniawan, Rudi
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2744

Abstract

Abstrak Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) memberikan dampak signifikan dalam bidang pertanian, khususnya pada deteksi dan klasifikasi penyakit tanaman. Penelitian ini mengusulkan model hibrid yang mengintegrasikan Residual Network (ResNet) sebagai ekstraktor fitur dengan Support Vector Machine (SVM) sebagai classifier utama untuk mengklasifikasikan penyakit pada tanaman jagung berbasis citra daun. Dataset yang digunakan mencakup empat kelas, yaitu Blight, Common Rust, Gray Leaf Spot, serta daun jagung Healthy atau sehat. Hasil pengujian menunjukkan bahwa model hibrid ResNet-SVM mampu mencapai akurasi akhir sebesar 94,61%. Berdasarkan laporan klasifikasi, performa terbaik ditunjukkan pada kelas Healthy dengan nilai precision, recall, dan f1-score mencapai 1,00. Kelas Common Rust juga memperoleh hasil tinggi dengan f1-score 0,96, sedangkan kelas Blight mencapai f1-score 0,92. Namun, kelas Gray Leaf Spot masih menjadi tantangan dengan f1-score 0,62 akibat jumlah data yang relatif lebih sedikit. Secara keseluruhan, nilai macro average f1-score tercatat sebesar 0,88, sementara weighted average f1-score mencapai 0,94. Temuan ini menunjukkan bahwa kombinasi ResNet dan SVM efektif dalam meningkatkan akurasi klasifikasi penyakit jagung, sekaligus memperkuat potensi penerapan metode hibrid deep learning dan machine learning dalam sistem deteksi penyakit tanaman berbasis citra digital. Kata kunci: Resnet, SVM, Model Hibrid, Klasifikasi, Penyakit Jagung Abstract The advancement of Artificial Intelligence (AI) has significantly impacted agriculture, particularly in plant disease detection and classification. This study proposes a hybrid model that integrates Residual Network (ResNet) as a feature extractor with Support Vector Machine (SVM) as the main classifier for classifying corn leaf diseases based on image data. The dataset consists of four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy leaves. Experimental results show that the hybrid ResNet-SVM model achieved a final accuracy of 94.61%. The best performance was obtained in the Healthy class with precision, recall, and f1-score of 1.00. The Common Rust class also achieved a high f1-score of 0.96, while the Blight class reached 0.92. However, the Gray Leaf Spot class remained more challenging, with an f1-score of 0.62 due to the relatively smaller number of samples. Overall, the model achieved a macro average f1-score of 0.88 and a weighted average f1-score of 0.94. These findings demonstrate that the combination of ResNet and SVM is effective in enhancing classification accuracy compared to single methods, highlighting its potential application in developing automated corn disease detection systems based on digital leaf images. Keywords: ResNet, SVM, hybrid model, classification, corn disease
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FLO DI GOOGLE PLAY STORE DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES Kurniawati, Eti; Irma Purnamasari, Ade; Ali, Irfan; Kurniawan, Rudi; Nurdiawan, Odi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8776

Abstract

Abstrak. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Flo pada Google Play Store menggunakan algoritma Multinomial Naive Bayes. Flo merupakan aplikasi mobile health (mHealth) populer yang digunakan untuk memantau siklus menstruasi dan kesehatan reproduksi. Data dikumpulkan melalui web scraping dan menghasilkan 10.000 ulasan yang setelah pembersihan menjadi 6.908 data valid. Proses pra-pemrosesan meliputi case folding, cleaning, normalisasi, tokenisasi, stopword removal, dan stemming menggunakan Sastrawi. Pelabelan sentimen dilakukan secara semi-otomatis berbasis lexicon InSet dan rating. Ekstraksi fitur menggunakan CountVectorizer menghasilkan representasi Bag-of-Words sebagai input model. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes mencapai akurasi sebesar 73,6% dengan nilai precision, recall, dan F1-score yang seimbang pada tiga kelas sentimen. Temuan ini menunjukkan bahwa Naive Bayes efektif digunakan dalam mengolah ulasan teks pendek dan informal berbahasa Indonesia. Penelitian ini berkontribusi dalam pemanfaatan machine learning untuk analisis sentimen aplikasi mHealth serta menyediakan wawasan yang dapat digunakan pengembang untuk meningkatkan kualitas layanan aplikasi Flo. Abstract. This study aims to analyze user reviews of the Flo application on Google Play Store using the Multinomial Naive Bayes algorithm. Flo is a popular mobile health (mHealth) application for tracking menstrual cycles and reproductive health. Data were collected using web scraping, obtaining 10,000 initial reviews, with 6,908 valid reviews after cleaning. Preprocessing included case folding, cleaning, normalization, tokenization, stopword removal, and stemming using Sastrawi. Sentiment labeling was performed semi-automatically using the InSet lexicon and rating-based rules. Feature extraction used CountVectorizer with the Bag-of-Words approach. The evaluation shows that Naive Bayes achieved an accuracy of 73.6% with balanced precision, recall, and F1-score across sentiment classes. These results indicate that Naive Bayes is effective for processing short and informal Indonesian text reviews. This research contributes to the application of machine learning in mHealth sentiment analysis and provides insights for developers to improve the quality of the Flo application.
Pengaruh Harga Dan Lokasi Terhadap Keputusan Pembelian Pada Fore Coffe Cirebon Muangsal; Kurniawan, Rudi
Jurnal Ekonomi Bisnis dan Manajemen Vol. 3 No. 2 (2025): EKOBIMA: Jurnal Ekonomi Bisnis dan Manajemen - Desember 2025
Publisher : POLITEKNIK LP3I

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/ekobima.v3i2.2721

Abstract

The purpose of this research is to analyze how price and location affect consumer purchasing decisions at Fore Coffee Cirebon by employing a quantitative methodological approach. A total of 100 respondents were selected through a non-probability sampling technique. The variables were assessed through a Likert-scale instrument, and the collected data were subsequently analyzed with SPSS version 22. The analysis process involved conducting validity tests, reliability assessments, multiple linear regression evaluations, and partial t-test examinations, simultaneous F-tests, and the coefficient of determination (R²). The results indicate that both price and location significantly affect consumer purchasing decisions, with price emerging as the most significant contributing factor. Overall, the research model accounts for 48.5% of the variation in purchasing decisions, while the remaining 51.5% is explained by other variables not examined in this study
Development of an Augmented Reality-Based History Learning Model to Improve Historical Literacy Among Secondary School Students Kurniawan, Rudi; Haminangan, Ryan; Avin Maulana, M.; Gunawan Romadon, Haris
IJORER : International Journal of Recent Educational Research Vol. 7 No. 1 (2026): January
Publisher : Faculty of Teacher Training and Education Muhammadiyah University of Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46245/ijorer.v7i1.1148

Abstract

Objective: This study aimed to develop a pedagogically validated Augmented Reality (AR)-based history learning model to enhance secondary school students' historical literacy, motivation, and engagement. This study addresses the pressing issue of low historical literacy, often exacerbated by conventional teaching methods perceived as monotonous and irrelevant. Method: This study employed a tailored Research and Development (R&D) approach adapted from the Borg and Gall model, encompassing three main phases: needs identification, development, and evaluation. Comprehensive data were collected from 30 students, teachers, and experts through surveys, interviews, observations, and historical literacy tests (pre- and post-tests). Results: The developed AR prototype successfully integrated 3D visualizations, interactive narratives, and gamified quizzes to create an immersive learning experience. The model's implementation yielded substantial improvement: the students’ average historical literacy score increased significantly by 22.4% (from 55.0% to 77.4%); furthermore, the model demonstrated high user acceptance, with a student satisfaction rate of 84%. Expert validation further affirmed the model’s pedagogical soundness, content accuracy, and curriculum alignment. Novelty: This study introduces an integrated AR framework explicitly designed for Indonesian history education, effectively transforming traditional instruction into an interactive and contextual experience. This approach supports Sustainable Development Goal 4 (SDG 4) objectives by promoting innovative and equitable access to quality education.
EfficientNet-Based Flower Recognition with LAB and CLAHE Enhancement Kurniawan, Rudi; Intan, Bunga
Jurnal Software Engineering and Computational Intelligence Vol 3 No 02 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i02.6197

Abstract

Accurate flower recognition is a challenging task in computer vision due to high intra-class variation, complex background textures, and illumination inconsistencies. This study proposes an enhanced image classification framework integrating LAB color space transformation and Contrast Limited Adaptive Histogram Equalization (CLAHE) with the EfficientNet architecture. The proposed approach aims to improve visual feature separability by enhancing color stability and local contrast prior to network training. Experiments were conducted using a 17-class flower dataset, and the model achieved an overall accuracy of 98.53%, a macro-averaged F1-score of 0.9704, and AUC values close to 1.00 for most species. Visual analysis through the confusion matrix and ROC curves confirmed the model’s robustness, with only minor misclassifications observed between morphologically similar classes such as Iris–Crocus and Daffodil–Tulip. These findings demonstrate that combining LAB and CLAHE preprocessing with EfficientNet significantly enhances model generalization and visual discriminability. The method provides a lightweight yet effective solution for applications in biodiversity monitoring, precision agriculture, and automated plant taxonomy.
Analisis Pengaruh Faktor Fundamental dan Makroekonomi terhadap Harga Saham Subsektor Makanan dan Minuman yang terdaftar di Bursa Efek Indonesia periode tahun 2020-2024 dengan Inflasi sebagai Variabel Moderasi Laila Safitri, Dina Destia; Jati Kusuma, Pradana; Whini Setyahuni, Suhita; Kurniawan, Rudi
El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Vol. 7 No. 1 (2026): El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmal.v7i1.10989

Abstract

This study examines the effect of fundamental factors (ROA, DER, CR, PBV, EPS) and macroeconomic factors (exchange rates, interest rates) on stock prices of food and beverage companies listed on the Indonesia Stock Exchange during 2020–2024, with inflation as a moderating variable. Using a quantitative approach and secondary data from financial statements and macroeconomic sources, the sample comprises 20 companies (100 observations). Results show that EPS and PBV have a positive and significant effect on stock prices, while ROA, DER, CR, exchange rates, and interest rates are not significant. Inflation moderates the relationship between ROA and PBV with stock prices, but not for other variables. The findings indicate that profitability and market valuation are the main factors considered by investors, while inflation plays a selective role.
Co-Authors A.A. Gede Agung Abdul Ghani, Raihan Abdul Latif Abdul Razak Munir Abdussamad . Adawiyah , Rabiatul Adinda Maharani, Azahra Aditia Aditia Putra Pranjaya Aditya Nugroho Adriani, Weni Yunisa Afifah, Izza Nur Agni, Vega Putra Dwi Agung Pranata AGUNG SEDAYU Agustin, Maulina Aisy, Rahadatul Aktavera, Beni Alba, Amru Alesia Hafidza Andini Alfirda Sofyan, Zahra Ali Ridho Barakbah Ali, Luthfi Gosan Alif Prayudha, Bimo Amaliah, Novi An-naziz Safaat, Wafik Ananda, Seprian Haris Andini, Selvi Andri Anto Tri Susilo, Andri Anto Andriani, Meri Andriyani, Wini Anggraeni, Anggi Annisa Desty Puspatriani Ansori, Ilman Anwari, Saeful APRIANDY, KEVIN ILHAM Aprianto, Wili Aprisusanti, Rani Purnama Aribah, Firyal Arief Rachman Arip Budiman, Arip Armanda, Dicky Armanto, Armanto Arna Fariza Aruni , Fidhia Aruni, Fidhia Arya Rudi Nasution Aryanto, Vincent Didiek Wiet asep gunawan Asep Gunawan Asmarani, Sri Utami Atun Farihatun Avin Maulana, M. Awaludin, Ade Ayu Endang Purwati Ayulinda, Arianisah P. Baehaqi Bahri, Saiful Belli Nasution Berizky, Kekieta Gustie Bima Sena Bayu Dewantara Bimastari Aviani, Tri Hasanah Chasanah, Amalia Nur Chulyatunni’mah D.A, Dyah Kuntorini Dadan Suhendar, Asep Dadang Sudrajat Dadet Pramadihanto Danar Dana, Raditya Darma Irawan, Bobi Darussalam, Luthvi Nurfauzi Daulay, Nelly Khairaini Dayanti, Resda Dermawan, Hibrizi Dzaky Dewiyana, Dewiyana Dheri Febiyani Lestari Dian Ade Kurnia Diana Puspitasari Diana Sari, Deuis Dinda, Dinda Ducha, Moh Syamsi Dwi Budi Santoso Elmayati, Elmayati Enik Sulistyowati Erdi Julham Ernawati Ernawati Esti Andarini ETI KURNIAWATI Fahmi Wardhani, Masitha Fajar Ramadhan, Fajar Falih, Alfi Rizqi Falih Fanny Rifqi El Fuad, Fanny Rifqi Fauzan . Febrianur Ibnu Fitroh Sukono Putra Ferrina Ermalina Rumbik, Ferrina Ermalina Rumbik Fery Riyanto Fikri, Achmad Firmansyah, Andan Firmansyah, Tegar Fitriani Fitriani Fitriati, Cut Annisa Fitriyani, Ida Fonna, Syarizal Frans Sudirjo Gapatra, Reja Gifthera Dwilestari Gilang Ramadhan Ginting, Helmina Br. Gunawan Romadon, Haris Haekal Susanto, Ahmad Hamida, Silfiana Nur Haminangan, Ryan Hamonangan, Ryan Hanifatul Lutfiah, Hanifatul Lutfiah Harapan Harapan Hasan Basri Hayati, Umi Heni Marliany Henri Setiawan Herdiana, Ruli Herwantono, Herwantono Hidayat, Asep Toyib Hidayat, Muhamad Taufiq Hidayat, Peri Hidayat, Zaids Syarif Huda, Achmad Thorikul I Made Tegeh Ichsan Ichsan Ida Farida Idris Winarno Ikhwan Fahruddin, Yusuf Ikramullah, Ikramullah Ima Sukmawati, Ima Inawati, Windi Indriastuti, Marlina Intan, Bunga Irfan Ali, Irfan Irma Purnamasari, Ade Islamiatik, Fena Ayu Ismail Ismail iwan Syarif j.ezugwu, umezurike Jannah, Zahratul Jati Kusuma, Pradana Jayawarsa, A.A. Ketut Jonathan, Fandy Junaidi Junaidi Kamil, Qatrunnada Kaslani Khairani Daulay, Nelly Komala, Wulan Kristianti, Veronica Ernita Kurniadi, Yudi Kurniawan, Efik Kusman Ibrahim Kusmiyaty, Agesty Kusuma, Fujianti Kusumawaty, Jajuk Laila Safitri, Dina Destia Lilian Lilian Lily Herlinah Linda Oktavianingsih Lingga Wijaya, Harma Oktafia Lorentiana Wijayanti, Rima M Rusdi M. Azka Kesuma Wardana Ma’sum, Hadiansyah Maelani, Imelda Maimun Syukri, Maimun Makhfud Syawaludin Manzis, Zian Masru’ah, Iim Imas Maulana Sujarwadi Misnatul Sa’ada2, Misnatul Sa’ada MJW, Endrian Mohamad, Fatma Susilawati Monica, Intan Muangsal Mubarok, Khoirul Mudatsir Mudatsir Muhaimin, Ahmad Muhamad Chairul Basrun Umanailo Muhammad Muhammad Muhsin Muhsin Muhyi, Adbdul Mujib, Miftachul Muktar, Muktar Mulyawan, Mulyawan Mustikasari, Julia Arum Mustofa Bisri, Mu'ammar Muzaki, Fazri Nanda Andreas Octavini Narasati, Riri Narasati Naufan, Muhammad Hilmy Ningtias, Restika Puspa Nining Rahaningsih Nodi Marefanda NOVIANTI, ELIS Noviati, Elis Novilia, Fitri Nur Amalia, Yustika Nur Hamidah, Nur Nur Hidayat Nurapandi, Adi Nurazijah, Wulan Nurbaiti, Seni Nurholipah, Titin Nurmalawati, Nurmalawati Nurmasyahyati Nurmasyahyati, Nurmasyahyati Nurul Huda Nurul Kamaly Odi Nurdiawan Oktafia Lingga Wijaya, Harma Oktavia, Vicky Othman bin Mohd Pamungkas, Angling Sadewo Aji Perdana, Tito Aditya Pratama, Annisa Salsabilah Pratama, Vishal Indu Prawitasari, Dian Prikurnia, Anas Khair Purusa, Nanda Adhi Purwo Suwignyo Puspitasari, Yeni Putri ayu, Putri Ayu Dara Sekarwangi Putri, Widia Rachmawati, Oktavia Citra Resmi Raden Mohamad Herdian Bhakti Rafila, Amal Julio Rafles Ginting Rahayu, Anisa Dinda Rahmad Nuthihar Rahmadiawan, Dieter Rahman Sahputra, Rizky Rahman, Sakilah Rahmawati, Yannisa Rai Fatkaozi, Ahmad Ramadhan, Teddy Muhammad Rano Raudotul Janah, Fina Regina Regina Ria Kurniawati Rifaai Alldi Ananda, Muhamad Rifatul Khasanah, Rifatul Khasanah Riyana, Iis Rizki Ani, Fitri Rizki Fauzi, Ahmad Rizki Lesmana, Ghali Rizki, Fido Rizqi, A Faisal Maulana Rocky Putra A Rohman, Idan Saepul Rusdiyanto Sabilla, Raissa Sadiyyah, Putri Aisyatus Saeful Anwar, Saeful Safitri, Maria Safrida Safrida Safrida Safrida, Nila Salma Munada, Mutiara Salman Alfarizi Salsabila, Denaya Bethary Samsul Anwar Samsuryadi Samsuryadi Sandi, Yudisa Diaz Lutfi Sandova, Firdaus Sanjaya, Handika Sari Rusmita Sari, Wisdalia Maya Sasabone, Luana Savero, Raihan Vito Savira Uswatun Khasanah, Savira Uswatun Khasanah Septian Nugraha, Titan Sesulihatien, Wahjoe Tjatur Setiani, Tia Setiawardhana, Setiawardhana Shalihah, Ghina Sholeha, Mia Sintaningrum, Picantia Bunga Siti Nurhamidah, Lena Situmeang, Alona sobri, ahmad Sofyan, Sarwo E. Sofyan, Verra Rosyalia Widia Sormin, Rahma Diani Srinayanti, Yanti Sugiyono . Suhanda Suhanda Suhita Whini Setyahuni Sumiarsih, Mia Sunardi, Lukman Supratati, Tati Suprihartini, Yayuk Suryawijaya, Tito Wira Eka Susana, Heliyanti Susanti, Puspasari Susilawati Mohamad, Fatma Syach Putra, Yanuar Syafruddin, Muhammad Andri Syahrial Sidik Syamsul Anwar, Syamsul Syifaul Huzni Taryana Taryana, Taryana Tati Suprapti Taufik Hidayat Tedjo Darmanto Tedjo Darmanto Tessy Badriyah, Tessy Tri Harsono Triswanto, Triswanto Usmiyatun Usmiyatun Vitriyan Espa Wabula, Abdul Latif Wahyudi Wahyudi Wahyunisari, Nina Waluyo, Endrian Mulyadi Justitia Waqar Mazhar, Muhammad Wardana, Affan Hanafi Whini Setyahuni, Suhita Wibowo, Mochammad Eric Suryakencana Widianto, Fuad Hassan Widiawati, Fitri Wijaya, Yudhistira Wijaya, Yudhitira Arie Winayah, Winayah Yudhistira Arie Wijaya Yudi Setia Rachmanda yulani, Yulani - Yulia, Yuli Yunitri, Ninik Yusnawati, Yusnawati Yusuf Sidiq, Yusuf Sidiq Zahara, Ana Zahrul Fuadi Zainullah Zakaria, Fakhmi Zulius, Antoni