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Analisis Sentimen Terhadap Isu Pemblokiran Thrifting Pada Platform TikTok Menggunakan Bidirectional Long Short-Term Memory Windi Astuti; Bambang Irawan; Nur Ariesanto Ramdhan
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3361

Abstract

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.
Analisis Kinerja Metode Long Short-Term Memory (LSTM) dalam Klasifikasi Sentimen Ulasan Pengguna Shopee Muhimatul Ifadah; Bambang Irawan
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3407

Abstract

User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.
Analisis Analisis Sentimen Ulasan eFootball pada Google Play Store Menggunakan Multinomial Naive Bayes dan Support Vector Machine Sasongko, Mohammad Umar Sasongko; Irawan, Bambang
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4282

Abstract

In the digital era, the use of mobile applications is increasing, so it is important to understand user satisfaction and dissatisfaction with the applications used. One of the popularmobile games in Indonesia is eFootball 2026, which has a lot of reviews from players on the Google Play Store. The large number of reviews allows sentiment analysis to be carried out to find out user opinions on the quality of the application. This study aims to analyze the sentiment of eFootball application user reviews using the Multinomial Naive Bayes method and Support Vector Machine. Review data is processed through the stages of text preprocessing, feature extraction using TF-IDF, and class imbalance handling with SMOTE. Model evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the Multinomial Naive Bayes method produced an accuracy of 76.72%, while the Support Vector Machine obtained an accuracy 74.92%, with relatively balanced precision, recall, and F1-score values. Based on these results, it can be concluded that the Multinomial Naive Bayes method has a better performance in analyzing the sentiment of eFootball app reviews on the Google Play Store and can be used as a basis for evaluation for future app development.
Perbandingan Analisis Sentimen Untuk Prediksi Kepuasan Ulasan Produk Kopi Pada Media Sosial Menggunakan Algoritma Svm Dan Naïve Bayes Pramuja, Trisena; Irawan, Bambang
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4284

Abstract

The development of social media has led to a significant increase in the number of consumer reviews of various types of products, including coffee products. To help manufacturers understand consumer satisfaction levels more efficiently, sentiment analysis is a relevant method because it is able to identify opinions automatically. This study compares the performance of two widely used algorithms, namely Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB), in predicting sentiment on consumer reviews related to coffee products on social media. The dataset was analyzed through the stages of text cleanup, TF-IDF transformation, and label encoding process. Both models are developed using a uniform pipeline with consistent parameters to ensure an objective performance comparison. The results show that SVM algorithms with linear kernels produce the highest accuracy compared to Naive Bayes. In addition, a confusion matrix is applied to evaluate the accuracy of predictions in each sentiment category. These findings confirm that SVM is more effective in short-text-based sentiment analysis tasks, such as product reviews on social media platforms.
Pemodelan Analisis Sentimen Ulasan Pengguna Aplikasi Info Bmkg Menggunakan Pendekatan Multinomial Naïve Bayes Syaogi, Moh.; Ramdhan, Nur Ariesanto; Bachri, Otong Saeful; Irawan, Bambang
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4285

Abstract

Info BMKG is one of several digital platforms that have been pushed by the fast evolution of IT to replace traditional methods of providing public services. Reviews on the Play Store can be used to determine user perceptions and levels of satisfaction with the application. Manual analysis is laborious and inefficient due to the high number of evaluations. Consequently, the purpose of this research is to use the Naive Bayes algorithm to categorize evaluations of the Info BMKG app as either positive or negative in order to do sentiment analysis. Using a web scraping approach, a total of 5,000 user evaluations were obtained for the study data. Next, the data underwent text preprocessing, word weighting using the TF-IDF technique, and sentiment classification with the Multinomial Naive Bayes algorithm. There was an 80:20 split between the dataset's training and testing sets. The experimental findings show that the Naive Bayes algorithm achieves an accuracy of 87.83% on the testing data when it comes to classifying user review emotions.
Prediksi Kanker Payudara Berbasis Machine Learning Dengan Analisis Probabilitas Klasisfikasi ardyansyah, luthfi; Irawan, Bambang
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4294

Abstract

Breast cancer is one of the diseases with a high mortality rate in women, so early detection is crucial to increase the chances of recovery. Unfortunately, conventional methods of diagnosis still rely on the interpretation of medical personnel and laboratory procedures which are time-consuming and costly. This study tries to present a machine learning-based approach to predict breast cancer, while adding a classification probability analysis to make the prediction more informative. The breast cancer dataset was used to train four models, namely Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbor. Evaluation was carried out using accuracy, confusion matrix, ROC curve, and AUC. The results showed that all four models were able to classify cancers with fairly high performance, while one model stood out with the highest accuracy and AUC values. Classification probability analysis provides additional perspective on the confidence level of predictions, which can help medical personnel make more objective clinical decisions.
Analisis Pola Konsumsi Energi Listrik Pelanggan Rumah Tangga Menggunakan Alogaritma K-Means Clustering Hilmi Mubarok; Irawan, Bambang
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4296

Abstract

The increase in household electricity consumption is one of the main challenges in national energy management. Diverse electricity usage patterns are influenced by social, economic, and behavioral characteristics of consumers. This study aims to analyze and cluster household electricity consumption patterns using the K-Means Clustering algorithm. The dataset consists of secondary data from 1,200 household customers with attributes including installed power capacity, monthly electricity consumption (kWh), peak usage time, and average daily load. The research stages include data cleaning, normalization using StandardScaler, determination of the optimal number of clusters using the Elbow Method, clustering with K-Means, and evaluation using the Davies-Bouldin Index (DBI). The results indicate that the optimal number of clusters is three, representing low, medium, and high electricity consumption groups. A DBI value of 0.71 indicates good clustering quality. These findings can support electricity providers in designing energy efficiency policies and household load management strategies.
KLASIFIKASI PENYAKIT KULIT WAJAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK EFFICIENTNET-B3: Riko Angga Bayu Kusuma; Bambang Irawan; Abdul Khamid
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.v14i1.8721

Abstract

Facial skin diseases are a common health issue that significantly affect an individual's quality of life. Early detection through image processing is a crucial step for timely treatment. This study applies Convolutional Neural Network with EfficientNet-B3 architecture to classify five types of facial skin diseases, namely acne, actinic keratosis, basal cell carcinoma, eczema, and rosacea. The model was developed through fine-tuning on an augmented image dataset, with training and testing data splits. Evaluation results show a testing accuracy of 96.61 percent, accompanied by average precision, recall, and F1-score values of 0.97. The confusion matrix indicates high classification performance with minimal errors between classes. This approach proves effective in improving detection accuracy, thus potentially supporting medical personnel in early diagnosis.
ANALISIS SENTIMEN ULASAN PENGGUNA TERHADAP GAME ZENLESS ZONE ZERO MENGGUNAKAN METODE BI-DIRECTIONAL LSTM Zahrotun Ni'mah; Bambang Irawan; Nur Ariesanto Ramdhan
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.v14i1.8722

Abstract

Perkembangan industri game mobile mengakibatkan meningkatnya jumlah ulasan pengguna di Google Play Store, yang mencerminkan persepsi dan pengalaman pengguna terhadap suatu game . Namun, keberagaman karakteristik bahasa dalam jumlah ulasan yang besar menjadikan proses analisis secara manual kurang efisien. Penelitian ini menggunakan metode analisis sentimen berbasis deep learning untuk menganalisis sentimen pengguna terhadap game Zenless Zone Zero. Data yang digunakan terdiri dari 6.000 ulasan berbahasa Indonesia yang dikumpulkan dari Google Play Store dengan memanfaatkan teknik web scraping. Tahapan penelitian meliputi prapemrosesan teks, pelabelan awal dengan menggunakan metode berbasis leksikon dengan InSet Lexicon, serta klasifikasi sentimen menggunakan model BiDirectional Long Short-Term Memory (Bi-LSTM). Klasifikasi yang diterapkan bagian ke dalam dua kategori, yaitu sentimen positif dan negatif. Dengan akurasi sebesar 91,41% dan nilai presisi, recall, dan F1-score antara 0,86 dan 0,92, hasil pelatihan model menunjukkan bahwa Bi-LSTM mampu bekerja secara efektif. Hasil tersebut menunjukkan bahwa kombinasi metode berbasis leksikon dan Bi-LSTM efektif digunakan dalam menganalisis sentimen ulasan aplikasi game berbahasa Indonesia, sekaligus mampu merepresentasikan persepsi pengguna terhadap game Zenless Zone Zero.
PENERAPAN ALGORITMA BI-LSTM DENGAN OPTIMASI THRESHOLD ADJUSTMENT UNTUK ANALISIS SENTIMEN ULASAN APLIKASI MOBILE JKN Malik, Adam; Irawan, Bambang
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.v14i1.8879

Abstract

Penelitian ini bertujuan untuk menerapkan algoritma Bidirectional Long Short-Term Memory (Bi-LSTM) dengan optimasi threshold adjustment dalam analisis sentimen ulasan pengguna aplikasi Mobile JKN pada platform Google Play Store. Data ulasan yang digunakan berasal dari file mobilejkn.csv dengan ribuan record, diproses melalui tahapan pre-processing yang mencakup pembersihan teks, tokenisasi, penghapusan stopword, dan stemming. Model memanfaatkan lapisan embedding, bidirectional LSTM, dropout, serta dense layer dengan aktivasi softmax. Evaluasi model Bi-LSTM mencapai akurasi 88,5% pada data validasi (setelah pelatihan 10 epoch dengan optimizer Adam), dengan peningkatan performa menjadi 90,2% setelah penerapan threshold adjustment (penyesuaian batas probabilitas maksimum <0,58 untuk klasifikasi netral). Nilai presisi rata-rata 89,1%, recall 88,7%, dan F1-score 88,9%. Hasil analisis menunjukkan dominasi sentimen negatif (sekitar 45-50%) terkait masalah teknis seperti kesulitan login, verifikasi OTP lambat, kegagalan booking antrian, serta proses registrasi yang rumit. Temuan ini sejalan dengan keluhan umum pada ulasan terbaru (rating rata-rata 4,3 dari 933 ribu ulasan). Penelitian ini merekomendasikan kepada BPJS Kesehatan untuk segera memperbaiki fitur autentikasi, stabilitas server, dan antarmuka pengguna agar meningkatkan kepuasan serta loyalitas peserta JKN.
Co-Authors Abdul Khamid Abdul Kolik Abdullah Karim Abdullah Karim Achmad Djumlani Achmad Fikri Achmad Nurmandi Adam Idris Adam Malik Adhani, Karenina Dwi Adigunawan, Adigunawan Agung Prayitno Agustin, Dania Safira Agustinus Lejiu Agustinus Suryantoro Ahmad Faqih Aisa Tri Agustini Ajeng Pratiwi Akbar, Paisal Albertus Maqnus Soesilo Alfiandri Alfiandri Alfiandri, Alfiandri Algopeng, Zozi Alifiansyah, Roby Fathan Amanda, Jesi Amar, Zafran Arifah Amelia Putri Dewi Nilam Sari Ananda, Alfin Andi Hafidz Khanz Andika Saputra Andin Ayu Oksilia Ramadhani Andri Prasetyo Angger Bagus Prasetiyo Anggit Murdani Ani Purwati Anthonius Margono Ardian Nugraha Putra Ardiano, Muhammad Rayyan Ardyansyah, Luthfi Arieyasmieta, Wildan Lutfi Arisandi, Bobi Ariz , Naufal Arniti, Ni Ketut Aryasatya, Muhammad Fathi Aryo Wibisono Asep Maulana, Asep Aspita Laila Astriani, Linda Axel Prasetyo Sudiro azharia, Jenny Clarisa azis, santowi Azizah, Enur Badarita, Badarita Ballianie, Novia Bambang Suharno Banun Binaningrum Barelang, Syaeful Bachri Bato, Bulan Erika Bhakti, M.Herdian Bhimo Rizky Samudro Budi Rahayu Budi Tjahjono Budiman Budiman Chairi, Minal Charles Raymond Jeffrey CHIANI, SARASWATI HAYLIAN Christina Nugroho Ekowati, Christina Nugroho Christover, Deandlles Cin, Muksin Cornelia, Geby Dan Buntu Paranoan Darmawan , Ade Daryono Daryono, Daryono Dayutama, Theodorus Dedy Miswar Dini Zulfiani Djumadi . Dudik Djaja Sidarta Dyah Mutiarin, Dyah Efriliyanti, Lia Eki Darmawan Eko Fuji Pangestu Eko Priyo Purnomo Elsa Junita, Ika Endang Nurcahyani Enos Paselle Erizal Eva Sri Handayani Pongtuluran Fadilah, Muhammad Rizky Faisal Nasar Bin Madi Fajar Apriani Farhanuddin Jamanie Farisi, Salman Fathurrahman Fathurrahman Fatullah, Iqbal Fauzi, Dzikri Ahmad Fauzia, Gina Febri Juita Anggraini Fernando Mersa Putra Fetia Harsa Fina Marshella Finnah Fourqoniah Firda Safira, Masnoni Firyal Nabila Ulya H.M Fransiska Xeviria Furqanul Hakim Gabriel Putra Gea Cita Meiratri Ghaffar, Dzaky Abdul Gintings, M. Fajar Mediyawan Gintings, Mohammad Fajar Mediyawan Giyarsi, Giyarsi Guspani, Reta Gusti Naufal Rizky Perdana Hamid Abdillah Hamzah, A. Hadian Pratama Hania Ayu Karin Hapidah, Hapidah Haqqi, Abdul Harfiani, Nabela H.N. Hariestya Viareco Haris Puspito Buwono Harmitalia, Mita Hartutiningsih . Hayati, Zahratul Hendra Wibowo Hului Hendra Wibowo Hului Hengki Satrisno Hernawan, Renaldy Herwanto, Agus Heryono Susilo Utomo Hidayat Hidayat Hidayat, Moh. Yusuf Hidayat, Muhammad Nizar Hilmi Mubarok Hutagalung, Winny Laura Christina Ibrahim, Adil Hasan Ibrahim, Adil Hassan Idris, Adam Ika Barokah Suryaningsih Ikhsanudin Ikhsanudin Ilham Yusuf Maulana Ilmi, Fikri Ikmalul Indah Purnamawati Indarto, Kus Iradhad Taqwa Sihidi Irmansah Isnaeni Yuliani Izma, Muhammad Athallah JAHIRUDIN, JAHIRUDIN Jani Master, Jani Jorgi Rivaldo Jubba, Hasse Juniarti, Eni Kadja , Deky Baleanus Kafa, Mushfi Abdulloh Kartika Kartika Kartini Kartini Kezia Arum Sary Khaerunnisa, Rindiyani Khair . Khairina, Etika Kris Witono Kundang Karsono Kurnia, Dian Ade Kus Indarto Kus, Indarto Kusdaryanto, Ardo Kustantina, Kustantina Latifatus Safariyah Leonardo, Nicholas Liky Faizal Lisdawati Lisdawati Liza Marina Loilatu, Mohammad Jafar Lustari, Reli Luthfi Sultan Jauhary Lito Made Kutanegara, Pande Mahfut Maimun Maimun Mappaelo Maradona Abdullah Mardeli Mardeli Martitah Martitah Masjaya . Maskud, Maskud Maulana, Muhammad Evan Maulida, Azkiyatul Mawaddah, Merisa Rahma Mawardi Mayasari, Windatania Meilinda, Nadia Mirawati Yanita Mochammad Iqbal Fadhlurrohman Moh. Hidayatullah Mohammad Taufik Mohammad Taufik Mohammad Taufik Mubarok, Muhammad Zaqi Muhamad Tamamul Iman Muhammad Bagus Sistriatmaja Muhammad Dhaffa Nugroho Muhammad Dinar, Yusadiningrat Muhammad Fahad Muhammad Fikri Setiawan Muhammad Guntur Muhammad Hidayat Muhammad Ilham Effendy Muhammad Imaduddin Muhammad Nizar Muhammad Noor Muhammad Raihan MUHAMMAD REZA FAHLEVY Muhammad Taufik Hidayat Muhammad Zaini Muhammad Zaki Muhimatul Ifadah Murni Murni Mutia Dewi Muzaki, Mochamad Najahatul Hananah Napitupulu, Richard RP Nasrudin, Ahmad Nida Handayani Nisa, Hoirun Nizirwan Anwar Nova Eliza Nova Sintia Dewi Sitorus Novalina Nurul Imama Noviandi Noviandi, Noviandi Noviantika, Stefanny Amalia Nugroho Susanto, Gregorius Nur Ariesanto Ramdhan Nur Fitriyah Nur Handayati Nur Hasan Nur Ilmiah Rivai Nurdin, Akhmad NURJANNAH, ANA Nurmawati, Subekti Nurrofiq, Ainan Zaky Nursaidah Nursaidah, Nursaidah Oktaviani, Ratna Ordas Dewanto Otong Saeful Bachri Pandoyo, Pandoyo Pidesia . Poppy Andriany Prabowo, Ary Pramuja, Trisena Pratama, Denni Pratama, Fristian Adi Pratama, Rizky Prawita Sari, Bening Premana, Agyztia Prihartono, Willy Prihartono Purnama Agustin, Kharisma Purwoko Purwoko Putra, Farrel Reyhan Putri, Cici Amelia Putri, Tasya Kamila Putri, Yoana Nabilah Qurani, Suci Ayu Qurrotaini, Lativa r. Patmiarsih Rabiatul Adawiyah Rachmatullah, Mochamad Miftah Raden Mohamad Herdian Bhakti Rahmat, Al Fauzi Rahmawati, Firda Devi Rahmi Dianita Ramadhan , Rafly Surya Ramadhan, Azki Ramadhan, Satrio Surya Ramdanis, Sapril Nurul Ramdhan, Nur Ariesanto Rande, Santi Rande, Santi Ratih Wirapuspita Wisnuwardani Reri Aprizal Reza Laksana Putra Riko Angga Bayu Kusuma Rini merliana, Setia Ririn Irmadariyani Rismayana . Rista Angraeni Rita Kalalinggi Riyan Ningsih Rizka, Yola Rizki Andre Handika Rizki Raja Putra Rizky Nugraha, Gilang Rizky Reynaldi Rochmah Agustrina Rodhiyah, Zuli Rohayu Rohayu Rohmah, Ainun Nimatu Rosid, Abdur Rumi, Nur Ananda Rusdiansyah Rusdiansyah Said Amaddin Saipul Saipul, Saipul Salahudin Salahudin Samsul Hadi Samuel ., Samuel Samuel Samuel Sandy Dwi Prasetyo Saprudin, Mohamad Sari, Mira Yulia Sari, Vita Komala Sarkani Sarkani Sarwoko, Jonathan Puji Sasongko, Mohammad Umar Sasongko Satrisno, Hengki Senobaan, Riska Tipa Septiria, Dalima Setiawan, Rafli Putra Shahib, Muhammad Umar Simamora, Nurcahaya Sintia, Delva Sinulingga, Samuel Mahesa Siti Kurniasih Siti Rodiyah Siti Triaminingsih Sofia Rahmasari Sonny Sudiar, Sonny Sri Wahyuningsih Steven Christian Subhaini Jakfar Sugeng Supriadi, Sugeng Suherlan Suhestiwi, Rini Sulistio, Alip Sulistiyono, Rovy SUMADI SUMADI Sumardi Sumardi . Sundari Meganingrum, Alpa Sundi, Venni Herli suratman Surpendi . Surya Nanda Situmorang Suryono Suryono Susilo Utomo, Heryono Syafitri, Ranny Aulia Syahrani . Syahrul Borman Syaifurrahim Azhari Syamsul Hadi Syaogi, Moh. Syifa Min Zaytun, Salwa tamlica, Agam Tampubolon, M. Ahsan Tan, Firwan Tasman, Alfadhli Thalita Rifda Khaerani Theresia, Maya Thriwaty Arsal Tiani, Lilis Tien Rohmatin, Tien Timothy Christian Tito Winnerson Sitanggang, Tito Winnerson Tjahjojo, Budi Tjokro Prasetyadi, Tjokro Totalia, Sherly Sustantien Tri Syukria Putra Tugiyono Tundjung Tripeni Handayani, Tundjung Tripeni Valentino Aris, Valentino Wibowo, Rahmat Catur Wijaya, Valentino Winda Khoritotul Jannah Windi Astuti Yofita Sandra Yogi Pasca Pratama, Yogi Pasca Yohani . Yosefa Sayekti Yulhendri Yulhendri Yulia Prihartini Yulianty Yulianty Zahidah, Athiya Zahriani, Nurul Zahrotun Ni'mah Zaky Mubarak, Ahmad Zhikry Fitrian Zulfikar, Iqbal Zulkifli, Zarina Zuly Qodir