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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.
KLASIFIKASI PENYAKIT TANAMAN PADI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR MOBILNETV2 Eko Fuji Pangestu; Bambang Irawan
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.v14i1.8900

Abstract

Diseases affecting rice plants are one of the major factors contributing to decreased agricultural productivity and potential losses for farmers. Conventional disease identification generally relies on expert knowledge and is often impractical to perform accurately and efficiently in the field. This study aims to develop an image-based classification system for rice leaf diseases using a Deep Learning approach with a Convolutional Neural Network architecture, specifically MobileNetV2. The dataset consists of five rice leaf condition classes, namely bacterial disease, brown spot, blast, tungro, and healthy leaves, obtained from the Roboflow platform. The research methodology includes data collection, image pre-processing, model training using a transfer learning approach, and performance evaluation. Experimental results demonstrate that the proposed MobileNetV2 model achieves an accuracy of 93.46% and shows strong performance across most disease categories. Although misclassification still occurs among classes with similar visual characteristics, the results indicate that the developed model has significant potential as an efficient and automated decision-support system for rice plant disease identification.
SISTEM REKOMENDASI METADATA LAGU BERDASARKAN DETEKSI EMOSI WAJAH MENGGUNAKAN VIT-B/16 Nurrofiq, Ainan Zaky; Bambang Irawan
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.v14i1.8950

Abstract

This study presents a music metadata recommendation system based on facial emotion detection using the Vision Transformer (ViT-B/16) model. The system classifies user emotions into seven categories using the KDEF facial dataset and matches them with music metadata (title, artist, genre, mood) labeled with corresponding emotional tags. The ViT-B/16 model was trained using transfer learning and evaluated with accuracy, precision, recall, and F1-score. The model achieved an accuracy of 89% and an average F1-score of 0.89. The recommendation system was assessed by 30 participants, with 87% indicating that the suggested song metadata matched the detected emotion. The system offers real-time emotion recognition and automatic mood-based song suggestions. However, classification accuracy for visually similar emotions such as “fear” and “angry” remains a challenge. Future development may include audio and lyric analysis, as well as user preference integration, to enhance recommendation relevance.
Co-Authors Abdul Khamid Abdul Kolik Abdul Mufid Abdullah Karim Abdullah Karim Achmad Djumlani Achmad Fikri Achmad Nurmandi Adam Idris Adam Malik Ade Adriadi Ade Nurdin Adelia, Nur Hidayah 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 Alfarisy, Fariz Kustiawan 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 Ankardiansyah Pandu Pradana Anthonius Margono Apriyadi, Muchsin Ardian Nugraha Putra Ardiano, Muhammad Rayyan Ardyansyah, Luthfi Arief Fahmi Lubis 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 Azizah, Maharani Azzahra, Fitriyani Badarita, Badarita Ballianie, Novia Bambang Suharno Bamer, Ircha Altri Banun Binaningrum Barelang, Syaeful Bachri Bato, Bulan Erika Bhakti, M.Herdian Bhimo Rizky Samudro Budi Rahayu Budi Tjahjono Budiman Budiman Cathas Teguh Prakoso 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 Desi Permata Sari Dewi, Dian Kemala Dini Zulfiani Djumadi . Dudik Djaja Sidarta Dyah Mutiarin, Dyah Edi Saputra Efriliyanti, Lia Eki Darmawan Eko Fuji Pangestu Eko Priyo Purnomo Elsa Junita, Ika Endang Nurcahyani Enos Paselle Erizal Erlangga, Anugrah Danial Eva Sri Handayani Pongtuluran Fadilah, Muhammad Rizky Faisal Nasar Bin Madi Fajar Apriani Fakhira, Dhia Akifah Farhanuddin Jamanie Farisi, Salman Fathurrahman Fathurrahman Fatullah, Iqbal Fauzi, Dzikri Ahmad Fauzia, Gina Fazriyas Febri Juita Anggraini Fernando Mersa Putra Fetia Harsa Fetriasih, Riska Fina Marshella Finnah Fourqoniah Firda Safira, Masnoni Firyal Nabila Ulya H.M Fitri, Yunika Fransiska Xeviria Furqanul Hakim Gabriel Putra Gea Cita Meiratri Ghaffar, Dzaky Abdul Gintings, M. Fajar Mediyawan Gintings, Mohammad Fajar Mediyawan Gita Antar Wulan Giyarsi, Giyarsi Guspani, Reta Gusri, Lailal Gusti Naufal Rizky Perdana Haikal, M. Arsyad Hamid Abdillah Hamzah, A. Hadian Pratama Hania Ayu Karin Hanif, Athal Hapidah, Hapidah Haqqi, Abdul Harfiani, Nabela H.N. Hariestya Viareco Haris Puspito Buwono Harmitalia, Mita Hartutiningsih . Hayati, Zahratul Hendra Wibowo Hului Hendra Wibowo Hului Hendri Busman Hengki Satrisno Heri Kuseri 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 Ilfan, Freddy Ilham Yusuf Maulana Ilmi, Fikri Ikmalul Indah Purnamawati Indah Susilowati Indarto, Kus Iradhad Taqwa Sihidi IRWANSYAH Isnaeni Yuliani Izma, Muhammad Athallah JAHIRUDIN, JAHIRUDIN Jani Master, Jani Jorgi Rivaldo Jubba, Hasse Juita, Febri Julianti, Manda Dwi Juniarti, Eni Jusman, Darren Kadja , Deky Baleanus Kafa, Mushfi Abdulloh Kartika Kartika Kartini Kartini Kezia Arum Sary Khaerudin, Akhmad Khaerunnisa, Rindiyani Khair . Khairina, Etika Khairun Nisa Kharisma Dwi Pratiwi Kris Witono Kundang Karsono Kurnia, Dian Ade Kus Indarto Kus, Indarto Kusdaryanto, Ardo Kustantina, Kustantina Laisah, Nur Latifatus Safariyah Leonardo, Nicholas Liky Faizal Lisdawati Lisdawati Liza Marina Loilatu, Mohammad Jafar Lustari, Reli Luthfi Sultan Jauhary Lito M Ghiffari Ramadhan Made Kutanegara, Pande Mahfut Maimun Maimun Mappaelo Maradona Abdullah Mardeli Mardeli Mardhotillah, Bunga Martitah Martitah Masjaya . Masjaya Masjaya Maskud, Maskud Maulana, Muhammad Evan Maulida, Azkiyatul Mawaddah, Merisa Rahma Mayasari, Windatania Meilinda, Nadia Mirawati Yanita Mochammad Iqbal Fadhlurrohman Moh. Hidayatullah Moh. Ikhwan Faidlur Ruhman Mohammad Rizki, Visal 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 Guntur, Muhammad 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 Muryunika, Rince Mutia Dewi Muzaki, Mochamad Nadia Nur Alifiana 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 Nur ‘Azah Nurdin, Akhmad Nurhakim, Bani NURJANNAH, ANA Nurmawati, Subekti Nurrofiq, Ainan Zaky Nursaidah Nursaidah, Nursaidah Oktaviani, Ratna Ordas Dewanto Otong Saeful Bachri Otong Saeful Batchri Pandoyo, Pandoyo Paselle, Enos Peratama, M. Bayu Pidesia . Poppy Andriany Prabowo, Ary Pramuja, Trisena Prasetiyo, Daniel Pratama, Denni Pratama, Fristian Adi Pratama, Rizky Prawita Sari, Bening Premana, Agyztia Prihartono, Willy Prihartono Purnama Agustin, Kharisma Purwanti, Lidya Lin Purwoko Purwoko Putra, Aris Pratama Putra, Farrel Reyhan Putra, Irman Putra, Tri Syukria Putri, Cici Amelia Putri, Nadia Ananda Putri, Tasya Kamila Putri, Yoana Nabilah Qurani, Suci Ayu Qurrotaini, Lativa r. Patmiarsih Rabiatul Adawiyah Rachmatullah, Mochamad Miftah Rachmi Masnilah Raden Mohamad Herdian Bhakti Raditio, Eriko Rahayu, Reni Rahmat, Al Fauzi Rahmawati, Firda Devi Rahmawati, Rahmawati 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 Riana Anggraini, Riana Rikardo, Ricki Riko Angga Bayu Kusuma Rini merliana, Setia Ririn Irmadariyani Riska Ayu Pratiwi, Riska Ayu Rismayana . Rista Angraeni Rita Kalalinggi Riya, Okta Riyan Ningsih Rizka, Yola Rizki Andre Handika Rizki Raja Putra Rizky Nugraha, Gilang Rizky Reynaldi Rochmah Agustrina Rodhiyah, Zuli Rohayu Rohayu Rohmah, Ainun Nimatu Rosa, Emantis Rosid, Abdur Rumi, Nur Ananda Rusdiansyah Rusdiansyah Sabrina, Najwah Safitri, Anisa Dwi Said Amaddin Saipul Saipul, Saipul Salahudin Salahudin Samsul Hadi Samuel ., Samuel Samuel Samuel Sandy Dwi Prasetyo Saprudin, Mohamad Sari, Mira Yulia Sari, Tiara Puspita Sari, Vita Komala Sarkani Sarkani Sarwoko, Jonathan Puji Sasongko, Mohammad Umar Sasongko Satrisno, Hengki Senobaan, Riska Tipa Septiria, Dalima Setiawan, Didik Bagus Setiawan, Rafli Putra Shahib, Muhammad Umar Simamora, Nurcahaya Sintia, Delva Sinulingga, Samuel Mahesa Siti Kurniasih Siti Rodiyah Siti Triaminingsih Sofia Rahmasari Sonny Sudiar, Sonny Steven Christian Subandi Subandi Subhaini Jakfar Sugeng Supriadi, Sugeng Suherlan Suhestiwi, Rini Sukadi, Erpon Sukamto, Ika Sumiyarsi Sulistio, Alip Sulistiyono, Rovy SUMADI SUMADI Sumardi Sumardi . Sundari Meganingrum, Alpa Sundi, Venni Herli suratman Suratman Umar Surpendi . Surya Nanda Situmorang Suryono Suryono Susilo Utomo, Heryono Syafitri, Ranny Aulia Syahrani . Syahrul Borman Syaifurrahim Azhari Syamsul Hadi Syaogi, Moh. Syapira, Syaiba Syaputra, Ravista Meizen sya’adi, Nur Syifa Min Zaytun, Salwa Tamaulina Br Sembiring 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 Utami, Intan Valentino Aris Viareco, Hariestya Wibowo, Rahmat Catur Wijaya, Valentino Wildan Zaman Winda Khoritotul Jannah Windi Astuti Yahya, Samuel Yanova, Shally Yofita Sandra Yogi Pasca Pratama, Yogi Pasca Yohani . Yoka, Yoka Yosefa Sayekti Yulhendri Yulhendri Yulia Prihartini Yulianty Yulianty Yulianty Zahidah, Athiya Zahriani, Nurul Zahrotun Ni'mah Zaky Mubarak, Ahmad Zhikry Fitrian Zulfikar, Iqbal Zulkifli, Zarina Zuly Qodir