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Bambang Irawan
Universitas Mulawarman
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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.
EVALUASI KLASSIFIKASI PENYAKIT DAUN TEH MENGGUNAKAN TRANSFER LEARNING EFFICIENTNETB0 azis, santowi; 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.v14i1.8954

Abstract

Penelitian ini mengevaluasi performa model EfficientNetB0 berbasis transfer learning untuk deteksi dini penyakit daun teh. Dataset Tea Leaf Disease yang tersedia secara publik digunakan, terdiri dari 5.867 gambar daun teh dengan enam kelas, yaitu algal spot, brown blight, gray blight, healthy, helopeltis, dan red spot. Dataset dibagi menjadi data latih (70%), validasi (15%), dan uji (15%). Model dilatih selama 30 epoch dengan laju pembelajaran 1×10⁻⁴, kemudian dilakukan fine-tuning selama 15 epoch tambahan menggunakan laju pembelajaran 1×10⁻⁵ disertai augmentasi data yang intensif. Hasil pengujian pada data uji menunjukkan akurasi sebesar 97%, dengan nilai macro-averaged precision, recall, dan F1-score masing-masing mencapai 0,97. Analisis confusion matrix mengindikasikan tingkat kesalahan klasifikasi yang rendah, meskipun masih terjadi kesalahan pada kelas-kelas yang memiliki kemiripan visual tinggi, seperti brown blight dengan gray blight serta helopeltis dengan healthy. Hasil ini menunjukkan bahwa EfficientNetB0 memiliki akurasi dan efisiensi yang tinggi, sehingga berpotensi untuk diimplementasikan pada aplikasi mobile sebagai sistem pendukung deteksi dini penyakit daun teh bagi petani.
ANALISIS SENTIMEN ULASAN APLIKASI GROK DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM) tamlica, Agam; 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.v14i1.9011

Abstract

Kemajuan dalam kecerdasan buatan telah memicu proliferasi berbagai aplikasi berbasis bahasa alami, yang dicontohkan oleh Grok, yang mengumpulkan banyak ulasan pengguna di Google Play Store. Evaluasi tersebut merangkum persepsi dan pengalaman pengguna, yang sangat penting untuk peningkatan kualitas layanan. Tujuan penelitian ini adalah untuk memeriksa sentimen ulasan pengguna yang terkait dengan aplikasi Grok menggunakan algoritma Long-Term Memory (LSTM), karena kemampuannya untuk pemahaman kontekstual yang mendalam dan analisis urutan kata. Data dikumpulkan melalui metodologi web scraping yang menggunakan pustaka google-play-scraper dan menjalani beberapa tahap pemrosesan, termasuk pembersihan, tokenisasi, penghapusan terminologi, stemming, dan klasifikasi sentimen. Model dilatih selama lima epoch dengan partisi data 80% untuk pelatihan dan 20% untuk validasi, menghasilkan tingkat akurasi 89,58%. Temuan menunjukkan bahwa model LSTM mahir dalam mengidentifikasi pola linguistik dan sentimen pengguna, khususnya dalam klasifikasi positif Hasil ini menggarisbawahi potensi penggunaan LSTM sebagai kerangka kerja analisis opini otomatis untuk aplikasi berbasis kecerdasan buatan di Indonesia.
Co-Authors Abdul Khamid Abdul Kolik Abdullah Karim Abdullah Karim Achmad Djumlani Achmad Fikri Achmad Nurmandi Adam Idris Adam Malik Adelia, Nur Hidayah Adhani, Karenina Dwi Adigunawan, Adigunawan Affan, Muhammad 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 Angela, Bigael Yunisca Angelia, Monica Angger Bagus Prasetiyo 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 Ary Prabowo Aryasatya, Muhammad Fathi Aryo Wibisono Asep Maulana, Asep Astriani, Linda Attaufiqi, Agil Fahmi 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 Bhimo Rizky Samudro Budi Rahayu Budi Tjahjono Cathas Teguh Prakoso Chairi, Minal Charles Raymond Jeffrey CHIANI, SARASWATI HAYLIAN Christina Nugroho Ekowati, Christina Nugroho Christover, Deandlles Cicih Ratnasih Cin, Muksin Cornelia, Geby Dan Buntu Paranoan Darmawan , Ade Daryono Daryono, Daryono Dayutama, Theodorus Dedy Miswar Desi Permata Sari Didik Bagus Setiawan Dini Zulfiani Djumadi . Dudik Djaja Sidarta Dyah Mutiarin, Dyah Dyah Rahayuning Perwitasari Efriliyanti, Lia Eki Darmawan Eko Fuji Pangestu Eko Priyo Purnomo 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 Fathurrahman Fathurrahman Fatullah, Iqbal Fauzi, Dzikri Ahmad Fauzia, Gina 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 Gea Cita Meiratri Ghaffar, Dzaky Abdul Gintings, M. Fajar Mediyawan Gintings, Mohammad Fajar Mediyawan Gita Antar Wulan Giyarsi, Giyarsi Guspani, Reta Gusti Naufal Rizky Perdana Hafizh Rahman Hakim, Muhammad Haikal, M. Arsyad Hamid Abdillah Hamzah, A. Hadian Pratama Hanif, Athal Hapidah, Hapidah Haqqi, Abdul Harfiani, Nabela H.N. Hariestya Viareco Harits, Dimaz Harmitalia, Mita Hartutiningsih . Hayati, Zahratul Hendra Wibowo Hului Hendra Wibowo Hului 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 Ilmi, Fikri Ikmalul Indah Purnamawati Indarto, Kus Innova, Zacky Iradhad Taqwa Sihidi Iranas, Ady Irwansyah Isnaeni Yuliani Izma, Muhammad Athallah JAHIRUDIN, JAHIRUDIN Jorgi Rivaldo Jubba, Hasse Juita, Febri Julianti, Manda Dwi Juniarti, Eni Jusman, Darren Kadja , Deky Baleanus Kafa, Mushfi Abdulloh Kartika Kartika Kartini Kartini Katiga Capricorna, Epsilona Kezia Arum Sary Khaerudin, Akhmad Khaerunnisa, Rindiyani Khair . Khairina, Etika Kharisma Dwi Pratiwi Kris Witono Kundang Karsono Kurnia, Dian Ade Kus Indarto Kus, Indarto Kusdaryanto, Ardo Kustantina, Kustantina Kusuma Handayani, Kusuma Laisah, Nur Latifatus Safariyah Leonardo, Nicholas Lidya Lin Purwanti Ligery, Finny 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 Mardianti, Yolanda Martitah Masjaya . Masjaya Masjaya Maskud, Maskud Maulana, Muhammad Evan Maulana, Wahyu Riski Maulida, Azkiyatul Mawaddah, Merisa Rahma Mayasari, Windatania Meilinda, Nadia Mirawati Yanita Mochammad Iqbal Fadhlurrohman Moh. Hidayatullah Moh. Ikhwan Faidlur Ruhman Mohammad Taufik Mohammad Taufik Mohammad Taufik Mubarok, Muhammad Zaqi Muhamad Tamamul Iman Muhammad Bagus Sistriatmaja Muhammad Dinar, Yusadiningrat 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 Salman Nasyirudien Muhammad Taufik Hidayat Muhammad Zaini Muhammad Zaki Muhimatul Ifadah Muktiono Murni Murni Mutia Dewi Mutmainni Nadia Nur Alifiana Najahatul Hananah Napitupulu, Richard RP Nida Handayani Nindita, Annisa Ayu Nisa, Hoirun Nizirwan Anwar Nova Eliza Nova Sintia Dewi Sitorus Novalina Nurul Imama 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 Pandoyo, Pandoyo Peratama, M. Bayu Pidesia . Poppy Andriany Pramuja, Trisena Prasetiyo, Daniel Pratama, Denni Pratama, Fristian Adi Pratama, Rizky Pratiwi, Intan Najma Prawita Sari, Bening Prayoga, Agung Premana, Agyztia Prihartono, Willy Prihartono Purnama Agustin, Kharisma Putra, Aris Pratama Putra, Farrel Reyhan Putra, Tri Syukria Putri, Cici Amelia Putri, Nadia Ananda Putri, Tasya Kamila Putri, Vany Alia Putri, Yoana Nabilah Qurani, Suci Ayu Qurrotaini, Lativa r. Patmiarsih R.M.Herdian Bhakti Rabiatul Adawiyah Rachmi Masnilah Raditio, Eriko Ragil Raditya Saputra Rahayu, Nurul Widyawati Rahayu, Reni Rahmadani, Dwi Rizki Rahmat, Al Fauzi Rahmawati, Firda Devi Rahmawati, Rahmawati Rahmi Dianita Ramadhan , Rafly Surya Ramadhan, Azki Ramadhan, Ridwan Ramadhan, Satrio Surya Ramdanis, Sapril Nurul Ramdhan, Nur Ariesanto Rande, Santi Rande, Santi Ratih Wirapuspita Wisnuwardani Renti Yasmar Reri Aprizal Reza Laksana Putra 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 Romahdoni, Rifky Oktri 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 Saputra, Dimas Sari, Mira Yulia Sari, Tiara Puspita Sari, Vita Komala Sarkani Sarkani Sarwoko, Jonathan Puji Sasongko, Mohammad Umar Sasongko Satrisno, Hengki Senobaan, Riska Tipa Setiawan, Rafli Putra Shahib, Muhammad Umar Sigit, Arief Bandoro Simamora, Nurcahaya Sintia, Delva Sinulingga, Samuel Mahesa Siti Kurniasih Siti Nurhayati Siti Rodiyah Siti Triaminingsih Sofia Rahmasari Sonny Sudiar, Sonny Steven Christian Subagiyo Subagiyo Subandi Subandi Subhaini Jakfar Sugeng Supriadi, Sugeng Suherlan Sukadi, Erpon Sukamto, Ika Sumiyarsi Sulistio, Alip Sulistiyono, Rovy SUMADI SUMADI Sumardi Sumardi . Sundari Meganingrum, Alpa Sundi, Venni Herli Supena, Stevy Hanny 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 Thoriq, Eaden Ahmed Thriwaty Arsal Tiani, Lilis Tien Rohmatin, Tien Tito Winnerson Sitanggang, Tito Winnerson Tjahjojo, Budi Tjokro Prasetyadi, Tjokro Totalia, Sherly Sustantien Tri Syukria Putra Tugiyono Tundjung Tripeni Handayani, Tundjung Tripeni Turkey, Mohamed Utami, Intan Valentino Aris Viareco, Hariestya Vinan Viyus Wahid, Muhammad Zainul Wibowo, Rahmat Catur Wijaya, Valentino Wildan Zaman Winda Khoritotul Jannah Windi Astuti Yahya, Samuel Yofita Sandra Yogi Pasca Pratama, Yogi Pasca Yohani . Yoka, Yoka Yosefa Sayekti Yulhendri Yulhendri Yulia Prihartini Yulianty Yulianty Yumarni, Asmara Zahidah, Athiya Zahriani, Nurul Zahrotun Ni'mah Zaky Mubarak, Ahmad Zhikry Fitrian Zulfikar, Iqbal Zulkifli, Zarina Zulmy, Ahmad Nijar Zuly Qodir