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Journal of Policy and Bureaucracy Management Jurnal Pengabdian kepada Masyarakat JUTECH : Journal Education and Technology Foremost Journal Journal of Government and Political Issues Jurnal Penelitian Inovatif Indo-MathEdu Intellectuals Journal Unram Journal of Community Service (UJCS) SRIWIJAYA JOURNAL OF ENVIRONMENT JSIP: Jurnal Studi Ilmu Pemerintahan International Journal of Science and Society (IJSOC) Edu Sociata : Jurnal Pendidikan Sosiologi Wisanggeni : Jurnal Pengabdian Masyarakat Jurnal Pajak Vokasi (JUPASI) Abiwara : Jurnal Vokasi Administrasi Bisnis Jurnal Riset Inossa : Media Hasil Riset Pemerintahan, Ekonomi dan Sumber Daya Alam Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Mathematics Instruction, Social Research and Opinion Jurnal Ilmiah Pendidikan Dasar (JIPDAS) An-Nizam: Jurnal Bakti Bagi Bangsa Jurnal Fuaduna: Jurnal Kajian Keagamaan dan Kemasyarakatan Kreasi: Jurnal Inovasi dan Pengabdian Kepada Masyaraka Kybernology : Journal of Government Studies Media Bina Ilmiah Literasi: Jurnal Pendidikan Guru Indonesia Jurnal Sintaks Logika (JSilog) Borneo Journal of Language and Education Jurnal Penelitian Ilmu Pendidikan Indonesia Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian Kepada Masyarakat JURNAL BIOLOGI PAPUA Innovative: Journal Of Social Science Research JURNAL KESEHATAN, SAINS, DAN TEKNOLOGI (JAKASAKTI) Ranah Research : Journal of Multidisciplinary Research and Development Paradigma: Junal Kalam dan Filsafat Enrichment: Journal of Multidisciplinary Research and Development Jurnal Pengabdian Sosial GEMBIRA (Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat dan Riset Pendidikan International Journal of Islamic Education (IJIE) ARKHAS: Journal of Arabic Language Teaching Jurnal Terobosan Peduli Masyarakat (TIRAKAT) Jurnal Intelek Insan Cendikia Journal of Innovation in Teaching and Instructional Media Journal of Evrimata: Engineering and Physics Jurnal Kebidanan dan Kesehatan Jurnal Ilmu Administrasi Publik PESHUM JESS (Journal of Educational Social Studies) Jurnal Pendidikan Teknik Mesin Jurnal El-Thawalib JURNAL MANAJEMEN DAN BISNIS INDONESIA ABHATS: Jurnal Islam Ulil Albab Nemui Nyimah Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat Ipso Jure Jurnal Manajemen FE-UB Jurnal Publikasi Ilmu Komputer dan Multimedia Jurnal Kendali Teknik dan Sains
Bambang Irawan
Universitas Mulawarman
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PENERAPAN METODE ANALYTICAL HIERARCHY PROCESS UNTUK PENILAIAN TENAGA KESEHATAN KOTA TEGAL Ghaffar, Dzaky Abdul; Bachri, Otong Saeful; Irawan, Bambang
JUTECH : Journal Education and Technology Vol 6, No 2 (2025): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i2.5292

Abstract

Penilaian kinerja tenaga kesehatan berperan penting dalam menjaga kualitas pelayanan kesehatan. Namun, penilaian manual sering kali subjektif dan kurang terstruktur, sehingga dapat memengaruhi objektivitas keputusan. Penelitian ini bertujuan merancang Sistem Pendukung Keputusan (SPK) berbasis metode Analytical Hierarchy Process (AHP) untuk menghasilkan evaluasi kinerja yang objektif dan terukur. Sistem ini diterapkan di Dinas Kesehatan Kota Tegal dengan sembilan kriteria penilaian berdasarkan nilai-nilai ASN BerAKHLAK dan indikator profesionalisme. Data dikumpulkan melalui observasi, wawancara, dan dokumentasi yang melibatkan 83 tenaga kesehatan. Metode AHP digunakan untuk menentukan bobot kriteria melalui perbandingan berpasangan, menghitung rasio konsistensi, dan menghasilkan peringkat akhir pegawai. Hasil penelitian menunjukkan bahwa SPK ini efektif dalam memproses data penilaian dan menghasilkan peringkat yang akurat. Sistem ini mendukung pengambilan keputusan terkait promosi, penghargaan, dan pengembangan kompetensi secara transparan dan objektif. Pengembangan di masa depan dapat mencakup integrasi dengan sistem manajemen kepegawaian dan fitur pelaporan otomatis.
PERANCANGAN CHATBOT MUSIK WEB UNTUK REKOMENDASI LAGU BERDASARKAN MOOD Leonardo, Nicholas; Ramadhan , Rafly Surya; Rumi, Nur Ananda; Nisa, Hoirun; Wijaya, Valentino; Ariz , Naufal; Irawan, Bambang
Journal of Information System Management (JOISM) Vol. 7 No. 2 (2026): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2026v7i2.2319

Abstract

Mayoritas sistem rekomendasi musik, termasuk fitur “Made For You” pada Spotify, masih mengandalkan riwayat pemutaran tanpa mempertimbangkan kondisi emosional pengguna secara real-time, sehingga menghasilkan saran yang kurang relevan. Penelitian ini merancang Chatwise, chatbot musik berbasis web yang memberikan rekomendasi lagu personal melalui percakapan interaktif berdasarkan mood pengguna. Sistem memproses input teks berisi pilihan suasana hati dan alasan singkat menggunakan teknik pencocokan kata kunci, kemudian mengintegrasikannya dengan Spotify API untuk memperoleh data lagu secara real-time. Pengembangan dilakukan dengan metode Agile Scrum dan diuji menggunakan black-box testing serta kuesioner kepada 10 responden. Hasil menunjukkan 70% responden memberikan penilaian positif pada aspek tampilan, kecepatan, akurasi deteksi mood, kemudahan penggunaan, dan variasi genre. Temuan menunjukkan Chatwise berhasil mengintegrasikan deteksi mood dengan rekomendasi musik real-time, memberikan solusi adaptif dan kontekstual, meskipun masih diperlukan pengembangan lebih lanjut untuk meningkatkan akurasi dan keragaman musik.
Klasifikasi Huruf Hijaiyah Berbasis Citra Digital Menggunakan Metode Convolutional Neural Network (CNN) Firyal Nabila Ulya H.M; Bambang Irawan; Abdul Khamid
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.v18i2.3308

Abstract

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.  
Klasifikasi Jenis Sampah Organik Dan Anorganik Menggunakan Convutional Neural Network Berbasis Citra Digital Nova Eliza; Bambang Irawan; Abdul Khamid
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.v18i2.3309

Abstract

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.
Prediksi Konsentrasi PM2.5 Resolusi 15 Menit di Kabupaten Brebes Menggunakan Transformer dan GEOS-CF NASA Muhammad Fikri Setiawan; 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.v18i2.3330

Abstract

Polusi udara partikulat halus (PM2,5) merupakan ancaman serius bagi kesehatan masyarakat di Kabupaten Brebes, Jawa Tengah. Faktor penyumbang utamanya adalah emisi kendaraan di jalur Pantura, aktivitas industri perikanan, serta konsentrasi tinggi selama musim kemarau (Juni–November). Tidak adanya model peramalan sub-jam yang akurat menghambat pengembangan sistem peringatan dini yang efektif. Penelitian ini mengembangkan dan mengevaluasi model deep learning berbasis Transformer untuk memprediksi konsentrasi PM2,5 dengan resolusi waktu 15 menit. Data yang digunakan berasal dari NASA GEOS-CF (band PM25_RH35_GCC) yang diakses melalui Google Earth Engine menggunakan API Python. Dataset mencakup periode 1 Januari hingga 22 November 2025, menghasilkan 7.813 observasi per jam, yang kemudian diinterpolasi linear menjadi 31.249 titik data dengan resolusi 15 menit. Arsitektur Transformer terdiri dari 3 lapis enkoder, 4 kepala perhatian multi-head, dimensi embedding 128, dimensi feed-forward 256, panjang sekuen 60 timestep, dan augmentasi fitur menggunakan rerata bergulir (*rolling mean*, jendela = 3) dan beda pertama (*first difference*). Pelatihan dilakukan dengan TensorFlow-Keras, pengoptimal Adam, penjadwal peluruhan kosinus (*cosine decay scheduler*), dan fungsi kerugian Huber. Pembagian data dilakukan secara kronologis: 70% pelatihan, 30% validasi. Evaluasi pada set uji independen (16 Agustus–21 November 2025, 9.357 observasi atau 97 hari 11 jam 15 menit) menghasilkan MAE 0,7691 µg/m³, RMSE 1,2052 µg/m³, R² 0,9945, dan *Explained Variance Score* 0,9948. Model ini mampu menggambarkan variasi diurnal dan anomali musiman secara akurat, jauh melampaui model LSTM dan GTWR konvensional. Penelitian ini memberikan kontribusi signifikan di bidang Teknologi Informasi melalui kerangka kerja pengolahan *big data* satelit untuk aplikasi lingkungan.
Klasifikasi Preferensi Destinasi Wisata Gunung dan Pantai Menggunakan Metode Deep Learning Andin Ayu Oksilia Ramadhani; 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.v18i2.3331

Abstract

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.
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.
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