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All Journal EKONOMIA Jurnal Informatika dan Teknik Elektro Terapan Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Psychology, Evaluation, and Technology in Educational Research INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi METIK JURNAL Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Scientific Journal of Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Didaktik : Jurnal Ilmiah PGSD STKIP Subang Reswara: Jurnal Pengabdian Kepada Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing BAKTI BANUA : JURNAL PENGABDIAN KEPADA MASYARAKAT Teknika Jurnal Informatika Teknologi dan Sains (Jinteks) Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Jurnal Bangkit Indonesia CONSEN: Indonesian Journal of Community Services and Engagement Jurtik STMIK Bandung Journal of Innovative and Creativity Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Buffer Informatika INOVTEK Polbeng - Seri Informatika JSE Journal of Science and Engineering Journal of Information Technology KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdimas Mahakam
Taghfirul Azhima Yoga Siswa
Universitas Muhammadiyah Kalimantan Timur
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Model Hybrid PSO, Feature Selection Correlation dan Logistic Regression untuk Deteksi Penyakit Jantung Hidayatullah, Muhammad Wahyu; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
INFOMATEK Vol 28 No 1 (2026): Juni 2026 (In Progress)
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v28i1.43123

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian baik di Indonesia maupun secara global sehingga diperlukan model deteksi dini yang akurat. Penelitian ini bertujuan meningkatkan kinerja Logistic Regression dengan regularisasi L2 melalui optimasi Particle Swarm Optimization (PSO) dan feature selection berbasis correlation. Metode yang digunakan meliputi pre-processing, standarisasi, seleksi fitur, serta evaluasi menggunakan K-10 Fold Cross Validation. Hasil pengujian menunjukkan bahwa Logistic Regression menghasilkan accuracy 82,47%, precision 80,31%, recall 88,56%, dan F1-score 84,10%. Setelah dioptimasi dengan PSO, performa meningkat menjadi accuracy 84,45%, precision 81,74%, recall 91,01%, dan F1-score 85,98%. Hasil tersebut menegaskan bahwa pendekatan hybrid yang diusulkan efektif dalam meningkatkan deteksi penyakit jantung.
Metode Hybrid SVR-GWO Untuk Prediksi Harga Saham PT. Aneka Tambang Tbk Muhammad Aditya Rahman; Taghfirul Azhima Yoga Siswa; Rofilde Hasudungan
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3586

Abstract

Fluctuating and unpredictable stock price movements pose a challenge for investors in their decision-making. This study aims to apply and analyze the performance of a hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) model in predicting the stock price of PT Aneka Tambang Tbk. The data used consists of daily stock prices from September 11, 2020, to September 11, 2025, totaling 1,202 data points, with a division of 70% training data and 30% testing data. The research stages include pre-processing, basic SVR modeling, and parameter optimization using GWO. The evaluation was carried out using RMSE, MAE, and MAPE. The results show that GWO optimization improved the model's performance from RMSE 99.78, MAE 55.70, and MAPE 2.61% to RMSE 77.27, MAE 48.97, and MAPE 2.37%. Thus, the SVR–GWO model is capable of improving the accuracy of stock price predictions and has the potential to support investment decision-making.Keyword: Grey Wolf Optimizer; Machine Learning; Prediction; Stock Price; Support Vector Re-gression AbstrakPergerakan harga saham yang fluktuatif dan sulit diprediksi menjadi tantangan bagi investor dalam pengambilan keputusan. Penelitian ini bertujuan menerapkan dan menganalisis kinerja model hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) dalam memprediksi harga saham PT Aneka Tambang Tbk. Data yang digunakan berupa harga saham harian periode 11 September 2020 hingga 11 September 2025 sebanyak 1202 data, dengan pembagian 70% data pelatihan dan 30% data pengujian. Tahapan penelitian meliputi pre-processing, pemodelan SVR dasar, serta optimasi parameter menggunakan GWO. Evaluasi dilakukan menggunakan RMSE, MAE, dan MAPE. Hasil menunjukkan bahwa optimasi GWO meningkatkan kinerja model dari RMSE 99.78, MAE 55.70, dan MAPE 2.61% menjadi RMSE 77.27, MAE 48.97, dan MAPE 2.37%. Dengan demikian, model SVR–GWO mampu meningkatkan akurasi prediksi harga saham dan berpotensi mendukung pengambilan keputusan investasi.Kata Kunci: Grey Wolf Optimizer; Harga Saham; Machine Learning; Prediksi; Support Vector Regression
Komparasi FastText dan TF-IDF Berbasis Random Forest pada Analisis Sentimen IKN di Youtube Fadhil Irsyad Ramadhani; Taghfirul Azhima Yoga; Naufal Azmi Verdhika
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9749

Abstract

The development of Indonesia's New Capital City (IKN) represents a significant national policy that has triggered diverse public responses, particularly across social media platforms like YouTube. This study aims to analyze public sentiment regarding the IKN project and compare the performance of two text feature extraction methods, FastText and Term Frequency-Inverse Document Frequency (TF-IDF), using the Random Forest algorithm. The primary objective is to identify which method is more effective in capturing the nuances of Indonesian-language public opinion. The dataset for this research includes 4,093 YouTube comments related to IKN, obtained using the YouTube Data API v3 in August 2025. The data were categorized into two classes, positive and negative, while neutral data were removed to minimize model bias. Data labeling was conducted manually and validated by a linguistic expert, followed by pre-processing stages such as data cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. The setting of a 200-vector dimension for FastText and a 5,000-feature limit for TF-IDF was based on findings from previous sentiment analysis research, proving that such configurations provide stable classification performance compared to other parameters, as they are statistically more effective in filtering irrelevant features without losing deep semantic information. Model performance was evaluated using the 10-Fold Cross-Validation method and Confusion Matrix based on accuracy, precision, recall, and F1-score metrics. Results indicate that the FastText method achieved an accuracy of 83.67%, precision of 84.01%, recall of 83.72%, and an F1-score of 80.83%, while TF-IDF yielded an accuracy of 80.53%. These findings conclude that FastText is more effective in representing the context and semantic meaning of Indonesian YouTube comments related to IKN. Furthermore, this method provides a balance in pattern recognition and the precision of sentiment classification results. This research contributes to assisting stakeholders and researchers in more accurately understanding public opinion toward IKN.
HYBRID SVR-GS UNTUK PREDIKSI SAHAM PT ANEKA TAMBANG TBK Muhammad Afif Aunur Rohman; Taghfirul Azhima Yoga Siswa; Rofilde Hasudungan
Jurnal Ilmiah Informatika Vol. 11 No. 1 (2026): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/.v11i1.9280

Abstract

Sektor pertambangan di Indonesia memiliki peran strategis dalam perekonomian nasional, namun pergerakan harga sahamnya bersifat fluktuatif akibat pengaruh faktor eksternal, seperti harga komoditas dan kondisi pasar global. Kondisi tersebut menjadikan prediksi harga saham sebagai permasalahan yang kompleks. Penelitian ini bertujuan untuk memprediksi harga saham PT Aneka Tambang Tbk (ANTM) menggunakan metode Support Vector Regression (SVR) dengan optimasi hyperparameter melalui Grid Search (GS), sehingga membentuk model hybrid SVR-GS. Data yang digunakan berupa data historis saham ANTM periode 2020–2025 sebanyak 1.202 data yang diperoleh dari Investing.com. Tahapan penelitian meliputi preprocessing data, feature engineering dengan pendekatan lag time, normalisasi Min-Max, pembagian data berbasis time series, serta evaluasi model. Kinerja model diukur menggunakan Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa SVR Default menghasilkan MAE 173,78 dan MAPE 9,65%, sedangkan SVR Semi-Tuned menurunkan kesalahan menjadi MAE 71,72 dan MAPE 3,19%. Model SVR-GS memberikan performa terbaik dengan MAE 45,16, RMSE 67,94, dan MAPE 2,24% pada rasio data 70:30. Dengan demikian, optimasi Grid Search terbukti meningkatkan akurasi prediksi harga saham ANTM secara signifikan.
Hybrid Support Vector Regression-Genetic Algorithm Model for Forecasting Stock Prices Muhammad Ulil Albab; Taghfirul Azhima Yoga Siswa; Rofilde Hasudungan
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The stock market exhibits a high level of volatility, which often leads to significant price fluctuations and increases the risk of financial losses for investors. Therefore, stock price prediction is an important tool to support investment decision-making, particularly for PT Aneka Tambang Tbk (ANTM.JK). This study aims to predict ANTM stock prices by applying the Support Vector Regression (SVR) method optimized using a Genetic Algorithm (GA). The data used in this study consist of 1202 historical stock price data of ANTM from September 11, 2020 to September 11, 2025, obtained from Investing.com, and the data are normalized using the Min-Max normalization method. The dataset is divided into training data and testing data using an 80:20 ratio, where 80% of the data are used for training and 20% for testing. The SVR model is constructed using the Radial Basis Function (RBF) kernel, while the GA is employed to optimize the SVR parameters in order to obtain the optimal parameter combination, with main GA parameters including population size of 50, 30 generations, crossover rate of 0.8, and mutation rate of 0.1. Model performance is evaluated by comparing the prediction results of SVR without optimization and GA-optimized SVR using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results indicate that the application of the GA improves the predictive performance of the model. The SVR model without optimization produces RMSE, MAE, and MAPE values of 85.48, 59.02, and 2.62%, respectively. After parameter optimization using GA, the model performance improves as indicated by reduced error values, with RMSE of 75.97, MAE of 52.42, and MAPE of 2.42%
Klasifikasi Kecelakaan Lalu Lintas Menggunakan Kombinasi Forward Selection, ADASYN, dan Random Forest Aspianur; Taghfirul Azhima Yoga Siswa; Naufal Azmi Verdikha
Buffer Informatika Vol. 11 No. 2 (2025): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v11i2.476

Abstract

Kecelakaan lalu lintas menjadi penyebab utama kematian bagi kelompok usia 15-29 tahun, dengan lebih dari 1,3 juta kematian setiap tahunnya. di Indonesia, data dari korps lalu lintas polri menunjukkan bahwa pada tahun 2023 terjadi lebih dari 100 ribu kasus kecelakaan, dengan korban jiwa mencapai 25 ribu orang. Penelitian ini bertujuan untuk mengembangkan model klasifikasi tingkat keparahan kecelakaan lalu lintas dengan mengintegrasikan metode Adaptive Synthetic Sampling (ADASYN) dan Forward Selection ke dalam algoritma Random Forest. Data yang digunakan merupakan data kecelakaan dari Polresta Samarinda periode 2020–2024 yang terdiri dari 35 fitur. Proses penelitian mencakup tahapan data preprocessing, feature selection dengan Forward Selection, dan pembagian data testing dan training dengan 10k-fold validation. Permodelan menggunakan algoritma Random Forest, dan evaluasi model menggunakan confusion matrix untuk mencari akurasi, precision, recall, dan f1-score. Hasil penelitian menunjukkan fitur yang paling signifikan terhadap klasifikasi kecelakaan lalu lintas adalah cuaca, jumlah luka ringan, jumlah luka berat, dan jumlah meninggal dunia. Penerapan Random Forest tanpa penanganan ketidakseimbangan dan tanpa seleksi fitur hanya menghasilkan akurasi 79,26%, precision 29,03%, recall 34,62%, dan f1-score 31,58%. Setelah diterapkan ADASYN, metrik evaluasi meningkat signifikan menjadi akurasi 84,26%, precision 81,82%, recall 84,62%, dan f1-score 83,20%. Peningkatan lebih besar tercapai setelah seleksi fitur Forward Selection, menghasilkan akurasi akhir 95,28%, precision 94,23%, recall 96,15%, dan f1-score 95,18%.
Analisis Kecelakaan Lalu Lintas Berbasis Decision Tree Terintegrasi Forward Selection dan Metode ADASYN Muhammad Wildan Hadinata; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Buffer Informatika Vol. 11 No. 2 (2025): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v11i2.477

Abstract

Di Indonesia, Angka kecelakaan lalu lintas juga menunjukkan situasi yang mengkhawatirkan. Pada tahun 2023, terjadi sebanyak 665 kasus kecelakaan lalu lintas di provinsi Kalimantan Timur menurut data BPS Provinsi Kaltim. Penelitian ini bertujuan untuk mengembangkan model klasifikasi tingkat keparahan kecelakaan lalu lintas dengan mengintegrasikan metode Adaptive Synthetic Sampling (ADASYN) dan Forward Selection ke dalam algoritma Decision Tree. Data yang digunakan merupakan data kecelakaan dari Polresta Kota Samarinda periode 2020-2024 yang terdiri dari 35 fitur. Proses penelitian mencakup tahapan data preprocessing, feature selection dengan Forward Selection, dan pembagian data testing dan training dengan 10k-fold validation. Permodelan menggunakan algoritma Decision Tree dan evaluasi model menggunakan confusion matrix untuk mencari akurasi, presisi, recall, dan f1-score. Hasil penelitian menunjukkan fitur yang paling signifikan terhadap klasifikasi kecelakaan lalu lintas adalah cuaca, jumlah meninggal dunia, jumlah luka ringan dan jumlah luka berat. Penerapan ADASYN berhasil meningkatkan akurasi dari 69,44% menjadi 76,83%. Sedangkan penambahan seleksi fitur Forward Selection lebih lanjut meningkatkan akurasi hingga 99,06%.
Analisis Komparasi TF-IDF dan Word2Vec pada Algoritma SVM Untuk Sentimen Pembangunan IKN di YouTube: Studi Kasus Komentar YouTube pada Kanal Pembangunan IKN Mi'raj Fattah; Taghfirul Azhima Yoga Siswa; Naufal Azmi Verdikha
Buffer Informatika Vol. 12 No. 1 (2026): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v12i1.551

Abstract

Pembangunan Ibu Kota Negara (IKN) Nusantara merupakan kebijakan strategis nasional yang menuai beragam reaksi publik, baik positif maupun negatif, terutama di media sosial seperti YouTube. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap opini masyarakat mengenai pembangunan IKN serta membandingkan kinerja akurasi antara dua metode ekstraksi fitur, yaitu Term Frequency-Inverse Document Frequency (TF-IDF) dan Word2Vec, menggunakan algoritma Support Vector Machine (SVM). Data penelitian terdiri dari 1.969 komentar bersih yang dikumpulkan melalui teknik scraping pada periode Juli hingga September 2025. Tahapan penelitian meliputi pengambilan data, pre-processing (pembersihan, case folding, normalisasi, tokenisasi, stopword removal, dan stemming), pelabelan data manual yang divalidasi ahli bahasa, ekstraksi fitur, serta klasifikasi menggunakan SVM dengan kernel Radial Basis Function (RBF). Evaluasi model dilakukan menggunakan metode 10-Fold Cross Validation dan Confusion Matrix. Hasil penelitian menunjukkan bahwa TF-IDF memberikan kinerja yang lebih baik dibandingkan Word2Vec pada dataset ini. SVM dengan TF-IDF menghasilkan rata-rata akurasi sebesar 74,40%, sedangkan SVM dengan Word2Vec menghasilkan akurasi sebesar 72,98%. Penelitian ini menyimpulkan bahwa representasi fitur berbasis frekuensi (TF-IDF) lebih efektif dibandingkan representasi semantik (Word2Vec) dalam mengklasifikasikan sentimen komentar singkat di YouTube terkait topik IKN.
Komparasi Ekstraksi Fitur TF-IDF dan Word2Vec pada Naïve Bayes untuk analisis Sentimen Pembangunan IKN di YouTube Mu. Aldi Rahmad Fahrozi; Taghfirul Azhima Yoga Siswa; Naufal Azmi Verdikha
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9198

Abstract

The development of Indonesia’s New Capital City (IKN) has generated diverse public responses on social media, particularly YouTube, making sentiment analysis necessary to map public perceptions. Previous studies have reported relatively low classification accuracy, reaching only 60%, indicating the need for more effective approaches to improve performance. This study aims to compare the performance of the Naïve Bayes algorithm in classifying public sentiment toward the IKN development using two feature extraction methods, namely TF-IDF and Word2Vec. The data were collected from YouTube comments and processed through preprocessing stages, expert-based labeling, and evaluation using 10-Fold Cross Validation. The results show that the TF-IDF-based Multinomial Naïve Bayes model achieves the best performance with an accuracy of 83%, a positive recall of 82%, and a negative F1-score of 85%, outperforming the Word2Vec-based Gaussian Naïve Bayes model, which attains an accuracy of 82% with a lower positive recall of 76%. These findings confirm that TF-IDF is more effective and stable in handling short-text comment characteristics than Word2Vec, which requires a larger corpus for optimal semantic representation.
Muhammadiyah-Based Community Engagement: Qur’anic Literacy, Worship Discipline, and Financial Empowerment: Keterlibatan Komunitas Berbasis Muhammadiyah: Literasi Al-Qur’an, Disiplin Ibadah, dan Pemberdayaan Keuangan Daryanto Daryanto; Bayu Wijayantini; Taghfirul Azhima Yoga Siswa; Alfi Arif; Nursaid Nursaid; Taufik Sobri; Triawan Adi Cahyanto
CONSEN: Indonesian Journal of Community Services and Engagement Vol. 6 No. 1 (2026): Consen: Indonesian Journal of Community Services and Engagement
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/consen.v6i1.2629

Abstract

This community service program aimed to strengthen Qur'anic literacy, worship discipline, and basic financial literacy among the congregation of Surau Wang Ulu, Kangar, Perlis, Malaysia, through a Muhammadiyah values-based empowerment approach. The ten-week program employed Participatory Action Research (PAR) across five stages: needs assessment, planning, implementation, evaluation, and follow-up. A total of 50 main participants took part, comprising 38 children aged 7–15 and 12 adults, with 25 parents and surau administrators serving as supporting participants. Interventions included talaqqi-based Qur'anic instruction, structured salah simulation, individual mentoring, parental engagement workshops, and basic financial literacy sessions. Data were collected through pre- and post-assessments, observation checklists, attendance records, and participant feedback, and analyzed descriptively. Post-programme assessments showed improvements across all measured indicators: tahsin recitation accuracy (72%), tajwid application (70%), beginner Qur'anic reading progression (65%), salah movement accuracy (68%), congregational prayer participation (75%), and basic financial literacy (60%). Community attendance throughout the programme reached 85%. These findings indicate that an integrated Muhammadiyah-based community service model combining spiritual, worship, and life-skills components can yield measurable improvements across religious and socioeconomic domains.
Co-Authors Abdul Rahim Abdul Rahim Abror, Irfan Fiqry Agustya Nanda Pratiwi Akbar, Zakaria Ihza Alfi Arif Anis Siti Nurrohkayati Anitasari, Dini Anton Prafanto Anton Saputra Arbansyah Arbansyah Ari Ahmad Dhani Ariyadi, Dedy Asnur Karima Aspianur Azwar Damari Bahrudin, Faizal Bayu Wijayantini, Bayu Betris Dea Maretta, Nanda Damari, Azwar Darmawan Setiya Budi Daryanto Dzul Rachman, Dzul Enriko Chiesa Sipahutar Fadhil Irsyad Ramadhani FAUZI Fendy Yulianto Fendy Yulianto Gubtha Mahendra Putra Haryadi, Rina Mashitoh Hasudungan, Rofilde Heri Abijono Hery Kurniawan Hidayati Ramadhani, Novia Hidayatullah, Muhammad Wahyu Istimaroh Istimaroh Jubaidi Khanisa Octavia Khatimah, Khusnul lia, Alvina Lidya Sari Mardiana Mardiana Mi'raj Fattah Mu. Aldi Rahmad Fahrozi Muhammad Aditya Rahman Muhammad Afif Aunur Rohman Muhammad Fadly Ramadhani Muhammad Najeri Al Syahrin Muhammad Norhalimi Muhammad Rhosyid Akhmad Muhammad Ulil Albab Muhammad Wildan Hadinata Naufal Azmi Verdhika Naufal Azmi Verdikha Naufal Azmi Verdikha Naufal Azmi Verdikha Nursaid Nursaid Pambudi, Faldy Alfareza Paula Mariana Kustiawan Pitoyo Pitoyo Pitoyo, Pitoyo Poernamawan, Ahmad Nugraha Prihandoko . Putri, Azzahra Namira Raenald Syaputra Rahman, Febrian Nor Ramadhani, Daib Jidan Renaldi Panji Wibowo Restu, Anggiq Karisma Aji Rivaldo, Vito Junivan Rizky Aspiah Rochman, Bagus Fathur Rofilde Hasudungan Rudiman, R Rudiman, Rudiman Salsabila, Cindy Azra Santi Yatnikasari Sarina Safitri Satria, Bima Sidiq, Reza June Siti Muawwanah Taufik Sobri Taufiq, Ilham Taufiqurrahman Taufiqurrahman Triawan Adi Cahyanto Wahyu Hidayat Wawan Joko Pranoto Wawan Joko Pranoto Wawan Joko Pranoto Wawan Joko Pranoto Wawan Joko Pranoto Widyastuti, Dessy Yoga Priantama