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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika Technomedia Journal Applied Technology and Computing Science Journal Building of Informatics, Technology and Science Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) GUYUB: Journal of Community Engagement Informatics, Electrical and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering Jurnal Informatika: Jurnal Pengembangan IT El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science
iwan Syarif
Politeknik Elektronika Negeri Surabaya
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Incremental News Mining Using Evolving Clustering with Functional Operators Hidayah, Amalia Wirdatul; Barakbah, Ali Ridho; Syarif, Iwan
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3197

Abstract

Online media publish journalistic products, one of which is news online (online news). This is in line with the findings of the Ministry of Communication and Informatics (Kemkominfo), that in 2018 there were 43,000 online media in Indonesia. On generally in getting actual news, humans tend to read the news on online media one by one. The activity is not effective because of the news that produced by online media have the same information with each other news. In this study, we propose an innovative solution to this issue by developing a news mining system that employs clustering based on an evolving system. This system has the potential to improve the effectiveness of news retrieval by grouping similar news together and identifying key information trends, ultimately enhancing the ability of individuals to obtain actual news. Based on research observations, the performance of news clustering using an evolving clustering system with functional operators is quite good, as evidenced by an accuracy of 83%.
METODE STATISTIK DAN MACHINE LEARNING UNTUK PREDIKSI HARGA BAHAN POKOK DI JAWA TIMUR Dzulfiqar, Achmad Fakhri; Ferry Astika Saputra; Iwan Syarif
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4625

Abstract

Price fluctuations of basic commodities impact economic stability and community welfare. This study compares predictive methods based on statistical approaches (Simple Moving Average, Linear Regression) and machine learning techniques (Support Vector Regression, Long Short-Term Memory) using data from SISKAPERBAPO, which records daily prices of 76 basic commodities across 119 central markets in 38 districts/cities in East Java. The study supports the role of Regional Inflation Control Teams (TPID) in maintaining stable and low inflation through coordinated policies. Evaluation based on Root Mean Square Error (RMSE) and Squared Correlation indicates that SVR performs best of 4 commodities (rice, sugar, chicken meat, chicken eggs), while LSTM excels for 3 commodities (cooking oil, beef, garlic). These findings recommend SVR and LSTM as the most efffective methods for price prediction and provide a reference for TPID and policymakers in developing for price control.
Implementation of Cyber Threat Intelligence on Intrusion Detection System using STIX Framework Mahardhika, Yesta Medya; Saputra, Ferry Astika; Syarif, Iwan; Wibowo, Prasetyo; Ardhani, Misbahul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6518

Abstract

Cyber threats are complex and diverse issues. Various types of threats emerge daily on the internet. In this research, we proposed a new Cyber Threat Intelligence platform to deal with the challenges above, using Snort as a tool for detecting anonymous network traffic and STIX as a serialization format and standardization of Cyber Threat Intelligence data. As a result, a Cyber Threat Intelligence based on Snort contains Apache Spark as the processing engine, MongoDB as the database, and STIX as the serialization format and data standardization. We test our platform by using two data sources, the CIC-IDS2017 dataset, and the real traffic. We successfully converted the snort alerts to STIX format and visualized them into graph. The graph shows indication of network traffic suspicious, the country of attacker come from, attribute information and attack pattern. The experiment shows that converting Snort data to STIX requires considerable time if the amount of data processed is getting bigger, Real Traffic needs 16 seconds of data preprocessing and 3 minutes of conversion time, while PCAP needs 35 seconds of preprocessing time and 13 minutes of conversion time.
EMASJID, APLIKASI CHATBOT ISLAMI DENGAN METODE RAG (RETRIEVAL-AUGMENTED GENERATION) Fathoni, Kholid; Sudaryanto, Aris; Syarif, Iwan; Darmawan, Zakha Maisat Eka; Yunanto, Andhik Ampuh; Prasetyo; Aji , Rendra Suprobo; Fauzy, Aryazaky Iman; Hakkun, Rizky Yuniar
Jurnal Teknik Elektro dan Informatika Vol 5 No 1 (2025): INFOTRON
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/infotron.v5i1.22894

Abstract

The development of a religious chatbot is crucial for fostering good understanding and mitigating potential social conflicts arising from religious misunderstandings. This study introduces the Intelligent Religious Assistant ChatBot, developed using the Retrieval-Augmented Generation (RAG) method within a Large Language Model (LLM). RAG combines information retrieval from authoritative sources integrated into a database with text generation to produce user-specific answers. The application has been fully developed and performance-tested, demonstrating its capability to provide accurate and contextually appropriate answers based on authoritative Islamic references endorsed by major Indonesian Islamic organizations, including Nahdlatul Ulama, Muhammadiyah, and the Indonesian Ulema Council (MUI).
Analysis of Mental Health Disorders via Social Media Mining Using LSTM and Bi-LSTM Kholifah, Binti; Syarif, Iwan; Badriyah, Tessy
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2205

Abstract

Mental health disorders are a growing global concern, with many individuals lacking early detection and appropriate treatment. Mental illness can impact a person’s quality of life and often goes undetected until symptoms worsen. One contributing factor to this problem is the limited ways to detect mental disorders in their early stages. Social media, especially platform X, offers the potential to analyze users’ emotional expressions that may indicate a mental disorder, such as depression or anxiety. Psychological symptoms can be explored more broadly using Natural Language Processing. This study optimizes several text preprocessing techniques to extract meaningful information from social media text. To convert words into numerical vectors, several word embedding methods are used, such as Word2Vec, FastText, and GloVe. Meanwhile, the classification process is carried out using LSTM and Bi-LSTM because they are considered capable of studying data sequence patterns, such as sentence structure, effectively. The results show that the addition of expanding contractions, emoticon handling, negation handling, repeated character handling, and spelling correction in the preprocessing text can improve the model performance. In addition, Bi-LSTM with pre-trained FastText shows better results than the other methods in all experiments, achieving 86% accuracy, 87.5% precision, 84% recall, and 85.71% F1-Score.
The Impact of Image Pre-processing for Tuberculosis Prediction System Based on Chest X-ray Images Kurniawan, Rudi; Badriyah, Tessy; Apriandy, Kevin Ilham; Syarif, Iwan
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9086

Abstract

With the rapid development of automated detection system using deep learning techniques on Chest X-ray (CXR) image datasets to the subjective assessment performed by healthcare professionals. Preprocessing is critical in medical image analysis as it helps highlight important anatomical features while suppressing irrelevant information, thus enabling the model to focus on meaningful patterns. In this paper, we investigate the impact of image preprocessing techniques on the performance of a tuberculosis prediction system based on CXR images using a deep learning approach. We used the “Tuberculosis Chest X-rays (Shenzhen)” dataset, which contains 1,344 CXR images (672 TB cases and 672 normal cases). We propose a five-step preprocessing pipeline consisting of resizing, heavy sharpen filtering, CLAHE (Contrast Limited Adaptive Histogram Equalization), horizontal flip augmentation, and data normalization. The findings indicate that the model utilising preprocessing markedly surpasses the one lacking it, attaining an accuracy, precision, recall, and F1-score of 71%, in contrast to 51%, 50%, 50%, and 36% without preprocessing, respectively.  This study enhances the existing research on the application of deep learning in medical diagnostics and emphasises the significance of preprocessing for attaining dependable, high-performance systems.
Analisis Kinerja Algoritma Mesin Pembelajaran untuk Klarifikasi Penyakit Stroke Menggunakan Citra CT Scan Sakinah, Nur; Badriyah, Tessy; Syarif, Iwan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020743482

Abstract

Stroke adalah suatu kondisi dimana pasokan darah ke otak terganggu sehingga bagian tubuh yang dikendalikan oleh area otak yang rusak tidak dapat berfungsi dengan baik. Penyebab stroke antara lain adalah terjadinya penyumbatan pada pembuluh darah (stroke iskemik) atau pecahnya pembuluh darah (stroke hemoragik). Pasien yang terkena stroke harus segera ditangani secepatnya karena sel otak dapat mati dalam hitungan menit. Tindakan penanganan stroke secara cepat dan tepat dapat mengurangi resiko kerusakan otak dan mencegah terjadinya komplikasi. Penelitian ini bertujuan untuk mengembangkan perangkat lunak yang dapat membaca dan menganalisis citra CT scan dari otak, dan kemudian secara otomatis memprediksi apakah citra CT scan tersebut stroke iskemik atau stroke hemoragik. Data citra CT scan berasal dari Rumah Sakit Umum Haji Surabaya yang diambil selama periode Januari-Mei 2019 dan berasal dari 102 pasien yang terindikasi stroke. Sebelum data gambar tersebut diolah dengan menggunakan beberapa algoritma mesin pembelajaran, data tersebut melalui tahap pre-processing yang bertujuan untuk meningkatkan kualitas citra meliputi konversi citra, pemotongan citra, penskalaan, greyscaling, penghilangan noise dan augmentasi. Tahap selanjutnya adalah ekstraksi fitur menggunakan metode Gray-Level Co-Occurrence Matrix (GLCM). Penelitian ini juga bertujuan untuk membandingkan kinerja lima algoritma mesin pembelajaran yaitu Naïve Bayes, Logistic Regression, Neural Network, Support Vector Machine dan Deep Learning yang diterapkan untuk memprediksi penyakit stroke. Hasil percobaan menunjukkan bahwa algoritma Deep Learning menghasilkan tingkat performansi paling tinggi yaitu nilai akurasi 96.78%, presisi 97.59% dan recall 95.92%. AbstractStroke is a condition in which the blood supply to the brain is interrupted so that parts of the body that are controlled by damaged brain areas cannot function properly. Causes of strokes include blockages in blood vessels (ischemic stroke) or rupture of blood vessels (hemorrhagic stroke). Stroke patients must be treated as soon as possible because brain cells can die within minutes. The handling of stroke patients quickly can reduce the risk of brain damage and prevent complications. This study aims to develop software that can read and analyze CT scan images from the brain, and then automatically predict whether the CT scan images are ischemic stroke or hemorrhagic stroke. The CT scan image data came from the Surabaya Hajj General Hospital which was taken during the January-May 2019 period and came from 102 patients who had indicated a stroke. Before the image data is processed using several machine learning algorithms, the data goes through a pre-processing phase which aims to improve image quality including image conversion, image cutting, scaling, greyscaling, noise removal and augmentation. The next step is feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM) method. This study also aims to compare the performance of five machine learning algorithms, namely Naïve Bayes, Logistic Regression, Neural Networks, Support Vector Machines and Deep Learning which are applied to predict stroke. The experimental results show that the deep learning algorithm produces the highest level of performance where the accuracy value is 96.78%, 97.59% precision and 95.92% recall.
Sistem Akuaponik untuk Peternakan Lele dan Tanaman Kangkung Hidroponik Berbasis IoT dan Sistem Inferensi Fuzzy Rozie, Fachrul; Syarif, Iwan; Al Rasyid, Muhammad Udin Harun; Satriyanto, Edi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814025

Abstract

Akuaponik adalah penggabungan sistem budidaya akuakultur dan hidroponik yang dapat menjadi solusi untuk mengatasi keterbatasan lahan, keterbatasan sumber air serta meningkatkan ketahanan pangan. Pada sistem akuaponik, kualitas air pada budidaya ikan merupakan salah satu syarat utama dalam keberhasilan proses budidaya. Penelitian ini mengkombinasikan peternakan lele dengan penanaman kangkung hidroponik. Kotoran ikan lele dan sisa makanan terakumulasi di air dan dapat menjadi racun bagi ikan lele karena mengandung kadar anomia yang tinggi sehingga sangat berbahaya jika tidak dibuang. Air ini kemudian dialirkan ke tanaman kangkung hidroponik melalui biofilter yang bermanfaat sebagai pengurai air kotor dari kolam menjadi nitrat dan nitrit yang berguna sebagai nutrisi tanaman. Selanjutnya setelah air menjadi bersih dan mempunyai kadar oksigen yang tinggi, air tersebut dialirkan kembali ke kolam ikan lele. Penelitian ini bertujuan untuk mengembangkan sistem cerdas pada budidaya akuaponik dengan memanfaatkan teknologi Internet of Things yang dilengkapi dengan beberapa jenis sensor untuk memantau dan mengendalikan kualitas air dengan menerapkan algoritma Sistem Inferensi Fuzzy / Fuzzy Inference System (FIS) untuk mengatur kecepatan sirkulasi air kolam agar menghemat daya listrik pada pompa. Peralatan ini juga dilengkapi dengan layanan pemberian pakan ikan secara otomatis yang dapat diprogram sesuai kebutuhan. Sistem akuaponik ini dapat dipantau melalui web maupun ponsel pintar berbasis android. Pengujian yang dilakukan terhadap perbandingan keputusan oleh pakar dan sistem FIS pada kecepatan sirkulasi air sistem akuaponik menunjukkan nilai akurasi 83,33%, dan hasil dari pengujian ketepatan alat pemberi pakan yang dibuat secara otomatis terhadap ketepatan pemberian pakan secara manual memiliki nilai akurasi 90,97%. AbstractAquaponics is a combination of aquaculture and hydroponic cultivation systems that can be a solution to overcoming limited land, limited water sources and increasing food security. In the aquaponics system, water quality in fish farming is one of the main requirements in the success of the cultivation process. This research combines catfish farming with hydroponic kale cultivation. Catfish feces and food scraps accumulate in water and can be toxic to catfish because they contain high levels of anomia so it is very dangerous if not disposed of. This water is then flowed to hydroponic kale plants through a biofilter which is useful as decomposing dirty water from the pond into nitrates and nitrites which are useful as plant nutrients. Furthermore, after the water becomes clean and has high oxygen levels, the water is flowed back into the catfish pond. This study aims to develop a smart system in aquaponic cultivation by utilizing Internet of Things technology which is equipped with several types of sensors to monitor and control water quality by applying the Fuzzy Inference System (FIS) algorithm to regulate the speed of pool water circulation in order to save electric power on the pump. This equipment is also equipped with an automatic fish feeding service which can be programmed as needed. This aquaponics system can be monitored via the web or an Android-based smart phone. Tests carried out on the comparison of decisions by experts and the FIS system on the water circulation speed of the aquaponics system show an accuracy value of 83.33%, and the results of testing the accuracy of the feeder that is made automatically against the accuracy of manual feeding have an accuracy value of 90.97% .
Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator Ningrum, Ayu Ahadi; Syarif, Iwan; Gunawan, Agus Indra; Satriyanto, Edi; Muchtar, Rosmaliati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834587

Abstract

Kualitas dan ketersediaan pasokan listrik menjadi hal yang sangat penting. Kegagalan pada transformator menyebabkan pemadaman listrik yang dapat menurunkan kualitas layanan kepada pelanggan. Oleh karena itu, pengetahuan tentang umur transformator sangat penting untuk menghindari terjadinya kerusakan transformator secara mendadak yang dapat mengurangi kualitas layanan pada pelanggan. Penelitian ini bertujuan untuk mengembangkan aplikasi yang dapat memprediksi umur transformator secara akurat menggunakan metode Deep Learning-LSTM. LSTM adalah metode yang dapat digunakan untuk mempelajari suatu pola pada data deret waktu. Data yang digunakan dalam penelitian ini bersumber dari 25 unit transformator yang meliputi data dari sensor arus, tegangan, dan suhu. Analisis performa yang digunakan untuk mengukur kinerja LSTM adalah Root Mean Squared Error (RMSE) dan Squared Correlation (SC). Selain LSTM, penelitian ini juga menerapkan algoritma Multilayer Perceptron, Linear Regression, dan Gradient Boosting Regressor sebagai algoritma pembanding.  Hasil eksperimen menunjukkan bahwa LSTM mempunyai kinerja yang sangat bagus setelah dilakukan pencarian komposisi data, seleksi fitur menggunakan algoritma KBest dan melakukan percobaan beberapa variasi parameter. Hasil penelitian menunjukkan bahwa metode Deep Learning-LSTM mempunyai kinerja yang lebih baik daripada 3 algoritma lain yaitu nilai RMSE= 0,0004 dan nilai Squared Correlation= 0,9690. AbstractThe quality and availability of the electricity supply is very important. Failures in the transformer cause power outages which can reduce the quality of service to customers. Therefore, knowledge of transformer life is very important to avoid sudden transformer damage which can reduce the quality of service to customers. This study aims to develop applications that can predict transformer life accurately using the Deep Learning-LSTM method. LSTM is a method that can be used to study a pattern in time series data. The data used in this research comes from 25 transformer units which include data from current, voltage, and temperature sensors. The performance analysis used to measure LSTM performance is Root Mean Squared Error (RMSE) and Squared Correlation (SC). Apart from LSTM, this research also applies the Multilayer Perceptron algorithm, Linear Regression, and Gradient Boosting Regressor as a comparison algorithm. The experimental results show that LSTM has a very good performance after searching for the composition of the data, selecting features using the KBest algorithm and experimenting with several parameter variations. The results showed that the Deep Learning-LSTM method had better performance than the other 3 algorithms, namely the value of RMSE = 0.0004 and the value of Squared Correlation = 0.9690.
Penerapan Aplikasi Klasifikasi Hukum Tajwid Menggunakan Image Processing Kindarya, Fabyan; Kusumaningtyas, Entin Martiana; Barakbah, Aliridho; Permatasari, Desy Intan; Al Rasyid, M. Udin Harun; Ramadijanti, Nana; Fariza, Arna; Syarif, Iwan; Sa'adah, Umi; Saputra, Ferry Astika; Ahsan, Ahmad Syauqi; Sumarsono, Irwan; Yunanto, Andhik Ampuh; Edelani, Renovita; Primajaya, Grezio Arifiyan; Kusuma, Selvia Ferdiana
El-Mujtama: Jurnal Pengabdian Masyarakat  Vol. 4 No. 2 (2024): El-Mujtama: Jurnal Pengabdian Masyarakat
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmujtama.v4i2.1930

Abstract

Tajwid is an important science that regulates the way of reading the verses of the Al-Qur’an properly. Learning Tajwid means knowing the meaning that corresponds to the correct recitation. Learning to read the Al-Qur’an tends to be done traditionally in a place of learning or by calling a teacher to the house. Learning in this way has some drawbacks, such as the limited availability of trained and competent teachers because not all areas have sufficient access to these teachers. Dependence on schedules and locations can be a constraint for students with limited mobility or busy schedules. The role of the teacher is still important in learning tajwid, especially in providing effective explanations, guidance, and feedback. However, to overcome these shortcomings, integration with independent and technology-based learning methods can help improve the accessibility, flexibility, and quality of tajwid learning. The classification of tajwid laws using image processing allows users to see the results of inputting images of verses of the Al-Qur’an into the type of detected nun sukun tajwid and how to recite it. The initial stage of this system in detecting tajwid laws from uploaded images is the input of images by users, which can be done in two ways, namely by directly taking pictures using a smartphone camera or uploading images from the gallery. This is followed by the OCR process to detect the Arabic text contained in the image and provide diacritics for that Arabic text. Finally, letter classification is carried out after nun sukun and classification of tajwid laws contained in accordance with the detected letters after nun sukun. This system has an accuracy rate of 92.18% from the classification results that have been carried out.
Co-Authors Adam Prugel-Bennett Afifah, Izza Nur Agung Muliawan Ahsan, Ahmad Syauqi Aidil Saputra Kirsan Aji , Rendra Suprobo Al Falah, Adam Ghazy Alfaqih, Wildan Maulana Akbar Ali Ridho Barakbah Alwan Fauzi Amalia Wirdatul Hidayah Amran, Osamah Abdullah Yahya Andhik Ampuh Yunanto APRIANDY, KEVIN ILHAM Ardhani, Misbahul Arna Fariza Assodiky, Hilmy Aziz, Adam Shidqul Bagas Dewangkara Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Darmawan, Zakha Maisat Eka Desy Intan Permatasari, Desy Intan Deyana Kusuma Wardani Dian Neipa Purnamasari Dimas Bagus Santoso Dona Wahyudi Dzulfiqar, Achmad Fakhri Edelani, Renovita Edi Satriyanto Entin Martiana Kusumaningtyas Fahrudin, Tresna Maulana Fakhri, Haidar Fathoni, Kholid Fauzy, Aryazaky Iman Ferry Astika S Ferry Astika Saputra Ferry Astika Saputra Fitri Setyorini Gary Wills Gunawan, Agus Indra Hamida, Silfiana Nur Hardiyanti, Fitriani Rohmah Hasan Basri Hidayah, Amalia Wirdatul Hidayah, Nadila Wirdatul Hilmy Assodiky Hisyam, Masfu Huda, Achmad Thorikul Idris Winarno Irsal Shabirin Khoirunnisa, Asy Syaffa Kholifah, Binti Kindarya, Fabyan Kusuma, Selvia Ferdiana M Udin Harun Al Rasyid, M Udin Harun Mahardhika, Yesta Medya Masfu Hisyam Maulana, Yufri Isnaini Rochmat Mayangsari, Mustika Kurnia Mufid, Mohammad Robihul Muhammad Fajrul Falah Muhlis Tahir Nadila Wirdatul Hidayah Nana Ramadijanti, Nana Ningrum, Ayu Ahadi Novie Ayub Windarko Nur Rosyid Mubtadai, Nur Rosyid Nur Sakinah Nur Ulima Rusmayani Prasetyo Primajaya, Grezio Arifiyan Rabiatul Adawiyah Rachmawati, Oktavia Citra Resmi Reesa Akbar Rengga Asmara Rengga Asmara Riyanto Sigit, Riyanto Rizky Yuniar Hakkun Rosmaliati, Rosmaliati Rozie, Fachrul Rudi Kurniawan Rulisiana Widodo S, Ferry Astika Sa'adah, Umi Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Sritrusta Sukaridhoto Sudaryanto, Aris Sumarsono, Irwan Susanti, Puspasari Tessy Badriyah, Tessy Tresna Maulana Fahrudin Tri Harsono Ubed, Imanullah Ali Utomo, Agus Priyo Walujo, Ivana Yudith Wibowo, Prasetyo Willy Sandhika Yufri Isnaini Rochmat Maulana