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All Journal Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknik Komputer AMIK BSI Bina Insani ICT Journal Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics IndoMath: Indonesia Mathematics Education JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL TEKNOLOGI DAN OPEN SOURCE JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science Infotekmesin JATI (Jurnal Mahasiswa Teknik Informatika) Respati Media Informatika Journal of Computer System and Informatics (JoSYC) Jurnal Sains Teknologi Transportasi Maritim Jurnal Sistem Komputer dan Informatika (JSON) Madani : Indonesian Journal of Civil Society MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknologi Informasi dan Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Jurnal Digit : Digital of Information Technology Mosharafa: Jurnal Pendidikan Matematika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jupiter Journal of Computer & Information Technology Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika dan Teknologi Informasi BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Smatika Jurnal : STIKI Informatika Jurnal
Odi Nurdiawan
STMIK IKMI Cirebon
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Komparasi Algoritma Naïve Bayes dan Algoritma K-Nearst Neighbor terhadap Evaluasi Pembalajaran Daring Odi Nurdiawan; Ruli Herdiana; Saeful Anwar
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i02.621

Abstract

Since the outbreak of the endemic caused by the Corona virus in Indonesia, many methods have been tried, one of which is conducting remote training and encouraging students to practice from home each time. The use of digital technology in the midst of the COVID-19 endemic has a big contribution to learning institutions by practicing online learning. Students are expected to be able to accept the procedures that have been implemented by the state. However, this condition does not guarantee that students agree or accept this stage. Therefore, measurements are needed to determine the level of student happiness in carrying out online learning. With that in mind, the author conducted an experiment on the ability of the algorithm first, namely the form of grouping with the Naïve Bayes Algorithm and the K-Nearst Neighbor Algorithm. The information used is the basic information, meaning that the information obtained from the results of the questionnaire circulars for students in semester 3(3) semester 5(5) and semester 7(7) amounted to 352 respondents. In the development of the form of the algorithm using the type 9.3 rapid miner tools with the operators used are retrive, multiply, cross validation, Naïve Bayes Algorithm and knn, apply form and performance. The results of the accuracy of the Naïve Bayes Algorithm are 91.45%. The results of the accuracy of the K-Nearst Neighbor Algorithm are 97, 72%. The accuracy of the K-Nearst Neighbor Algorithm is greater than the Nave Bayes algorithm, so it can be concluded that the K-Nearst Neighbor Algorithm has good ability in grouping.
ANALISIS SENTIMEN ULASAN APLIKASI BANK JAGO MENGGUNAKAN SUPPORT VECTOR MACHINE DAN NEURAL NETWORK Mariyani, Dinda; Irma Purnamasari, Ade; Ali, Irfan; Nurdiawan, Odi; Nurdiawan, Rudi
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.v14i1.8775

Abstract

Abstrak. Pertumbuhan layanan perbankan digital di Indonesia menjadikan ulasan pengguna pada Google Play Store sebagai sumber penting untuk mengevaluasi kualitas aplikasi, termasuk Bank Jago. Namun, ulasan tersebut bersifat tidak terstruktur, informal, dan mengandung noise sehingga menyulitkan analisis sentimen. Penelitian ini bertujuan memberikan gambaran objektif kecenderungan opini pengguna serta membandingkan kinerja algoritma Support Vector Machine (SVM) dan Neural Network (MLPClassifier). Sebanyak 10.000 ulasan dikumpulkan melalui scraping dan direduksi menjadi 7.946 ulasan setelah penghapusan duplikasi. Data diproses melalui tahapan preprocessing meliputi cleaning, case folding, normalisasi slang, tokenisasi, stopword removal, dan stemming. Pelabelan sentimen dilakukan menggunakan lexicon InSet, sedangkan ekstraksi fitur menggunakan CountVectorizer berbasis Bag-of-Words. Hasil penelitian menunjukkan bahwa SVM memperoleh akurasi tertinggi sebesar 91,2%, lebih unggul dibandingkan Neural Network dengan akurasi 89,8%. Temuan ini menegaskan bahwa pemilihan preprocessing dan representasi fitur yang tepat berperan penting dalam meningkatkan performa analisis sentimen pada ulasan aplikasi perbankan digital. Abstract. The growth of digital banking services in Indonesia has made user reviews on the Google Play Store an important source for evaluating application quality, including Bank Jago. However, these reviews are unstructured, informal, and noisy, creating challenges for sentiment analysis. This study aims to provide an objective overview of user sentiment and to compare the performance of Support Vector Machine (SVM) and Neural Network (MLPClassifier). A total of 10,000 reviews were collected through scraping and reduced to 7,946 reviews after duplicate removal. The data were processed through preprocessing stages including cleaning, case folding, slang normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using the InSet lexicon, while feature extraction employed a Bag-of-Words approach with CountVectorizer. The results show that SVM achieved the highest accuracy of 91.2%, outperforming the Neural Network model with 89.8%. These findings highlight the importance of appropriate preprocessing and feature representation for improving sentiment analysis performance in digital banking application reviews.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FLO DI GOOGLE PLAY STORE DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES Kurniawati, Eti; Irma Purnamasari, Ade; Ali, Irfan; Kurniawan, Rudi; Nurdiawan, Odi
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.v14i1.8776

Abstract

Abstrak. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Flo pada Google Play Store menggunakan algoritma Multinomial Naive Bayes. Flo merupakan aplikasi mobile health (mHealth) populer yang digunakan untuk memantau siklus menstruasi dan kesehatan reproduksi. Data dikumpulkan melalui web scraping dan menghasilkan 10.000 ulasan yang setelah pembersihan menjadi 6.908 data valid. Proses pra-pemrosesan meliputi case folding, cleaning, normalisasi, tokenisasi, stopword removal, dan stemming menggunakan Sastrawi. Pelabelan sentimen dilakukan secara semi-otomatis berbasis lexicon InSet dan rating. Ekstraksi fitur menggunakan CountVectorizer menghasilkan representasi Bag-of-Words sebagai input model. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes mencapai akurasi sebesar 73,6% dengan nilai precision, recall, dan F1-score yang seimbang pada tiga kelas sentimen. Temuan ini menunjukkan bahwa Naive Bayes efektif digunakan dalam mengolah ulasan teks pendek dan informal berbahasa Indonesia. Penelitian ini berkontribusi dalam pemanfaatan machine learning untuk analisis sentimen aplikasi mHealth serta menyediakan wawasan yang dapat digunakan pengembang untuk meningkatkan kualitas layanan aplikasi Flo. Abstract. This study aims to analyze user reviews of the Flo application on Google Play Store using the Multinomial Naive Bayes algorithm. Flo is a popular mobile health (mHealth) application for tracking menstrual cycles and reproductive health. Data were collected using web scraping, obtaining 10,000 initial reviews, with 6,908 valid reviews after cleaning. Preprocessing included case folding, cleaning, normalization, tokenization, stopword removal, and stemming using Sastrawi. Sentiment labeling was performed semi-automatically using the InSet lexicon and rating-based rules. Feature extraction used CountVectorizer with the Bag-of-Words approach. The evaluation shows that Naive Bayes achieved an accuracy of 73.6% with balanced precision, recall, and F1-score across sentiment classes. These results indicate that Naive Bayes is effective for processing short and informal Indonesian text reviews. This research contributes to the application of machine learning in mHealth sentiment analysis and provides insights for developers to improve the quality of the Flo application.
ANALISIS POLA KETERKAITAN PRODUK TOKO SEMBAKO IBU IYU DENGAN ALGORITMA FP-GROWTH Suripno; Nining Rahaninsih; Irfan Ali; Martanto; Odi Nurdiawan
INFOTECH journal Vol. 11 No. 2 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v11i2.16632

Abstract

Toko Sembako Ibu Iyu merupakan toko ritel tradisional yang menghasilkan data transaksi dalam jumlah besar setiap hari, sehingga diperlukan teknik pengolahan data yang mampu mengekstraksi informasi bernilai guna mendukung pengambilan keputusan. Penelitian ini menerapkan metode data mining menggunakan algoritma association rule mining, khususnya FP-Growth, untuk mengidentifikasi pola keterkaitan produk dan memahami kecenderungan pembelian konsumen. Data yang digunakan mencakup transaksi periode Januari hingga Juni 2024 yang berisi kode transaksi, tanggal, serta daftar produk yang dibeli. Tahapan penelitian meliputi seleksi data, pembersihan duplikasi, standarisasi penamaan, dan transformasi ke format basket transaction sebelum dianalisis menggunakan FP-Growth dengan minimum support 0,01 dan minimum confidence 0,6. Hasil penelitian menghasilkan 11 aturan asosiasi, dengan aturan terbaik menunjukkan bahwa konsumen yang membeli Marlboro Kretek cenderung membeli Cheetos BBQ/Jagung Bakar, dengan nilai support 1,2% dan confidence 95,7%. Temuan ini dapat dimanfaatkan untuk strategi penataan produk, promosi bundling, dan optimalisasi manajemen persediaan sehingga mendukung peningkatan efisiensi operasional toko ritel tradisional.
Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun Pada Posyandu Melati Dengan Menggunakan Algoritma Decision Tree Ahmad Zam Zami; Odi Nurdiawan; Gifthera Dwilestari
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 3 (2022): Maret 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i3.3892

Abstract

One of the health problems in Cirebon is about nutritional status. This happens because the increase and decrease in the number of children under five who experience nutritional status problems each year is uncertain. Toddlers are a group of people who are vulnerable to nutrition. The incidence of malnutrition if not addressed will cause a bad impact for toddlers. The impacts include death and chronic infection. Early detection of undernourished children (malnutrition and malnutrition) can be done with an examination of weight for age (W/U) to monitor the child's weight, the parameters used to calculate the nutritional status of toddlers include age, weight and height/length. To classify the nutritional status of children under five, a knowledge or scientific study is needed that can classify data based on the data from measurements and weighing. This study uses 8 criteria, namely Name, Address, Mother's Name, Gender, Age, Weight, Height, Status Classification. The accuracy results obtained are 98.86% with details, namely the Prediction Results of Malnutrition and it turns out that the True Malnutrition is 13 data. Poor Nutrition Prediction Results and turns out to be True Normal by 1 Data. Normal Prediction Results and it turns out to be True Malnutrition is 1 Data. Normal Prediction Results and turns out to be True Normal of 161 data. The results of the classification of infant levels based on age, infants aged 0 months to 10 months had normal nutrition, while infants aged 10 months to 19.5 months were prone to malnutrition for infants, and those aged more than 19.5 months had poor nutrition. normal.
OPTIMIZING VGG-16 CONVOLUTIONAL NEURAL NETWORK FOR PAP SMEAR IMAGE CLASSIFICATION IN CERVICAL CANCER DETECTION Odi Nurdiawan; Heliyanti Susana; Ade Rizki Rinaldi; Ahmad Asyraful Hijrah; Indah Diniarti
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7131

Abstract

Early detection of cervical cancer through Pap smear image analysis plays a crucial role in reducing mortality rates associated with this disease. This study aims to optimize the VGG16 architecture to improve the classification accuracy of Pap smear images. The proposed method employs transfer learning with pre-trained ImageNet weights, customization of fully connected layers, and data augmentation techniques to enhance the diversity of training images. Experimental results demonstrate a significant improvement in training accuracy, reaching 98.50%, while validation accuracy remained stable at 88.24%, indicating potential overfitting. Performance testing on unseen data yielded an accuracy of 80%, with high precision for the negative class but low recall for the positive class, suggesting a bias toward the majority class. These findings highlight the need for additional strategies, such as data balancing and hybrid method integration, to improve sensitivity to positive cases. This research contributes to the development of adaptive deep learning-based classification models that support clinical decision-making in cervical cancer screening and opens opportunities for further research on model optimization and dataset expansion.
SOLAR-POWERED IOT-BASED BEHAVIORAL VALIDATION SYSTEM FOR SUSTAINABLE RAT PEST CONTROL IN RURAL RICEFIELDS Willy Prihartono; Ade Rizki Rinaldi; Cep Lukman Rohmat; Odi Nurdiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7247

Abstract

Rice-field rats (Rattus argentiventer) continue to cause substantial rice yield losses in Indonesia, reaching up to 30% per season. This study presents a solar-powered IoT-based ultrasonic deterrent system designed for autonomous operation in off-grid rural environments. The system integrates PIR motion detection, PWM-controlled ultrasonic emission (16–20 kHz; 85–95 dB), and a solar-battery energy subsystem to ensure continuous nocturnal functionality. Field validation involving 21 rats demonstrated measurable short-term behavioral disruption, with 42.9% avoidance and 33.3% panic responses. Electrical testing confirmed stable night-time performance, with an average power output of 26.8 W during peak rodent activity. Statistical analysis showed χ²(2, N = 21) = 2.38, p = 0.30. While statistical significance was not achieved, the observed effect size (Cramer’s V = 0.24) indicates a moderate behavioral association, supporting practical deterrent potential under field conditions. Unlike prior studies that evaluate sensing or energy components separately, this research integrates renewable energy autonomy, real-field behavioral validation, and IoT-based automation within a single operational framework. The findings establish a foundation for adaptive, machine-learning-driven pest control systems to enhance sustainable rice-field management
Clusterization of Family Planning Participants Based on Pregnancy Risk Using K-Means Algorithm in Ciherang Village Melva Regina Arpratika; Nana Suarna; Agus Bahtiar; Martanto; Odi Nurdiawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2248

Abstract

This study aims to group family planning (KB) participants in Ciherang Village based on pregnancy risk levels using the K-Means clustering algorithm. The identification of pregnancy risk is still performed manually, resulting in less effective analysis. Therefore, a data mining approach is applied to improve decision-making accuracy. The data used in this study were obtained from KB cadres, including variables such as age, number of children, education, occupation, and contraceptive methods. The research method follows the Knowledge Discovery in Database (KDD) stages: data selection, preprocessing, transformation, data mining, and evaluation. The K-Means algorithm is used for clustering, while the Davies–Bouldin Index (DBI) is applied to evaluate clustering quality. The results show that the optimal number of clusters is K = 2 with a DBI value of 0.721. The first cluster represents low pregnancy risk participants, while the second cluster represents high pregnancy risk participants. Age and number of children are identified as the most influential factors. This study provides useful insights for healthcare providers in developing targeted strategies for family planning programs. Keywords: Data Mining; Davies–Bouldin Index; K-Means Clustering; Pregnancy Risk; Family Planning
Real-Time Face Attendance System Using CNN Mobilenet and MTCNN Hajijin Amri; Odi Nurdiawan; Arif Rinaldi Dikanda
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4403

Abstract

This research presents the development of a real-time attendance system utilizing facial recognition, which incorporates three main components: the MobileNet Convolutional Neural Network (CNN) for classification, Multi-task Cascaded Convolutional Networks (MTCNN) for face detection, and Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing. The model was trained on a curated subset of the Labeled Faces in the Wild (LFW) dataset, containing 20 categories with 50 images each, and evaluated using a locally captured dataset. Training was conducted on Google Colab using a pre-trained MobileNet model that was fine-tuned with 800 images, while 200 images were used for validation. System performance was assessed through several metrics, including accuracy, precision, recall, F1-score, and a confusion matrix. The model achieved a validation accuracy of 86% and an average F1-score of 0.85, reflecting high classification accuracy. To enhance usability, the system was implemented within a Python-based graphical user interface (GUI), which automates attendance tracking and records data directly into Excel spreadsheets. This study highlights the potential of integrating lightweight CNN architectures with effective preprocessing techniques and real-time GUI applications to create a reliable, efficient, and practical biometric attendance system
Optimizing Multimodal Health Chatbots through the Integration of Medical Text and Images Danar Dana, Raditya; Mulyawan, Mulyawan; Bahtiar, Agus; Nurdiawan, Odi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5328

Abstract

This study is motivated by the growing need for image-classification systems that remain accurate despite variations in image quality commonly found in real-world environments. Differences in image resolution often lead to decreased performance of Convolutional Neural Network (CNN) models, particularly in scenarios involving limited acquisition devices. This research aims to analyze the effect of image-resolution variations on CNN robustness by applying an adaptive augmentation strategy. An experimental approach was employed by manipulating independent variables namely image-resolution levels and augmentation techniques and observing their impact on accuracy, validation stability, and model generalization. The results show that medium-resolution images (128×128 px) combined with adaptive augmentation produce the best performance, yielding the highest validation accuracy and reduced overfitting compared to other configurations. The urgency of this study lies in its practical contribution to developing efficient image-classification models suitable for resource-constrained environments. Scientifically, the findings provide a structured mapping of the relationship between resolution, augmentation, and model stability, offering a foundation for designing more robust CNN architectures adaptable to real-world data variability.
Co-Authors Abdul Rauf Chaerudin Abdul Robi Padri abdullah, nur syarief Ade Irma Purnamasari Ade Irma Purnamasari Ade Irma Purnamasari Ade Kurnia, Dian Ade Rizki Rinaldi Adisty Tri Putra Agis Maulana Robani Agung Nugraha Agus Surip Ahmad Asyraful Hijrah Ahmad Faqih Ahmad Faqih Ahmad Faqih Ahmad Faqih Ahmad Zam Zami Ainnur Rahman, Rizal Amar, Mohammad Rosihin Amarda, Juan Amri, Hajijin Ananda Rafly Andi Setiawan Andi Setiawan Anwar Musaddad Aria Pratama Arif Fitriyanto, Goffar Arif Rinaldi Dikananda Arif Rinaldi Dikanda baihaqqi, Farisky Bambang Irawan Basysyar, Fadhil Muhammad Basysyar, Muhammad Fadhil Cep Lukman Rohmat Cep Lukman Rohmat Cep Lukman Rohmat Dadang Sudrajat Danar Dana, Raditya Deasiva, Imanda Denni Pratama Dian Ade Kurnia Dias Bayu Saputra Dikananda, Arif Rinaldi Dilla Eka Lusiana Dita Rizki Amalia Dodi Solihudin Dwi Teguh Afandi Edi Tohidi Edi Wahyudin Eko Wiyandi ETI KURNIAWATI Fadhil M. Basysyar Fadrin Helmi FANDI ACHMAD Fathurrohman Fathurrohman Fathurrohman Fatihanursari Dikananda Faturrohman, Faturrohman Fauzi Fauzi Febriansyah, Feggy Fidya Arie Pratama Fidya Arie Pratama Firmansyah Firmansyah Fitriyani, Nur Sifa Gifthera Dwilestari Haidar Fakhri Hajijin Amri Hamonangan, Ryan Hanafi, Muhammad Salman Hayati, Umi Heliyanti Susana Herdiana, Ruli Herdiana, Rully Heriyawan, Ikhsan Himawan, Irvan Hira Wahyuni Azizah Ibnu Ubaedila Indah Diniarti Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Purnamasari, Ade Irvandi Irvandi IRVANDI, IRVANDI Jaelani Sidik Jamalul'ain, Abdul Jayawarsa, A.A. Ketut Julia Eka Yanti Juliadi, Diky Karlina, Lita Kaslani Khamim Surya Hadi Kusuma Al Atros Khoirul Insan, Moh Khoirul Kurniawan Fajar Abdulloh Laturrizqi, Washi Lukmanul Hakim M. Basyisyar, Fadhil M. Iqbal Fadhilah, Aji Mamluah, Karimatul Mariyani, Dinda Martanto . Mauludin, Muhammad Rifqi Medina Aprilia Putri Melia Melia Melia Melia Melisa Hikari Melva Regina Arpratika Mia Fijriani Muchamad Sobri Sungkar, Muchamad Sobri Muhalim, Alvy Muhammad Adithya Pratama Mulyana Mulyana Mulyawan Mulyawan Mulyawan, Mulyawan Musliyadi, Mar'i Nana Suarna Nana Suarna Nana Suarna Nanda Permatasari Nenda Alfadil Seputra Nining Rahaningsih Nining Rahaninsih Noval Salim NoviFirda Aini Nur Atikah Nurcholis, Rifki Nurdiawan, Rudi Nurhadiansyah Nurrohmat, Iman Pratama, Fidya Arie Pratama, Irfan Pratiwi, Fitriyani Prihartono, Willy Purnamasari, Ade Irma Putri, Haidah R, Nining Riansah, Adam Rinaldi Dikananda, Arif Rinaldi Dikanda, Arif Riyan Suryatana Riyan, Ade Bani Rizki, Dicky Miftakhul Rohmat, Cep Lukman Rokhmatan Khaerullah, Rizal Rudi Hartono Rudi Kurniawan Rudi Kurniawan Ruli Herdiana Ruli Herdiana Rully Pramudita Saeful Anwar Saeful Anwar Saeful Anwar, Saeful Saepul Hadi Salsa Billa Agistina Suarna, Nana Subandi, Husein Suripno Syafi'i, Syafi'i Tati Suprapti Taufik Hidayat Tengku Riza Zarzani N Tio Prasetiya Tio Prasetya TOMAS TOMAS Topan Hadi Tuti Hartati Tuti Hartati Willy Prihartono Wiyandi, Eko Yudhistira Arie Wijaya Yunus, Shofian