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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Rang Teknik Journal ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Systematics Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Digital Business Management JUKI : Jurnal Komputer dan Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Backpropagation Neural Network Untuk Prediksi Kebutuhan Pemakaian Obat (Kasus Di RSUD dr. Adnaan WD) Hazlita, H; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.736

Abstract

Artificial Intelligence which is developing increasingly rapidly makes it possible to make predictions. Predictions are made using one of the Artificial Intelligence systems, namely Artificial Neural Networks. Predicting the need for drug use is a problem currently being faced by RSUD dr. Adnaan WD Payakumbuh so that the service is not optimal. This research aims to design an Artificial Neural Network architecture and determine the resulting level of accuracy in predicting the need for drug use. The method used in this research is the Backpropagation method. The stages in the Backpropagation algorithm include the initial weight initialization process, activation stage, weight change and iteration stage. The data processed in this research is drug use data obtained from the Pharmacy Installation at dr. Adnaan WD Payakumbuh Hospital. The results of this research show that the best network architecture is 12-12-1 with a relatively small Mean Squared Error (MSE) value of 0.00685, a Mean Absolute Percentage Error (MAPE) value of 0.1696% and a high level of accuracy reaching 99 .83% for the prediction of Paracetamol 150 mg. The results of this research can help health service centers optimize their services
Penerapan Metode Rough Set Dalam Memprediksi Penjualan Pada PT. Jaya Framex Bengkulu Lubis, Fitri Amelia Sari; Lubis, Siti Sahara; Agustin, Riris; Karmanita, Deti; Defit, Sarjon
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.758

Abstract

So far, in predicting sales at PT. Jaya Framex Bengkulu, only relies on manual calculations. There are no calculations that use a system to help predict sales at PT. Jaya Framex Bengkulu in the future. As more and more entrepreneurs emerge, it requires entrepreneurs to plan sales strategies. So that what is produced does not decrease further, and is not less competitive with other entrepreneurs, to avoid this, it is necessary to have sales predictions to predict sales so that you can plan future sales strategies. Based on the research conducted, the author can draw the conclusion that predicting the number of food products using Data Mining is very helpful in processing data that has been classified such as product supply, product type and capabilities so that it produces rules that support a decision which can later be used as support for sales prediction decisions. to be more optimal. From 13 sample data of the Data Mining sales process using the rough set method, 5 Reducts were produced which were extracted into knowledge of 11 Generate Rules, thereby producing a decision that was conveyed from the resulting rules. The results of this research can be used by developers to predict future sales. It is hoped that adding new variables can produce more varied decisions and more useful knowledge as decision support
Penerapan Metode TOPSIS Untuk Pemberian Bantuan Bedah Rumah Di Nagari Lunang Selatan Fitriyani, Intan Nur; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.738

Abstract

Indonesian government seeks to improve people's welfare by holding various poverty reduction programs, one of which is providing assistance to uninhabitable houses (RTLH). Equitable development of the welfare of Indonesian society must be comprehensive and even, starting from the smallest scope, namely the village. One of the villages in Indonesia that has implemented a program to provide assistance for uninhabitable houses is Nagari Lunang Selatan which is located in Lunang sub-district, Pesisir Selatan Regency, West Sumatra Province. The implementation of the uninhabitable housing assistance program in Nagari Lunang Selatan has so far still used a manual system so it is not effective because the final results are not objective. There are 5 criteria and 10 alternatives as sample data used in this research. These criteria include the number of dependents, total expenses, total income, land ownership status, and condition of the house. For this reason, this research provides a solution by implementing a decision support system for providing assistance for uninhabitable housing using the Technique For Order of Preference by Similarity to Ideal Solution method, known as TOPSIS, the TOPSIS method is suitable for solving semi-structural problems such as the problem of providing assistance for inadequate housing. inhabit. The aim of this research is to produce a system that can facilitate decision making regarding providing assistance for uninhabitable housing. The results obtained from the test calculation process on sample data of 10 alternatives with 5 criteria provide accurate results. From this test, the results obtained for 3 alternatives as recipients of house renovation assistance
Rancang Bangun Sistem Pakar Backward Chaining Untuk Antisipasi Hama Tumbuhan Kedelai Resnawita, R; Siregar, Diffri Solihin; Adawiyah, Quratih; Defit, Sarjon
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.759

Abstract

Soybeans are an important source of protein in Indonesia. Soybean cultivation in Indonesia faces difficulties posed by climate-related elements that create an ideal environment for the proliferation of various pest species, including those that attack leaves and pods. An expert system is a computing system designed to replicate all aspects of expert capacity in decision making. The Backward Chaining method is an approach where the reasoning process begins with the goal or conclusion to be achieved. This research aims to build a system to provide anticipation in dealing with soybean plant diseases and pests. The results of this expert system research support consultations by uploading photos as a consultation medium. Administrators have the ability to manage soybean crop data in the knowledge base, including adding, editing, and deleting data. The expert system is able to display diagnoses accompanied by complete prevention and management solutions for identified symptoms
Metode Multi Attribute Utility Theory (MAUT) Untuk Penilaian Kinerja Guru Yamin, Abdul Yamin; Defit, Sarjon; Sumijan, Sumijan
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.5920

Abstract

The performance assessment of teachers is a foundation or basis for the development decisions in terms of promotion and career of teachers in a madrasah or school. Currently, teacher performance assessment at Pondok Pesantren MTI Canduang is limited to teachers who are civil servants (PNS) or have obtained certification. In an effort to improve the quality of education, it is important to evaluate the performance of all teachers, including those who are not civil servants. The conventional method of assessment using paper-based evaluation sheets is considered inaccurate and inefficient due to the large number of teachers being assessed. Furthermore, there is no appropriate method for making decisions regarding teacher reward programs. Therefore, the purpose of this research is to apply the Multi Attribute Utility Theory (MAUT) method for teacher performance assessment. This method aims to provide a basis for decision-making in recommending teachers who deserve rewards in each assessment period. Based on the test results using the MAUT method with 40 teacher data and 12 defined assessment criteria, it was found that 3 data points for Tsanawiyah level had the highest value of 0.797 and the lowest value of 0.332, while 3 data points for Aliyah level had the highest value of 0.874 and the lowest value of 0.386. Thus, the research results can help the madrasah determine the best alternatives according to predefined criteria and weights. The resulting web-based application can facilitate the assessment process by making it easier, faster, and more accurate.
Implementasi Naïve Bayes dalam M-Series 4 Mobile Legends untuk Prediksi Kemenangan Tamaza, Muhammad Abyanda; Defit, Sarjon; Sumijan, Sumijan
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6707

Abstract

Mobile Legends is a game made by a developer from China called Moontoon which implements the Multiplayer Online Battle Arena (MOBA) system which is currently popular. The popularity of this game is proven by the holding of low, middle and high level tournaments. Recently a high level or international tournament called the M-Series World Championship was held in Indonesia. This game is played by two teams consisting of five players with the aim of destroying enemy targets in the form of towers. The problem in this game is winning and losing. One of the factors that determines victory or defeat is the choice of hero. The wrong hero composition during the draft pick stage can make it difficult for your team to play and lead to unexpected results. This research aims to predict the percentage level of Mobile Legends wins based on the drafted heroes. Prediction is the process of minimizing errors in systematically estimating the future based on past information. The technique used in this research is the Naïve Bayes algorithm. The Naïve Bayes algorithm is a classification method based on probability. This method consists of four stages, namely data understanding, data preparation, data analysis, and results analysis. This research dataset is provided by Youtube MPL Indonesia. The dataset consists of 880 training data and 90 test data for M-Series 4 Mobile Legends. The results of this research provide a percentage value in the form of prediction of 96.67%, precision of 95.65% and recall of 97.78%. The results of an accuracy rate of 96.67% using the Naïve Bayes algorithm show that predictions using the Naïve Bayes algorithm can be applied to predict win ratios in M-Series 4 Mobile Legends.
Glaucoma detection in retinal fundus images using residual network architecture Islami, Fajrul; Sumijan, Sumijan; Defit, Sarjon
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7621

Abstract

Glaucoma is a significant eye disease that can lead to irreversible vision loss if not detected and treated early. This research focuses on developing an automated glaucoma detection system using a combination of a convolutional neural network (CNN) with the residual network 18 (ResNet18) architecture, locality sensitive hashing (LSH), and Hamming distance calculation. The CNN model is trained to extract meaningful features from retinal images, while LSH enables efficient indexing and retrieval of similar images. Hamming distance calculations are utilized to measure the dissimilarity between binary codes obtained from LSH. A dataset of 506 retinal images, consisting of 117 glaucoma images, 19 glaucoma suspect images, and 370 healthy images. The proposed glaucoma detection system achieved an average accuracy of 99.96%, sensitivity of 99.97%, and specificity of 99.94% during training, and 82.37% accuracy, 86.78% sensitivity, and 73.55% specificity during testing. Comparative analysis demonstrated its superiority over traditional methods. Further research should focus on larger datasets and explore multi-class classification for different glaucoma stages. The proposed system has potential for early glaucoma detection, facilitating timely intervention, and preventing vision loss.
Analisa Data Profil Pelanggan Menggunakan Algoritma FP-Growth Suryani, Vivi; Defit, Sarjon; Yunus, Yuhandri
Jurnal Informasi dan Teknologi 2020, Vol. 2, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v2i1.8

Abstract

The number of bouquet orders is quite varied sometimes increased and decreased. The number of hikes certainly carries the goodness but the amount of decline certainly has an impact for Wawa Florist because it can not fulfill the number of bouquet order. The purpose of this research is to know how Data Mining techniques with Fp-Growth algorithm methods and designing the grouping of customer data of Wawa Florist with the FP-Growth algorithm method to obtain better and more effective analysis results. The result of the order data of the wreaths in Wawa Florish can be obtained which area information most booked wreaths, the most ordered bouquet of flowers are: D02 (Lubuk Buaya), D04 (Lubuk Minturun), D01 (Pariaman) and D03 (Lubuk Alung ). These results are obtained based on the appearance of the itemset of the bouquet booking data. Meet minimum confidence 60%.
Implementasi Metode Backpropagation untuk Memprediksi Tingkat Kelulusan Uji Kopetensi Siswa Syofneri, Nandel; Defit, Sarjon; Sumijan
Jurnal Informasi dan Teknologi 2019, Vol. 1, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v1i4.13

Abstract

Vocational High School (SMK) 2 Pekanbaru is a Vocational School in Industrial Technology. At present there are 2400 students with 14 majors. In students the level of will in students is still low. Resulting in a low graduation rate for students. This happened because of the difficulty in predicting the level of competency examination passing at SMK Negeri 2 Pekanbaru. The purpose of this study is to assist in predicting the passing level of competency exams so as to produce predictions of student graduation. The method used is the Backpropagation method. With this method data processing can be done using input values and targets that you want to produce. So that it can predict the graduation of students in the expertise competency test. Furthermore, the data to be managed is a recapitulation of the average vocational values majoring in computer network engineering from semester 1 to semester 5 with aspects of knowledge on the target students of 2017 Academic Year and 2018 Academic Year obtained from the sum of all subjects in each semester. The results of calculations using the Backpropagation method with the Matlab application will be predictive in producing grades for students' graduation rates in the future. So that the accuracy value will be obtained in the prediction. With the results of testing the accuracy of prediction student competency tests with patterns 5-4-1 reaching 85%, with patterns 5-6-1 reaching 95%, patterns 5-8-1 reaching 70%, patterns 5-10-1 reaching 85% % and with 5-12-1 patterns it reaches 85%. Of the five patterns, the best accuracy rate of 5-6-1 is 95%. The prediction results using the Bacpropagation method can become knowledge in the next year. So that the system parameters used in testing can be recognized properly.
Implementation Of The ARIMA Method In Predicting LQ 45 Stock Prices (UNTR Issuer) Hadiyanto, Tegas; Defit, Sarjon; Sovia, Rini
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5656

Abstract

The implementation of technology is used in running businesses or activities that generate profits, such as predicting investments on the stock exchange through transaction data in the transaction data base. Machine learning is an algorithm that produces an approximation function that connects input variables so that it has the potential to be implemented in stock predictions. Stock investment has the characteristics of high risk - high return. Losses are caused by investors' lack of knowledge. Stock value analysis is divided into two, namely fundamental analysis and technical analysis. Technical analysis uses data or records about the market to try to access the demand and supply of a particular stock or the market as a whole. Based on the problems found by investors or bankers, this research will use the autoregressive integrated moving average (ARIMA) method to predict stock price movements. The Arima method consists of four stages, namely identifying time series methods, estimating parameters for alternative methods, testing methods and estimating time series values. Based on these problems, the ARIMA method will be used to predict stock movements. The Arima model (1,0,2) with RMS: 2200.576849857124 successfully predicted for the next 180 days
Co-Authors Abdul Azis Said Adawiyah, Quratih Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Afriyadi, Iqbal Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zamsuri, Ahmad Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andin, Silfia Andri Nofiar Angga Putra Juledi Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Bosker Sinaga Breinda, Engla Bufra, Fanny Septiani Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devita, Retno Dhena Marichy Putri Dila, Rahmah Dinda Permata Sukma Dwi Utari Iswavigra Dwiki Aulia Fakhri Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma eriwandi Fadlul Hamdi Faisal Roza Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus, Muhammad Bambang Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra, Guslendra Hadiyanto, Tegas Halifia Hendri Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Hidayat, Rahmadani Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Juledi, Angga Putra Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Syahputra Mardayatmi, Suci Mardison Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Menhard, Menhard Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen MUHAMMAD TAJUDDIN Muhammad, L. J. Mulyanda, Sandy Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun, Nori Novi Yanti Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Parinduri, Rezti Deawinda Pati, Muhammad Ibnu Pebriyanti, Defi Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi, Nadya Alinda Ramadhan, Mukhlis Ramadhanu, Agung Ramdani Bayu Putra Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rian Kurniawan Rianti, Eva Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Rusdianto Roestam Rustam, Camila S Sumijan Said, Abdul Azis Sandrawira Anggraini Sani, Rafikasani Saputra, Dhio Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Siregar, Diffri Solihin Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Suri, Ghea Paulina Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki, Syafrika Deni Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva Tukino, Tukino Veri, Jhon Veza, Okta Virgo, Ismail Vitriani, Vitriani Wahyu, Fungki Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yamin, Abdul Yamin Yemi, Leonardo Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulvitri, Z Zuqron, M. Iqbal Zurni Mardian