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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah Kursor Jurnal Mahasiswa Jurusan Arsitektur Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Management Research and Studies Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Fitra Abdurrachman Bachtiar
Fakultas Ilmu Komputer - Universitas Brawijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Physics Social Sciences

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Segmentasi Pelanggan Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Pada Klinik Kecantikan (Studi Kasus : Belle Crown Malang) Aulia Dewi Savitri; Fitra Abdurrachman Bachtiar; Nanang Yudi Setyawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Belle Crown is one of new aesthetic centers in Malang has not applied CRM strategy (Customer Relationship Management) by giving different service for all of its costumers. Segmentation is a process undergo to identify costumers with similar characteristics, therefore, it can help to explore more information on profitable costumers. The costumer's business behaviour could be seen from Recency (last transaction range), Frequency (the number of transactions), and Monetary (the amount of money spent) or it is known as RFM (Recency, Frequency, Monetary). One of data clustering method is K-Means that is used to do the segmentation. The graphics result from Elbow method is used to determine the number of segments intuitively during the application of K-Means method. The data used in this research is transaction history taken from May-October 2017 and it includes 21.513 transactions and 4716 costumers. In its application, the research results two kinds of segments including 2 segments and 3 segments. The analysis based on RFM value showed that the first rate is the profitable customer as it has bigger RFM compared to other segments. The superficial of this research is to produce dashboard visualization as the result of costumers segmentation with some graphics based on RFM value of Belle Crown's service.
Support Vector Regression Untuk Peramalan Permintaan Darah: Studi Kasus Unit Transfusi Darah Cabang - PMI Kota Malang M. Raabith Rifqi; Budi Darma Setiawan; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PMI is responsible for meeting blood demand from hospitals. The management of the blood storage center has a very important task, to predict the requirement of blood components to minimize the ex less and the lack of blood supply. Blood has only a life span of 35 days since donated. If it is past the time then it can not be used anymore. Excess or lack of blood supply at the site should not occur, because it can affect the number of patients death. In order to reduce the losses that if it occurs, it is necessary to do research that uses the prediction method of blood predict that is implemented in a system. One of them with Support Vector Regression method that is suitable for blood demand forecasting. Implement SVR using normalized min - max data and use RBF kernel function. Based on the test results for the SVR method that has been done, the result of the minimum MAPE value is 3.899% with the parameter value lambda = 10, sigma = 0.5, cLR = 0.01, C = 0.1, epsilon = 0.01, number of data features = 4 and number of iterations of 5000, of the 12 test data used. The resulting MAPE value is <10% and can be categorized as good for predicting the amount of blood demand.
Klasifikasi Spam Pada Twitter Menggunakan Metode Improved K-Nearest Neighbor Dea Zakia Nathania; Indriati Indriati; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a service application that is popular because it can be used to interact and communicate in everyday life. A lot of various new types of automation software increases to disseminate information immediately. Twitter does not strictly check the automation tweet, therefore there is no prevention of the use of bot on a regular basis. Low restriction of the use of automation services on Twitter led to the emergence of market Spam-as-a-Service consisting of counterfeiting program, abridgement ad-based on service and sales account. Each of these services allows spammers to do the spam deployment process by using automation tweet services. So it is necessary to do a research on the classification of the tweet to know the type of category is included in the category of spam or not spam. Spam classification process begins with the preprocessing consists of several stages, namely; cleansing, case folding, tokenization, filtering and stemming. The next step are process of term weighting, until the process of classification using Improved K-Nearest Neighbor method. The results obtained on the basis of implementation and testing research of the classification of Spam on Twitter produces an average Precision of 0.8946, Recall of 0.9405, F-Measure of 0.9155 and results accuracy of 89.57%. Where is the number of documents, a comparison or balance the proportion of training data and the determination of k-values that are used too well or whether the process of classification of the document.
Implementasi Naive Bayes Dengan Certainty Factor Untuk Diagnosis Penyakit Anjing Desy Setya Rositasari; Nurul Hidayat; Fitra A. Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The interest of Indonesians in having dog as pets is high. Dogs become favorite pets because dogs have funny and adorable habits. In addition, taking care of dogs is quite easy. Prevention and detection of diseases that infect dogs is necessary, so the infected dogs can be taken care of immediately to prevent transmission of the disease to other dogs and to human. Diagnosing the diseases could be a bit difficult sometimes because some diseases have similar symptoms. Another problem is that there are not many veterinary clinics that open for 24 hours so it would be difficult for dog owners if they found out that their dog was sick outside of the clinics' working hours. The system of dog diseases diagnosis is made to assist veterinarians in diagnosing dog diseases, in addition the system is expected to assist the community in making an initial diagnosis of their dogs. This system is Android-based and applies the method of Naive Bayes and Certainty Factor. The Naive Bayes method is used to classify dog diseases based on the usual pattern of symptoms, while the Certainty Factor method is used to determine the value of certainty of classification results from the Naive Bayes method. Based on the accuracy test that was done for five times, the average accuracy value obtained was 97.2%.
Particle Swarm Optimization Untuk Optimasi Bobot Extreme Learning Machine Dalam Memprediksi Produksi Gula Kristal Putih Pabrik Gula Candi Baru-Sidoarjo Eka Yuni Darmayanti; Budi Darma Setiawan; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sugar demand will increase in line with the increase in population, income, and growth in food and beverage processing industry. Therefore, in order for the sugar production process is always increasing in accordance with needs of the sugar itself, hence need for production planning. Accurate forecasting can help companies in taking decisions to determine the amount of sugar to be produced, the materials needed and determine the price of the goods. One method that can be used to do the prediction algorithm is Extreme Learning Machine. But that method in a selection of input and weight bias is chosen randomly, this can lead to the results obtained in the calculation less maximum. This need for a combination of Particle Swarm Optimization algorithms that can perform optimization the input value weight and bias optimally. This research uses data 45 milled sugar production with 5 features. Based on the research that has been performed, the obtained optimal parameters, namely the number of population size 50, 80% training data comparison (36), the number of hidden neurons 10, weighs of inertia 0.5, and a maximum of iterations 250. The parameter value is obtained from the average MAPE of 0.59%. From the average MAPE results obtained, shows that the addition of the PSO algorithm on ELM can determine the value of the input of weight and optimal bias.
Penerapan Metode Analitycal Hierarchy Process-Simple Additive Weighting (AHP-SAW) dalam Penentuan Varietas Padi yang Unggul Dona Adittia; Nurul Hidayat; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rice (Oryza sativa L.) is a very important food crop in the world after wheat and corn. During the development of agricultural research appeared several varieties of rice. Variety itself is one important component that has a major contribution in increasing production and income of rice farming. Therefore, it takes a computer system to help farmers decide the varieties that will be planted in accordance with the environmental conditions of planting by considering some aspects of the criteria. In the design using Analytic Hierarchy Process - Simple Additive Weighting in order to give consideration / advice to farmers to determine the superior varieties. The result of this method is the rank of the varieties. Method AHP function to determine the value of the vector of weight of some rise varieties that criteria then made reference in is the rank of the varieties produced by the process SAW. In this research, the test is done by measuring the accuracy level with the result reaching accuracy above 80%. So the system that is created by using the method AHP-SAW can be applied as a supporting decision making in determining superior rice varieties.
Optimasi Travelling Salesman Problem Pada Angkutan Sekolah Dengan Algoritme Particle Swarm Optimization M. Khusnul Azhari; Imam Cholissodin; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Currently, The implementation of school transport has been done a lot of school, private and even the government. One of them is MI Salafiyah Kasim school. Although the school transport system has been implemented for years, there are obstacles such as students who are delivered not always the same every day, the driver delays in delivering to the destination, the school driver who always prioritizes personal experience and fund of transportation operations that are still unstable . To overcome these problems, the authors use the Particle Swarm Optimization Algorithm in the optimization to get the order of delivery of students with the shortest route that can be passed by the school driver. The results of this study compared actual sample data one day delivery with the system that has been designed. Of the five experiments applied to each kloter, three of them are able to produce a better route recommendation than the usual driver. Once reviewed overall, the system is considered to work well and produce a fairly optimal solution.
Implementasi Algoritme Improved Particle Swarm Optimization Untuk Optimasi Komposisi Bahan Makanan Untuk Memenuhi Kebutuhan Gizi Penderita Penyakit Diabetes Melitus Gregorius Dhanasatya Pudyakinarya; Imam Cholissodin; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Diabetes Mellitus is one of the diseases with the highest number of casualties in Indonesia. The high diabetics in Indonesia are due to the lack of public knowledge about healthy food controls that result in poor diet. As a result, many people have not met the balance of nutritional intake that is the most important part in managing a good and healthy diet. Information on the right diet is needed for diabetics to improve their health condition. The Particle Swarm Optimization (PSO) algorithm is often used in performing optimization cases with good and optimal results, in particular, there is development to Improved Particle Swarm Optimization (IPSO) which further improves PSO performance. Therefore, this study designs an optimization system for the composition of food ingredients for the nutritional needs of people with Diabetes Mellitus using Improved-PSO algorithm. The results obtained from this study are optimized Improved-PSO parameters that are population number = 150, acceleration coefficient value = 2, 1, and convergent system on iteration to 550. In addition, from the results of global analysis shows that the nutrient calculation of the system can meet the nutritional needs of patients with a difference of tolerance ± 10% of expert calculations.
Implementasi Data Mining untuk Prediksi Mahasiswa Pengambil Mata Kuliah dengan Algoritme Naive Bayes Indra Kurniawan Syahputra; Fitra Abdurrachman Bachtiar; Satrio Agung Wicaksono
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Faculty of Computer Science of Brawijaya University's academic division has tasks for scheduling and determining courses every semester offered for students. However, the scheduling process has some problems such as, many of classes are offered while the students who are interests in that course are very low or vice verca. Therefore, a system is needed that can predict students will take a course or not. One of the solutions is using data mining classification. Based on student's attributes values, grade points, grade point average, semester credit units, cumulative semester credit units, and the semester is used to classify whether the student will take certain courses. Result of the classification divided into two classes that are ‘Yes' for student who take the class and No class for student who put off the class. Classification process is performed using Naive Bayes Classification (NBC) algorithm. The process used data from the odd semester in 2014 to even semester in 2015 for training and from odd semester in 2016 for testing. Prediction result using two courses as sample, the result of accuracy score for Customer Relationship Management course is 85,88%, while for Wireless Network course is 44,92%. The output of this research is a web-based dashboard that displays a comparison of actual dan predict values of each course in certain year and semester.
Prediksi Produktivitas Padi Menggunakan Jaringan Syaraf Tiruan Backpropagation Gandhi Ramadhona; Budi Darma Setiawan; Fitra A. Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rice is very important for human beings, especially to the ASEAN community. Indonesia is one of the ASEAN countries that cultivate rice. In 2015, Indonesia ranked as the third-highest in terms of the world's largest rice producer. However, Indonesia still have to import rice every year due to its high demand and to fulfil Indonesian's per-capita consumption. The other reason is the different amount of harvest on each areas resulting in a scarcity of rice because the country can not be able to optimize the farming techniques that are used. This research use the methods of backpropagation neural network to predict the results of the rice productivity. In its implementation, the data is normalized using the min - max normalization and weighting initialization using Nguyen - Widrow. Based on the results of testing the parameters for the method of backpropagation, shows the most minimum RMSE i.e. 8.6918 with parameter values learning rate = 0.8, hidden layer neurons, hidden = 3 = 4 with the number of epoch 10000 against 135 training and 13 test data. Based on result of 5 fold cross validation against the stability testing data gets an average RMSE of 8.2126.
Co-Authors AA Sudharmawan, AA Abidatul Izzah Abu Wildan Mucholladin Achmad Arwan Achmad Basuki Achmad Basuki Achmad Fahlevi Achmad Firmansyah Sulaeman Achmad Hanim Nur Wahid Achmad Ridok Adam Hendra Brata Adam Syarif Hidayatullah Adhia, Nabila Nur Fajri Adinugroho, Sigit Aditya Rachmadi Aditya Rachmadi, Aditya Admaja Dwi Herlambang, Admaja Dwi Afida, Latansa Nurry Izza Afifurrijal Afifurrijal Agus Wahyu Widodo Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Fairuzabadi Aisyah Awalina Aisyatul Maulidah Akhmad Lazuardi Al Ikhsan, Mochammad Dearifaldi Alaikal Fajri Nur Alfian Aldi Fianda Putra Aldo, Muhammad Alfi Nur Rusydi Alfian, Kharis Alfin Taufiqurrahman Alfirsa Damasyifa Fauzulhaq Alhasyimi, Dana Mustofa Amadea, Karina Amalia Kusuma Akaresti Amrillah, Muhammad Ifa Andi Alifsyah Dyasham Anggit Chalilur Rahman Anita Rizky Agustina Anita Rizky Agustina Anjasari, Ni Luh Made Beathris Anjumi Kholifatu Rahmatika Annuranda, Ramansyah Eka Apriyanti -, Apriyanti Ardi Wicaksono ari kusyanti Arieftia Wicaksono Arifien, Zainal Aulia Dewi Savitri Aulia Nurrahma Rosanti Paidja Aulia Septi Pertiwi Awalina, Aisyah Azhar Izzannada Elbachtiar Azizah, Rizky Adinda Azzam Syawqi Aziz Azzam, Ja'far Shidqul Baharudin Yusuf Widiyanto Bangse, Ni Nyoman Dinda Permata Putri Barlian Henryranu Prasetio Bayu Aji Firmansyah Bayu Priyambadha Benni A. Nugroho Bere, Stevania Biabdillah, Fajerin Bianca Pingkan Nevista Bintang Fajrianti Budi Darma Setiawan Budi Setiawan Cahya, Reiza Adi Cinthia Vairra Hudiyanti Dariswan Janweri Perangin-Angin Darmawan, Riski Dary Ardiansyah Haryono Dea Zakia Nathania Dedi Romario Delpiero, Rangga Raditya Desy Setya Rositasari Dewi, Elok Nuraida Kusuma Dian Eka Ratnawati Dika Imantika Dimas Angga Nazaruddin Dinda Adimanggala Dito William Hamonangan Gultom Diva Fardiana Risa Djoko Pramono Dona Adittia Dyah Ayu Wulandari Dyah Ayu Wulandari Dyah Ayu Wulandari Dzar Romaita Eka Devi Prasetiya Eka Yuni Darmayanti Eko Laksono Eko Setiawan Fabiansyah Cahyo Kuncoro Pradipta Fahrezy, Ahmad Faizatul Amalia Fajar Pradana Faranisa, Puspa Ayu Fardan Ainul Yaqiin Farhan Setya Dhitama Farhansyah, Brahma Hanif Farid Syauqi Nirwan Fasya Ghassani Hadiyan Fatwa Ramdani, Fatwa Ferdian Maulana Akbar Ferry Ardianto Rismawan Ficry Agam Fathurrachman Fikar Mukamal Gandhi Ramadhona Gembong Edhi Setyawan Giga Setiawan Gregorius Dhanasatya Pudyakinarya Gultom, Dito William Hamonangan Gunawan, Alifi Haikal, Raihan Hanggara, Buce Trias Hanif Prasetyo Maulidina Hanifah Khoirunnisak Hanifah Muslimah Az-Zahra Hanifah Muslimah Az-Zahra, Hanifah Muslimah Haryowinoto Rizqul Aktsar Hasyir Daffa Ibrahim Hayashi, Yusuke Herman Tolle Heryana, Ana Hidayatullah, Adam Syarif Hirashima, Tsukasa Holiyanda Husada Hutamaputra, William Ihza Razan Alghifari Ikhsan Putra Arisandi Ikrom Septian Hadi Ilham Pambudi Imam Cholissodin Imam Cholissodin Indra Kurniawan Syahputra Indriati Indriati Indriati Indriati Indriati Indriati Indriati Intan Yusuf Habibie Iqbal Taufiq Ahmad Nur Irfani, Ilham Irma Nurvianti Irwan Suprianto Irwanto, M. Sofyan Issa Arwani Istanto, Raga Saputra Heri Ivqonnada Al Mufarrih Joseph Ananda Sugihdharma Joseph Ananda Sugihdharma Julia Ferlin Kartiko, Erik Yohan Katrina Puspita Kevin Gusti Farras Fari&#039; Utomo Kharis Alfian Khoirullah, Habib Bahari Kresna Hafizh Muhaimin Krisnabayu, Rifky Yunus Krisnandi, Dikdik Kuncahyo Setyo Nugroho Kurnia Fakhrul Izza Kurniawan, Rafi Athallah Kusumo, R. Budiarianto Suryo Lailil Muflikhah Larasati, Sza Sza Amulya Lathania, Laela Salma Ludgerus Darell Perwara Luthfi Afrizal Ardhani M Reza Syahputra A M. Ali Fauzi M. Khusnul Azhari M. Raabith Rifqi Mar'i, Farhanna Marji Marvel Timothy Raphael Manullang Maulidah, Aisyatul Mawarni, Marrisaeka Moch Irfan Prayudha Adhianto Mochamad Chandra Saputra Mochamad Havid Albar Purnomo Mochammad Dearifaldi Al Ikhsan Moh Iqbal Yusron Mufidatun Nuha Muh. Edo Aprillia Andilala Muhammad Ferdyandi Muhammad Tanzil Furqon Muhammad Taufik Dharmawan Muhammad Wafi Muhammad Zulfikarrahman Nabila Leksana Putri Nabila Lubna Irbakanisa Nadifa, Rahajeng Mufti Nainggolan, Cesilia Natasya Nanang Yudi Setiawan Nanang Yudi Setiawan Nanang Yudi Setyawan Nanda Ajeng Kartini Nanda Samsu Dhuha Nasita Ratih Damayanti Nevista, Bianca Pingkan Nourman Hajar Novanto Yudistira Novi Sunu Sri Giriwati Novianti, Siska Nur Wahyu Melliano Hariyanto Nur, Iqbal Taufiq Ahmad Nurafifah Alya Farahisya Nurkhoyri, Ageng Nurul Hidayat Oddy Aulia Rahman Nugroho Okta Dwi Ariska Pamungkas, Gilang Alif Pangestu, Gusti Pradana , Fajar Pranata, Arya Yudha Kusuma Priyambadha, Bayu Pryono, Muhammad Adam Pulungan, Vallery Puras Handharmahua Purnomo, Fawwaz Anrico Putra Pandu Adikara Rafif Taqiuddin Rafif Taqiuddin Rafly, Andi Rahman, Rafli Rahmat Adi Setiawan Ramadhan, Muhammad Fitrah Ramadhianti, Fatiha Randy Cahya Wihandika Randy Cahya Wihandika Ratih Kartika Dewi Refi Fadholi Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Renavitasari, Ivenulut Rizki Diaz Retno Indah Rokhmawati Retno Indah Rokhmawati, Retno Indah Revanza, Muhammad Nugraha Delta Reza Syahputra Rezka Aditya Nugraha Hasan Rezky Dermawan Rhobith, Muhammad Rian Nugroho Ridwan Adi Setiabudi Rifky Akhsanul Hadi Risa, Diva Fardiana Riski Darmawan Riza Setiawan Soetedjo Rizal Setya Perdana Rizkey Wijayanto Rizkia Desi Yudiari Rizky Adinda Azizah Rizky Muhammad Faris Prakoso Robi Dwi Setiawan Rochmawanti, Ovy Rona Salsabila Said Atharillah Alifka Alhabsyi Salsabila, Rona Samuel Arthur Satrio Agung Wicaksono Satrio Agung Wicaksono Satrio Hadi Wijoyo Satrio Hadi Wijoyo Satrio Wicaksono Satyawan Agung Nugroho Satyawan Agung Nugroho Shinta Aprilisia Sifaunnufus Ms, Fi Imanur Sigit Adinugroho Sinana, Admi Rut Sintiya, Karena Siswahyudi, Puad Siti Mutdilah Sofyanda, Erika Yussi Sri Wulan Utami Vitandy Sueddi Sihotang Sugihdharma, Joseph Ananda Sulandri, Sulandri Sutawijaya, Bayu Syahidi, Aulia Akhrian Syahputra, Indra K. Taufik Hidayat Timothy Julian Tirana Noor Fatyanosa, Tirana Noor Titus Christian Ubaydillah, Achmad Afif Utaminingrum, Fitri Vasha Farisi Sarwan Halim Very Sugiarto, Very Vivy Junita Wafi, Muhammad Wahyu Ardiansyah, Mohammad Wahyu Satriyo Wibowo Wahyudi, Hafif Bustani Wayan F. Mahmudy Wayan Firdaus Mahmudy Welly Purnomo Whita Parasati Wicaksono, Satrio A. Wicky Prabowo Juliastoro Windy Adira Istiqhfarani Wiratama Ahsani Taqwim Wirdhayanti Paulina Yoga Tika Pratama Yudi Muliawan Yuita Arum Sari Zafira, Sabrina Ella Zayn, Afta Ramadhan Zulfikarrahman, Muhammad