Garuda - Garba Rujukan Digital

Rayi Pradono Iswara

UIN Syarif Hidayatullah Jakarta

Author-ID : 2584333

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Pengembangan Algoritma Unsupervised Learning Technique Pada Big Data Analysis di Media Sosial sebagai media promosi Online Bagi Masyarakat Nurhayati Buslim; Rayi Pradono Iswara
JURNAL TEKNIK INFORMATIKA Vol 12, No 1 (2019): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

ABSTRAKKumpulan data yang besar atau dikenal dengan istilah big data dapat dianalisis dengan berbagai macam teknik. Salah satu teknik untuk mengolah big data adalah Unsupervised Technique. Ada berbagai macam algoritma yang menerapkan teknik ini. Setiap algoritma memiliki cara dan karakteristik masing-masing. Penelitian ini berfokus pada pengembagan algoritma yang menerapkan unsupervised learning technique salah satunya algoritma K-Means dengan mengambil sample data pada masyarakat yang melakukan usaha kreatif dan mandiri. Masyarakat dalam yang memanfaatkan usaha online dan offline dalam pemasarannya. Peneliti melakukan uji eksperimen dan simulasi terhadap algoritma tersebut dengan menghasilkan output berupa aplikasi software serta tabel dan grafik yang mampu menggabungkan data yang didapat dari media social dan kuesioner secara ofline. Hasil analisa pengolahan Data tersebut dapat di gunakan sebagao DSS (Decicion Support System) oleh masyarakat dalam mengambil keputusan pengembangan pemasaran produksinya selanjutnya. ABSTRACTLarge data collection or known as big data can be analyzed with various techniques. One technique for processing big data is Unsupervised Technique. There are various kinds of algorithms that apply this technique. Each algorithm has its own ways and characteristics. This study focuses on developing an algorithm that implements an unsupervised learning technique, one of which is the K-Means algorithm by taking data samples to people who are doing creative and independent efforts. The Society utilized online and offline business in marketing. The researcher conducted an experimental test and simulation of the algorithm by producing output in the form of software applications as well as tables and graphs that were able to combine data obtained from social media and questionnaires fromline. The results of the analysis of data processing can be used as a DSS (Decion Support System) by the community in making their next production marketing development decisions.

THE MODELING OF "MUSTAHIQ" DATA USING K-MEANS CLUSTERING ALGORITHM AND BIG DATA ANALYSIS (CASE STUDY: LAZ) Nurhayati Buslim; Rayi Pradono Iswara; Fajar Agustian
JURNAL TEKNIK INFORMATIKA Vol 13, No 2 (2020): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v13i2.19610

There are a lot of Mustahiq data in LAZ (Lembaga Amil Zakat) which is spread in many locations today. Each LAZ has Mustahiq data that is different in type from other LAZ. There are differences in Mustahiq data types so that data that is so large cannot be used together even though the purpose of the data is the same to determine Mustahiq data. And to find out whether the Mustahiq data is still up to date (renewable), of course it will be very difficult due to the types of data types that are not uniform or different, long time span, and the large amount of data. To give zakat to Mustahiq certainly requires speed of information. So, in giving zakat to Mustahiq, LAZ will find it difficult to monitor the progress of the Mustahiq. It is possible that a Mustahiq will change his condition to become a Muzaki. This is the reason for the researcher to take this theme in order to help the existing LAZ to make it easier to cluster Mustahiq data. Furthermore, the data already in the cluster can be used by LAZ managers to develop the organization. This can also be a reference for determining the zakat recipient cluster to those who are entitled later. The research is "Modeling using K-Means Algorithm and Big Data analysis in determine Mustahiq data ". We got data Mustahiq with random sample from online and offline survey. Online data survey with Google form and Offline Data survey we got from BAZNAS (National Amil Zakat Agency) in Indonesia and another zakat agency (LAZ) in Jakarta. We conducted by combining data to analyzed using Big Data and K-Means Algorithm. K-Means algorithm is an algorithm for cluster n objects based on attributes into k partitions according to criteria that will be determined from large and diverse Mustahiq data. This research focuses on modeling that applies K-Means Algorithms and Big Data Analysis. The first we made tools for grouping simulation test data. We do several experimental and simulation scenarios to find a model in mapping Mustahiq data to developed best model for processing the data. The results of this study are displayed in tabular and graphical form, namely the proposed Mustahiq data processing model at Zakat Agency (LAZ). The simulation result from a total of 1109 correspondents, 300 correspondents are included in the Mustahiq cluster and 809 correspondents are included in the Non-Mustahiq cluster and have an accuracy rate of 83.40%. That means accuracy of the system modeling able to determine data Mustahiq. Result filtering based on Gender is “Male” accuracy 83.93%, based on Age is ”30-39” accuracy 71,03%, based on Job is “PNS” accuracy 83.39%, based on Education is “S1” accuracy 83.79%. The advantaged of research expected to be able to determine quickly whether the person meets the criteria as a mustahik or Muzaki for LAZ (Amil Zakat Agency). The result of modeling is K-Means clustering algorithm application program can be used if UIN Syarif Hidayatullah Jakarta want to develop LAZ (Amil Zakat Agency) too.

Rancang Bangun Aplikasi Informasi Nama Stasiun Kereta Jurusan Jakarta – Cirebon Berbasis Smartphone Android Rohadi Rohadi; Husni Teja Sukmana; Rayi Pradono Iswara
Jurnal Pseudocode Vol 1, No 2 (2014)
Publisher : Universitas Bengkulu

Diantara banyaknya jasa pelayanan masyarakat, adalah kendaraan umum seperti Kereta Api (KA) dan Kereta Rel Listrik (KRL) yang banyak sekali digunakan sebagai kendaraan alternatif dalam melakukan perjalanan, karena harga perjalanan yang terjangkau juga waktu perjalanan yang relatif stabil karena memiliki jalur khusus, meski keterlambatan waktu kadang terjadi tapi tingkatnya lebih rendah dibanding dengan angkutan umum seperti bus, mikrolet, atau angkot. Sarana yang ditawarkan oleh kereta Api (KA) dan Kereta Rel Listrik (KRL) adalah waktu yang stabil dan harga yang terjangkau karena terdapat kelas-kelas untuk jenis KA dan KRL yaitu kelas Ekonomi, Bisnis, Eksekutif. Dikarenakan jalur khusus KA dan KRL ini lebih banyak mengambil jalur yang jauh dari kawasan penduduk (pertimbangan akibat resiko kecelakaan) atau jalan umum yang besar ditambah dengan kecepatan KA atau KRL yang konstan mengakibatkan penumpang sulit mencari informasi tentang daerah atau lokasi yang tengah dilewati dan yang akan dituju. Sehingga penumpang kebingungan dimana harus turun, jika adapun informasi di stasiun tidak memadai dan kurang layak untuk sebuah kenyamanan informasi. Ada beberapa aplikasi yang sudah ada seperti Google Map. Aplikasi tersebut memang dapat melihat lokasi peta secara real time, akan tetapi aplikasi ini tidak dapat memberikan notifikasi menggunakan suara. Disini kami akan menggabungkan notifikasi agar penumpang dapat mengetahui dimana posisi kereta walaupun device dalam keadaan stand by, dengan memanfaatkan teknologi GPS sebagai penentu koordinat dari setiap stasiun singgah yang telah di tentukan.Kata kunci: Transportasi, Kereta Api (KA), Kereta Rel Listrik (KRL), Smartphone, Android, Trayek, Google Map, GPS

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