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All Journal Telematika JUITA : Jurnal Informatika Jurnal Rekayasa Informasi
Imam Tahyudin
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Computer Science & IT Education

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Data Mining Method to Determine a Fisherman's Sailing Schedule Using Website Dwi Ayu Mutiara; Alung Susli; Didit Suhartono; Dani Arifudin; Imam Tahyudin
Telematika Vol 14, No 2: August (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i2.1193

Abstract

Some of Cilacap people live in coastal areas as fishermen who utilize the seafood to meet the needs of life. One of the fishermen supporters in the cruise is the information of Meteorological, Climatological, and Geophysical Agency (BMKG). This information is important for safety such as wind speed and wave height. For addressing the problem, research is conducted to determine the sailing schedule of fishermen using data mining method with the website based. The proposed method is using Support Vector Machine (SVM) classification algorithm. This research uses data from BMKG Cilacap from 2015 until 2017. Test data is part of data that is 30% randomly fetched from the overall data used. From model testing, get value with performance results from datasets that generate accuracy of 88%, 87% precision and 89% recall. This solution is followed by constructing the website in order to easy to access of sailing information. Therefore, the researcher created a website of fisherman sailing scheduling system based on SVM algorithm.
Pencarian Rute Terbaik pada Obyek Wisata di Kabupaten Banyumas Menggunakan Algoritma Genetika Metode TSP Imam Tahyudin; Ika Susanti
JUITA : Jurnal Informatika JUITA Vol. 3, Nomor 4 Nopember 2015
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (409.713 KB) | DOI: 10.30595/juita.v3i4.872

Abstract

There are many wonderful tourist attractions in Banyumas. Based on data which are obtained, its number are 25 locations while in this research focus on 11 objects as experiment. The purpose of this study was to determine the best path that connects the eleventh object using Genetic Algorithms especially TSP method. Based on the results are the best path length is 0.878 units of Cartesian with a population size is 25 and the probability of mutations is 0,005. The sequence paths are Purwokerto square, Andang pangrenan Parks, Baturraden, Dreamland Waterboom Ajibarang, Cipendok waterfall, educational tours of STMIK AMIKOM Purwokerto, Depo Bay Sokaraja, Goa Maria Kaliori, Museum of General Sudirman, BRI Museum, and Bale Kemambang
ANALISIS EKSPLORASI DAN VISUALISASI DIVIDEN UNTUK REKOMENDASI INVESTASI PADA SEKTOR PERBANKAN Puji Lestari; Imam Tahyudin; Retno Waluyo
JURNAL REKAYASA INFORMASI Vol 12 No 2 (2023): JURNAL REKAYASA INFORMASI Vol 12 No 2 Tahun 2023
Publisher : PROGRAM STUDI SISTEM INFORMASI INSTITUT SAINS DAN TEKNOLOGI NASIONAL (ISTN)

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Abstract

An investment is a binding in a sum of money or other assets that is being made at this time with the hope of obtaining a profit in the future. The banking sector is a financial sector that can be used as an investment option. The most expected profit when someone makes an investment is the acquisition of dividends. Before making an investment, it is important for investors to carry out an analysis of dividend data distributed by bank companies so that investors can find out the picture of the profits they get. In this research, an exploratory analysis and visualization of dividend data were taken from the official dividend record platform which aims to provide bank recommendations that can be used as a place to invest and get maximum profits for investors. The sample was selected using purposive sampling method, 7 bank samples were obtained in this study. The data processing method is carried out using Python tools and visualization using Tableau Desktop. The results obtained from the analysis in this study are recommendations for banks as a place to invest, namely PT. Bank Asia Central Tbk., and PT. Bank Mandiri (Persero) Tbk.
Co-Authors Alung Susli Dani Arifudin Didit Suhartono Dwi Ayu Mutiara Ika Susanti PUJI LESTARI Retno Waluyo