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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) IJISTECH (International Journal Of Information System & Technology) IJISTECH Teknikom: Teknologi Informasi, Ilmu Komputer dan Manajemen Jurnal INFOTEL
Adnan Purwanto
STT Telematika Telkom (ST3 Telkom)
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Aplikasi Peta ATM Dengan Menggunakan Aplikasi GPS Pada Handphone Android Tantan Taryono; Adnan Purwanto; Tenia Wahyuningrum
JURNAL INFOTEL Vol 5 No 1 (2013): May 2013
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v5i1.109

Abstract

Seiring meningkatnya mobilitas masyarakat dan tingginya kebutuhan informasi berupa peta, Geographic Information System (GIS) bukan hanya sekadar tren teknologi, tetapi telah menjadi sebuah kebutuhan. Ketatnya persaingan para vendor perangkat bergerak (mobile device) dengan berbagai fitur dan keunggulan, secara tidak langsung berdampak positif bagi konsumen teknologi mobile salah satunya operating system (OS) Android. Dengan adanya operating system (OS) Android yang memberikan banyak keuntungan salah satunya adalah OS Android yang bersifat open source, para pengembang aplikasi Android dapat membuat sebuah aplikasi sesuai kebutuhan termasuk pembuatan aplikasi Peta ATM. Disamping itu pada handphone Android juga telah tersedia GPS sebagai penetuan posisi pengguna. Peta ATM dengan menggunakan aplikasi GPS merupakan sebuah aplikasi yang dapat digunakan oleh banyak kalangan. Banyak manfaat yang dapat dirasakan dengan menggunakan aplikasi ini, diantaranya adalah dengan mengetahui sebuah lokasi dimana posisi user berada. Begitu juga dengan aplikasi peta Automatic Teller Machine (ATM) dapat memberikan kemudahan bagi para pengguna handphone berbasis operating system Android untuk mengetahui dimana letak posisi ATM BNI yang berada di wilayah Purwokerto dan sekitarnya. Dengan adanya aplikasi peta ATM BNI diharapkan para pengguna yang belum mengetahui secara lengkap kota Purwokerto dapat dengan mudah dalam mencari lokasi ATM.
Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines Adnan Purwanto; Lindung Parningotan Manik
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.39865

Abstract

Purpose: Effort Estimation is a process by which one can predict the development time and cost to develop a software process or product. Many approaches have been tried to predict this probabilistic process accurately, but no single technique has been consistently successful. There have been many studies on software effort estimation using Fuzzy or Machine Learning. For this reason, this study aims to combine Fuzzy and Machine Learning and get better results.Methods: Various methods and combinations have been carried out in previous research, this research tries to combine Fuzzy and Machine Learning methods, namely Logarithmic Fuzzy Preference Programming (LFPP) and Least Squares Support Vector Machines Machine (LSSVM). LFPP is used to recalculate the cost driver weights and generate Effort Adjustment Point (EAP). The EAP and Lines of Code values are then entered as input for LSSVM. The output results are then measured using the Mean Magnitude of Relative Error (MMRE) and Root-Mean-Square Error (RMSE). In this study, COCOMO and NASA datasets were used.Result: The results obtained are MMRE of 0.015019 and RMSE of 1.703092 on the COCOMO dataset, while on the NASA dataset the results of MMRE are 0.007324 and RMSE are 6.037986. Then 100% of the prediction results meet the 1% range of actual effort on the COCOMO dataset, while on the NASA dataset, the results show that 89,475 meet the 1% range of actual effort and 100% meet the 5% range of actual effort. The results of this study also show a better level of accuracy than using the COCOMO Intermediate method.Novelty: This study uses a combination of LFPP and LSSVM, which is an improvement from previous studies that used a combination of FAHP and LSSVM. The method used is also different where LFPP produces better output than FAHP and all data in the dataset is used for training and testing, whereas in previous research it only used a small part of the data.
Co-Authors Alfin Hikmaturokhman Anggun Fitrian Isnawati Aril Apria Susanto Aril Apria Susanto Bangun Harizal Bangun Harizal Bayu Hatmoko Bayu Hatmoko Dwiza Riana Dybio Dompu Hot Asih Eka Wahyudi Eka Wahyudi Lindung Parningotan Manik Muhammad Akbar Setiawan Muhammad Ubaidilah Muhammad Ubaidilah, Muhammad Nahar Mahardiyanto Rendy Munadi Sari Setyaning Tyas Siti Aisyah Sri Hadianti Tantan Taryono Tantan Taryono Teguh Iklas Teguh Iklas, Teguh Tenia Wahyuningrum Wahyu Pamungkas Wahyu Pamungkas Yuyun Khairunisa