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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Jurnal Infomedia IJISTECH (International Journal Of Information System & Technology) IJISTECH
Solikhun Solikhun
AMIK Tunas Bangsa
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : IJISTECH

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Analysis of Weight Product (WP) Algorithms in the best Go Car Driver Recommendations at PT. Maranatha Putri Bersaudara Roni Kurniawan; Agus Perdana Windarto; M Fauzan; Solikhun Solikhun; Irfan Sudahri Damanik
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v3i1.28

Abstract

This study aims to rank the best Go Car Driver. The problem arises because of the inaccuracy in giving value to the driver which results in the decision being given incorrectly so that the assessment tends to be subjective. This research was conducted at PT. Maranatha Putri Bersaudara. Sources of data obtained by observing, interviewing. The settlement method used is a decision support system with the Weight Producted method. The assessment criteria used are Performance (C1), Number of orders (C2), Rating (C3), Attitude (C4), Rating (C5) and Appearance (C6) where the alternatives used are 4 samples. The results obtained using the Weighted Product method are Alternative1 and Alternative4 which are recommended as the best go car driver with the assessment results of 0.0307 and 0.0272. It is expected that research results can be input to the relevant parties in recommending the best go car driver so as to minimize subjective judgment.
Improving Adaptive Learning Rate With Backpropogation on Retail Rice Price Prediction in Traditional Markets Erwin Binsar Hamonangan Ompusunggu; Solikhun Solikhun; Iin Parlina; Sumarno Sumarno; Indra Gunawan
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v3i1.26

Abstract

Rice is the most important staple food and carbohydrate food in the world especially people in Indonesia. This study aims to predict the retail price of rice in traditional markets using backpropogation by improvising Adaptive Learning Rate to increase the value of accuracy. Data sources were obtained from the Central Statistics Agency (BPS) in 33 provinces in Indonesia for the retail price of rice in the traditional market (Rupiah / kg) for the past 6 years (2011-2016). The results of the study state that the improvised learning rate uses 2 models: 2-10-1 and 2-15-1 (LR= 0,1; 0,5; 0,9) that the best architectural models are 4-15-1 (LR= 0.9) with an accuracy of 82%, Training MSE 0,000999936, Testing MSE 0.016051433 and Epoch 20515. The results of this study are expected to provide input to the government in providing input on predictions of retail rice prices that have an impact on the stability of rice prices in Indonesia.
Co-Authors Agus Perdana Windarto Ahmad Revi Ahmad Revi Erwin Binsar Hamonangan Ompusunggu Iin Parlina Indra Gunawan Irfan Sudahri Damanik M Fauzan Nani Hidayati Rina Novita Sari Roni Kurniawan Saifullah Saifullah Samuel Palentino Sinaga Sri Wardani Sumarno Sumarno Syahrul Ramadan Wanto, Anjar