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All Journal JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri Jurnal Teknologi Terpadu Jurnal Komunikasi, Sains dan Teknologi (JKST) Journal of Informatics and Advanced Computing Journal of Applied Research In Computer Science and Information Systems Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Maspiyanti, Febri
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Laptop Price Prediction Using Extreme Gradient Boosting Algorithm Adrianty, Syahrani; Maspiyanti, Febri
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.173

Abstract

The laptop is a support for many people in doing all activities. The number of laptop outputs with various models can affect the price of laptops. The presence of various online and offline stores causes different laptop prices and it becomes difficult to compare prices that are close to the low price range. Based on these problems, a system is needed that can predict laptop prices based on laptop specifications that are useful for people in finding a cheap price range. Data collection in this study came from bhinneka.com with 560 data and pemmz.com with 319 data collected by scrapping method. This research uses the Extreme Gradient Boosting method with evaluation techniques in the form of cross-validation resulting in an R2 score at the Bhinneka store of 0.98 and RMSE of 1250363.29 with the best cross-validation of 8. At Pemmz store produces an R2 score of 0.98 and RMSE of 1073090.92 with the best cross-validation of 6. Both results use data with outliers.
Comparison of Apriori and Fp-Growth Algorithms in Determining Package Menus at Sate Perawan Restaurant Sawangan Raya Shabrina Putri; Ninuk Wiliani; Maspiyanti, Febri
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 2 (2024): December 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i2.183

Abstract

The culinary creative industry holds promising prospects as it is a necessity for society. However, the variety of menu items and high customer demand lead to slow ordering processes, which hinder service at Rumah Makan Sate Perawan. Additionally, some menu items are less popular among customers. To address these issues, a system is needed to assist in determining food and beverage package menus based on association rules. This system aims to facilitate business owners in organizing packages and improving sales. This study employs the Apriori and FP-Growth algorithms, using sales transaction data collected over a four-month period. The research applies a minimum support of 0.1 for food, 0.01 for beverages, and a minimum confidence of 0.6 for both categories. The results indicate that there is no significant difference between the two algorithms in terms of the generated packages, lift ratio evaluation, and runtime. In the food category, 5 association rules were generated with an average lift ratio of 1.1929, while in the beverage category, 2 rules were generated with an average lift ratio of 1.8990.
KLASIFIKASI FASE PERTUMBUHAN PADI BERDASARKAN CITRA HIPERSPEKTRAL DENGAN MODIFIKASI LOGIKA FUZZY Maspiyanti, Febri; Fanany, M. Ivan; Arymurthy, Aniati Murni
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3272

Abstract

Remote sensing is a technology that is capable of overcoming the problems of measurement data for fast and accurate information. One of implementation of remote sensing technology in the field of agriculture is in hyperspectral image data retrieval to find out the condition and age of the rice plant. It is necessary for the estimation of rice yield in order to support Government policy in conducting imports rice to meet food needs in Indonesia. To have a good prediction model in estimation of rice yield that has high accuracy must be preceded by the determination of the phase of the rice plant. The selection of the appropriate classifier must also supported the selection of just the right features to get the optimum accuracy. In this study, we conducted a comparison between Fuzzy Logic and Modified Fuzzy Logic to perform the classification on nine rice growth stages based on hyperspectral image. Modified Fuzzy Logic have the same procedure with Fuzzy Logic but with extra crisp rules given in Fuzzy Rules which is expected to increase the accuracy achievement. In this study, Modified Fuzzy Logic proved to be able to improve the accuracy of up to 10% compared to Fuzzy Logic.
Co-Authors Adam Nugroho Adrianty, Syahrani Andini Putri, Andini Aniati Murni Arymurthy Deni Septiadi Dona Ines, Monika Effatha, Eidelwise Gloria Erick Fernando B311087192 Fanany, M. Ivan Gatc, Jullend Hadiyanti, Nadya Hanifa, Faiqa Hadya Ionia Veritawati Mastra, Riadika Murtako, Amir Ninuk Wiliani Shabrina Putri