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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.
IDENTIFICATION OF FOOD DIVERSIFICATION ON JAVA ISLAND USING ARCGIS Murtako, Amir; Hanifa, Faiqa Hadya; Effatha, Eidelwise Gloria; Nursari, Sri Rezeki Candra; Maspiyanti, Febri
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6570

Abstract

Indonesia is addressing the challenges of food security and consumer preference also known as Food diversification. The research aims to analyze the potential of various local food sources as alternatives to rice, which is the dominant staple food in Indonesia, with a particular focus on geographic implications. Although local carbohydrate sources like corn, potatoes, and tubers are available, their adoption is limited and understudied in relation to geographic distribution and consumer behavior. This study integrates survey data and GIS-based spatial analysis to evaluate local food diversification potential. Findings show that while 100% of respondents consume rice, 48.7% have tried alternatives, with limited availability (41.03%) and higher costs (17.95%) as key barriers. With 94.7% expressing willingness to adopt new staples, the results suggest GIS-based decision support systems can guide effective, region-specific food policy interventions.
IMPLEMENTASI COMPUTER VISION UNTUK DETEKSI SAMPAH LAUT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Hanifa, Faiqa; Maspiyanti, Febri
Journal of Informatics and Advanced Computing (JIAC) Vol 6 No 2 (2025): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/sch9tm40

Abstract

Marine debris has become a serious and growing threat to marine ecosystems, human health, and maritime activities and economies. Various manual monitoring efforts that has been carried out so far are often limited in terms of spatial coverage, efficiency, and resource effectiveness. With the rapid advancement of remote sensing technology and the integration of artificial intelligence, marine debris monitoring can now be automated through computer vision approaches. This study develops a computer vision-based system for marine debris detection using Sentinel-2 satellite imagery and Convolutional Neural Network (CNN) to adhere to the blue economy framework. The proposed approach applies semantic segmentation using the U-Net architecture. The primary dataset used in this study is provides multispectral imagery with a spatial resolution of 10 meters and annotations for four main classes: marine debris, organic material, seawater, and other objects. The imagery enhanced using spectral indices such as the Floating Debris Index (FDI) and the Normalized Difference Vegetation Index (NDVI) helps distinguish spectral characteristics between debris and non-debris classes more clearly. Model performance is evaluated using metrics including accuracy, precision, recall, Intersection-over-Union (IoU), and F1-Score. The best model achieved scores of 0.95, 0.81, 0.67, and 0.77 for each respective metric, demonstrating U-Net's effectiveness in detecting marine debris. The final system is deployed through an interactive Streamlit interface, allowing users to upload satellite imagery, view segmentation results, visualize spectral indices, and preview bounding boxes that highlight detected debris locations. This approach is expected to serve as an effective and adaptive tool to support sustainable marine environmental policies and decision-making.
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 Hanifa, Faiqa Hadya Ionia Veritawati Mastra, Riadika Murtako, Amir Ninuk Wiliani Shabrina Putri