Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Teknologi dan Sistem Komputer
Annisa Annisa
Department Of Computer Science, Faculty Of Mathematics And Natural Sciences, IPB University. Jl. Meranti Wing 20 Level 5, Kampus IPB Darmaga, Bogor 16680|IPB University
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Searching and Visualization of References in Research Documents Firnas Nadirman; Ahmad Ridha; Annisa Annisa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 2: June 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i2.74

Abstract

This research aims to develop a module for information retrieval that can trace references from bibliography entries of research documents, specifically those based on Bogor Agricultural University (IPB)’s writing guidelines. A total of 242 research documents in PDF from the Department of Computer Science IPB were used to generate parsing patterns to extract the bibliography entries. With modified ParaTools, automatic extraction of bibliography entries was performed on text files generated from the PDF files. The entries are stored in a database that is used to visualize author relationship as graphs. This module is supplemented by an information retrieval system based on Sphinx search system and also provides information of authors’ publications and citations. Evaluation showed that (1) bibliography entry extraction missed only 5.37% bibliography entries caused by incorrect bibliography formatting, (2) 91.54% bibliography entry attributes could be identified correctly, and (3) 90.31% entries were successfully connected to other documents.
Location Selection Based on Surrounding Facilities in Google Maps using Sort Filter Skyline Algorithm Annisa Annisa; Salsa Khairina
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.12939

Abstract

Selecting a good location is an essential task in many location-based applications. Intuitively, a place is better than another if there are many good facilities around it. The most popular location selection platform today is Google Maps. Unfortunately, Google Maps has not provided the location selection based on the number of surrounding facilities. Assume a situation when a college student wants to let a house near his campus. Besides the distance from the campus, the student certainly will consider amenities surrounding it, such as food courts, supermarkets, health clinics, and places of worship. The rent house will become a better choice if there are more of these facilities around. Skyline query is a well-known method to select interesting desirable objects. We applied the Sort Filter Skyline (SFS) Algorithm on Google Maps to get a small number of attractive locations based on the number of nearby facilities. This study has succeeded in developing a web-based application that facilitates Google Maps users to search for places based on the figure of surrounding facilities. The time required to do a location search using SFS in Google Maps will increase with the number of surrounding facility types considered by the user.
Identifikasi protein signifikan pada interaksi protein-protein penyakit Alzheimer menggunakan algoritme top-k representative skyline query Mohammad Romano Diansyah; Wisnu Ananta Kusuma; Annisa Annisa
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 3, Year 2021 (July 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13985

Abstract

Penyakit Alzheimer merupakan penyakit neurodegeneratif yang paling umum terjadi. Kajian ini bertujuan melakukan analisis protein-protein interaction (PPI) yang dapat memberikan pemahaman lebih baik terhadap penyakit neurodegeneratif dan bisa digunakan untuk menemukan protein yang memiliki peran signifikan pada penyakit Alzheimer. Data PPI diperoleh dari eksperimen dan prediksi komputasional. PPI dapat dianalisis menggunakan centrality measures. Metode Top-k RSP digunakan untuk menemukan protein signifikan dengan menggunakan aturan dominansi dan digunakan pada sumber data interaksi eksperimen dan eksperimen+prediksi. Hasil penelitian ini menunjukkan bahwa APP dan PSEN1 merupakan protein signifikan untuk penyakit Alzheimer. Selain itu, kedua sumber data (eksperimen+prediksi) dan algoritme Top-k RSP terbukti dapat digunakan untuk analisis PPI dari penyakit Alzheimer.
Sentinel-1A image classification for identification of garlic plants using decision tree and convolutional neural network Risa Intan Komaraasih; Imas Sukaesih Sitanggang; Annisa Annisa; Muhammad Asyhar Agmalaro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The Indonesian government launched a garlic self-sufficiency program by 2033 to reduce imports by monitoring garlic lands in several central garlic areas. Remote sensing using satellite imageries can assist the monitoring program by mapping the garlic lands. A previous study has classified Sentinel-1A satellite imageries to identify garlic lands in Sembalun Lombok Indonesia using the decision tree C5.0 algorithm with three scenarios data input and produced a model with an accuracy of 78.45% using scenarios with two attributes vertical-vertical (VV) and vertical-horizontal (VH) bands. Therefore, this study aims to improve the accuracy of the classification model from the previous study. This study applied two classification algorithms, decision tree C5.0 and convolutional neural network (CNN), with two new scenarios which used two new combinations of attributes). The results show that the use of new data scenarios as input for C5.0 can not increase the previous model's accuracy. While the use of the CNN algorithm shows that it can improve the previous study's accuracy by 7.91% because it produced a model with an accuracy of 86.36%. This study is expected to help garlic land identification in the Sembalun area to support government programs in monitoring garlic lands.
Co-Authors Ahmad Ridha Firnas Nadirman Imas Sukaesih Sitanggang Mohammad Romano Diansyah Muhammad Asyhar Agmalaro Risa Intan Komaraasih Salsa Khairina Wisnu Ananta Kusuma