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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika ILKOM Jurnal Ilmiah
Annisa Annisa
Institut Pertanian Bogor
Computer Science & IT

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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.
Association of single nucleotide polymorphism and phenotype in type 2 of diabetes mellitus using Support Vector Regression and Genetic Algorithm Ratu Mutiara Siregar; Wisnu Ananta Kusuma; Annisa Annisa
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1283.194-202

Abstract

Precision Medicine is used to improve proper health care and patients' quality of life, one of which is diabetes. Diabetes Mellitus (DM) is a multifactorial and heterogeneous group of disorders characterized by deficiency or failure to maintain normal glucose homeostasis. About 90% of all DM patients are Type 2 Diabetes Mellitus (T2DM). Biological characteristics and genetic information of T2DM disease were obtained by looking for associations in Single Nucleotide Polymorphism (SNP) which allows for determining the relationship between phenotypic and genotypic information and identifying genes associated with T2DM disease. This research focuses on the Support Vector Regression method and Genetic Algorithm to obtain SNPs that have previously calculated the correlation value using Spearman's rank correlation. Then do association mapping on the SNP results from the SVR-GA selection and check pastasis interaction. The results produced 14 SNP importance. Evaluation of the model using the mean absolute error (MAE) obtained is 0.02807. If the value of MAE is close to zero, then a model can be accepted. The genes generated from the association can be used to assist other researchers in finding the right treatment for T2DM patients according to their genetic profile.
Co-Authors Ahmad Ridha Firnas Nadirman Ratu Mutiara Siregar Rizky Maulidya Afifa Salsa Khairina Wisnu Ananta Kusuma