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Kartono Kartono
Department Of Mathematics, Faculty Of Science And Technology, Airlangga University, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen Berbasis Aspek Ulasan Aplikasi Mobile JKN dengan Lexicon Based dan Naïve Bayes Salsabila Roiqoh; Badrus Zaman; Kartono Kartono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6194

Abstract

Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan is a legal entity that provides social health insurance programs for the public released application called Mobile JKN to support various health services activities using users devices. Mobile JKN has not fully received a positive public perception and still has many shortcomings. It is necessary to conduct a deeper evaluation and analysis of the Mobile JKN. This study focuses on aspect-based sentiment analysis of user reviews on the Google Play Store to evaluate the Mobile JKN. The review data used are the last two versions, 4.2.3 and 4.3.0. This study was carried out by modeling aspects/topics using the Latent Dirichlet Allocation method and sentiment analysis using Naïve Bayes and Lexicon-Based methods. This research resulted in 3 aspects, namely Services and Features, Register and Login, and User Satisfaction. This was obtained based on the model with the highest coherence score of 0.6392 obtained in the model looping with the number of topics from 1 to 9, random state = 42, passes =50, and iteration = 60. Meanwhile, based on the sentiment analysis results, the Naïve Bayes method is better than the Lexicon-Based (Inset Lexicon) method. This is evident from performance of the Naïve Bayes with the highest accuracy score of 94.75% and Lexicon Based with Inset Lexicon obtained an accuracy score of 59.99%.
Optimizing Uncapacitated Facility Location Problem with Cuckoo Search Algorithm based on Gauss Distribution Mohammad Agung Nugroho; Eto Wuryanto; Kartono Faqih
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2467

Abstract

The objective of this study was to assess the capability of the Gauss distribution-based Cuckoo Search algorithm (GCS) in solving the Uncapacitated Facility Location Problem (UFLP). UFLP is an optimization problem that there are number of locations available to be built a facility so that it can serve number of customers, assuming each facility has no limits to serve customers and only a single facility is allowed to provide services to each customer. The objective function of UFLP is to minimize the combined costs of constructing facilities in an area and providing services to customers. UFLP falls under the category of NP-Hard Problems, where the computation complexity increases with the size of the data. The Cuckoo Search algorithm, which mimics the breeding behavior of Cuckoo birds, has been extensively used to tackle optimization problems. GCS was introduced to overcome the weaknesses of Cuckoo Search algorithm in terms of computational time and search accuracy. GCS used Gaussian distribution instead of Levy Flight which based on Levy distribution. In this study, the GCS algorithm was implemented using JavaScript and the dataset used was obtained from ORLib. The research outcomes showed that the GCS algorithm could achieve optimal result in all dataset.
Co-Authors Badrus Zaman Eto Wuryanto Mohammad Agung Nugroho Salsabila Roiqoh