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Laying Chicken Algorithm (LCA) Based For Clustering Iwan Tri Riyadi Yanto; Ririn Setiyowati; Nursyiva Irsalinda; - Rasyidah; Tri Lestari
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.467

Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.

Hidden Markov Model for Sentiment Analysis using Viterbi Algorithm Nursyiva Irsalinda; Haswat Haswat; Sugiyarto Sugiyarto; Meita Fitrianawati
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 2, ISSUE 1, February 2021
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol2.iss1.art3

Data mining is an activity to extract the knowledge from large amounts of data as very important information. The type of data in the era of 4.0 is data in the form of text, which is very much derived from social media. Recently, text becomes very important in some applications, such as the processing and the conclusion of a person's review and analysis of political opinion which is very sensitive in almost all countries, including Indonesia. Online text data that circulating on social media has several shortcomings that could potentially hinder the analysis process. One of the drawbacks is the people can post their own content freely, so the quality of their opinions cannot be guaranteed such as spam and irrelevant opinions. The other drawback is the basic truth of the online text data is not always available. Basic truth is more like a particular opinion, indicating whether the opinion is positive, negative and neutral. Therefore, the main objective of this study is to improve the forecasting accuracy of online text data analysis from social media. The method used os Hidden Markov Model (HMM) with Viterbi Algorithm that applied to extract the dataset sentiment at the 2015 elections in Surabaya from the popular site micro blogging called Twitter. The result of the study is Viterbi algorithm has predicted the best route with the candidate Tri Rismaharini gained a prediction of neutral sentiments, whereas ratio candidates gained sentiment negative predictions as well. The proposed Model is accurate to predict candidate features. It also helps political parties to introduce candidates based on reviews so that they can increase candidate performance or they can manage broad publicity to promote candidates.

A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN Sugiyarto Sugiyarto; Joko Eliyanto; Nursyiva Irsalinda; Zhurwahayati Putri; Meita Fitrianawat
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 5, No 1 (2021): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v5i1.3766

Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output.

Forecasting Tourist Visit Using the Vector Autoregressive Exogenous Method (VARX) Erni Muschilati; Nursyiva Irsalinda
Jurnal Ilmiah Matematika Vol 7, No 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v7i2.19608

Forecasting is an activity to predict what will happen in the future by paying attention to information from the past and the present. A regression model that explains the past movement of the variable itself and also all other variables without distinguishing which endogenous and exogenous variables are called Vector Autoregressive (VAR). But in practice, endogenous variables are supported by exogenous variables. The Vector Autoregressive Exogenous (VARX) model is a development of the VAR with the addition of exogenous variables. The purpose of this study is to form the best model in the VAR method with the addition of an exogenous variable in the form of an effect calendar for forecasting the number of tourists coming to the Special Region of Yogyakarta (DIY). The data used in this study are time series data for 10 years from January 2009 to December 2018 in the form of tourist visit data in the Special Region of Yogyakarta (DIY). The results obtained indicate that the effect calendar variable that affects tourist visitor data in DIY is at Christmas. After being analyzed using MAPE, the best model is the VARX (1.0) model which produces a smaller. So, it can be concluded that the VARX model with the addition of an effects calendar is suitable for predicting tourist visits

MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL Nursyiva Irsalinda; sugiyarto Surono
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 10 No 1 (2018): Jurnal Ilmiah Matematika dan Pendidikan Matematika
Publisher : Jurusan Matematika FMIPA Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2018.10.1.2833

Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

Pattern Recognition using Multiclass Support Vector Machine Method with Local Binary Pattern as Feature Extraction Nursyiva Irsalinda; Sugiyarto Surono; Indah Dwi Ratna Sary
Science and Technology Indonesia Vol. 7 No. 3 (2022): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.

MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL Nursyiva Irsalinda; sugiyarto Surono
Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Vol 10 No 1 (2018): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2018.10.1.2833

Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

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