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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Herman Yuliansyah
Universitas Ahmad Dahlan, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Bee Colony Optimization Pada Pemilihan Centroid (Klaster Pusat) Dalam Algoritma K-Means Ika Arfiani; Herman Yuliansyah; Muhammad Dzikrullah Suratin
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): Maret 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (436.104 KB) | DOI: 10.47065/bits.v3i4.1446

Abstract

Clustering is a method that is used to divide the data into several groups of parts. K-means (KM) is an algorithm that is often used in clustering, only just the result of KM often times get stuck in local optima i.e. the optimal solution (both maximum or minimal) on the candidate solution in the nearest neighbor only, not the whole of all existing solutions or what is commonly called the global optima. In this study aims to do improve the cluster determination process on the Kmeans algorithm using the Bee Colony Optimization (BCO) algorithm. BCO is an algorithm that works based on the way the bees search for food , BCO is famous for being able to escape from the local optima trap by recognizing which results are best from a series of optimal results . Combining BCO with KM begins with selecting a source of food early in random and using KM to resolve all the problems of clustering at every step BCO next and keep sources of food best in each iteration. The result of this research is that the BCOKM method has been proven to be able to solve the problem of data sharing, where the BCOKM method is able to form a good cluster, as shown by the resulting fitness value (the lowest value is 1221.53 and the highest value is 1233.28) all of which are better than the fitness value using K-means (1251.42). Likewise in terms of accuracy, where the use of BCOKM all showed better results (83.16%-83.30%) than the use of only K-means (83.09%)
Sentiment Analysis of the Waste Problem based on YouTube comments using VADER and Deep Translator Herman Yuliansyah; Surahma Asti Mulasai; Sulistyawati Sulistyawati; Fanani Arief Ghozali; Bambang Sudarsono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6918

Abstract

The waste problem is a severe problem that significantly affects the environment and public health. To effectively determine the public’s perception of the waste problem, it is necessary to examine public sentiment toward waste management. This research aims to develop a sentiment analysis model using VADER and deep-translator and analyze the Yogyakarta waste emergency problem. This research was conducted in two phases, namely, the first phase was developing a sentiment analysis model by evaluating its performance based on public data. Then, the second phase classifies public comments from YouTube regarding the waste problem to understand public perceptions and evaluations by identifying positive, negative, and neutral sentiments. The model evaluation results show that sentiment analysis using VADER and deep translator can achieve Accuracy, Precision, Recall, and F1-score values of 0.716, 0.837, 0.853, and 0.738, respectively. The sentiment results from YouTube comments obtained positive, neutral, and negative sentiments of 30.0%, 31.7%, and 37.3%, respectively. The results of the sentiment analysis are neutral sentiment discussing waste management, disappointment in negative sentiment, and hope for waste management in positive sentiment.
Co-Authors Fanani Arief Ghozali Ika Arfiani Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Sudarsono, Bambang Sulistyawati Sulistyawati Surahma Asti Mulasai