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All Journal Jurnal Teknologi Terpadu Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Nurfitriana, Diana
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Penerapan K-Means dan Rank Order Centroid pada Proporsi Individu dengan Keterampilan Teknologi Informasi dan Komputer Nurfitriana, Diana; Voutama, Apriade
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.608

Abstract

Technological developments occur so quickly, resulting in continuous changes that qualified human resources are needed to support the endless times that run. This study will classify individuals with information technology and computer skills in Indonesia based on region. This research used K-Means clustering, the Rank Order Centroid method, and the Davies-Bouldin Index clustering evaluation method to assess accuracy. K-means clustering is a simple algorithm and does not require a target class. There are areas for improvement in the K-Means process, namely at the initial centroid determination stage. Therefore, the ROC method is used. Based on data taken from the website of Badan Pusat Statistik Nasional about the proportion of productive age individuals 15-59 years who have Information and Computer Technology skills by the province during 2017-2021. It produces 3 clusters, including a high-level cluster in which there are 8 provinces, a medium-level cluster in which there are 22 provinces, and a low-level cluster in which there are 4 provinces, and obtained a DBI value of 0.163625 which is close to 0, meaning that the quality of the accuracy of the clustering results is good. Based on clustering results with good accuracy, using K-Means can be combined with ROC and is quite effective. The government can use the results of this study to prioritize improving the quality of human resources in areas with low-level information and computer technology skills. Suggestions for further research using other clustering algorithms and ROC as a comparison.
Analisis Opini Terhadap Aplikasi Riliv di Twitter Menggunakan Algoritma Naïve Bayes dan Random Forest Nurfitriana, Diana; Ridwan, Taufik; Voutama, Apriade
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 1 (2024): Maret 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i1.526

Abstract

In the current era of technological advancement and the internet, people can easily access various information. This technological advancement brings innovation in the mental health field, such as services in the form of apps. This research conducts sentiment analysis using the Naïve Bayes and Random Forest algorithms. The study aims to analyze Twitter users’ opinions regarding the Riliv apps and compare the results of classification using Naïve Bayes and Random Forest. This research methodology uses the AI Project Cycle method. The data used is tweet data from Twitter with the keyword 'aplikasi riliv’. The dataset consisted of 1035 data, which was processed to produce 273 positive, 273 neutral, and 39 negative sentiments data. The Naïve Bayes and Random Forest algorithms were applied to compare the classification results of the two. The most optimal classification results are Naïve Bayes with SMOTE with the division of 90% training data and 10% testing data, which results in an accuracy value of 82.72%, a value of precision is 82.89% and a value of recall is 82.72%. Based on the results of the distribution of sentiment data, most users gave positive reviews and were knowledgeable about the Riliv application, while only a few were disappointed
Co-Authors Apriade Voutama Taufik Ridwan, Taufik