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All Journal Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Matrik Jurnal Sisfokom (Sistem Informasi dan Komputer) Informatik : Jurnal Ilmu Komputer INTEGRITAS : Jurnal Pengabdian SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan
Ria Astriratma
Universitas Pembangunan Nasional Veteran Jakarta
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Klasifikasi Jenis Pantun dengan Metode Support Vector Machines (SVM) Helena Nurramdhani Irmanda; Ria Astriratma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.268 KB) | DOI: 10.29207/resti.v4i5.2313

Abstract

This study aims to create a model for categorizing pantun types and analyze the accuracy of support vector machines (SVM). The first stage is collecting pantun that have been labeled with pantun category. The pantun categories consist of pantun for children, pantun for young people, and pantun for elder. After collecting data, the next stage is pre-processing. This pre-processing stage makes data ready to be processed on the extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stop word removal, and stemming. The feature extraction stage is intended to analyze potential information and represent terms as a vector. Separating training data and testing data is necessary to be conducted before the classification process. Then the classification process is done by using multiclass SVM. The results of the classification are evaluated to obtain accuracy and will be analyzed whether the classification model is proper to be used. The results showed that SVM classified the types of pantun with accuracy of 81,91%.
Implementasi Algoritma K-Means untuk Clustering Project Health pada PT XYZ Ajeng Arifa Chantika Rindu; Ria Astriratma; Ati Zaidiah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5181

Abstract

Indonesia has several companies that are involved in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. The potential of a project can increase company revenue and productivity. On the other hand, there are some risks that need to be considered for every project when it is about to start. Project data is recorded from start to finish so that the project's progress and improvements can be monitored and analyzed. As the project runs, the project team at one of Indonesia's telecommunication companies, which is responsible for the processes leading to project success, requires a project health category. Therefore, this study is conducted to develop a clustering project health process, which is included in a type of unsupervised learning that runs on unlabeled data. One of the clustering algorithms is K-Means, which groups data based on similar criteria. Researchers also use dimensionality reduction with the principal component analysis (PCA) method to determine its impact on the clustering process with the K-Means algorithm. From this study, the researcher obtained three groups or project health categories, consisting of groups 0, 1, and 2. The evaluation results with the Calinski-Harabasz index showed that the K-Means model in the PCA dimensionality reduction data performed better than the standard K-Means model with a Calinski-Harabasz index value of 55633,12776405707, which is higher than 25914,578262576793.
Co-Authors Agyei Fosu Ajeng Arifa Chantika Rindu Andhika Octa Indarso Arif Maulana Rahman Helena Nurramdhani Irmanda Helena Nurramdhani Irmanda Henki Bayu Seta I Wayan Widi Pradnyana Ika Nurlaili Isnainiyah Jayanta Jayanta Kukuh Bagas Permadi Luqman Imam Nurul Chamidah Santoni, Mayanda Mega Sarika Afrizal Shinta Widyastuti Widya Cholil Yulnelly Yulnelly Zaidiah, Ati