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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Journal of ICT Research and Applications Indonesian Journal on Computing (Indo-JC) Majalah Ilmiah UNIKOM
Ruskanda, Fariska Zakhralativa
Widyatama University
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Study on the Effect of Preprocessing Methods for Spam Email Detection Fariska Zakhralativa Ruskanda
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 1 (2019): Maret, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2019.4.1.284

Abstract

The use of email as a communication technology is now increasingly being exploited. Along with its progress, email spam problem becomes quite disturbing to email user. The resulting negative impacts make effective spam email detection techniques indispensable. A spam email detection algorithm or spam classifier will work effectively if supported by proper preprocessing steps (noise removal, stop words removal, stemming, lemmatization, term frequency). This research studies the effect of preprocessing steps on the performance of supervised spam classifier algorithms. Experiments were conducted on two widely used supervised spam classifier algorithms: Naïve Bayes and Support Vector Machine. The evaluation is performed on the Ling-spam corpus dataset and uses evaluation metrics: accuracy. The experimental results show that different preprocessing steps give different effects to different classifier.
Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning Fariska Zakhralativa Ruskanda; Dwi Hendratmo Widyantoro; Ayu Purwarianti
Journal of ICT Research and Applications Vol. 14 No. 1 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2020.14.1.4

Abstract

The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.
Sentiment Analysis of Sentence-Level using Dependency Embedding and Pre-trained BERT Model Fariska Zakhralativa Ruskanda; Stefanus Stanley Yoga Setiawan; Nadya Aditama; Masayu Leylia Khodra
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.6938

Abstract

Sentiment analysis is a valuable field of research in NLP with many applications. Dependency tree is one of the language features that can be utilized in this field. Dependency embedding, as one of the semantic representations of a sentence, has shown to provide more significant results compared to other embeddings, which makes it a potential way to improve the performance of sentiment analysis tasks. This study aimed to investigate the effect of dependency embedding on sentence-level sentiment analysis through experimental research. The study replaced the Vocabulary Graph embedding in the VGCN-BERT sentiment classification system architecture with several dependency embedding representations, including word vector, context vector, average of word and context vectors, weighting on word and context vectors, and merging of word and context vectors. The experiments were conducted on two datasets, SST-2 and CoLA, with more than 19 thousand labeled sentiment sentences. The results indicated that dependency embedding can enhance the performance of sentiment analysis at the sentence level.
Asesmen kerja tim berbasis web dengan online teamkit Ruskanda, Fariska Zakhralativa
Majalah Ilmiah UNIKOM Vol. 13 No. 01 (2015): Majalah Ilmiah Unikom
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (660.921 KB)

Abstract

Dunia pendidikan saat ini hampir selalu memberi kesempatan mahasiswanya untuk bekerja sama dalam sebuah tim. Kerja sama tim ini bisa dalam bentuk latihan pengambilan keputusan singkat atau bahkan proyek manajemen atau simulasi bisnis yang berlangsung selama perkuliahan. Namun pada kebanyakan kasus, hanya sedikit perhatian yang diberikan pada keterampilan yang diperlukan agar dapat sukses bekerja dalam tim. Evaluasi kadang dilakukan secara subjektif dan tidak integral, serta lebih ditekankan pada hasil teknis bukan keberhasilan sebagai sebuah tim. Untuk mempelajari keterampilan ini, diantaranya diperlukan a) pemahaman atas konsep dan prinsip dasarnya, b) kesempatan untuk melatihnya, dan c) jalan untuk memperoleh feedback. Berdasarkan latar belakang permasalahan tersebut, saat ini diperlukan suatu sistem atau mekanisme yang berbasis web dimana mahasiswa yang terlibat dalam kerja tim dapat memperoleh umpan balik atas peran yang dimainkannya dalam tim. Formatnya harus dirancang agar dapat memberikan lingkungan pembelajaran yang nyaman baik bagi pemberi maupun penerima feedback. Online TeamKit adalah online feedback system software, yang dirancang untuk membantu instruktur untuk mengasah interpersonal skill para mahasiswanya. Jjika digunakan dengan semestinya, proses feedback ini akan mampu meningkatkan komunikasi antar anggota tim dan meningkatkan kinerja tim.
Co-Authors Ayu Purwarianti Dwi Hendratmo Widyantoro Masayu Leylia Khodra Nadya Aditama Stefanus Stanley Yoga Setiawan