Identifikasi Komentar Spam Pada Instagram

Chrismanto, Antonius Rachmat; Lukito, Yuan

doi:10.24843/lkjiti.2017.v08.i03.p08

Cited by 12 publications

(15 citation statements)

References 8 publications

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“…Solusi yang penulis bangun dilakukan dengan menggunakan teknik deteksi komentar spam secara otomatis menggunakan sistem klasifikasi supervised learning. Pada tahap awal penulis melakukan implementasi metode klasifikasi dan menguji beberapa metode Support Vector Machine dan Naïve Bayes [3] sehingga terpilihlah hasil algoritma terbaik yaitu algoritma k-NN [4]. Pada tahap kedua, penulis melanjutkan dengan pengembangan web service berbasis REST yang mengimplementasikan metode k-NN sebelumnya.…”

Section: Kata Kunci-firefox Extension Deteksi Komentar Spam Instagrunclassified

Firefox Extension untuk Klasifikasi Komentar Spam pada Instagram Berbasis REST Services

Chrismanto¹,

Raharjo²,

Lukito³

2019

JEPIN

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Klasifikasi komentar spam pada Instagram (IG) hanya dapat digunakan oleh pengguna melalui sistem yang berjalan di sisi client, karena data IG tidak dapat dimanipulasi dari luar IG. Dibutuhkan sistem yang dapat memanipulasi data dari sisi client dalam bentuk browser extension. Penelitian ini berfokus pada pengembangan browser extension untuk Firefox yang memanfaatkan web services REST pada layanan cloud dengan platform Amazon Web Services (AWS). Browser extension yang dikembangkan menggunakan 2 algoritma klasifikasi, yaitu KNN dan Distance-Weighted KNN (DW-KNN). Extension ini mampu menandai komentar spam dengan mengubah Document Object Model (DOM) IG menjadi berwarna merah dengan dicoret (strikethrough). Metode pengembangan extension dilakukan dengan metode Rapid Application Development (RAD). Pengujian pada penelitian ini dilakukan pada hasil implementasi browser extension dan pengukuran akurasi web service (algoritma KNN & DW-KNN). Pengujian implementasi browser extension menggunakan metode pengujian fungsionalitas, dimana setiap fitur yang telah diimplementasikan diuji apakah sudah sesuai dengan spesifikasi yang telah ditentukan sebelumnya. Pengujian akurasi web service dilakukan dengan bantuan tool SOAPUI. Hasil pengujian extension adalah: (1) pengujian extension pada sembarang halaman web berhasil 100%, (2) pengujian pada halaman awal (default) IG berhasil 100%, (3) pengujian pada halaman profile suatu akun IG berhasil 100%, (4) pengujian pada suatu posting IG dan komentarnya, tidak selalu berhasil karena dipengaruhi oleh kemampuan algoritma pada web services, (5) pengujian untuk bahasa bukan Indonesia tidak selalu berhasil karena bergantung pada library bahasa, (6) pengujian untuk load more comments pada IG tidak selalu berhasil karena bergantung pada algoritma pada web services, dan (7) pengujian pilihan algoritma pada options extension berhasil 100%. Hasil akurasi rata-rata tertinggi algoritma KNN adalah 80% untuk k=1, sedangkan DW-KNN adalah 90% untuk k=2.

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Section: Kata Kunci-firefox Extension Deteksi Komentar Spam Instagrunclassified

Firefox Extension untuk Klasifikasi Komentar Spam pada Instagram Berbasis REST Services

Chrismanto¹,

Raharjo²,

Lukito³

2019

JEPIN

Self Cite

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“…After data cleansing process, the final data used are 17.000 postings. From this datasets, some experiments were conducted using different algorithms and it was concluded that K-Nearest Neighbors (k-NN) gave the best results with 88.4% of accuracy [17], followed by Support Vector Machine with 78.5% [18], and Naïve Bayes with 75.5% [16]. Next, a REST-based web service to detect Indonesian language Instagram spam comments using k-NN algorithm design was designed and deployed on top of AWS platform and evaluate the performance based on response time.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, k-NN algorithm is used based on previous work [17] that gives best results compared to other algorithms (Support Vector Machine [18] and Naïve Bayes [16]). K-NN is learning directly while performing classification process by finding some adjacent data object or patterns based on the input and choose a class with the highest number of patterns [19].…”

Section: B Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Integration of REST-Based Web Service and Browser Extension for Instagram Spam Detection

Chrismanto¹,

Raharjo²,

Lukito³

2018

ijacsa

Self Cite

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In this paper, a REST-based Web Service developed in previous work was integrated with a newly developed browser extension that works in modern browser (Firefox and Google Chrome) using Greasemonkey. It uses previous collected datasets which comprised of 17.000 postings and comments from 10 Indonesian actresses whom followers are more than 10 million on Instagram. The performance of the developed web services has been evaluated and the average response time is 1678.133ms using AWS platform located in Ohio (US East 2). The proposed work is working as expected and in accuracy test, it has reached 63.125% in overall, 72% for nonstemmed data and 70% for stemmed data using 1000 test data with a processing time needed for classification is under 2s. The new extension works in Firefox and Chrome and it can utilize the web services to classify spam comments in Instagram.

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“…In other cases, non-informative reviews are also called spam reviews. These type of reviews tend not to be related to the content being discussed [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Mobile Application Reviews using Word Embedding and Convolutional Neural Network

Parwita¹,

Siahaan²

2019

LKJITI

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The app reviews are useful for app developers because they contain valuable information, e.g. bug, feature request, user experience, and rating. This information can be used to better understand user needs and application defects during software maintenance and evolution phase. The increasing number of reviews causes problems in the analysis process for developers. Reviews in textual form are difficult to understand, this is due to the difficulty of considering semantic between sentences. Moreover, manual checking is time-consuming, requires a lot of effort, and costly for manual analysis. Previous research shows that the collection of the review contains non-informative reviews because they do not have valuable information. Non-informative reviews considered as noise and should be eliminated especially for classification process. Moreover, semantic problems between sentences are not considered for the reviews classification. The purpose of this research is to classify user reviews into three classes, i.e. bug, feature request, and non-informative reviews automatically. User reviews are converted into vectors using word embedding to handle the semantic problem. The vectors are used as input into the first classifier that classifies informative and non-informative reviews. The results from the first classifier, that is informative reviews, then reclassified using the second classifier to determine its category, e.g. bug report or feature request. The experiment using 306,849 sentences of reviews crawled from Google Play and F-Droid. The experiment result shows that the proposed model is able to classify mobile application review by produces best accuracy of 0.79, precision of 0.77, recall of 0.87, and F-Measure of 0.81.

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Identifikasi Komentar Spam Pada Instagram

Cited by 12 publications

References 8 publications

Firefox Extension untuk Klasifikasi Komentar Spam pada Instagram Berbasis REST Services

Firefox Extension untuk Klasifikasi Komentar Spam pada Instagram Berbasis REST Services

Integration of REST-Based Web Service and Browser Extension for Instagram Spam Detection

Classification of Mobile Application Reviews using Word Embedding and Convolutional Neural Network

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