Alif Akbar Fitrawan, Alif Akbar
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Published : 3 Documents

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Deteksi Bot Spammer pada Twitter Berbasis Sentiment Analysis dan Time Interval Entropy Aditya, Christian Sri Kusuma; Hani’ah, Mamluatul; Fitrawan, Alif Akbar; Arifin, Agus Zainal; Purwitasari, Diana
Jurnal Buana Informatika Vol 7, No 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v7i3.656


Abstract. Spam is an abuse of messaging undesired by recipients. Those who send spam are called spammers.  Popularity of Twitter has attracted spammers to use it as a means to disseminate spam messages. The spams are characterized by a neutral emotional sentiment or no particular users’ preference perspective. In addition, the regularity of tweeting behavior periodically shows automation performed by bot. This study proposes a new method to differentiate between bot spammer and legitimate user accounts by integrating the sentiment analysis (SA) based on emotions and time interval entropy (TIE). The combination of knowledge-based and machine learning-based were used to classify tweets with positive, negative and neutral sentiments. Furthermore, the collection of timestamp is used to calculate the time interval entropy of each account. The results show that the precision and recall of the proposed method reach up to 83% and 91%. This proves that the merging SA and TIE can optimize overall system performance in detecting Bot Spammer.Keywords: bot spammer, twitter, sentiment analysis, polarity, entropy Abstrak. Spam merupakan penyalahgunaan pengiriman pesan tanpa dikehendaki oleh penerimanya, orang yang mengirimkan spam disebut spammer. Ketenaran Twitter mengundang spammer untuk menggunakannya sebagai sarana menyebarluaskan pesan spam. Karakteristik dari tweet yang dikategorikan spam memiliki sentimen emosi netral atau tidak ada preferensi tertentu terhadap suatu perspektif dari user yang memposting tweet. Selain itu keteraturan waktu perilaku saat memposting tweet secara periodik menunjukkan otomatisasi yang dilakukan bot. Pada penelitian ini diusulkan metode baru untuk mendeteksi antara bot spammer dan legitimate user dengan mengintegrasikan sentimen analysis berdasarkan emosi dan time interval entropy. Pendekatan gabungan knowledge-based dan machine learning-based digunakan untuk mengklasifikasi tweet yang memiliki sentimen positif, negatif dan tweet netral. Selanjutnya kumpulan timestamp digunakan untuk menghitung time interval entropy dari tiap akun. Hasil percobaan menunjukan bahwa precision dan recall dari metode yang diusulkan mencapai 83% dan 91%. Hal ini membuktikan penggabungan Sentiment Analysis (SA) dan Time Interval Entropy (TIE) dapat mengoptimalkan performa sistem secara keseluruhan dalam mendeteksi Bot Spammer.Kata Kunci:  bot spammer, twitter, sentiment analysis,  polarity, entropy
Pengenalan Wajah Menggunakan Implementasi T-shape Mask pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine Musthafa, Ahmad Reza; Fitrawan, Alif Akbar; Supria, Supria
Jurnal Buana Informatika Vol 7, No 1 (2016): Jurnal Buana Informatika Volume 7 Nomor 1 Januari 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v7i1.478


Abstract. Face recognition is the identification process to recognize a persons face. Many studies have been developing face recognition methods, one of which is the Two Dimensional Linear Discriminant Analysis (TDLDA) which has pretty good accuracy results with the method of classification Support Vector Machine (SVM). With more training data can add computational time. TDLDA using all the piksel image as input to be processed for feature extraction. Though not all the objects in the area of the face is a significant feature in face recognition. In this study, the proposed use of the T-shape with only use a significant part is the eyes, nose, and mouth are integrated with TDLDA and SVM. The result could reduce computing time on face recognition 21.56% faster than TDLDA method. The accuracy of the results in this study was 91% -96% which is close to the level of accuracy without using a mask on the face.Keyword: face recognition, T-shape, TDLDA, Support vector machine. Abstrak. Pengenalan wajah merupakan proses identifikasi untuk mengenali wajah seseorang. Telah Banyak penelitian yang mengembangkan metode pengenalan wajah, salah satunya adalah Two Dimensional Linear Discriminant Analysis (TDLDA) yang memiliki hasil akurasi yang cukup baik dengan metode klasifikasi Support Vector Machine (SVM). Dengan semakin banyak data training dapat menambah waktu komputasinya. TDLDA menggunakan semua piksel citra sebagai masukan yang akan diproses untuk ekstrasi fitur. Padahal tidak semua objek pada area wajah merupakan fitur yang signifikan dalam pengenalan wajah. Dalam penelitian ini diusulkan penggunaan T-shape dengan hanya menyimpan bagian yang signifikan yaitu mata, hidung, dan mulut yang diintegrasikan dengan TDLDA dan SVM. Hasilnya dapat mengurangi waktu komputasi pada pengenalan wajah 21,56% lebih cepat daripada metode TDLDA. Hasil akurasi pada penelitian ini adalah 91%-96% yang mendekati tingkat akurasi tanpa menggunakan mask pada wajah.Kata Kunci: pengenalan wajah, T-shape, TDLDA, Support vector machine.
Detection Object on Sea Surface to Avoid Collision with Post-Processed in Background Subtraction Image Fitrawan, Alif Akbar; Shodiq, Mohammad Nur; Kusuma, Dedy Hidayat
JOIV : International Journal on Informatics Visualization Vol 3, No 2 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1110.18 KB) | DOI: 10.30630/joiv.3.2.207


Data on shipping accident investigations from the National Transportation Safety Committee (NTSC) throughout 2010-2016 of fifty-four accident cases at sea, seventeen of which were accidents caused by collisions on ships in Indonesian waters, act to avoid a collision by detecting an object on the sea surface. Detection object is challenging because so many varieties object on the sea surface. Illumination variations with different seasons, periods, illumination intensity and direction affect the detection of objects directly. A rough sea is seen as a dynamic background of moving objects with size order and shape. All these factors make it difficult to object detection. Therefore, it is possible to conclude that background subtraction on sea surface problem remains open and a definitive robust solution is still missing. In this paper, we have applied a selection of background subtraction algorithms with post-processed to the problem. Experimental results with our dataset verify the high efficiency of our proposed method