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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Jurnal Repositor
Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya
Department of Informatics Engineering, Faculty of Information Technology, Sepuluh Nopember Institute of Technology Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia
Computer Science & IT Engineering

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CORTICAL BONE SEGMENTATION USING WATERSHED AND REGION MERGING BASED ON STATISTICAL FEATURES Mamluatul Hani`ah; Christian Sri Kusuma Aditya; Aryo Harto; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 8, No 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.002 KB) | DOI: 10.21609/jiki.v8i2.305

Abstract

Research on biomedical image is a subject that attracted many researchers’ interest. This is because the biomedical image could contain important information to help analyze a disease. One of the existing researches in his field uses dental panoramic radiographs image to detect osteoporosis. The analyzed area is the width of cortical bone. To analyze that area, however, we need to determine the width of the cortical bone. This requires proper segmentation on the dental panoramic radiographs image. This study proposed the integration of watershed and region merging method based on statistical features for cortical bone segmentation on dental panoramic radiographs. Watershed segmentation process was performed using gradient magnitude value from the input image. The watershed image that still has excess segmentation could be solved by region merging based on statistical features. Statistical features used in this study are mean, standard deviation, and variance. The similarity of adjacent regions is measured using weighted Euclidean distance from the statistical feature of the regions. Merging process was executed by incorporating the background regions as many as possible, while keeping the object regions from being merged. The segmentation result has succeeded in forming the contours of the cortical bone. The average value of accuracy is 93.211%, while the average value of sensitivity and specificity is 93.858% and respectively.
Deteksi Bot Spammer pada Twitter Berbasis Sentiment Analysis dan Time Interval Entropy Christian Sri Kusuma Aditya; Mamluatul Hani’ah; Alif Akbar Fitrawan; Agus Zainal Arifin; Diana Purwitasari
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

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
Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia Abdul Hadiy Dyo Fatra; Nur Hayatin Hayatin; Christian Sri Kusuma Aditya
Jurnal Repositor Vol 2 No 7 (2020): Juli 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v2i7.937

Abstract

Penelitian ini mengajukan sebuah klasfikasi terhadap respon masyarakat terhadap keputusan pemerintah untuk memindahkan Ibu kota Indonesia menggunakan metode lexicon. Hasil akurasi pengujian diukur dengan menggunakan confusion matrix. Data pada penelitian ini menggunakan data dari twitter yang berupa tweet. Data berisi tweet respon masyarakat terhadap keputusan perpindahan Ibu kota Indonesia. Data melewati 5 roses preprocessing yaitu case folding, punctuation removal, stopword removal, stemming, dan tokenizing. Lexicon digunakan karena menghasilkan nilai akurasi yang baik. Pada penelitian ini juga akan mencari kamus yang memiliki hasil klasifikasi paling baik. Hasil penelitian ini menunjukan hasil klasifikasi yang yang baik dengan mendekati hasil oleh pakar.
Co-Authors Abdul Hadiy Dyo Fatra Agus Zainal Arifin Alif Akbar Fitrawan Aryo Harto Aryo Harto Diana Purwitasari Mamluatul Hani`ah Mamluatul Hani`ah Mamluatul Hani’ah Nur Hayatin