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
Madrasah Muhammadiyah Al-Munawarroh adalah madrasah yang terletak di Malang dan salah satu madrasah yang berkembang. Untuk menunjang perkembangan tersebut, dibuat sebuah website profile yang dapat memberikan informasi kepada masyarakat secara cepat. Metode yang digunakan dalam pembuatan website adalah metode RAD (Rapid Application Development). Metode ini digunakan karena kami ingin melibatkan pihak madrasah dalam pembuatan website, agar website yang dibuat dapat bermanfaat secara penuh dan sesuai dengan kebutuhan penyebaran informasi bagi madrasah. Hasil dari pembuatan website profile menunjukkan bahwa pihak madrasah dapat dengan mudah menyebarkan informasi-informasi penting seperti jadwal pendaftaran siswa, informasi mengenai madrasah hingga informasi lowongan pekerjaan yang ada di madrasah.
In mid-2019 President of the Republic of Indonesia officially decided that the capital city be moved outside of Java. This has caused many responses from the public who responded to this decision. We have seen many of these community responses on social media, especially in Twitter. To see the reality of the response of the Indonesian people requires a study that can draw conclusions from the number of community responses. So from this problem this study was conducted to find the truth of the community response related to the decision to move the Indonesian capital by using the lexicon method. This study also wants to see a comparison of the effect of the stemming process on sentiment analysis. To measure the performance of the Lexicon method, this research will be tested by an expert. Then the results of the experts will be entered into the confusion matrix. From the calculations with the confusion matrix, the results showed that the response of many Indonesian people who agree with the decision to move the Indonesian capital.
Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized using the Particle Swarm Optimization method for the SVM parameters selection in the hope of optimizing the performance generated by the SVM algorithm in sentiment analysis. The results of the study using 10 k-fold cross-validations using the SVM algorithm resulted in an accuracy of 92,99%, a precision of 93,24%, and a recall of 93%. Meanwhile, the SVM and PSO algorithms produce an accuracy of 95%, precision of 95,08%, and recall of 94,97%. The results show that the Particle Swarm Optimization method can overcome the weaknesses of the Support Vector Machine algorithm in the problem of parameter selection and has succeeded in improving the resulting performance where the SVM-PSO is more superior to SVM without optimization in sentiment analysis.
This study proposes a classification of public response to the government's decision to move the Indonesian capital using the lexicon method. The results of testing accuracy are measured using a confusion matrix. The data in this study use data from Twitter in the form of tweets. The data contains tweets of community responses to the decision to move the Indonesian capital. Data passes through 5 preprocessing processes, namely case folding, punctuation removal, stopword removal, stemming, and tokenizing. Lexicon is used because it produces good accuracy values. In this study also will look for a dictionary that has the best classification results. The results of this study show the results of a good classification by approaching the results by experts.
AbstrakAsrama Mahasiswa Kalimantan Selatan (AMKS) Mandastana Malang merupakan fasilitas yang diberikan oleh Pemerintah Provinsi Kalimantan Selatan untuk mahasiswa yang menempuh pendidikan di kota Malang. Pengolahan data merupakan komponen penting dalam suatu organisasi. Pengelolaan data dan penyampaian informasi yang lambat juga akan menjadi kendala yang akan datang dan semua pelaporan data atau informasi juga belum terkomputerisasi. Semua proses yang masih menitik beratkan kepada sistem manual membuat pengolahan data dan informasi menjadi kurang efesien. Pada penelitian ini dilakukan perancangan sebuah sistem informasi berbasis web pada AMKS Mandastana Malang yang berfungsi untuk pengelolaan data dan informasi dari kegiatan-kegiatan organisasi asrama serta pelaporan data kepada Pemerintah Provinsi Kalimantan Selatan. Sistem ini menggunakan framework codeigniter modular extensions atau sering disebut HMVC (Hierarchical, Model,View,Controller) sebagai struktur yang memudahkan untuk perancangan, perawatan dan pengembangan. Metode waterfall digunakan sebagai metode pengembangan dalam sistem ini. Pengujian sistem menggunakan blackbox testing, requirement test dan UAT (User Acceptance Test) menghasilkan sistem yang berjalan dengan baik.South Kalimantan Student Dormitory (AMKS) Mandastana Malang is a facility provided by the provincial government of South Kalimantan for students who are educated in the city of Malang. Data processing is an important component of an organization. Slow data management and information delivery will also become an upcoming constraint and all data or information reporting is also not computerized. All processes that are still centered on manual systems make data processing and information become less efficient. In this research conducted the design of a web-based information system in AMKS Mandastana Malang which serves for the management of data and information from the activities of dormitory organizations and data reporting to the provincial government South Kalimantan. The system uses a modular extensions CodeIgniter framework or is often called HMVC (Hierarchical, Model, View, Controller) as a structure that makes it easy to design, care and develop. The waterfall method is used as a development method in this system. Testing the system using Blackbox testing, requirement test and UAT (User Acceptance Test) produces a system that runs well.
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