SPAMMER DETECTION BASED ON ACCOUNT, TWEET, AND COMMUNITY ACTIVITY ON TWITTER

Spammers are the activities of users who abuse Twitter to spread spam. Spammers imitate legitimate user behavior patterns to avoid being detected by spam detectors. Spammers create lots of fake accounts and collaborate with each other to form communities. The collaboration makes it difficult to detect spammers' accounts. This research proposed the development of feature extraction based on hashtags and community activities for the detection of spammer accounts on Twitter. Hashtags are used by spammers to increase popularity. Community activities are used as features for the detection of spammers so as to give weight to the activities of spammers contained in a community. The experimental result shows that the proposed method got the best performance in accuracy, recall, precision and g-means with are 90.55%, 88.04%, 3.18%, and 16.74%, respectively.  The accuracy and g-mean of the proposed method can surpassed previous method with 4.23% and 14.43%. This shows that the proposed method can overcome the problem of detecting spammer on Twitter with better performance compared to state of the art

PENERAPAN WAVELET HAAR DAN BACKPROPAGATION UNTUK PENGELOMPOKAN DIABETIK RETINOPATI BERDASARKAN CITRA RETINA MATA

Diabetik retinopati merupakan penyakit yang menyerang retina mata dan dapat menyebabkan kebutaan. Tingkat keparahan diabetik retinopati terbagi atas empat yaitu normal, Diabetik Retinopati Non-proliferative (NPDR), Diabetik Retinopati Proliferative (PDR) dan Makula Endema (ME). Pada penelitian ini diabetik retinopati dapat dikelompokkan dengan mengkombinasikan metode wavelet haar dan backpropagation. Jumlah data yang digunakan 612 citra (data seimbang setiap kelas 153 data). Ukuran citra 2304�1536, 2240�1536 dan 1440�960. Ekstraksi ciri citra digital yang digunakan yaitu wavelet haar pada citra warna red, green dan blue (RGB)pada level 1 dan level 4 pada subband LL serta pengelompokan dengan backpropagation dengan learning rate 0,1; 0,01 dan 0,001; persentase pembagian data latih dan data uji adalah 70:30, 80:20, 90:10 dan 95:5, nilai MSE yang digunakan adalah 10-6, maksimum epoch 100.000 iterasi. Hasil penelitian ini adalah akurasi pengujian tertinggi yang diperoleh sebesar 56,25% dengan ukuran citra 2440�1448, haar level ke-4 serta persentase perbandingan data latih dan data uji 95:5, Learning rate 0,1;0,01 dan 0,001. Dengan demikian, algoritma wavelet haar tidak mampu mengenali ciri dari diabetik retinopati dan proses dekomposisi akan banyak menghilangkan informasi dari diabetik retinopati, serta hasil normalisasi LL1 memiliki perbedaan yang sangat dekat sehingga mempersulit pengelompokan dengan backpropagation. Kata Kunci: Backpropagation, Diabetik Retinopati, Retina Mata, RGB, Wavelet Haa

Analisis Performa Link Stability dari Faktor Kecepatan untuk Dinamisasi Zona pada Zone Routing Protocol

Zone dynamization is carried out in the Zone Routing Protocol to allow the adaptation of the routing protocol to VANET network conditions. Zone dynamization is accomplished by periodically updating the radius within a configured time period. The value of link stability from the factors that influence network conditions is used as a reference in the radius value’s renewal process. From the test and simulation results, speed is the most dominant factor in link stability composition. Comparison between ZRP and zone dynamics against traditional ZRP shows better performance than ZRP with zonal dynamics when measured from metric analysis of packet delivery ratio, delay, and routing overhead. The increase in ZRP performance can occur because the zoning dynamics carried out make ZRP more adaptive to network conditions so that it does not work too proactively or reactively.

PERBANDINGAN IMPUTASI DAN PARAMETER SUPPORT VECTOR REGRESSION UNTUK PERAMALAN CUACA

Curah hujan adalah informasi penting di bidang transportasi, pertanian, industri dll. Dengan mengetahui informasi curah hujan, tindakan dapat diambil secara tepat di beberapa bidang tersebut. sehingga tidak ada kerugian karena kesalahan dalam informasi curah hujan. Makalah ini bertujuan untuk menemukan metode yang sesuai dalam peramalan curah hujan yang terkait dengan metode pemrosesan data imputasi dan nilai parameter dalam Support Vector Regression (SVR). Hasil percobaan menunjukkan bahwa metode preprocessing data imputasi terbaik diperoleh untuk digunakan ke dalam SVR berdasarkan nilai Mean Squared Error (MSE) dan Mean Absolute Error (MAE). Berdasarkan hasil MSE, k-nearest neighbor adalah metode terbaik yang digunakan untuk preprocessing data imputasi. Data preprocessing menghasilkan eksperimen pada SVR Polinomial dengan parameter C 1000, toleransi 0,001, epsilon 0,01 dan iterasi tak terbatas. Di sisi lain, hasil MAE menunjukkan bahwa Artificial Neural Network (ANN) adalah metode terbaik dalam imputasi data preprocessing. ANN dengan radial basis function kernel, gamma 0,001, C 1000, toleransi 0,001 dan iterasi tanpa batas. JST diuji pada RBF SVR dengan gamma 0,001, C 1000, toleransi 0,001 dan iterasi tak terbatas

PENDAMPINGAN PENGELOLAAN KEUANGAN BERBASIS SYARIAH PADA MASYARAKAT PELAKU USAHA MITRA BWM FATAHA DI KAMPUNG MAREDAN BARAT KECAMATAN TUALANG

Pengabdian ini dilaksanakan bagi nasabah Bank Wakaf Mikro (BWM) Fataha, Kampung Maredan, Kecamatan Tualang, Kabupaten Siak, tujuan dari pengabdian masyarakat ini adalah untuk mengetahui tingkat pemahaman Nasabah Bank Wakaf Mikro (BWM) Fataha Kampung Maredan Barat Kecamatan Tualang tentang Akad-Akad Pembiayaan Syariah dan mengetahui tingkat keberhasilan pengenalan nasabah Bank Wakaf Mikro Fataha Kampung Maredan Barat Kecamatan Tualang. Metode yang digunakan dalam kegiatan pengabdian masyarakat ini a dalah dengan penyuluhan, presentasi dan diskusi. Hasil pengabdian menunjukkan bahwa: Program pengabdian kepada masyarakat nasabah BWM Fataha, Kecamatan Tualang, Perawang ini dapat diselenggarakan dengan baik dan berjalan dengan lancar sesuai dengan rencana kegiatan yang telah disusun, hasil dari pengabdian ini disimpulakan bahwa: Pertama, Nasabah BWM Fataka pemahaman tentang transaksi keuangan syariah beragam, ada yang sudah faham, ada yang masih ragu-ragu, bahkan ada yang belum faham, Kedua, Ketercapaian tujuan program kegiatan pengabdian kepada masyarakat nasabah BWM Fataha keseluruhan program yang telah dilakukan dengan kolaborasi antara pemilik usaha dan pengabdi telah dilakukan semua dan sesuai dengan roundown acara maupun waktu yang telah ditentukan sebelumnya

Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price

Stock trading is a popular financial instrument worldwide. In Indonesia, the stock market is known as the Indonesia Stock Exchange (BEI), and one actively traded stock is PT Bank Central Asia (BBCA). However, predicting stock price movements is challenging due to various influencing factors. Investors use fundamental and technical analyses for decision-making, but results often vary. Machine learning, particularly random forest regression and linear regression algorithms, can be used for stock price forecasting. In this paper, we compares these two machine learning methods to forecast BBCA stock prices, aiming to provide more accurate and effective solutions for investor's investment and trading decisions. The evaluation results of cross-validation mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for linear regression were 0.12848, 0.35807, 0.29570, and 0.0036%, respectively, while for random forest regression were 27473.76, 158.04, 142.70, and 1.7153%. These findings indicate that linear regression outperforms in forecasting performance

Automatic image slice marking propagation on segmentation of dental CBCT

Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively

Deteksi Akun Spammer Berdasarkan Hashtag Dan Aktifitas Komunitas Pada Twitter

Spammer meniru pola perilaku pengguna yang sah untuk menghindari terdeteksi oleh pendeteksi spam. Spammer membuat banyak account palsu dan berkolaborasi satu sama lain untuk membentuk komunitas. Kolaborasi antar account menggunakan tweet berbeda-beda tetapi menggunakan hashtag untuk mencapai tujuan. Kolaborasi tweet berbeda ini akan membuat seperti hashtag natural yang banyak diperbincangkan orang di Twitter. Kolaborasi ini mempersulit deteksi account spammer karena berada di antara account legitimate yang membuat tweet dengan hashtag. Penelitian ini mengusulkan pengembangan ekstraksi fitur berdasarkan hashtag dan aktivitas komunitas untuk mendeteksi account spammer di Twitter. Proses penentuan komunitas menggunakan perangkat lunak gephi dengan data following account yang membuat tweet pada account Twitter Komisi Pemberantasan Korupsi Republik Indonesia dari tanggal 01 Agustus 2018 hingga 10 September 2019. Data tersebut dilakukan proses ekstraksi fitur account, tweet, dan komunitas. Tweet dilakukan dengan menggunakan hashtag. Hashtag digunakan oleh spammer untuk meningkatkan popularitas. Pada fitur account, tweet, dan komunitas dilakukan seleksi fitur untuk mendapatkan fitur optimal dengan menggunakan Recursive Feature Elimination. Hasil seleksi fitur digunakan untuk mendapatkan nilai legitimate atau spammer menggunakan multi-layer perceptron. Hasil nilai dilakukan penggabungan dengan menggunakan pembobotan untuk penentuan spammer. Hasil percobaan menunjukkan bahwa pengembangan ekstraksi fitur berdasarkan hashtag dan aktivitas komunitas berhasil untuk mendeteksi account spammer di Twitter. Metode yang diusulkan mendapatkan nilai akurasi, recall, presisi, dan g-mean masing-masing 90,6%, 88,0%, 3,2%, dan 16,7%. Hasil fitur seleksi pada fitur tweet setiap pembagian persentase dataset tidak mengalami perubahan yaitu 6 fitur. Pada fitur tweet terdapat fitur jumlah hashtag dan unique hashtag. Unique hashtag yang tinggi menunjukan bahwa account melakukan hashtag berbeda-beda yang tidak memiliki keterkaitan terhadap spammer. Hal ini membuktikan bahwa hashtag digunakan oleh spammer dalam aktivitasnya. Bobot fitur account dan tweet memiliki keseimbangan dalam keberhasilan penentuan spammer. Fitur komunitas dilihat berdasarkan bobot yang digunakan, dengan hasil yang bagus yaitu lebih kecil dari 0,3. ================================================================================================================== Spammers imitate legitimate user behavior patterns to avoid being detected by spam detectors. Spammers create lots of fake accounts and collaborate with each other to form communities. Collaboration between accounts uses different tweets but using hashtags to achieve goals. This different tweet collaboration will make it look like a natural hashtag that many people talk about on Twitter. This collaboration makes it difficult to detect spammers' accounts because they are among legitimate accounts that tweet with hashtags. This research proposed the development of feature extraction based on hashtags and community activities to detect spammer accounts on Twitter. The process of determining the community uses Gephi software by using the following account data which tweeted on the Indonesian Republic's Corruption Eradication Commission's Twitter account from 01 August 2018 to 10 September 2019. Data was extracted about the account, tweet, and community. Tweets done contain a hashtag. Hashtags are used by spammers to increase popularity. Features about accounts, tweets, and community features are selected to get optimal features using Recursive Feature Elimination. The feature selection results are used to get legitimate values or spammers using multi-layer perceptron. The results of the value are merged using weighting to determine the spammer's account. The experimental results show that the development of feature extraction based on hashtags and community activities has succeeded in detecting spammers' accounts on Twitter. The proposed method obtains accuracy, recall, precision, and g-mean with 90.6%, 88.0%, 3.2% and 16.7% respectively. The results of the selection feature on the tweet feature per division of the dataset does not change, namely 6 features. In the tweet feature there is a hashtag and unique hashtag feature. High unique hashtags indicate that accounts have different hashtags that are not related to spammers. This proves that the hashtag is used by spammers in their activities. The weight of account and tweet features has a balance in determining the success of spammers. Community features based on weights are used that good results are smaller than 0.3

TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets

Twitter is a social media service that is often used (popular) as a means of communication between users. Twitter's popularity makes spammers spam for personal purposes and gains. Bot spammers are user abuse on Twitter social media. Spammers spread spam repeatedly to other users. This spam is done with the aim of achieving trending topics. Spam activity is carried out by imitating the behavior patterns of real users so that they are not detected as acts of Twitter abuse. in this paper proposed a TF-IDF weighting to detect spammer accounts on Twitter based on tweets and retweet representation of tweets. The purpose of this study is to detect Bot Spammers or Humans using a classification technique using the Naive Bayes algorithm. The best experimental results in the division of 70% training data and 30% test data obtained 92% accuracy with precision and recall of 100% and 87.5%, respectively. This shows that it has successfully detected spammer accounts on Twitter

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices