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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pendidikan Matematika (Kudus) Jurnal Surya Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Jurnal Surya Masyarakat Journal of Soft Computing Exploration Jurnal Komputer dan Teknologi Informasi
Muhammad Sam'an
Department of Informatics, Universitas Muhammadiyah Semarang
Religion Humanities Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Mathematics Public Health Other

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Journal : International Journal of Electrical and Computer Engineering

 1 
The cross-association relation based on intervals ratio in fuzzy time series Etna Vianita; Muhammad Sam'an; A. Nafis Haikal; Ina Salamatul Mufaricha; Redemtus Heru Tjahjana; Titi Udjiani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2040-2051

Abstract

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modified steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross-association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals.
Hybrid deep learning model for YouTube spam comment detection Sam'an, Muhammad; Imaddudin, Khrisna
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3313-3319

Abstract

Social media platforms, including YouTube and Facebook, allow users to interact through comments and videos. However, the openness of these platforms also makes them susceptible to spammers engaging in phishing, malware distribution, and advertisement dissemination. In response, our study introduces an innovative technique for detecting features indicative of spam within comments associated with shared videos. The initial phase involves data collection from the University of California, Irvine (UCI) machine learning repository and preprocessing using tokenization and lemmatization. Subsequently, a rigorous feature selection process is executed, and experiments are conducted with various proposed classification models. The performance evaluation demonstrates outstanding accuracy in identifying spam comments on YouTube: convolutional neural network with gated recurrent unit (CNN-GRU) at 95.92%, convolutional neural network with long short-term memory (CNN-LSTM) at 95.41%, convolutional neural network with bidirectional long short-term memory (CNN-biLSTM) at 96.43%, gated recurrent unit (GRU) at 95.41%, long short-term memory (LSTM) at 94.13%, and bidirectional long short-term memory (biLSTM) at 96.94% and convolutional neural network (CNN) at 94.64%. These results highlight the substantial contribution of our approach to spam detection and the fortification of online security.
Co-Authors A. Nafis Haikal A. Nafis Haikal Adinullhaq, Juyus Muhammad Ahmad Ilham Akhmad Fathurrohman Akhmad Fathurrohman Ardhani, Yevi Alviatul Arfido Ramadhani Ariyanto, Nova Asdani Kindarto Bayu Surarso Etna Vianita Etna Vianita Fahrezi, Andrian Febrianto Febrianto Febrianto Febrianto, Febrianto Hasbi Ardianto Pratama Ilham, Ahmad Imaddudin, Khrisna Ina Salamatul Mufaricha Irvan Nurmantoro Juyus Muhammad Adinulhaq Juyus Muhammad Adinullhaq Kindarto, Asdani Luqman Assaffat Luqman Assaffat Much Aziz Muslim Muhammad Munsarif Muhammad Sholeh Sarwono Nova Ariyanto Nurmantoro, Irvan Ramadhani, Arfido Redemtus Heru Tjahjana Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Samsudi Raharjo Sefrina Ainun Susilo Hariyanto Syafrie Abdunnasir Jawad Titi Udjiani Yahya Nur Ifriza Yevi Alviatul Ardhani Yosza Dasril