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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Infortech Infotek : Jurnal Informatika dan Teknologi Jurnal Responsif : Riset Sains dan Informatika IMTechno: Journal of Industrial Management and Technology
Hamdun Sulaiman
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Library & Information Science

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Journal : Infotek : Jurnal Informatika dan Teknologi

 1 
Deteksi Spam Email dengan Metode Naive Bayes dan Particle Swarm Optimization (PSO) Muhamad Abdul Ghani; Hamdun Sulaiman
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 1 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i1.7049

Abstract

Internet-based technology has become a primary need. Based on the survey results from the Central Statistics Agency in collaboration with APJII, email sending and receiving activities have outperformed social media positions by reaching 95.75%. Very intense use of email can have both positive and negative effects. Because apart from being a communication tool, in reality not everyone uses email well and there are even so many misuses of email that have the potential to harm others. This misused email is commonly known as spam or junkmail (junk email) which contains advertisements, scams and even viruses. In this study, data processing from gmail emails with text mining was carried out and then tested with several data mining classification methods including the Naïve Bayes Algorithm, SVM, Random Forest and combined with Partical Swarm Optimization in predicting spam emails with the aim that the selected algorithm is the most accurate. From the test results by measuring the performance of the four algorithms using Confusion Matrix and ROC, it is known that the Naïve Bayes algorithm with Partical Swarm Optimization (PSO) has the highest accuracy value, namely 81.40% and AUC 0.78
Implementasi Machine Learning Dengan Metode Text Mining Pada Twitter Hamdun Sulaiman; Muhamad Ryansyah; Kudiantoro Widianto; Sidik Sidik; Andria Nugraha
Infotek: Jurnal Informatika dan Teknologi Vol. 7 No. 1 (2024): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v7i1.23734

Abstract

Currently PT. Telkom Indonesia (Indihome), uses the role of social media as a form of concern for its customers to handle complaints. Tweets from indihome customers on social media twitter are handled by the customer service division of Indihome. The manual of the categorization process carried out by the customer service division of Indihome on every narration of the "complain" complaint tweet that  goes  to  @indihome  twitter,  makes  the  process  considered  inefficient.  The purpose of this research is to provide solutions related to the problem of categorizing complaint tweets and to develop tools that can extract the narration of "complain" tweets in Indonesian. The research method used is comparative. On the other hand, gataframework and rapidminer tools are also used in this research to assist in preprocessing and cleaning of datasets to help create corpus and sentiment analysis. The total dataset after cleansing and preprocessing is 1,510. Based on the method proposed in this study on the Support Vector Machine classification algorithm, the highest  category  was  found  to  have  82.42%  accuracy,  75.33%  precision,  and 98.75% recall with an AUC of 0.826
Co-Authors Ami Rahmawati Andria Nugraha Dede Wintana Gunawan Gunawan Kudiantoro Widianto, Kudiantoro Mira Retaniama Muhamad Abdul Ghani Muhamad Ryansyah Mustofa Mustofa Sari Susanti Satia Suhada, Satia Sidik Sidik Sinta Rukiastiandari