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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Masyarakat Borneo JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Computer Science and Research
Jamaluddin
Universitas Methodist Indonesia
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Journal : METHOMIKA: Jurnal Manajemen Informatika

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TEXT MINING DAN KLASIFIKASI MULTI LABEL MENGGUNAKAN XGBOOST Rimbun Siringoringo; Jamaluddin Jamaluddin; Resianta Perangin-angin
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (601.925 KB) | DOI: 10.46880/jmika.Vol6No2.pp234-238

Abstract

The conventional classification process is applied to find a single criterion or label. The multi-label classification process is more complex because a large number of labels results in more classes. Another aspect that must be considered in multi-label classification is the existence of mutual dependencies between data labels. In traditional binary classification, classification analysis only aims to determine the label in the text, whether positive or negative. This method is sub-optimal because the relationship between labels cannot be determined. To overcome the weaknesses of these traditional methods, multi-label classification is one of the solutions in data labeling. With multi-label text classification, it allows the existence of many labels in a document and there is a semantic correlation between these labels. This research performs multi-label classification on research article texts using the ensemble classifier approach, namely XGBoost. Classification performance evaluation is based on several metrics criteria of confusion matrix, accuracy, and f1 score. Model evaluation is also carried out by comparing the performance of XGBoost with Logistic Regression. The results of the study using the train test split and cross-validation obtained an average accuracy of training and testing for Regression Logistics of 0.81, and an average f1 score of 0.47. The average accuracy for XGBoost is 0.88, and the average f1 score is 0.78. The results show that the XGBoost classifier model can be applied to produce a good classification performance.
PENGONTROLAN KEAMANAN SISTEM KOMPUTER CLIENT DARI SERANGAN HACKER DAN VIRUS KOMPUTER SECARA JARAK JAUH (REMOTE SERVER) DENGAN MENGGUNAKAN SSH Jamaluddin Jamaluddin; El Rahmat Jaya Hulu; Rimbun Siringoringo
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 1 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No1.pp123-127

Abstract

Computer system security is very important to be considered by computer users to protect their computers from attacks such as hackers and computer viruses that can take data and damage the user's computer system. Hacker is a person or party who has the skill in breaking through and sneaking to access a computer without the user's permission and can take data and even damage the system on the user's computer. And a Computer Virus is a computer program that copies and inserts copies into the program and can damage the computer system. So that by using SSH (Secure Shell) can control and check computer security from hacker attacks and computer viruses without having to come to the location where there is a client computer or done remotely (Remote Server).
MODEL BIDIRECTIONAL LSTM UNTUK PEMROSESAN SEKUENSIAL DATA TEKS SPAM Siringoringo, Rimbun; Jamaluddin, Jamaluddin; Perangin-angin, Resianta; Harianja, Eva Julia Gunawati; Lumbantoruan, Gortap; Purba, Eviyanti Novita
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp265-271

Abstract

This study examines the LSTM-based model for processing spam in text data. Spam poses several dangers and risks, both for individuals and organizations. Spam can be a nuisance that hampers both individual and organizational productivity. Much spam contains fraudulent or phishing attempts to obtain sensitive information. Spam detection using deep learning involves the utilization of algorithms and deep neural network models to accurately classify messages as either spam or not spam. Typically, spam detection systems use a combination of these methods to improve the accuracy of identifying spam messages. This study applies the Bi-LSTM deep learning model to sequentially process text (sequencing). The performance of the model is determined based on the loss and accuracy. The data used are the Spam SMS and Spam Email datasets. The test results show that the Bi-LSTM model demonstrates better performance on all tested datasets. Bi-LSTM is able to capture textual patterns from both the context and the text itself, as it can combine information from both directions. The test results prove that the Bi-LSTM model is more effective in text comprehension. So we need to use Snort to maintain network security. Snort is a useful software for observing activity in a computer network. Snort can be used as a lightweight Network Intrusion Detection System (NIDS). Detection is carried out based on the rules that have been described by the administrator in the directory rules contained in the configuration file. Snort can analyze real time alerts, where the mechanism for entering alerts can be in the form of a user syslog, file or through a database. So we can detect attacks on computer networks early.
Co-Authors Adela Bintang Asari Alex Leo Nardo Sipayung Alexander Simanullang Angely Sinaga Apriani Magdalena Sibarani Arina P. Silalahi Aritonang, Mendarissan Arthur Simanjuntak Br. Tarigan, Emia Edi Ulina Mahdalena Christiani Sinaga Darwis Robinson Manalu Dimita Hemalli Premasari Purba Doli Hasibuan Edison Sagala Efrianses F. H. Sinaga El Rahmat Jaya Hulu Elda Rotio Harahap Elida Pane Emma Rosinta Simarmata Endi Juli Anto Eva Julia Gunawati Harianja Eva Julia Gunawati Harianja Eva Julia Gunawati Harianja, Eva Julia Gunawati Eviyanti N. Purba Gea, Asaziduhu Gortap Lumbantoruan Harahap, Elda Rotio Harianja, Eva Julia G. Hiras Sinaga Hutahean, Lisna Hutapea, Marlyna I. Imelda S. Dumayanti Ira Mirantika Br. Ginting Jepryanta Natanael Brahmana Jonathan H. Saragih Jujur Marentha Nababan Junika Napitupulu Kartika Dewi Lian Adriawan Lidia Marisa Sianturi Lince R. Panataria Lisna Hutahean Lyna M. N. Hutapea Marpaung, Flora Melanthon Rumapea Melky Alessandro Purba Merry Anna Napitupulu, Merry Anna Moris Raichel Sitanggang Mufria J. Purba Mulatua P. Silalahi Mulatua Silalahi Naikson Fandier Saragih Nainggolan, Rena Nancy Lusiana Damanik Ndruru, Yufita Friska Paiman Nababan Panjaitan, Calvin Nicolas Perangin-angin , Resianta Posma S. M. Lumbanraja Purba, Eviyanti N. Purba, Eviyanti Novita Reka Putri Halawa Rena Nainggolan Resianta Perangin-Angin Rijois I. E. Saragih Rimbun Siringoringo Rimbun Siringoringo, Rimbun Roni Jhonson Simamora Sagala, Edison Saragih, Rijois Iboy E. Shara Asima Putri Sibarani Sianturi, Angel Maisya Sidabutar, Dewi Purnama Sihaloho, Senta Egrioni Simarmata, Ruth Elovani Sinambela Marzuki Sion Sela Simorangkir Sitanggang, Rini Inriani Katarina Sitorus, Hegi Audria Sofya C. Sitompul Stevani L. Z. Simanjuntak Stevani Laura Zois Br. Simanjuntak Tarigan, Riandi Pratama Thomson J. Napitupulu Winda Silalahi Yosephine Sembiring Yosevina Tarigan Yufita Friska Ndruru Zalukhu, Delianus