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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Journal Information System Development METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Pengabdian Masyarakat Nauli
Indra Kelana Jaya
Universitas Methodist Indonesia
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Journal : Journal Information System Development

 1 
ENSEMBLE LEARNING DENGAN METODE SMOTEBAGGING PADA KLASIFIKASI DATA TIDAK SEIMBANG Siringoringo, Rimbun; Jaya, Indra Kelana
Journal Information System Development (ISD) Vol 3, No 2 (2018): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

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Abstract

Unbalanced data classification is a crucial problem in the field of machine learning and data mining. Data imbalances have a poor impact on classification results where minority classes are often misclassified as a majority class. Conventional machine learning algorithms are not equipped with the ability to work on unbalanced data, so the performance of conventional algorithms is always not optimal. In this study, ensemble learning using SMOTEBagging method was applied to classify 11 unbalanced datasets. SMOTEBagging performance is also compared with three types of conventional classification algorithms namely SVM, k-NN, and C4.5. By applying the 5 cross-validation scheme, the AUC value generated by SMOTEBagging is higher at 10 datasets. The mean values of the lowest to highest AUC were obtained by SVM, k-NN, C4.5 and SMOTEBagging algorithms with values 0.638, 0.742, 0.770 and 0.895. By applying Friedman test it was found that the performance of AUC SMOTEBagging differed significantly with the other three conventional methods SVM, k-NN and C4.5ENSEMBLE LEARNING DENGAN  METODE  SMOTEBagging PADA KLASIFIKASI DATA TIDAK SEIMBANG
Analisa Alokasi Memori dan Kecepatan Kriptograpi Simetris Dalam Enkripsi dan Dekripsi Perangin-angin, Resianta; Jaya, Indra Kelana; Rumahorbo, Benget; Marpaung, Berlian Juni R
Journal Information System Development (ISD) Vol 4, No 1 (2019): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

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Abstract

Currently the focus of cryptography is on the security and speed of data transmission. Cryptography is the study of how to secure information. This security is done by encrypting the information with a special key. This information before being encrypted is called plaintext. After being encrypted with a key called ciphertext. At present, AES (Advanced Encryption Standard) is a cryptographic algorithm that is safe enough to protect confidential data or information. In 2001, AES was used as the latest cryptographic algorithm standard published by NIST (National Institute of Standard and Technology) in lieu of the DES (Data Encryption Standard) algorithm that has expired. The AES algorithm is a cryptographic algorithm that can encrypt and decrypt data with varying key lengths, namely 128 bits, 192 bits, and 256 bits. From the results of tests carried out for speed and classification memory, it can be concluded that the AES cryptographic algorithm is superior or faster if the size or size of the plaint text is not so large, because for the smaller AES algorithm the speed ratio in terms of encryption will become more fast, it becomes very different for the Blowfish algorithm itself where for large sizes plaint text can be encrypted faster than AES but for smaller sizes Blowfish is certainly slower in that case, for memory allocation in this case from the tests performed it can be concluded that AES requires more storage space or larger memory allocation compared to the blowfish algorithm
Analisa Alokasi Memori dan Kecepatan Kriptograpi Simetris Dalam Enkripsi dan Dekripsi Resianta Perangin-angin; Indra Kelana Jaya; Benget Rumahorbo; Berlian Juni R Marpaung
Journal Information System Development Vol 4, No 1 (2019): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Currently the focus of cryptography is on the security and speed of data transmission. Cryptography is the study of how to secure information. This security is done by encrypting the information with a special key. This information before being encrypted is called plaintext. After being encrypted with a key called ciphertext. At present, AES (Advanced Encryption Standard) is a cryptographic algorithm that is safe enough to protect confidential data or information. In 2001, AES was used as the latest cryptographic algorithm standard published by NIST (National Institute of Standard and Technology) in lieu of the DES (Data Encryption Standard) algorithm that has expired. The AES algorithm is a cryptographic algorithm that can encrypt and decrypt data with varying key lengths, namely 128 bits, 192 bits, and 256 bits. From the results of tests carried out for speed and classification memory, it can be concluded that the AES cryptographic algorithm is superior or faster if the size or size of the plaint text is not so large, because for the smaller AES algorithm the speed ratio in terms of encryption will become more fast, it becomes very different for the Blowfish algorithm itself where for large sizes plaint text can be encrypted faster than AES but for smaller sizes Blowfish is certainly slower in that case, for memory allocation in this case from the tests performed it can be concluded that AES requires more storage space or larger memory allocation compared to the blowfish algorithm
ENSEMBLE LEARNING DENGAN METODE SMOTEBAGGING PADA KLASIFIKASI DATA TIDAK SEIMBANG Rimbun Siringoringo; Indra Kelana Jaya
Journal Information System Development Vol 3, No 2 (2018): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Unbalanced data classification is a crucial problem in the field of machine learning and data mining. Data imbalances have a poor impact on classification results where minority classes are often misclassified as a majority class. Conventional machine learning algorithms are not equipped with the ability to work on unbalanced data, so the performance of conventional algorithms is always not optimal. In this study, ensemble learning using SMOTEBagging method was applied to classify 11 unbalanced datasets. SMOTEBagging performance is also compared with three types of conventional classification algorithms namely SVM, k-NN, and C4.5. By applying the 5 cross-validation scheme, the AUC value generated by SMOTEBagging is higher at 10 datasets. The mean values of the lowest to highest AUC were obtained by SVM, k-NN, C4.5 and SMOTEBagging algorithms with values 0.638, 0.742, 0.770 and 0.895. By applying Friedman test it was found that the performance of AUC SMOTEBagging differed significantly with the other three conventional methods SVM, k-NN and C4.5ENSEMBLE LEARNING DENGAN  METODE  SMOTEBagging PADA KLASIFIKASI DATA TIDAK SEIMBANG
Co-Authors Albert Harianja Ambarita, Indah Aritonang, Mendarissan Azli, Putri Berlian Juni R Marpaung Dandi Daniel Siregar Darwis Robinson Manalu DEWI SARTIKA Dian Agaventa, Chrissandro Dumayanti, Imelda Sri Ericho Elovando Surbakti Eva Julia Gunawati Harianja Eva Julia Gunawati Harianja, Eva Julia Gunawati Fajar Fati Gratianus Nafiri Larosa Feruche, Melani Gea, Asaziduhu Girsang, Jhon Vebrianto Harlen G. Simanullang Harlen Gilbert Simanullang Helen Fransisca Simanungkalit Humuntal Rumapea Imelda Sri Dumayanti Jeudi A. T. P. Sianturi Jimmy F. Naibaho Larosa, Fati G. N. Larosa, FGN Larosa Lyna M. N. Hutapea Manurung, Samuel Mariana Tri Susanti Marpaung, Berlian Juni R Marzuki Sinambela Marzuki, Sinambela Melani Feruche Mufria J. Purba Naibaho, Jimmy F. Naikson Fandier Saragih Nine Situmeang Nova Soraya Simanjuntak Panjaitan, Efdi Sarjono Pujiastuti, Maria Putra, Reza Ananda Rambe, Lima Hartima Resianta Perangin-Angin Rika Rosnelly, Rika Rimbun Siringoringo, Rimbun Rumahorbo, Benget Sagala, Tamado Simon Samuel Van Basten Manurung Saragih, Naikson F. Silalahi, Arina Prima Silalahi, Veraci Simanullang, Harlen Simanullang, Harlen Gilbert Simbolon, Anita Ira Agustina Sinambela Marzuki Siska Narulita Siboro Surianto Sitepu Surianto Sitepu Susanti, Mariana Tri Tarigan, Kerista Widya Ompusunggu Yohanna, Margaretha Yolanda Y. P. Rumapea yolanda yulianti pratiwi rumapea Zai, Andreas