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All Journal Jurnal TIMES Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Journal Information System Development METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Masyarakat Borneo Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Rimbun Siringoringo, Rimbun
Universitas Methodist Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health Social Sciences Other

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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
KLASIFIKASI DATA TIDAK SEIMBANG MENGGUNAKAN ALGORITMA SMOTE DAN k-NEAREST NEIGHBOR Siringoringo, Rimbun
Journal Information System Development (ISD) Vol 3, No 1 (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. k-Nearest Neighbor is one of the most popular and simple classification methods but it is not equipped with the ability to work on unbalanced datasets. In this study, the Synthetic Minority Over-Sampling Technique (SMOTE) was applied to solve the class imbalance problem on the Credit Card Fraud dataset. By applying the 10-cross-validation evaluation scheme, it was found that SMOTE increases the mean of  G-Mean by 53.4% to 81.0% and the mean of  F-Measure by 38.7 to 81.8%Keywords: Class imbalance, Synthetic Minority Over-sampling Technique, k-Nearest Neighbor
PEMODELAN TOPIK BERITA MENGGUNAKAN LATENT DIRICHLET ALLOCATION DAN K-MEANS CLUSTERING Siringoringo, Rimbun; Jamaluddin, Jamaluddin; Gea, Asaziduhu
Journal Information System Development (ISD) Vol 5, No 1 (2020): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

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Abstract

Mayoritas pengguna internet saat ini melakukan penelusuran internet untuk mengetahui berita atau informasi yang sedang berkembang. Pertumbuhan internet dan media sosial telah mendorong munculnya ratusan portal atau berita online dengan topik berita yang sangat beragam. Menelusuri topik berita secara manual merupakan metode yang tidak efektif serta menghabiskan waktu yang banyak.  Pada penelitian ini dilakukan pemodelan topik berita menggunakan Latent Dirichlet Allocation (LDA). Sebelum penerapan model LDA, juga diterapkan proses-proses pendukung yaitu tokenisasi, lemmatisasi, faktorisasi tf-idf, dan non-negative matrix factorization. Hasil penelitian menunjukkan bahwa LDA dapat diterapkan untuk memodelkan topik berita dengan baik dengan nilai  skor loglikelihood -13615.912 dan skor perplexity 378.958. Selain menggunakan LDA, pemodelan topik juga dilakukan dalam bentuk klaster dengan menerapkan k-means clustering. Dengan metode elbow diperoleh jumlah klaster yang ideal untuk k-means clustering adalah 5 klaster serta performa nilai silhouette 0.62
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
KLASIFIKASI DATA TIDAK SEIMBANG MENGGUNAKAN ALGORITMA SMOTE DAN k-NEAREST NEIGHBOR Rimbun Siringoringo
Journal Information System Development Vol 3, No 1 (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. k-Nearest Neighbor is one of the most popular and simple classification methods but it is not equipped with the ability to work on unbalanced datasets. In this study, the Synthetic Minority Over-Sampling Technique (SMOTE) was applied to solve the class imbalance problem on the Credit Card Fraud dataset. By applying the 10-cross-validation evaluation scheme, it was found that SMOTE increases the mean of  G-Mean by 53.4% to 81.0% and the mean of  F-Measure by 38.7 to 81.8%Keywords: Class imbalance, Synthetic Minority Over-sampling Technique, k-Nearest Neighbor
Co-Authors Angely Sinaga Apriani Magdalena Sibarani Arina P. Silalahi Aritonang, Mendarissan Br Nadapdap, Askeline Ruthkenera Br. Hombing, Betseba Br. Siagian, Rut Magdalena Darwis Robinson Manalu Dedy Arisandi Delvi Natalina Br Tarigan Donda Sari Tiur Maida Situmorang Edi Kurniawan El Rahmat Jaya Hulu Emma Rosinta Simarmata Ericho Elovando Surbakti Erna Budhiarti Nababan Eva Julia G. Harianja Eva Julia Gunawati Harianja Eva Julia Gunawati Harianja, Eva Julia Gunawati Fati Gratianus Nafiri Larosa Gea, Asaziduhu Giska Yufani Gortap Lumbantoruan Harianja, Eva J. G. Harianja, Eva Julia G. Helen Fransisca Simanungkalit Hutagalung, Estri Aprilia Hutapea, Marlyna I. Imelda S. Dumayanti Indra Kelana Jaya Ira Mirantika Br. Ginting Jamaluddin Jamaluddin Jamaluddin Jepriyanta N. Brahmana Jimmy F. Naibaho Jonathan H. Saragih Jujur Marentha Nababan Junika Napitupulu Laia, Sadarman Lyna M. N. Hutapea Mahendra Tlapta Sitepu Marpaung, Flora Merry Anna Napitupulu, Merry Anna Moris Raichel Sitanggang Mufria J. Purba Nababan, Maria Tesalonika Naikson Fandier Saragih Nainggolan, Rena Napitupulu, Thomson Januari Ndruru, Yufita Friska Nduru, Yiska Sonia Kristin Nova Soraya Simanjuntak Panjaitan, Calvin Nicolas Perangin Angin, Resianta Perangin-angin , Resianta Petty Exclesia Pardosi Posma S. M. Lumbanraja Purba, Eviyanti N. Purba, Eviyanti Novita Rajagukguk, Marshanda Febyola Rasmulia Sembiring Reka Tini Sipayung Sipayung Rena Nainggolan Resianta Perangin Angin Resianta Perangin-Angin Rijois I. E. Saragih Rumahorbo, Benget Sibagariang, Roida Ferawati Sidabutar, Dewi Purnama Silalahi, Calvin Matius Simanjuntak, Stevani L. Z. Sitindaon, Ester Sitorus, Hegi Audria Stevani L. Z. Simanjuntak Sutarman Thomson J. Napitupulu Tobing, Putra Halomoan Widya Ompusunggu Winda Sari Sitanggang Yessy Dearni C. Saragih Yohana Angelita Manullang Yosephine Sembiring Zakarias Situmorang Zalukhu, Delianus