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All Journal Jupiter Jurnal Teknologi Format : Jurnal Imiah Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJISCS (International Journal Of Information System and Computer Science) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Informasi Interaktif Jurnal Teknologi Komputer dan Sistem Informasi bit-Tech Respati Jurnal E-Komtek Teknika Jurnal Sosial dan Teknologi Tamansiswa International Journal in Education and Science (TIJES) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Journal of Intelligent Software Systems Jurnal Teknologi Informasi Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Kohesi: Jurnal Sains dan Teknologi Jurnal Ilmiah Mahasiswa Agrokomplek Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Teknomatika: Jurnal Informatika dan Komputer VISA: Journal of Vision and Ideas Jurnal Wicara Desa JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Femi Dwi Astuti
Universitas Teknologi Digital Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

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Journal : Informasi Interaktif

 1 
PENGARUH SMOTE DAN FORWARD SELECTION DALAM MENANGANI KETIDAKSEIMBANGAN KELAS PADA ALGORITMA KLASIFIKASI Ika Nur Fajri; Femi Dwi Astuti
Informasi Interaktif Vol 7, No 1 (2022): Jurnal Informasi Interaktif Vol. 7 No. 1 Januari 2022
Publisher : Universitas Janabadra

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Abstract

A high accuracy value in the classification process can ideally be obtained if the number of classes in the dataset is balanced. In fact, the data obtained do not all have a balanced number of classes, thus reducing the performance of the classification algorithm. In addition to the problem of an unbalanced number of classes, the attributes involved in the calculation also affect the accuracy value, so it is necessary to choose which attribute is the most influential. In this study, one method of feature selection is used, namely Forward Selection. This method is used to select which features are the most influential. SMOTE, which is one of the over-sampling algorithms, makes data with fewer classes equal to those with many classes. The results show that in the car evolution dataset the use of SMOTE can increase accuracy by 6.12% and the use of SMOTE with forward selection can increase accuracy by 6.09%. In the glass identification dataset the use of SMOTE can increase accuracy by 9.65% and the use of SMOTE with forward selection can increase accuracy by 12.6%. The use of forward selection with SMOTE is more effective for datasets that have a small number of classes.Keywords: Forward Selection, K-NN, Klasifikasi, SMOTE
SELEKSI FITUR FORWARD SELECTION PADA ALGORITMA NAIVE BAYES UNTUK KLASIFIKASI BENIH GANDUM Femi Dwi Astuti
Informasi Interaktif Vol 3, No 3 (2018): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

Abstract - Wheat (Triticum aestivum L) is one of the staple food ingredients besides rice. The demand for the wheat in the world until 2020 is estimated to increase by 1.6% per year. The data processing for wheat seeds has been done a lot, one of them is by using data mining classification techniques. The feature selection is used before the classification process to optimize the accuracy values from the classification results. The feature selection used in this research is forwarding the selection which is applied to the Naive Bayes algorithm to classify the wheat seeds.The results of this study indicate that the value of the accuracy and the wheat classification  after using the feature selection has a higher value of 93.81% compared to the condition before using the feature selection of 90.48%. The precision results also increased from 91.49% to 94.81%. Keywords: Forward Selection, Naive Bayes, Classification, Gandum.
Co-Authors Adiguna, I Gede Divbodhi Adiningtyas, Baiq Faras Sukma Agustinus Agustinus Andriyani, Widyastuti Arghyanita, Ni Luh Made Swasti Arrio Saputra Bagas Antuk Pramukti Bambang Budi Santoso Bambang Purnomosidi Dwi Putranto Danny Kriestanto Danny Kriestanto, Danny Dwi Noorma Putri Erranaomi, Muhammad Brilian Fajri, Ika Nur Fauzi, Arma Febri Nova Lenti Guntara, Mohammad Hudaimi Husain, Nadia Salsabila I Komang Damar Jaya Indra Yatini Buryadi Indra Yatini Buryadi Jayaputra Maharani, Kadek Dyah Aurelia Nugroho, Muhammad Agung Nurwiyati, Fransisca Wiwiek Pius Dian Widi Anggoro Ponco Nugroho, Edo Septian Ramadhani, Nur Revianti, Siska Lidya Rikie Kartadie Rosit Sanusi Salahuddin, Muamar Omar Salwa, Nabila Widhi Suharijanto, Richardo Amos Sumiyatun Syamsu Windarti Utami, Firmania Dwi Wahab, Alvionita Safira Wibowo, Dwi Winarno, Basuki Heri Wiwi Widayani, Wiwi Yogo Hermawan Yuli Astuti