Article Per Year (5 Year)
p-Index From 2021 - 2026
3.063
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Jurnal Teknik ITS Jurnal Sains dan Seni ITS JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Mikrotek Jurnal Simantec Jurnal Teknobisnis Jurnas Nasional Teknologi dan Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI SISFO SPIRIT Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Sewagati
Arif Djunaidy
Departemen Sisitem Informasi Institut Teknologi Sepuluh Nopember
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : spirit

 1 
KLASIFIKASI BERBASIS GRAVITASI DATA DAN PROBABILITAS POSTERIOR Muhamad Arief Hidayat; Arif Djunaidy
SPIRIT Vol 7, No 1 (2015): SPIRIT
Publisher : STMIK YADIKA BANGIL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (755.016 KB) | DOI: 10.53567/spirit.v7i1.23

Abstract

The classification method based on data gravitation (DGC) is one of the new classification techniques that uses data  gravitation as the criteria of the classification. In the case of DGC, an object is classified on the basis of the class that creates the largest gravitation in that object. However, the DGC method may cause inaccurate result when the training data being used suffer from the class imbalanced problem. This may be caused by the existence of the training data containing a class having excessively big mass that will in turn tend to classify an uknown object as a member of that class due to the high degree of the data gravitation produced, and vice versa. In this research, a modification to the DGC method is performed by constructing a classificaion method that is based on both the data gravitation and posterior probability (DGCPP). In DGCPP, the mass concept defined in the DGC method as the prior probability is replaced by the posterior probability. By using this modification, data gravitation calculation process is expected to produce more accurate results in compared to those produced by the DGC method. In addtion, by improving the data gravitation calculation, it is expected that the DGCPP method willproduce more accurate classification results in compared to those produced by the DGC method for both normal dataset as well as dataset having class imbalanced problems. A thorough tests for evaluating the classification accuracy are performed using a ten-fold cross-validation method on several datasets containing both normal andimbalanced-class datasets. The results showed that DGCPP method produced positive average of accuracy differences in compared to those produced by the DGC method. For the tests using the entire normal datasets showed that the average of accuracy differences are statistically significant with a 95% confidence level. In addition, results of the tests using the four imbalanced-class datasets also showed that the average accuracy differences are statistically significant with a 95% confidence level. Finally, results of the tests for evaluating the computing times required by the classification program showed that the additional computing time needed by DGCPP method to perform the classification process is insignificant and less than the human response time, in compared to that needed by DGC method for running all datasets being used.  Keywords—data gravitation-based classification, class imbalanced problem,posterior probability 
Co-Authors Absari, Dhiani Achmad Holil Noor Ali Adhita Pratiwi Affindi Mario Bagaskara Ahmad Mukhlason Ahmad Mukhlason Ahmad Mukhlason Ahmad Muklason Akbar, Izzat Aulia Akhmad Tajuddin Tholaby MS Amalia Utamima Amalinda Jayanty Aulia Sidharta, Hanugra Barry Nuqoba, Barry Daniel Oranova Siahaan Devie Rosa Anamisa Dewi Septina Br Pelawi Diah Risqiwati Dio Afriansyah Putra Pradana Divka, Princessa Sissy Dwija Wisnu Brata Edio da Costa Faizal Mahananto Gede Aditra Pradnyana Ghinarosa Fitrinanda Hanif Mitsal Mahatta Hardhika Propitadewa Imamah h Jeremy Diaz Prasetyo Jihaan Harvi Habibah Kusumawardhani, Renny Pradina Lailatul Husniah Laily Rahmadhani Laura Wilhelmina Theresia M. Fiqri Ananda Mardiyanto Mardiyanto Meilani, Maulidiya Mochamad Zidan Aqila Kamil Muchammad Husni Muhamad Arief Hidayat Muhammad Ali Fikri Muhammad Arif Nuriman Muhammad Daffa Maulana Muhammad David Rahman Muhammad Hisyam Nabihah Hanun Atikah Nafitra, Hasna Dhiya Naufal Rafiawan Basara Nazriyah Deni Tsaniyah Nisa Dwi Angresti Nisrina Fadhilah Fano Nova Eka Budiyanta Nur Khofifah Nur Laila Nur Nindya Risnina Purnama, I Putu Adhitya Pratama Mangku Rahma Fauzia Rahmanisa, Fathia Rahmi, Mery Yulinda Raras Tyas Nurita Raras Tyasnurita Retno Aulia Vinarti Rico Bayu Wiranata Rifki Fahrial Zainal Rikasakomara, Edwin Riksakomara, Edwin Rully Soelaiman Septica Tiara Indah Permatasari Sidharta, Hanugra Aulia Sri Herawati SUGIYANTO Sugiyanto - Tony Dwi Susanto Tri Achmadi Untung B Santoso Wiga Ayu Puspaningrum Wiwik Anggraeni Wiwik Anggraeni Wiwik Anggraeni Yoga Yuniadi