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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL MAGISTER HUKUM UDAYANA Jurnal Ilmiah Hukum dan Dinamika Masyarakat Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Jurnal Online Mahasiswa (JOM) Bidang Ilmu Hukum Jurnal Pembangunan dan Alam Lestari Berkala Ilmu Kesehatan Kulit dan Kelamin Jurnal Pendidikan Biologi Indonesia Jurnal Promkes: The Indonesian Journal of Health Promotion and Health Education Jurnal Ilmiah Universitas Batanghari Jambi Jurnal Ilmiah Inovasi Musamus Journal of Livestock Science Jurnal Pengembangan Penyuluhan Pertanian JURNAL GIZI DAN KESEHATAN Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Triton Jurnal Implementa Husada Indian Journal of Forensic Medicine & Toxicology Teknika Journal of Soft Computing Exploration INDONESIAN MIDWIFERY HEALTH AND SCIENCES JOURNAL PROFESSIONAL HEALTH JOURNAL Langit Biru: Jurnal Ilmiah Aviasi International Journal of Health Science (IJHS) Jurnal Akta Notaris Journal of Global Pharma Technology Jurnal Penelitian Rumpun Ilmu Teknik Journal of Complementary in Health Seminar Nasional Teknologi dan Multidisiplin Ilmu Corona: Jurnal Ilmu Kesehatan Umum, Psikolog, Keperawatan dan Kebidanan Genitri: Jurnal Pengabdian Masyarakat Bidang Kesehatan International Journal Of Public Health International Journal of Health and Medicine CYBER-Techn(Jurnal Informatika dan Industri) Business, Entrepreneurship, and Management Journal Journal of Advances in Information Systems and Technology
Budi Prasetyo
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Journal : Journal of Soft Computing Exploration

 1 
Improving Algorithm Accuracy K-Nearest Neighbor Using Z-Score Normalization and Particle Swarm Optimization to Predict Customer Churn Muhammad Ali Imron; Budi Prasetyo
Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v1i1.7

Abstract

Due to increased competition in the business world, many companies use data mining techniques to determine the loyalty level of customers. In this business, data mining can be used to determine the loyalty level of customers. Data mining consists of several research models, one of which is classification. One of the most commonly used methods in classification is the K-Nearest Neighbor algorithm. In this study, the data which used are from German Credit Datasets obtained from UCI machine learning repository. The purpose of this study is to find out how Z-Score works to normalize the data and Particle Swarm Optimization to find the most optimal K value parameters, so the performance of the K-Nearest Neighbor algorithm is more optimal during the classification. The methods which were used to normalize the data are Z-score and Particle Swarm Optimization to determine the most optimal K value. The classification was tested using confusion matrix to determine the generated accuracy. From the finding of this study, the application of Z-score normalization and Particle Swarm Optimization with the K Nearest Neighbor algorithm succeed in increasing the accuracy up to 14%. The initial accuracy was 68.5%, and after applying the normalization of Z-Score and Particle Swarm Optimization, the accuracy became 82.5%.
Improve the Accuracy of C4.5 Algorithm Using Particle Swarm Optimization (PSO) Feature Selection and Bagging Technique in Breast Cancer Diagnosis Raka Hendra Saputra; Budi Prasetyo
Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v1i1.9

Abstract

Breast cancer is the second leading cause of death due to cancer in women currently. It has become the most common cancer in recent years. In early detection of cancer, data mining can be used to diagnose breast cancer. Data mining consists of several research models, one of which is classification. The most commonly used method in classification is the decision tree. C4.5 is an algorithm in the decision tree that is often used in the classification process. In this study, the data used was the Breast Cancer Wisconsin (Original) Data Set (1992) obtained from the UCI Machine Learning Repository. The purpose of this study was to select features that will be used and overcome class imbalances that occur, so that the performance of the C4.5 algorithm worked more optimal in the classification process. The methods used as feature selection are PSO and bagging to overcome class imbalances. Classification was tested using the confusion matrix to determine the accuracy that was generated. From the results of this study, the application of PSO as a feature selection and bagging to overcome class imbalances with the C4.5 algorithm succeeded in increasing accuracy by 5.11% with an initial accuracy of 93.43% to 98.54%.
Co-Authors - Nurkholis Afiyah, R. Khairiyatul Agus Hadi Prayitno Agus Widodo Ahmad Raihan Anak Agung Gede Sugianthara Anang Efendi Anang Endaryanto Andi Sutrisno Andri Siswo Utomo Aprilia Dian Evasari Aris Dwi Gunawan Aris Prio Agus Santoso Asharinnisa Salsabila Aziz Rahatur Abdusukri Bagyo Yanuwiadi Bambang Purwanto Chatarina Umbul Wahyuni Cita Rosita Sigit Prakoeswa Damayanti Damayanti Deltaningtyas Tri Cahyaningrum Dessy Artina Dewi Asri Dewi Kusumawati Dewi Setyowati Dharwin Siswantoro Diah Indriani Dian Shalma Kartika Dodi Haryono Dwi Santosa Dyah Ayu Kusuma Wardani Dzul Fahmi Afriyanto EKO SANTOSO Endyka Erye Frety Erfan Kustiawan Evy Ervianti Farkhan Setyawan Flora Ramona Sigit Prakoeswa Fransiska Niken Hapsari Gayuh Syaikhullah Hardyanto Soebono Hari Basuki Notobroto Hariadi Subagja Heny Nurjanah Hidayat, Endik Ilyas Azzindani Imamatul Faizah Indah Purnamasari Inggit Winarni Ivon Diah Wittiarika Kukuh Ardian Laksana, Muhammad Ardian Cahya Linda Astari, Linda Lutfi Agus Salim M. Yulianto Listiawan Maria Yuliana Mita Kusumawardani Muh Rais Prasetyo Muhammad Ali Imron Muhammad Bagus Qomaruddin Muhammad langlang Tahta Raharja Nafiatus Sintya Deviatin Natasha Tiara Bernadette Ningrum, Astika Gita Noorlaila Ainunnissa Pepy D. Endraswari Pramesti, Nabila Virgin Pramesti Putri Puspita Amalia Rahmat Hidayat Raka Hendra Saputra RatnaYunita Sari Reikha Rahmasari Rina Dewi Rina Hartati Risnanditiar Hirsya Priambodo Rista Rista Rizqi Febrian Pramudita Robiataul Adawiyah Saelan Saelan Santi Martini Setya Yuwana Shrimarti Rukmini Devy Siti Anisa Siti Nurul Kholif Solihin Solly Aryza Sony Wibisono Sri Kurniati Sri Utami Suharjito Suharjito Sukarni Sunarno Sundoro Supriyadi Susi Sulistiana Timothy John Pattiasina Trisma Jaya Saputra Ucca Fajrin Wicitra Putri Umdatus Soleha Wahyul Anis Wida Wahidah Mubarokah Willy Prasetya Yohanes Aditya Adhi Satria Yuni Rohyani Yusnita Rahayu Yustisia, Griselda