Article Per Year (5 Year)
p-Index From 2021 - 2026
4.201
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Pangabdhi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ComTech: Computer, Mathematics and Engineering Applications Jurnal ABDINUS : Jurnal Pengabdian Nusantara G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) Jurnal Statistika dan Aplikasinya Elastisitas Jurnal Ekonomi Pembangunan Journal of Mathematics Education and Science Indonesian Community Journal Eksponensial Limits: Journal of Mathematics and Its Applications
Elly Pusporani
Universitas Airlangga
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Inferensi

 1 
Klasifikasi Pasien Penderita Penyakit Liver dengan Pendekatan Machine Learning Elly Pusporani; Siti Qomariyah; Irhamah Irhamah
Inferensi Vol 2, No 1 (2019): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (798.153 KB) | DOI: 10.12962/j27213862.v2i1.6810

Abstract

Liver atau hati adalah organ yang perannya sangat vital dalam tubuh manusia. Penyakit liver sering dianggap sebagai silent killer (pembunuh diam-diam) karena adanya kemungkinan tidak timbul gejala. Permasalahan yang terjadi adalah sulitnya mengenali penyakit liver sejak dini., bahkan saat penyakit ini sudah menyebar pun masih sulit untuk dideteksi. Padahal penderita perlu mengetahui adanya gejala penyakit liver sejak dini agar dapat segera melakukan pengobatan. Adanya diagnosa penyakit liver sejak dini mampu meningkatkan kelangsungan hidup pasien. Pada penelitian ini diterapkan metode untuk klasifikasi penyakit liver menggunakan machine learning dan dibandingkan hasilnya dengan metode klasik. Data yang digunakan adalah Indian liver patients dataset (ILPD)yang diambil dari UCI machine learning. Terdapat beberapa tahapan preprocessing yang dilakukan, antara lain pengecekan missing value, imputasi, feature selection, dan resampling untuk mengatasi data imbalance. Setelah dilakukan preprocessing, selanjutnya dilakukan analisis menggunakan metode regresi logistik, decision tree, naïvebayes, k-nearest neighbor, dan support vector machine. Berdasarkan nilai akurasi dan presisi, maka metode SVM memberikan hasil yang terbaik, tapi berdasarkan recall maka metode K-Nearest Neighbor memberikan hasil terbaik. Walaupun SVM memberikan hasil nilai akurasi dan presisi tertinggi tetapi terdapat ketimpangan yang besar antara nilai presisi dan recall yang dihasilkan, jika dibandingkan selisih nilai akurasi dan recall dari metode K-Nearest Neighbor.
Prediction of Nike’s Stock Price Based on the Best Time Series Modeling Sari, Adma Novita; Zuleika, Talitha; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21737

Abstract

Nike is one of the world's largest shoe, clothing, and sports equipment companies. The more modern the development of the era, the more diverse the fashion. Of course, investors can consider this when deciding whether to invest in Nike's brand shares. Stock prices constantly fluctuate up and down, so investors need to implement strategies to minimize losses in investing to achieve economic growth. This supports the Sustainable Development Goals (SDGs) in point 8 regarding the importance of sustainable economic growth and investment in infrastructure development to improve economic welfare. Investors can minimize losses by predicting or forecasting stock prices. Stock prices can be analyzed using specific methods. The update that will be brought in this study is the Nike brand stock price prediction for the 2020-2024 period using the best model from the time series method comparison conducted using classical nonparametric, which consists of the kernel estimator method and the Fourier series estimator method and modern nonparametric using the Support Vector Regression (SVR) method. Based on the analysis method, the best method is selected through the minimum MAPE value. A comparison of the results of Nike brand stock price predictions using several methods shows that the MAPE value of the Nike brand stock price data analysis is the minimum obtained using the kernel estimator approach, which is 1.564%. Thus, the kernel estimator approach predicts the Nike brand stock price much better. Predictions using the best methods can be recommendations and evaluations for economic actors to prepare better economic planning.
Co-Authors Adinda Tries Melati Afifah Nur Makkiyah Ailsa Shafa Salsabila Ain, Dzuria Hilma Qurotu Ainaya Zakiyah Nabila Alexandra, Victoria Anggia Alfredi Yoani Ana, Elly Audilla, Marfa Aufa Muhammad Yogi Riyanto Aulia Ramadhanti Ayuning Dwis Cahyasari Ayuning Dwis Cahyasari Azizah Dewi Ariyani Azzah Nazhifa Wina Ramadhani Bagas Maulana Christopher Andreas Deby Victoria Diana Nurlaily Dita Amelia Dwitya, Shabrina Nareswari Elly Ana Fadillah Mardianto, M. Fariz Fajrina, Sofia Andika Nur Farida Nur Hayati Farizi, Muhammad Fikry Al Fauzi, Doni Muhammad Ferissa Maulida Ismi Fidela Sahda Ilona Ramadhina Fitri, Marfa Audilla Fitriana Nur Afifa Grace Lucyana Koesnadi Haq, Affan Fayzul Helfira Lady Ari Pramesti I Kadek Pasek Kusuma Adi Putra Idrus Syahzaqi Indrasta, Irma Ayu Irhamah - Ismi, Ferissa Maulida Jannah, Sa'idah Zahrotul Koesnadi, Grace Lucyana Lu'lu'a, Na'imatul Lu’lu’a, Na’imatul M. Fariz Fadillah Mardianto Marcel Laverda Subiyanto Marcelena Vicky Galena Mardianto, M. Fariz Fadillah Mardianto, Muhammad Fariz Fadillah Maula, Sugha Faiz Al Nabila Rahma Na’ifa, Ariza Nadya Lovita Hana Trisa Nashwa Carista Nitasari, Alfi Nur Nurrohmah, Zidni ‘Ilmatun Nurul Fajriah Deswani Sangadji Permana, Made Riyo Ary Pratama, Bagas Shata Previan, Anggara Teguh Putri, Farah Fauziah Putri, Ferdiana Friska Rahmana Putri, Refa Berliana Rahmat Agung Ibrahim Rani, Lina Nugraha Rasyid, Mochamad Rohayah, Dewi Sa'idah Zahrotul Jannah Sa'idah Zahrotul Jannah Salsabila, Fatiha Nadia Sari, Adma Novita Sari, Adma Novita Sasy Okti Karima Sa’idah Zahrotul Jannah Sediono, Sediono Setiawan, Nicoletta Almira Dyah Sheila Sevira Asteriska Naura Simamora, Antonio Nikolas Manuel Bonar Siregar, Naufal Ramadhan Al Akhwal Siti Maghfirotul Ulyah Siti Qomariyah Steven Soewignjo Toha Saifudin Tsabita Amalia Shofa, Nayla Valida, Hanny Wieldyanisa, Ezha Easyfa Yuliati, Intan Yuniar, Muhammad Alvito Dzaky Putra Zah, Alfian Iqbal Zuleika, Talitha Zuleika, Talitha