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Journal : Inferensi

Prediksi Harga Ekspor Non Migas di Indonesia Berdasarkan Metode Estimator Deret Fourier dan Support Vector Regression Chaerobby Fakhri Fauzaan Purwoko; Sediono Sediono; Toha Saifudin; M Fariz Fadillah Mardianto
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.15558

Abstract

Economic growth is one of the indicators in the Sustainable Development Goals (SDGs) on increasing economic activity.  One of the activities that supports the running of the economy is trade between countries, such as exports.  In Indonesia, non-oil and gas exports have played an important role in total exports in recent years, including coal exports being the main export.  Therefore, price predictions for Indonesia's non-oil and gas exports are very important as material for evaluating policies to encourage economic growth.  This is the main focus of this research.  In this study, non-oil and gas export price forecasts are made taking into account current issues such as the COVID-19 pandemic and the Russia-Ukraine war.  The accuracy of the model obtained from the Fourier series estimator and Support Vector Regression (SVR) is investigated by comparing the Mean Absolute Percentage Error (MAPE) value to predict Indonesia's non-oil and gas export prices.  The results of the study show that the COVID-19 pandemic and the Russia-Ukraine war have had a significant impact on non-oil and gas export prices. The SVR model with the Radial Basis Function (RBF) kernel shows better accuracy than the Fourier series estimator model of the cos sin function, with MAPE values of 9.29 and 15.26% for each test data, respectively.  Therefore, this study is expected to be the basis for formulating policies related to regulating non-oil and gas export processes to support economic growth in Indonesia.
Comparison of Logistic Regression and Support Vector Machine in Predicting Stroke Risk Safitri, Lensa Rosdiana; Chamidah, Nur; Saifudin, Toha; Firmansyah, Mochammad; Alpandi, Gaos Tipki
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20420

Abstract

The issue of health is the third goal of Indonesia's Sustainable Development Goals (SDGs) which is state to ensuring a healthy life and promoting prosperity for all people at all ages. One of the SDGs’s concerns is deaths caused by non-communicable diseases (NCDs) including strokes. One prevention that can be done is by making a prediction of stroke for early detection. There are various methods available which are statistical methods and machine learning methods. In this research work, we aim to compare the two methods based on statistical method and machine learning method on stroke risk prediction. The data used in this research is primary data from Universitas Airlangga Hospital (RSUA) from June until August 2023. In this research, we compare the statistical method that is Logistic Regression (LR), and the machine learning method which is Support Vector Machine(SVM). We use Phyton to analyze all methods in this research. The results show that SVM with Radial Basis Kernel is better than LR in predicting stroke risk based on three goodness criteria namely sensitivity, F-1 score and accuracy where these three goodness criteria values of SVM are greater than those of LR.
Co-Authors Abdul Aziz Aditya Syarifudin Akbar Adyatma, Isryad Yoga Afifa, Fitriana Nur Aflaha, Nabila Shafa Aisharezka, Mutiara Aisyah, Arlisya Shafwan Al Hasri, Ilham Maulana Aldawiyah, Najwa Khoir Alfi Nur Nitasari Alfredi Yoani Alpandi, Gaos Tipki Ana, Elly Angga Kusuma Bayu Viargo Aniq Atiqi Any Tsalasatul Fitriyah Ardi Kurniawan Ardi Kurniawan Ariani, Fildzah Tri Januar Ariyawan, Jovansha Arrofah, Aini Divayanti Aulia, Niswa Faizah Auliyah, Nina Ayuning Dwis Cahyasari Azis, Aurelia Islami Azizah, Khansa Baihaqi, Mochamad Belindha Ayu Ardhani Budijono, Gabriella Agnes Chaerobby Fakhri Fauzaan Purwoko Christiano Ginzel, Bryan Given Christopher Andreas Dewanti, Maria Setya Dewanty, Sanda Insania Diah Puspita Ningrum Dita Amelia Dita Amelia, Dita Easyfa Wieldyanisa, Ezha Elly Ana Elly Pusporani Erfiana Erfiana Faiza, Atikah Fajrina, Sofia Falasifah, Sabrina Fatmawati Fatmawati Fauzi, Doni Muhammad Fauziah, Nathania Fina Insyiroh Firmansyah, Mochamad FIRMANSYAH, MOCHAMMAD Fitriani, Mubadi'ul Fortunata, Regina Gaos Tipki Alpandi Gaos Tipki Alpandi Hardiansyah, Fernanda Rizky Herdianto, Muhammad Hendra Ilma Amira Rahmayanti Indrasta, Irma Ayu Insania Dewanty, Sanda Januarta, R. Arya Khairian, Farhan Aldan Kholidiyah, Azizatul Leni Sartika Panjaitan Lensa Rosdiana Safitri M. Fariz Fadillah Mardianto Maelcardino Christopher Justin Mahadesyawardani, Arinda Maharani, Prima Makhbubah, Karina Rubita Marisa Rifada Marpaung, Josua Ronaldo Davico Marshanda Aprilia Marthabakti, CitraWani Mediani, Andini Putri Mochamad Rasyid Aditya Putra Muhammad Rosyid Ridho Az Zuhro Muzakki, Naufal Nahar, Muhammad Hafidzuddin Naura, Sheila Sevira Asteriska Nugraha, Galuh Cahya Nur Chamidah Nur chamnidah Nur Rahmah Miftakhul Jannah Nurdin, Nabila Nurrohmah, Zidni 'Ilmatun Oktavia, Sabrina Salsa Panjaitan, Leni Sartika Pratama, Fachriza Yosa Purnama, Titania Faisha Puspasari, Laili Rahayu, Rizky Dwi Kurnia Ramadhani, Azzah Nazhifa Wina Ramadhanti, Aulia Ramadhanty, Devira Thania Ramadhina, Fidela Sahda Ilona Recylia, Rien Risky Wahyuningsih Sa'idah, Andini Safitri, Lensa Rosdiana Salma Bethari Andjani Sumarto Salsabila, Fatiha Nadia Sa’idah Zahrotul Jannah Sediono, Sediono Sentosa, Martha Ayu Setyawan, Muhammad Daffa Bintang Shalwa Oktavrilia Kusuma Siagian, Kimberly Maserati Sihite, Rivaldi Siti Maghfirotul Ulyah Sugha Faiz Al Maula Suliyanto Suliyanto Suliyanto Syaugi Sungkar, Salman Tiani Wahyu Utami Trisa, Nadya Lovita Hana Ubadah, Mohammad Noufal Valida, Hanny Victory, Johanna Tania Wahyuli, Diana Widyawati, Ayu Wieldyanisa, Ezha Easyfa Wulandari, Indana Zulfa Yan Dwi Zhafira, Azizah Atsariyyah