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All Journal Abdimas POSITIF UNEJ e-Proceeding Fountain of Informatics Journal Jurnal Ilmiah KOMPUTASI Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Indonesian Journal of Applied Informatics Mathvision : Jurnal Matematika Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) International Journal of Quantitative Research and Modeling Pattimura Proceeding : Conference of Science and Technology Emerging Statistics and Data Science Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Abduh Riski
Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Jember
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Principal Component Regression in Statistical Downscaling with Missing Value for Daily Rainfall Forecasting M Dika saputra; Alfian Futuhul Hadi; Abduh Riski; Dian Anggraeni
International Journal of Quantitative Research and Modeling Vol. 2 No. 3 (2021): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v2i3.151

Abstract

Drought is a serious problem that often arises during the dry season. Hydrometeorologically, drought is caused by reduced rainfall in a certain period. Therefore, it is necessary to take the latest actions that can overcome this problem. This research aims to predict the potential for a drought to occur again in the Kupang City, Indonesia by developing a rainfall forecasting model. Incomplete daily local climate data for Kupang City is an obstacle in this analysis of rainfall forecasting. Data correction was then carried out through imputed missing values using the Kalman Filter method with Arima State-Space model. The Kalman Filter and Arima State-Space model (2,1,1) produces the best missing data imputation with a Root Mean Square Error (RMSE) of 0.930. The rainfall forecasting process is carried out using Statistical Downscaling with the Principal Component Regression (PCR) model that considers global atmospheric circulation from the Global Circular Model (GCM). The results showed that the PCR model obtained was quite good with a Mean Absolute Percent Error (MAPE) value of 2.81%. This model is used to predict the daily rainfall of Kupang City by utilizing GCM data.
Sistem Biometrik Pengenalan Wajah dengan Metode Grey Level Co-Occurrence Matrix dan Support Vector Machine Adhitiyah Redaya Kusuma Bhakti; Abduh Riski; Ahmad Kamsyakawuni
IJAI (Indonesian Journal of Applied Informatics) Vol 7, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v7i2.69069

Abstract

Abstrak Teknologi biometrik wajah dikembangkan untuk mengenali seseorang secara unik. Pada penelitian ini biometrik diaplikasikan pada aplikasi pengenalan wajah dengan citra wajah manusia sebagai objeknya menggunakan metode Grey Level Co-Occurrence Matrix dan Support Vector Machine. Metode GLCM merupakan metode yang digunakan untuk proses ekstraksi fitur citra. Sedangkan SVM digunakan untuk proses pengenalan/identifikasi. Tujuan dari penelitian ini adalah mendapat hasil akurasi yang baik untuk pengenalan wajah melalui kedua metode yang digunakan. Hasil yang diperoleh dari penelitian ini adalah akurasi pada data pelatihan sebesar 93% dengan total 200 citra wajah. Sedangkan pada data pengujian diperoleh akurasi sebesar 90% untuk 50 citra wajah.===================================================AbstractFacial biometric technology was developed to uniquely recognize a person. In this research, biometrics was applied to face recognition applications with human face images as objects using the Gray Level Co-Occurrence Matrix and Support Vector Machine methods. The GLCM is a method used for the image feature extraction process. While SVM is used for the identification process. The purpose of this research is to get good accuracy results for face recognition through the two methods used. The results obtained from this research are the accuracy of the training data by 93% with a total of 200 face images. While the test data obtained an accuracy of 90% for 50 face images.
Co-Authors Adhitiyah Redaya Kusuma Bhakti Agustina Pradjaningsih Ahmad Husnan Fanani Ahmad Kamsyakawuni Ahmad Saiful Rizal Ahmad Tanto Wiraga Alfian Futuhul Hadi Anisa Wahyu Illahi Ari Susiana Azimatul Matsniya Bella Audina Cahya Ramadhani Azhar Dian Anggraeni Durratul Sayyidah Adilah Munawaroh Febia Zein Aziza Heri Purwantoro I Made Tirta Irma Dwi Anggraeni Kiswara Agung Santoso Kosala, Kosala Dwidja Purnomo Lilian Atino Eyta Makin M Dika saputra M Ziaul Arif, M Ziaul Mohamad Fatekurohman Shofia Nabila Azzahra Shofia Nabila Azzahra Sholikhah, Nining Taufiqi Fadhilah Amieni