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Journal : Journal of Information Technology and Computer Science

Audit System Development for Government Institution Documents Using Stream Deep Learning to Support Smart Governance Cholissodin, Imam; Soebroto, Arief Andy; Sutrisno, Sutrisno
Journal of Information Technology and Computer Science Vol. 4 No. 1: June 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1246.411 KB) | DOI: 10.25126/jitecs.20194173

Abstract

Document audit system is a means of evaluating documents on the results of delivering information, administrative documentary evidence in the form of texts or others. Currently, these activities become easier with the presence of computer technology, smartphones, and the internet. One of the examples is the documents created by various government institutions whether local, city and central government. The instance is online-published documents that are shaded by certain government institutions. Before the documents are published or used as an archive or authentic evidence for reporting or auditing activities, the documents must go through the editing stage to correct if there are errors and deficiencies such as spelling errors or incomplete information. In the editing process, however, a person may not be able to escape from making mistakes that result in the existence of writing errors after the editing process before the submission. Word spelling mistakes can change the meaning of the conveyed knowledge and cause misunderstanding of information to the readers, especially for assessors or the audit team. Based on the problem, the researcher intends to assist the work of the audit preparation team in document analysis by proposing a system capable of detecting word spelling errors using the Dictionary Lookup method from Information Retrieval (IR) and Natural Language Processing (NLP) science combined with Stream Deep Learning algorithms. Dictionary Lookup method is considered effective in determining the spelling of words that are true or false based on Lexical Resource. In addition, String Matching method that has been developed can correct word-writing errors correctly and quickly.Keywords: spelling mistake detection, dictionary lookup, audit of government institution documents, stream deep learning
Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting Yulianto, Fendy; Mahmudy, Wayan Firdaus; Soebroto, Arief Andy
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1148.218 KB) | DOI: 10.25126/jitecs.20205374

Abstract

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.
Co-Authors Achmad Arwan Achmad Ridok Adam Hendra Brata Ade Wija Nugraha Adi Setyo Nugroho Admaja Dwi Herlambang Agi Putra Kharisma Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmad Mustafirudin Ahmad Shofi Nurur Rizal Aizul Faiz Iswafaza Alfarisi, Muhammad Asnin Ali Akbar Alysha Ghea Arliana Amira Ibtisama Ana Kusuma Ardani Andreas Tommy Christiawan Andri Wijaya Kusuma Asrul Syawal Asrul, Divanda Arya Inasta Asus Maizar Suryanto H Austenita Pasca Aisyah Baghaz, Renanda DSP Bambang Gunadi Brilliansyach, Raihan Fikri Candra Dewi Candra Dewi Canny Amerilyse Caesar Catur Ari Setianto Dama Yuliana Deby Putri Indraswari Denny Sagita Rusdianto Destyana Ellingga Pratiwi Destyana Ellingga Pratiwi Dhea Azahria Mawarni Dian Eka Ratnawati Djoko Pramono Dwi Cindy Herta Turnip Dwi Puri Cemani Dzikrullah, Muhammad Aulia Fachruz Edy Santoso Eka Miyahil Uyun Eko Ari Setijono Marhendraputro Eko Arisetijono Elza Fadli Hadimulyo Enggar Septrinas Enggarsita Auliasin Eugenius Yosep Korsan N Evi Irhamillah Azza Faisal Roufa Rohman Faizatul Amalia Fajar Pradana Fauziah Mayasari Iskandar Febrianita Indah Perwitasari Fendy Yulianto Ferdy Wahyurianto Fildzah Amalia Galuh Mazenda Guruh Prayogi Willis Putra Habib Yafi Ardi Hanafi, Andy Hastian Bayu Hendra Darmawan Herman Syantoso Himawan Sutanto I Gede Adi Brahman Nugraha I Putu Bagus Arya Pradnyana Ibnu, Mohammad Ibrahim Kusuma Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indra Ekaristio P Indriana Candra Dewi Indriati Indriati Indriati Indriati Ishak Panangian Sinaga Ismiarta Aknuranda Issa Arwani Issa Arwani Karmia Larissa Br Pandia Khoifah Inda Maula Khrisna Widhi Dewanto Krisna Wahyu Aji Kusuma Lailatul Rizqi Ramadhani Lailil Muflikhah Laode Muhamad Fauzan Latifah Hanum Mahdi Fiqia Hafis Maria Tenika Frestantiya Maria Tenika Frestantiya Maria Tenika Frestantiya, Maria Tenika Maya Febrianita Mohammad Imron Maulana Muh. Arif Rahman Muhammad Iqbal Kurniawan Muhammad Rois Al Haqq Muhammad Rouzikin Annur Muhammad Tanzil Furqon Muhammad Taruna Praja Utama Mutia Ayu Sabrina Nadya Rahmasari Nadya Sylviani Niftah Fatiha Armin Niken Hendrakusma Wardani Nizar Rahman Kusworo Nurannisa, Nadhira Nuriya Fadilah Nurudin Santoso Nurul Faizah Nurul Faridah, Nurul NURUL HIDAYAT Nurul Hidayat Nurul Hidayat Nurul Hidayat Odhia Yustika Putri Priyambadha, Bayu Randy Cahya Wihandika Raymond Gunito Farandy Junior Rekyan Regasari Restia Dwi Oktavianing Tyas Reynald Daffa Pahlevi Ridwan Fajar Widodo Rio Andika Dwiki Adhi Putra Rio Arifando Risda Nur Ainum Riski Ida Agustiyan Risqi Nur Ifansyah Rivaldy Raihan Syams Rizal Setya Perdana Rizal Setya Perdana Saiful Kirom, Muhammad Ihsan Santoso, Nurudin Sativandi Putra Satrio Agung Wicaksono Sitepu, Yosua Christiansen Stefan Levianto Sukamto, Anjas Pramono Surya Wirawan SUTRISNO Sutrisno Sutrisno Sutrisno, Sutrisno Teddy Syach Pratama Thareq Ibrahim Tiara Rossa Diassananda Tryse Rezza Biantong Vasya, M Azka Obila Vicky Virdus Vivien Fathuroya, Vivien Wayan Firdaus Mahmudy Welly Purnomo Wijaya, Aldi Rahman Wildan Ziaulhaq Wildan Ziaulhaq Wildansyah Maulana Rahmat Yearra Taufan Ardy Rinaldy Yusril Iszha Eginata Zaien Bin Umar Alaydrus Ziya El Arief Ziya El Arief Ziya El Arief, Ziya El