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All Journal Jurnal Sistem Komputer KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Tech-E Jurnal Komputasi Jurnal Teknoinfo Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Tekno Kompak Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Teknologi dan Sistem Tertanam Journal Social Science And Technology For Community Service International Journal of Electronics and Communications Systems Jurnal Inovasi Pembangunan Jurnal Pepadun Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) IJCOSIN : Indonesian Journal of Community Service and Innovation Journal of Artificial Intelligence and Technology Information Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Scientific Journal of Informatics
YOHANA TRI UTAMI, YOHANA TRI
Jurusan Ilmu Komputer, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Lampung
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : Jurnal Teknoinfo

 1 
DETEKSI RODA KENDARAAN DENGAN CIRCLE HOUGH TRANSFORM (CHT) DAN SUPPORT VECTOR MACHINE (SVM) Sri Dianing Asri; Desi Ramayanti; Ade Dwi Putra; Yohana Tri Utami
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.1952

Abstract

In the digital image processing, many methods have been developed, the purpose of developing these methods is how computers can detect and recognize objects in an image with a precisely and process them in a relatively short time. Wheels are components that are always present in every vehicle, whether the vehicle is a bus, car or truck, it must have wheels with the same shape. If a wheel can be detected and recognized then the vehicle recognition and classification can be determined. This research focuses on capturing circle images, detecting wheel circles by applying Circle Hough Transformation (CHT). This transformation is able to recognize the object based on its boundaries and is resistant to noise. After obtaining the image of the circle, the next step is to classify it into Wheels and Non Wheels using the Support Vector Machine (SVM) method. The development of the wheel circle detection model on the side view image of this vehicle can be used as one of the first steps in research on wheel-based automatic vehicle recognition and classification systems.
HOTSPOT PREDICTIVE MODELING USING REGRESSION DECISION TREE ALGORITHM Dewi Asiah Shofiana; Yohana Tri Utami; Yunda Heningtyas
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.2051

Abstract

Forest fires had always become an international issue influencing many life sectors, including environmental, social, and economic. The forest fire in 2013 was regarded as one of the worst forest fire tragedies in history, not only in Indonesia but also in the world. Detection of hotspots on the earth's surface by the satellite can be an indication of land and forest fire occurrence. This research aims to build a predictive model of monthly hotspots in Rokan Hilir Regency using the regression tree algorithm. Several variables related to weather information are included, such as rainfall, sea surface temperature, and southern oscillation index. This research used 245 training data and 43 testing data, resulting a predictive model with a correlation of 0.875 and an error rate of 0.166. Based on the values, we can conclude that the performance of the model is considerably good.
Co-Authors ., Rusliyawati Ade Dwi Putra Ade Surahman Adhie Thyo Priandika Agnes Pramudani Agus Wantoro Akmal Junaidi Alita, Debby Amelia Jasmine andika Saputra Anisa Raden Arry Verdian Astria Hijriani AULIYA RAHMAN ISNIAN, AULIYA RAHMAN Bagus Rahmat Riadi Bidari Tasya Salsabila Desi Ramayanti Devi Nur Anisa Dewi Asiah Shofiana Dian Pitaloka Dina Eka Nurvazly Dwi Sakethi Dwi Sakethi Faradila, Dita Febi Eka Febriansyah febi eka febriansyah, febi eka ferdi yulianto Ferdi Yulianto Fitri Handayani Harco Leslie Hendric, Spit Warnars Heningtyas, Yunda Humairoh Ratu Ayu Imam Ahmad Imam Ahmad Irawati, Anie Rose Irwan Adi Pribadi Irwan Adi Pribadi Jasmine, Amelia Lisa Adelia Lumbanraja, Favorisen R M Rizky Apriawan Machudor Yusman Muhammad Saleh Muhaqiqin Noval Aditya Marlon Pasaribu, A. Ferico Octaviansyah Prabowo, Rizky Ramadhanti, Karina Adityas Rangga Agustiantino Rangkas Andreansyah Rd. Irwan Adi Pribadi Riki Sofyan Sanriomi Sintaro Shofiana, Dewi Asiah Sigit Tunggul Waskito Siti Laelatul Chasanah Siti Laelatul Chasanah Sri Dianing Asri SUAIDAH SUAIDAH, SUAIDAH Tasya Aprilla Almuqaramah Yuton Taufik, Rahman Wartariyus, Wartariyus Wulan Seftiani Yulianto, Ferdi Yuri Rahmanto