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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi Telematika : Jurnal Informatika dan Teknologi Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Wikrama Parahita : Jurnal Pengabdian Masyarakat JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) ILKOMNIKA: Journal of Computer Science and Applied Informatics Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) ETAM: Jurnal Pengabdian kepada Masyarakat Jurnal Masyarakat Madani Indonesia Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Agus Qomaruddin Munir
Fakultas Teknik, Universitas Negeri Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : JITK (Jurnal Ilmu Pengetahuan dan Komputer)

 1 
EFFECTIVE BREAST CANCER DETECTION USING NOVEL DEEP LEARNING ALGORITHM Irawadi Buyung; Agus Qomaruddin Munir; Putra Wanda
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1363.386 KB) | DOI: 10.33480/jitk.v8i2.4077

Abstract

Ultrasound is one of the most common screening tools for breast cancer detection. However, the lack of qualified radiologists causes the diagnosis process to become a challenging task. Deep learning's promising achievement in various computer vision problems inspires us to apply the technology to medical image recognition problems. We propose a detection model based on the Rapid-CNN to detect breast cancer quickly and accurately. We conduct this experiment by collecting breast cancer datasets, pre-processing, training models, and evaluating the model performance. This model can detect breast cancer with bounding boxes based on the experiment result. In this model, it is possible to detect the bounding box that is more than what it should be, so we applied NMS to eliminate the prediction of the bounding box that is less precise to increase accuracy.
RAINFALL PREDICTION USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GEOGRAPHIC INFORMATION SYSTEM APPROACH Agus Qomaruddin Munir; Heru Ismanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4180

Abstract

Rainfall is one indicator to determine the estimated adequacy of groundwater on agricultural land. The groundwater availability produced by rain can determine cropping patterns in an area. The availability of rainfall data depends on the accuracy of information on current climate conditions. This case causes the related parties to find difficulty determining the classification of cropping patterns in the future. Accurate rainfall prediction models are needed to overcome the problem of shifting rain patterns. Rainfall prediction models in determining cropping patterns are recommended by FAO, such as linear regression, which is still widely used today. This study aims to develop a new model of rainfall prediction by using the method SARIMA to determine cropping patterns to increase crop yields. Rainfall data was used from 2010 to 2020 from seven rainfall collection stations in Sleman Regency, and they are used as training data to predict future rainfall. The output of the data analysis is a prediction of rainfall in the range of January-April, which is predicted to be high, May-August, which is predicted to be low; and September-December, which is predicted to be moderate. In addition, based on the identified cropping patterns, recommendations can be given to farmers to set cropping schedules and strategies to increase the productivity of the farmland. The testing of accuracy forecasting used relative mean absolute error (RMAE) for 12 months. The results of the forecasting accuracy test for 12 months in Sleman Regency showed RMAE average of 1.46 was considered low, for it was still below 10%.
Co-Authors Agus Prayitno Aini, Farida Nur Andri Syafrianto Anindita Septiarini, Anindita Anny Kartika Sari Bagus Subekti Nuswantoro Bayu Indra Wahyudi Bekti Wulandari Budi Rahmani Daud Andang Pasalli, Daud Andang Desty Ervira Puspaningtyas Dewanto, Satriyo Agung Dharmawan, Dhimas Arief Didit Suprihanto, Didit Dina Fitri Septarini, Dina Fitri Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari, Evrita Lusiana Hafidhah, Naufal Naja Hamdani Hamdani HERU Heru Ismanto Ikhwan Mustiadi Indra Listiawan Irawadi Buyung Irawadi Buyung, Irawadi Jayanti, Rafika Latifah Listyalina Listiawan, Indra Listyalina, Latifah Muh. Ridho Wahid Solihin Nurkhamid , Nurkhamid Nurkhamid, Nurkhamid Prastowo Kristiyanto Putra Wanda Putu Sugiartawan Rahman, Dzul Fadli Ratna Wardani Ratnaningsih, Wahyu Sabdullah, Mursid Setiyawan, Ramadhana TAUFIQ HIDAYAT Wijaya, Nurhadi Yusriadi Yusriadi Yusril Eka Saputra Zaenal Mustofa Zaidir