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All Journal Jurnal Ilmiah Kursor INTEGER: Journal of Information Technology Jurnal Matematika: MANTIK Network Engineering Research Operation [NERO] BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) Zeta - Math Journal DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Contemporary Mathematics and Applications (ConMathA) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Lentera: Jurnal Ilmiah Kependidikan STLISTIKA: Jurnal Bahasa, Sastra, dan Pengajarannya Journal Focus Action of Research Mathematic (Factor M) NERO (Networking Engineering Research Operation)
Kuzairi
Universitas Islam Madura
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : NERO (Networking Engineering Research Operation)

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PERBANDINGAN METODE FUZZY TIME SERIES CHEN DAN METODE EXPONENTIAL SMOOTHING DALAM MEMPREDIKSI CURAH HUJAN DI KABUPATEN PAMEKASAN Tamam, Moh. Badrit; Kuzairi, Kuzairi; Yulianto, Toni; Faisol, Faisol; Yudistira, Ira; Amalia, Rica
NERO (Networking Engineering Research Operation) Vol 9, No 2 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27986

Abstract

This research aims to predict rainfall in Pamekasan Regency, Madura, East Java, using two prediction methods: Fuzzy Time Series Chen and the Exponential Smoothing (ES) method, specifically Double Exponential Smoothing (DES). The data used in this study consists of monthly rainfall data from January 2011 to December 2023, covering a period of 13 years. The data was sourced from reliable records that regularly track rainfall in the region. In the analysis, both methods were applied to generate accurate predictions of rainfall patterns in Pamekasan Regency. Based on the calculations and performance evaluation, the best method for predicting rainfall in this region was found to be Double Exponential Smoothing Holt. This method uses two key parameters: alpha at 0.4 and beta at 0.6. After applying this method, a Mean Absolute Percentage Error (MAPE) of 1.479 was obtained, indicating a very low and acceptable level of prediction error. Therefore, it can be concluded that the Double Exponential Smoothing Holt method is an effective and accurate approach for predicting rainfall in Pamekasan Regency based on the historical data used..Keywords: Rainfall; Pamekasan Regency; Prediction; Chen's Fuzzy Time Series and Exponential Smoothing (ES) Method
Implementation Of Convolutional Neural Network Algorithm For Tobacco Pest Detection Tamam, Moh Badri; Chafid, Nurul; Hozairi, Hozairi; Aini, Qurrotul; Santoso, Teguh Budi; Kurniawan, Wawan; Kuzairi, Kuzairi
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30044

Abstract

Agriculture plays a vital role in increasing Gross Domestic Product (GDP), providing employment, contributing to foreign exchange earnings, and supporting environmental conservation. Indonesia has great potential as an agricultural country where population majority relies on agricultural sector for their livelihood. Pamekasan Regency is center of tobacco production development in East Java, with a tobacco plantation area of over 30,000 hectares. However, pest attacks such as caterpillars often damage tobacco plants, reducing productivity and leaf quality. This study implemented AI technology, specifically Convolutional Neural Networks (CNN), to detect caterpillar pests in tobacco plants in Pamekasan. The main focus is on AI development in computer vision using deep learning techniques. The CNN training process involves several stages: convolution, ReLU layers, subsampling/pooling layers, and fully connected layers. The test scenario was conducted by dividing data by 85% training, 10% validation, and 5% testing, as well as tuning parameters for the learning rate and epochs. The model achieved a maximum accuracy of 85% without overfitting at a learning rate of 0.001 and epochs 15. This demonstrates that the CNN deep learning method can effectively identify disease features in tobacco plants. The application of this technology can increase productivity and efficiency in the agricultural sector, supporting a sustainable economy and ecology.Keywords: convolutional neural network, image detection, tobacco pest.
Co-Authors A.A. Ketut Agung Cahyawan W Alifandi, Mahrus Amalia, Rica Anekawati, Anik Anisa Anisa Anwari, Anwari Buhari Buhari Chafid, Nurul Dian Islami Fachry Abda El Rahman Faisol Faisol, Faisol Hidya Maulida, Hidya Hozairi Ira Yudistira Ira Yudistira Islami, Dian Jamilah, Ismiyatul Lilik Safitri M. Fariz Fadillah Mardianto Mamdudah, Siti Moh. Badri Tamam Moh. Yaqin Muhammad Luthfi Noer Azizah Nur Halimah Nurmadhani, Nadya Pramiswari, Titin Qurrotul Aini Ramadani, Nia Rica Amalia Riyatun Hasanah Rohman, Naylur Sahidah, Sahidah Santoso, Teguh Budi Sholiha, Anisatus Siti Amiroch, Siti Sumiatun Sumiatun Suyibah, Suyibah Titin Pramiswari Tony Yulianto Ukhrowi, Putri Ulfah, Ifadatul Ummi Nur Yatun Hasanah Wawan Kurniawan Yaqin, Moh Yudhistira, Ira Yudistira, Ira Yulianto, Toni