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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmu Dasar BERKALA SAINSTEK CAUCHY: Jurnal Matematika Murni dan Aplikasi Prosiding Seminar Matematika dan Pendidikan Matematik POSITIF UNEJ e-Proceeding Fountain of Informatics Journal Sinkron : Jurnal dan Penelitian Teknik Informatika InComTech: Jurnal Telekomunikasi dan Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Indonesian Journal of Applied Informatics Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Eurekamatika Unnes Journal of Mathematics Journal of Computers and Digital Business Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Ahmad Kamsyakawuni
Jurusan Matematika, FMIPA Universitas Jember
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

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RAINFALL PREDICTION IN JEMBER REGENCY WITH ADAPTIVE NEURO FUZZY INFERENCE SYSTEM BASED ON GSMaP SATELLITE DATA Riski, Abduh; Haqqi, Wakhidatun Nafi’u; Kamsyakawuni, Ahmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1713-1724

Abstract

Rainfall is very influential in daily life, including in agriculture. According to the Jember Regency Government, the majority of the economic activities of the Jember people come from the agricultural sector. Significant changes in rainfall conditions will adversely affect the agricultural sphere. The Water Resources Office of Jember Regency measures rainfall directly. Precipitation measurement can also be made indirectly using the Global Satellite Mapping of Precipitation (GSMaP), a project promoted by the Japan Aerospace Exploration Agency (JAXA) to produce rainfall accumulation globally. Rainfall predictions are urgently needed to address rainfall-related issues. The Adaptive Neuro-Fuzzy Inference System (ANFIS) method is an effective method for prediction because its working principle combines adaptive methods of artificial neural networks and fuzzy logic. The RMSE in the ANFIS training and testing process on daily rainfall was 12.7464 and 14.6268. Furthermore, RMSE in ANFIS training and testing on monthly rainfall was 7.6336 and 8.1456. The predicted daily rainfall in Jember Regency on January 1, 2023, is 3.1971 mm. Meanwhile, the predicted monthly rainfall in Jember Regency in January 2023 is 19.9114 mm.
PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM Kamsyakawuni, Ahmad; Sholihah, Walidatush; Riski, Abduh
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2597-2610

Abstract

Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process.
Prediction of Rice Production in Jember Regency Using Adaptive Neuro Fuzzy Inference System (ANFIS) Riski, Abduh; Putriana, Novia Ayu; Fadri, Firda; Kamsyakawuni, Ahmad; Pradjaningsih, Agustina; Santoso, Kiswara Agung; Sari, Merysa Puspita
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2797.262-275

Abstract

Jember Regency is the fourth largest rice-producing regency/city in East Java, so Jember Regency dramatically contributes to increasing the agricultural sector in East Java Province. However, the level of rice production can fluctuate, which is influenced by other factors such as rainfall. A prediction system is needed to anticipate a decrease in rice production. This research aims to predict rice production in the Jember Regency using the Adaptive Neuro Fuzzy Inference System (ANFIS), highlighting the impact of key variables like rainfall, harvested area, and land productivity. This research consists of three stages: training, testing, and prediction. The input variables used in this research are rainfall (mm), harvested area (Ha.), and land productivity (Kw/Ha.), while the output variable is rice production (tons). The membership functions used are generalized Bell and Gaussian, with several combinations of many membership functions. The best model obtained from this research is a model that uses generalized bell membership functions with three membership functions for rainfall variables and two membership functions for harvest area and land productivity variables. The epoch (iteration) used to achieve minimum error is 100 epochs. The best model achieved high accuracy, producing a MAPE value of 0.080% in training and 1.525% in testing, indicating its strong potential for reliable agricultural production forecasting. The predicted amount of rice production in Jember Regency in 2024 was 922,136.8317 tons.
Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network Shofia Nabila Azzahra; Ahmad Kamsyakawuni; Abduh Riski
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p04

Abstract

The ripeness of crystal guava fruit is currently sorted conventionally by analyzing the colour of the rind visually with the human eye. However, this method has several weaknesses that result in low accuracy and inconsistency. Therefore, automatic determination of ripeness level is necessary to increase accuracy and obtain precise information. This research uses the HSI colour space as an interpretation of fruit image characteristics and uses the Backpropagation algorithm to perform classification. This study utilizes image data of crystal guava fruit, categorizing them into four stages of ripeness: unripe, half-ripe, ripe, and very ripe. There are 140 fruit image data with 35 data for each ripeness category. Each image will be processed with median filter, cropping and segmentation. The HSI value will be taken from the image and processed at the classification stage using the Backpropagation algorithm. In classification using Backpropagation Neural Network, the best network model in this study was achieved in the 3 10 4 network architecture with a binary sigmoid activation function, learning rate = 0.3, and batch size = 64. This model produces a loss value of 0.5364 with an accuracy of 0.9 in testing process.
Co-Authors Abduh Riski Abduh Riski Abduh Riski, Abduh Abdullah, Rinaldy Ahmad Adhitiyah Redaya Kusuma Bhakti Agustina Pradjaningsih Ahmad Husnan Fanani Ahmad Khoirul Umam Ahmad Saiful Rizal Anggoro, Vani Krismo Ari Susiana Arif, M. Ziaul Asti, Melinda Astuti, Lestari Fidi Azimatul Matsniya Cahya Ramadhani Azhar Dian Anggraeni Eko Adi Sarwoko Fadri, Firda Fakih, Muhammad Fahmil Farida, Yasmin Febia Zein Aziza Firdaus Ubaidillah, Firdaus Fismaya, Florencia Wahyu Ganda Gusti, Dwi Rahmasari Kinasih Haqqi, Wakhidatun Nafi’u Heri Purwantoro Hermanto Hermanto Hidayah, Annisa Yuniar Ikhsanul Halikin, Ikhsanul Innafajri Insyirah Juliyanto, Bagus Kaesardi, Dinda Septika Khumairoh, Anisa Binti Khurnia Palupi Kiswara Agung Santoso Kosala Dwidja Purnomo Kusno Kusno Lestari, Vidiyanti Lilian Atino Eyta Makin M Ziaul Arif, M Ziaul M. Ziaul Arif Maulana, Mohammad Iqbal Maulana, Muhammad Iqbal Nadzira, Annisa Ayu Nanang Suparyono, Nanang Putriana, Novia Ayu Rachmad Gernowo Rahayu, Rivi Tri Ramadhani, Lutvia Citra Regita, Yulia Dewi Retno Wulandari Rizki Gangsar Septiono Santoso, Ahmad Rico Sari, Merysa Puspita Seggaf, Muhammad Shofia Nabila Azzahra Shofia Nabila Azzahra Sholihah, Walidatush Siti Virna Rohmatul Izza Sri Ratnawati Sukmawati, Rika Ayu Tri Hartati, Tri Wulandari, Eka Yuni Zainul Anwar