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All Journal Jurnal Ilmu Dasar BERKALA SAINSTEK Abdimas Prosiding Seminar Matematika dan Pendidikan Matematik Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Sinkron : Jurnal dan Penelitian Teknik Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA SISTEM INDUSTRI ILKOM Jurnal Ilmiah Range : Jurnal Pendidikan Matematika Majalah Ilmiah Matematika dan Statistika (MIMS)
Abduh Riski, Abduh
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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APPLICATION FUZZY MAMDANI TO DETERMINE THE RIPENESS LEVEL OF CRYSTAL GUAVA FRUIT Kamsyakawuni, Ahmad; Riski, Abduh; Khumairoh, Anisa Binti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.239 KB) | DOI: 10.30598/barekengvol16iss3pp1087-1096

Abstract

Crystal guava is one of Indonesia's flora diversity. The rind of the unripe crystal guava fruit is green, and the rind of the ripe crystal guava fruit is yellowish green. However, it is difficult to determine the ripeness of crystal guava due to the similar color of the fruit skin. Determining fruit ripeness is uncertain and therefore requires a way to deal with this uncertainty. One of the methods you can use is fuzzy Mamdani. In this study, the ripeness level of crystal guava is determined using fuzzy Mamdani. Crystal guava fruits fall into four ripeness categories: raw, half ripe, ripe, and very ripe. The data used is in the form of an RGB image separated from the background so that only crystal guava fruit objects are captured. The image of the fruit object was then extracted by looking for the median red, green, and blue at each pixel of the image. This value is used as input for the fuzzy Mamdani process. The fuzzy set and fuzzy rules that have been formed can be applied to determine the maturity level of crystal guava fruit by validating the results of 140 image data with an accuracy of 83,5%.
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.
Co-Authors Abdullah, Rinaldy Ahmad Agustina Pradjaningsih Ahmad Kamsyakawuni Ahmad Khoirul Umam Anak Agung Gede Sugianthara Anggoro, Vani Krismo Arif, M. Ziaul Dwidayanti, Frisca Puji Erna Apriliani Fadri, Firda Fismaya, Florencia Wahyu Ganda Haqqi, Wakhidatun Nafi’u Hidayah, Annisa Yuniar Kaesardi, Dinda Septika Khumairoh, Anisa Binti Kiswara Agung Santoso Maulana, Mohammad Iqbal Maulana, Muhammad Iqbal Mohammad Isa Irawan Nilasari, Laylatul Febriana Nuranggraini, Laras Vegi Putriana, Novia Ayu Rahayu, Rivi Tri Rohmatul Aulia, Indriyani Santoso, Ahmad Rico Sari, Merysa Puspita Sholihah, Walidatush Sukma Lailatul Fadillah Sukmawati, Rika Ayu Umama, Nadya