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All Journal 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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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.
Sistem Biometrik Pengenalan Wajah dengan Metode Grey Level Co-Occurrence Matrix dan Support Vector Machine Bhakti, Adhitiyah Redaya Kusuma; Riski, Abduh; Kamsyakawuni, Ahmad
IJAI (Indonesian Journal of Applied Informatics) Vol 7, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v7i2.69069

Abstract

Abstrak Teknologi biometrik wajah dikembangkan untuk mengenali seseorang secara unik. Pada penelitian ini biometrik diaplikasikan pada aplikasi pengenalan wajah dengan citra wajah manusia sebagai objeknya menggunakan metode Grey Level Co-Occurrence Matrix dan Support Vector Machine. Metode GLCM merupakan metode yang digunakan untuk proses ekstraksi fitur citra. Sedangkan SVM digunakan untuk proses pengenalan/identifikasi. Tujuan dari penelitian ini adalah mendapat hasil akurasi yang baik untuk pengenalan wajah melalui kedua metode yang digunakan. Hasil yang diperoleh dari penelitian ini adalah akurasi pada data pelatihan sebesar 93% dengan total 200 citra wajah. Sedangkan pada data pengujian diperoleh akurasi sebesar 90% untuk 50 citra wajah.===================================================AbstractFacial biometric technology was developed to uniquely recognize a person. In this research, biometrics was applied to face recognition applications with human face images as objects using the Gray Level Co-Occurrence Matrix and Support Vector Machine methods. The GLCM is a method used for the image feature extraction process. While SVM is used for the identification process. The purpose of this research is to get good accuracy results for face recognition through the two methods used. The results obtained from this research are the accuracy of the training data by 93% with a total of 200 face images. While the test data obtained an accuracy of 90% for 50 face images.
Co-Authors Abduh Riski Abduh Riski, Abduh Abdullah, Rinaldy Ahmad 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 Bhakti, Adhitiyah Redaya Kusuma 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 Maulana, Mohammad Iqbal Maulana, Muhammad Iqbal Merysa Puspita Sari 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 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