Article Per Year (5 Year)
p-Index From 2021 - 2026
3.822
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Dasar KADIKMA Jurnal Teknologi Informasi dan Ilmu Komputer Prosiding Seminar Matematika dan Pendidikan Matematik UNEJ e-Proceeding BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Teorema: Teori dan Riset Matematika Indonesian Journal of Combinatorics Jurnal Ilmiah Pendidikan Matematika Al Qalasadi Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Pattimura Proceeding : Conference of Science and Technology Journal of Computers and Digital Business
Kiswara Agung Santoso
Jurusan Matematika FMIPA Universitas Jember
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology 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 Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
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.
Analisis Variasi Parameter terhadap Optimasi Produksi Bakso dengan Pendekatan Metode Interior-Point Pradjaningsih, Agustina; Islamiyah, Syayidah Umrotul; Santoso, Kiswara Agung; Soepardi, Apriani
Teorema: Teori dan Riset Matematika Vol 11, No 1 (2026): Maret
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/teorema.v11i1.20560

Abstract

UMKM Pentol Bakso Bu Nurus, bergerak di bidang produksi bakso beku dengan berbagai varian. Ketidakstabilan pasokan bahan baku dan tingginya kompetisi pasar mendorong perlunya penerapan optimasi guna memaksimalkan pemanfaatan sumber daya yang terbatas demi mencapai keuntungan tertinggi. Penelitian ini mengimplementasikan dua algoritma dari metode titik interior yaitu algoritma Affine Scaling dan Karmarkar dengan mempertimbangkan data seperti jenis produk, biaya produksi, takaran bahan, dan profit per unit. Analisis sensitivitas kemudian dilakukan untuk mengidentifikasi sejauh mana jumlah daging dapat berubah tanpa mempengaruhi solusi optimal. Hasil optimasi menunjukkan kedua algoritma memberikan peningkatan keuntungan sebesar 20,9%. Sedangkan dari analisis sensitivitas, diperoleh interval stabilitas untuk penggunaan daging, yakni 206.550 gram < b₂ ≤ 250.000 gram, di mana solusi optimal tidak berubah selama pasokan daging berada dalam rentang itu.
A COMPARATIVE ANALYSIS OF COLOR SPACES FOR TOMATO RIPENESS CLASSIFICATION USING MACHINE LEARNING AND DEEP LEARNING APPROACHES Firda Fadri; Yoyok Yulianto; Kiswara Agung Santoso
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2631-2644

Abstract

The classification of tomato ripeness is crucial for post-harvest processing, quality assurance, and agricultural automation, as manual evaluation is often subjective, inconsistent, and time-consuming. This research investigated the impact of color space selection and hyperparameter optimization on tomato ripeness classification using machine learning (SVM, Random Forest, K-NN, GNB) and deep learning (CNN) approaches. Evaluation results indicated that YCbCr was the best-performing color space for classical models, with SVM achieving the highest accuracy (91.24%) and RF following closely (89.54%), whereas HSV yielded optimal performance for CNN (90.46%), highlighting differences in feature extraction mechanisms. Confusion matrix and ROC curve analyses demonstrated that models capturing nonlinear and interdependent color features, such as SVMs and CNNs, achieved superior class separability, particularly for the Ripe and Unripe classes. Dominant channel analysis revealed that chrominance channels, Cb in YCbCr and H in HSV, played a critical role in ripeness discrimination. These findings emphasized the importance of preprocessing for feature selection and provided guidance on selecting appropriate models and color spaces to improve the accuracy and reliability of automated tomato ripeness classification.
Co-Authors Abduh Riski Abduh Riski, Abduh Afifah, Zakiyatul Agustina Pradjaningsih Ahmad Kamsyakawuni Ahmad Tanto Wiraga Andora, Ela Anggraeni, Dyan Mei Apriani Soepardi Ardelia Nani Vidatiyasa Asti, Melinda Astuti, Lestari Fidi Bagus Arief Setiawan D. Dafik Delenia, Erick Dian Anggraeni Djoni Budi Sumarno Endang Murihani Fadri, Firda Fahreza, Faizal Rifky Fakih, Muhammad Fahmil Firda Fadri Gusti, Dwi Rahmasari Kinasih Ikhsanul Halikin, Ikhsanul Innafajri Insyirah Islamiyah, Syayidah Umrotul Itaniza Rosyadi Juwitarty, Novita Anggraini K Kusno Kosala Dwidja Purnomo Kristiana Wijaya Kusbudiono Kusbudiono, Kusbudiono Kusno Kusno Lestari, Vidiyanti Maulana, Nur Ridwan Merysa Puspita Sari Moh Hasan Moh. Hasan Najah, Maulidyah Lailatun Nila Al Indiani Nilamsari, Fania Tasya Nilasari, Laylatul Febriana Putriana, Novia Ayu Reggy Valentinnes Septa Jeniusa Regita, Yulia Dewi Rizki Gangsar Septiono S Slamin Seggaf, Muhammad Setiawan, Bagus Arief Siti Virna Rohmatul Izza Sochif Prasetiya Wahyu, Ria Ammelia Yoyok Yulianto Yusnita, Ade Ratna Zakiyatul Afifah