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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Pertanian Indonesia Agromet MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Pustakawan Indonesia FORUM STATISTIKA DAN KOMPUTASI Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Pembangunan Wilayah dan Kota Agrikultura Jurnal Keteknikan Pertanian Proceedings of KNASTIK Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Forum Agribisnis SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) International Journal of Advances in Intelligent Informatics Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Widyariset JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang Jurnal ULTIMATICS CYBERNETICS BHUMI: Jurnal Agraria dan Pertanahan Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JURNAL METEOROLOGI DAN GEOFISIKA Building of Informatics, Technology and Science Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Tanah dan Iklim Widyariset Aiti: Jurnal Teknologi Informasi Jurnal Pustakawan Indonesia Makara Journal of Science Eduvest - Journal of Universal Studies J-Icon : Jurnal Komputer dan Informatika Jurnal Sistem Informasi
Agus Buono
Department Of Computer Science, Faculty Of Mathematics And Natural Science, Bogor Agricultural University, Jalan Meranti, Kampus IPB Darmaga, Bogor 16680,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering 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 Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Other

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PENGGUNAAN METODE FUZZY SIMILARITY DALAM PENENTUAN CAKUPAN WILAYAH INDEKS CURAH HUJAN Woro Estiningtyas; Agus Buono; Rizaldi Boer; Irsal Las
Jurnal Meteorologi dan Geofisika Vol 14, No 2 (2013)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v14i2.155

Abstract

Dalam pengembangan asuransi indeks iklim, diperlukan informasi berapa luas cakupan indeks iklim yang disusun dari suatu stasiun hujan yang dapat mewakili berlakunya suatu indeks. Penelitian ini menyajikan suatu pendekatan penentuan cakupan indeks hujan menggunakan metode Fuzzy Similarity (FS). Metode FS tergolong baru dalam aplikasi cakupan indeks hujan ini. Dalam analisisnya, metode FS tidak memerlukan periode data yang sama pada setiap stasiun hujan. Hal ini sangat membantu karena seringkali satu stasiun hujan hanya memiliki data yang pendek sementara ada stasiun lain yang cukup panjang datanya. Untuk analisis ini digunakan stasiun Cikedung, Lelea, Terisi dan Kandanghaur yang semuanya tercakup dalam wilayah administratif Kabupaten Indramayu, Jawa Barat. Masing-masing stasiun referensi dikorelasikan dengan 41 stasiun di seluruh Kabupaten Indramayu. Cakupan wilayah indeks hujan ditetapkan berdasarkan nilai korelasi lebih dari 0.45. Hasil penelitian menunjukkan bahwa cakupan wilayah untuk stasiun pewakil Terisi adalah yang paling luas. Sekitar 53.8% dari seluruh stasiun di Kabupaten Indramayu memiliki kemiripan data dengan stasiun Terisi. Sebaliknya stasiun pewakil Kandanghaur, hanya berlaku untuk stasiun itu sendiri karena korelasinya yang sangat rendah terhadap stasiun lainnya. This research provides an option method of determining the coverage area of the rainfall station for the implementation of climate indices with Fuzzy Similarity (FS). Four rainfall station selected for each sub district as reference station is Cikedung, Lelea, Terisi and Kandanghaur, Indramayu District, West Java. Each reference station was correlated with 41 stations across the district Indramayu. The result shows that the coverage area for the Terisi station was the most extensive. Approximately 53.8% of all stations in Indramayu district have similarities with the Terisi rainfall station data. Whilst for Kandanghaur station, it only covers Kandanghaur because there is low correlation with another rainfall station.
Fuzzy Learning Vector Quantization Untuk Klasifikasi Citra Daging Oplosan Berdasarkan Ciri Warna dan Tekstur Lidya Ningsih; Agus Buono; Mushthofa; Toto Haryanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.932 KB) | DOI: 10.29207/resti.v6i3.4067

Abstract

Beef consumption is quite high and expensive in the world. In Indonesia, beef prices are relatively expensive because the meat supply chain from farmers to the market is quite long. The high demand for beef and the difficulty of obtaining meat are factors in the high price of meat. This makes some meat traders cheat by mixing beef and pork (oplosan). Mixing beef and pork is detrimental to beef consumers, especially those who are Muslim. In this paper, we proposed a new strategy for identifying beef, pig, and mixed meat utilizing Fuzzy learning vector quantization (FLVQ) Based on the color and texture aspects of the meat. The HSV (Hue saturation value) approach is used for color features, whereas the GLCM (Gray level co-occurrence matrix) method is used for texture features. This study makes use of primary data collected from the Pasar Bawah Tourism and Cipuan Market in Pekanbaru, Riau Province. The data set consists of 600 photos, 200 each of beef, pork, and mixed. Based on the test scenario, the coefficient of fuzzyness and learning rate affect the accuracy of meat image identification. The proposed strategy has succeeded in classifying pork, beef and mixed meat with the best percentage of accuracy results in theclasses of beef and pork, beef and mixed, pork and mixed meat, respectively, at 100%, 97.5%, and 95%. This demonstrates that the proposed strategy has succeeded in classifying the image of pork, beef, and mixed.
Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house Siti Kania Kushadiani; Agus Buono; Budi Nugroho
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p116-125

Abstract

As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production of publications. This study aimed to enhance a fuzzy inference system (FIS) parameters using the genetic algorithm (GA). The enhancements led to optimally predict the number of LIPI Press publications for the following year. The predictors used were the number of work units, the number of workers, and the publishing process duration. The dataset covered a five years range of total production of LIPI Press. Firstly, an expert set up the parameters of the fuzzy inference system denoted as a FIS expert. Next, we performed a FIS GA by applying the genetic algorithm and K-fold validation in splitting the training data and testing data. The FIS GA revealed optimum prediction with parameters that were composed of both population size (30), the probability of crossover (0.75), the probability of mutation (0.01), and the number of generations (150). The experiment results show that our enhanced FIS GA outperformed FIS expert approach.
Modeling Singular Value Decomposition and K-Means of Core Image in Clasification of Potential Nickel Agung Prajuhana Putra; Agus Buono; Bib Paruhum Silalahi
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Exploration is a main process in the nickel mining activities. One of the most important steps in exploration is obtain soil samples (cores) to determine the potential of nickel in the soil. Laboratory testing is a way to know how much the nickel content on the core. This research aims to utilize the core image of the statistical characteristics of color and texture, Biplot analysis using SVD, K-Means and identification using SVM method with RBF kernel and polynomial to determine the potential of nickel.DOI: http://dx.doi.org/10.11591/telkomnika.v13i3.7197
Downscaling Modeling Using Support Vector Regression for Rainfall Prediction Sanusi Sanusi; Agus Buono; Imas S Sitanggang; Akhmad Faqih
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp6423-6430

Abstract

Statistical downscaling is an effort to link global scale to local scale variable. It uses GCM model which usually used as a prime instrument in learning system of various climate. The purpose of this study is as a SD model by using SVR in order to predict the rainfall in dry season; a case study at Indramayu. Through the model of SD, SVR is created with linear kernel and RBF kernel. The results showed that the GCM models can be used to predict rainfall in the dry season. The best SVR model is obtained at Cikedung rain station in a linear kernel function with correlation 0.744 and RMSE 23.937, while the minimum prediction result is gained at Cidempet rain station with correlation 0.401 and RMSE 36.964. This accuracy is still not high, the selection of parameter values for each kernel function need to be done with other optimization techniques.
Optimization of Support Vector Regression using Genetic Algorithm and Particle Swarm Optimization for Rainfall Prediction in Dry Season Gita Adhani; Agus Buono; Akhmad Faqih
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i11.pp7912-7919

Abstract

Support Vector Regression (SVR) is Support Vector Machine (SVM) is used for regression case. Regression method is one of prediction season method has been commonly used. SVR process requires kernel functions to transform the non-linear inputs into a high dimensional feature space. This research was conducted to predict rainfall in the dry season at 15 weather stations in Indramayu district. The basic method used in this study was Support Vector Regression (SVR) optimized by a hybrid algorithm GAPSO (Genetic Algorithm and Particle Swarm Optimization). SVR models created using Radial Basis Function (RBF) kernel. This hybrid technique incorporates concepts from GA and PSO and creates individuals new generation not only by crossover and mutation operation in GA, but also through the process of PSO. Predictors used were Indian Ocean Dipole (IOD) and NINO3.4 Sea Surface Temperature Anomaly (SSTA) data. This research obtained an SVR model with the highest correlation coefficient of 0.87 and NRMSE error value of 11.53 at Bulak station. Cikedung station has the lowest NMRSE error value of 0.78 and the correlation coefficient of 9.01.
Similarity Measurement for Speaker Identification Using Frequency of Vector Pairs Inggih Permana; Agus Buono; Bib Paruhum Silalahi
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp6205-6210

Abstract

Similarity measurement is an important part of speaker identification. This study has modified the similarity measurement technique performed in previous studies. Previous studies used the sum of the smallest distance between the input vectors and the codebook vectors of a particular speaker. In this study, the technique has been modified by selecting a particular speaker codebook which has the highest frequency of vector pairs. Vector pair in this case is the smallest distance between the input vector and the vector in the codebook. This study used Mel Frequency Cepstral Coefficient (MFCC) as feature extraction, Self Organizing Map (SOM) as codebook maker and Euclidean as a measure of distance. The experimental results showed that the similarity measuring techniques proposed can improve the accuracy of speaker identification. In the MFCC coefficients 13, 15 and 20 the average accuracy of identification respectively increased as much as 0.61%, 0.98% and 1.27%.
Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target Iis Rodiah; Medria Kusuma Dewi Hardhienata; Agus Buono; Karlisa Priandana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4201

Abstract

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.
SELEKSI FITUR YANG BERPENGARUH MENGGUNAKAN NILAI MEAN PADA KLASIFIKASI FRAGMEN METAGENOME Arini Aha Pekuwali; Wisnu Ananta Kusuma; Agus Buono
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 1 (2020): Maret 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i1.2188

Abstract

Pekuwali (2018) has conducted research into the classification of metagenome fragments using spaced k-mers. Optimize the arrangement of features using Genetic Algorithms. Pekuwali (2018) concluded that the best arrangement of features or called chromosomes is 111111110001 with a fitness value of 85.42. Chromosome 111111110001 produces 336 features of extracting DNA fragments. This research aims to find out which features influence classi fi cation and the resulting accuracy. The method used is the Mean value. The mean value method was chosen because the data distribution is normal or close to normal. This study concludes that the influential features in the classification are features 22 to 27 with an accuracy of 78.83% and features 38 to 43 with an accuracy of 79.67%.
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Agus Buono; Karlisa Priandana; Bib Paruhum Silalahi; Elisabeth Sri Hendrastuti
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Co-Authors Ade Fruandta Adi Rakhman Aditya Cipta Raharja Agung Prajuhana Putra Akhmad Faqih Alif Kurniawan Alvin Fatikhunnada Anang Kurnia Angga Wahyu Pratama Aries Maesya Arini Aha Pekuwali Arini Pekuwali Astuti, Indah Puji Atik Pawestri Sulistyo Aziz Kustiyo Aziz Rahmad Bahukeling, Trukan Sri Benyamin Kusumoputro Bib Paruhum Silalahi Budi Nugroho Cece Sumantri DEWI APRI ASTUTI Dhany Nugraha Ramdhany Dian Kartika Utami Edi Santosa Ekowati Handharyani Elisabeth Sri Hendrastuti Endang Purnama Giri Erliza Hambali Erliza Noor Ernan Rustiadi Fadhilah Syafria Fadhilah Syafria Fajar Delli Wihartiko Fildza Novadiwanti Firdaus, Husni Firdaus, Nova Fredicia Fredicia Galih Kurniawan Sidik Galih Kurniawan Sidik Galih Kurniawan Sidik Gendut Suprayitno Gita Adhani GUNARSO GUNARSO Hardhienata, Medria Kusuma Dewi Harry Dhika, Harry Hastuadi Harsa Herianto Herianto Hidayat Hidayat Hidayat I Wayan Astika Ibrahim, Firmansyah Iis Rodiah Imam Suroso, Arif Imas Sukaesih Sitanggang Indah Prasasti Indah Puji Astuti Indra Jaya Inggih Permana Irman Hermadi Irmansyah . Irsal Las Irsal Las ISKANDAR ZULKARNAEN SIREGAR Kana Saputra S Karlisa Priandana Kikin H Mutaqin Kudang Boro Seminar Laila Sari Lubis Laila Sari Lubis Lailan Syaufina Lidya Ningsih Liyantono . M. Cholid Mawardi M. Mukhlis Marcelita, Faldiena Medria Kusuma Dewi Hardhienata Mindara, Gema Parasti Mohamad Solahudin Muhammad Ardiansyah Muhammad Rafi Muttaqin Mushthofa Mustakim Mustakim Mustakim Mustakim Muttaqin, Muhammad Rafi Niswati, Za'imatun Noviyanti, Inna Nurhayati, Yosi Popong Nurhayati Pratistya, Sayu Desty Puspita Kartika Sari Puspita Kartika Sari Putri Yuli Utami Raehan, Siti Raharja, Aditya Cipta Rahmat Hidayat Rizal Syarief Rizaldi Boer Rizki, Arviani RR. Ella Evrita Hestiandari Santo, Deni Sanusi Sanusi Sari Agustini Hafman Savitri, Siska Shelvie Nidya Neyman Sholihah, Walidatush Sidik, Galih Kurniawan Siregar, Ardinsyah Sitanggang, Imas S. Siti Kania Kushadiani Sony Hartono Wijaya Sri Dianing Asri Sri Hendrastuti, Elisabeth Sri Nurdiati Sri Wahjuni Stephane Douady Suharno Suharno Suharno Sumanto, Sumanto Syeiva Nurul Desylvia Taufik Djatna Thoyyibah Tanjung Toto Haryanto Uliniansyah, Mohammad Teduh Vicky Zilvan Wisnu Ananta Kusuma Wisnu Jatmiko Woro Estiningtyas Woro Estiningtyas Woro Estiningtyas Yan Mitha Djaksana Yandra Arkeman Yenni Vetrita Yoanda, Sely