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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 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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Pengembangan Model Pengenalan Wajah Manusia dengan Teknik Reduksi Dimensi Bi2DPCA dan Support Vector Machine sebagai Classifier Fredicia Fredicia; Agus Buono; Endang Purnama Giri
Ultimatics : Jurnal Teknik Informatika Vol 8 No 1 (2016): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.244 KB) | DOI: 10.31937/ti.v8i1.497

Abstract

This paper presents the modeling of face recognition using feature extraction based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) as a classifier. Three PCA techniques were compared, they are 1DPCA, 2DPCA and Bi-2DPCA. Meanwhile, three type of SVM kernel functions-linear, polynomial, and radial basis function (RBF) were used. The experiment used the ORL Face Database AT&T Laboratory, which contain 400 images with 10 images per each person. The leave one out method is used for validating each pair of extraction and classifier method. The highest accuracy is obtained by a combination of linear kernel and Bi-2DPCA85%, with 94.25%, and also the fastest computation time, is 15.34 seconds. Index Terms— Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation
Purchase Recommendation and Product Inventory Management using Content Based Filtering with Sequential Pattern Mining Approach Aditya Cipta Raharja; Imas Sukaesih Sitanggang; Agus Buono
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 4, November 2018
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.795 KB) | DOI: 10.22219/kinetik.v3i4.663

Abstract

Today, the product sales at XYZ Bookstore are increase in accordance to the trend in society. In that case, high sales must be supported by good supply and on target. Product sold based on needs of consumers will make possibility to achieve high sales. Using the Sequential Pattern Mining approach, we can specify sales patterns of products in relation to another products. SPADE (Sequential Pattern Discovery using Equivalence classes) is an algorithm that can be used to find sequential patterns in a large database. This algorithm finds frequent sequences of the sales transaction data using database vertical and join process of the sequence. The results of SPADE algorithm is frequent sequences which are used to form the rules. Those can be used as predictors of other items that will be purchased by consumers in the future. The result of this study is a lot of unique sequence appears that can provide the best advice for Merchandiser Officer, for example, there are 1.468 sequences that prove the customer who bought the product in Children’s Book category will always bought the same thing in the others day. This research produce some recommendation, one of the recommendation is Children's Book category has a very high chance of being a Best Seller for a long time so that the purchasing officer on XYZ bookstore should ensure that the product's supply of the category is always safe throughout the year. It means SPADE is successfully used to provide the advice and Merchandiser Officer must ensure the stock of that product is always available to avoid Lost Sales.
Peningkatan Performansi Multi Objektif NSGA-II Dengan Operator Mutasi Adaptif Pada Kasus Portofolio Reksadana Saham Putri Yuli Utami; Yandra Arkeman; Agus Buono; Irman Hermadi
CYBERNETICS Vol 3, No 02 (2019): CYBERNETICS
Publisher : Universitas Muhammadiyah Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29406/cbn.v3i02.2194

Abstract

Non-dominated sorting genetic algorihm (NSGA-II) merupakan salah algoritma pencarian solusi optimal dengan mengurutkan solusi berdasarkan pareto-front untuk mengindentifikasi feasible solutions. Performansi algoritme NSGA-II sangat dipengaruhi oleh operator parameter. Salah satu parameter adalah operator mutasi yang memegang kendali untuk diversitas kandidat solusi. Pada riset ini operator mutasi dibuat adaptif dengan menggunakan distribusi probabilitas polinomial (parameter nm). Parameter ini mengontrol kekutatan mutasi dan mengubah nilai mutasi secara adaptif serta mengubah probabilitas mutasi secara dinamik untuk mengatur banyaknya gen yang mengalami mutasi. Berdasarkan hasil penelitian nilai standar deviasi mutasi non-adaptif lebih kecil daripada mutasi adaptif. Nilai standar deviasi merepresentasikan varians sehingga mutasi adaptif memiliki varians yang beragam dibandingkan dengan mutasi non-adaptif. Mutasi adaptif dapat meningkatkan diversitas kromosom sehingga mencapai konvergensi kromosom agar terhindar dari konvergensi dini dengan waktu komputasi yang lebih efektif. Pada kasus portofolio reksadana saham menghasilkan standar deviasi yang lebih besar sehingga solusi yang dihasilkan semakin beragam.
IDENTIFIKASI DAN DELINEASI WILAYAH ENDEMIK KEKERINGAN UNTUK PENGELOLAAN RISIKO IKLIM DI KABUPATEN INDRAMAYU Woro Estiningtyas; Rizaldi Boer; Irsal Las; Agus Buono
Jurnal Meteorologi dan Geofisika Vol 13, No 1 (2012)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.575 KB) | DOI: 10.31172/jmg.v13i1.114

Abstract

Tulisan ini menyajikan hasil analisis, survey dan wawancara dengan petani di Kabupaten Indramayu terkait dengan kejadian kekeringan. Klasifikasi dan peta  tingkat endemik kekeringan dianalisis berdasarkan plot antara anomali luas kekeringan dan anomali frekuensi kejadian kekeringan. Berdasarkan survey di Kabupaten Indramayu, kekeringan menjadi penyebab utama gagal panen (79,8%). Kekeringan paling sering terjadi selama 6 bulan dan bulan Juni adalah bulan yang dominan terjadi kekeringan. Sebaran rata-rata luas kekeringan per kecamatan adalah 26 Ha sampai dengan 1602,5 Ha, dengan rata-rata 406 Ha/per kecamatan. Jumlah kejadian kekeringan berkisar antara 1-9 kejadian dan rata-rata 4 kejadian kekeringan dalam kurun waktu 2005-2011. Peta endemik kekeringan menghasilkan sebaran wilayah dengan klasifikasi endemik kekeringan tinggi, agak tinggi, agak rendah dan rendah. Beberapa pilihan teknologi untuk pengelolaan risiko iklim   diusulkan dalam penelitian ini   berdasarkan peta endemik kekeringan, karakteristik dan diskripsi setiap wilayah. Wilayah endemik tinggi merupakan prioritas pertama penanganan apabila terjadi bencana kekeringan. Pada wilayah ini dapat diterapkan teknik irigasi bergilir teratur, penggunaan varietas sangat genjah dan toleran kekeringan. Untuk sawah tadah hujan digunakan padi gogorancah pada MH dan walik jerami pada MK,  pergiliran varietas dan pengaturan pola tanam. This paper presents the results of analysis, surveys and interviews with farmers in Indramayu district. Drought becomes a major cause of crop failure (79,8%). Classification and map of drought were analysis based on anomaly drought area and frequency drought data.. Distribution of average drought in Indramayu district is 406 ha and 4 incidents in 2005-2011. Map of endemic drought is produce four classification : high, middle   high, middle low and low. Several technologies for managing climate risk in this research can be designed based on the map of endemic drought, the characteristics and description of each area. Highly endemic areas is the first priority handling in case of drought. In this irrigation techniques can be applied to regular rotation, the use of very early maturing varieties and drought tolerant. For rainfed land, gogorancah can be applied during wet season, and walik jerami in dry season, rotating varieties and cropping patterns. 
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.
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 Arif Imam Suroso Arini Aha Pekuwali Arini Pekuwali Astuti, Indah Puji Atik Pawestri Sulistyo Aziz Kustiyo Aziz Rahmad Benyamin Kusumoputro Bib Paruhum Silalahi Budi Nugroho Cece Sumantri Dhany Nugraha Ramdhany Dian Kartika Utami Djaksana, Yan Mitha 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 Firmansyah Ibrahim Fredicia Fredicia Galih Kurniawan Sidik Galih Kurniawan Sidik Galih Kurniawan Sidik Gema Parasti Mindara Gendut Suprayitno Gita Adhani GUNARSO GUNARSO Hastuadi Harsa Herianto Herianto Hidayat Hidayat Hidayat I Wayan Astika Ibrahim, Firmansyah Iis Rodiah Imas Sukaesih Sitanggang Indah Prasasti Indah Puji Astuti Indah Puji Astuti Indra Jaya Inggih Permana Inna Noviyanti 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 Mohamad Solahudin Muhammad Ardiansyah Muhammad Rafi Muttaqin Mushthofa Mustakim Mustakim Mustakim Mustakim Muttaqin, Muhammad Rafi Niswati, Za'imatun Nova Firdaus Nurhayati, Yosi Popong Nurhayati Pratistya, Sayu Desty Puspita Kartika Sari Puspita Kartika Sari Putri Yuli Utami Raharja, Aditya Cipta Rahmat Hidayat Rizal Syarief Rizaldi Boer Rizki, Arviani RR. Ella Evrita Hestiandari Santo, Deni Sanusi Sanusi Sari Agustini Hafman Savitri, Siska Sholihah, Walidatush Sidik, Galih Kurniawan Siregar, Ardinsyah Sitanggang, Imas S. Siti Kania Kushadiani Siti Raehan Sony Hartono Wijaya Sri Dianing Asri Sri Nurdiati Sri Wahjuni Stephane Douady Suharno Suharno Suharno Sumanto, Sumanto Syeiva Nurul Desylvia Taufik Djatna Thoyyibah Tanjung Toto Haryanto Trukan Sri Bahukeling Uliniansyah, Mohammad Teduh Vicky Zilvan Wisnu Ananta Kusuma Wisnu Jatmiko Woro Estiningtyas Woro Estiningtyas Woro Estiningtyas Yandra Arkeman Yenni Vetrita Yoanda, Sely