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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,

Author-ID : 221408

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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Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification Arini Pekuwali; Wisnu Ananta Kusuma; Agus Buono
Journal of ICT Research and Applications Vol. 12 No. 2 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.2.2

K-mer frequencies are commonly used in extracting features from metagenome fragments. In spite of this, researchers have found that their use is still inefficient. In this research, a genetic algorithm was employed to find optimally spaced k-mers. These were obtained by generating the possible combinations of match positions and don't care positions (written as *). This approach was adopted from the concept of spaced seeds in PatternHunter. The use of spaced k-mers could reduce the size of the k-mer frequency feature's dimension. To measure the accuracy of the proposed method we used the naÃ¯ve Bayesian classifier (NBC). The result showed that the chromosome 111111110001, representing spaced k-mer model [111 1111 10001], was the best chromosome, with a higher fitness (85.42) than that of the k-mer frequency feature. Moreover, the proposed approach also reduced the feature extraction time.

ANALISIS HUBUNGAN KODE-KODE SPBK (SISTEM PERINGKAT BAHAYA KEBAKARAN) DAN HOTSPOT DENGAN KEBAKARAN HUTAN DAN LAHAN DI KALIMANTAN TENGAH Indah Prasasti; Rizaldi Boer; Muhammad Ardiansyah; Agus Buono; Lailan Syaufina; Yenni Vetrita
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 2 No. 2 (2012): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.2.2.101

Land and forest fire is one of causes ofland degradation in Central Kalimantan. Remote sensing dataapplications, especially READY-ARL NOAA and CMORPH data, are benefit forthe available climate observation data. The objectives of this research are: (1) to analyzis relationship between hotspots, FDRS and occurences of land and forest fire, and (2) to develop the estimation model of burned area from hotspot and FDRS codes. The result of this research showed that burned area can not be estimated by using number of hotspots. The drought code (DC) wich is one of FDRS codes has correlation with burned area. So, burned area can be estimated using drought code (DC) (R-sq = 58%) by using the following formula: Burned Area (Ha) = -62.9 + 5.14 (DC – 500).Keywords: land and forest fire, NOAA, CMORPH, hotspot

PENGARUH INCOMPLETE DATA TERHADAP AKURASI VOTING FEATURE INTERVALs-5 (VFI5) Aziz Kustiyo; Agus Buono; Atik Pawestri Sulistyo
KOMPUTASI Vol 4, No 8 (2007): Vol. 4, No. 8, Juli 2007
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Permasalahan mengenai data hilangan merupakan masalah umum yang tejadi pada lingkungan medis. Data hilangan dapat disebabkan beberapa hal yaitu salah memasukan data, data nya tidak valid dan peralatan yang di gunakan untuk mengambil data tidak berfungsi dengan baik. Voting Feature Intervals merupakan algoritma klasifikasi yang di kembangkan oleh Gulsen Demiroz dan H.Altay Guvenir pada tahun 1997. Algoritma ini dapat mengatasi data hilang dengan mengabaikan data hilang tersebut . Pada penelitian ini dilakukan penerapan algoritma Voting Feature Intervals-5 (VFI5) sebagai algoritma klasifikasi pada kasus data hilang. Data yang di gunakan adalah data ordinal (data Dermatology) dan data interval (data lonosphere). Untuk mengatasi data hilang di gunakan tiga metode yaitu mengabaikan data hilang dengan mean atau modus. Rata-rata tingkat akurasi data ordinal tertinggi sebesar 93.81% dan Rata-rata tingkat interval tertinggi sebesar 79.89%. Hasil penelitian menunjukan rata-rata tingkat akurasi yang tertinggi dicapai ketika data hilang dengan mean atau modus.

Optimasi CNN dengan GA Pada Prediksi Awal Musim Hujan Berdasarkan Data GCM: Kabupaten Pacitan1) (CNN Optimization Using GA for Rainy Season Onset Prediction Based on GCM Output:Pacitan District) Fildza Novadiwanti; Agus Buono; Akhmad Faqih
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) Vol 41, No 1 (2017)
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v41n1.2017.69-77

Abstrak: Di Indonesia, pertanian menjadi sektor penting dalam pembangunan nasional dan pembangunan ekonomi. Awal musim hujan merupakan salah satu variabel iklim yang dapat memengaruhi produksi pertanian. Perubahan awal musim hujan dapat berdampak pada terjadinya gagal panen. Penelitian ini mengembangkan model untuk memprediksi awal musim hujan menggunakan cascade neural network yang dioptimasi menggunakan genetic algorithm berdasarkan data global circulation model pada Kabupaten Pacitan. Data observasi menggunakan data awal musim hujan dari 3 stasiun cuaca, yaitu Arjosari, Kebon Agung, dan Pringkuku. Data prediktor menggunakan data global circulation model antara tahun 1983 – 2011 dari 3 model, yaitu CMC1-CanCM3, CMC1-CanCM4, dan NCEP-CSFv2. Optimasi cascade neural network dengan genetic algorithm dilakukan dengan mengoptimasi jumlah hidden neuron dan menghasilkan peningkatan nilai koefisien korelasi (r). Penelitian ini menghasilkan model terbaik dari setiap stasiun cuaca dengan parameter yang berbeda. Nilai r stasiun Arjosari adalah 0.89. Nilai r stasiun Kebon Agung adalah 0.86. Nilai r stasiun Pringkuku adalah 0.87. Abstract. In Indonesia, agriculture becomes an important sector for national development and national economy. The onset of the rainy season is one of the rainfall variables that affect agricultural production. The changing of the onset of rainy season can impact on crop failure. This research aims to develop a model for predicting the onset of rainy season using optimized cascade neural network with genetic algorithm based on global circulation model in Pacitan district. Observational data used is the onset of rainy season of 3 weather stations in Pacitan: Arjosari, Kebon Agung, and Pringkuku. Predictor data used is global circulation model output data between 1983 – 2011 from 3 models: CMC1-CanCM3, CMC1-CanCM4, and NCEP-CSFv2. Optimization of cascade neural network with genetic algorithm has been done by optimizing the amount of hidden neuron and obtained an increase value of correlation coefficient (r). This research obtained the best model from each weather stations with different parameters. R value of Arjosari weather station is 0.89. R value of Kebon Agung weather station is 0.86. R value of Pringkuku weather station is 0.87.

Pendekatan Usability Engineering pada Sistem Manajemen Pengetahuan Bibit Kelapa Sawit Thoyyibah Tanjung; Agus Buono; Irman Hermadi
Jurnal Informatika Universitas Pamulang Vol 1, No 2 (2016): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Penggunaan sistem manajemen pengetahuan sangat diperlukan untuk pertukaran informasi. Banyaknya penggunaan sistem tersebut diiringi dengan berkembangnya benih kelapa sawit di Indonesia, yang mana ketersediaan dalam negeri adalah ± 160 juta, sedangkan permintaan terhadap benih kelapa sawit dalam negeri adalah ± 230 juta benih, sehingga terjadi kekurangan benih di Indonesia. Untuk menanggulangi kekurangan tersebut dibutuhkan sebuah sistem informasi yaitu sistem manajemen pengetahuan. Tujuan penelitian ini untuk mengembangkan Knowledge Management System (KMS) dengan metode pendekatan usability engineering. Dimana dengan menggunakan usability bisa menentukan karakteristik dan kelayakan suatu sistem. Hasil dari penelitian ini berupa sistem manajemen pengetahuan pemilihan bibit sawit yang mana pengetahuannya meliputi cakupan data benih dan bibit kelapa sawit, pengetahuan dari pakar termasuk praktisi, ilmuan dan petani.

SPEAKER IDENTIFICATION USING HYBRID MODEL OF PROBABILISTIC NEURAL NETWORK AND FUZZY C-MEANS Vicky Zilvan; Agus Buono; Sri Nurdiati
Widyariset Vol 16, No 2 (2013): Widyariset
Publisher : Pusbindiklat - LIPI

A hybrid model of Probabilistic Neural Network and Fuzzy C-Means has been developed. The model has been applied using Mel Frequency Cepstrum Coefficients (MFCC) as feature extraction for identification. In addition to the natural voice, the effect of noise has also been taken into account. It has been shown that the model has good accuracy at 96% for voice without noise, 85.5% for voice with noise at the level of signal to noise ratio 30, and 60% for voice with noise at the level of signal to noise ratio 20. It has also been concluded that the clustering procedure using Fuzzy C-Means could improve the accuracy up to 96% for large number of training data.

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

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

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

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

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.

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

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