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All Journal E-Jurnal Matematika The Journal of Experimental Life Sciences (JELS) Telematika : Jurnal Informatika dan Teknologi Informasi Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Natural B Jurnal Berkala Epidemiologi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Prosiding Seminar Nasional Teknoka BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Irigasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya

Anam, Syaiful

Brawijaya University

Author-ID : 1175744

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation

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Backpropagation with BFGS Optimizer for Covid-19 Prediction Cases in Surabaya Zuraidah Fitriah; Mohamad Handri Tuloli; Syaiful Anam; Noor Hidayat; Indah Yanti; Dwi Mifta Mahanani
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5454

Covid-19 is a new type of corona virus called SARS-CoV-2. One of the cities that has contributed the most to infected Covid-19 cases in Indonesia is Surabaya, East Java. Predicting the Covid-19 is the important thing to do. One of the prediction methods is Artificial Neural Network (ANN). The backpropagation algorithm is one of the ANN methods that has been successfully used in various fields. However, the performance of backpropagation is depended on the architecture and optimization method. The standard backpropagation algorithm is optimized by gradient descent method. The Broyden - Fletcher - Goldfarb - Shanno (BFGS) algorithm works faster then gradient descent. This paper was predicting the Covid-19 cases in Surabaya using backpropagation with BFGS. Several scenarios of backpropagation parameters were also tested to produce optimal performance. The proposed method gives better results with a faster convergence then the standard backpropagation algorithm for predicting the Covid-19 cases in Surabaya.

Peningkatan Kemampuan Perangkat Desa Gondowangi Kecamatan Wagir Kabupaten Malang Dalam Pengelolaan Sistem Informasi Data Kependudukan Terintegrasi Website Zuraidah Fitriah; Noor Hidayat; Trisilowati Trisilowati; Syaiful Anam; Candra Dewi
Journal of Innovation and Applied Technology Vol 7, No 1 (2021)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2021.007.01.15

Dalam observasi awal diperoleh informasi tentang pengelolaan sistem informasi data kependudukan di desa Gondowangi belum dilakukan secara terintegrasi, dalam hal ini hanya dilakukan secara manual. Desa Gondowangi telah memiliki website, namun pengelolaan dilakukan oleh pihak luar perangkat desa, sehingga penyampaian informasi melalui website tersebut belum optimal. Agar pengelolaan website bisa lebih optimal, maka harus dilakukan peningkatan kemampuan perangkat desa dalam mengelola website (sebagai admin) dan mengintegrasikan hasil pengolahan data kependudukan dengan website. Dalam makalah ini diuraikan tentang upaya meningkatkan kemampuan perangkat desa Gondowangi dalam pengelolaan sistim informasi data kependudukan yang terintegrasi dengan website Desa Gondowangi. Pengelolaan dan pengolahan data dilakukan dengan menggunakan aplikasi yang tersedia pada Google, dalam hal ini Google Application.

Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps Onky Prasetyo; Ahmad Afif Supianto; Syaiful Anam; Hilman Ferdinandus Pardede; Vicky Zilvan; R. Budiarianto Suryo Kusumo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 1: Februari 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022915579

Digitalisasi proses pembelajaran memungkinkan untuk dihasilkannya rekaman terhadap setiap aktivitas siswa selama belajar. Rekaman yang dihasilkan tersebut dapat digunakan untuk mengelompokkan siswa berdasarkan pola dari proses belajar yang dilakukan. Hasil pengelompokkan yang peroleh dapat digunakan untuk melakukan penyesuaian komponen pembelajaran ataupun metode pembelajaran bagi siswa. Salah satu metode pengelompokan yang sering digunakan adalah Self-Organizing Maps (SOM), SOM merupakan metode jaringan syaraf tiruan dengan tujuan untuk mempertahankan topologi data ketika data input multidimensi diubah menjadi data output dengan dimensi yang lebih rendah. Neuron SOM pada dimensi input diperbaharui sepanjang proses pelatihan, sedangkan neuron pada dimensi output tidak mendapatkan pembaruan sama sekali, hal ini menyebabkan struktur neuron yang digunakan pada tahapan inisialisasi akan tetap sama hingga akhir proses pengelompokan. Pada penelitian ini menggunakan metode Adaptive Moving Self-Organizing Maps (AMSOM) yang menggunakan struktur neuron lebih fleksibel, dengan dimungkinkannya terjadi perpindahan, penambahan dan penghapusan dari neuron menggunakan data 12 assignments dari media pembelajaran MONSAKUN. Hasil penelitian menunjukkan terdapat perbedaan yang signifikan secara statistik antara nilai quantization error dan nilai topographic error dari algoritme AMSOM dengan algoritme SOM. Metode AMSOM menghasilkan rata-rata nilai quantization error 27 kali lebih kecil dan rata-rata nilai topographic error 54 kali lebih kecil dibandingkan dengan metode SOM.AbstractThe digitization of the learning process makes it possible to produce recordings of each student's activity during learning. The resulting record can be used to group students based on the pattern of the learning process. The grouping results can be used to make adjustments to the learning components or learning methods for students. One of the most frequently used clustering methods is Self-Organizing Maps (SOM), SOM is a neural network method to maintain data topology when multidimensional input data is converted into output data with lower dimensions. The SOM neurons in the input dimension are updated throughout the training process, while the neurons in the output dimension do not get updated at all, this causes the neuron structure used in the initialization stage to remain the same until the end of the grouping process. In this study, the Adaptive Moving Self-Organizing Maps (AMSOM) method uses a more flexible neuron structure, allowing for the transfer, addition and deletion of neurons using 12 assignments of data from MONSAKUN learning media. The results showed that there was a statistically significant difference between the quantization error and the topographic error of the AMSOM algorithm and the SOM algorithm. The AMSOM method produces an average quantization error 27 times smaller and an average topographic error 54 times smaller than the SOM method.

Hibridisasi Algoritma Genetika Dengan Variable Neighborhood Search (VNS) Pada Optimasi Biaya Distribusi Asyrofa Rahmi; Wayan Firdaus Mahmudy; Syaiful Anam
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 2: Juni 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakProses distribusi dianggap sangat penting bagi perusahaan karena menjadi salah satu faktor yang mempengaruhi perolehan keuntungan. Besarnya biaya yang dikeluarkan serta kompleksnya permasalahan dalam proses distribusi menjadikan permasalahan distribusi sebagai topik yang perlu diteliti lebih mendalam lagi. Karena algoritma genetika (AG) sudah terbukti mampu memberikan solusi terbaik pada berbagai macam permasalahan optimasi dan kombinatorial, maka algoritma ini digunakan untuk menyelesaikan permasalahan distribusi pada penelitian ini. Namun, penerapan GA klasik memiliki kekurangan yaitu belum mencapai titik optimum global sehingga perlu dihibridisasi menggunakan algoritma variable neighborhood search (VNS). Algoritma ini dipilih karena selain mencari solusi secara global, algoritma ini juga mencari solusi secara lokal sehingga mampu menutupi kekurangan dari GA. Dengan menggunakan hibridisasi GA dengan VNS maka biaya yang diperoleh adalah 32392960 yang dibuktikan dengan penghematan biaya sebesar 323190 jika dibandingkan dengan GA klasik yaitu 32716150. Namun, dilihat dari waktu komputasi, GA-VNS membutuhkan waktu yang relatif sama dengan GA klasik yaitu 279332 ms (milisecond) dan 265091 ms.Kata kunci: distribusi, algoritma genetika, variable neighborhood searchAbstractThe distribution process is considered importantly for the company as one of the factors that affects profitability. The costs incurred as well as the complexity of the distribution problems makes the distribution problems as a topic that need to be examined more deeply. Since the wide range of combinatorial and optimization problems have been ever solved by using genetic algorithm (GA) well then it is used to resolve the distribution problems in this study. However, the implementation of classical GA has the disadvantage that has not yet reached the global optimum so that needs to be hybridized by using variable neighborhood search (VNS) algorithm. The VNS algorithm has been chosen because its ability either to search the global solutions or local solutions. The local search of VNS algorithm is able to cover the shortage of the GA. By using hibridization of GA with VNS, the cost accrued is 32392960 as evidenced by cost savings of 323190 in comparison with the classical GA is 32716150. However, the computational time of GA-VNS is equal to its classical GA relatively.Keywords: distribution, genetic algorithm, variable neighborhood search

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur M. Chandra Cahyo Utomo; Wayan Firdaus Mahmudy; Syaiful Anam
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 3: September 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakPrakiraan curah hujan merupakan salah satu tanggung jawab penting yang dilakukan oleh layanan meteorologi di seluruh dunia. Permasalahan utama dalam hal analisis dan prakiraan adalah tingkat kesalahan yang semakin meningkat dari waktu ke waktu. Hal ini dapat terjadi karena kondisi ketidakpastian juga meningkat seiring dengan perubahan musim dan iklim. Penelitian ini mencoba mengombinasikan dua metode yaitu Logika Fuzzy untuk menghadapi kondisi-kondisi yang tidak pasti dan Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat. Penelitian ini juga menggunakan algoritma Particle Swarm Optimization untuk menentukan kebutuhan secara otomatis. Kebutuhan yang perlu ditentukan secara otomatis adalah bobot-bobot awal dalam Jaringan Syaraf Tiruan multi-layer sebelum akhirnya melakukan proses pelatihan algoritma. Penelitian ini menggunakan studi kasus di empat area Jawa Timur yaitu Puspo, Tutur, Tosari, dan Sumber untuk memprakirakan curah hujan di area Puspo. Data yang digunakan merupakan curah hujan timeseries yang dicatat selama 10 tahun oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Hasil penelitian ini menunjukkan bahwa kombinasi dari Logika Fuzzy dengan Jaringan Syaraf Tiruan multi-layer mampu memberikan tingkat RMSE sebesar 2.399 dibandingkan dengan hanya menggunakan regresi linear dengan tingkat RMSE sebesar 7.211.Kata kunci: fuzzy, hujan, hybrid, jaringan syaraf, optimasi, timeseriesAbstractRainfall forecasting is one of the important responsibilities that carried out by meteorological services in the worldwide. The main problem in terms of analysis and forecasting is the error rate is almost increasing from time to time. This caused by the uncertainty conditions are also increasing with the change of seasons and climate. This study tried to combine two methods of Fuzzy Logic for the problem solved of uncertain conditions and multi-layer Artificial Neural Network for the problem solved of the uncertainty that continues to increase. Particle Swarm Optimization algorithm also is used to determine the requirement automatically. The requirement that needs to be determined automatically is initial weights in multi-layer Artificial Neural Networks before the process of algorithm training. This study uses a case study in four areas of East Java that are Puspo, Tutur, Tosari, and Sumber. The data are a time series of rainfall rate that recorded in the 10 years by Badan Meteorologi Klimatologi dan Geofisika (BMKG). The results of this study indicate that the combination of Fuzzy Logic with Multi-Layer Neural Networks is capable of providing an RMSE level of 2,399 compared to only using linear regression with an RMSE level of 7,211.Keywords: fuzzy, hybrid, neural networks, optimization, rainfall, time series

Enzymatic Reaction Model Parameter Estimation of Biodiesel Synthesis Using Particle Swarm Optimization Syaiful Anam; Indah Yanti; Wuryansari Muharini K.
Natural B, Journal of Health and Environmental Sciences Vol 1, No 1 (2011)
Publisher : Natural B, Journal of Health and Environmental Sciences

The increasing number of vehicles and industries that emit exhaust gas emissions that cause air pollution close to the threshold of a dangerous man. Oil exploration major cause rapid depletion of petroleum. The discovery of biodiesel provides an alternative solution to the above, because biodiesel can reduce exhaust emissions and is a renewable alternative energy. Synthesis biodiesel can be done through an enzyme reaction that utilizes so-called biodiesel synthesis enzymatic reaction. Valid model enzymatic reaction is the key in the process of biodiesel synthesis reaction. This enzymatic reaction model contains the parameters to be estimated. Therefore, the determination of the parameters (parameter estimation) is an important enzyme kinetic. Parameter estimation can be performed using local optimization algorithms, but this algorithm has the major drawback is the optimal value obtained is a local optimal value. Therefore, in this research have been applied to global optimization algorithm, Particle Swarm Optimization for parameter estimation because it has the ability to find solutions quickly. Based on the simulation results obtained by the best parameter estimates as follows: k1=0.05000000000, k2=0.11000000000, k3=0.215000000000, k4=1.22799999999995, k5=0.24200000000000, k6=0.007000000000 and Sum Square Error is 2.51 x 10-27.

MODIFIED ARMIJO RULE ON GRADIENT DESCENT AND CONJUGATE GRADIENT ZURAIDAH FITRIAH; SYAIFUL ANAM
E-Jurnal Matematika Vol 6 No 3 (2017)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2017.v06.i03.p166

Armijo rule is an inexact line search method to determine step size in some descent method to solve unconstrained local optimization. Modified Armijo was introduced to increase the numerical performance of several descent algorithms that applying this method. The basic difference of Armijo and its modified are in existence of a parameter and estimating the parameter that is updated in every iteration. This article is comparing numerical solution and time of computation of gradient descent and conjugate gradient hybrid Gilbert-Nocedal (CGHGN) that applying modified Armijo rule. From program implementation in Matlab 6, it's known that gradient descent was applying modified Armijo more effectively than CGHGN from one side: iteration needed to reach some norm of the gradient (input by the user). The amount of iteration was representing how long the step size of each algorithm in each iteration. In another side, time of computation has the same conclusion.

Multispectral Imaging and Convolutional Neural Network for Photosynthetic Pigments Prediction Kestrilia Prilianti; Ivan C. Onggara; Marcelinus A.S. Adhiwibawa; Tatas H.P. Brotosudarmo; Syaiful Anam; Agus Suryanto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

The evaluation of photosynthetic pigments composition is an essential task in agricultural studies. This is due to the fact that pigments composition could well represent the plant characteristics such as age and varieties. It could also describe the plant conditions, for example, nutrient deficiency, senescence, and responses under stress. Pigment role as light absorber makes it visually colorful. This colorful appearance provides benefits to the researcher on conducting a nondestructive analysis through a plant color digital image. In this research, a multispectral digital image was used to analyze three main photosynthetic pigments, i.e., chlorophyll, carotenoid, and anthocyanin in a plant leaf. Moreover, Convolutional Neural Network (CNN) model was developed to deliver a real-time analysis system. Input of the system is a plant leaf multispectral digital image, and the output is a content prediction of the pigments. It is proven that the CNN model could well recognize the relationship pattern between leaf digital image and pigments content. The best CNN architecture was found on ShallowNet model using Adaptive Moment Estimation (Adam) optimizer, batch size 30 and trained with 15 epoch. It performs satisfying prediction with MSE 0.0037 for in sample and 0.0060 for out sample prediction (actual data range -0.1 up to 2.2).

Pemetaan Trase Jaringan Irigasi Melalui Analisis Geospasial (Studi Kasus Daerah Irigasi Cibuluh, Jawa Barat) Abu Bakar Sambah; Dwi Agus Kuncoro; Syaiful Anam
Jurnal Irigasi Vol 12, No 1 (2017): Jurnal Irigasi
Publisher : Balai Teknik Irigasi

Planning of irrigation canal has always faced the problems due to the overlapping of different land use. Irrigation planning should consider the irrigation canals surrounding different land use. Optimization of the determination of the irrigation network must be applied through the assumption of the physical condition of topographical as well as the proximity between irrigation canal and area of irrigation. The aims of this study were: (1) Mapping existing condition of irrigation canals in DI Cibuluh related to the land use and topography of the study area; (2) Mapping and determining the optimal trace irrigation networks based on spatial analysis of the existing land use and topographical characteristics; (3) Establish a simulation concepts of re-classification related to irrigation services area based on the elevation of the study area using geospatial analysis. The study was conducted through geospatial analysis methods in Geographic Information Systems. Digital Elevation Models (DEM) were the basic data in simulating irrigation services area. The results showed that there were two overlapping land use type (forests and industrial areas) that should be subtracted from the irrigated areas. Alignment of Irrigation network was planned without overlapping forest and industrial area, so that the planning was more focus on simulation based on the control points by processing adjustments as well as high elevation contour and water height.

Leaders and followers algorithm for constrained non-linear optimization Helen Yuliana Angmalisang; Syaiful Anam; Sobri Abusini
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp162-169

Leaders and Followers algorithm was a novel metaheuristics proposed by Yasser Gonzalez-Fernandez and Stephen Chen. In solving unconstrained optimization, it performed better exploration than other well-known metaheuristics, e.g. Genetic Algorithm, Particle Swarm Optimization and Differential Evolution. Therefore, it performed well in multi-modal problems. In this paper, Leaders and Followers was modified for constrained non-linear optimization. Several well-known benchmark problems for constrained optimization were used to evaluate the proposed algorithm. The result of the evaluation showed that the proposed algorithm consistently and successfully found the optimal solution of low dimensional constrained optimization problems and high dimensional optimization with high number of linear inequality constraint only. Moreover, the proposed algorithm had difficulty in solving high dimensional optimization problem with non-linear constraints and any problem which has more than one equality constraint. In the comparison with other metaheuristics, Leaders and Followers had better performance in overall benchmark problems.

Co-Authors A.S. Adhiwibawa, Marcelinus Abdi Negara Guci Abu Bakar Sambah Agus Suryanto Agus Suryanto Agus Widodo Ahmad Afif Supianto Asyrofa Rahmi Candra Dewi Corina Karim, Ari Andari Dian Eka Ratnawati Dwi Agus Kuncoro Dwi Febry Indarwati Dwi Mifta Mahanani Faiz Anggiananta Winantoro Fery Widhiatmoko Fery Widhiatmoko Helen Yuliana Angmalisang Hidayat, Noor Hilman Ferdinandus Pardede Ibnu Mansyur Hamdani Indah Yanti Indah Yanti Indah Yanti Isti Marlisa Fitriani Ivan C. Onggara Kestrilia Rega Prilianti Komang Agus Arta Wijaya M. Chandra Cahyo Utomo M. Rafael Andika Putra Marcelinus A.S. Adhiwibawa Mila Kurniawaty Mochamad Hakim Akbar Assidiq Maulana Mochamad Hakim Akbar Assidiq Maulana Mochammad Iskandar Ardiyansyah Rochman Mohamad Handri Tuloli Mohamad Handri Tuloli Noor Hidayat Nur Khilmiyatul Ilmiyah Nur Shofianah Nur Shofianah Nyimas Ayu Widi Indriana Onggara, Ivan C. Onky Prasetyo Onky Prasetyo R. Budiarianto Suryo Kusumo Raden Rizky Widdie Tigusti Raqqasyi Rahmatullah Musafir Revi Anistia Masykuroh Ririn Nirmalasari Sherly Witanto Sobri Abusini Suhhy Ramzini Suryanto, Agus Syamsumar Bustamin Tatas H.P. Brotosudarmo Trisilowati, Trisilowati Vicky Zilvan Wayan Firdaus Mahmudy Wuryansari Muharini K. Yunita Dwi Alfiyanti Zuraidah Fitriah ZURAIDAH FITRIAH

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