Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm

Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results

Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation

This study investigates Particle Swarm Optimization (PSO) hybridization with Simulated Annealing (SA) to optimize Support Vector Machine (SVR). The optimized SVR is used for software effort estimation. The optimization of SVR consists of two sub-problems that must be solved simultaneously; the first is input feature selection that influences method accuracy and computing time. The next sub-problem is finding optimal SVR parameter that each parameter gives significant impact to method performance. To deal with a huge number of candidate solutions of the problems, a powerful approach is required. The proposed approach takes advantages of good solution quality from PSO and SA. We introduce SA based acceptance rule to accept new position in PSO. The SA parameter selection is introduced to improve the quality as stochastic algorithm is sensitive to its parameter. The comparative works have been between PSO in quality of solution and computing time. According to the results, the proposed model outperforms PSO SVR in quality of solutio

Review: A State-of-the-Art of Time Complexity (Non-Recursive and Recursive Fibonacci Algorithm)

Abstract. Solving strategies in the computation the time complexity of an algorithm is very essentials. Some existing methods have inoptimal in the explanations of solutions, because it takes a long step and for the final result is not exact, or only limited utilize in solving by the approach. Actually there have been several studies that develop the final model equation Fibonacci time complexity of recursive algorithms, but the steps are still needed a complex operation. In this research has been done several major studies related to recursive algorithms Fibonacci analysis, which involves the general formula series, begin with determining the next term directly with the equation and find the sum of series also with an equation too. The method used in this study utilizing decomposition technique with backward substitution based on a single side outlining. The final results show of the single side outlining was found that this technique is able to produce exact solutions, efficient, easy to operate and more understand steps. Keywords: Time Complexity, Non-Recursive, Recursive, Fibonacci Algorith

Prediction of Rainfall using Simplified Deep Learning based Extreme Learning Machines

Prediction of rainfall is needed by every farmer to determine the planting period or for an institution, eg agriculture ministry in the form of plant calendars. BMKG is one of the national agency in Indonesia that doing research in the field of meteorology, climatology, and geophysics in Indonesia using several methods in predicting rainfall. However, the accuracy of predicted results from BMKG methods is still less than optimal, causing the accuracy of the planting calendar to only reach 50% for the entire territory of Indonesia. The reason is because of the dynamics of atmospheric patterns (such as sea-level temperatures and tropical cyclones) in Indonesia are uncertain and there are weaknesses in each method used by BMKG. Another popular method used for rainfall prediction is the Deep Learning (DL) and Extreme Learning Machine (ELM) included in the Neural Network (NN). ELM has a simpler structure, and non-linear approach capability and better convergence speed from Back Propagation (BP). Unfortunately, Deep Learning method is very complex, if not using the process of simplification, and can be said more complex than the BP. In this study, the prediction system was made using ELM-based Simplified Deep Learning to determine the exact regression equation model according to the number of layers in the hidden node. It is expected that the results of this study will be able to form optimal prediction model. Keywords: prediction, rainfall, ELM, simplified deep learnin

Pelatihan Budidaya Multi-Culture Farming Berbasis Teknologi Sistem Pakar serta Optimasi untuk Kemandirian Ekonomi dan Ketahanan Pangan Masyarakat Indonesia

Poverty alleviation efforts in Indonesia are still challenging due to the lack of job opportunities. Therefore, creative efforts are needed that are easy to do at home, especially during this Covid-19 pandemic. In this service activity, the creative business was introduced, which was packaged in multi-culture farming cultivation training. The community service activities were preceded by conducting a field survey on three partners, i.e., the farmer groups in Sunge Geneng Village, Sekaran District, Lamongan Regency, RT 3 / RW III Kauman Village Malang City, and Poncokusumo District Malang Regency. Since the survey plan was carried out during the Covid-19 Pandemic and the frequent Public Activity Restrictions (PPKM), the team could only survey the initial two partners. However, during the implementation process, the second partner was the only one to reach the implementation stage of Multi-Culture Farming with non-AI technology and introduce the use of AI Technology. Therefore, the second partner, RT 03 / RW 03 Kauman Village, Klojen District, Malang City, also became the leading partner. The main results of activities were ten budikdamber tools, catfish feed, and training modules about optimizing plant fertilizer nutrition. The other results were land-use using AI Engine and educational videos that received an excellent response from the partners and non-partners being sustainable to create a kind of fostered village, especially in entrepreneurship that utilizes digital technology.ABSTRAKUpaya pengentasan kemiskinan di Indonesia masih sulit dilakukan, karena minimnya lapangan pekerjaan. Oleh karena itu dibutuhkan usaha kreatif yang mudah dilakukan di rumah, terutama dimasa pandemi Covid-19 ini. Dalam kegiatan pengabdian ini, dikenalkan usaha kreatif tersebut yang dikemas dalam bentuk pelatihan budidaya multi-culture farming. Rangkaian kegiatan pengabdian masyarakat tersebut didahului dengan melakukan survey lapangan terhadap tiga Mitra, yaitu di kelompok Tani desa Sunge Geneng Kecamatan Sekaran Kabupaten Lamongan, Kampung Kauman RW/RT III/03 dan Poncokusumo Malang. Pada rencana survey tersebut dikarenakan saat itu masih di tengah Pandemi Covid-19 dan juga sering ada PPKM. Maka tim hanya mampu melakukan survey terhadap 2 Mitra awal, dan ketika proses pelaksanaan hanya Mitra yang ke-2 yang sampai pada tahapan implementasi Multi-Culture Farming dengan teknologi non-AI, tetapi sudah dikenalkan juga pada saat pelatihan menggunakan Teknologi AI, di mana Mitra ke-2 tersebut tepatnya pada RT 03, RW-03 kelurahan Kauman kecamatan Klojen kota Malang yang sekaligus menjadi Mitra utama. Hasil utama kegiatan berupa pemberian bantuan 10 alat budikdamber, pakan lele serta modul pelatihan optimasi pupuk tanaman, penggunaan lahan dan video edukasi yang telah mendapatkan respon sangat baik dari tanggapan Mitra sekaligus juga dari Non Mitra untuk terus dapat berkelanjutan sampai membuat semacam kampung binaan terutama dalam hal wirausaha yang memanfaatkan Teknologi digital

Optimization of Healthy Diet Menu Variation using PSO-SA

Abstract. Optimal healthy diet in accordance with the allocation of cost needed so that the level of nutritional adequacy of the family is maintained. The problem of optimal healthy diet (based on family budget) can be solved with genetic algorithm. The algorithm particle swarm optimization (PSO) has the same effectiveness with genetic algorithm but PSO is superior in terms of efficiency, PSO algorithm has a lower complexity than genetic algorithm. However, genetic algorithms and PSO have a problem of local optimum because these algorithm associated with random numbers. To overcome this problem, PSO algorithm will be improved by combining it with simulated annealing algorithm (SA). Simulated annealing algorithm is a numerical optimization algorithms that can avoid local optimal. From our results, optimal parameter for PSO-SA are popsize 280, crossover rate 0.6, mutation rate 0.4, first temperature 1, last temperature 0.2, alpha 0.9, and generation size 100. Keywords: PSO, SA, optimization, variation, healthy diet menu

Integration Method of Local-global SVR and Parallel Time Variant PSO in Water Level Forecasting for Flood Early Warning System

Flood is one type of natural disaster that can’t be predicted, one of the main causes of flooding is the continuous rain (natural events). In terms of meteorology, the cause of flood is come from high rainfall and the high tide of the sea, resulting in increased the water level. Rainfall and water level analysis in each period, still not able to solve the existing problems. Therefore in this study, the proposed integration method of Parallel Time Variant PSO (PTVPSO) and Local-Global Support Vector Regression (SVR) is used to forecast water level. Implementation in this study combine SVR as regression method for forecast the water level, Local-Global concept take the role for the minimization for the computing time, while PTVPSO used in the SVR to obtain maximum performance and higher accurate result by optimize the parameters of SVR. Hopefully this system will be able to solve the existing problems for flood early warning system due to erratic weather

Sistem Monitoring Aliran Sungai dan Lingkungan Berbasis Smart Environment di RW 03 Kelurahan Kauman Kota Malang

Monitoring of the rivers state and the environment of roads in the city center is often still inadequate. For example, garbage is often found in the river, while on the roads, there is still not yet a sound security system. Kauman RT 03 RW III Klojen Malang is one of the densely populated regions and is located in the center (point of zero) of Malang city at the time ago still does not have a security system or security guard and there is a river flow which is often found garbage piling up and often causes flooding when it rains heavy. Based on field conditions in Kauman and meetings with residents represented by several RT heads in RW 03 Kauman, Klojen Malang requires the use of a smart environment and CCTV technology integration. Therefore the result of dedication to society to apply CCTV's technology, so it has been used at Kauman for environmental and security monitoring. Considering the high level of the busyness of the urban at Kauman, with providing it, they can be monitoring the environment by automatically systems continuously 24 hours every day. Therefore, the system has been being able to facilitate and help people to monitor the environment and river flow to be more effective, efficient, and modern. ABSTRAKMonitoring keadaan sungai dan lingkungan ruas jalan pada masyarakat tengah kota seringkali masih belum memadai. Di aliran sungai misalnya, masih sering dijumpai sampah yang menumpuk, sedangkan di ruas jalan masih belum dijumpai sistem keamanan yang baik. Kampung Kauman RT 03 RW III kecamatan Klojen Kota Malang merupakan salah satu kampung yang padat penduduk dan berada di pusat (titik nol) kota saat ini belum memiliki sistem keamanan ataupun satpam dan terdapat aliran sungai yang seringkali dijumpai sampah menumpuk bahkan sering menyebabkan banjir bila hujan deras. Berdasarkan kondisi lapangan di kampung Kauman dan pertemuan dengan warga yang diwakili oleh beberapa ketua RT di wilayah RW 03 Kauman yang membutuhkan pemanfaatkan integrasi teknologi smart environment dan teknologi CCTV. Hasil kegiatan pengabdian masyarakat telah dapat secara optimal dimanfaatkan untuk memenuhi kebutuhan pengawasan ataupun monitoring lingkungan tersebut. Mengingat tingkat kesibukan masyarakat perkotaan yang tinggi, dengan adanya sistem monitoring mereka dapat mengambil manfaat besar dengan dikembangkannya sistem pengawasan aliran sungai dan lingkungan yang bisa bekerja secara otomatis dan kontinyu selama 24 jam. Sistem yang dibuat telah mampu memudahkan sekaligus membantu masyarakat untuk monitoring lingkungan dan aliran sungai secara lebih efektif, efisien, dan modern.

IMPLEMENTASI CITRA DIGITAL UNTUK IDENTIFIKASI PENYAKIT PADA DAUN PADI MENGGUNAKAN ANFIS

Salah satu faktor yang dapat mengurangi produksi padi adalah penyakit yang menyerang pada daun padi, sehingga identifikasi awal penyakit ini sangat penting dilakukan agar tidak meluas serangannya. Proses identifikasi selain dapat dilakukan menggunaka cara manual oleh ahli, juga dapat dilakukan menggunakan alat bantu alat berupa aplikasi berbasis citra daun padi. Pada penelitian ini dilakukan identifikasi jenis penyakit yang menyerang daun padi menggunakan citra daun padi yang berpenyakit dengan metode Adaptive Neuro Fuzzy Inference System (ANFIS). Proses identifikasi penyakit pada daun padi diawali dengan thresholding citra menggunakan metode Otsu dan dilanjutkan dengan pengelompokan data menggunakan metode K-Means untuk mendapatkan nilai koefisien adaptif yang diperlukan pada proses di lapisan pertama ANFIS. Kemudian dilakukan pelatihan dan uji coba untuk mengidentifikasi jenis penyakit yang menyerang daun padi menggunakan ANFIS. Pengujian dilakukan untuk mengenali dua jenis penyakit yaitu Bercak Coklat Sempit dan Blas. Hasil pengujian terhadap 40 data uji menghasilkan akurasi terbaik sebesar 84.5% dengan maksimum error sebesar 1x10-12. Dengan demikian dapat dikatakan bahwa metode ini cukup efektif untuk digunakan sebagai langkah awal dalam mengenali penyakit yang menyerang daun pad