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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Habitat Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer JAST : Jurnal Aplikasi Sains dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science
Imam Cholissodin
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Other

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Prediction of rainfall using improved deep learning with particle swarm optimization Imam Cholissodin; Sutrisno Sutrisno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.14665

Abstract

Rainfall is a natural factor that is very important for farmers or certain institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include Deep Learning (DL) and Particle Swarm Optimization (PSO). The use of the Deep Learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.
Integration Method of Local-global SVR and Parallel Time Variant PSO in Water Level Forecasting for Flood Early Warning System Arief Andy Soebroto; Imam Cholissodin; Maria Tenika Frestantiya; Ziya El Arief
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.6772

Abstract

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 Sutrisno Sutrisno; Imam Cholissodin; Arief Andy Soebroto; Muh Arif Rahman
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 5, No 1 (2021): EDISI JUNI 2021
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v5i1.2259

Abstract

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. 
Deteksi Emosi pada Tweet Berbahasa Indonesia tentang Pembelajaran Jarak Jauh Menggunakan K-Nearest Neighbor dengan Pembobotan Kata Term Frequency-Inverse Gravity Moment Fira Sukmanisa; Yuita Arum Sari; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

In December 2019 in the city of Wuhan, China, a case known as coronavirus disease 2019 (Covid-19) emerged and spread rapidly throughout the world. The Indonesian government implements a distance learning policy (PJJ) to minimize the spread of Covid-19. Opinions about PJJ are conveyed by the public via tweets. Emotion detection is the process of classifying tweets into emotion classes. Term weighting is a basic problem in text classification because it can affect accuracy. TF-IDF is one of the most frequently used term weightings, but TF-IDF is not the most effective because it ignores class labels. Therefore, emotion detection in tweets is carried out in order to find out emotions about PJJ. In this study, emotion detection will go through several processes, namely preprocessing, weighting of the Term Frequency-Inverse Gravity Moment (TF-IGM), cosine similarity, classification using the K-Nearest Neighbor (KNN) method, and evaluation using confusion matrix. Based on the test results using an imbalanced dataset, the optimal TF-IGM weighting coefficient is 9 which produces the highest accuracy of 0.55 at k = 25. The use of the TF-IGM weighting coefficient provides an accuracy that is less stable when compared to the TF-IGM without the weighting coefficient. The weighted words TF-IGM and TF-IDF have the same highest accuracy value, and the distance between evaluation results is small for each k tested.
Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture Arief Andy Soebroto; Imam Cholissodin; Destyana Ellingga Pratiwi; Guruh Prayogi Willis Putra
HABITAT Vol. 33 No. 1 (2022): April
Publisher : Department of Social Economy, Faculty of Agriculture , University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.habitat.2022.033.1.9

Abstract

In the smart agriculture system based on green-based technology of artificial intelligence (AI), flooding can be predicted early by forecasting the water surface and good agricultural irrigation. The process of rising and falling of the water surface in a water basin area can be explained theoretically, but since there are many related variables and the complexity of dependencies between variables, the mathematical model is difficult to construct. Forecasting water surface in the field of irrigation needs too many variable parameters, such as cross-sectional area, depth, volume of rivers and so on. Based on patterns in each period, forecasting can be done using a statistical method and AI. This study uses the support vector regression (SVR) method, regression, multiple linear regression, and algorithm backpropagation, all compared to one another. The results of tests carried out between SVR and multiple linear regression show that SVR is superior. This can be seen from the result of the mean square error (MSE) obtained for each method. SVR 0.03 and for multiple linear regression, 0.05. The result is also supported by the best MSE result in the regression method, which is 0.338, and the best MSE value in artificial neural network (ANN), which is 0.428.
Single nucleotide polymorphism based on hypertension potential risk prediction using LSTM with Adam optimizer Lailil Muflikhah; Imam Cholissodin; Nashi Widodo; Feri Eko Herman; Teresa Liliana Wargasetia; Hana Ratnawati; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1126-1139

Abstract

Recent healthcare research has focused a great deal of interest on using genetic data analysis to predict the risk of hypertension. This paper presents a unique method for accurately predicting the vulnerability to hypertension by utilizing single nucleotide polymorphism (SNP) data. We present a novel neural network design utilizing the adaptive moment (Adam) optimizer to describe the intricate temporal correlations in SNPs. The study used a dataset with carefully preprocessed SNP data from a broad cohort for model input. The long short-term memory (LSTM) network was methodically built and trained with hyper-parameter and fine-tuning using the Adam optimizer to converge on ideal weights. Our findings indicate encouraging predictive performance, highlighting the suggested methodology’s usefulness in determining hypertension risk factors. The result showed that the proposed method achieved stability in the performance of 89% accuracy, 96% precision, 88% recall, and 92% F1-score. Due to its higher accuracy and greater predictive power, our SNP-based LSTM methodology is superior to the conventional machine learning method. By providing a novel framework that uses genetic data to predict the risk of hypertension, this research makes substantial contribution to the field of predictive healthcare. This framework helps with early intervention and customized preventative efforts.
Co-Authors Arief Andy Soebroto Destyana Ellingga Pratiwi Feri Eko Herman Fira Sukmanisa Guruh Prayogi Willis Putra HANA RATNAWATI Lailil Muflikhah Maria Tenika Frestantiya Muh. Arif Rahman Nashi Widodo Riyanarto Sarno SUTRISNO Yuita Arum Sari Ziya El Arief