Articles
<![CDATA[The Control Design of Ship Formation with the Presence of a Leader ]]>
<![CDATA[M. Miswanto]]>;
<![CDATA[I. Pranoto]]>;
<![CDATA[H. Muhammad Mhammad]]>;
<![CDATA[D. Mahayana]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 4, No 1: March 2015 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v4i1.pp53-62
<![CDATA[Formation control is an important behavior for multi-agents system (swarm). This paper addresses the optimal tracking control problem for swarm whose agents are ships moving together in a specific geometry formation. We study formation control of the swarm model which consists of three agents and one agent has a role as a leader. The agents of swarm are moving to follow the leader path. First, we design the control of the leader with Pontryagin Maximum Principle. The control of the leader is designed for tracking the desired path. We show that the tracking error of the path of the leader tracing a desired path is sufficiently small. After that, geometry approach is used to design the control of the other. We show that the positioning and the orientation of each agent can be controlled dependent on the leader. The simulation results show to illustrate of this method at the last section of this paper.]]>
<![CDATA[Optimasi Bobot K-Means Clustering untuk Mengatasi Missing Value dengan Menggunakan Algoritma Genetica ]]>
<![CDATA[Bain Khusnul Khotimah]]>;
<![CDATA[Muhammad Syarief]]>;
<![CDATA[Miswanto Miswanto]]>;
<![CDATA[Herry Suprajitno]]>
<![CDATA[ Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 4: Agustus 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer, Universitas Brawijaya ]]>
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DOI: 10.25126/jtiik.2021844912
<![CDATA[Nilai yang hilang membutuhkan preprosesing dengan teknik imputasi untuk menghasilkan data yang lengkap. Proses imputasi membutuhkan initial bobot yang sesuai, karena data yang dihasilkan adalah data pengganti. Pemilihan nilai bobot yang optimal dan kesesuaian nilai K pada metode K-Means Imputation (KMI) merupakan masalah besar, sehingga menimbulkan error semakin meningkat. Model gabungan algoritma genetika (GA) dan KMI atau yang dikenal GAKMI digunakan untuk menentukan bobot optimal pada setiap cluster data yang mengandung nilai yang hilang. Algoritma genetika digunakan untuk memilih bobot dengan menggunakan pengkodean bilangan riel pada kromosom. Model hybrid GA dan KMI dengan pengelompokan menggunakan jumlah jarak Euclidian setiap titik data dari pusat clusternya. Pengukuran kinerja algoritma menggunakan fungsi kebugaran optimal dengan nilai MSE terkecil. Hasil percobaan data hepatitis menunjukkan bahwa GA efisien dalam menemukan nilai bobot awal optimal dari ruang pencarian yang besar. Hasil perhitungan menggunakan nilai MSE =0.044 pada K=3 dan replika ke-5 menunjukkan kinerja GAKMI menghasilkan tingkat kesalahan yang rendah untuk data hepatitis dengan atribut campuran. Hasil penelitian dengan menggunakan pengujian tingkat imputasi menunjukkan algoritma GAKMI menghasilkan nilai r = 0.526 lebih tinggi dibandingkan dengan metode lainnya. Penelitian ini menunjukkan GAKMI menghasilkan nilai r yang lebih tinggi dibandingkan metode imputasi lainnya sehingga dianggap paling baik dibandingkan teknik imputasi secara umum. AbstractMissing values require preprocessing techniques as imputation to produce complete data. Complete data imputation results require the appropriate initial weights, because the resulting data is replacement data. The choice of the optimal weighting value and the suitability of the network nodes in the K-Means Imputation (KMI) method are big problems, causing increasing errors. The combined model of Genetic Algorithm (GA) and KMI is used to determine the optimal weights for each data cluster containing missing values. Genetic algorithm is used to select weights by using real number coding on chromosomes. GA is applied to the KMI using clustering calculated using the sum of the Euclidean distances of each data point from the center of the cluster. Performance measurement algorithms using the fitness function optimally with the smallest MSE value. The results of the hepatitis data experiment show that GA is efficient in finding the optimal initial weight value from a large search space. The results of calculations using the MSE value = 0.04 for K = 3 and the 5th replication. So, GAKMI resulted in a low error rate for mixed data. The results of research using imputation level testing performed GAKMI produced r = 0.526 higher than the other methods. Thus, the higher the r value, the best for the imputation technique.]]>
<![CDATA[Analisis Kestabilan Model Matematika Penyebaran Penyakit Schistosomiasis dengan Saturated Incidence Rate ]]>
<![CDATA[Elda Widya]]>;
<![CDATA[Miswanto Miswanto]]>;
<![CDATA[Cicik Alfiniyah]]>
<![CDATA[ Contemporary Mathematics and Applications (ConMathA) Vol. 2 No. 2 (2020) ]]>
Publisher : <![CDATA[Universitas Airlangga ]]>
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DOI: 10.20473/conmatha.v2i2.23851
<![CDATA[Schistosomiasis is a disease caused by infections of the genus Schistosoma. Schistosomiasis can be transmitted through schistosoma worms that contact human skin. Schistosomiasis is a disease that continues to increase in spread. Saturated incidence rates pay attention to the ability to infect a disease that is limited by an increase in the infected population. This thesis formulates and analyzes a mathematical model of the distribution of schistosomiasis with a saturated incidence rate. Based on the analysis of the model, two equilibrium points are obtained, namely non-endemic equilibrium points (E0) and endemic equilibrium points (E1). Both equilibrium points are conditional asymptotically stable. The nonendemic equilibrium point will be asymptotically stable if rh > dh, rs > ds and R0 < 1, while the endemic equilibrium point will be asymptotically stable if R0 > 1. Sensitivity analysis shows that there are parameters that affect the spread of the disease. Based on numerical simulation results show that when R0 < 1, the number of infected human populations (Hi), the number of infected snail populations (Si), the amount of cercaria density (C) and the amount of miracidia density (M) will tend to decrease until finally extinct. Otherwise at the time R0 > 1, the number of the four populations tends to increase before finally being in a constant state.]]>
<![CDATA[Analisis Kestabilan Model Matematika Predator-Prey pada Dinamika Sosial ]]>
<![CDATA[Laurensia Regina Bestari Gepak]]>;
<![CDATA[Miswanto Miswanto]]>;
<![CDATA[Cicik Alfiniyah]]>
<![CDATA[ Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 1 (2022) ]]>
Publisher : <![CDATA[Universitas Airlangga ]]>
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DOI: 10.20473/conmatha.v4i1.34147
<![CDATA[In social life, difference and diversity is something that cannot be denied by anyone. Starting from differences horizontally concerning ethnicity, language, customs to religion and vertically concerning the political, social, cultural to economic fields. The existence of these many differences can certainly bring positive and negative impacts in social life. With diversity, interaction in society is dynamic, but it results in the emergence of negative attitudes such as egoism and competition between groups. From the occurrence of this can trigger the problem of social inequality in the community. Social inequality can occur because of national development efforts that only focus on economic aspects and forget about social aspects. The purpose of this thesis is to discuss the stability analysis of the predator-prey mathematical model on social dynamics with the Holling type II functional response. From this model analysis, we obtained four equilibrium points, which are the equilibrium point for the extinction of all population (E0) which is unstable, then the equilibrium point for the extinction of the non-poor population and the poor (E1) and the extinction of the non-poor population (E2) which are stable with certain conditions and coexistence (E3) which is to be asymptotically stable. Also in the final section, we perform the numerical simulation to supports the analytical result.]]>
<![CDATA[Model Matematika Persaingan Dua Spesies dengan Toksisitas dan Pemanenan Selektif ]]>
<![CDATA[Puja Nur Audria]]>;
<![CDATA[Miswanto Miswanto]]>;
<![CDATA[Fatmawati Fatmawati]]>
<![CDATA[ Limits: Journal of Mathematics and Its Applications Vol 16, No 2 (2019) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/limits.v16i2.5255
<![CDATA[Persaingan merupakan interaksi biologi antar makhluk hidup untuk bersaing mendapatkan sumber energi yang terbatas, misalnya makanan yang dibutuhkan untuk tumbuh dan bertahan hidup. Beberapa spesies mempunyai strategi tersendiri dalam bersaing, diantaranya adalah kemampuan mengeluarkan racun. Pada jurnal ini, dikaji dua model predator-prey yang dipengaruhi oleh adanya toksisitas dan pemanenan selektif. Model pertama mengkaji model persaingan dua spesies dengan adanya toksisitas dan pemanenan selektif, sedangkan model kedua mengkaji model persaingan dua spesies dengan adanya toksisitas dan pemanenan selektif dengan Holling tipe III. Dari model pertama diperoleh 4 titik setimbang yaitu dan Dari model kedua juga diperoleh 4 titik setimbang, yaitu dan . Titik setimbang dan tidak stabil, sedangkan , dan stabil dalam kondisi tertentu. Hasil simulasi numerik menunjukkan bahwa kedua spesies pada model kedua mengalami peningkatan dibandingkan dengan model pertama. Hal tersebut dikarenakan adanya kecenderungan untuk mencari musuh yang lain ketika jumlah musuh mulai berkurang]]>
<![CDATA[Analisis Model Matematika Orde Fraksional Penyebaran Worm Berbasis Wi-Fi Pada Smartphone ]]>
<![CDATA[Mohammad Imam Utoyo]]>;
<![CDATA[Er Ayu Nurafifah]]>;
<![CDATA[Miswanto Miswanto]]>
<![CDATA[ Limits: Journal of Mathematics and Its Applications Vol 15, No 2 (2018) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/limits.v15i2.4304
<![CDATA[Worm merupakan suatu program atau software (perangkat lunak) yang memiliki kemampuan mereplikasi diri dan dapat menyebabkan kerusakan pada jaringan komputer. Pada umumnya worm menginfeksi jaringan komputer, namun seiring dengan perkembangan teknologi menyebabkan munculnya worm jenis baru yaitu worm berbasis Wi-Fi (Wireless Fidelity) yang dapat menginfeksi smartphone. Salah satu upaya penanggulangan worm adalah dengan menambahkan sebuah node baru pada jaringan Wi-Fi yaitu node karantina untuk meminimalisir penyebaran worm pada smartphone. Model matematika penyebaran worm berbasis Wi-Fi pada smartphone dapat digunakan untuk mengetahui dinamika penyebaran worm. Melalui dinamika penyebaran worm, dapat dipelajari faktor penghambat infeksi worm. Pada penelitian ini dilakukan analisis kestabilan titik setimbang model matematika orde fraksional penyebaran worm berbasis Wi-Fi pada smartphone dengan orde turunan fraksional α∈(0,1]. Berdasarkan analisis model, diperoleh dua titik setimbang yaitu titik setimbang bebas worm〖 P〗_0 dan titik setimbang endemik 〖 P〗_1. Titik setimbang bebas worm stabil asimtotis lokal jika basic reproduction number R_0<1, sedangkan titik setimbang endemik stabil asimtotis lokal jika basic reproduction number R_0>1. Kemudian dilakukan analisis sensitivitas dan simulasi numerik dengan variasi nilai orde fraksional α untuk mengetahui dinamika penyebaran worm berbasis Wi-Fi pada smartphone. Berdasarkan hasil simulasi numerik diperoleh hasil bahwa penambahan node karantina pada jaringan Wi-Fi dapat menurunkan populasi node terinfeksi dan meningkatkan populasi node yang pulih.]]>
<![CDATA[Analisis kestabilan dan kontrol optimal model matematika penyebaran penyakit Ebola dengan variabel kontrol berupa karantina ]]>
<![CDATA[Erzalina Ayu Satya Megananda]]>;
<![CDATA[Cicik Alfiniyah]]>;
<![CDATA[Miswanto Miswanto]]>
<![CDATA[ Jambura Journal of Biomathematics (JJBM) Volume 2, Issue 1: June 2021 ]]>
Publisher : <![CDATA[Department of Mathematics, State University of Gorontalo ]]>
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DOI: 10.34312/jjbm.v2i1.10258
<![CDATA[Ebola disease is an infectious disease caused by a virus from the genus Ebolavirus and the family Filoviridae. Ebola disease is one of the most deadly diseases for human. The purpose of the thesis is to analyze the stability of the equilibrium point and to apply the optimal control of quarantine on a mathematical model of the spread of ebola. Model without control has two equilibria, non-endemic equilibrium and endemic equilibrium. The existence of endemic equilibrium and local stability depends on the basic reproduction number (R0). The non-endemic equilibrium is asymptotically stable if R0 1 and endemic equilibrium tend to asymptotically stable if R0 1. The problem of optimal control is solved by Pontryagin’s Maximum Principle. From the numerical simulation, the result shows that control is effective enough to minimize the number of infected human population and to minimize the cost of its control.]]>
<![CDATA[Analisis Kestabilan dan Kontrol Optimum pada Model Penyebaran Penyakit Influenza dengan Adanya Populasi Cross-Immune ]]>
<![CDATA[Bertha Aurellia Pamudya Fajar]]>;
<![CDATA[Miswanto]]>;
<![CDATA[Windarto]]>
<![CDATA[ Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022) ]]>
Publisher : <![CDATA[Universitas Airlangga ]]>
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DOI: 10.20473/conmatha.v4i2.39168
<![CDATA[Influenza is a respiratory tract infection known as flu. Caused by an RNA virus from Orthomyxoviridae family. This thesis aims to analyze the stability of the equilibrium point in the mathematical model of influenza transmission with Cross-Immune population and applying optimal control variables in the form of prevention and treatment. In this mathematical model of influenza transmission with Cross-Immune population, we obtain two equilibriums namely, the non- endemic equilibrium and the endemic equilibrium. Local stability and the existence of endemic equilibrium depend on the basic reproduction number (R0). The spread of influenza does not occur in the population when R0 < 1 and the spread of influenza persist in the population when R0 > 1. Furthermore, the problem of control variables in the mathematical model of influenza transmission is determined through the Pontryagin Maximum Principle method. The numerical simulation results show that treatment efforts are more effective in suppressing the spread of influenza disease than prevention efforts. However, giving control variables in the form of prevention and treatment at the same time is very effective in minimizing the number of human populations expose to and infected with influenza.]]>
<![CDATA[Pembuatan Sistem Informasi Desa (SID) untuk Menunjang Pelayanan di Desa Klangon, Madiun: Village Information System Set Up in Klangon Village, Madiun, to Support Services ]]>
<![CDATA[Nashrul Millah]]>;
<![CDATA[Miswanto Miswanto]]>;
<![CDATA[Cicik Alfiniyah]]>
<![CDATA[ PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 8 No. 1 (2023): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat ]]>
Publisher : <![CDATA[Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya ]]>
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DOI: 10.33084/pengabdianmu.v8i1.4160
<![CDATA[The quality of village services provided to the community can be raised with the implementation of an effective information system. Klangon Village, Saradan District, Madiun Regency, is the program's partner. Due to a lack of information system management skills, the Village Information System has not been implemented in an ideal manner.An alternative solution offered is to provide training and assistance to local government staff in Klangon village to develop and manage Information Systems. The process starts with a partner survey to identify the underlying causes of the issue at hand, followed by training, mentorship, and program evaluation. The outcomes of this civic engagement project include the creation of a website serving as the village's information system and an improvement in the village of Klangon's capacity to maintain it.]]>
<![CDATA[Analisis Kestabilan dan Kontrol Optimal Model Matematika Penyebaran Leptospirosis dengan Saturated Incidence Rate ]]>
<![CDATA[Miswanto]]>;
<![CDATA[Nisrina Firsta Ammara]]>;
<![CDATA[Windarto]]>
<![CDATA[ Contemporary Mathematics and Applications (ConMathA) Vol. 5 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Airlangga ]]>
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DOI: 10.20473/conmatha.v5i2.49379
<![CDATA[Leptospirosis is a disease caused by the bacteria Leptospira inchterohemorrhagiaea. Leptospirosis can attack humans and other animals, through rodents, especially rats. This research aims to analyze the stability of the equilibrium point in the mathematical model of the spread of Leptospirosis and apply optimal control variables in the form of prevention and treatment efforts. Based on the results of the mathematical model analysis of the spread of Leptospirosis, two equilibrium points were obtained, there are the non-endemic equilibrium point and the endemic equilibrium point. Local stability and the existence of an equilibrium point depend on the basic reproduction number ????0. The non-endemic equilibrium point is local asymptotically stable if ????0 < 1, while the endemic equilibrium point tends to be asymptotically stable if ????0 > 1. Next, the problem of control variables in the model is determined using Pontryagin's Maximum Principle. Numerical simulation results show that providing control in the form of prevention efforts and treatment efforts simultaneously provides effective results in minimizing the population of individuals exposed to and infected by Leptospirosis at the cost of providing optimal control.]]>
