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<![CDATA[MODEL PENGENDALIAN ALAMI PENYAKIT EMBUN JELAGA OLEH JAMUR CAPNADIUM SP PADA TANAMAN CENGKEH MENGGUNAKAN KUMBANG HELM CYCLONEDA SPP SEBAGAI PREDATOR KUTU DAUN (COCCOUS VIRIDIS GREEN ]]>
<![CDATA[Sudirman, Sudirman]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Puspita, J W]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 16 No. 1 (2019) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2019.v16.i1.12758
<![CDATA[Soot dew disease is one of the clove plant diseases caused by fungi Capnadium sp. fungus Capnadium sp living on filth of aphids Coccous Viridis Green. The fungus is spread by vectors of black ants that exist on a clove vulnerable. To control the disease naturally, people utilize the helmet beetles Cycloneda spp as a pest predator of aphids Coccous Viridis Green. The mathematical models that represent the natural control of the disease was adapted from the SI model. The model provides 9 exiting critical points which describes the state of the system. The results of the stability analysis of the critical points using the method of Linearization and Routh-Hurwitz shows that there are 4 disease-free critical points such that the solution can be maintained in the neighbourhood of the critical points. All endemic critical points are unstable such that the solution will leave the critical points. Simulation at the endemic critical points indicates the existence of helmet beetles Cycloneda spp population that able to suppress the spread of this disease by preying aphids Coccus Viridis Green.Keywords : Dew Soot, Helmet Beetles, Aphids, Mathematical Models.]]>
<![CDATA[Model Matematika Penyebaran Penyakit Bakteri Pumbuluh Kayu Cengkeh (BPKC) ]]>
<![CDATA[Chijra]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Hajar]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 16 No. 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2019.v16.i2.14985
<![CDATA[ABSTRACTClove wood vessels is one of the most damaging diseases of clove plants. This disease is caused by the bacterial Ralstonia Syzygii. the bacterial Rasltonia Syzygii lives in clove wood vessels. The bacterial Ralstonia Syzygii ispread through the Hindola Spp vector. The matemathical model that represents the spread of the disease isdeveloped from the SEI model (Suspectible, Exposed, Infected). The model gives 4 critical points ð‘‡1, ð‘‡2, ð‘‡3 and ð‘‡4 exist interaction between bacterial population Ralstonia Syzygii and Hindola Spp vector is less than the level of vulnerable clove recruitman divided by carrying capacity of Ralstonia Syzygii bacterial multiplied by Hindola Spp carrying capacity. The results of system stability analysis at the critical point using linearization give unstable three critical points ð‘‡1, ð‘‡2, ð‘‡3which describes equilibrium conditions and a stable ð‘‡4 critical point which describes endemic conditions. Numerical simulations are carried out to describe temporary disease-free conditions, and stable endemic conditionsKeywords : Clove Wood vessel Disease, Linierization Method, SEI Model]]>
<![CDATA[Rancang Bangun Aplikasi Pengontrolan Kupon BBM Kendaraan Dinas Dan Pelaporan Konsumsinya Dengan Sms Gateway Berbasis Barcode ]]>
<![CDATA[Tadjuka, M A]]>;
<![CDATA[Jaya, A I]]>;
<![CDATA[Ratianingsih, R]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 16 No. 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2019.v16.i2.14988
<![CDATA[ABSTRACTThis study offers design of SMS Gateway application based a barcode- SMS Gateway for controlling theconsumption of fuel and reporting the consumption of official vehicles of Tadulako University, Department ofHygiene of Palu City and POLSEK of Easten Palu City. Barcode is an encoded information in form of thin and wide stripe with white line spacing in between. Controlling the fuel consumption is done by recapitulation the data that is send by SMS. It is contain information about the rest of quota for the current month. This application use open source softwares such us Gammu, MySql, Xampp, and PHP. The resulting application only transmit direct current month quota to the institution of Tadulako University, Department of Hygiene of Palu City and POLSEK of Easten Palu City.Keywords : Barcode, Gammu, MySql, PHP, SMS Gateway, Xampp.]]>
<![CDATA[Analisis Kestabilan Penyebaran Penyakit Antraks Pada Populasi Hewan Dengan Pemberian Vaksinasi: Studi Kasus Untuk Infeksi Pada Populasi Manusia ]]>
<![CDATA[Megawati]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Hajar]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 16 No. 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2019.v16.i2.14989
<![CDATA[ABSTRACTAnthrax is an infectious disease that caused by the Bacillus anthracis bacteria. The disease attacks animals such as cows in acute and preacute stage. Anthrax is a zoonotic disease that can be transmitted to humans through three types of media that are skin, digestive and respiratory tracts. To overcome the high death risk, treatment and vaccination of the period 6 – 12 months are conducted. The aims of this study is developing a mathematical model of anthrax spread in animal populations with vaccination treatment. The model is also consider human populations, such that the SIRSV model (susceptible, Infected, Recovered, susceptible and Vaccine) is used for animal population and SI model (susceptible, Infected) is used for human population. The stability of model is analyzed at the critical points by linearization method. The free-disease unstable critical point and the stable endemic critical point are derived. The simulation shous that the number of infected animal and infected human population is not significantly different and indicates that the vaccination treatment could overcome the spread of anthrax succesfully.Keywords : Anthrax, Critical Point Endemic, Critical Point Non Disease, linearization method, Mathematical Models]]>
<![CDATA[Kendali Optimal Model Prognosis Sindrom Metabolik Dengan Faktor Resiko Obesitas Dan Diabetes Melitus Tipe II Menggunakan Minimum Pontryagin ]]>
<![CDATA[Nurannisa]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Puspita, J W]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 16 No. 2 (2019) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2019.v16.i2.14990
<![CDATA[ABSTRACTMetabolic syndrome (SM) is a compound of risk factors of cordiovascular disease occurancy. Obesity and type IIdiabetes mellitus are the main two of the risk factors. The epidemiological data shous that the prevalence ofmetabolic syndrome in the world is 20-25%. The objective of these research is control to minimize the prognosisof the disease among the SM population that have obesity and type II DM risk factors. The pontryagin minimumprinciple is used to determine the optimal solution of the prognosis model that the optimal control. The solution is derived from the state and co-state state equations that are evaluated of the drug that give to the sufferer in stationary conditions. The performance Index was designed to minimize the number of SM population that suffer obesity and type II diabetes mellitus and the use of sulfonilurea that given as the normoweighted populations and biguanid for obese populations. The simulation of the optional solution shows that the optimal control was derived to control the number SM that have population of the optional solution obesity and type II DM risk with optimal biguanide 500 mg and sulfonilurea 5 mg as much.Keywords : Metabolic Syndrome, Minimum Pontryagin, Obesity, Stability ,Type II Diabetes Mellitus.]]>
<![CDATA[Model Matematika Pengendalian Penyebaran Penyakit Schistosomiasis Menggunakan Itik Sebagai Musuh Alami Bagi Keong Perantara Schistosomiasis ]]>
<![CDATA[Karini, I]]>;
<![CDATA[Ratianingsih, R]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 17 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2020.v17.i1.15163
<![CDATA[In Indonesia Schistosomiasis is only found in Central Sulawesi Province, in the highlands of Lindu, the Napu plateau and the Bada plateau, Poso Regency. The disease is caused by the Schistosoma japonicum worm which requires an intermediary host, namely the Oncomelania hupensis lindoensis snail, which is an endemic animal in the area. This study examined mathematically the control of the spread of Schistosomiasis by using ducks as natural enemies for intermediate snails. The human population is divided into vulnerable human subpopulations and a subpopulation of infected humans. Interactions between snail populations and duck populations are expressed as interactions between Predator and Prey. The Schistosoma japonicum worm population is seen as a population growth cycle model. The stability of the model is analyzed using the Jacobi matrix, which is evaluated at a critical point. The model has two critical points ð‘‡1 and ð‘‡2 which represent a disease-free conditions, while ð‘‡3 represents endemic point. Mathematical model simulations controlling the spread of Schistosomiasis. The simulation is using ducks with early populations indicate that disease control by using ducks is less effective because it takes a very long time to be estimated at 55 years. Keywords : Conch Oncomelania Hupensis Lindoensis, Duck, Schistosomiasis, Schistosoma Japonicum Worm.]]>
<![CDATA[Mengkaji Perputaran Uang Bank Melalui Model Kaldor-kalecki: Tinjauan Numerik Untuk Sistem Kartu Kredit ]]>
<![CDATA[Sehani, A]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Puspita, J W]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 17 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2020.v17.i1.15166
<![CDATA[Credit Card is a card payment instrument, where the cardholder's payment obligation is firstly fulfilled by the acquirer or publisher which use third party funds in the form of investments to pay the obligation payment of cardholder. These investment funds are managed as the initial fund of credit card customers. Some of the generated profits could be saved as bank deposits, while others are used for joint capital investment funds. The preview description shows the circulation of bank deposit of the credit card system which mathematically corresponds to the concept represented by Kaldor-Kalecki model. The aims of this study is that money circulation process is represented numerically by such model solution using the Runge-Kutta method. The interpretation of the numerical solution of the Kaldor-Kalecki model of the credit card system is simulated for Bank Mega's financial report data in 2017, the results shows that Bank Mega was found a decline of the number of credit card production. It could be said that the numerical solutions well represented the condition of the credit card system issued by Bank Mega. Negative values of numerical solution also reviews as period of the Bank investment]]>
<![CDATA[Model Matematika Kendali Optimal Intensitas Cahaya Dan Nutrisi Pada Pertumbuhan Mikroalga Dengan Menggunakan Metode Pontryagin ]]>
<![CDATA[Azim]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Nacong, N]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 17 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2020.v17.i1.15173
<![CDATA[Microalgae are the most primitive plant-sized cellular organisms commonly known as phytoplankton. The habitat of its life is in waters or humid places. This organism is a primary producer of water that has any capability to photosynthesis like any other high-level plants. This study examines mathematically the optimal control of light intensity and liquid waste nutrition in microalgae growth. Growth liter is done by setting the intensity of light in the process of glucose formation and nutrition tofu liquid waste, tapioca, industry, and households as the additional nutrients of microalgae. The Pontryagin maximun principles is used to determine the optimal control solution. The solution is solved from the state and co-state equation that stationery evaluated using the indexed performance maks ð½[ð‘¢1 + ð‘¢2 ] = ∫ ðº(ð‘¡) − ð‘¡ð‘“ ð‘¡0 ð‘†(ð‘¡) − 1 2 ð‘¢1 (ð‘¡) 2 − 1 2 ð‘¢2 (ð‘¡) 2ð‘‘ð‘¡ with the stationer condition that gives the optimal control ð‘¢1 ∗ = ð›¾2ð›¼2ð‘„ðµ and ð‘¢2 ∗ = −ð›¾5ðœŒ1ð‘†. The results shows that before the optimal control of light intensity and nutrition of liquid waste is applied, the concentration of microalgae biomass becomes 5.915 g / liter on the 20th day stayed at the 105th day. The lipid quota with an initial value of 0.6 g/liter will decrease to 0.2 g / liter at 4th day which is the equilibrium point. Optimal control of the regulation of light intensity of 2-9 klux and liquid waste nutrition provided a significant increase in the amount of microalgae biomass and lipid quota, with the regulation of light intensity of 2- 9 klux and tofu liquid waste nutrition which gave the largest increase in the amount of microalgae biomass and lipid quota]]>
<![CDATA[Kestabilan Model Matematika Infeksi Primer Penyakit Varicella Dan Infeksi Rekuren Penyakit Herpes Zoster Oleh Virus Varicella Zoster ]]>
<![CDATA[Hardiyanti]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Hajar]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 17 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2020.v17.i1.15180
<![CDATA[Varicella and herpes zoster are two infectious skin diseases of human that caused by varicella zoster virus, where varicella disease is a primary infection that often infected younger people while herpes zoster disease is a recurrent disease that often infected older people because of reactivation of latent varicella-zoster virus. If the pain caused by herpes zoster after recurrent phase is a appeared then the condition is known as postherpetic neuralgia. This study builds a mathematical model of primary infection (varicella disease) and recurrent infection (herpes zoster disease) developed from the SIR model (Susceptible, Infected, Recovered). The human population is divided into seven subpopulations, namely susceptible, infection, recovered of varicella, herpes zoster and postherpetic neuralgia subpopulation. Stability analysis at the critical point by linearization method gives a critical point ð‘‡1 that guaranted to exist and unstable if 𛼠ðœ‡(ð›½1+ðœ‡) ð´ , while the critical point ð‘‡1 does not have any reqruitment. Stability analysis at the endemic disease-free critical point is represented ð‘‡1 that will be unstable if ð‘‡2 exist and stable ð‘‡1 if ð‘‡2 exist. Numerical simulations by simulated to describe such temporary disease-free conditions and an endemic stable conditions.]]>
<![CDATA[Sistem Pendukung Keputusan Untuk Mendeteksi Penyakit Diabetes Melitus Tipe 2 Menggunakan Metode Learning Vector Quantization (LVQ) ]]>
<![CDATA[Aliyanti, N]]>;
<![CDATA[Ratianingsih, R]]>;
<![CDATA[Puspita, J W]]>
<![CDATA[ JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 17 No. 2 (2020) ]]>
Publisher : <![CDATA[Program Studi Matematika, Universitas Tadulako ]]>
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DOI: 10.22487/2540766X.2020.v17.i2.15336
<![CDATA[ABSTRACT Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body can not effectively use the insulin that is produced. Diabetes mellitus can be divided into two types: Type 1 diabetes mellitus and diabetes mellitus type 2. This study aims to detect diabetes mellitus and may predict the development status (Metabolic Syndrome) using Learning Vector Quantization. The data needed to detect type 2 diabetes are blood sugar levels, genetics, age, physical activity, diet, smoking habits, body mass index, gender and abdominal circumference. In addition, the data also used HbA1C and cholesterol levels to detect the status of the development of type 2 diabetes mellitus (Metabolic Syndrome). The classification process is divided into two stages: stage 1 to determine the type 2 diabetes or Non diabetes mellitus, and phase 2 to predict the prognosis of type 2 diabetes into Metabolic Syndrome or Non Metabolic Syndrome (the patient is still in the category of type 2 diabetes) performed on 200 data respectively divided into 80 training data and 120 testing data. Best detection results at stage 1 that is equal to 96.67% can be obtained using learning rate (α) of 0.7, and the rate of decrement (decα) of 0.75.While the best detection results at stage 2 average accuracy rate of 92.5% using a variety of learning rate (α) and the rate of decrement (decα). Error detection in stage 2 occurs only in the Metabolic Syndrome data detected as Type 2 diabetes mellitus. Keywords : Accuracy, Diabetes Mellitus, Learning Vector Quantization]]>
