Indonesian Journal of Mathematics and Natural Sciences最新文献

{"title":"Efek Pemberian Pakan dengan Tambahan Overripe Tempe terhadap Jumlah Escherichia coli dan Bakteri Asam Laktat (BAL) pada Ayam Petelur","authors":"Khoirun Najah, S. H. Bintari","doi":"10.15294/ijmns.v44i1.32698","DOIUrl":"https://doi.org/10.15294/ijmns.v44i1.32698","url":null,"abstract":"Overripe tempe memiliki kandungan nutrisi dan komponen bioaktif yang dihasilkan bakteri asam laktat dan kapang Rhizopus oligosporus. Bakteri asam laktat (BAL) dapat menghasilkan beberapa senyawa metabolit, seperti hidrogen peroksida, diasetil, asam laktat dan bakteriosin. Penelitian ini bertujuan untuk mengkaji efek pemberian pakan dengan tambahan overripe tempe terhadap jumlah total Escherichia coli dan BAL pada ayam petelur. Penelitian ini menggunakan rancangan acak lengkap (RAL) satu faktor dengan 4 kelompok perlakuan masing-masing 7 kali pengulangan. Penelitian ini menggunakan 28 ekor ayam petelur umur 94 minggu. Pemberian perlakuan pakan dengan tambahan overripe tempe terdiri atas P0 (pakan tanpa overripe tempe), P1 (overripe tempe 7,5%), P2 (overripe tempe 15%) dan P3 (overripe tempe 22,5%). Parameter yang diamati adalah penurunan E. coli dan peningkatan BAL pada hari ke-20 dan ke-40. Data hasil penelitian dinalisis dengan Anova dan dilanjut dengan uji Tuckey dengan signifikasi 5%. Hasil penelitian pada hari ke-20 menunjukkan pemberian overripe tempe berpengaruh terhadap penurunan E. coli dan peningkatan BAL. Pada hari ke-40, pemberian overripe tempe berpengaruh terhadap penurunan E. coli namun tidak berpengaruh terhadap peningkatan BAL. Simpulan dari penelitian ini yaitu pemberian overripe tempe yang optimal pada feses ayam petelur fase layer adalah 7,5%.Overripe tempe contains nutrients and bioactive components obtained from lactic acid bacteria and Rhizopus oligosporus mold. Lactic acid bacteria (LAB) can produce several metabolites, such as hydrogen peroxide, diacetyl, lactic acid and bacteriocins. This study aims to examine the effect of feeding with the addition of overripe tempe on the total amount of Escherichia coli and lactic acid bacteria in laying hens. This study used a one-factor completely randomized design (CRD) with 4 treatment groups with 7 repetitions. This study used laying hens that were 94 weeks old and totaled 28 chickens. Provision of feed treatment with additional overripe tempe consisted of P0 (feed without overripe tempe), P1 (giving overripe tempe 7,5%), P2 (giving overripe tempe 15%) and P3 (giving overripe tempe 22,5%). The parameters observed were a decrease in Escherichia coli and an increase in lactic acid bacteria on the 20th and 40th days. The research data were analyzed using the Anova test at 5% significance, if the results had a significant effect, then the Tuckey test was followed with a significance of 5%. The results of the study on the 20th day showed that giving overripe tempe influenced on decreasing E. coli bacteria and influenced increasing LAB. The results of the study on the 40th day showed that giving overripe tempe influenced on reducing E. coli bacteria but did not affect the increase in LAB. The conclusion from this research is that the optimal application of overripe tempe in layer phase layer chicken feces is 7,5%.","PeriodicalId":412942,"journal":{"name":"Indonesian Journal of Mathematics and Natural Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131749616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ketepatan Klasifikasi Metode Regresi Logistik dan Metode Chaid dengan Pembobotan Sampel","authors":"Puspa Juwita, S. Sugiman, P. Hendikawati","doi":"10.15294/ijmns.v44i1.32699","DOIUrl":"https://doi.org/10.15294/ijmns.v44i1.32699","url":null,"abstract":"Tujuan penelitian ini adalah menentukan ketepatan metode regresi logistik dan CHAID dengan pembobotan sampel pada klasifikasi status angkatan kerja Kabupaten Temanggung 2015. Populasi dalam penelitian ini adalah angkatan kerja Kabupaten Temanggung 2015. Data dalam penelitian ini diperoleh dari Sakernas Kabupaten Temanggung 2015. Variabel dependen dalam penelitian ini adalah angkatan kerja, sedangkan variabel independennya adalah klasifikasi desa/kelurahan, hubungan dengan kepala rumah tangga, jenis kelamin, umur, status pernikahan, pendidikan, pelatihan kerja, dan pengalaman kerja. Dari analisis regresi logistik diperoleh persamaan, sedangkan anlalisi CHAID menghasilkan pohon klasifikasi. Persamaan dan pohon klasifikasi tersebut dapat digunakan untuk memprediksi variabel dependen. Kesalahan klasifikasi dihitung menggunakan APER (Apparent Error Rate), kemudian ketepatan klasifikasi dapat diperoleh dengan rumus 1 – APER. Ketepatan regresi logistik dan CHAID dengan pembobotan sampel secara berturut-turut adalah 96,4% dan 96,6%. Hal ini menunjukkan ketepatan metode CHAID pada klasifikasi status angkatan kerja Kabupaten Temanggung 2015 lebih tinggi dibandingkan regresi logistik.The purpose of this study is to determine the accuracy of logistic regression and CHAID with sample weighting on Temanggung regency labor status classification in 2015. The population of this study is labor of Temanggung Regency in 2015. The data of this study is obtained from Sakernas of Temanggung Regency in 2015. The dependent variable of this study is labor status, whereas the independent variables of this study are domicile region, relation with family head, gender, age, marriage status, education level, job training, and job experience. Logistic regression analysis results a mathematic equation, and CHAID method result a classification tree. Those result can predict the dependent variable. Classification error is calculated using APER (Apparent Error Rate), then the accuracy can be calculated by 1- APER. Accuracy of logistic regression and CHAID with sample weighting respectively are 96,4% and 96,6%. This show that accuracy of CHAID is greater than logistic regression.","PeriodicalId":412942,"journal":{"name":"Indonesian Journal of Mathematics and Natural Sciences","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125041037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Studi Komputasi Aktivitas Senyawa Turunan Santon Sebagai Antikanker Leukemia Myeloid Kronik K562","authors":"Lestari Agustina, K. Kasmui","doi":"10.15294/ijmns.v44i1.32702","DOIUrl":"https://doi.org/10.15294/ijmns.v44i1.32702","url":null,"abstract":"Analisis hubungan kuantitatif struktur aktivitas (HKSA) terhadap 10 senyawa turunan santon telah dilakukan berdasarkan metode regresi multilinier. Jenis deskriptor yang digunakan adalah deskriptor sterik, deskriptor hidrofobik, dan deskriptor elektronik. Terhadap setiap senyawa dilakukan optimasi geometri dengan metode DFT B3LYP bassis set 6-31G*, kemudian dihitung nilai deskriptornya menggunakan software MarvinBeans dan NWChem. Data aktivitas antikanker IC50 diperoleh dari literatur dan dinyatakan sebagai Log 1/IC50. Data perhitungan deskriptor diolah menggunakan IBM SPSS 21, diperoleh persamaan KHSA sebagai berikut: Log 1/IC50 = 2,759+0,001 Indeks Wiener-8,306 Gap HOMO-LUMO + 0,202Log P + 0,017 PSA + 35,995 LUMO-0,437 Indeks Balaban -0,021 Indeks Harary +0,001Indeks Szeged, dengan n= 10; R=1,00 ; R2=1,00 ; SE= 0; PRESS= 0,00. Dari persamaan HKSA didapatkan prediksi senyawa yang berpotensi sebagai antikanker, yaitu senyawa 6-etoksi-3,4,5,8-tetrahidroksi-2,7-dimetoksi-santon dengan nilai Log 1/IC¬50 4,24588.Quantitative structure activity relationship (QSAR) analysis of 10 xanthone derivatives was carried out based on the multilinier regression method. The types of descriptors used are steric descriptors, hydrophobic descriptors, and electronic descriptors. Geometry optimization is done with each DFT B3LYP bassist set 6-31G * method, then the descriptor values are calculated using MarvinBeans and NWChem software. IC50 anticancer activity data were obtained from literature and stated as Log 1 / IC50. Descriptor calculation data is processed using IBM SPSS 21. Obtained by the QSAR equation as follows: Log 1/IC50 = 2,759+0,001 Indeks Wiener-8,306 Gap HOMO-LUMO + 0,202Log P + 0,017 PSA + 35,995 LUMO-0,437 Indeks Balaban -0,021 Indeks Harary +0,001Indeks Szeged, with n= 10; R=1,00 ; R2=1,00 ; SE= 0; PRESS= 0,00. From the QSAR equation, prediction of potential compounds as anticancer is 6-etoxy-3,4,5,8-tetrahidroxy-2,7-dimetoxy-xhanthone dengan with Log 1/IC50 values 4.245881.","PeriodicalId":412942,"journal":{"name":"Indonesian Journal of Mathematics and Natural Sciences","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133903968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potensi Antidiabetik Ekstrak Kulit Lidah Buaya Pada Tikus Hiperglikemik yang Diinduksi Aloksan","authors":"R. Susanti, Amalia Nor Rohmah, A. Yuniastuti","doi":"10.15294/ijmns.v44i1.32700","DOIUrl":"https://doi.org/10.15294/ijmns.v44i1.32700","url":null,"abstract":"Hiperglikemia menyebabkan tingginya radikal bebas sehingga terjadi stres oksidatif. Pada kondisi tersebut, perlu antioksidan eksogen. Penelitian ini bertujuan untuk menganalisis pemberian ekstrak kulit lidah buaya terhadap kadar malondialdehid (MDA) dan kadar superoksid dismutase (SOD) tikus hiperglikemia yang induksi aloksan. Sebanyak 25 ekor tikus strain Wistar jantan diambil secara acak dan dibagi menjadi 5 kelompok. Kelompok pertama sebagai kelompok kontrol negatif (K-). Kelompok kedua adalah kontrol positif (K+), hanya diberi aloksan. Kelompok ketiga (KP I), keempat (KP II) dan kelima (KP III), diberi aloksan dan ekstrak kulit lidah buaya berturut-turut dosis 87,5 mg/kgBB, 175 mg/kgBB) dan 350 mg/kgBB. Aloksan sebagai inducer hiperglikemia diberikan secara Intra Peritoneal dosis 120 mg/kgBB. Setelah 4-7 hari, diberi ekstrak kulit lidah buaya secara oral selama 28 hari. Data MDA dan SOD masing-masing dianalisis secara statistik dengan uji Anova, dan dilanjutkan uji Turkey. Hasil penelitian menunjukkan perbedaan signifikan kadar MDA dan SOD antara kelompok kontrol positif dan kelompok perlakuan. Potensi ekstrak kulit lidah buaya sebagai antidiabetik, ditunjukkan dengan menurunnya kadar MDA dan meningkatnya kadar SOD tikus hiperglikemik. Dosis ekstrak kulit lidah buaya yang paling efektif adalah 350 mg/kgBB (KP III), sehingga kadar MDA dan SOD tidak berbeda nyata dengan kelompok kontrol negatifHyperglycemia causes oxidative stress by free radicals. Exogenous antioxidants are needed to offset the impact. This research would observe Aloe vera peel extract to malondialdehyde (MDA) content and superoxide dismutase (SOD) level of diabetic rat. A total of 25 male Wistar rats were taken randomly and divided into 5 groups. The first group as a negative control group (K-). The second group is positive control (K +), given alloxan only. The third (KP I), fourth (KP II) and fifth (KP III) group were given alloxan and aloe vera pell extract at a dose of 87.5mg/kgBW, 175mg/kgBW and 350mg/kgBW, respectively. Alloxan as an inducer of hyperglycemia, was given intra-peritoneally at a dose of 120mg/kgBW. After 4-7 days, the aloe vera peel extract was given orally for 28 days. MDA and SOD data were statistically analyzed with the Anova test, followed by the Turkey test. The results showed a significant difference in MDA and SOD levels between the positive control group and the treatment group. The potential of aloe vera peel extract as an antidiabetic was shown by decreasing MDA levels and increasing levels of SOD in hyperglycemic rats. The most effective dose of aloe vera peel extract was 350mg/kgBW, it was able to reduce MDA and increase SOD until it was not significantly different from the negative control group.","PeriodicalId":412942,"journal":{"name":"Indonesian Journal of Mathematics and Natural Sciences","volume":"456 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124550917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peramalan Volatilitas Risiko Berinvestasi Saham Menggunakan Metode GARCH–M dan ARIMAX–GARCH","authors":"Wella Cintya Pradewita","doi":"10.15294/ijmns.v44i1.32701","DOIUrl":"https://doi.org/10.15294/ijmns.v44i1.32701","url":null,"abstract":"Model GARCH–M merupakan pengembangan model GARCH yang dimasukkan variansi bersyarat ke dalam persamaan mean. Model ARIMAX–GARCH merupakan penggabungan model ARIMAX dan GARCH. Kedua model tersebut dapat digunakan untuk mengatasi masalah heteroskedastisitas pada data. Penelitian ini bertujuan menemukan model terbaik untuk peramalan volatilitas risiko berinvestasi saham. Penelitian ini menggunakan literature dengan tahapan perumusan masalah, pengumpulan data, pengolahan dan analisis data, serta penarikan kesimpulan. Dalam analisis dan pembahasan meliputi statistika deskriptif, uji stasioneritas, pembentukan dan menentukan model terbaik kedua model, pembandingan kedua model, dan peramalan volatilitas saham. Dari hasil penelitian ini diperoleh model terbaik untuk peramalan volatilitas saham yaitu GARCH (1,1) – M dengan nilai MAPE=118,0299 lebih kecil dibanding nilai MAPE pada model ARIMAX (2,1,2)– GARCH (1,1) =191,3115. Berdasarkan model terbaik tersebut diperoleh hasil peramalan volatilitas saham sebesar 0,07629 dan apabila dana yang dialokasikan oleh investor saham sebesar Rp 200.000.000, 00 maka nilai VaR yang diperoleh sebesar Rp 85.615.826,00.GARCH-M is an expansion of the GARCH model that entered conditional variance into the mean equation. ARIMAX - GARCH is combination of ARIMAX model and GARCH model. Both models can be used to solve the problem of heteroscedasticity on data. The purpose of this research was to find the best model for forecasting of the risk of investing in stocks. The method of this research was problem formulation, data collection, data processing and analysis, and conclusions. In the analysis and discussion include descriptive statistics, stationary test, estimate and determine the best models of both models, comparison of both models, and stock volatility forecasting. The results of this research obtained the best model for forecasting of stock volatility is GARCH (1,1) - M with MAPE value = 118.0299 smaller than MAPE value of ARIMAX (2,1,2) - GARCH (1,1) = 191, 3115. Based on the best model is obtained forecasting of stock volatility is 0.07629 and if the fund allocated by investors are Rp 200,000,000.00, so the value of VaR obtained Rp 85.615.826,00.","PeriodicalId":412942,"journal":{"name":"Indonesian Journal of Mathematics and Natural Sciences","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128893768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}