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Journal : international journal of computing science and applied mathematics-ijcsam

Zika Virus Mutation and The Spreading to Indonesia Mohammad Syaiful Pradana; Siti Amiroch
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 4 No. 1 (2018)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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

Abstract

More than 13 countries in the Americas have reported sporadic Zika virus infection that show very rapid geographic expansion. While in Indonesia, the euphoria is also increasingly prevalent virus discussed especially after the discovery of Jambi positive patients infected with the virus Zika on January 26, 2016 last.Viruses transmitted by mosquito bites to humans are the same mosquito transmits dengue fever, chikungunya and yellow fever with symptoms that have similar resemblance to the zika virus. Based on similarity of symptoms of infection with dengue virus, can be analyzed using sequence alignment to get identical percentage, local alignment calculation, genetic mutation and the spread of the zika virus to Indonesia. From the whole process, Smith Waterman algorithm can be used to align dengue virus type 1, type 2, type 3, and type 4 with zika virus (Jambi). Mutations between dengue virus and zika virus on average 28% of dissimilarity sequences in the same position between the sequence. Overall, the dengue virus type 1 mutation to the zika virus was 28.2723%, the dengue virus type 2 mutation to the zika virus was 28.1984%, the dengue virus type 3 mutation to the zika virus 27.9373% and the mutation of dengue virus type 4 to the zika virus of 28.7206%. By looking at all mutations, from the simulation results note that the mutations of both viruses include mutation type I. The phylogenetic tree showed the spread of the Zika virus to Indonesia, originally from South Africa, the islands of Chile, Caledonia, the Philippines, Yap Micronesia, Thailand, Cambodia, and finally reached Indonesia. Zika virus jambi suspected of dengue virus mutation because a few years earlier there was a dengue virus outbreaks in a long time in Jambi, it turns out the virus zika jambi is not from mutation dengue virus but the virus comes from the Asian region.
Early Risk Prediction of Rheumatoid ArthritisUsing a Hybrid Restricted Boltzmann Machine Riska Rahma Yanti; Siti Amiroch; Awawin Mustana Rohmah; Dicka Yale Kardono
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.9705

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmunedisease that requires early detection to prevent permanent jointdamage. The main challenge in predicting RA risk lies in thecomplexity of clinical data, which is multidimensional and oftenimbalanced. This study employs a Hybrid Restricted BoltzmannMachine (H-RBM) approach to improve the accuracy of earlyRA risk prediction. The RBM method is used as a featureextractor to map non-linear relationships within clinical data,which is then combined with three classification algorithms:Support Vector Machine (SVM), Random Forest (RF), andLogistic Regression (LR). The research data was sourced fromKaggle, comprising 4,425 samples that were balanced using theSynthetic Minority Oversampling Technique (SMOTE). Testingwas conducted with three data split ratios (70:30, 80:20, and90:10) using GridSearchCV for hyperparameter tuning. Theexperimental results show that the RBM-SVM model dominatesabsolutely with the highest performance at the 90:10 ratio,yielding an accuracy of 99.82%, an AUC score of 1.0000, and arecall of 100%. The perfect recall rate indicates that the modelis capable of identifying all positive patients without any missedcases (zero false negatives). Meanwhile, the RBM-RF and RBM-LR models achieved optimal performance at an 80:20 ratio withaccuracies of 93.95% and 83.42%, respectively. Overall, RBMintegration proved significant in extracting more informativelatent information compared to raw clinical features, therebymaking a crucial contribution to a precise RA early detectiondecision support system