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[ABSTRAK
Latar Belakang. SNP IL-28B mempunyai peran penting dalam pencapaian SVR pada pengobatan Hepatitis C kronik antarras manusia dan berpotensi untuk memprediksi keberhasilan terapi Peg-IFN/RBV maupun penyembuhan spontan Hepatitis C akut. Hingga saat ini, mekanisme molekular yang mendasari kaitan SNP IL-28B dengan respons terapi masih belum jelas meskipun diperkirakan terkait dengan ekspresi IFN-l3 dan reseptor IFN-l3 di jaringan hati. Tujuan. Mengetahui hubungan SNP IL-28B dan SVR serta ekspresi IFN-l3 dan reseptor IFN-l3 di jaringan hati serta mendapatkan kemaknaan klinis SNP IL-28B dan kovariat SVR dalam memprediksi respons terapi Peg-IFN/RBV. Metode. Penelitian ini terbagi menjadi dua bagian. Pertama, penelitian potong lintang pada pasien Hepatitis C kronik yang telah selesai menjalani terapi Peg-IFN/RBV dengan melakukan pengambilan data dasar dan sampel darah. Kedua, penelitian kasus kontrol pasien yang menjalani biopsi hati dan pewarnaan imunohistokimia. Hasil. Pencapaian SVR yang lebih tinggi ditemukan pada pasien dengan alel CC SNP IL28B (p=0,014). Alel CC SNP IL28B mempunyai ekspresi IFN-l3 lebih tinggi dibandingkan dengan alel non-CC (p = 0,018). Meskipun demikian, tidak ditemukan adanya perbedaan bermakna antara ekspresi IFN-l3 (p = 0,237) maupun reseptor IFN-l3 dengan SVR (p = 0,237). Pada penelitian ini, diformulasikan persamaan faktor risiko pencapaian SVR sebagai p = 1 / (1 + e-y); e = 2,7, y = -2,498 + 2,652 (SNP IL-28B) + 2,029 (trombosit) untuk praterapi dan sedangkan untuk masa terapi y = -0,223 + 2,621 (RVR). Simpulan. SNP IL-28B merupakan faktor risiko praterapi yang penting dalam pengobatan hep C kronik G1 menggunakan terapi dua kombinasi. Alel mayor IL28B mengekspresikan IFN-l3 dan reseptornya lebih banyak sebagai respons adanya VHC, namun tidak ditemukan adanya hubungan hal tersebut dengan pencapaian SVR. RVR merupakan faktor masa terapi terbaik untuk memprediksi SVR. Penelitian lanjutan diperlukan untuk membuktikan adanya faktor lain yang berperan dalam pencapaian SVR.;

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ABSTRACT
Background: SNP IL-28B played an important role in achieving sustained virological response (SVR) among different ethnics in chronic Hepatitis C patients and is considered potential in predicting treatment response of Pegylated interferon/ribavirin (Peg-IFN/RBV) combination and spontaneous clearance in acute hepatitis. Up to date, molecular mechanism underlying correlation between SNP IL28B and SVR has not been fully understood yet although it is predicted to be related to IFN-λ3 and IFN-λ3 receptor in liver tissues. Aim: Understanding the association between SNP IL-28B and SVR in chronic Hepatitis C treatment and expression of IFN-l3 and IFN-l3 receptor in liver tissues to evaluate clinical importance of SNP IL-28B examination in Hepatitis C treatment of Peg-IFN/RBV through SVR prediction model. Methods: This study consisted of two parts. First, a cross-sectional study on chronic Hepatitis C patients who completed Peg-IFN/RBV therapy. The second part was case control study on patients underwent liver biopsy and immunohistochemical staining. Results: Sustained virological response was significantly higher in CC allele of SNP IL-28B compared to non CC allele (p = 0.015). Higher expression of IFN-l3 was found in CC allele compared to non CC allele (p = 0.018). On the other hand, there is no significant difference between SVR and expression of IFN-l3 (p = 0.237) and IFN-l3 receptor (p = 0.237). Risk factor for SVR probability were formulated into p = 1 / (1 + e-y); e = 2.7, y = -2.498 + 2.652 (SNP IL-28B) + 2.029 (thrombocytes) for pretreatment while for on treatment risk factor y = -0.223 + 2.621 (RVR) Conclusion: SNP IL-28B was important pretreatment risk factor in genotype 1 chronic Hepatitis C treated with dual therapy. Major allele of IL-28B expressed more IFN-l3 and its receptor in response to HCV although no association between both factors was found. RVR was the best on treatment factor for SVR. Further evaluation study was required to find other possible factors affecting SVR achievement , Background: SNP IL-28B played an important role in achieving sustained virological response (SVR) among different ethnics in chronic Hepatitis C patients and is considered potential in predicting treatment response of Pegylated interferon/ribavirin (Peg-IFN/RBV) combination and spontaneous clearance in acute hepatitis. Up to date, molecular mechanism underlying correlation between SNP IL28B and SVR has not been fully understood yet although it is predicted to be related to IFN-λ3 and IFN-λ3 receptor in liver tissues. Aim: Understanding the association between SNP IL-28B and SVR in chronic Hepatitis C treatment and expression of IFN-l3 and IFN-l3 receptor in liver tissues to evaluate clinical importance of SNP IL-28B examination in Hepatitis C treatment of Peg-IFN/RBV through SVR prediction model. Methods: This study consisted of two parts. First, a cross-sectional study on chronic Hepatitis C patients who completed Peg-IFN/RBV therapy. The second part was case control study on patients underwent liver biopsy and immunohistochemical staining. Results: Sustained virological response was significantly higher in CC allele of SNP IL-28B compared to non CC allele (p = 0.015). Higher expression of IFN-l3 was found in CC allele compared to non CC allele (p = 0.018). On the other hand, there is no significant difference between SVR and expression of IFN-l3 (p = 0.237) and IFN-l3 receptor (p = 0.237). Risk factor for SVR probability were formulated into p = 1 / (1 + e-y); e = 2.7, y = -2.498 + 2.652 (SNP IL-28B) + 2.029 (thrombocytes) for pretreatment while for on treatment risk factor y = -0.223 + 2.621 (RVR) Conclusion: SNP IL-28B was important pretreatment risk factor in genotype 1 chronic Hepatitis C treated with dual therapy. Major allele of IL-28B expressed more IFN-l3 and its receptor in response to HCV although no association between both factors was found. RVR was the best on treatment factor for SVR. Further evaluation study was required to find other possible factors affecting SVR achievement ]

Abstrak :
Prediksi harga saham merupakan hal yang sangat penting bagi investor karena sangat berguna untuk menentukan nilai masa depan dari suatu perusahaan yang sahamnya sedang diperdagangkan di bursa efek. Investor akan mendapatkan keuntungan yang besar dengan prediksi yang tepat, sebaliknya investor akan mendapatkan kerugian jika prediksi yang digunakan tidak tepat. Pada skripsi ini, akan dibahas pembuatan model prediksi Adaptive Neuro Fuzzy Inference System ANFIS dengan menggunakan variabel indikator teknikal terbaik berdasarkan Support Vector Regression SVR yang dilihat dari kecenderungan data historis saham 25 perusahaan dari sub sektor Bank, sektor Keuangan, yang tercatat di Bursa Efek Indonesia. Melalui metode ini, akan didapatkan nilai akurasi model yang cukup baik sedemikian sehingga dapat menjadi rekomendasi bagi investor dalam melakukan prediksi harga saham berdasarkan variabel indikator teknikal terpilih. ......Forecasting stock price has become an important issue for stock investors because it is very useful to determine the future value of a company whose its share are traded on the stock exchange. Investors will get a profit with a sharp predictions, otherwise they will get loss if the predictions is inappropriately used. This undergraduate thesis will study how to make a model prediction Adaptive Neruo Fuzzy Inference System ANFIS using the best technical indicators. These technical indicators chosen by using Support Vector Regression SVR referred from the tendencies of stock time series data for 25 companies of Banking sub sector, Financial sector, that listed on Indonesian Stock Exchange. Through this method, analyst will get the value of the model rsquo s accuracy, that is good enough. So that it could be a recommendation for investors for forecasting the stock prices using this method with the selected technical indicators.

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ABSTRAK Sistem prediksi berbasis citra VNIR mampu untuk memprediksi parameter tertentu pada suatu objek. Parameter seperti kadar fenolik dari daun bisbul dapat diprediksi dengan sistem prediksi berbasis citra VNIR. Citra VNIR daun bisbul diakuisisi menggunakan kamera hiperspektral dengan rentang 400 hingga 1000 nm. Model regresi yang digunakan pada sistem prediksi ini meliputi Support Vector Regression (SVR), Partial Least Square Regression (PLSR), serta Decision Tree Regression (DTR). Dari ketiga model tersebut didapatkan nilai error yang menunjukkan performa sistem prediksi yang dibuat. Error berupa koefisien determinasi (R2) dan Root Mean Square Error (RMSE). Nilai koefisien determinasi (R2) sebesar 0,95 (SVR); 0,91 (PLSR); dan 0,90 (DTR). Serta untuk RMSE sebesar 2,66 (SVR). 3,60 (PLSR), dan 3,90 (DTR). Berdasarkan hasil koefisien korelasi dari ketiga model tersebut, dapat disampaikan bahwa kadar fenolik dari daun bisbul dapat diprediksi dengan menggunakan model SVR untuk performa yang baik dan menggunakan parameter fungsi kernel polinomial orde 3. Nilai prediksi kadar fenolik rata-rata dari ketiga model sebesar 32,72 GAE(µg/mg) untuk DTR; 32,46 GAE(µg/mg) untuk PLSR; dan 32,27 GAE(µg/mg) untuk SVR.

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ABSTRACT Prediction systems based on VNIR images are able to predict certain parameters on an object. Parameters such as the phenolic content of Diospyros discolor Willd leaf can be predicted by this system. VNIR images of Diospyros discolor Willd leaf acquired using a hyperspectral camera with a range of 400 to 1000 nm. The regression model to predict the content used Support Vector Regression (SVR), Partial Least Square Regression (PLSR), and Decision Tree Regression (DTR). Based on three models, an error value is obtained that indicates the performance of the predictive system. The error value such as coefficient correlation (R) and Root Mean Square Error (RMSE). The value of R from the models are 0,95 (SVR); 0,91 (PLSR), and 0,90 (DTR). The value of RMSE from the models are 2,66 (SVR). 3,60 (PLSR), and 3,90 (DTR). Value of predicted total phenolic content from the models are 32,72 GAE(µg/mg) for DTR; 32,46 GAE(µg/mg) for PLSR; dan 32,27 GAE(µg/mg) for SVR. Based on the coefficient correlation, phenolic content can be predicted using SVR model for best result with kernel function polynomial 3 order.

Abstrak :
Dengan terus bertambahnya industri pada berbagai bidang, membuat konsumen memiliki banyak pilihan ketika memilih barang atau jasa. Oleh karena itu dibutuhkan suatu cara agar dapat menghasilkan barang dengan tepat. Hal inilah yang menjadi peranan dari sebuah metode peramalan permintaan. Terdapat banyak cara dalam melakukan peramalan, namun cara manakah yang dapat memberikan hasil yang terbaik. ANN dan SVR merupakan salah satu cara yang memberikan hasil peramalan terbaik. Dalam penelitian ini, dibandingkan antara metode ANN dan SVR dengan metode tradisional. Dari enam jenis data yang digunakan empat menunjukan ANN memberikan peramalan terbaik, dan satu menunjukan SVR memberikan peramalan terbaik. ......With the continuous increase of industry in many fields, making consumers to have many choices when choosing goods or services. Because of that, we need some way to produce good with the correct amount. This is the role of a demand forecast method. There are many methods in demand forecast, but which method that give the best result. ANN and SVR are one of many methods that will give the best forecast result. In this research, ANN and SVR method will be compared to the traditional methods. From six kinds of data that is used, four show that ANN give the best forecast result, and one shows SVR give the best forecast result.

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ABSTRAK
Latar Belakang: Penyakit hepatitis C kronik merupakan masalah kesehatan global yang dapat menyebabkan morbiditas serta mortalitas yang tinggi pada kondisi sirosis dan karsinoma hepatoseluler. Adanya terapi sofosbuvir-daclatasvir yang bersifat pangenotipik diharapkan dapat mengatasi penyakit ini. Namun, didapatkan hasil pencapaian SVR 12 yang bervariasi dan lebih rendah pada genotipe 3 dibandingkan genotipe 1. Di Indonesia sendiri belum ada data mengenai pencapaian SVR 12 pada kedua genotipe ini yang menggunakan terapi sofosbuvir-daclatasvir. Tujuan: Mengetahui pencapaian SVR 12 pasien hepatitis C Kronik genotipe 3 dibandingkan genotipe 1 yang mendapatkan terapi sofosbuvir-daclatasvir. Metode: Penelitian ini merupakan studi kohort retrospektif dengan menggunakan data sekunder yang melibatkan 209 pasien hepatitis C kronik genotipe 3 dan 1. Dilakukan analisis dengan membagi pasien menjadi dua kelompok yaitu genotipe 3 dan 1 serta dibandingkan dengan pencapaian keberhasilan SVR 12 menggunakan uji chi-square. Faktor sirosis hepatis dan usia yang dianggap dapat memengaruhi keberhasilan SVR 12 dianalisis dengan menggunakan uji chi-square kemudian dilanjutkan dengan analisis regresi logistik. Hasil: Sampel berjumlah 209 pasien yang terdiri dari 45 pasien genotipe 3 dan 164 pasien genotipe 1. Pencapaian keberhasilan SVR 12 pada genotipe 3 dan 1 yaitu 84,4% dan 98,8%. Kelompok pasien genotipe 3 memiliki keberhasilan SVR 12 lebih rendah dibandingkan kelompok pasien genotipe 1 dengan adjusted OR=0,065 (IK95% 0,013-0,330) dan ARR 14,4%. Sirosis hepatis dan usia tidak memengaruhi keberhasilan SVR 12 (p=1,00 dan p=0,72). Sejumlah 5 dari 9 pasien yang mengalami kegagalan memiki koinfeksi dengan HIV. Simpulan: Pasien hepatitis C kronik genotipe 3 yang menggunakan terapi sofosbuvir-daclatasvir memiliki keberhasilan SVR 12 lebih rendah dibandingkan genotipe 1.

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ABSTRACT
Background. Chronic hepatitis C is a global health problem with high morbidity and mortality in the condition of cirrhosis and hepatocellular carcinoma. sofosbuvir-daclatasvir is pangenotypic therapy that expected to overcome this disease. However, the achievement of SVR 12 was varied and lower in genotype 3 compared to genotype 1. In Indonesia, there is no data about achievement SVR 12 in both genotypes using sofosbuvir-daclatasvir. Objectives. To know SVR 12 achievement between genotype 3 and 1 chronic hepatitis C patients that using sofosbuvir-daclatasvir therapy. Methods. This study is a retrospective cohort using secondary data of 209 hepatitis C chronic genotype 3 and 1. Samples were divided into two groups according to its genotype and compared with achievement of SVR 12 then analyzed using chi-square test. Hepatic cirrhosis and age factors that are considered to affect the achievement SVR 12 were analyzed using chi-square test and logistic regression test. Results. 209 patients participated in this study consisting of 45 genotype 3 and 164 genotype 1. Achievement of SVR 12 succeed in genotypes 3 and 1 were 84,4% and 98,8%. Genotype 3 patients had lower SVR 12 achievement compared to genotype 1 patients with adjusted OR=0,065 (95% CI 0,013-0,330) and ARR 14,4. Hepatic cirrhosis and ages did not affect SVR 12 (p= 1.00 and 0,72, respectively). Five from nine patients who failed have co-infection with HIV. Conclusions. Chronic hepatitis C patients using sofosbuvir-daclatasvir theraphy had lower SVR 12 achievement in genotype 3 than genotype 1.

Abstrak :

Peramalan permintaan berguna sebagai permulaan dalam perencanaan yang memiliki tujuan untuk memenuhi permintaan pelanggan di masa depan. Pada awalnya, metode peramalan dikembangkan berbasis statistik. Namun, seiring berjalannya waktu, metode peramalan saat ini juga ada yang menggunakan metode machine learning karena bertambahnya kompleksitas data dengan variabilitas permintaan yang tinggi. Industri FMCG, khususnya produk pembersih rumah tangga memiliki pola permintaan yang cukup dinamis. Hal ini menjadi tantangan tersendiri untuk perusahaan sehingga mampu meramalkan secara akurat pola permintaan pada industri ini. Dalam penelitian ini, peneliti melakukan peramalan permintaan untuk produk pembersih rumah tangga menggunakan metode deret waktu, yaitu Autoregressive Integrated Moving Average (ARIMA), dan metode machine learning, yaitu Support Vector Regression (SVR). Objek penelitian yang dipilih oleh peneliti adalah data permintaan dari 8 SKU produk pembersih rumah tangga yang dijual oleh PT Wijaya Agung Hutama sejak Januari 2014 hingga Desember 2022. Hasil penelitian menunjukkan bahwa metode SVR unggul untuk 6 dari 8 SKU yang diramalkan, dengan rata-rata Mean Absolute Percentage Error (MAPE) sebesar 31,06%. Sementara itu, ARIMA mendapatkan rata-rata MAPE sebesar 33,52%. Metode yang diajukan peneliti juga dapat menurunkan error pada peramalan perusahaan hingga 8,31%. ......Demand forecasting is useful as a starting point in planning with the objective of meeting customer demand in the future. Initially, forecasting methods were developed based on statistics. However, over time, current forecasting methods also incorporate machine learning techniques due to the increasing complexity of data and high demand variability. The Fast-Moving Consumer Goods (FMCG) industry, particularly household cleaning products, has a dynamic demand pattern. This poses a challenge for companies to accurately forecast demand in this industry. This study conducted demand forecasting for household cleaning products using time series methods, namely Autoregressive Integrated Moving Average (ARIMA), and machine learning methods, specifically Support Vector Regression (SVR). The research focused on the demand data for 8 SKU (Stock Keeping Unit) of household cleaning products sold by PT Wijaya Agung Hutama from January 2014 to December 2022. The results of the study showed that the SVR method outperformed ARIMA for 6 out of 8 forecasted SKUs, with an average Mean Absolute Percentage Error (MAPE) of 31.06%. Meanwhile, ARIMA obtained an average MAPE of 33.52%. The proposed method by the researchers also reduced the forecasting error for the company by 8,31%. These findings indicate that the suggested forecasting methods can help companies plan production and inventory more accurately. 

Abstrak :
Korosi dalam industri menyebabkan kerugian ekonomi signifikan dengan mengurangi masa pakai peralatan. Upaya pencegahan seperti penggunaan inhibitor korosi telah dilakukan, tetapi pemilihannya memakan waktu dan biaya, terutama untuk aplikasi di lingkungan asam. Oleh karena itu, pendekatan machine learning (ML) diperlukan untuk mengatasi masalah ini. ML digunakan untuk memprediksi sifat elektronik senyawa turunan pirimidina-pirazol. Sifat elektronik tersebut didapatkan melalui simulasi Teori Fungsional Kerapatan (DFT, Density Functional Theory) yang kemudian dapat digunakan untuk mengetahui efisiensi inhibitor senyawa karena memiliki korelasi yang linier. Model ML yang akan digunakan adalah K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Gradient Boosting, Extra Trees, dan Artificial Neural Network (ANN) dengan menggunakan deskriptor SMILES dan AlvaDesc sebagai fitur untuk menjelaskan struktur kimia dari senyawa organik. Model terbaik telah diidentifikasi yang dapat memprediksi sifat elektronik senyawa turunan pirimidina-pirazol dengan akurasi tertinggi dan jumlah fitur yang optimal. Proses validasi model dilakukan dengan membandingkan hasil prediksi dengan data dari literatur. Hasil penelitian menunjukkan model terbaik adalah model XGB dengan akurasi rata-rata yang mencapai 96,50%. ......Corrosion in industry causes significant economic losses by reducing the lifespan of equipment. Various strategies, like corrosion inhibitors, are used, but selecting effective inhibitors for acidic environments is costly and time-consuming. To address this, machine learning (ML) was applied to predict the electronic properties of pyrimidine-pyrazole derivatives using Density Functional Theory (DFT) simulations. ML models employed are K-Nearest Neighbors (KNN), Support Vector Regression (KNN), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra Trees (ET), and Artificial Neural Network (ANN) using SMILES and AlvaDesc descriptors as features to elucidate the chemical structure of organic compounds. From the aforementioned models, the optimal model was identified that could predict the electronic properties of pyrimidine-pyrazole derivative compounds with the highest accuracy and optimal number of features. The model validation process involved a comparison of the prediction results with data from previous studies. The results demonstrated that the optimal model was XGB, with an average accuracy of 96.50%.

Abstrak :
The largest region that produces oil palm in Indonesia has an important role in improving the welfare an economy of the society. Oil palm production has increased significantly in Riau Province in every period. To determine the production development for the next few years, we proposed a prediction of the production results. The dataset were taken to be the time series data of the last 8 years (2005-2013) with the function and benefits of oil palm as the parameters. The study was undertaken by comparing the performance of Support Vector Regression (SVR) method and Artificial Neural Network (ANN). From the experiment, SVR resulted the better model compared to the ANN. This is shown by the correlation coefficient of 95% and 6% for MSE in the kernel Radial Basis Function (RBF), whereas ANN resulted only 74% for R2 and 9% for MSE on the 8th experiment with hidden neuron 20 and learning rate 0,1. SVR model generated predictions for next 3 years which rose 3%-6% from the actual data and RBF model predictions

Daerah penghasil kelapa sawit terbesar di Indonesia mempunyai peranan penting dalam peningkatan kesejahteraan dan ekonomi masyarakat. Produksi kelapa sawit mengalami peningkatan yang signifikan di Provinsi Riau dalam setiap kurun waktu, untuk menentukan perkembangan produksi beberapa tahun ke depan, kami mengusulkan suatu prediksi dari hasil produksi. Dataset yang diambil adalah data time series dari data yang diperoleh selama 8 tahun terakhir (2005-2013) dengan fungsi dan manfaat kelapa sawit sebagai parameter. Dalam implementasinya peramalan dilakukan dengan membadingkan kinerja metode Support Vector Regression (SVR) dan Artificial Neural Network (ANN). Dari percobaan, SVR menghasilkan model terbaik dibandingkan dengan ANN yaitu ditunjukkan dengan koefisien korelasi sebesar 95% dan MSE 6% pada kernel Radial Basis Function (RBF), sedangkan ANN hanya menghasilkan R2 sebesar 74% dan MSE 9% pada percobaan ke-8 dengan hidden neuron 20 dan learning rate 0,1. SVR model menghasilkan prediksi untuk 3 tahun kedepan yang memiliki kenaikan antara 3%-6% dari data aktual dan prediksi model RBF

Abstrak :
Background: HIV infection in HCV-infected patients accelerates disease progression and reduces the success rate of Peg-IFN/RBV treatment. HCV mutation in NS5A-ISDR/PKR-BD region improved the outcome in HCV monoinfection treated with Peg-IFN/RBV. SNP-IL28B polymorphism is predicted to have an effect on HCV quasispecies evolution. However, the role of NS5A mutation and SNP IL-28B in HIV-HCV coinfection is still unclear. The aim of the study is to determine the role of HCV NS5A-ISDR/PKR-BD mutation and SNP IL-28 polymorphism on the successfulness of Peg-IFN/RBV therapy in HCV-HIV coinfection. Methods: prospective cohort was performed in this study. Plasma sample were obtained from 30 and 8 patients with HCV-HIV coinfection and HCV monoinfection, respectively. PCR nucleotide sequencing was performed after RNA virus extraction and cDNA synthesis. Protein secondary structure and prediction of mutation function were analyzed using PredictProtein (PP) program. Results: sixteen HCV-HIV coinfected patients and none from eight HCV patients achieved sustained virological response (SVR). ≥1 non-neutral mutation was found in 24/30 HCV-HIV coinfection and more frequent in SVR group (14 patients). ≥1 non-neutral mutation were found statistically significant for overall SVR achievement (p<0.05) in all patients regardless of coinfection or monoinfection status. Of the 27 HCV-HIV coinfected patients with CC-gene, 21 subjects had non-neutral mutation. The structure which was expected as NS5A binding site structure was different from consensus (wild type) in SVR group, while the structure was similar to consensus in non-SVR group. Conclusion: having ≥1 non-neutral mutation was associated with SVR achievement in Peg-IFN/RBV therapy, regardless of monoinfection and coinfection status.