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Deteksi Diabetik Retinopati menggunakan Regresi Logistik Tyasnurita, Raras; Pamungkas, Adhi Yoga Muris
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.578.130-135

Retinopathy diabetic is a disease caused by diabetes mellitus complications that can cause damage to the retina of the eye. It has a direct impact on the disruption of the vision of the patient. Detecting this disease is very important to prevent total blindness on diabetes mellitus patients. One method to do the detection is by using machine learning. This research uses feature extraction data from an image that contains signs of retinopathy diabetic or not. In this research, we focus on retinopathy diabetic classification. We applied logistic regression algorithm for classification. There is four training condition in a machine learning model: using the default parameter, feature standardization, feature selection, and hyper-parameter tuning. The model used a regularization control (C) value of 11.288, iterations 200, and a regularization penalty (l1). The experimental results show that this proposed model with full features produced 80,17% accuracy in data validation.

Stock price forecast of macro-economic factor using recurrent neural network M. Reza Pahlawan; Edwin Riksakomara; Raras Tyasnurita; Ahmad Muklason; Faizal Mahananto; Retno A. Vinarti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp74-83

The stock market is one of the investment choices that always have traction from time to time. Aside from being a means of corporate funding, investing in the stock market can benefit investors. Investing also has a higher risk because the pattern of stock prices is volatile, which is caused by internal and external factors. One external factor that affects stock prices is the macro-economic, where these factors are events that occur in a country where one of the economic sectors affected is stock prices. Investors often feel confused about the right time in decisions making related to buying or selling stock. One way to look at how the prospect of stock prices is a stock price forecasting activity. For this study, we will be making use of the recurrent neural network (RNN) to forecast stock prices for the next periods. This research involves two variables: the closing stock price and the rupiah exchange rate against the dollar for the daily period. We achieve a MAPE value of 1.546% for RNN model without the variable foreign exchange rate and 1.558% for the RNN model that uses the foreign exchange rate against the dollar.

Solving examination timetabling problem within a hyper-heuristic framework Shinta Dewi; Raras Tyasnurita; Febriyora Surya Pratiwi
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.2996

Scheduling exams in colleges are a complicated job that is difficult to solve conventionally. Exam timetabling is one of the combinatorial optimization problems where there is no exact algorithm that can answer the problem with the optimum solution and minimum time possible. This study investigated the University of Toronto benchmark dataset, which provides 13 real instances regarding the scheduling of course exams from various institutions. The hard constraints for not violate the number of time slots must be fulfilled while paying attention to fitness and running time. Algorithm of largest degree, hill climbing, and tabu search within a hyper-heuristic framework is investigated with regards to each performance. This study shows that the Tabu search algorithm produces much lower penalty value for all datasets by reducing 18-58% from the initial solution.

Pembuatan Aplikasi Pendukung Keputusan untuk Peramalan Persediaan Bahan Baku Produksi Plastik Blowing dan Inject Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average) di CV. Asia Amira Herwindyani Hutasuhut; Wiwik Anggraeni; Raras Tyasnurita
Jurnal Teknik ITS Vol 3, No 2 (2014)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v3i2.8114

Persediaan bahan baku memiliki peranan penting bagi perusahaan karena akan berpengaruh pada kemampuan perusahaan untuk memenuhi permintaan pelanggan. Berbagai kendala dapat muncul akibat kurangnya bahan baku untuk produksi, seperti kekurangan bahan baku untuk produksi sehingga menimbulkan keterlambatan terhadap pemenuhan permintaan pelanggannya. Akibat keterlambatan tersebut akhirnya perusahaan mengalami beberapa kerugian. Untuk mengatasi hal tersebut, perusahaan memerlukan perencanaan di berbagai hal, khususnya perencanaan yang berhubungan dengan persediaan. Salah satu bentuk perencanaan persediaan yaitu meramalkan persediaan bahan baku untuk setiap waktu. Metode ARIMA merupakan metode yang disarankan untuk kasus CV. Asia karena memiliki sifat yang fleksibel, yaitu mengikuti pola data yang ada. Metode ARIMA memiliki tingkat akurasi yang tinggi dan cenderung memiliki nilai error yang kecil karena prosesnya yang terperinci. Model ARIMA yang diperoleh nantinya akan diimplementasikan dalam Microsoft Excel menggunakan Visual Basic for Application, dengan mempertimbangkan kondisi kekinian perusahaan yang menggunakan Microsoft Excel untuk proses bisnis kesehariannya, sehingga dapat membantu CV. Asia untuk meramalkan persediaan bahan bakunya dan melakukan pengambilan keputusan.

Aplikasi Kombinasi Heuristik dalam Kerangka Hyper-Heuristic untuk Permasalahan Penjadwalan Ujian Gabriella Icasia; Raras Tyasnurita; Etria Sepwardhani Purba
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

Deteksi Diabetik Retinopati menggunakan Regresi Logistik Raras Tyasnurita; Adhi Yoga Muris Pamungkas
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.578.130-135

Retinopathy diabetic is a disease caused by diabetes mellitus complications that can cause damage to the retina of the eye. It has a direct impact on the disruption of the vision of the patient. Detecting this disease is very important to prevent total blindness on diabetes mellitus patients. One method to do the detection is by using machine learning. This research uses feature extraction data from an image that contains signs of retinopathy diabetic or not. In this research, we focus on retinopathy diabetic classification. We applied logistic regression algorithm for classification. There is four training condition in a machine learning model: using the default parameter, feature standardization, feature selection, and hyper-parameter tuning. The model used a regularization control (C) value of 11.288, iterations 200, and a regularization penalty (l1). The experimental results show that this proposed model with full features produced 80,17% accuracy in data validation.

IDENTIFICATION OF CHRONIC KIDNEY DISEASE USING NAIVE BAYES, ADABOOST, AND RANDOM FOREST LEARNING METHODS Raras Tyasnurita; Shafira Widya Hapsari
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 6 No 1 (2020): JITK Issue August 2020
Publisher : LPPM Nusa Mandiri

Chronic kidney disease is a decrease in function in the kidneys where the condition leads to kidney damage. This disease causes damage to the body's immunity, because the body fails to maintain fluid balance. Therefore, it becomes a critical need to identify whether a patient is a sufferer of chronic kidney disease or not. The classification methods used in this study are Naive Bayes, AdaBoost, and Random Forest. Recently, proper early recognition is needed to detect chronic kidney disease to prevent delays in its treatment. Given the large number of chronic kidney disease cases that occur, this study is expected to be an effort to control the increase in sufferers. The results showed that the Naive Bayes approach achieved 95.4% accuracy, which increased to 98.6% after AdaBoost was implemented, and Random Forest led at 99.3%.

Forecasting Carbon Emissions Using the SARIMA and LSTM Methods Syifa Ilma Nabila Suwandi; Raras Tyasnurita; Hanifan Muhayat
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 6 No 1 (2022): June 2022
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v6i1.436

The majority of greenhouse gas (GHG) effects are caused by very high levels of carbon emissions in the world. Therefore, it is necessary to take action to control the levels of carbon emissions in the world. In this study, the world's carbon emission levels were forecasted based on time series data on carbon emissions from 1949 to 2018 in North America. This study uses 2 forecasting methods, namely SARIMA and LSTM, with the consideration that both methods are considered capable of providing good results. Forecasting results show that the best parameter for SARIMA is [(0,1,0) (1,1,0)12] with a MAPE of 1.995%. Meanwhile, if you use the LSTM method with parameters 1 input, 4 hidden layers, and output 1, it produces a MAPE of 0.540%. This condition makes the LSTM method more optimal for predicting carbon emission levels in the world.

Website Urun Daya untuk Meningkatkan Product Knowledge pada Konsumen UMKM Sentra Oleh – Oleh Khas Daerah Rully Agus Hendrawan; Ika Nurkasanah; Algracevian Andrea Gibran Syahrial; Andhika Prasandy Rachman; M. Yusuf Sulaiman; Jessica Patricia Halim; Raffly Andico Devara; Erma Suryani; Arif Wibisono; Raras Tyasnurita; Faizal Mahananto; Rizka Wakhiddatus Sholikah
Sewagati Vol 7 No 3 (2023)
Publisher : Pusat Publikasi ITS

Indonesia merupakan negara kepulauan dengan berbagai macam makanan daerah yang beraneka macam, salah satunya adalah buah tangan/oleh-oleh. Namun, berbagai macam tantangan seperti persaingan bisnis dan pandemi COVID-19 berdampak cukup besar dalam industri pariwisata dan oleh-oleh. Usaha Mikro, Kecil, dan Menengah (UMKM) yang bergerak di sektor oleh-oleh mengalami kendala dan penurunan transaksi dalam bisnisnya. Oleh karena itu, program pengabdian masyarakat ini bertujuan untuk meningkatkan visibilitas UMKM di sektor oleh-oleh dengan platform website yang dikembangkan dengan metode design thinking. Metode tersebut dipilih berdasarkan kondisi saat ini, dimana masyarakat gemar mencari informasi yang lengkap sebelum melakukan pembelian produk. Metode pelaksanaan program pengabdian masyarakat ini diawali dengan tahap analisis kebutuhan mitra dan pengguna, kemudian dilanjutkan dengan tahap desain arsitektur website, pengembangan website, uji coba website, dan diakhiri dengan tahap sosialisasi dan pendampingan penggunaan website kepada mitra. Hasil program pengabdian masyarakat ini mampu membantu UMKM di sektor oleh-oleh untuk melakukan pemasaran produk mereka secara online, dan memperluas jangkauan pasar mereka. Bagi calon pelanggan, design thinking method website ini memungkinkan pencarian informasi terkait harga, rasa, komposisi, manfaat dan informasi lainnya terkait produk yang ditawarkan oleh mitra.

GOLD PRICE FORECASTING USING MULTIPLE LINEAR REGRESSION METHOD Raras Tyasnurita; Rifqi Rahmadrian Luthfiansyah; Muhamad Rayhan Brameswara
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 9, No 3 (2023): Juni 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v9i3.1748

Abstract – Price forecasting is a part of economic decision making. Forecasting the daily rise and fall of gold prices can help investors decide when to buy or sell the commodity. The price of gold depends on many factors such as the price of other precious metals, the price of crude oil, the performance of the stock exchange, and the exchange rate of currencies. This study discusses gold price forecasting using the multiple linear regression method. The results of this study indicate that the best model is in the data distribution of 70%: 30% for training and testing, with a MAPE of 4.7% Based on these results, it can be concluded that the use of multiple linear regression method produces a fairly good model for gold prices forecasting. Besides, the correlation analysis show that the price of other precious metals greatly influences the price of gold where in this case the silver price whose correlation value is 0.87. Keywords: forecasting, gold investment, multiple linear regression Abstrak: Peramalan harga merupakan bagian dari pengambilan keputusan ekonomi. Melakukan peramalan terhadap kenaikan dan penurunan harga emas harian dapat membantu investor memutuskan kapan harus membeli atau menjual komoditas. Harga Emas bergantung pada banyak faktor seperti harga logam mulia lainnya, harga minyak mentah, kinerja bursa saham, dan nilai tukar mata uang. Penelitian ini membahas peramalan harga emas dengan menggunakan metode regresi linear ganda. Hasil dari penelitian ini menunjukkan bahwa model terbaik terdapat pada pembagian data pelatihan 70% dan pengujian 30%, dengan MAPE sebesar 4.7%. Berdasarkan hasil tersebut dapat diambil kesimpulan bahwa penggunaan metode regresi linear ganda menghasilkan model yang cukup baik untuk peramalan harga emas. Selain itu, analisis korelasi menunjukkan bahwa harga logam mulia lainnya sangat mempengaruhi harga emas dimana dalam hal ini variabel harga perak yang nilai korelasinya 0.87. Kata kunci: peramalan; investasi emas, regresi linear ganda

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