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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Jurnal Teknologi dan Sistem Komputer JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INTEK: Jurnal Penelitian Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat Mosharafa: Jurnal Pendidikan Matematika Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Jurnal Abdimas Jurnal Komtika (Komputasi dan Informatika) LOGIC: Jurnal Penelitian Informatika
Irma Palupi
Universitas Telkom
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Nursing Public Health Social Sciences Other

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Journal : Journal of Computer System and Informatics (JoSYC)

 1 
Candlestick Patterns Recognition using CNN-LSTM Model to Predict Financial Trading Position in Stock Market Aditya Ramadhan; Irma Palupi; Bambang Ari Wahyudi
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2133

Abstract

Investors need analytical tools to predict the price and to determine trading positions. Candlestick pattern is one of the analytical tools that predict price trends. However, the patterns are difficult to recognize, and some studies show doubts regarding the robustness of the recognizing system. In this study, we tested the predictive ability of candlestick patterns to determine trading positions. We use Gramian Angular Field (GAF) to encode candlestick patterns as images to recognize 3-hour and 5-hour of 6 candlestick patterns with Convolutional Neural Network (CNN), coupled with the Long short-term memory (LSTM) model to predict the close price. The trading position consists of buying and selling position with a hold period of several hours. Our results show CNN successfully detected 3-hour and 5-hour GAF candlestick patterns with an accuracy of 90% and 93%. LSTM can predict the close price trend with 155.458 RMSE scores and 0.9754% MAPE with 10-hour look back. With a hold duration of three hours and CNN-LSTM as an additional model, the test data's 85 candlestick patterns are recognized with 82.7% accuracy, compared to 60% accuracy of profitable trading positions when CNN candlestick pattern recognition is used alone. Compared to employing CNN candlestick pattern identification alone, the CNN-LSTM model combination can improve the prediction power of candlestick patterns and offer more lucrative trading positions.
Image Detection for Common Human Skin Diseases in Indonesia Using CNN and Ensemble Learning Method Fauzi Dzulfiqar Wibowo; Irma Palupi; Bambang Ari Wahyudi
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2151

Abstract

Skin disease is a common health problem throughout the world which is one of the main causes of global disease. Skin and subcutaneous diseases managed to contribute 1.79% of global diseases and also became the fourth leading cause of the burden of non-fatal diseases and disability in 2013. Indonesia was ranked 29th out of 195 countries in Asia which indirectly contributed to in contributing to the transmission of skin diseases due to several causes such as lack of access to health care services, poor hygiene conditions, and also population density. Based on the information revealed in the book entitled illustrated guide on various skin diseases commonly found in Indonesia, it is stated that skin diseases ranging from herpes, ringworm, chickenpox, scabies, to psoriasis are often found in Indonesia. With current technological advances, it is possible for humans to be able to recognize various skin diseases with the help of the Convolutional Neural Network (CNN) Method. A total of 1203 images containing types of skin diseases such as herpes simplex, pityriasis, psoriasis, tinea corporis, scabies, and also vitiligo will be a class in the classification process, but because most images are still unbalanced and do not have strong object elements, it is necessary to do this. data preparation and data balancing is also needed so that the architectural model will not be difficult to learn. By using k-fold cross validation and carrying out the ensemble method, the results of the model evaluation will be in the form of an accuracy matrix where the results of each model will be compared and it will be determined which model is the best based on the results obtained. The test results that produce Cross Validation show that the RGB image is superior where the accuracy value obtained is 49% and the Grayscale image has an accuracy of 47%. however, when compared with the ensemble results, Grayscale images have superior accuracy results, namely the accuracy results are 93% and RGB images produce only 86.
Co-Authors Aditya Ramadhan Adiwijaya Adriani, Zahrina Aulia AL Mamuda, Naila Andri Saputra Anton Sri Haryanto Apriani, Iklima Ardhia Pringgowati Ayu Shabrina Ayu Shabrina Aziz, Kemal Bambang Ari Wahyudi Brigita Tenggehi Deni Saepudin Diah Fitri Wulandari Diani Sarah Kamilial Diani Sarah Kamilial Diyas Puspandari Erwin Budi Setiawan Fauzi Dzulfiqar Wibowo Firdaus Maringga Firdaus Maringga Firdaus Maringga Fitriyani Fitriyani Haikal, Muhamad Fachri Hamonangan, Ricardo Harry Susilodharma Husain Athfal Hidayat I Wayan Ade Sugisnawan Dandysmara I Wayan Ade Sugisnawan Dandysmara Indra Utama Sitorus Indwiarti Iryanto Iryanto Jondri Jondri Khairi, Muhammad Daffa Mahmud Imrona Megi Rahma Dony Muh. Arfan Arsyad Muhammad Faizal Muhammad Saddam Salsabillah Muhammad Saddam Salsabillah Muhammad Saddam Salsabillah Naila Al Mahmuda Naila AL Mamuda Ni Putu Winda Ardiyanti Nur Roza Fitriyana Nurul Ikhsan Panuluh, Bagus Prasetyo, Huda Rizky Priscilia Lovita Paelongan Putranto, Muhammad Fadhlan Putu Harry Gunawan Rian Febrian Umbara Rinaldi Wibiyanto Riyadi Lazuardi Sirait Rohidi Rohidi Sadewa, I Made Aditya Putra Satrio, M Siti Saadah Suleman, Annisa Rahmawati varid vaya yusuf Wibowo, Muhammad Raihan Yuliant Sibaroni