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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek Semesta Teknika Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Wikrama Parahita : Jurnal Pengabdian Masyarakat Jurnal Pilar Nusa Mandiri Indonesian Journal of Information System JMM (Jurnal Masyarakat Mandiri) Journal of Information Systems and Informatics JIKA (Jurnal Informatika) Insearch: Information System Research Journal RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Hastomo, Widi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19 Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Kalbuana, Nawang; Nisfiani, Ervina; ETP, Lussiana
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 7, No 2 (2021): Volume 7 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v7i2.47411

Abstract

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.
Identification of 29 Types of Plant Diseases using Deep Learning EfficientNetB3 Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Kusuma Wardhana, Indra Sari; Sutarno, Sutarno; Arif, Dodi
Insearch: Information System Research Journal Vol 2, No 02 (2022): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v2i02.4389

Abstract

To supply the world's food needs in the midst of the existing food crisis, farmers urgently need to expand crop production. By establishing it simple to recognize the kind of plant disease so that earlier control efforts could be conducted, farmers' harvest failures driven on by disease attacks must be prevented. In this study, one of the Convolutional Neural Network (CNN) architectures known EfficeintNetB3 is applied to generate a classification model for 29 different types of plant diseases. A model is created after 3,170 image data are used for validation and 57,067 image data were utilized for training. 3,171 image data tests were conducted as part of the model testing phase, and the total test results were produced an extraordinarily high accuracy score of 0.99 percentage and an F1-score
Optimizing Investment: Combining Deep Learning for Price Prediction and Moving Average for Return-Risk Analysis Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Masriyanda, Masriyanda; Sestri, Ellya; Kardian, Aqwam Rosadi; Azis, Nur; Dewanto, Ignatius Joko; Rasyiddin, Ahmad; Sundoro, Aries; Kamilia, Nada
Jurnal Teknik Elektro Vol 14, No 2 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i2.45002

Abstract

The ability to analyze predictions marks something going up or down, as well as the level of possible risk taken into account by much-needed stock investors. In a study, this analysis of risk and correlation between shares was calculated using the method of moving averages (MA). Besides that, a dataset of 4 stocks (Apple, Google, Microsoft, and Amazon) also performed prediction mark stock in period time next (future) with the use of the neural network method (deep learning) Long Short-Term Memory (LSTM) model. The result of programming in the Python language is several visualizations for easy graph-reading information. This article presents new research that aims to fill the gap in understanding investment analysis for beginners by visualizing risk and return analysis on shares. The results reveal that changes in stock sales volume did not occur significantly, although the short and long-term MA charts for the four stocks tended to fluctuate, offering new insights into investment analysis and providing a basis for future development. The best accuracy results were on MSFT shares, with an achievement of 0.9532 and a loss value of 0.0014. Thus, MSFT shares can be used as a priority for investment. Therefore, this research adds a new dimension to the literature and paves the way for further investigations in risk and return analysis and stock prediction using deep learning.
Classification of cervical spine fractures using 8 variants EfficientNet with transfer learning Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Surawan, Tri; Lamandasa, Serlia Raflesia; Usuli, Sudarto; Kapuy, Holmes Rolandy; Digdoyo, Aji
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7065-7077

Abstract

A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%.
PENERAPAN TEKNOLOGI VIRTUAL REALITY DALAM PERMAINAN ULAR TANGGA Razi, Fahrul; Arman, Shevti Arbekti; hastomo, Widi
Jurnal Informatika Vol 8, No 3 (2024): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v8i3.11861

Abstract

Perkembangan teknologi informasi dan komunikasi saat ini telah berkembang dengan sangat pesat. Hal ini dapat dilihat dari banyaknya pengguna internet. Teknologi Virtual Reality Mobile pada era saat ini dapat diimplementasikan ke berbagai bidang pada kehidupan sehari-hari. Salah satunya adalah bidang permainan anak–anak. Virtual Reality pada Game membuat permainan menjadi lebih menarik dan interaktif. Permainan yang dapat dimainkan salah satunya yaitu permainan ular tangga yang mana permainan ini sudah jarang dimainkan pada saat ini. Game ular tangga adalah game yang dirancang dengan menerapkan Virtual Teknologi realitas yang akan mempermudah dan memaksimalkan ke seruan saat bermain Game Ular Tangga. Game ini di rancangan dengan menggunakan engine (pemrograman game) Unity 3D. Dengan memadukan teknologi virtual reality dan permainan ular tangga dapat meningkatkan minat anak–anak untuk memainkannya. Hasil yang didapat dari perancangan dan pembangunan ini adalah suatu Game ular tangga yang berbasis Virtual Reality (VR). Dimana dengan adanya permainan ini anak–anak dapat bermain sekaligus belajar tentang teknologi Virtual Reality yang didapatkan dari informasi yang disediakan dalam area permainan.
Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Sestri, Ellya; Terisia, Vany; Yusuf, Diana; Arman, Shevty Arbekti; Arif, Dodi
Semesta Teknika Vol 27, No 1 (2024): MEI
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v27i1.19691

Abstract

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.
Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks Sujatna, Yayat; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Yuningsih, Nia; Arif, Dody; Handayani, Sri Setya; Kardian, Aqwam Rosadi; Wardhani, Ire Puspa; Rere, L.M Rasdi
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p215-250

Abstract

The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of LSTM and GRU. Each model performs the training process in four epochs (200, 500, 750, and 1000). The results of observations in this study show that long-term predictions (28 days ahead) using four stacks of LSTM-GRU and daily training accumulation techniques produce better accuracy than the general method (using multiple outputs). From the observations we have made for predictions for the next 28 days, the model with the LGLG stack arrangement (LSTM-GRU-LSTM-GRU) produces the best accuracy at epoch 750 with an MSE LSTM-GRU 63.43762863. This study will undoubtedly continue in order to achieve even better precision, either by utilizing a new design or by further improving the technology we are now employing.
Transformasi Perilaku Menuju Zero Waste Melalui Edukasi Penggunaan Tumbler Indra Bakti; Hastomo, Widi; Saputro, Ahmad Eko; Hudaa, Syihaabul; Ambardi, Ambardi; Chufran, Indra Bakti; Fitriansyah, Reza
Wikrama Parahita : Jurnal Pengabdian Masyarakat Vol. 8 No. 2 (2024): November 2024
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jpmwp.v8i2.7695

Abstract

Kampanye tumbler telah berhasil meningkatkan kesadaran siswa dan komunitas sekolah tentang dampak negatif limbah plastik terhadap lingkungan. Melalui kampanye ini, pola konsumsi siswa dan seluruh entitas sekolah mengalami perubahan positif, dengan lebih banyak orang beralih dari penggunaan plastik sekali pakai ke penggunaan tumbler yang ramah lingkungan. Komitmen siswa terhadap penggunaan tumbler mencapai 93%, yang membuktikan pentingnya pendidikan dalam membentuk perilaku berkelanjutan dan gaya hidup ramah lingkungan. Kampanye ini berhasil meningkatkan pemahaman tentang pentingnya tumbler sebagai alternatif pengganti plastik sekali pakai, serta diharapkan dapat mendorong sekolah untuk menerapkan kebijakan pengurangan limbah plastik di lingkungan sekolah. Kegiatan pengabdian masyarakat ini merupakan integrasi kampanye tumbler dalam dunia pendidikan, di mana sekolah memiliki kesempatan untuk melibatkan siswa sebagai agen perubahan. Melalui edukasi yang menyeluruh, siswa dapat memahami dampak besar dari perubahan kecil yang mereka lakukan, seperti beralih menggunakan tumbler. Namun, dampak kampanye ini belum dapat diukur secara menyeluruh karena hanya melibatkan dua kelas (X-XI). Agar kegiatan ini dapat berkelanjutan, partisipasi aktif dari guru sangat diperlukan. Kampanye selanjutnya dapat melibatkan orang tua dan masyarakat sekitar sekolah, serta menggunakan konten visual yang menarik untuk menyampaikan pesan lebih efektif.
Predicting Crime Time Intervals Using Machine Learning Models Deswandi, Arief; Hastomo, Widi
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.881

Abstract

Understanding the time interval of crime can help optimize patrols and guards to identify crime-prone areas and estimate the time prone to crime. The urgency of this research lies in the need to develop more efficient methods for analyzing and preventing crime. By understanding the time pattern of crime, law enforcement can improve more effective prevention and law enforcement strategies. The methods used are DT, XGBoost, and CatBoost. This method was chosen because of its superior ability to handle large, complex, and unbalanced datasets. The evaluation was carried out using MAPE to measure the level of accuracy of crime clock predictions. The results show that XGBoost successfully predicts the time pattern of crimes with a MAPE of 8.29%, indicating a high level of accuracy. These results can be effectively applied to predict time-based crimes, helping to make better preventive decisions and improving the efficiency of security resource allocation.
ENHANCING SOLAR ENERGY EFFICIENCY: PREDICTIVE MODELING WITH XGBOOST AND LINEAR REGRESSION Hastomo, Widi; Digdoyo, Aji; Bayangkari Karno, Adhitio Satyo; Arif, Dodi
Jurnal Informatika Vol 9, No 1 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i1.12713

Abstract

Abstract Improving the reliability of the power grid system and operational efficiency is essential to facing future energy challenges. This study aims to provide added value to the management of the power grid, especially solar photovoltaic power plants (PLTS), by developing a more accurate predictive model for estimating energy output. By utilizing two real-time data sets, namely weather data and PLTS data, as well as machine learning methods, this study compares the performance of the XGBoost and Linear Regression (LR) models. We built the model to overcome high variability in energy output and maintain the stability of the power grid. The results show that XGBoost has a better performance with an MAE value of 38.08 compared to linear regression, which has an MAE of 80.23, indicating the superiority of XGBoost in predicting PLTS energy output. This study also opens up opportunities for further research with a focus on the application of other models such as random forests and neural networks, as well as improving data quality and parameter optimization to further improve prediction reliability and operational efficiency. The best-performing XGBoost model enables more efficient energy utilization and enhances the operational efficiency of PV solar power plants.
Co-Authors Adhitio Satyo Bayangkari Karno Ambardi, Ambardi Arif, Dody Aryo Nur Utomo Asy-Syifa, Zahwa Zia Azis, Nur Bakti, Indra Bayangkari Karno, Adhitio Satyo Chufran, Indra Bakti Daruningsih, Kukuh Deon Strydom Deswandi, Arief Digdoyo, Aji Dodi Arif Fahrul Razi Fahrul Razi Fiqhri, Zul Fitriyani Fitriyani Handayani, Sri Setya Holmes Rolandy Kapuy Hudaa, Syihaabul Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Ire Puspa Wardhani Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Karno, Adhitio Satyo Bayangkari Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti Lussiana ETP Lyscha Novitasari Maeda, Serly Masriyanda, Masriyanda Muhammad Mardani, Muhammad Nisfiani, Ervina Nurhidayati, Aulia Putra, Yoga Rarasto Putri, Dhea Ananda Rahman, Ibadu Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Saputro, Ahmad Eko Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Soleha, Maratus Sudarto Usuli Sudarwanto, Pantja Sundoro, Aries Surawan, Tri Sutarno Sutarno Syamsu, Muhajir Vany Terisia Yayat Sujatna, Yayat Yulianti Muthmainnah, Yulianti Yuningsih, Nia YUSUF, DIANA