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All Journal International Journal of Electrical and Computer Engineering Sainteks Jurnal Teknik Elektro Jurnal Edukasi dan Penelitian Informatika (JEPIN) Semesta Teknika Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Jurnal Pilar Nusa Mandiri CogITo Smart Journal Indonesian Journal of Information System Justek : Jurnal Sains Dan Teknologi Jurnal ULTIMA InfoSys Jurnal Teknologi Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Infotekmesin RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi JIKA (Jurnal Informatika) Journal of Telecommunication, Electronics and Control Engineering (JTECE) Insearch: Information System Research Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Journal of Informatics and Information Security Prosiding SeNTIK STI&K RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi

Bayangkari Karno, Adhitio Satyo

Universitas Gunadarma

Author-ID : 1568479

Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology 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

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.

Deteksi Tumor Otak Dengan CNN Resnet-152 Aji Digdoyo; Tri Surawan; Adhitio Satyo Bayangkari Karno; Dyah Ruri Irawati; Yasin Effendi
Jurnal Teknologi Vol 9, No 2 (2022): Jurnal Teknologi
Publisher : Universitas Jayabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31479/jtek.v9i2.128

Penyakit tumor di Indonesia menduduki tingkat kematian terbesar ke-5 setelah diabetes, stroke, ginjal dan darah tinggi. Kurangnya penanganan dini, jumlah paramedis dan peralatan yang minim adalah penyebab utama tingginya tingkat kematian. Artificial Intelligence (AI) memiliki potensi tinggi untuk berkontribusi membantu pasien dan para medis mendiagnosa tumor secara langsung, cepat dan murah. Salah satu metode AI dipergunakan dalam penelitian ini adalah CNN dengan arsitektur ResNet-152. Dengan melakukan training dan validasi sejumlah 2.870 image menghasilkan nilai akurasi masing-masing 99% dan 81%. Untuk lebih memastikan hasil yang diinginkan, maka model yang diperoleh dari training dipergunakan kembali untuk pengujian, dengan hasil nilai akurasi sebesar 96% dan akurasi untuk tiap kelas adalah glioma (97%), meningioma (95%), no_tumor (98%) dan pituaty (96%).

Analisis Data Time Series Menggunakan LSTM (Long Short Term Memory) Dan ARIMA (Autocorrelation Integrated Moving Average) Dalam Bahasa Python. Adhitio Satyo Bayangkari Karno
ULTIMA InfoSys Vol 11 No 1 (2020): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Abstract — This study aims to predict time series data by using two methods, the first method commonly used is statistics Autocorrelation Integrated Moving Average (ARIMA model) and the second method which is relatively new, namely machine learning Long Short Term Memory (LSTM). Before the data is processed by both methods, data cleaning and data optimization are carried out. Data optimization is a transformation process to eliminate elements of trends and variations from data. The transformation consists of 7 results of a combination from Log processes, Moving Average (MA), Exponential Weigh Moving Average (EWMA), and Differencing (Diff). The seven processes are each used in the ARIMA and LSTM processes. So that 14 predictions will be obtained (7 from the ARIMA process and 7 from the LSTM process). From the 14 prediction results obtained the smallest RMSE value for ARIMA is 2% and the smallest RMSE value for LSTM is 1%. The results of this study using 7 combinations of transformation processes, can increase the level of accuracy of predictions from ARIMA and LSTM. Where the accuracy of LSTM learning machines by using Telkom's stock data has higher accuracy than ARIMA.

Mengatasi Ketimpangan Data Deep Neural Network dengan Pelipatan Fitur Data Klasifikasi Spektroskopi Darah Widi Hastomo; Adhitio Satyo Bayangkari Karno; Sutarno Sutarno; Dodi Arif; Eka Sally Moreta; Sudjiran Sudjiran
Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Vol 8 No 2 (2022): Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Muhammadiyah Buton

Permasalahan utama dalam penelitian ini adalah ketimpangan data masukan menghasilkan dampak negatif yang signifikan terhadap hasil prediksi dari model Deep Neural Network (DNN). Kemampuan klasifikasi DNN sangat akurat hanya untuk dataset yang berimbang, namun DNN pada awalnya tidak di rancang untuk menangani ketimpangan data. Ketimpangan data merupakan hal yang sering dijumpai dalam dunia nyata, menjadikan ini sebagai tantangan besar dalam prediksi klasifikasi menggunakan model DNN. Penelitian ini berfokus untuk memprediksi tingkat kandungan kolesterol tinggi, kolesterol rendah dan hemoglobin, menggunakan data kasus di kompetisi Zindi Blood Spectroscopy Classification Challenge. Dengan melakukan analisa data, cleansing outlier, fine tunning, model neural network, jaringan pengelompokan data target dengan kategori sejenis, urutan pemrosesan, pemilihan nilai pelipatan (7 pelipatan) yang tepat terhadap data input train dan data test serta epoch 60, dapat meningkatkan hasil nilai score prediksi yang cukup tinggi sebesar 0.94594.

Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management Widi Hastomo; Nur Aini; Adhitio Satyo Bayangkari Karno; L.M. Rasdi Rere
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Indonesia is committed to reducing greenhouse gas (GHG) emissions through a nationally determined contribution (NDC) scheme. The target to reduce GHG emissions is 29% through the business as usual (BAU) scheme or 41% with international aid. These ambitious targets require transformations in energy, food, and land-use systems, which need to cope with the potential trade-offs among many targets, such as food security, energy security, avoided deforestation, biodiversity conservation, land use competition, and freshwater use. Mitigation and adaptation have complementary roles in responding to climate change at both temporal and spatial scales. This paper aims to perform simulations and predictions on manure management emissions producing CO2eq using machine learning methods of long short-term memory (LSTM) and gated recurrent unit (GRU). The hidden layer architecture used was six combinations, while the dataset was obtained from the fao.org repository. The optimizer used in this paper was RMSprop, with a graphical user interface using the Streamlit dashboard. The results of this study are (a) cattle with fifteen epochs using hidden layer four combinations (LSTM, GRU, LSTM, GRU) yielded RMSE 450,601; (b) non-dairy cattle with fifteen epochs and one hidden layer (GRU, GRU, GRU, GRU) yielding RMSE 361.421; (c) poultry birds with twelve epoch values and three hidden layers (GRU, GRU, LSTM, LSTM) resulted in an RMSE value of 341.429. The challenges faced were the determination of epochs, the combination of hidden layers, and the characteristics of the relatively small number of datasets. The results of this study are expected to provide added value for developing better decision support tools and models to assess emission trends in the livestock sector and develop CO2eq emission mitigation strategies that lead to sustainable fertilizer management practices.

Exloratory Data Analysis Untuk Data Belanja Pelanggan dan Pendapatan Bisnis Widi Hastomo; Adhitio Satyo Bayangkari Karno; Sudjiran; Dodi Arif; Eka Sally Moreta
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1547

A more quantifiable perspective is assuming the role of mechanistic management in an effort to enhance business based on its capacity to transform data into knowledge and insight. The industry has not completely supported its business strategy also with driven data. Using a transaction dataset taken from one of the Kaggle.com challenges, this experiment attempts to determine consumer spending patterns and Retail Fashion business revenues (H&M Personalized Fashion Recommendations). The results of the experiment are the number of transactions based on customer age, the most sales product and one-time purchased item, and the type of product that generates the highest and smallest income. The approach employed is EDA using the Python language. In order for businesses to generate analytical findings that provide future perspectives and to help identify the gap by delivering analytical results in the form of suggestions that can be perpetuated, the findings of this experiment are intended to support the capabilities of simulation. The challenge in this experiment is the abundance of datasets, which necessitates a suitable operating environment.

Deep Learning - Prediksi dan Resiko Investasi Enam Saham Bank di Indonesia Nurdina Widanti; Tri Surawan; Adhitio Satyo Bayangkari Karno; Aji Digdoyo; Nani Kurniawati; Rachmat Nursalam; Harini Agusta
Jurnal Teknologi Vol 10, No 1 (2022): Jurnal Teknologi
Publisher : Universitas Jayabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31479/jtek.v10i1.194

Stock prediction and risk level are important for investors, but this ability can only be done by experts and takes a long time. The rapid development of technology today demands that decisions be made quickly and precisely. Deep Learning (DL) is one of the Artificial Intelligence (AI) methods that are able to analyze and predict stock values accurately, in real-time, and without much human intervention. Analysis of risk and correlation between stocks by calculating daily returns using the moving average (MA) method. Dataset of 6 bank shares obtained from yahoo-finance, namely Bank Central Asia (BBCA), Bank Rakyat Indonesia (BBRI), Bank Mandiri (BMRI), Bank Nasional Indonesia (BBNI), Bank Rakyat Indonesia Syariah (BRIS), and Bank Tabungan Negara (BBTN). The volume of share sales increased significantly only in BBRI and BBNI shares, although 5 bank shares (except BRIS) experienced price increases. The highest correlation occurred between BBNI and BMRI shares with a value of 97%, 92% between BMRI and BBCA shares, and 91% between BBNI and BBCA. Analysis of risk and expected return shows that BRIS has the highest risk and expected return of 0.042245 and 0.002986, respectively. BBCA shares have the lowest risk and expected return at 0.015392 and 0.000695, respectively. The results show that future predictions have decreased, namely BBRI, BBNI, and BBTN, and rose for BBCA, BMRI, and BRIS stocks.

MobilenetV2 Architecture To Detect Covid-19 X-Ray Imagery Widi Hastomo; Adhitio Satyo Bayangkari Karno; Ellya Sestri; Eva Karla; Stevianus Stevianus; Dodi Arif
Justek : Jurnal Sains dan Teknologi Vol 5, No 2 (2022): November
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v5i2.11820

Abstract: The COVID-19 pandemic has hit all over the world, in the last two years and has changed the pace, structure and nature of social life. This study aims to detect COVID-19 using a chest x-ray image dataset sourced from kaggle.com, which is divided into 4 categories. The proposed method is CNN with MobileNetV2 architecture, by dividing 80% train data and 20% test data into 224x224 and batch size 32. The optimizer uses SGD, lr 0.005, momentum 0.9 and epoch 20. The results of the study with the achievement of precision values for the covid category 0.99, lung opacity 0.98, normal 0.96 and viral pneumonia category reached 0.99. Further studies can use the development of the CNN model and can try with other optimizers.Abstrak: Pandemi covid-19 telah melanda diseluruh dunia, dalam dua tahun terakhir dan mengubah langkah, struktur dan sifat kehidupan bermasyarakat. Penelitian ini bertujuan untuk mendeteksi covid-19 menggunakan dataset citra chest x-ray yang bersumber dari kaggle.com, yang dibagi menjadi 4 kategori. Metode yang diusulkan yaitu CNN dengan arsitektur MobileNetV2, dengan membagi data train 80% dan data test 20% ukuran citra menjadi 224x224 dan batch size 32. Optimizer menggunakan SGD, lr 0.005, momentum 0.9 serta epoch 20. Hasil penelitian ini dengan capaian nilai presisi untuk kategori covid 0.99, lung opacity 0.98, normal 0.96 dan kategori viral pneumonia mencapai 0.99. Studi selanjutnya dapat menggunakan pengembangan dari model CNN serta dapat mencoba dengan optimizer yang lain.

REAL-TIME STRUCTURAL ANALYSIS BASED ON MACHINE LEARNING FOR CUSTOM PRODUCT DESIGN: A CASE STUDY OF ORTHOPEDIC FIXATOR PRODUCT Aji Digdoyo; Adhitio Satyo Bayangkari Karno; Widi Hastomo; Agita Tunjungsari; Nada Kamilia; Indra Sari Kusuma Wardhana; Nia Yuningsih
J-ICON : Jurnal Komputer dan Informatika Vol 11 No 1 (2023): Maret 2023
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v11i1.9919

Mass customization is related to increasing the balance between the needs of companies that are focused on customers on conditions of production flexibility and efficiency. Product adjustment according to customer needs can increase the company's competitiveness. However, special production processes and adjustments are time consuming and cost inefficient. Parametric product modeling is a fairly popular technique for dealing with this problem. However, it still has challenges related to the high cost of software and a workforce that has special expertise in the field of quality control. In addition, product-specific designs cannot be tested quickly, resulting in a long production time. This study proposes a machine learning (ML) method that aims to obtain a fast time structure to analyze the production of orthopedic fixators. This research process requires a collection of training data with product attributes, physical characteristics, quality, selected ML techniques, and determination of the appropriate set of hyperparameters. Optimization results were obtained using the gradient boosting method with a value of . With these results, the orthopedic fixation device can be used in the case study of developing this machine learning model.

Brain Tumor Classification Using Four Versions of EfficientNet Widi Hastomo; Adhitio Satyo Bayangkari Karno; Dody Arif; Indra Sari Kusuma Wardhana; Nada Kamilia; Rudy Yulianto; Aji Digdoyo; Tri Surawan
Insearch: Information System Research Journal Vol 3, No 01 (2023): 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.v3i01.5810

Medical image processing approaches for detecting brain cancers are still primarily done manually, with low accuracy and taking a long period. Furthermore, this task can only be done by professionals with a high degree of medical competence, and the number of experts is obviously restricted in comparison to the large number of patients who need to be treated. With the growth of artificial intelligence and the rapid development of computers in terms of processing speed and storage capacity, it is feasible to assist doctors in classifying the existence of tumors in the head. This study employs four variations of the EfficientNet architecture to train a model on a variety of MRI imaging data. The model version B1 was shown to be the best in this investigation, with 98% accuracy, 99% precision, 95% recall, and 97% f1 score from versions B0 to B3 (4 versions). These results are excellent, but they do not rule out additional study utilizing various forms of design.

Co-Authors Agita Tunjungsari Ahmad Eko Saputro Aji Digdoyo Aji Digdoyo Aji Digdoyo Arif, Dody Aryo Nur Utomo Azis, Nur Bakti, Indra Deon Strydom Diana Yusuf Digdoyo, Aji Dodi Arif Dodi Arif Dodi Arif Dody Arif Dyah Ruri Irawati Eka Sally Moreta Eka Sally Moreta Elliya Sestri Eva Karla, Eva Fahrul Razi Faqihudin Faqihudin Handayani, Sri Setya Harini Agusta Holmes Rolandy Kapuy Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Ire Puspa Wardhani Iwan Setiawan Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti LM Rasdi Rere LM Rasdi Rere Lussiana ETP Masriyanda, Masriyanda Meika Syahbana Rusli Nada Kamilia Nada Kamilia Nada Kamilia Nani Kurniawati Nia Yuningsih Nia Yuningsih Nisfiani, Ervina Nur Aini Nurdina Widanti Rachmat Nursalam Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Reza Fitriansyah Rudy Yulianto Rudy Yulianto Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Stevianus Stevianus Sudarto Usuli sudjiran Sudjiran Sukardi, Sukardi Sundoro, Aries Surawan, Tri Sutarno Sutarno Sutarno Sutarno Tri Surawan Tri Surawan Vany Terisia Widi Hastomo Yasin Effendi Yayat Sujatna, Yayat Yuningsih, Nia YUSUF, DIANA

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