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All Journal Jurnal Teknik Elektro Semesta Teknika Jurnal Pilar Nusa Mandiri Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jutech: Jurnal Teknologi Informasi Jurnal Sistem Informasi
Sestri, Ellya
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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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.
Metode Decision Tree Dalam Klasifikasi Kredit Pada Nasabah PT Bank Perkreditan Rakyat (Studi Kasus : PT BPR Lubuk Raya Mandiri) Yusuf, Diana; Sestri, Ellya
Jurnal Sistem Informasi (JUSIN) Vol 1 No 1 (2020): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v1i1.855

Abstract

Data mining is a new technology that has been successfully applied in many fields. Many problems are solved by data mining algorithms C4.5 as a supporter.Classification is one of the methods contained in data mining and not a few researchers who use the classification methods in solving problems. Credit analysis will be done with the digging data against existing data customer credits based on atribut-atributnya with tekanik data mining algorithm C4.5. The algorithm C4.5 is itself a group of decision tree algorithm. This algorithm has input in the form of training and samples. Data mining technique used to classify loans with algorithm C4.5. Analysis and processing of data use applied tools of RapidMiner v 7.3
Sistem Pakar Diagnosa Penyakit Tetelo Menggunakan Metode Certainty Factor Terisia, Vany; Sestri, Ellya; Raihan, Dendi
Jurnal Sistem Informasi (JUSIN) Vol 3 No 1 (2022): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v3i1.1646

Abstract

In today's technological era, computers are not only used as a tool to complete human work but can also run applications designed to access information quickly. Expert systems are one part of artificial intelligence. An Expert Systems application is an application that transfers expert knowledge to a computer. That way the computer can also solve problems as is usually done by experts. Expert systems have been widely developed in various fields, including the field of animal husbandry. Detection of tetelo disease can use an expert system application. One of them is to detect Tetelo disease. Therefore, breeders can access information about Tetelo disease that is diagnosed with the help of an expert system that uses the Certainty Factor method. The Certainty Factor method serves to determine the degree of certainty of the diagnosis result.
Islam Dan Sains Teknologi Modern Pribadi, Sarli Amri Teguh; Sestri, Ellya
Jurnal Teknologi Informasi (JUTECH) Vol 1 No 1 (2020): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v1i1.850

Abstract

Tema agama dan sains teknologi selalu menjadi wacana diskusi yang menarik. Sains dalam kehidupan manusia selalu berkembang dan berubah. Sains dan bahkan seolah tidak pernah terprediksikan sebelumnya. Sains dan teknologi di Barat mengalami perkembangan pesat pada abad 17-18 sejak revolusi keilmuan terhadap otoritas keagamaan pada abad 12-13.[1]Sains Islam adalah sains yang dikembangkan oleh kaum muslimin sejak abad Islam ke-2 hingga ke-9, merupakan peradaban yang paling produktif dibandingkan dengan peradaban manapun diwilayah sains, dan sains Islam berada di garda depan berbagai kegiatan keilmuan, mulai dari bidang kedokteran sampai astronomi. Dan disinlah menjadi penting bahwa antara sains dan Islam memiliki hubungan yang sangat erat, karena sains Islam adalah lahir dari worldview dan pandangan hidup Islam yang terderivasi dari al-Qur’an dan Hadits sebagai otoritas kebenaran.
Analisis Faktor Penentu Preferensi Masyarakat Milenial Dalam Menggunakan Sistem Pembayaran Secara Cashless Di Tangerang Selatan Husnayetti, Husnayetti; Sestri, Ellya; Novida, Irma
Jurnal Teknologi Informasi (JUTECH) Vol 1 No 1 (2020): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v1i1.851

Abstract

The study focuses on the determinants of the millennial community preference in using cashless payment. The aim of research to identify factors that considered the millennial to use cashless payment. In theory, the factors that encourage people to adopt and use electronic payment system as follows: Socio-demographic consisting of age, sex, education and language, Financial, Technology (frequency of use of mobile banking phone, a personal computer, the Internet, PDAs and the use of the service over the phone) and Supply-side by counting the number of POS (Pont Off Sale) and ATM. Furthermore, the high range of the technology and the Internet and systems digital payment (cashless)The earliest touching the millennial so in this study conducted in the millennial who use cashless payment in South Tangerang method is purposive sampling, the data obtained through questionnaires distributed to 100 respondents. The questionnaire prepared by the Likert scale and analyzed using factor analysis and descriptive analysis. From the results of the study showed that the determinants of the millennial use payment cashless is the ability of financial (X1) with a loading factor 0.736, Convenience Payment (X2) with loading factor of 0.732, Ease of transacting (X3) with a value of 0.875 and Special Promo (X4) with a value of 0.826-factor loading.
Mengiplementasikan Vector Space Model Similarity Euclidean Distance Menggunakan TFIDF Pada Klasifikasi Teks Bahasa Indonesia Fitriansyah, Reza; Sestri, Ellya; Terisia, Vany
Jurnal Teknologi Informasi (JUTECH) Vol 3 No 2 (2022): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v3i2.2034

Abstract

Weighting based on the term with stemming techniques to get the basic word form term in question. This will the application of the Indonesian language text classification machine using the K-Nearest Neighbor algorithm and the Vector Space Model method on the TFIDF frequency weighting of the number of words and the Euclidean Distance function. comparison between the test documents and the test sample collection Using news documents as learning documents, a total of 10 (10) documents with 3 (three) categories, produces an Precision and Recall 90.00% for k = 5 using frequency weighting in words with the Euclidean Distance function.
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.
PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY Setiawati, Popong; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Sestri, Ellya; Kasoni, Dian; Arif, Dodi; Razi, Fahrul
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6126

Abstract

In countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar power plants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar power plants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems.
Implementasi Teknik Clustering Untuk Pengelompokan Mobil Bekas Berdasarkan Grade Pada Mobi Auto Yusuf, Diana; Sestri, Ellya; Razi, Fahrul
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 6 No. 2 (2023): J-SISKO TECH EDISI JULI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v6i2.8352

Abstract

Penelitian ini bertujuan untuk mengimplementasikan teknik clustering dalam pengelompokan mobil bekas berdasarkan grade pada perusahaan Mobi Auto. Metode clustering yang digunakan ialah K-Means Clustering untuk mengelompokkan mobil bekas ke dalam  3 (tiga) grade yakni grade A (Mobil Kualitas Sangat Baik), grade B (Mobil Kualitas Baik), dan grade C (Mobil Kualitas Rata-Rata). Data fitur kendararaan, kondisi fisik, riwayat perawatan, harga dan atribut tambahan dikumpulkan dan digunakan sebagai variabel dalam analisis klasterisasi. Hasil penelitian ini dapat memberikan pengelompokkan mobil bekas sesuai dengan grade yang ditetapkan oleh perusahaan. Hal ini akan membantu Mobi Auto dalam mengelola stok mobil bekas dengan lebih efisien, memberikan informasi yang akurat kepada pelanggan dan meningkatkan pengalaman pembelian mobil bekas. Implementasi K-Means Clustering ini dapat menjadi alat yang bermanfaat dalam pengelompokkan mobil bekas berdasarkan grade di perusahaan Mobi Auto. Penelitian ini memberikan dasar bagi pengembangan sistem pengelompokkan yang lebih canggih dan efektif di masa depan, serta memberikan manfaat dalam pengolahan dan analisis data secara keseluruhan untuk perusahaan penjualan mobil bekas.    
Co-Authors Azis, Nur Dodi Arif Fahrul Razi Husnayetti, Husnayetti Ignatius Joko Dewanto, Ignatius Joko Kamilia, Nada Kardian, Aqwam Rosadi Karno, Adhitio Satyo Bayangkari Kasoni, Dian Masriyanda, Masriyanda Novida, Irma Pribadi, Sarli Amri Teguh Raihan, Dendi Rasyiddin, Ahmad Reza Fitriansyah Setiawati, Popong Shevti Arbekti Arman Sundoro, Aries Vany Terisia Widi Hastomo YUSUF, DIANA