Garuda - Garba Rujukan Digital

PENGARUH PRODUK DOMESTIK BRUTO, JUMLAH UANG BEREDAR, INFLASI DAN BI RATE TERHADAP INDEKS HARGA SAHAM GABUNGAN DI INDONESIA PERIODE 2007 - 2013 Dodi Arif
Jurnal Ilmiah Ekonomi Bisnis Vol 19, No 3 (2014)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (574.103 KB)

The capital market is an alternative investment that can provide the return on investment income and income shares of other investment securities that are traded in the stock market. To obtain the optimal stock returns in accordance with the risk that it contains, an investor should also know the actual stock price index is the index number of prices of stocks that have been compiled and calculated so as to produce a trend of stock price changes. By knowing the stock price index, investors can determine the condition of the capital markets in general. In capital markets, changes in stock prices are influenced by factors external systematic risk as the Gross Domestic Product (GDP), the money supply (M2), the level of inflation, SBI, and others. By using multiple linear regression techniques will be the factors that affect the bersiko external systematic changes in stock prices in Indonesia during the period 2007 to 2013. The results of the analysis carried out will be seen how big factors that will affect the systematic risk of the stock price in Indonesia. Keywords: Risk, Stock price.JUB, GDP, BI Rate

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

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

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

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.

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 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.

Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.

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

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.

Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet Adhitio Satyo Bayangkari Karno; Dodi Arif; Indra Sari Kusuma Wardhana; Eka Sally Moreta
Journal of Informatic and Information Security Vol. 2 No. 1 (2021): Juni 2021
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/abbs9m42

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%).

Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.

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.

Title

Found 9 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 9 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 9 Documents
Search