Article Per Year (5 Year)
p-Index From 2021 - 2026
2.282
P-Index
This Author published in this journals
All Journal International Journal of Artificial Intelligence Research ComTech: Computer, Mathematics and Engineering Applications bit-Tech eCo-Buss Algor eCo-Fin
Riki Riki
Universitas Buddhi Dharma
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search
Journal : bit-tech

 1 
Business Intelligent Method For Academic Dashboard Niki Destiandi; Aditiya Hermawan; Riki Riki
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (793.382 KB) | DOI: 10.32877/bt.v1i2.42

Abstract

Business Intelligence Lifecycle is a method for developing effective business intelligence (BI) decision support applications such as the Academic Dashboard. There are six steps in the BI life cycle from the beginning to implementation such as Justification, Planning, Business Analysis, Design, Construction, and Deployment, where each step is developed to be more detailed in accordance with BI's environmental needs (L. T. Moss). Management of tertiary institutions in Indonesia requires appropriate and fast academic reports that make it possible to make strategic decisions and in order to improve the quality of education. Academic evaluations can be presented with the dashboard being easy for decision making. The dashboard is a page that displays graphics as a KPI from an organization and provides everything needed to make key research results [4]. Problems that occur there are a lot of academic data that is stored but when turning it into a report at the time of evaluation academic activities are difficult and require a long time and require monitoring, evaluation and measurement tools that can measure the performance of universities. The Business Intelligence Lifecycle can be used to provide information to produce high resolution by adding KPI components.
Implementation of Naïve Bayes Algorithm for Classification of Mental Health of Social Media Users Aditiya Hermawan; Riki Riki
bit-Tech Vol. 4 No. 2 (2021): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v4i2.282

Abstract

Social media has become a human need to interact in everyday life. Apart from being a means of communication, social media also has the additional function of exchanging information on the internet in various forms including writing, images and videos. One of the social media that has many users is Instagram, where Instagram offers information sharing features in the form of images, photos and short videos. The purpose of this feature is for users to express themselves and attract the attention of others, thereby creating feelings of happiness and increasing self-confidence. In addition to positive impacts, there are also negative impacts on users, for example excessive use that causes addiction so that it can cause mental health disorders. Mental health needs to be handled properly so that it does not continue to get worse, but there are several obstacles in seeing a psychiatrist in mental health, including limited access and also negative stigma if someone sees a psychiatrist. Therefore, a tool is needed that can be an early indication in knowing the level of mental agitation, especially in the use of Instagram. Classification in data mining can help provide initial information on a person's condition in his mental health. The Naïve Bayes algorithm provides an accuracy rate of 92.5% in classifying mental health on data sets that have been clustered. Good accuracy can help social media users know their mental health condition.
Implementation of Linear Regression Algorithm to Predict Stock Prices Based on Historical Data Jelvin Putra Halawa; Aditiya Hermawan; Junaedi .; Riki Riki
bit-Tech Vol. 5 No. 2 (2022): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v5i2.616

Abstract

Stock investment is in great demand by investors because it can provide large profits with large risks or losses, in accordance with the investment principle of low risk low return, high risk high return. Stock prices that fluctuate in a very short time make it difficult for investors to predict stock prices in the future, so investors must pay more attention and gather as much information as possible regarding the shares to be bought or sold. This study aims to create a data mining model using a Linear Regression algorithm that can predict daily stock closing prices to provide information that supports investors in stock transactions. The data used is historical data on daily stock prices for 10 companies in the last 8 years for the period 25 February 2013 – 25 February 2021. Historical stock price data will be prepared using the Noving Average method and create a data mining model using the linear regression method to generate stock price prediction models. The resulting model can be used to predict stock prices well enough to assist investors in making investment decisions to obtain large profits with low risk.
Clustering Mental Health pada Pengguna Instagram Menggunakan Algoritma K-Means Yuliastati Putri Sugiarta Karlim; Aditiya Hermawan; Ardiane Rossi Kurniawan Maranto; Riki Riki
bit-Tech Vol. 6 No. 1 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i1.880

Abstract

The use of Instagram too often can have an impact on the mental health of its users. Mental health that is not good requires early treatment so that it does not have a widespread impact on other health. Mental illness requires a professional to treat it as an effort to prevent a disease from getting worse. However, the stigma attached to sufferers is one of the significant causes behind the reluctance to seek treatment. Therefore we need a way so that Instagram users can find out for themselves the condition of their mental health. One way is to do Clustering the use of Instagram so that it can provide an early indication of a person's mental health. From the proposed model we can find out the categories of 600 respondents who were collected using a questionnaire with 10 main attributes. The proposed model is k-means with 3 clusters determined using the elbow method. In this study, the last centroid obtained through calculations was used to evaluate the k-means by comparing the results of the k-means calculations with the results of psychologists. The results of the K-means evaluation have an accuracy of 73.83% so that the last centroid can be applied to web-based applications that have been created. This mental health clustering model is expected to be able to help the community to get mental health conditions early and reduce the negative stigma that exists and can be used as evaluation material in using social media more wisely.
Optimizing Virtual Culinary Tours Using Character-based Interaction and Finite State Machine (FSM) Margaretha Natalya; Aditiya Hermawan; Riki Riki
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1886

Abstract

This research focuses on the development of a Virtual Tour application utilizing Finite State Machine (FSM) to enhance the interaction of a Non-Playable Character (NPC) as a tour guide in a 3D virtual environment. The primary issue addressed is the significant impact of the COVID-19 pandemic on the tourism industry, which led to travel restrictions and limited opportunities for visitors to explore tourist destinations physically. The aim of the study is to create a virtual tourism experience as an alternative solution, enabling users to explore historical sites like Pasar Lama Tangerang remotely through Google Cardboard VR. To achieve this, the NPC’s behavior is controlled using FSM, allowing the character to transition between states—idle, walking, and talking—based on user interactions. Data was collected through user testing with a Likert scale questionnaire, evaluating user satisfaction and the effectiveness of the FSM method. The results revealed a 74.35% positive user rating, categorized as Good, demonstrating the potential of FSM to provide an interactive, engaging, and educational virtual tour experience. These findings highlight the effectiveness of FSM in creating a dynamic and user-responsive virtual tour, offering significant benefits to the tourism sector by providing an innovative, accessible, and immersive way for potential visitors to explore destinations during travel restrictions. This research contributes to the growing field of e-tourism, showcasing the potential of virtual reality and FSM to transform the tourism industry in times of crisis.
Enhancing Stock Price Forecasting: Optimizing Neural Networks with Moving Average Data Aditiya Hermawan; Stanley Ananda; Junaedi; Edy; Riki Riki
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2196

Abstract

This research focuses on optimizing a neural network model for stock price prediction using Particle Swarm Optimization (PSO), considering the inherent risks and potential high returns associated with stock investment. Given the challenges posed by stock price volatility, this study combines Moving Average (MA) a fundamental statistical technique in stock market analysis with advanced data mining approaches, specifically neural networks and PSO, to enhance prediction accuracy. The primary objective is to improve the efficiency of neural networks by minimizing error rates and equipping investors with more reliable tools for financial decision-making. The proposed methodology involves converting historical stock price data into a Simple Moving Average (SMA) over a 5-day period, followed by optimizing a neural network model using PSO. This optimization process fine-tunes key parameters, particularly the weight distributions of various stock market indicators, including Open SMA, High SMA, Low SMA, and Close SMA. Model performance is evaluated using Root Mean Square Error (RMSE) as a validation metric. The findings indicate a significant enhancement in the predictive accuracy of the neural network model after PSO optimization. The optimal configuration is identified in a two-layer neural network with a specific node arrangement. This optimized model not only improves stock price forecasting precision but also has practical implications for investors and financial analysts in risk management and profit maximization.
Co-Authors Aditiya Hermawan Aditiya Hermawana Ardiane Rossi Kurniawan Maranto Ceng Giap Yo Edy Hariyanto Hariyanto Jelvin Putra Halawa Junaedi Margaretha Natalya Niki Destiandi Novita Trisetyo Oscar Hasan Putra Robby Williams Satria Abadi Setyawan Widyarto Stanley Ananda Yuliastati Putri Sugiarta Karlim Yusuf Kurnia Yusuf Kurniaa