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INDONESIA
PROCEEDING IC-ITECHS 2014
Published by Sekolah Tinggi Informatika dan Komputer Indonesia Malang
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Core Subject : Science, Education,
Computer Science & IT Education
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Articles 235 Documents
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Assistive Eyewear: Arduino Powered Eyeglasses for Blind Individuals Fernandez, Jane Motas; valencia, armie; daniel, villano; Cunanan, Delbert Carlos D
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1671

Abstract

The quality of life for individuals with visual impairments has been significantly enhanced by assistive technologies. This paper introduces a new solution called Arduino Powered Eyeglasses (APE) for Blind Individuals tailored specifically for blind individuals. APE integrates sophisticated sensors, microcontrollers, and audio feedback systems into a wearable device, enhancing spatial awareness and navigation capabilities. Utilizing ultrasonic sensors, the system detects obstacles in the user's path and delivers real-time audio feedback through a buzzer embedded in the eyeglasses frame. Furthermore, it has an embedded vibration motor that acts as a feedback system as well when navigating an outdoor and indoor environment that is noisy. The design, implementation, and preliminary evaluation of APE are presented herein, emphasizing its effectiveness in aiding blind individuals with various daily tasks and promoting independence and mobility. APE's affordability and user-friendly interface position it as a promising solution for empowering individuals with visual impairments to navigate the world with increased confidence and autonomy.
Heartcare: Predictive Analytics for Early Detection and Prevention Rosli, Daniyal; Din, Mazura Mat; Idrus, Zanariah
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1672

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, often due to late detection and prevention. Heartcare aims to leverage predictive analytics to facilitate early detection and prevention of heart diseases. By integrating machine learning algorithms such as Decision Trees, Random Forests, and Logistic Regression, Heartcare provides healthcare professionals with a powerful tool for patient health monitoring. This study focuses on developing a predictive model to assess heart disease risk using patient-specific data, such as age, sex, BMI, and lifestyle factors. The outcomes will enable healthcare professionals to make informed decisions, potentially saving lives and reducing healthcare costs.
The Role of Data Science in Optimizing Business Decisions in the Digital Era Samudra, Dewa; Ulya, Annisa Zulfa; Sulitiani, Heni
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1673

Abstract

Data science has become a key element in business transformation in the digital era. With the ability to analyze large and diverse datasets, data science helps organizations identify patterns, make predictions, and support strategic decision-making. The application of technologies such as machine learning, big data, and artificial intelligence enables companies to enhance operational efficiency, understand consumer behavior, and create relevant innovations. This article discusses the role of data science in supporting the growth of digital businesses, the challenges faced, and the opportunities companies can leverage to achieve a competitive advantage.
Early Prediction of Mental Health Disorder Among Higher Education Students Using Machine Learning Mohd Asni, Muhammad Luqman Hakim; Mohd Zukhi, Mohd Zhafri; Mat Din, Mazura
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1674

Abstract

In spite of the fact that mental health illnesses are quite common among students in higher education, early detection continues to be a difficult task. This study seeks to determine the use of machine learning to forecast the occurrence of mental health issues in this group. Various machine learning methods were explored to analyze the data collected from higher education students and to identify potential risk factors associated with mental health issues. Through the development of a model that is capable of accurately predicting the risk of mental health illnesses, the project intends to facilitate early intervention and improve the overall well-being of their student population.
Intelligence Book Recommendation System Using Collaborative Filtering Nabilah, Nisa; Zanariah, Zanariah
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1675

Abstract

The rapid growth of online literary material has changed the way users discover books, revealing the limitations of traditional recommendation algorithms. This paper presents a review about an intelligent book recommendation system that uses collaborative filtering (CF) and artificial intelligence techniques to address major obstacles such as cold-start issues, data scarcity, and privacy concerns. The suggested method guarantees customized, accurate, and diversified recommendations by merging hybrid approaches such as CF with content-based filtering and matrix factorization. To measure performance, the researchers employ publicly accessible datasets, rigorous preprocessing approaches, and assessment criteria like as accuracy, recall, and F1-score. This project intends to rethink the book discovery process by solving basic issues and applying a privacy-conscious design, while also providing a scalable and user-friendly platform for tailored recommendations.
Cryptocurrency Price Forecasting Using ARIMAX: Conceptual Framework Mohamad Fuad, Muhammad Fauzan; Din, Mazura Mat
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1676

Abstract

Cryptocurrency, a digital or virtual currency secured by cryptography, has become a dynamic and volatile asset class, presenting both opportunities and challenges for traders and investors. This study aims to develop a web-based application prototype for forecasting Bitcoin prices using the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The ARIMAX model, known for its ability to integrate external factors into time series forecasting, is applied to historical Bitcoin price data, utilizing the Crypto Fear and Greed Index as an exogenous variable. By addressing the inherent volatility and unpredictability of Bitcoin's price movements, this study seeks to enhance the reliability of price predictions. Model performance will be evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The resulting web application will deliver real-time price forecasts and analyses, empowering users to make informed decisions and manage risks more effectively in cryptocurrency trading. This research ultimately aims to contribute to advancements in predictive modeling techniques within financial technology, providing traders and investors with a valuable tool for navigating the complex cryptocurrency market.
SHIELD: Symptom-Based Hybrid Intelligent Early Learning for Disease Prediction Fazil Akashah, 'Asrul 'Azeem; Tajuddin, Taniza
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1677

Abstract

Traditional diagnostic approaches often face delays and inaccuracies, while standalone machine learning models fail to account for individual uniqueness. The SHIELD system leverages hybrid machine-learning models to enhance disease prediction based on patient symptoms. This study integrates Gradient Boosting, Decision Trees, and Random Forest models, combining their strengths using an ensemble voting approach. A comprehensive dataset from Kaggle, enriched with symptom severity mappings, enables accurate and personalized predictions. The system delivers practical outputs, including disease names, descriptions, and home remedies, through a user-friendly web interface. Achieving an accuracy of approximately 99.59% with the ensemble model, SHIELD demonstrates its potential to revolutionize early disease detection, aligning with global health objectives.
Early Detection on Company Bankruptcy: a Comparison of Neural Networks and Logistic Regression Ahmad Shukri, Muhammad Fairus; Abdul Razak, Nor Hafizah; Mat Din, Mazura
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1678

Abstract

Detecting firm insolvency at an early stage is crucial for financial analysis and risk management. This study compares the efficacy of two widely used bankruptcy prediction techniques: Neural Networks (NN) and Logistic Regression (LR). We evaluate each approach based on its accuracy, computing efficiency, and interpretability, aiming to identify a suitable predictive model that aligns with specific objectives, data characteristics, and the need for interpretability in financial decision-making. This research indicates that NN provides superior prediction accuracy but is accompanied by increased computing demands and reduced interpretability. In contrast, LR offers more speed, requires fewer processing resources, and provides explicit understanding of variable correlations; however, it may not perform well with intricate and nonlinear data. This study confirms the significance of choosing a suitable predictive model that balances competing demands of accuracy, efficiency, and interpretability in financial decision-making.
Deep Learning for Meal Recognition and Calorie Estimation Ahmad Fariz, Ahmad Nabil Bin; Abdul Razak, Nor Hafizah; Mat Din, Mazura
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1679

Abstract

Accurate calorie estimates from foods are prerequisite for diet following and health monitoring. Manual calorie estimations according to age-old methods mostly tend to be inaccurate. This paper proposes the use of convolutional neural networks (CNNs) for precise identification from food images and prediction of meal calories to solve the concern. Therefore, the objective is to create a model capable of recognizing foodstuff besides estimating their caloric content. Developing a model that could correctly identify food ingredients and calculate their energy value through training and testing was important in this project. Our aim here was to verify the accuracy of the model using systematic reviewing means as well as an interface where it can be tested. A dataset of 1,337 high-quality images divided into 12 culinary classes cake, hamburger, noodles, spaghetti, pizza, chicken curry, croissant, French fries, fried chicken, roast chicken, lobster nasi goreng, and waffle was obtained from Roboflow Universe and used for this project. The selection of technique which is YOLO (You Only Look Once) model architecture and flow design because it proved to be highly efficient for real-time object recognition.
Automated Recognition of Medicinal Plants in the Wild: A Leaf-centric Approach Ahmad Zaki, Muhammad Ammar; Mohd Zukhi, Mohd Zhafri; Din, Mazura Mat
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1680

Abstract

This study explores the use of technology to simplify the identification of medicinal plants in the wild by focusing on leaf characteristics. Using convolutional neural networks (CNNs), the research aims to develop a mobile-friendly system tailored to Malaysia’s rich biodiversity and traditional medicine heritage. Key steps include collecting a diverse range of plant data, enhancing image quality through pre-processing, and testing various CNN models to determine the most effective one. Designed for use by both experts and non-experts, such as rural communities and herbalists, the tool integrates advanced AI with traditional knowledge to preserve cultural practices, promote safe natural remedies, and raise awareness about medicinal plants’ role in healthcare and conservation. By addressing the decline in herbal knowledge, this project aims to deliver a practical and accessible solution that supports public health and environmental sustainability.

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