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Indonesian Journal of Artificial Intelligence and Data Mining
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : 26143372     EISSN : 26146150     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) is an electronic periodical publication published by Puzzle Research Data Technology (Predatech) Faculty of Science and Technology UIN Sultan Syarif Kasim Riau, Indonesia. IJAIDM provides online media to publish scientific articles from research in the field of Artificial Intelligence and Data Mining. IJAIDM will be published 2 (two) times a year, in March and September, each edition contains 7 (seven) articles. Articles may be written in English or Indonesia.
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Articles 30 Documents
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Search results for , issue "Vol 8, No 1 (2025): March 2025" : 30 Documents clear
Evaluation of Ensemble and Hybrid Models for Predicting Household Energy Consumption: A Comparative Study of Machine Learning Approaches Airlangga, Gregorius
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.32819

Abstract

Accurately predicting household energy consumption is critical for efficient energy management, particularly as global energy demands rise. This study explores the predictive performance of various machine learning models, including linear regression, Ridge regression, Lasso regression, Random Forest, Gradient Boosting, XGBoost, CatBoost, and a hybrid model combining Long Short-Term Memory (LSTM) networks with Random Forest regression. The models were evaluated on a dataset consisting of minute-level energy readings over a 350-day period. Key performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (?2) were used to assess model accuracy. The results demonstrate that ensemble models, particularly Random Forest and CatBoost, outperformed traditional regression models in terms of error minimization. CatBoost achieved the lowest MSE among all models, highlighting its effectiveness in handling non-linearities and categorical data. However, none of the models achieved a positive (?2) score, indicating their limitations in fully explaining the variance within the dataset. The hybrid LSTM + Random Forest model, despite its expected strength in capturing temporal dependencies, performed worse than simpler models, suggesting issues with feature extraction and model integration.These findings suggest that while ensemble methods are well-suited for energy consumption prediction, more advanced modeling techniques or enhanced feature engineering are needed to improve performance. Future research could explore deeper neural networks or time-series models such as ARIMA to better capture the temporal patterns in household energy consumption.
Implementation of Apriori Algorithm in Determining the Layout of Items Putry, Yollanda; Andri Agus, Raja Tama; Sena, Maulana Dwi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31206

Abstract

Shopping at mini markets is now becoming popular as people's shopping orientation changes. This results in consumers preferring to shop in modern markets rather than to stalls or traditional shops. Because consumers want to shop comfortably and practically or in terms of finding goods in an easy way and knowing the location of the desired item. Risaga Jaya is a retail business that sells various needs such as staples, cosmetics, stationery, various snacks, During its operation, many goods are not in demand and there is a buildup of goods in the warehouse because there is no strategy for placing the position of goods that are more attractive to consumers. The purpose of this research is to create a strategy for placing goods to maximize sales using data mining with the apriori algorithm. By using the apriori algorithm, it can organize and organize the layout of an item by bringing related items closer together so that it can increase sales at the store based on consumer purchasing patterns, namely if consumers buy chitato then buy snack candy with a support given of 10% and a confidence given of 75%.
Implementation of Supervised Learning Method In Grapevine Leaf Classification Lifindra, Stevanie Aurelia; Yuadi, Imam
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31633

Abstract

Grapevine leaves are a type of leaf variety that is difficult to identify because it will take time if processed manually so research will be carried out using the help of machine learning. This research aims to classify 5 varieties of grapevine leaves using orange data mining and several classification methods namely k-Nearest Neighbors (kNN), logistic regression, random forest and support vector machine (SVM). The dataset used is 500 images and 5 classes where each class consists of 100 images, namely Ak (100), Buzgulu (100), Ala_Idris (100), Dimnit (100), and Nazli (100). The stages in the analysis process are to enter the image into orange data mining by passing several stages so that the image dataset can be processed and read on the test and score so that the confusion matrix can be obtained. The results of the research conducted using orange data mining show that classification using logistic regression gives the best results at a precision value of 0.848% and a recall value of 0.847%. This research shows that classification using orange data mining also provides good results, besides that this research can also help in the classification process so that it does not require a long time.
Implementation of Fingerprint Biometrics on Smart Door Entrance Access Integrated with Internet of Things-based PINs Handini, Wulan Tri; Endri, Jon; Salamah, Irma
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31738

Abstract

Security is something that must often be ignored by most people and think it is safe, but it turns out that someone can still lose their valuables. In this final project, we will design an Internet of Things (IoT)-based smart door access tool that uses a fingerprint and pin password using the Optical Scanner Sensor method. The purpose of making and designing a smart door tool based on the Internet of Things (IoT) is one of them to apply the optical method as a method used to recognize fingerprint biometric identification. By using a smartphone (android) as a controller using the NodeMCU contained in the ESP2866 WiFi module via an internet connection connected to an application made with MIT App Inventor. In the application of fingerprint sensors using the optical method, the scanning process is obtained through finger scanning based on the effect of light reflection that occurs on the optical sensor on the fingerprint.   So as to produce digital image retrieval on identified fingerprints. The communication that uses the fingerprint sensor and Arduino uno as a data processing unit uses serial data communication. When the command has run according to its function, the results of the data obtained enter in realtime at the data processing place.
Clothing Inventory Forecasting System at Kagas Using the Weighted Moving Average Method Sulistiani, Indah; Sembiring, Muhammad Ardiansyah; Akmal, Akmal
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31498

Abstract

Information systems are made in stores so that they can easily process data and produce the information needed quickly, accurately, precisely, effectively and efficiently in spending costs. Kagas is a clothing store engaged in fashion that has been established since 2020. The purpose of this study is to apply the Weighted Moving Average method to the forecasting system in determining sales of robe clothes.  The results of calculating the stock of gamis clothes manually and calculating using a forecasting system using the previous year's data from May 2023 to April 2024 are the same. Forecasting of gamis clothes for the May 2024 period is 175 with a MAD value of 8.04, an MSE value of 135.33 and a MAPE value of 4.7%. With a forecasting system using the weight moving average method, it makes it easier for Toko Kagas to forecast the stock of gamis clothes inventory in the following month.
Identifying Twitter Topics Using K-Means Clustering and Association Rule Mining for Improved Insights Lengari, Cristiany Gunu; Puspitasari, Ira
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31720

Abstract

The annual growth in social media users has led businesses to increasingly leverage these platforms for marketing, promotion, and addressing public complaints. Twitter, now known as X, stands out as one of the most widely used social media platforms. It serves as a forum for various opinions and complaints regarding services provided by businesses. This study focuses on analyzing public opinions related to Indihome services, as expressed on the @indihomecare Twitter account. These opinions range from expressions of support to complaints about internet services and Indihome's responses to these issues. This study employs a text clustering approach using the K-means algorithm on Twitter data, complemented by association rules to identify topics related to Indihome customer complaints. The optimal number of clusters is determined using the Elbow method, while Word Cloud visualizations are utilized to illustrate frequently occurring words within each cluster. The application of association rules revealed that the most frequently appearing words, with a support value of 0.057, were "indihome," "account," "whatsapp," and "channel." These findings provide insights into the primary concerns and communication channels used by Indihome customers on Twitter
Design and Development of An Intelligent Automatic Tilapia Fish Farming Device in A Bucket Based on Internet of Things Suni, Gina Amanah; Fadhli, Mohammad; Rose, Martinus Mujur
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31809

Abstract

The cultivation of various freshwater fish species, such as catfish, tilapia, carp, and sepat, can be effectively managed through the budikdamber technique, where fish and vegetables are grown together in a single container. This research introduces an alternative method designed to control water temperature, automate fish feeding, and cover the container automatically when it rains. By integrating monitoring and control devices, budikdamber owners can manage automated feeding, monitor water temperature, measure pH levels, control water depth, and automatically activate rain covers. This smart device is expected to enhance budikdamber management efficiency, contributing to the improved welfare of the fish and overall system sustainability.
Single Moving Average Method Forecasting to Predict Skincare Sales Marhama, Siti; Sembiring, Muhammad Ardiansyah; Sena, Maulana Dwi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.32028

Abstract

Nada Nadine Skincare Store is a business engaged in the sale of beauty skincare. Nada Nadine Skincare Store was established in 2023 and is located on Jalan Lingkungan IV Air Joman, Asahan Regency. The problem at Nada Nadine Skincare Store is that it has difficulty in estimating the level of demand for beauty skincare (Wardah) for inventory because it is still guessing and experience from the production department of Nada Nadine Skincare Store and there is no beauty skincare sales forecasting system that will make it easier for Nada Nadine Skincare Store to determine the amount of beauty skincare (Wardah) inventory. The purpose of this system is to be able to apply the single moving average method to the beauty skincare (Wardah) sales demand forecasting system at the Nada Nadine Skincare Store and to be able to design a website-based beauty skincare (Wardah) sales demand forecasting system at the Nada Nadine Skincare Store using the PHP programming language and MySql database. From the problem analysis, the author tries to predict the amount of beauty skincare demand using the Single Moving Average (SMA) method. The conclusion that can be drawn from research on the Single Moving Average (SMA) based skincare sales forecasting system forecasts the sales demand for beauty skincare (wardah) at the Nada Nadine Skincare Store so The system calculation results are the same as the manual calculation forecast July 2024 is 33,50, forecast August 2024 is 32,75, MAD value is 2,70, MSE value is 11,31 and MAPE value is 8,84%.
Performance Evaluation of Machine Learning Models for Predicting Household Energy Consumption: A Comparative Study Airlangga, Gregorius
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.32791

Abstract

Accurate prediction of household energy consumption is critical for improving energy efficiency and optimizing resource allocation in smart grids. This study evaluates the performance of several machine learning regression models, including Linear Regression, Ridge Regression, Lasso Regression, Random Forest, Gradient Boosting, XGBoost, CatBoost, and LightGBM, for predicting daily household energy consumption. The models were trained and tested on time series data, and their performance was measured using four key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R². Results show that non-linear models, especially ensemble-based methods such as Random Forest and CatBoost, outperformed traditional linear regression models. Random Forest achieved the lowest MAE (0.1682) and competitive RMSE (0.2450), making it the best overall model. CatBoost, with its advanced gradient boosting algorithm, also demonstrated superior predictive accuracy, achieving an RMSE of 0.2421 and an MAE of 0.1830. In contrast, linear models struggled to capture the complex patterns in the data, with Linear Regression showing the worst performance. The negative R² scores across all models indicate challenges in explaining the variance in the dataset, which may be attributed to external factors or noise not captured by the models. This study highlights the importance of choosing appropriate machine learning models for time series forecasting and recommends further exploration of deep learning models and external features to improve prediction accuracy.
Unlocking the Future: EFL Students' Insights on Artificial Intelligences for Academic Writing Assapari, Muhammad Mugni; Hidayati, Rosyadi; Mukti, Siti Raudatul Warni
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.30307

Abstract

Lately, the widely used and fiercely debated ChatGPT has attracted the interest of researchers, professors, lecturers, and administrators. Additionally, English as a Foreign Language (EFL) learners require AI feedback on scientific writing to improve their writing abilities. This quantitative study investigated Indonesian EFL learners' perceptions and challenges in a university English program using ChatGPT tools. This study investigated undergraduate students' viewpoints on using AI-powered ChatGPT tools in English academic writing. It focused on the main goal, results, and feedback to improve their second language writing skills. Data were collected from (n=80) students using questionnaires and semi-structured interviews to analyze their impressions. A survey was administered to EFL undergraduates in the English Language Education Study Program at Mataram State Islamic University, Indonesia. Writing ability was assessed online using Google Forms. However, students reported the advantages and difficulties of using ChatGPT for academic writing. These results demonstrate that AI-enabled digital tools can enhance student performance in EFL, academic writing, and other disciplines. The benefits and drawbacks of artificial intelligence must be studied and evaluated, and its implications for academic writing must be developed

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