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Knowledge Engineering and Data Science
Published by Universitas Negeri Malang
ISSN : -     EISSN : 25974637     DOI : http://dx.doi.org/2597-4637
Core Subject : Science, Engineering,
Computer Science & IT Engineering
The journal welcomes experimental and theoretical findings on data science and knowledge engineering along with their applications to real-life situations.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 117 Documents
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Evidence of Students’ Academic Performance at the Federal College of Education Asaba Nigeria: Mining Education Data Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Emordi, Frances; Ako, Rita Erhovwo; Adigwe, Winifred; Anazia, Kizito Eluemonor; Geteloma, Victor
Knowledge Engineering and Data Science
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Abstract

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining techniques in the context of higher education by offering a data mining model for the higher education system in the university. In this research, the classification task is used to evaluate student’s performance, and as many approaches are used for data classification, the decision tree method is used here. By this, we extract data that describes students’ summative performance at semester’s end, helps to identify the dropouts and students who need special attention, and allows the teacher to provide appropriate advising/counseling.
EEG Classification while Listening to Murottal Al-Quran and Classical Music using Random Forest Method Sumarti, Heni; Septiani, Fahira; Sudarmanto, Agus; Caesarendra, Wahyu; Edison, Rizki Edmi
Knowledge Engineering and Data Science
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This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music. The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification. The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal al-Quran stimulation were 45.32% delta, 31.60% alpha, 17.02 theta and 6.05% beta. Meanwhile, the average brain waves of teenagers when given classical music stimulation were 46.54% delta, 28.64% alpha, 19.21% theta and 5.50% beta. Classification is obtained with the best value that frequently appears (mode) from the prediction results for each sample using random forest methods. The accuracy, precision, and recall of classifying adolescent brain waves when given murottal and classical music stimuli using the Random Forest method with cross-validation technique (optimum at k-fold=5) were 65.38%, 76.92%, and 70.00%, respectively. The results of this study show that stimulation using murottal al-Quran and classical music effectively improves adolescent relaxation conditions.
Spatial Analysis and Machine Learning Integration for Nutritional Status Mapping Using ANN and Random Forest Models Anggraini, Desi Anis; Kurniawan, Fachrul; Nugroho, Fresy; Koeshardianto, Meidya; Iqbal Bachtiar, Mohammad
Knowledge Engineering and Data Science
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Abstract

Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify high-risk zones. The integration of machine learning and spatial analysis proved effective in enhancing classification accuracy, improving data interpretation, and supporting data-driven nutritional policy and regional health decision-making.
Mapping of Product Sales Potential Based on Brands in The East Kalimantan Region using Hybrid Analytical Framework Gaffar, Achmad F O, Mr; Mulyanto, Mulyanto, Mr; Putra, Arief BW, Mr; Sumadi, Muhammad Taufiq, Mr; Gaffar, Emmilya Umma Aziza
Knowledge Engineering and Data Science
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In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means Clustering is then applied to group sales potential, with the optimal number of clusters determined using the Elbow Method and validated by the Silhouette Score. Two sales potential scenarios are evaluated: with Weighted Operational Cost (WOC) and without WOC under a fixed pricing assumption. The results show notable differences between the two scenarios. With WOC, overall sales potential is predominantly Medium (64.84%), whereas without WOC it is predominantly High (51.39%). Several districts, including Berau, East Kutai, and Balikpapan, remain consistently high-potential areas, particularly for Dewalt and Stanley products. These findings highlight the importance of incorporating operational cost considerations into data-driven sales potential mapping.
A Comparative Study of Machine Learning Models for Javanese Wuku Classification: Exploring SVM, Naïve Bayes, and CNN for Cultural Texts Sulistyo, Danang Arbian; Prasetya Wibawa, Aji; Prasetya, Didik Dwi; Ahda, Fadhli Almu'iini; Utama, Agung Bella Putra
Knowledge Engineering and Data Science
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This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) under repeated stratified 5-fold cross-validation (10 repeats; 50 runs) to ensure robust estimates. CNN achieved the best performance with Accuracy = 0.92 ± [SD], Macro-F1 = 0.90 ± [SD], and AUC = 0.93 ± [SD], outperforming SVM (Accuracy: 0.87; F1-score: 0.84) and Naïve Bayes (Accuracy: 0.82; F1-score: 0.78). The results underscore CNN’s strong effectiveness for nuanced, context-rich text classification, offering a vital contribution to cultural heritage preservation and advancing Natural Language Processing (NLP) for under-resourced languages. From a knowledge-engineering perspective, predicted Wuku labels can serve as structured metadata to support computational indexing and retrieval of Wuku narratives in cultural information systems. Methodologically, our CNN is a lightweight, small-corpus design that uses tuned regularization (dropout/early stopping) and multi-scale convolution to capture culturally salient n-gram cues, rather than relying on a fixed default TextCNN configuration. Future work involves expanding the dataset and exploring advanced deep learning architectures.
Prediction of Audit Findings Using Deep Learning with Financial and Non-Financial Data: A Case Study in Province X Setiawan, Fery Yohan; Yuniarno, Eko Mulyanto; Rachmadi, Reza Fuad
Knowledge Engineering and Data Science
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Abstract

The implementation of the audit from the local government financial statements by The Audit Board of The Republic of Indonesia (BPK RI), especially for the Province X representative, are frequently faced by the various limitations, one of them being the required audit time. At this moment, the BPK RI representative of Province X doesn’t have the tools that are able to help the accurate of sample determination for the pick test, which resulted in this study proposing the application of multi-label classification to predict the findings of financial statement (Laporan Keuangan, LK) audits based on financial and non financial ratio. The multi-label classification approach used is a traditional approach and deep learning. The model selection was based on model performance evaluation, using metrics such as accuracy, hamming loss, average precision, average recall, and F1 Score, resulting in the best model being DNN. The DNN model achieved an accuracy of 0.7728, a Hamming loss of 0.1750, an average precision of 0.8393, an average recall of 0.9120, and an F1 Score of 0.8740. The DNN model can be used to predict audit findings in determining the audit sample, thereby minimizing the limitations, particularly time constraints, often encountered during LK audits.
The Effect of the Number of Hidden Layers on The Performance of Deep Q-Network for Traveling Salesman Problem Hanif, Benzfica; Larasati, Aisyah; Nurdiansyah, Rudi; Le, Trung
Knowledge Engineering and Data Science
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Abstract

The Traveling Salesman Problem (TSP) effectively represents the complex distribution issues encountered by couriers, who must carefully plan a route that includes all customer addresses while minimizing the distance traveled. As the magnitude of deliveries and the range of destinations expand, the courier's responsibility becomes progressively challenging. In this particular context, the objective of our research is to expand the existing knowledge and explore the complete capabilities of Deep Q-Network (DQN) models in order to achieve the most efficient route determination. This endeavor can potentially bring about significant changes in the courier and delivery service sector. The foundation of our unique methodology relies on an empirical inquiry, utilizing a comprehensive dataset including 178 observations obtained from motorcycle-based package delivery agents. Our research is carefully planned and executed using a comprehensive factorial experimental design. This design incorporates three crucial factors: the number of hidden layers, episodes, and epochs. The hidden layer parameter is set to a singular level, while the episode parameter is configured to explore five levels, and the epoch parameter is designed to travel four levels. The evaluation of our DQN models' performance is conducted utilizing the MSE metric as a measure. This assessment is carried out at every iterative cycle, ensuring thorough scrutiny. The central focus of our research centers on the intricate connection between episodes and epochs, and their influence on MSE. The findings of our study reveal that the association between episodes, epochs, and errors is not statistically significant although different level of episodes and epochs produces slightly different level of error.
A Hybrid Soft Voting and Stacking-Based Meta-Learning Approach for Sentiment Analysis of Bangkalan Batik Wahyudi, Moh. Imron; Muflikhah, Lailil; Perdana, Rizal Setya
Knowledge Engineering and Data Science
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Abstract

Sentiment analysis is an important field in Natural Language Processing (NLP) that focuses on processing consumer opinions to gain useful insights. The information generated from sentiment analysis can be used as a basis for business decision-making, service quality evaluation, and the formulation of more effective marketing strategies. In the local context, Bangkalan Batik, as one of Madura's distinctive cultural products, has high economic value and cultural identity. However, consumer reviews available online, for example through Google Maps, are still rarely utilized optimally by MSMEs as a source of strategic information. Therefore, this study was conducted to develop a sentiment classification model capable of overcoming the limitations of small data and unbalanced class distribution. The research dataset consisted of 1,000 Bangkalan Batik consumer reviews categorized into three sentiment classes: positive, neutral, and negative. After the preprocessing stage, text representation was performed using TF-IDF. Next, three basic algorithms, Naïve Bayes, Logistic Regression, and Support Vector Machine, were combined through the Soft Voting approach. To improve performance, Logistic Regression meta-classification was used as an additional layer of stack-based Meta-Learning. In addition, the Synthetic Minority Oversampling Technique (SMOTE) was applied to overcome class imbalance so that the model was more sensitive to minority opinions. The results of experiments with stratified k-fold cross-validation show that the proposed model performs better than both single and conventional ensemble models. The developed hybrid model achieves 87% accuracy and an F1 score of 88%, and shows a significant improvement in remembering minority classes. This research contributes to the development of ensemble-based text classification methods for small and imbalanced datasets.
Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Akbari, Ade Kurnia Ganesh; Fadhilla, Akhmad Fanny; Triono, Alfiansyah Putra Pertama; Paramarta, Andien Khansa’a Iffat; Setyaputri, Faradini Usha; Hernandez, Leonel
Knowledge Engineering and Data Science
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Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.
Classification of Indonesian Sign Language (SIBI) Using Data Mining Algorithms K-Nearest Neighbor and Random Forest Wirawan, Muhammad Zaki; Afif, Achmad; Handayani, Anik Nur; Hitipeuw, Imanuel; Fukuda, Osamu
Knowledge Engineering and Data Science
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Abstract

This study aims to address the communication hallenges faced by the Indonesian deaf community by developing an automatic classification model for Sistem Bahasa Isyarat Indonesia (SIBI) using data mining techniques. The main objective is to identify a practical algorithm for recognizing SIBI hand gestures to enhance accessibility and inclusiveness in digital communication. A comprehensive dataset consisting of 32,850 gesture samples representing SIBI alphabet signs was collected and processed through feature extraction, data cleaning, and normalization using Z-Transform and Min-Max methods. Two classification algorithms, K-Nearest Neighbor (KNN) and Random Forest, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results show that both algorithms achieved high classification performance, with Random Forest obtaining a superior accuracy of 94.33% under Min-Max normalization compared to KNN’s 93.67%. These findings highlight the potential of Random Forest as an effective method for SIBI recognition. The research contributes to the advancement of automatic sign language recognition technology in Indonesia and supports the development of more inclusive communication tools for individuals with hearing impairments.

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