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Journal of Data Insights
Published by Universitas Muhammadiyah Semarang
ISSN : -     EISSN : 29882109     DOI : https://doi.org/10.26714/jodi
Core Subject : Science, Education,
Computer Science & IT Mathematics
The Journal of Data Insights is an open access publication for peer-reviewed scholarly journals. The Journal of Data Insights focuses on the processing, analysis and interpretation of data for data-driven decisions and solutions in industry, hospitals, government and universities. All articles should contain a validation of the proposed idea, e.g. through case studies, experiments, or a systematic comparison with other already practiced approaches. Two types of papers will be accepted: (1) a short paper discussing a single contribution to a particular new trend or idea, and; (2) a longer paper outlining a specific Research trends. As part of our commitment to scientific advancement, Journal of Data Insights follows an open access policy, which makes published articles freely available online without subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 51 Documents
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Stock Price Forecasting of PT. Bank Rakyat Indonesia (Persero) Tbk. Using Long Short-Term Memory (LSTM) Method Lydia Nur Sa'adah; Nasyiatul Izzah; Kamilah Citra Khumairoh; M. Al Haris; Ihsan Fathoni Amri
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.847

Abstract

Stock price forecasting is a major challenge in financial market analysis due to the volatility and unpredictability of price movements. The limitations of traditional statistical methods in capturing nonlinear patterns and long-term temporal dependencies have encouraged the adoption of deep learning–based approaches. This research aims to predict the stock price of PT Bank Rakyat Indonesia (Persero) Tbk. (BBRI) using the Long Short-Term Memory (LSTM) method, which is effective at handling problems with fading information and identifying long-term trends in time series data. The dataset comprises historical BBRI share prices from April 16, 2015, to April 16, 2025, with 80% of the data used for training and 20% for testing. LSTM’s model was trained for 10 epochs with a batch size of 32 using the Adam optimizer. The results prove that the LSTM model can effectively capture stock price movement patterns, achieving a mean absolute error (MAE) of 8.42 and a mean absolute percentage error (MAPE) of 1.50%, indicating a high level of accuracy. The visualization of the prediction results reveals a trend that closely aligns with the actual values. These findings reinforce LSTM’s position as a reliable approach to stock price forecasting and highlight its potential as a strategic tool for investors and policymakers in managing market risk.
Sentiment Analysis of YouTube User Comments on Government Policies Using the Naïve Bayes Method: Analisis Sentimen Komentar Pengguna Youtube Terhadap Kebijakan Pemerintah Menggunakan Metode Naïve Bayes Trisnawadi Ismardani; Zaihol Fatah
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.876

Abstract

This research endeavors to analyze public sentiment expressed in YouTube user comments regarding the government's policy pertaining to the confiscation of undeveloped land after a two-year period of non-utilization. The methodology employed leverages the Naïve Bayes algorithm for classification, implemented within the Google Colaboratory environment. Data were systematically collected from specific YouTube videos discussing the aforementioned land confiscation policy. The research workflow encompassed comprehensive stages: data acquisition, rigorous text preprocessing, feature weighting utilizing the Term Frequency-Inverse Document Frequency (TF-IDF) technique, and final classification using the Naïve Bayes algorithm. Evaluation results demonstrate that the proposed model achieved a high accuracy level of 90%, with the highest F1-score recorded within the neutral sentiment class. However, an imbalance in the dataset's class distribution led to comparatively lower precision and recall values for both the positive and negative classes. Overall, this study confirms the high efficacy of the Naïve Bayes algorithm in analyzing Indonesian-language text data from social media platforms, specifically YouTube comments, and provides a crucial foundation for the future development of more balanced sentiment models.
Analysis of Data Mining in Predicting Poverty Levels in Indonesia Using the Decision Tree Method : Analisa Data Mining Dalam Memprediksi Tingkat Kemiskinan Masyarakat Indonesia Dengan Metode Decision Tree Ahsin Ilallah; Zaihol Fatah
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.878

Abstract

This study aims to examine the application of the Decision Tree method in predicting poverty levels in Indonesia using the RapidMiner software. Poverty is a complex issue influenced by social, economic, and educational factors. Through a data mining approach, this research seeks to identify patterns within poverty data to support more accurate decision-making. The research data were obtained from the public platform Kaggle and include key variables such as individual expenditure, the Human Development Index (HDI), average study time, access to proper sanitation and safe drinking water, as well as the open unemployment rate. The results show that the Decision Tree model achieved an accuracy of 94.90%, with a precision of 95.24% and a recall of 93.75%, based on the confusion matrix. The use of RapidMiner also facilitates the analysis, as the results are presented visually and are easy to understand. This model is recommended for implementation in government information
Geographically Weighted Regression Modeling Using Fixed and Adaptive Kernel Weights for the Human Development Index Case in West Java Province: Pemodelan Regresi Berbobot Geografis Menggunakan Bobot Kernel Tetap dan Adaptif untuk Studi Kasus Indeks Pembangunan Manusia di Provinsi Jawa Barat Karin Karin; Alwan Fadlurohman; Dannu Purwanto
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.887

Abstract

This study aims to analyze the factors influencing the Human Development Index (HDI) in West Java Province using the Geographically Weighted Regression (GWR) approach. The independent variables used in this study are the Open Unemployment Rate (TPT), School Participation Rate for ages 16–18 (APS_16_18), Population Density, and Gross Regional Domestic Product per Capita (PPK). The modeling was carried out by comparing various kernel functions, namely Gaussian, Bisquare, and Tricube, as well as two bandwidth approaches: fixed and adaptive. The results indicate that the GWR model with a Gaussian kernel and a fixed bandwidth approach provides the best performance based on the lowest AIC value. Compared to the classical Ordinary Least Squares (OLS) model, the GWR model offers a better explanation of spatial variation in HDI across the study area. Although the GWR model was not statistically significant overall based on the ANOVA test, local analysis showed that the variables TPT and PPK had significant effects in all districts and cities, while APS_16_18 and Population Density were not significant in any region. These findings demonstrate that the GWR model is capable of capturing spatial heterogeneity that is not detected by the global regression model.
K-Nearest Neighbor Algorithm in Classification of Stunting Detection Dataset: Algoritma K-Nearest Neighbor dalam Klasifikasi Dataset Deteksi Stunting Lea Angelina; Saeful Amri; M Al Haris; Rochdi Wasono; Erna Julia Nanga; Faninda Aidina Fitri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.752

Abstract

Stunting is a nutritional problem that can affect children's physical growth and cognitive development and has a long-term impact on the quality of future generations. Early detection of stunting is crucial to enable timely and effective interventions. As technology advances, machine learning algorithms such as K-Nearest Neighbors (KNN) offer potential solutions to improve the accuracy of stunting risk classification. This study aims to design a classification model based on the K-Nearest Neighbors (KNN) algorithm in the early detection of stunting risk in toddlers. This research uses the 2024 stunting dataset obtained from Kaggle. The data is analyzed through the stages of cleaning, transformation, and division into training and testing data. The KNN model was tested with various K values to determine the optimal value. The results showed that the KNN model with a value of K=8 resulted in an accuracy of 93.80%, F1-Score of 93.65%, precision of 93.63%, and recall of 93.79%. This shows that KNN is reliable in classifying the nutritional status of toddlers and can be applied in stunting prevention efforts using more accurate data. This research contributes to developing machine learning-based classification systems that can support decision-making in public health programs, especially in reducing stunting rates.
Comparison of Decision Tree and Random Forest Performance for Sentiment Analysis of Public Service App Reviews: Perbandingan Kinerja Decision Tree dan Forest Performance untuk Analisis Sentimen Ulasan Aplikasi Layanan Publik Aditya Rizky Purnama; Galet Guntoro Setiaji; Ahmad Rifa'i
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1112

Abstract

The Info BMKG application is a government-developed digital public service platform designed to provide real-time weather, seismic, and climate information to Indonesian citizens. The substantial volume of user reviews accumulated on the Google Play Store holds significant potential as a service evaluation resource; however, the limitations of manual review processes necessitate an efficient computational approach. This study proposes a machine learning-based sentiment analysis framework to classify user reviews of the Info BMKG application, while systematically comparing the performance of two algorithms Decision Tree and Random Forest using a dataset of 10,000 reviews collected via web scraping. The data underwent text preprocessing, rating-based sentiment labeling, and TF-IDF feature extraction, followed by evaluation using accuracy, precision, recall, F1-score, cross-validation, and computational time metrics. Experimental results demonstrate that Random Forest achieved 81% accuracy with a 77% F1-score, outperforming Decision Tree which attained 78% accuracy and 75% F1-score. In terms of efficiency, Decision Tree exhibited faster testing time (0.114 seconds) compared to Random Forest (0.201 seconds), while Random Forest proved more efficient in training time (7.347 seconds versus 12.421 seconds). These findings confirm that Random Forest represents the more optimal algorithm for sentiment classification tasks involving public service application user reviews.
Sentiment Analysis of FlyGaruda Review Using Support Vector Machine and Naive Bayes Algorithm: Analisis Sentimen Ulasan FlyGaruda Menggunakan Algoritma Support Vector Machine dan Naive Bayes Gibran Masta Pangestu Baskoro; Galet Guntoro Setiaji; Ahmad Rifa’i
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1117

Abstract

FlyGaruda is an official digital application owned by Garuda Indonesia that provides ticket booking and online check-in services for users. This study analyzed the sentiment of reviews on the Google Play Store by comparing the performance of Support Vector Machine and Multinomial Naive Bayes. The methods used include scraping, text preprocessing, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature, and evaluation using the Confusion Matrix. The dataset used totaled 4,790 reviews with positive, negative, and neutral categories. The results showed that both models obtained an accuracy of 82.25%. However, the Support Vector Machine produces a weighted precision of 77.66% and an F1-Score of 78.91%, better at handling data imbalances. Meanwhile, Multinomial Naive Bayes excels in computing efficiency with a training time of 0.08 seconds compared to 90.60 seconds on the Support Vector Machine. In conclusion, although it is slower, the Support Vector Machine provides more consistent and accurate classification performance. This research contributes to the development of a machine learning-based opinion analysis system to improve the quality of aviation digital services in a sustainable manner. These findings can serve as a reference in the selection of the best algorithms between accuracy and computational speed in large text data and support data-driven decision-making in the modern air transportation industry in the current era of global sustainable digital transformation
Development of a Decision Support System for Regional Competitiveness Policy Recommendations Based on Explainable Artificial Intelligence (XAI): Pengembangan Sistem Pendukung Keputusan untuk Rekomendasi Kebijakan Daya Saing Regional Berdasarkan Explainable Artificial Intelligence (XAI) Sintha Istikomah; Dwi Purnomo Putro; Sholihul Ibad; Aditya Hermawan
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1141

Abstract

Enhancing regional competitiveness is a critical factor in driving economic growth, investment, and community welfare. However, the utilization of Regional Competitiveness Index (Indeks Daya Saing Daerah/IDSD) data in Indonesia has largely been limited to ranking purposes, thus failing to provide specific, data-driven policy recommendations. This study aims to develop a Decision Support System (DSS) for regional competitiveness policy recommendations by combining machine learning and Explainable Artificial Intelligence (XAI) within a Design Science Research (DSR) framework. The dataset originates from provincial IDSD data spanning 2022–2025, encompassing 12 assessment pillars as predictor variables. Three regression algorithms were examined: Linear Regression, Random Forest, and XGBoost. A Variance Inflation Factor (VIF) analysis was conducted to verify the absence of severe multicollinearity among the predictor variables. Based on performance evaluation, XGBoost was selected as the final model due to its superior predictive performance and stability, yielding an R² of 0.8712 on the 2025 test data and a mean 5-fold cross-validation R² of 0.7723. To enhance model transparency, SHapley Additive exPlanations (SHAP) was employed. Interpretation results revealed that Innovation Capability (Pillar 12), Adoption of Information and Communication Technology (Pillar 3), and Market Size (Pillar 10) are the most influential factors affecting regional competitiveness scores. Building on these findings, the developed system delivers context-specific, priority policy recommendations through an interactive dashboard. This study demonstrates that the integration of XGBoost and XAI constitutes a more objective, transparent, and adaptive data-driven decision-making solution for supporting regional competitiveness improvement in Indonesia.
Hate Speech Detection on X Using K-Nearest Neighbor with TF–IDF and Cosine Similarity: Deteksi Ujaran Kebencian pada X Menggunakan K-Nearest Neighbor dengan TF–IDF dan Kesamaan Kosinus Faiq Madani; Arvanida Feizal Permana; Abdul Karim; Riyagung Nuryusufa Tranggono Adi Prasetya; Wendy Sarasjati
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1149

Abstract

The rapid growth of social media has increased online interactions but has also accelerated the spread of hate speech content that may negatively impact individuals and communities. X (formerly Twitter), as one of the largest social networking platforms, enables users to share opinions publicly, making automatic hate speech detection increasingly important. This research proposes a hate speech classification approach using the K-Nearest Neighbor (KNN) algorithm combined with Term Frequency–Inverse Document Frequency (TF–IDF) weighting and Cosine Similarity. The dataset consists of 900 social media posts collected through the platform API and manually labeled into hate speech and non-hate speech categories, consisting of 675 training data and 225 testing data. Prior to classification, text preprocessing techniques including tokenization, stopword removal, and stemming were applied to improve text quality. Model evaluation was conducted using 10-fold cross validation to assess classification performance. Experimental results showed that the KNN algorithm with Cosine Similarity distance measurement and K=3 parameter achieved an accuracy of 78.22% in hate speech detection tasks. The findings indicate that KNN combined with TF–IDF and Cosine Similarity provides a reliable approach for social media text classification and can support automated hate speech detection systems.
ADASYN-Based Multiclass Support Vector Machine for Village Development Index Classification in North Maluku Province: Support Vector Machine Multikelas Berbasis ADASYN untuk Klasifikasi Indeks Pembangunan Desa di Provinsi Maluku Utara Tiani Wahyu Utami; Lea Angelina; Saeful Amri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1154

Abstract

Class imbalance is a significant constraint that can diminish the performance of classification models. This study implements the integration of Adaptive Synthetic Sampling (ADASYN) and Multiclass Support Vector Machine (SVM) to classify the 2024 Village Development Index (IDM) in North Maluku Province. The dataset comprises 684 villages, utilizing the Social Resilience Index (IKS), Economic Resilience Index (IKE), and Environmental Resilience Index (IKL) as predictor variables. The data was partitioned using a ratio of 80% for training and 20% for testing. An extreme imbalance was identified in the "independent village" category (0.88%); therefore, ADASYN was applied to the training data to generate 862 synthetic samples to balance the class distribution. The optimal model yielded by the process was a linear kernel SVM with a Cost value of 100, yielding an accuracy of 98.54%, precision of 98.26%, recall of 99.4%, and an F1-score of 98.83%. Of the total 137 villages evaluated, only two villages were misclassified: Salimuli Village and Dowongimaiti Village. These findings demonstrate the effectiveness of the ADASYN-SVM combination in producing accurate classifications to support village development policies in island regions.

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