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Saeful Amri
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jodi@unimus.ac.id
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Jl. Kedungmundu No. 18 Semarang Indonesia
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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 34 Documents
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Stock Price Forecasting of PT. Bank Rakyat Indonesia (Persero) Tbk. Using Long Short-Term Memory (LSTM) Method Sa'adah, Lydia Nur; 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 Ismardani, Trisnawadi; Fatah, Zaihol
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 Ilallah, Ahsin; Fatah, Zaihol
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; Fadlurohman, Alwan; Purwanto, Dannu
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.

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