https://journal.ittelkom-pwt.ac.id/index.php/dinda/issue/feedJournal of Dinda : Data Science, Information Technology, and Data Analytics2025-01-28T14:10:23+08:00Nur Ghaniaviyanto Ramadhan, S. Kom., M.Kom.ghani@ittelkom-pwt.ac.idOpen Journal Systems<p><strong>Journal of Dinda : Data Science, Information Technology, and Data Analytics</strong> as a publication media for research results in the fields of Data Science, Information Technology, and Data Analytics, but not implicitly limited. Published 2 times a year in <strong>February</strong> and <strong>August</strong>. The journal is managed by the Data Engineering Research Group, Faculty of Informatics, Telkom Purwokerto Institute of Technology. ISSN Number is <a href="https://issn.lipi.go.id/terbit/detail/20220107221364737">2809-8064</a></p>https://journal.ittelkom-pwt.ac.id/index.php/dinda/article/view/1723Analysis of Student Academic Performance to Identify New Patterns Using Linear Regression Algorithm2025-01-28T14:09:20+08:00Adelia Putri Septianiadelia14822@gmail.comAkhmad Khanif Zyenakhmadkhanifzyen@unisnu.ac.idBuang Budi Wahonobuangbudiwahono@unisnu.ac.id<h1>Abstract</h1> <p>This research aims to analyze and identify new patterns in student academic performance using linear regression algorithms. Using data from 1001 respondents, this study analyzes the relationship between various variables such as study hours, previous scores, extracurricular activities, sleep hours, and learning practices on academic performance index. The research methodology employs a quantitative approach with linear regression analysis to identify relationships between variables. The results show significant correlations with an R-squared value of 0.783, indicating that 78.3% of the variation in performance index can be explained by the studied variables. Key findings reveal a synergistic effect between study hours and active learning practices, with performance improvements of up to 23%. The research also identifies a threshold effect on study hours above 6 hours which no longer provides significant impact. Optimal sleep patterns of 7-8 hours show positive correlation with highest academic performance. This study provides important contributions to understanding the factors influencing academic performance and can be used as a basis for developing more effective learning strategies.</p> <p>Keywords: academic performance, linear regression, learning patterns, educational data analysis, performance index.</p>2025-01-28T14:09:20+08:00##submission.copyrightStatement##https://journal.ittelkom-pwt.ac.id/index.php/dinda/article/view/1649Integration of RFM Method and K-Means Clustering for Customer Segmentation Effectiveness2025-01-28T14:09:53+08:00Nafissatus Zahronzahro44@gmail.comNadia Annisa Maorinadia@unisnu.ac.idGentur Wahyu Nyipto Wibowogentur@unisnu.ac.id<p><span style="font-weight: 400;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Penelitian ini bertujuan untuk mengintegrasikan metode RFM dan K-Means Clustering untuk segmentasi pelanggan. Rumusan masalah yang diajukan adalah bagaimana mengintegrasikan kedua metode ini agar segmentasi pelanggan lebih efektif. Data transaksi pelanggan ADAPTA.Id tahun 2022 yang meliputi 2.252 transaksi pelanggan dianalisis untuk menghasilkan nilai RFM, dinormalisasi, dan diklaster menggunakan K-Means. Dua klaster optimal diidentifikasi dengan skor silhouette sebesar 0,8511. Dari total 2.252 transaksi pelanggan, terdapat dua klaster utama: klaster pertama berisi 10 pelanggan dengan frekuensi pembelian tinggi dan nilai transaksi signifikan, sedangkan klaster kedua terdiri dari 918 pelanggan dengan frekuensi dan nilai transaksi lebih rendah. Mayoritas pelanggan berada di klaster kedua. Segmentasi ini memungkinkan perusahaan untuk merancang strategi pemasaran yang lebih efektif dengan memfokuskan sumber daya untuk mempertahankan pelanggan bernilai tinggi dan meningkatkan aktivitas pembelian di klaster bernilai rendah. Pendekatan ini menawarkan wawasan mendalam untuk strategi bisnis yang lebih efisien, serta meningkatkan kepuasan dan loyalitas pelanggan. Skor silhouette yang tinggi menegaskan validitas klaster.</span></span></span></p>2025-01-28T14:09:53+08:00##submission.copyrightStatement##https://journal.ittelkom-pwt.ac.id/index.php/dinda/article/view/1719Climate Change Sentiment Analysis using LSTM2025-01-28T14:10:23+08:00Marchel Yusuf Rumlawang Arpipimarchel.535210039@stu.untar.ac.idTeny Handhayanitenyh@fti.untar.ac.idJanson Hendrylijansonh@fti.untar.ac.id<p>This research aims to observe the sentiment of Indonesian people towards climate change using the Long Short-Term Memory (LSTM) methods. The data samples used in this study are primary data that have been collecting by using the Twitter Application Programming Interface (API) that provides by a platform known as RapidAPI. This data sample is text data with 2425 total samples obtained during the time period from 01 January 2020 to 25 August 2024. The sentiment is classified as positive, negative, and neutral. The performance of the LSTM model is evaluate using accuracy, precision, recall, F1-score, and confusion matrix and then compare with other models such as Ensemble Model, Naive Bayes, and Linear SVC. By conducting Exploratory Data Analysis (EDA), it is reveals that the distribution of public sentiment towards climate change in Indonesia from the collected data is mostly positive. However, there are not many individuals that are still ignorant and skeptical about the issue, resulting in a negative sentiment that can be fatal to the environment and its surroundings. When comparing the Ensemble Model, Naive Bayes, and Linear SVC, the LSTM model successfully identifies the perception patterns between sentences according to their sentiments. LSTM obtains an accuracy of 60% and outperforms Ensemble Model, Naive Bayes, and Linear SVC. This research also highlights the technical challenges in processing and analyzing dynamic and diverse data so that the results obtained are better, especially in terms of data quality before further processing.</p>2025-01-28T14:10:23+08:00##submission.copyrightStatement##