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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Arif Siswandi
Universitas Pelita Bangsa, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Identifikasi Indikasi Risiko Depresi pada Unggahan Media Sosial X Menggunakan Natural Language Processing dan Algoritma Random Forest Arif Siswandi; Arif Susilo
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1026

Abstract

Depression among university students has become an important mental health concern due to its potential impact on quality of life and academic performance. Social media platform X, as a text-based communication medium, provides a space for spontaneous expression that may reflect users’ emotional states. This study aims to analyze linguistic patterns associated with indicative depressive expressions in social media posts using a Natural Language Processing (NLP) approach and the Random Forest algorithm. Data were collected through web scraping between January and November 2024 using keywords conceptually derived from the Patient Health Questionnaire-9 (PHQ-9) indicators and adapted to linguistic expressions commonly used in social media communication. From an initial collection of 36,081 posts, several filtering stages were conducted, including duplicate removal, language filtering, and elimination of irrelevant content, resulting in a final dataset of 1,070 posts used in this study. The high filtering rate indicates that many scraped posts did not directly represent relevant emotional expressions. The dataset was manually labeled into three indicative categories of depressive expressions: mild, moderate, and severe. The analytical process included text preprocessing, TF-IDF feature extraction, and classification modeling using the Random Forest algorithm. The evaluation results show an accuracy of 97%. However, this value should be interpreted cautiously because model performance may be influenced by dataset characteristics and the manual labeling process. Therefore, the proposed model should be regarded as an exploratory approach for identifying linguistic patterns associated with emotional expressions in social media text rather than a clinical diagnostic tool for depression.
Implementasi K-Means Clustering Berbasis RapidMiner untuk Optimalisasi Segmentasi Penjualan Produk dalam Meningkatkan Efektivitas Strategi Pemasaran Sufajar Butsianto; Arif Siswandi
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8439

Abstract

The Indonesian electronic retail industry is experiencing rapid growth along with digital transformation. However, available sales data is often only stored as transaction records without further analysis, so it has not been optimally utilized for marketing decision making or customer segmentation. This study aims to implement the RapidMiner-based K-Means Clustering algorithm to analyze segmentation patterns of electronic products at XYZ Store. The dataset used includes the variables Transaction_ID, Product_ID, Product_Name, Category, Quantity, Unit_Price, Revenue, and Recency. The research stages include data collection, preprocessing (filtering, aggregation, and Z-Score normalization), K-Means application, and interpretation of clustering results. Determination of the number of clusters in this study uses the Elbow Method, which shows an optimal point at K = 3, so that number of clusters is chosen for the data grouping process. Based on the results of the application of the K-Means algorithm with the three clusters, the following are obtained: (1) Cluster 0 (High Sales & High Revenue) consisting of Smartphones, Laptops, and Tablets as superior products with a contribution of almost 60% of total revenue; (2) Cluster 1 (Medium Sales & Moderate Revenue) includes Televisions, Refrigerators, and Smartwatches with a stable contribution of around 27%; and (3) Cluster 2 (Low Sales & Low Revenue) contains Washing Machines, Speakers, Headphones, and Printers with a low contribution of only 14%. These findings provide a strategic basis for management in making business decisions, such as procurement priorities, seasonal promotions, product bundling, and clearance strategies. This study proves that the application of data mining with K-Means Clustering is effective in increasing operational efficiency and supporting the competitiveness of the electronics retail business in Indonesia.
Prediksi Indeks Pembangunan Manusia Menggunakan Support Vector Regression dengan Optimasi Particle Swarm Optimization Arif Siswandi; Arif Susilo; Rizki Muhammad Mukti
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10535

Abstract

The Human Development Index (HDI) is a key indicator for measuring regional development performance and serves as an essential reference for evidence-based policy formulation. Accurate HDI prediction is crucial to support effective development planning and decision-making. This study aims to develop an HDI prediction model using Support Vector Regression (SVR) optimized with Particle Swarm Optimization (PSO) to improve prediction accuracy. The dataset was obtained from Statistics Indonesia (BPS), covering 38 provinces during the 2015–2025 period with a total of 421 observations. The research process consisted of data preprocessing, Min-Max Scaling normalization, an 80:20 train-test split, SVR model development, parameter optimization using PSO, and performance evaluation based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results show that the baseline SVR model achieved an MAE of 0.069187, RMSE of 0.093548, and R² of 0.492454. After PSO optimization, the model performance improved, achieving an MAE of 0.060864, RMSE of 0.084224, and R² of 0.588583. These findings demonstrate that PSO effectively enhances the predictive performance of SVR by identifying optimal parameter combinations. The main contribution of this study is the development and validation of an optimized SVR-PSO framework for HDI prediction using multi-provincial socioeconomic data in Indonesia, providing a more accurate machine learning-based approach to support data-driven human development planning and policy formulation.
Co-Authors Arif Susilo Rizki Muhammad Mukti Sufajar Butsianto