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All Journal Jurnal Pilar Nusa Mandiri Jurnal Penelitian dan Pengabdian Masyarakat Journal of Informatics Development
Maysas Yafi Urrochman
Institut Teknologi dan Bisnis Widya Gama Lumajang
Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science Social Sciences Other

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PERFORMANCE COMPARISON OF RANDOM FOREST REGRESSION, SVR MODELS IN STOCK PRICE PREDICTION Urrochman, Maysas Yafi; Asy'ari, Hasyim; Hizham, Fadhel Akhmad
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6072

Abstract

The stock market is characterized by high volatility and complexity, making it an intriguing and challenging subject for researchers and practitioners. This study aims to predict stock prices by comparing the performance of two machine learning models: Random Forest Regression and Support Vector Regression (SVR). These models were selected for their ability to handle complex data and high volatility. The dataset used in this study consists of BNI stock data over the last five years (2019–2024), comprising a total of 1,211 data points. Testing was conducted using a cross-validation approach, and model performance was evaluated based on several metrics, including MSE, R², RMSE, MAPE, MAE, and Score. The results indicate that Random Forest Regression outperforms SVR. The model achieved an MAE of 17.766, an RMSE of 22.376, and an R² of 0.997. These findings suggest that Random Forest Regression is more effective in predicting stock prices, particularly in unstable market conditions. This study recommends Random Forest Regression as a reliable model for stock price prediction, with potential applications in other stock markets with similar characteristics.
Digital Transformation of School Administration through Human Resource Information System Implementation Masyhuri Masyhuri; Maysas Yafi Urrochman
Jurnal Penelitian dan Pengabdian Masyarakat Vol. 4 No. 3 (2026): August 2026
Publisher : Yayasan Pondok Pesantren Sunan Bonang Tuban

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61231/10g2dk18

Abstract

This community service program aims to improve administrative management effectiveness through the implementation of a web-based information system at SMK Al-Maliki Sukodono Lumajang. The main problem faced by the schools was the use of manual recording systems, which resulted in low efficiency, high risk of data errors, and limited access to information. The methods employed included needs analysis, system design, implementation, as well as user assistance and evaluation. The results indicate that the developed system successfully improved work efficiency, data organization, and reporting processes. User evaluation revealed a very high level of satisfaction, with 73% of respondents being very satisfied with the system’s benefits, 67% with access speed, and 60% with ease of use. In addition, the system encouraged a shift toward technology- and data-driven work practices. Therefore, the implementation of the web-based information system is proven effective in enhancing school administrative governance and has strong potential to be replicated in other educational institutions with similar characteristics.
Cluster Analysis of Alumni Job Waiting Times Using the K-Means Algorithm with Elbow and Silhouette Methods Abdur Ro’uf; Hasyim Asy’ari; Maysas Yafi Urrochman
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2080

Abstract

Evaluating graduate employability is a key indicator for assessing the quality of higher education institutions. One effective approach involves analyzing alumni tracer study data using data mining techniques. This study aims to categorize alumni data based on the waiting period for employment after graduation using the K-Means algorithm. To determine the optimal number of clusters, two methods were employed: the Elbow method (Sum of Squared Errors/SSE) and the Silhouette Score. The research process included data preprocessing—comprising data selection, transformation, and normalization—to enhance data quality prior to clustering. Test results indicated that the Elbow method yielded an optimal cluster count of k=4, whereas the Silhouette method showed the best result at k=3, with a score of 0.62. Consequently, k=3 was selected as the optimal number of clusters due to superior cluster quality. The clustering results revealed that the majority of alumni (67.6%) experienced a short waiting period (averaging ±2 months), while 16.5% faced a long waiting period (±8 months), and 15.9% fell into the medium category (±5 months). These findings are expected to serve as a basis for higher education institutions to evaluate and improve graduate quality, ensuring better alignment with the demands of the job market.
Zero Crossing Rate (ZCR) Feature Extraction in Speech Emotion Recognition Using a Convolutional Neural Network Silviana Widya Lestari; Maysas Yafi Urrochman; Cahyasari Kartika Murni
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2083

Abstract

Speech Emotion Recognition (SER) plays a pivotal role in enhancing human-computer interaction (HCI), healthcare, and automated feedback systems (Hashem et al., 2023). However, extracting relevant acoustic features from complex speech signals remains a significant challenge (Lieskovská et al., 2021). This paper investigates the effectiveness of the Zero Crossing Rate (ZCR) feature extraction method combined with a 1D Convolutional Neural Network (CNN) architecture for speech emotion classification. By evaluating emotional speech data across benchmark datasets (RAVDESS, CREMA, SAVEE, and TESS), audio signals were preprocessed using data augmentation techniques, including noise injection, time stretching, pitch shifting, and time shifting. The ZCR feature representation was fed into a CNN model to classify seven core emotional states: anger, disgust, fear, happiness, sadness, surprise, and neutral. The proposed ZCR-CNN approach achieved a classification accuracy of 61.96% with a training loss of 0.8214 and testing loss of 0.8123. Although ZCR effectively captures high-frequency and noisiness variations indicative of specific emotional states, its performance highlights the trade-offs of relying on single-domain time features compared to spectral features (Singh & Goel, 2022).
Co-Authors Abdur Ro’uf Asy'ari, Hasyim Cahyasari Kartika Murni Hasyim Asy’ari Hizham, Fadhel Akhmad Masyhuri Masyhuri Silviana Widya Lestari