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All Journal Jurnal Pilar Nusa Mandiri Enthusiastic : International Journal of Applied Statistics and Data Science Journal of Informatics Development
Choiri, Achmad Firman
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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GENERATION Z'S AND ANDROID OS: HOW USER EXPERIENCE, SECURITY, AND SYSTEM PERFORMANCE SHAPE SATISFACTION Murni, Cahyasari Kartika; Choiri, Achmad Firman; 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.v21i1.6082

Abstract

This paper investigates Generation Z's opinion of the Android OS through an analysis of system performance, security, and user experience and their influence on general satisfaction. With Technology Adaptation (Z) as a mediator, the study focuses on three main factors: User Experience (X1), Security and Privacy (X2), and System Performance and Stability (X3), specifically among students who use Android devices. Data were collected through a structured questionnaire, and the analysis was conducted using the Partial Least Squares (PLS) method to evaluate the relationships between the variables. The findings reveal that, in addition to frequent updates, Generation Z's satisfaction is significantly influenced by the accessibility, performance, and security features of Android. The results highlight the importance of a positive user experience and robust security measures in enhancing user satisfaction. Continuous development in these areas is crucial for improving user engagement and contentment with Android devices.
Product Demand Forecasting in E-Commerce with Big Data Analytics: Personalization, Decision Making and Optimization Murni, Cahyasari Kartika; Choiri, Achmad Firman; Rahmawati, Febriane Devi
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1548

Abstract

This study explores the role of Big Data in forecasting product demand in the e-commerce sector through the application of machine learning and time series methods. A quantitative descriptive approach is used, involving data collection, preprocessing, analysis, and model evaluation. Forecasting techniques applied include ARIMA for time series prediction and XGBoost for supervised learning to identify key demand factors. Model performance is evaluated using accuracy metrics such as RMSE, MAE, and MAPE. The results indicate that the XGBoost model provides the highest forecasting accuracy at 89%, while the ARIMA model achieves 78%. These findings demonstrate that Big Data significantly supports strategic decision-making in e-commerce by enhancing personalization, optimizing inventory, and enabling data-driven marketing strategies.
Implementation of Artificial Neural Network for IoT-Based Water Quality Classification in Fish Ponds Choiri, Achmad Firman; Murni, Cahyasari Kartika
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1752

Abstract

This study presents the implementation of an Artificial Neural Network (ANN) to classify water quality in fish ponds using a dataset derived from a fuzzy inference-based IoT system. The previous fuzzy system utilized three sensor parameters—pH, Total Dissolved Solids (TDS), and temperature—to determine water quality (good, moderate, poor) through rule-based reasoning. Although the fuzzy approach produced accurate and interpretable results, it lacked adaptability to new data variations and required manual rule adjustments. In this research, the ANN model was trained using MATLAB’s Neural Network Toolbox with 120 dataset samples obtained from the fuzzy system’s outputs. The model architecture consisted of three input neurons (pH, TDS, temperature), one hidden layer with ten neurons using a tansig activation function, and one output neuron with purelin. Training of the model was conducted using the Levenberg–Marquardt backpropagation algorithm, employing a dataset split of 80% for training, 10% for validation, and 10% for testing. The results showed that the ANN achieved a classification accuracy of 94.8%, with a Mean Squared Error (MSE) of 0.85942 and a regression coefficient (R) of 0.94, indicating a strong correlation between predicted and actual data. Compared to the fuzzy inference method, the ANN model demonstrated better adaptability to unseen data and a higher level of generalization. This system can be integrated into IoT-based monitoring platforms for real-time, intelligent, and adaptive water quality prediction to support sustainable aquaculture.
Integration of Natural Language Processing in a Web-Based Translanguaging System for Arabic-Indonesian Language Learning Reknadi, Danang Bagus; Abidin, Mohammad Mansyur; Choiri, Achmad Firman
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1753

Abstract

Arabic–Indonesian language learners often face challenges in understanding the contextual meaning of texts due to differences in morphological and syntactic structures between the two languages. To address this, this study proposes the development of a web-based translanguaging system integrated with Natural Language Processing (NLP) to help users understand and translate texts more meaningfully. This system was developed using the Waterfall model with stages of requirements analysis, design, implementation, testing, and maintenance. The implemented NLP module includes tokenization, part-of-speech tagging, and sentence structure analysis to produce translations that consider context, not just literal word equivalents. The implementation results show that the system is able to improve user comprehension of Arabic–Indonesian texts with a simple and accessible interface. Furthermore, the translation history feature supports continuous self-learning. Although the system still has limitations in handling idiomatic text and complex sentence structures, the NLP integration has proven effective in improving the quality of translanguaging. This research contributes to the development of bilingual learning technology and can be further developed using deep learning models such as BERT or mBERT to improve semantic and contextual accuracy.
Fuzzy Logic Algorithm Optimization for Safe Distance Control on Arduino-Based Reverse Parking System and SRF04 Sensor Choiri, Achmad Firman
Journal of Informatics Development Vol. 2 No. 2 (2024): April 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v2i2.1336

Abstract

This research aims to develop a smart parking system that can accurately control the distance between vehicles and obstacles during reverse parking maneuvers. By integrating fuzzy logic algorithms into the system, this study seeks to improve the precision and reliability of distance control, thereby improving the overall safety of parking operations. The utilization of the Arduino platform and the SRF04 sensor allows real-time data processing and accurate distance measurement, i.e. this research contributes to the effectiveness of the proposed system. The application of fuzzy logic optimization in this context is expected to provide a powerful solution for safe reverse parking, offering potential benefits in terms of comfort and accident prevention in parking scenarios, especially for cars that still do not have obstacle detection sensors at the rear of the car
Tourist Preference Analysis Based on Google Reviews Using the DBSCAN Method Qori'atunnadyah, Marita; Murni, Cahyasari Kartika; Choiri, Achmad Firman; Marianto, Hadi; Yazid, Muhammad
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art7

Abstract

Tourism is a strategic sector contributing to regional economic growth. Although Lumajang Regency offers prominent natural destinations, data-based insights into tourist preferences remain limited. This study analyzed tourist preferences using Google Reviews through a text mining approach that integrated the density-based spatial clustering of applications with noise (DBSCAN) algorithm and lexicon-based sentiment analysis. Data were collected via web scraping from six major destinations, yielding 16,904 reviews, of which 9,800 contained analyzable text. The text data were preprocessed using the term frequency-inverse document frequency (TF–IDF) to generate numerical representations prior to clustering. Using DBSCAN with parameters ε = 0.8 and MinPts = 4, one main cluster comprising 9,353 reviews and 447 outliers was identified. The main cluster was dominated by keywords such as waterfall, beautiful, and scenery, emphasizing the visual appeal of Tumpak Sewu as Lumajang’s tourism icon, while the outliers reflected reviews from international visitors and practical travel information. Sentiment analysis showed that most reviews were positive (68.0%), followed by neutral (24.1%) and negative (7.9%). These findings indicate a predominantly positive perception of Lumajang tourism, though accessibility and facilities require improvement. The study demonstrates the potential of digital review data for developing data-driven tourism management and promotion strategies.
Co-Authors Abidin, Mohammad Mansyur Danang Bagus Reknadi Hizham, Fadhel Akhmad Marianto, Hadi Muhammad Yazid Murni, Cahyasari Kartika Qori'atunnadyah, Marita Rahmawati, Febriane Devi