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Jurnal ICT : Information and Communication Technologies
Published by Marq & Cha Institute
ISSN : 20867867     EISSN : 28089170     DOI : https://doi.org/10.35335/jict
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering
Jurnal ICT : Information and Communication Technologies (p-ISSN: 2086-7867) is a scientific journal and open access journal published by Pusat Penelitian Teknoligi, Marqcha Institute, Indonesia. Jurnal JICT covers the field of Informatics, Computer Science, Information Technology and Communication.It was firstly published in 2010 for a printed version. The aims of Jurnal JICT are to disseminate research results and to improve the productivity of scientific publications. Jurnal JICT is published two times a year (April and October).
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 96 Documents
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Design of an IoT-Based Earthquake Vibration Detection System Using MPU6050 Sensors with Real-Time Monitoring via a Mobile App Dwiputra, Audry Zaky; Nazry S, Hevlie Winda
Jurnal ICT : Information and Communication Technologies Vol. 17 No. 1 (2026): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Advances in Internet of Things (IoT) technology enable electronic devices to communicate and exchange data in real time. This study aims to design and implement an IoT-based earthquake vibration detection system using an MPU6050 sensor and an ESP32 microcontroller with real-time monitoring via a mobile application. The research method used is engineering research with an iterative prototype approach. The system consists of an MPU6050 sensor as a vibration detector, ESP32 as a data processor and communication module, an IoT server, an LCD display, a buzzer, and a mobile application as the user interface. Acceleration data on the X, Y, and Z axes is processed using a threshold-based method to distinguish between normal conditions and earthquake events. The test results show that the system achieves a detection accuracy of 92.6%, with an average response time of 1.2 seconds from vibration detection to mobile notification delivery. In addition, the system demonstrates a data transmission reliability of 98.3%, indicating stable communication between the device and the IoT server. The system is capable of detecting vibration changes effectively, transmitting data in real time, and displaying monitoring information through a mobile application. Early warning notifications are successfully generated when vibration values exceed the defined threshold. Based on the results, the proposed system provides a low-cost, efficient, and easy-to-implement solution for earthquake vibration monitoring on a local scale. However, improvements are still required in adaptive threshold optimization and large-scale field testing to enhance system robustness and reliability under real-world conditions.
Classification of Oil Palm Fruit Ripeness Levels Based on Digital Image Feature Extraction Using the Catboost Algorithm Ardhini, Setya Eka; Hutagalung, Fatma Sari
Jurnal ICT : Information and Communication Technologies Vol. 17 No. 1 (2026): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v17i1.327

Abstract

Determining the ripeness level of oil palm fruit is essential for improving palm oil production quality. Manual assessment methods are often subjective and inconsistent because they rely on workers’ experience and environmental conditions. Therefore, this study proposes an automatic image-based classification system using the CatBoost algorithm. The novelty of this research lies in the integration of CatBoost with RGB color and Gray Level Co-occurrence Matrix (GLCM) texture feature extraction for multiclass oil palm fruit ripeness classification. The dataset consisted of 1000 images categorized into four classes: unripe, under-ripe, ripe, and overripe. The research stages included image preprocessing, feature extraction, classification, and web-based implementation using the Flask framework. Experimental results showed that the proposed system achieved high performance based on accuracy, precision, recall, and F1-score metrics, demonstrating the effectiveness of CatBoost in classifying oil palm fruit ripeness while reducing overfitting. The developed web-based system can assist plantation workers in determining fruit ripeness automatically, objectively, and efficiently, thereb
Application of the Analytical Hierarchy Process and GIS in a Decision Support System for Determining the Location of the Final Disposal Site (FDS) for the City of Medan in Deli Serdang Regency Hartono, Arya Danu; Tanjung, Mahardika Abdi Prawira
Jurnal ICT : Information and Communication Technologies Vol. 17 No. 1 (2026): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

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Abstract

Indonesian Sign Language (BISINDO) is the primary communication medium for the deaf community, yet limited public understanding often leads to communication barriers. Previous sign language recognition studies have generally been conducted offline, lacked real-time web integration, and produced only text-based outputs without multimodal interaction. To address these limitations, this study proposes a real-time web-based BISINDO translator system using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model, integrated with a Text-to-Speech (TTS) feature. The novelty of this research lies in the combination of CNN for spatial feature extraction and LSTM for temporal sequence learning within a fully deployed web application framework (NzSignify), enabling real-time end-to-end sign language translation with both text and voice output. The dataset consists of primary video recordings from three subjects, covering 11 gesture classes with 1,000 grayscale frames per class at a resolution of 100×89 pixels. The proposed model is implemented using a React.js and Node.js-based system to support real-time inference. Experimental results show that the hybrid CNN-LSTM model achieves a classification accuracy of 96% based on Confusion Matrix evaluation. In real-time testing, an 80% confidence threshold effectively filters misclassified gestures and improves translation reliability into text and speech outputs. Compared to previous studies that mainly rely on standalone CNN or traditional machine learning methods with offline processing, the proposed approach demonstrates improved capability in capturing both spatial and temporal features of sign gestures as well as supporting real-time deployment. These findings indicate that the developed system provides a more practical, accurate, and interactive solution for BISINDO translation, enhancing communication accessibility between deaf and hearing communities through a real-time multimodal platform.
Application of The Random Forest Algorithm in Classifying the Tendency of Impulsive Purchasing Behavior Among Gen Z Consumers in E-Commerce Based on Flash Sale Features Nurfadhilah, Syifa; Azhari , Mulkan
Jurnal ICT : Information and Communication Technologies Vol. 17 No. 1 (2026): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v17i1.329

Abstract

The rapid growth of e-commerce in Indonesia, particularly on Shopee, has significantly influenced consumer behavior through promotional strategies such as flash sales. This study aims to classify impulsive buying tendencies among Generation Z, identify key influencing factors, and develop a web-based classification system for behavioral analysis. A quantitative data mining approach was applied using the Random Forest algorithm. The dataset consisted of 420 Gen Z respondents collected through a Likert-scale questionnaire using purposive sampling, and model evaluation was conducted using 10-fold cross-validation to ensure reliability. The results show that the Random Forest model achieved an accuracy of 83.16%, outperforming Decision Tree (78.42%) and Logistic Regression (75.08%), indicating its effectiveness in handling complex behavioral patterns. Feature importance analysis revealed that limited stock availability (39.85%) and discount magnitude (33.21%) are the most dominant factors influencing impulsive buying behavior, followed by promotional duration and notification attractiveness. These findings emphasize the role of urgency and scarcity in driving impulsive purchases among Gen Z consumers. Additionally, a web-based system was developed using the Flask framework in Python to support automated data processing, model training, and visualization of results. The system enables real-time behavioral analysis and decision support for digital marketing strategies. Overall, the study demonstrates that machine learning, particularly Random Forest, provides a more accurate and objective approach for analyzing impulsive buying behavior compared to conventional statistical methods, while also offering a practical tool for e-commerce analytics and strategy optimization.
Analysis Of The Distribution Of Livestock Disease Cases By Region Based On Data From The Ministry Of Trade’s Animal Health Center Using The Dbscan Clustering Method In Bandar District Dzakiyya, Muhammad Naufal; Gultom , Zuli Agustina
Jurnal ICT : Information and Communication Technologies Vol. 17 No. 1 (2026): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v17i1.330

Abstract

Livestock farming serves as a vital economic pillar for the community in Bandar District, Simalungun Regency. However, the high intensity of livestock activities is accompanied by a significant risk of disease transmission, which has historically been managed through conventional recording methods that lack spatial integration. This research aims to analyze the spatial distribution patterns of livestock diseases by implementing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method integrated into a web-based Geographic Information System (GIS). Using a quantitative approach, the study processed 200 case records from December 2025 to January 2026. Spatial distances were calculated using the Haversine formula to ensure geographic accuracy. The results indicate that the optimal parameters for the DBSCAN algorithm are an epsilon ($\epsilon$) of 3.0 km and a minimum points (MinPts) of 2. These parameters successfully identified two primary clusters with zero noise, encompassing all 200 cases. Cluster 1 (98 cases) is concentrated in the west-central region, dominated by cattle and goats with diverse pathologies such as Scabies and BEF. Cluster 2 (102 cases) is located in the east-northern region and exhibits a more heterogeneous livestock profile, including rabies cases in dogs. High-density areas requiring priority intervention were identified in Pematang Kerasaan Rejo and Perdagangan II. The developed web-based GIS provides an interactive visualization platform that enhances early warning capabilities and supports data-driven decision-making for livestock disease surveillance and regional control.
Analysis of Public Sentiment Toward Mental Health on Social Media Using Naïve Bayes Wiji Lestari Sitorus; Zuli Agustina Gultom
Jurnal ICT : Information and Communication Technologies Vol. 17 No. 1 (2026): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v17i1.333

Abstract

Mental health is a global issue that has garnered significant public attention on social media. The platform X (formerly Twitter) is widely used by the public to openly express emotional conditions, yielding vast amounts of unstructured textual data. This research aims to analyze public sentiment regarding mental health issues on social media X using the Multinomial Naïve Bayes algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) word weighting. The dataset consists of 9,000 tweets written in Indonesian, collected between February 15 and 27, 2025, using the keywords kesehatan_mental (mental health), stress (stress), kecemasan (anxiety), and depresi (depression). To enhance data quality, a comprehensive text preprocessing pipeline was implemented, including cleaning, case folding, word normalization (using a 59-entry mapping dictionary), tokenizing, stopword removal, and stemming. The performance of the classification model was evaluated using a confusion matrix on 1,800 test data. The results demonstrate that the Multinomial Naïve Bayes model achieved a high accuracy of 90.78% and a macro average F1-score of 90.75%. Specifically, the positive sentiment class yielded a precision of 96.22% and a recall of 84.89%, while the negative sentiment class achieved a precision of 86.48% and a recall of 96.67%. Furthermore, this study integrates the classification model into a web-based system equipped with an explainability feature that visualizes word contributions to the sentiment outcomes. This research contributes an interpretative, informative, and efficient computational approach for monitoring public sentiment trends toward mental health issues on Indonesian social media.

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