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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 24 Documents
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Search results for , issue "Vol 10, No 1 (2025)" : 24 Documents clear
Menggunakan Metode Machine Learning Untuk Memprediksi Nilai Mahasiswa Dengan Model Prediksi Multiclass Setiawan, Moh. Arif Ma'ruf; Kusrini, Kusrini; Hartono, Anggit Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8334

Abstract

This study aims to predict students' final GPA and study duration using machine learning methods. The model applied in this study is the Random Forest Regressor, which was trained using a dataset that includes various factors such as semester GPA, socio-economic background, demographics, learning activities, and the difficulty level of courses. The results of the study show that the model produces less accurate predictions, with a Mean Squared Error (MSE) of 0.34 for the final GPA and 3.83 for the study duration. Furthermore, the R² Score for the predictions of final GPA and study duration are -0.079 and -0.055, respectively, indicating that the model's prediction performance is not optimal. In the multiclass classification section, the model is able to classify students into several categories based on their final GPA, such as Cum Laude, Very Satisfactory, Satisfactory, and Fair. From the testing results, the model predicts a final GPA of 2.92 for a new student example, which is classified into the "Satisfactory" category, with a predicted study duration of 8 semesters. The conclusion of this study indicates that the regression model used requires improvement to achieve better accuracy. Other factors, such as feature optimization or the use of alternative algorithms, can be explored in future research to enhance the prediction results.
Sistem Smart Home untuk Deteksi Potensi Kebakaran Berbasis Internet of Things dengan Notifikasi WhatsApp Maulana, Fahmi; Widiyono, Widiyono; Taryadi, Taryadi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8176

Abstract

The security and comfort of homes are fundamental needs that have become increasingly urgent with the advancement of technology. According to fire data released by the Pekalongan City Government in 2023, there were 101 reported cases of fires in Pekalongan, a threefold increase from 38 incidents in 2022. This study aims to design and implement a smart home system for detecting potential fires based on the Internet of Things (IoT) using NodeMCU ESP8266, ThingSpeak, and sensors including MQ2, flame sensors, and DHT11. The development method employs a prototyping model, supported by interviews with firefighters to identify relevant fire variables and ensure the system design meets user needs through hardware experimentation. Testing results indicate that the flame sensor can detect flames of 1.5 cm in length at a distance of up to 15 cm, with an average response time of 7.22 seconds to send notifications to WhatsApp. It can also detect flames of 3 cm in length at a distance of up to 50 cm, with an average response time of 8.79 seconds. The MQ2 sensor successfully detects gas concentrations above a value of 35, sending notifications to WhatsApp with an average response time of 8.89 seconds. Sensor data is visualized in real-time through ThingSpeak. Based on usability testing results, 68% of respondents expressed agreement, 24% were neutral, and 8% disagreed. The conclusion of this study is that the system can serve as an innovative alternative to create a safer and more efficient home environment. This research is expected to contribute to the development of smart home technology in Indonesia
Evaluasi Performa Website Rumah Sakit CSH Mempergunakan User Acceptance Test Setyadi, Resad; Fauzi, Muhammad Andre
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.6287

Abstract

User Acceptance Testing (UAT) is a crucial step to ensure that the solutions implemented in a system align with user needs. Unlike system testing, UAT focuses on the functionality of the solution for end users. In this context, testing user acceptance becomes an essential element to assess the performance and user satisfaction of a website. The CSH Hospital website faces the challenge of lacking a scientific analysis to evaluate its performance and usability. Therefore, the UAT method is applied using the ISO 9126 dimensions and the Likert scale. The information system employed facilitates routine transactions, data processing, operational support, and provides relevant information to users. The evaluation results show that the CSH Hospital website achieved a score of 87%, reflecting a high level of user acceptance and comfort in using the website. However, identifying areas for improvement, such as simplifying and accelerating the online registration process, can enhance user experience and streamline services in the future.This aligns with the Sustainable Development Goal (SDG) 3, "Good Health and Well-Being." By improving digital services like the hospital's website, the community can gain easier access to healthcare services, increase registration efficiency, and improve the overall patient experience. Ultimately, this supports efforts to achieve universal access to quality healthcare services, as mandated by SDG 3.
Deteksi Tepi Menggunakan Metode Operator Prewitt dan Kirsch pada Citra Uang Kertas Sulistyo, Wicaksono Yuli; Arifah, Amalina Nur; Pratiwi, Septia Ayu
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.6292

Abstract

The importance of edge detection in image processing, especially on banknote objects, prompted this research to carry out an analysis of two edge detection methods, namely the Prewitt and Kirsch operators. five images of banknotes with different denominations (2000, 5000, 10000, 20000 and 50000) were taken as research objects. The edge detection method is implemented using MATLAB, utilizing both Prewitt and Kirsch operators. Image quality assessment uses PSNR, Histogram and Pixel value parameters. The comparison results show that the Prewitt and Kirsch operators provide optimal edge detection results, producing clear and sharp edges in the banknote image. The edge detection quality assessment was carried out through the PSNR metric, and both showed PSNR values above 30 dB, indicating good quality in terms of clarity and accuracy. Comparison of the Histogram and Pixel values shows that the Kirsch method has a higher Histogram and the Prewitt method has a higher Pixel value.

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