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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 431 Documents
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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.
Child Presence Detection for Child Safety with Deep Neural Networks Hidayat, Sidiq Syamsul; Aprilia, Dita; Hadwi, Sindung; Mujahidin, Irfan; Prabowo, M. Cahyo Adi; Rakhman, Fikri Arif
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.6540

Abstract

Accidents and injuries to children often occur due to lack of supervision. This research develops a child presence detection system using Computer Vision technology and the Age Estimation method to improve child safety in dangerous areas. The system was tested with a Canon EOS M50 camera at various distances, camera heights, and light intensity. The analysis using anova obtained a data confidence level of 95% for light intensity, and the age estimation method showed performance with a success of 84.72%. This research can be applied to supervise and improve safety in children, especially outdoors.
Pengembangan Aplikasi Presensi QR Code Berbasis Website Dengan Metode Agile Assyafa, Izza; Budi, Setyo
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8472

Abstract

Attendance recording for students at Pondok Pesantren Mahasiswa (PPM) Al-Hikmah Semarang is still conducted manually, making it prone to recording errors and time-consuming when compiling attendance data. This study aims to develop a QR Code-based attendance system to improve the efficiency and accuracy of attendance recording. The method used involves designing and developing a web-based system using the Laravel framework and the Agile methodology. The system is designed to be used by both students and class supervisors during learning activities at the dormitory. The research results show that the system can automate student attendance through QR Code scanning, store data in a structured manner, and provide accurate attendance reports that are easily accessible to the dormitory administrators. Additionally, features such as schedule management, student data management, and attendance reporting based on specific criteria have been implemented to support more effective administration. The system is also equipped with a feature to print attendance recap reports. With the implementation of this system, student attendance management becomes more efficient, transparent, and less prone to errors compared to the previously used manual method.
Integrasi Backend Golang-Echo pada Aplikasi Greenly sebagai Solusi Teknologi Pengelolaan Sampah Digital Anggraeni, Mutia Dwi; Utomo, Fandy Setyo; Marcos, Hendra
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8227

Abstract

The waste problem in Indonesia is increasingly pressing, with 35.7% of the 31.9 million tons of national waste in 2023 not being managed properly. This study develops the Greenly web application backend as a digital solution to support waste reporting, recycling education, and increasing community participation through a gamification system. The methodology used is the Waterfall model, including needs analysis, design with Entity Relationship Diagram (ERD), implementation, and testing. The backend is built using the Golang and Echo frameworks, then packaged in Docker and deployed on the AWS EC2 service. The Continuous Integration/Deployment (CI/CD) process is carried out using GitHub Actions, with Nginx as a reverse proxy. Testing is carried out through Integration Test to ensure the reliability of key features such as CRUD data, waste reporting, and gamification. The results show that the backend system runs stably, safely, and efficiently, with an automatic CI/CD flow that is successfully executed without errors. The main contribution of this study is the provision of an adaptive and reliable backend as the foundation for a digital waste management system based on community participation.
Comparison of Machine Learning Algorithm for Enzyme Production Optimization from Industrial Waste Bastian, Ade; Fitriyani, Rofi; Susandi, Dony; Pangestu, Arki Aji; Mardiana, Ardi; Sujadi, Harun
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8212

Abstract

The manufacture of industrial enzymes from trash provides a sustainable remedy for environmental issues. This work investigates machine learning methods to enhance enzyme production from industrial waste by examining critical factors such as waste type and chemical makeup. Three algorithms—Linear Regression, Decision Tree, and Neural Network—were used to estimate and forecast enzyme production. Evaluation criteria, such as Mean Squared Error (MSE) and Coefficient of Determination (R²), were used to evaluate model performance. The results indicated that the Decision Tree method was the most effective, exhibiting lowest error and enhanced accuracy in selecting ideal production factors such as fermentation temperature and time. This method improves efficiency, lowers operating expenses, and encourages sustainable waste management practices. The results highlight the potential of machine learning to convert trash into useful industrial goods, providing a route to more sustainable biotechnology. Future study may enhance hybrid algorithms, include new waste factors, and facilitate real-time implementation for wider industrial applicability.  
Perbandingan Inisialisasi Bobot Random dan Nguyen-Widrow Pada Backpropagation Dalam Klasifikasi Penyakit Diabetes Guswanti, Widya; afrianty, iis; budianita, elvia; syafria, fadhilah
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8618

Abstract

Diabetes is a metabolic disorder that occurs when the pancreas is unable to produce adequate amounts of insulin or the body has difficulty in utilizing it optimally. This condition has the potential to cause various health complications. Therefore, early diagnosis of diabetes is very important to reduce the mortality rate due to these complications. Backpropagation Neural Network (BPNN) is an approach in Artificial Neural Network (ANN) that is commonly applied for disease classification, including diabetes. However, the BPNN method has drawbacks, namely its slow convergence rate and the possibility of getting stuck at a local minimum due to random weight initialization. To overcome these problems, this study applies the Nguyen-Widrow weight initialization method to improve the performance of BPNN in diabetes classification. The data source in this study comes from Kaggle, consisting of 768 data with 8 parameters. Model testing was conducted using k-fold cross-validation with K=10, and exploring various numbers of neurons in the hidden layer and learning rate (lr). The results showed that weight initialization using the Nguyen-Widrow method improved the accuracy of BPNN compared to random weight initialization. The best model was obtained with lr 0.001 and 15 neurons in the hidden layer, resulting in an accuracy of 91.23%, higher than the random weight initialization which only reached 89.91%. Thus, the Nguyen-Widrow method is proven effective in improving the performance of BPNN for diabetes classification.Diabetes merupakan gangguan metabolik yang terjadi ketika pankreas tidak mampu menghasilkan insulin dalam jumlah yang memadai atau tubuh mengalami kesulitan dalam memanfaatkannya secara optimal. Kondisi ini berpotensi menimbulkan beragam komplikasi kesehatan. Oleh karena itu, diagnosis dini penyakit diabetes sangat penting untuk menekan angka kematian akibat komplikasi tersebut. Backpropagation Neural Network (BPNN) adalah pendekatan dalam Jaringan Syaraf Tiruan (JST) yang umum diterapkan untuk klasifikasi penyakit, termasuk diabetes. Namun, metode BPNN memiliki kekurangan, yaitu laju konvergensinya yang lambat dan kemungkinan terjebak pada minimum lokal akibat inisialisasi bobot yang dilakukan secara random. Untuk mengatasi permasalahan tersebut, penelitian ini menerapkan metode inisialisasi bobot Nguyen-Widrow guna meningkatkan performa BPNN dalam klasifikasi diabetes. Sumber data dalam penelitian ini berasal dari Kaggle, terdiri dari 768 data dengan 8 parameter. Pengujian model dilakukan menggunakan k-fold cross-validation dengan K=10, serta mengeksplorasi berbagai jumlah neuron dalam hidden layer dan learning rate (lr). Hasil penelitian menunjukkan bahwa inisialisasi bobot menggunakan metode Nguyen-Widrow meningkatkan akurasi BPNN dibandingkan dengan inisialisasi bobot random. Model terbaik diperoleh dengan lr 0,001 dan 15 neuron pada hidden layer, menghasilkan akurasi sebesar 91,23%, lebih tinggi dibandingkan inisialisasi bobot random yang hanya mencapai 89,91%. Dengan demikian, metode Nguyen-Widrow terbukti efektif dalam meningkatkan performa BPNN untuk klasifikasi diabetes.
Prediksi Harga Rumah di Bandung 2024 Menggunakan Ensemble Learning: Analisis Komparatif dan Interpretabilitas Hibatulloh, Muh Naufal; Prakoso, Gilang Danu; Putri Yunus, Adiestiana Dwi; Putra, Tommy Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8200

Abstract

House price prediction plays a crucial role in investment decision-making and financial planning, particularly in developing cities like Bandung with its complex property market dynamics. This study aims to evaluate and compare the performance of various ensemble learning techniques in predicting house prices in Bandung for the year 2024, with a specific focus on model interpretability analysis. The data was collected through web scraping from www.rumah123.com in March 2024, covering attributes such as location, number of rooms, land area, and building area. The evaluated ensemble techniques include Random Forest, Gradient Boosting Machines, Xtreme Gradient Boosting, Linear Regression, and Stacking Ensemble. Model performance was assessed using MAE, RMSE, and R-squared metrics, while interpretability analysis was conducted using SHAP values. The Model Stacking Ensemble shows the most optimal results with R² 0.9076, RMSE 0.311, and MAE 0.216 in experiments involving location features. Features such as land size, building size, and location have proven to have the greatest impact in predicting prices based on SHAP analysis. This model has been successfully integrated into a Flask website for interactive price predictions.

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