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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 28 Documents
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Search results for , issue "Vol 10, No 2 (2025)" : 28 Documents clear
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.
Prediksi Kesehatan Mental Remaja Berdasarkan Faktor Lingkungan Sekolah Menggunakan Machine Learning Rahma, Mutiara; Fikry, Muhammad; Afrillia, Yesy
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.v10i2.8556

Abstract

Adolescent mental health is a crucial aspect that affects academic performance, social relationships, and overall well-being. The school environment is one of the primary factors influencing adolescents' mental conditions. This study aims to predict adolescent mental health levels based on school environmental factors using the Random Forest algorithm. Data were collected from 229 adolescents in Lhokseumawe and categorized into four classes of mental health conditions. The research methodology includes data preprocessing, model training, and performance evaluation using accuracy and other relevant metrics. The results show that the model achieved an accuracy of 80.43%, with the highest F1-score of 0.90 in the category indicating no mental health issues. Feature importance analysis identified loneliness, feelings of worthlessness, academic pressure, and home-related stress as the most influential factors in the predictions. While the model effectively classified most data, some misclassifications occurred at certain mental health levels. Thus, the Random Forest model proves to be an effective predictive tool for detecting potential adolescent mental health issues. The findings of this study can serve as a reference for educational institutions in designing more targeted intervention strategies to support adolescent mental well-being.
Prediksi Stok Barang di Toko Eko Helm Menggunakan Metode Time series Analysis Fadillah, Betran Dwi; Hendrastuty, Nirwana
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.v10i2.8584

Abstract

Eko Helm Store located in South Lampung, faces challenges in managing helmet inventory, particularly in determining the optimal stock levels for two categories: affordable and premium helmets. This study aims to forecast helmet stock requirements for the year 2024 using the ARIMA method. Weekly sales data from January to December 2024 were analyzed through stationarity testing using the Augmented Dickey-Fuller (ADF) test and differencing, followed by parameter identification based on ACF and PACF plots. The best-fitting models were identified as ARIMA(2,1,0) for premium helmets, with a Mean Squared Error (MSE) of 24.5101 and an Akaike Information Criterion (AIC) of 249.4062, and ARIMA(1,1,0) for affordable helmets, with an MSE of 32.6102 and an AIC of 250.5381. ARIMA was selected due to its ability to capture trends and seasonal fluctuations more effectively than methods such as moving average or exponential smoothing. The forecasting results estimate a stock requirement of 112 units for affordable helmets and 64 units for premium helmets over the next four weeks. The ARIMA model is integrated into an automated forecasting system that runs scheduled scripts without manual intervention. This system supports timely and precise inventory procurement decisions.
Perancangan Ulang Desain UI/UX Aplikasi I-Nusaplant Dengan Metode Design Thinking dan A/B Testing Saputra, M Ari; Khaira, Ulfa; Saputra, Edi
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.v10i2.8236

Abstract

I-Nusaplant is a mobile-based application that can detect types of medicinal plants. The I-Nusaplant application was designed by Information Systems students using Android-based leaf images. This application aims to help the community in detecting medicinal plants. The ease of detecting medicinal plants using the application must also be supported by a good appearance and user experience. I-Nusaplant has 3 menus in it, namely Home, Detection, and About. The I-Nusaplant application has shortcomings after conducting interviews with medicinal plant experts, medicinal plant enthusiasts, and general users. The majority of respondents chose the I-Nusaplant application to be redesigned for several reasons related to user experience in running the application. Ease of obtaining information, time to move around each menu, and some features that users need. In doing a redesign, it is necessary to have an in-depth design using UI/UX. The design process in solving problems and generating user needs, researchers use the Design Thinking method to generate ideas to solve user problems. At the analysis stage, problem analysis is carried out, finding solutions to user problems, analyzing user needs. In the design stage, UI/UX design is produced, namely architecture information, user flow, and interface design. UI/UX design will be tested using the Maze tool. After that, compare the results of the old and new I-Nusaplant interface designs using the A/B Testing method. This method aims to see the performance of the old and new application designs. Our A/B testing revealed that the new design, while more complex, is just as efficient as the old one, both scoring 99 this shows that the new design is easy to use by users despite its different design.
Klasifikasi Pertanyaan Quora Menggunakan Metode Keyword-based dan Analisis Sentimen dengan ComplementNB Adiuntoro, Alwan; Hendrawan, Aria
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.v10i2.7965

Abstract

Text classification is a fundamental task in Natural Language Processing (NLP) that supports the categorization of data based on predefined labels. This study aims to evaluate the effectiveness of keyword-based labeling and sentiment analysis methods for text classification using the Quora Questions dataset. The dataset comprises 16,921 samples with imbalanced class distribution, where the opinion category dominates, while the hypothetical category is a minority class. The labeling process utilized a keyword-based approach for the fact and hypothetical categories, while the opinion category was labeled using sentiment analysis with the Vader Lexicon library. TF-IDF was employed as the feature representation method, with two approaches explored: n-gram range tuning (1–3) and without tuning. ComplementNB, designed for handling imbalanced datasets, was utilized for classification, with a training-test split of 70:30. The results show that the approach without n-gram tuning achieved the highest accuracy of 93.89%, with zero variance in cross-validation. Evaluation revealed that ComplementNB effectively handles class imbalance, as demonstrated by high precision and recall in the minority class. This study demonstrates that a simple approach combining keyword-based labeling and sentiment analysis can be effectively implemented for category-based text classification tasks, particularly in platforms like Quora. These findings are relevant for similar applications requiring real-time text classification with minimal complexity.

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