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INDONESIA
Jurnal Pengembangan Sistem Informasi dan Informatika
Published by Training & Research Institute Jeramba Ilmu Sukses
ISSN : -     EISSN : 27461335     DOI : 10.47747
Core Subject : Science, Social,
Computer Science & IT Social Sciences
Jurnal Pengembangan Sistem Informasi dan Informatika (Jurnal-PSII) is a media for lecturers and students to publish research results dedicated to all aspects of the latest outstanding developments in the field of information systems and informatics. Areas of research include, but are not limited to the information systems, information technology, informatics and computer science, and industrial engineering and its Applications.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 99 Documents
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Penerapan Metode Support Vector Machine Dalam Menganalisis Sentimen Pengguna Aplikasi Sirekap 2024 Di Google Playstore Iqrom, Redho Aidil; Syahril, Muhammad; Jakak, Pamuji Muhamad; Irawan, Indra; Febyani, Yanita
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 1 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i1.2565

Abstract

Sirekap is a mobile application that was built to help the public monitor and oversee the development of the 2024 elections held in Indonesia. The research aims to apply the Support Vector Machine algorithm in analyzing sentiment about the use of the Sirekap application in 2024. The Support Vector Machine method is used to classify user sentiment into classes, namely positive, negative, and very negative. The amount of data used is 15,000 data sourced from Sirekap application reviews on Google PlayStore, with more detailed research stages including data collection, data preprocessing, data labeling, visualization, word weighting, and testing and analysis. The results show that the Support Vector Machine algorithm provides an accuracy of 88% for the Sirekap 2024 application. These results are expected to help developers to develop further the Sirekap 2024 application in improving the quality of the application and providing better user comfort Based on the results of the sentiment analysis of Sirekap 2024 application users on the Google Play store using the Support Vector Machine (SVM) method, an accuracy rate of 88% was obtained in classifying the sentiment of reviews into positive, negative, and very negative. This shows that the Support Vector Machine method is quite accurate for sentiment analysis of Indonesian text data. Overall, most reviews are very negative with a percentage reaching 76.9%, followed by negative reviews at 12.6%, and the least are positive reviews at 11%.
A GAN-Based Approach for Identifying Fake Accounts on Twitter Zain, M Syafrizal; Swengky, Better; Wisesa, Bradika Almandin; Putri, Vivin Mahat
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 1 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i1.2671

Abstract

The multiple security threats on the network make the need for robust security measures a major concern. The increasing presence of fake accounts and malicious actors on online platforms poses significant challenges, requiring sophisticated detection techniques to maintain network integrity. To address these issues, we propose a novel method for detecting fake accounts by leveraging Generative Adversarial Networks (GANs). By analyzing data extracted from platform APIs, our approach leverages the unique characteristics of GANs to improve the accuracy and efficiency of the detection process. In this study, we develop a GANs-based model specifically designed to detect fake accounts. The model is built through several key stages: first, we collect a comprehensive dataset, then perform data processing and preprocessing to make it suitable for machine learning applications. Next, the model is trained using various hyperparameters to optimize accuracy, thus learning the underlying patterns associated with fake accounts. After the training stage, the model is tested on previously unseen data to evaluate its generalization and performance in real-world scenarios. Experimental results show that our model achieves a threshold value of 0.0054779826. This value plays a crucial role in determining the accuracy of the detection system. The smaller the threshold value, the higher the model accuracy, as it shows a lower error rate in distinguishing between real and fake accounts. The ability of GANs-based models to adaptively learn from data during the training process contributes to high precision in detecting anomalies as well as minimizing false positives.
Implementasi Convolutional Neural Network (CNN) dalam Diagnosa Penyakit Daun Padi Berdasarkan Citra Digital Irawan, Indra; Wathan, M.Hizbul; Swengky, Better; Ramadani, Ardi
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2756

Abstract

This study investigates the implementation of Convolutional Neural Network (CNN) in classifying rice leaf diseases based on digital images. The model classifies three types of diseases: Bacterial Leaf Blight, Rice Blast, and Rice Tungro Virus. A dataset of 240 images was obtained from Kaggle, with 80 images per class. Four training scenarios were applied using 25, 50, 75, and 100 epochs. Preprocessing steps included resizing all images to 150x150 pixels and normalizing pixel values. Evaluation results show that classification accuracy increases with the number of training epochs. The best model was achieved at 100 epochs, yielding a validation accuracy of 91.67% and testing accuracy of 92%. These results demonstrate that CNN is effective in diagnosing rice leaf diseases and can support early detection efforts to strengthen national food security.
Klasifikasi Mata Katarak dan Mata Normal Menggunakan Algoritma Dasar Convolutional Neural Network (CNN) Swengky, Better; Wathan, M Hizbul; Irawan, Indra; Aulia, Rosaura
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2758

Abstract

Eye diseases encompass a wide range of conditions, from mild visual impairments to complete blindness, with cataracts being one of the leading causes. Despite advances in medical imaging, automated classification of cataract versus normal eye images remains a challenging task. This study proposes a classification method using a Convolutional Neural Network (CNN) to distinguish between cataract-affected eyes and normal eyes accurately. The approach involves collecting and preprocessing a labeled dataset, extracting features such as color and vein patterns (including average RGB values), and training the CNN model with optimized parameters. Experimental results demonstrate that the proposed model achieves a high classification accuracy of 95.1%. These findings indicate that CNN-based image classification is a promising tool for supporting automated cataract detection and early diagnosis
InfusCare: Smart Infusion Monitoring System with Real-Time Notifications via ESP32 and Blynk Wathan, M Hizbul; Irawan, Indra; Swengky, Better; Cahyadi, Irsan
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2759

Abstract

Manual infusion monitoring in medical settings can lead to errors and delays in care steps that can put patients at risk. Internet of Things (IoT) technology provides a solution that enhances the accuracy and efficiency of real-time infusion monitoring. This study develops an IoT-based infusion monitoring system with the HX711 module and ESP32 microcontroller, using a connected load cell sensor as a monitoring interface through the Blynk application. This system can accurately measure the volume of infusion fluid and provide automatic notifications when the fluid volume approaches the minimum limit. Tests were conducted with infusion fluid simulation, load cell sensor calibration, and system calibration integration testing. The test results indicate that the system can display data on fluid weight in real time with an accuracy level of 98.5%, and when the fluid volume reaches 1 second in average response time, you can send notifications at the right time. Therefore, this system is expected to be implemented in various medical facilities as a solution for patient safety and the effectiveness of infusion care, as well as for automatic and reliable infusion monitoring.
Prediksi Harga Saham Malindo Feedmill Tbk. (MAIN) Menggunakan Jaringan Saraf Tiruan Long Short-Term Memory (LSTM) Putri, Vivin Mahat; Zain, M Syafrizal; Darma, Satria Agus
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2789

Abstract

Stock price prediction presents a significant yet intricate challenge in financial forecasting, primarily due to volatile market dynamics and the nonlinear nature of data. This study investigates the efficacy of the Long Short-Term Memory (LSTM) model, a specialized Recurrent Neural Network (RNN), for forecasting the stock price of PT. Malindo Feedmill, Tbk., a publicly listed agribusiness firm on the Indonesia Stock Exchange. A five-year historical dataset of daily stock prices (open, high, low, close, volume) was utilized. Pre-processing involved data normalization, the application of a sliding window approach, and partitioning the data into training and testing subsets. The LSTM model was trained on sequential closing prices to effectively learn and model long-term dependencies inherent in stock price movements. The model's predictive performance was rigorously assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. Our results reveal that the LSTM model adeptly captures price trends, yielding a low MAPE of 3.47% on the test set. Comparative analysis against traditional models like linear regression confirms that LSTM provides superior accuracy and robustness, especially under volatile market conditions. This research highlights the significant potential of deep learning models in facilitating smarter investment decisions within the Indonesian agricultural sector. Subsequent work will aim to integrate sentiment analysis and macroeconomic indicators to further improve real-time predictive accuracy.
Penerapan YOLOv11 untuk Penghitungan Otomatis Jumping Jack pada Video Latihan Fisik Wisesa, Bradika Almandin; Putri, Vivin Mahat; Faristasari, Evvin; Duli, Sirlus Andreanto Jasman; Irawan, Indra; Agustin, Silvia
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2795

Abstract

The Jumping Jack Counter is an image processing-based application developed to automatically count the number of jumping jack movements in exercise videos. This study aims to implement the YOLOv11 model to detect and count jumping jack movements by analyzing body posture. YOLOv11 is utilized to identify body positions categorized into two main classes: "open" (arms and legs spread apart) and "closed" (arms and legs together). The dataset consists of 15,000 video frames collected from various exercise videos, with research stages including data collection, data labeling, preprocessing, model training, and testing. The results demonstrate that YOLOv11 achieves a 92% accuracy rate in counting jumping jack movements. These findings are expected to assist coaches and users in monitoring physical exercise in real-time, thereby enhancing training effectiveness. The majority of movement detections (78%) were for the open position, followed by the closed position (20%), with 2% detection errors attributed to lighting variations or camera angles. [1].
Pembangunan Sistem Informasi Persediaan Barang Di Qaisar Mart Berbasis Desktop Abditio, Abditio
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 2 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i2.3040

Abstract

Inventory of goods is always needed in Qaisar Mart activities. The existence of inventory on the one hand is a waste so it can be said to be a burden that must be eliminated. The aim of this research is to develop a desktop-based inventory information system at Qaisar Mart. The method in this proposed research is a system development method using the Waterfall method. This method was chosen by researchers because the Waterfall method is a sequential software development method so that there will be no repetition, starting from system analysis, system design, system development and repeated trials so that the time used will be more efficient. A computerized inventory system will be very helpful if implemented because merchandise inventory management becomes easier, and owners can directly monitor their merchandise inventory through tables and reports. The inventory information system held by the admin and owner can find out the inventory of the goods.
Rancang Bangun Sistem Informasi Pendaftaran Calon Member Komara Fitness Berbasis Web Putra, Bagus Raka
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 2 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i2.3041

Abstract

The registration system is still manual through a ledger that is recorded and does not use technology to store data on existing members. In this research method, the authors use descriptive and qualitative methods. And in solving problems, the authors are guided by software engineering to simplify the design process, the authors use the prototype method. The results of this study can make it easier for admins and members to register web-based members using the prototype method with a UML design model supporting design tools, namely use cases, class diagrams, activity diagrams, the programming language used is PHP and MySQL database, there are several the menu on the web is home, articles, events, promos, service, class, record, user, training schedule and about us.
Sistem Informasi Berbasis Web Sebagai Media Promosi Pada Toko Sinar Baru Grande Prabumulih Valentine, Lola
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 2 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i2.3042

Abstract

A web-based information system as a promotional medium is a system that provides information services in the form of promotions. The researcher's aim is to make it easier to promote goods. The research objectives resulted in several uses consisting of practical and academic uses. The method used in this research uses a quantitative approach that is descriptive in nature to describe and describe a variable, symptom, situation or certain social phenomenon and the Waterfall development method, the analytical tools used are Use case diagrams, Class diagrams, Activity diagrams. . This information system was built to make it easier for consumers to view the latest products more easily and quickly.

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