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Muhammad Wali
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International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 284 Documents
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Estimating Distributor Demand for Fishing Gear Products Using Linear Regression Algorithm Keswanto; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2864

Abstract

Fishing equipment plays a critical role in both recreational and commercial fishing activities across various aquatic environments. The challenge of managing inventory effectively is heightened by the fluctuating demand and the need to avoid overstocking, which can result in increased operational costs. To address this, a linear regression algorithm is utilized to predict demand for fishing products, using relevant independent variables to model the relationship with dependent variables such as monthly sales figures. This predictive model aims to provide actionable insights that can assist businesses in making informed decisions regarding inventory management and distribution strategies. The study employs the RapidMiner Studio application to develop and evaluate the model's performance, with the analysis yielding a Root Mean Square Error (RMSE) of 140.200. This relatively low RMSE value demonstrates the model's accuracy and effectiveness in forecasting demand, suggesting that the algorithm can be a valuable tool for optimizing inventory levels and ensuring product availability while minimizing excess stock.
Predicting Consumer Demand Based on Retail Stock Using the K-Nearest Neighbors Algorithm Putri N.A, Anindya; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2865

Abstract

Inefficient stock management, such as improper stock management, will result in excess or shortage of goods. Excess stock can cause high storage costs and the risk of unsold goods. Predict consumer needs based on stock. Analyze inefficient stock to improve shortages. One effective method for making this prediction is using the K-Nearest Neighbors (K-NN) algorithm. The K-NN algorithm is a simple but powerful machine-learning technique that can be used for classification and regression. The model scenario results show 24 objects in the Low-needs group and 14 in the High-needs group. Evaluation and performance testing using the Rapid Miner tool can also produce a relevant picture of the modelled scenario. The model implemented using the K-NN algorithm has an Accuracy value of 97.50% with a Standard Deviation of +/- 750%, then a Precision value of 100%, and a Recall value of 950%. By measuring model performance with cross-validation, the resulting accuracy has a standard deviation value, which aims to see the distance between the average accuracy and the accuracy of each experiment (iteration)
Data Mining Modeling Using the K-Means Algorithm to Analyze the Impact of New Media on Early Childhood Psychology at Bimba Rainbow Kids Sukmajaya Sugiyono; Haryati; Sarimole, Frencis Matheos; Tundo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2874

Abstract

New media, particularly the internet, has become an integral aspect of contemporary life, fundamentally altering the ways in which individuals interact, learn, play, and access information. The continuous evolution of new media, driven by technological advancements, exerts a profound influence on its users, with implications that span various dimensions of human experience. This study aims to analyze and classify the psychological impact of new media on early childhood, specifically within the context of Bimba Rainbow Kids Sukmajaya, utilizing the K-Means data mining method. This research employs a qualitative approach to uncover the underlying factors that shape the psychological effects observed in young children. The anticipated outcomes of this study are expected to contribute significantly to the academic discourse on the influence of new media on early childhood psychology. Moreover, the findings hold potential relevance for educators, parents, teachers, policymakers, and the general public who are invested in comprehending the broader implications of new media on the psychological development of early childhood
Analysis of the Effectiveness of IoT-Based Automatic Street Lighting Control Using Linear Regression Method Saputra, Tino; Surapati, Untung
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2878

Abstract

Public street lighting (PJU) is a crucial component of infrastructure that ensures security during nighttime. This research aims to design an automatic PJU control system utilizing Internet of Things (IoT) technology, employing light and motion sensors integrated with an ESP32 microcontroller. The system enables remote control of PJU lamps via a web-based platform, offering significant flexibility for users. The ESP32 microcontroller is linked to a PIR sensor that detects motion, which triggers an increase in the intensity of the PJU lamps. Conversely, when no motion is detected, the light intensity is reduced to conserve energy. Users can manage the PJU lamps from any internet-connected device. Experimental results demonstrate a notable improvement in energy efficiency, with an average reduction in power consumption of 13.77 watts and an efficiency increase of 42.67%. The linear regression model employed yields an R-squared value of 0.629, indicating a reasonably good fit in explaining the variability in power consumption. This system offers real-time monitoring and autonomous operation of street lights, contributing to the advancement of smarter and more efficient PJU systems.
Sentiment Analysis of BMKG Weather Information Service Using K-Nearest Neighbor Method Sodiq, Muhamad Fajar; Amin, Fatkhul
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2881

Abstract

The Meteorology, Climatology, and Geophysics Agency (BMKG) is a government institution that provides information related to air quality, climate, dry days, satellite imagery, wave forecasts, wind forecasts, and fire potential. This information is disseminated not only through BMKG's official website but also via the social media platform X, making it easier for the public to access up-to-date information. This study aims to classify user sentiment towards the weather information services provided by BMKG using the K-Nearest Neighbor (KNN) method. Data was collected through a web crawling technique, resulting in 1,031 data points analyzed in this research. The data processing stages included Pre-Processing and sentiment calculation using Vader's Sentiment and Random Forest. The classification results using the KNN algorithm showed an accuracy rate of 96%, with an average precision of 96%, an average recall of 96%, and an average f-measure of 96%. These findings indicate that the KNN model can effectively classify user sentiment towards BMKG's services.
Data Transfer Security in IoT Communication Based on Attribute-Based Cryptography Abidullah, Adel; Rahmani, Khoshal Rahman; Wadeed, Wali Mohammad; Hakimi, Musawer
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2887

Abstract

Due to the drastic growth, the Internet of Things (IoT) has become an inevitable form of human life. However, IoT communication is subjected to a vast range of security breaches in the vulnerable environment, which leads to the demand for appropriate technology security issues in IoT communication. The cryptography technique exhibits effective security characteristics that promise promising results for identifying security breaches in IoT. This paper proposes attribute-based elliptical curve cryptography (ATB_ECC) to improve security in IoT communication. IoT devices perform communication to overcome security issues in IoT based on defined attributes of access points. Attributes are involved in the characteristics of trusted nodes in the network. The cryptography technique utilizes Elliptical Curve Cryptography (ECC) to transmit messages securely in the IoT environment. Through integrating attribute factors and cryptography techniques, the IoT network can distinguish variations in the active data communication and threats in the IoT network. Simulation performance is examined for different critical structures, such as low, medium, and high. The proposed ATB_ECC is examined for attack prediction scenarios considering real-time servers. The examined results stated that the proposed ATB_ECC has effectively prevented attacks, especially brute force attacks. Analysis of results stated that low-key structure exhibits minimal complexity, but the security level is minimal. A high-key structure consumes vast energy and has increased complexity, but the security is significantly improved. The comparative analysis of various key structure results illustrated that the proposed attributes-based ECC exhibits improved performance at 15% for throughput.
Implementation of a Web-Based Raw Material Inventory Information System Using the Prototype Method: A Case Study at PT. XYD Susanto, Dede Agus; Firmansyah, Andri; Nawangsih, Ismasari
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2926

Abstract

PT. XYD is a manufacturing company in the automotive industry and serves as a key vendor for the Wuling car brand, producing parts for models such as Confero, Cortez, Almaz, Air Ev, and Alvez. As production volume and the complexity of managing raw materials have increased, PT. XYD faces challenges in maintaining the accuracy of inventory data, particularly in the warehouse. Errors in data entry can disrupt the production process, leading to delays in product delivery to customers. To address this issue, this study developed and implemented a web-based raw material inventory information system using the prototype method. This method was chosen for its ability to accelerate the development process through iterative improvements based on user feedback. The system was designed using Unified Modeling Language (UML) to illustrate the system’s model and structure. The implementation of this system aims to enhance efficiency and accuracy in managing inventory data, ultimately minimizing the risk of errors, increasing productivity, and ensuring smooth company operations. The results of the study demonstrate that the system successfully facilitates the digitalization of ordering and inventory reporting processes, reducing reliance on manual processes and improving the speed and accuracy of data processing at PT. XYD.
Implementation of Digital Signatures in the Integrated Patient Progress Notes System at XYZ Hospital Bandung Supriadi, Tasya Saldira Putri; Syahidin, Yuda; Yunengsih, Yuyun
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2936

Abstract

This study aims to implement a digital signature system for filling out Integrated Patient Progress Notes (CPPT) using QR-Code technology. The research method employed is the waterfall development model. The stages of the study include requirements analysis, data collection from various sources, system design, implementation according to the design, testing, and reporting. The study was conducted at XYZ Hospital in Bandung, where it was found that the CPPT signing process is still manually done using a pen, which is considered inefficient as it takes a long time for authorization and is prone to manipulation, thereby reducing the document's validity. The implementation of a digital signature aims to simplify the document signing process quickly without the need for printing, sending, or waiting for physical documents, allowing it to be done digitally and automatically, thereby increasing speed and efficiency. This system also provides authentication assurance by the rightful owner, ensures document security, reduces the risk of damage or loss, and minimizes the possibility of manipulation, thus providing convenience for staff and doctors in terms of time and data accuracy.
Implementation of a Web-Based Village Information System Using the Waterfall Method in Hegarmukti Village Ferdiansyah, Febby; Anshor, Abdul Halim; Widodo, Edy
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2986

Abstract

The dissemination of information at the village level is crucial for ensuring transparency to the residents. To facilitate the quick and easy communication of such information, a web-based village information system was developed. This system can be accessed by all community members, both within and outside the village, as long as they have an internet connection. Currently, Hegarmukti Village does not have an effective web-based information system, leading to delays in information delivery and a lack of accuracy. Moreover, the potential of the village remains under-promoted. This study aims to design and implement a web-based village information system for Hegarmukti Village using the Waterfall method. The system is developed using HTML for programming and MySQL for the database, with the goal of enhancing transparency and the efficiency of information dissemination. The system provides features such as village profiles, news updates, and population data searches, improving the accessibility and reliability of village information.
Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions Munawir; Sulistyawati, Upik Sri
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2606

Abstract

This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.

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