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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 535 Documents
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Predicting Peak Ground Acceleration Using RNN and LSTM Model on MCGuire’s Empirical Calculation Irna Purwanti; Taswanda Taryo; Ferhat Aziz
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7268

Abstract

Peak ground acceleration (PGA) is an important parameter in seismic hazard assessment because it represents the maximum ground acceleration generated by an earthquake and informs structural design and disaster mitigation. Predicting PGA remains challenging because seismic-wave propagation is nonlinear and affected by geological heterogeneity. This study developed, evaluated, and spatially mapped recurrent neural network (RNN) and long short-term memory (LSTM) models for predicting PGA values calculated using the McGuire empirical equation in BMKG Regional II, Indonesia, which extends from South Sumatra to West Java. A historical earthquake catalog covering 1971–2025 was used to generate a spatial dataset comprising 3,727 grid points based on surface-wave magnitude and hypocentral distance. The dataset was divided sequentially into 70% training data and 30% testing data. On the test set, the LSTM produced an RMSE of 55.1559, an MAE of 31.0443, and an R² of 0.7112, whereas the RNN produced an RMSE of 56.7773, an MAE of 36.5842, and an R² of 0.6940. The spatial results also showed that the LSTM reproduced high PGA values more closely than the RNN, which produced smoother estimates in areas with abrupt PGA variations. Within the dataset and model configuration used in this study, the LSTM therefore provided better predictive performance than the RNN for reconstructing the spatial distribution of McGuire-based PGA. The resulting model may support regional seismic hazard assessment, but validation against observed ground-motion records and the inclusion of local site parameters remain necessary before its use in engineering or regulatory applications.
Web-Based Motorcycle Spare Part Stock Management Information System at Geleng Classic Shop Toti Fernanda Suprapto; Moh. Muhtarom; Bondan Wahyu Pamekas
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7292

Abstract

Advances in information technology have encouraged businesses to adopt more effective inventory management systems. Geleng Classic Store still managed motorcycle spare part inventory manually using record books, resulting in recording errors, delays in updating information, inconsistencies between physical and recorded stock, and difficulties in monitoring inventory. These problems affected the efficiency of stock management and the availability of spare parts. This research aimed to design and develop a web-based inventory information system to support spare part stock management at a small and medium-sized enterprise. The system was developed using the waterfall model within the System Development Life Cycle (SDLC), consisting of requirements analysis, system design, implementation, testing, and maintenance. The system was developed using the Laravel framework, PHP programming language, and MySQL database, with the First In, First Out (FIFO) method applied to inventory management. The developed system provides computerized recording of incoming and outgoing goods, real-time stock monitoring, minimum stock notifications, and role-based access according to user requirements. PIECES analysis indicated improvements in the management of performance, information, economics, control, efficiency, and service compared with the previous manual system. Black-box testing of ten main system features showed that all tested functions produced the expected results. The implementation of FIFO prioritizes older stock for release, supporting more orderly stock rotation and reducing the possibility of older items remaining in storage for extended periods. The developed system therefore provides a practical tool for supporting motorcycle spare part inventory management at Geleng Classic Store.
Analysis of Multilayer Perceptron, Long Short-Term Memory, and Temporal Convolutional Network Modeling Algorithms for Rainfall Prediction at Soekarno–Hatta Airport Eko Widyantoro; Eko Widyantoro HS; Sajarwo Anggai; Sudarno Wiharjo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7332

Abstract

Tropical atmospheric variability poses challenges for daily precipitation prediction at Soekarno–Hatta International Airport, with implications for aviation safety. This study aimed to compare the performance of three deep learning architectures—Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Temporal Convolutional Network (TCN)—for daily precipitation prediction using meteorological observations from the BMKG Soekarno–Hatta Station for the period 2019–2025. A quantitative experimental approach was employed using a time-series split scheme to preserve the temporal structure of the data and reduce the risk of data leakage. Air temperature, relative humidity, atmospheric pressure, and wind speed were used as input variables, while precipitation was used as the target variable. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results showed that TCN achieved the best predictive performance, with an RMSE of 10.32 mm, MAE of 7.15 mm, MAPE of 18.27%, and R² of 0.87, followed by LSTM and MLP. Permutation-based sensitivity analysis identified relative humidity and air temperature as the two most influential variables for model prediction. Overall, TCN demonstrated stronger performance in capturing temporal patterns in daily precipitation than LSTM and MLP and showed potential for future application in operational precipitation forecasting and extreme-weather early warning systems at airport environments.
Analysis and Design of a Stock and Sales Management System Using the FIFO Method at Pakis Medika Utama Pharmacy Annisa Zalzabilla Rahmawati; Eko Purwanto; Sundari Sundari
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7341

Abstract

Apotek Pakis Medika Utama still uses manual methods for managing drug inventory and recording sales, resulting in several problems, including recording errors, difficulties in monitoring stock levels in real time, delays in preparing reports, and the absence of notifications for drugs approaching their expiration dates. In addition, the pharmacy frequently receives drug supplies from Pharmaceutical Wholesalers (PBF) with relatively short expiration periods, requiring more structured inventory management to minimize the risk of stock accumulation and losses caused by expired drugs. This study aims to analyze and design a drug inventory and sales management system using the FIFO (First In, First Out) method by considering drug batch numbers and expiration dates. The system development method used is Prototyping, which includes requirements gathering through observation, interviews, and documentation, followed by system design. The proposed system is designed using the Laravel framework and MySQL database. The design results indicate that the proposed system is designed to manage drug data, support sales transactions based on the FIFO method, monitor drug stock and expiration dates, provide notifications for minimum stock levels and drugs approaching expiration through the dashboard and email, and generate inventory and sales reports automatically. This system design is expected to support more effective, integrated, and structured inventory and sales management at Apotek Pakis Medika Utama.
WebGIS-Based K-Nearest Neighbor Classification for Agricultural Land Suitability Assessment Across Multiple Commodities in Curah Kalak Village, Situbondo Regency Moh. Alfian Husni Mubarok; A. Hamdani; Adi Susanto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7343

Abstract

Agricultural land suitability assessment plays an important role in supporting agricultural planning and ensuring that land resources are used according to environmental characteristics and crop requirements. In Curah Kalak Village, Situbondo Regency, land suitability assessment is generally conducted manually, resulting in limited efficiency and difficulties in accessing spatial information for decision-making. This study aims to develop a WebGIS-based agricultural land suitability classification system by integrating the K-Nearest Neighbor (KNN) algorithm with Geographic Information System (GIS) technology. The classification process uses environmental and spatial parameters, including slope, soil pH, soil type, rainfall, altitude, soil depth, and irrigation distance. A total of 669 agricultural land records were used as the dataset, and land suitability classes were classified using KNN with K = 31 based on Euclidean distance. The developed system classified land suitability for sugarcane, chili, corn, and rice commodities. The classification results indicated that chili and sugarcane were categorized as Highly Suitable (S1), whereas corn and rice were categorized as Moderately Suitable (S2). Performance evaluation using an 80:20 train-test split showed that the KNN model achieved an accuracy of 73.00%, weighted precision of 68.00%, weighted recall of 73.00%, and weighted F1-score of 70.00%, indicating moderate classification performance. Furthermore, the WebGIS provides interactive digital maps for visualizing classification results and spatial information. Black-box testing confirmed that all implemented system functions operated according to the specified requirements. The novelty of this study lies in the integration of KNN-based multi-commodity land suitability classification with WebGIS visualization within a village-level platform for agricultural land assessment. The proposed system can support agricultural land management and spatially informed decision-making in Curah Kalak Village.
Web-Based Laundry Information System with WhatsApp Gateway and Chatbot for Sales Reporting Faqih Nur Rahman; Achmad Baijuri; A. Hamdani
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7352

Abstract

Small service businesses require efficient operational management and timely access to business information to support daily operations. Grandma Laundry Sumberkolak previously relied on manual transaction recording, handwritten receipts, conventional WhatsApp messaging, and manual sales recapitulation using a calculator, which made transaction handling less efficient and complicated sales report monitoring. This study aimed to design and implement a web-based laundry information system integrated with a WhatsApp Gateway and a chatbot for sales reporting. The study employed an applied research approach and used the Waterfall model, consisting of requirement analysis, system design, implementation, testing, and maintenance. The system was developed using Laravel, PHP, and PostgreSQL, with a WhatsApp Gateway service and a chatbot interface connected to an external GPT-4o-based large language model for natural-language access to sales data. The implemented system supported customer and service management, transaction processing, order status updates, automatic WhatsApp notifications, and interactive sales reporting. Functional testing and post-use user validation showed that the main system functions operated as expected; all 15 respondents gave affirmative responses to the seven post-use validation items, while four of five routine chatbot reporting prompts matched direct PostgreSQL reference results exactly. One routine prompt produced a partial result because of a timestamp-boundary issue in the generated SQL query. The findings indicate that the developed system can provide practical support for operational management, automated customer communication, and access to sales information in the studied laundry business
Implementation of One-to-One Face Matching for an Internal Face Verification Server in a Mobile Attendance System at the Secretariat General of DPD RI Anang Lesmana; Agus Sulistyanto; Anton Zulkarnain Sianipar
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7353

Abstract

The mobile attendance system at the Secretariat General of the Regional Representative Council of the Republic of Indonesia previously relied on Google ML Kit for face verification services. This approach introduced challenges related to reliance on third-party services, limited configuration flexibility, and institutional control over biometric data. This study aims to develop an internal Face Verification Server using the One-to-One Face Matching method as an alternative to third-party face verification services. The system was developed using the Waterfall software development methodology, covering requirements analysis, system design, implementation, testing, and maintenance. Face verification is performed by comparing a captured facial image with a stored facial embedding associated with an employee identification number. Quantitative evaluation was conducted using 40 verification samples, consisting of 20 genuine and 20 impostor samples, with a threshold value of 0.5. The evaluation achieved 100% accuracy, a False Acceptance Rate (FAR) of 0%, and a False Rejection Rate (FRR) of 0% on the tested samples. The results indicate that the proposed system successfully distinguished matching and non-matching facial images according to the predefined threshold. The internal deployment also reduces reliance on third-party services and enables the face verification service to operate within the institution's internal network infrastructure. This study contributes an independently managed Face Verification Server architecture for mobile attendance systems in a government institutional environment.
Machine Learning-Based Prediction of Shallow Foundation Bearing Capacity Incorporating Slope Inclination as a Predictive Feature Afifah Yurisa Putri; Lindung Zalbuin Mase; Muharram Nur Fikri; Rena Misliniyati; Aidil Fitriansyah
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7430

Abstract

Accurate prediction of the ultimate bearing capacity of shallow foundations is important for safe geotechnical design, particularly under varying slope and soil conditions where conventional analytical methods may be limited by simplifying assumptions. This study developed and evaluated machine learning models for predicting the ultimate bearing capacity of shallow foundations using slope inclination, footing width, foundation depth, and soil friction angle as input variables. A dataset of 399 samples obtained from a previously validated finite element method (FEM)-based investigation was used to train and test Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models. Model performance was evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), and 10-fold cross-validation. Unlike previous studies that primarily considered conventional foundation and soil parameters, this study incorporates slope inclination within a unified machine learning framework and compares statistical, kernel-based, and ensemble learning approaches under the same evaluation conditions. XGBoost achieved the highest predictive performance, with a testing R² of 0.9938 and an RMSE of 31.669, followed by SVR with an R² of 0.9534 and an RMSE of 86.900. MLR showed comparatively lower performance, with an R² of 0.8396 and an RMSE of 161.158. The 10-fold cross-validation results further indicated stable XGBoost performance, with a mean R² of 0.991 and a standard deviation of 0.003. These results indicate that XGBoost provides high predictive performance for the evaluated dataset and may support bearing capacity estimation for shallow foundation design.
Face Image Authenticity Detection for ASN Attendance Using CNN MobileNetV2: A Case Study of Tangerang City Government Suryadi Suryadi; Sajarwo Anggai; Sudarno Wiharjo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7459

Abstract

Face recognition-based attendance systems can improve the convenience of attendance processes, but they remain vulnerable to presentation attacks, in which facial images displayed on another device are used to simulate a user's physical presence. At the Tangerang City Government, the Tangerang AYO application is used as the main attendance channel for civil servants (ASN) across government agencies (OPD), with attendance data linked to the calculation of Additional Employee Income (TPP). This study develops a face liveness detection model to distinguish between real and fake facial images using a CNN MobileNetV2 architecture with a transfer learning and two-phase fine-tuning approach. The dataset was constructed from actual ASN attendance data and consisted of real and fake images representing facial displays through a secondary device. The model was evaluated using a confusion matrix, accuracy, precision, recall, specificity, F1-score, ROC curve, and AUC. On the testing data (n=301), the model achieved an accuracy of 97.34%, precision of 96.13%, recall of 98.68%, specificity of 96.00%, F1-score of 97.39%, and AUC of 0.9934 at a threshold of 0.5. Grad-CAM analysis showed that the model produced different activation patterns between real and fake images, including contextual visual information in real images and screen-surface characteristics in fake images. A FastAPI-based REST API prototype achieved an average response time of 306.48 ms under warm conditions without a dedicated GPU, indicating the potential feasibility of the model as an additional verification layer for an ASN attendance system.
Forecasting Public Interest Toward FIFA World Cup 2026 in Indonesia Using Google Trends and ARIMA Model Eko Tri Asmoro; Sri Mardiyati; Ulfa Pauziah; Munich Heindari Ekasari
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7471

Abstract

Public interest in major international sporting events can be examined through online search behavior, which provides an indication of public attention and anticipation. This study aimed to analyze and forecast public interest in the FIFA World Cup 2026 in Indonesia using Google Trends data. Weekly search data from June 2021 to May 2026 were collected from Google Trends, focusing on the keyword “FIFA World Cup 2026” and related search terms. A quantitative time-series approach was employed, including descriptive statistics, correlation analysis, the Augmented Dickey-Fuller (ADF) stationarity test, Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses, and Autoregressive Integrated Moving Average (ARIMA) modeling. The results showed that search interest increased as the tournament approached, with several notable spikes observed during 2024–2026. Descriptive statistics indicated substantial fluctuations in search activity, while the ADF test confirmed that the series was stationary, with an ADF statistic of −8.1497 and a probability value below 0.05. Several ARIMA specifications were evaluated, and ARIMA(1,0,2) was identified as the selected forecasting model based on the lowest Akaike Information Criterion (AIC = 8.1480) and Schwarz Criterion (SC = 8.2162) values. Forecast evaluation produced an RMSE of 16.482, an MAE of 10.525, and a Theil Inequality Coefficient of 0.571. The forecasting results suggest that search interest in the FIFA World Cup 2026 will remain elevated as the tournament approaches. These findings indicate that Google Trends data combined with ARIMA modeling can be used to analyze and forecast search-interest patterns related to major international sporting events.

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