Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika

Prediction and Analysis of Factors Affecting Marketplace Sales Using a Bidirectional LSTM Model for Inventory Estimation

Indah Cahyani — 2026-07-31

Forecasting weekly sales at the product level can help marketplace sellers avoid both excess inventory and stock shortages. The purpose of this study is to investigate the sales forecasting of the Shopee store antianshop by applying the Bidirectional Long Short-Term Memory (BiLSTM) method in conjunction with correlation-based feature selection to predict the inventory. The raw dataset contained 5,426 weekly product records from Shopee Seller Centre covering 22 May 2023 to 31 May 2025; 5,312 records remained after preprocessing. For each product, the data were ordered by week and converted into ten-week input sequences. Pearson correlation showed that Add to Cart (r = 0.5835$) and Enter Cart (r = 0.5579$) were the strongest retained predictors of weekly Units Sold. BiLSTM was then compared with a unidirectional LSTM under the same experimental settings for ten products. LSTM recorded slightly lower average errors, with MAE of 1.7462 units, RMSE of 2.3977 units, and non-zero MAPE of 69.57%, while BiLSTM produced 1.8505 units, 2.4611 units, and 71.18%, respectively. The results indicate that BiLSTM was competitive but did not outperform the simpler LSTM model consistently. We combined the two models in a Streamlit dashboard that shows product forecasts and weekly inventory guidance. Generalizability of the findings should be done cautiously since the analysis used one store, relatively short product histories, and no systematic hyperparameter search.

Multi-Horizon LSTM Forecasting of Digital Product Transactions in Retail Counter

Dervio Rahmatdianto — 2026-07-31

This study proposes a multi-horizon forecasting approach using a stacked Long Short-Term Memory (LSTM) neural network to predict digital retail transaction volumes in a micro-scale counter environment. Historical transaction data were obtained from MyFastbiller Telegram logs and transformed into a 739-day daily time series containing 27,540 valid transactions. Temporal feature engineering, normalization, and 60-day sequence generation were applied prior to model training. The LSTM architecture was evaluated across three operational forecasting horizons: H-1, H-7, and H-30.

Results show consistent prediction accuracy with MAPE values of 15.24%, 15.14%, and 16.35% for H-1, H-7, and H-30 respectively, all below the 20% operational threshold. The H-7 horizon achieved the best performance due to alignment with weekly seasonality patterns in transaction behavior. Visualization results indicate that predicted transaction series closely follow actual temporal dynamics across all horizons.

The trained model was integrated into a web-based decision-support dashboard to provide multi-horizon transaction forecasts for operational planning. The findings demonstrate that multi-horizon LSTM forecasting effectively captures seasonal and intermittent characteristics of digital retail transactions and provides practical value for data-driven balance management in micro-scale retail counters.

Solar Panel Energy Analysis on Remote Control Mobile Robots

Agus Ismangil — 2026-07-31

The ever-increasing energy demand due to technological and industrial developments is driving the search for environmentally friendly energy solutions. Renewable energy, particularly solar energy, offers great potential to replace dependence on dwindling fossil fuels. As a manifestation of innovation in the field of robotics, this Android-based remote-controlled mobile robot is designed to use a 2WP monocrystalline solar panel as the main energy source, with a battery as additional power storage. This robot system integrates an Arduino Uno microcontroller for data processing and an HC-05 Bluetooth module that can control the robot remotely through an Android application. By utilizing solar energy through the solar panel, the robot can reduce dependence on conventional batteries, while increasing energy efficiency. Trials were conducted to evaluate system performance, including measuring the voltage, current, and efficiency of the solar panel. This research aims to contribute to the development of environmentally friendly robotics technology utilizing solar energy. The results of this study indicate that the designed system can work according to plan and has the potential for further development.

Civil Servant Mutation Determination System Based on Performance and Competence Using the Method Organization, Rangement and Synthesis of Relational Data (Oreste)

Victor Ilyas Sugara Sugara — 2026-07-31

This study discusses the development of a Decision Support System (DSS) to determine the mutation of Civil Servants (PNS) at the Center for Standardization of Food Crop Instruments (PSITP) Bogor using the methodOrganization Rangement Et Synthese De Donnes Relationnelles (ORESTE).The previous transfer process was still done manually using Excel, making it prone to calculation errors, lacking objectivity, and taking a long time. This research aims to develop a web-based system capable of assessing employee performance and competency in a more accurate, transparent, and structured manner. The data used includes 40 employees and their performance and competency scores, obtained through interviews and field research. MethodsORESTESused because it is able to process rankings withBesson Rankwhen there are the same criteria values, then calculateDistance Scoreto produce a preference value (Vi) which determines the final ranking of employees who are worthy of being transferred. The system development uses the methodWaterfall which includes needs analysis, system design, implementation withPHP And MySQL, testing, and maintenance. The resulting system provides login features for admins, employees, and center heads; performance and competency data management; and automatic calculations usingORESTES; and the display of employee ranking results. Structural, functional, and validation tests showed that the system ran as required and was able to provide accurate calculation results. The research results concluded that the methodORESTESIt is effective in civil servant transfer decision-making because it produces objective rankings based on a combination of performance and competency. The developed decision support system also speeds up the transfer process, minimizes human error, and increases transparency and accuracy in determining which employees are eligible for transfer.

Implementation of Artificial Intelligence in Health Screening Systems for Category-Based Fitness and Nutrition Recommendations

Nasrul — 2026-07-31

The increasing prevalence of non-communicable diseases, such as diabetes, hypertension, and metabolic disorders, highlights the need for accessible health screening services. However, existing health screening systems generally provide examination results without offering automated educational recommendations to support preventive healthcare. This study proposes an Artificial Intelligence (AI)-based health screening information system that automatically generates fitness and nutrition recommendations based on categorized health screening results. The research employed a Research and Development (R&D) approach using the Extreme Programming (XP) software development methodology. The proposed system was developed using the Laravel framework and the Filament administration panel and integrates a Large Language Model (LLM) through the OpenAI API. The proposed architecture combines structured prompt engineering, predefined AI guardrails, SHA-256 prompt hashing, and recommendation caching to improve recommendation consistency and computational efficiency. The system was evaluated using User Acceptance Testing (UAT) and an AI Recommendation Consistency Evaluation. The UAT results showed that all functional requirements were successfully fulfilled. The consistency evaluation demonstrated that repeated processing of identical health screening data produced stable recommendations, achieving an average qualitative consistency score of 90.4% and an average TF–IDF cosine similarity of 0.784. These findings indicate that the proposed architecture is capable of generating reliable and consistent educational recommendations while reducing redundant AI requests through recommendation caching. This study contributes to the development of intelligent health information systems by introducing an efficient AI recommendation architecture that supports digital health screening and preventive healthcare services.

Assessing Information Security Resilience Using ISO/IEC 27001 and KAMI Index 5.0

Fitri Safnita — 2026-07-31

Digital transformation in the government sector has increased the use of information technology in delivering public services. This condition requires government organizations to ensure adequate information security to protect data and systems from various cyber threats. This study aims to assess the level of information security resilience at the Communication and Informatics Office of Padang City in supporting digital transformation by referring to the ISO/IEC 27001 standard and using the KAMI Index 5.0 evaluation instrument. The research employed a descriptive approach with data collection techniques including observation, interviews, and document analysis in the KAMI Index assessment process. The results show that the level of compliance with the information security framework achieved a score of 518, indicating that the organization has met the basic framework for implementing information security based on the ISO/IEC 27001 standard. However, the implementation maturity level remains at Level II, indicating that several information security management processes have been implemented but are not yet fully documented and optimally managed. Therefore, improvement recommendations are proposed to enhance the maturity level, comprising specific recommendations across seven areas: information security governance (5), risk management (14), security framework (5), asset management (13), technology and security (6), personal data protection (11), and supplementary (6). These recommendations are expected to serve as a foundation to improve information security management effectiveness, thereby supporting the sustainability of local government digital services.

Decision Tree-Based Anomaly Traffic Detection for Local Area Network (LAN) Security Using Wireshark and Nmap Data Analysis

Rizki Prasetyo — 2026-07-31

The development of information technology encourages the use of LAN networks as primary infrastructure in educational environments. The high intensity of network usage at the Faculty of Engineering and Computer Science increases security risks such as unauthorized access, open ports, and anomalous traffic, making comprehensive network security analysis necessary. This study aims to analyze LAN network security at the Faculty of Engineering and Computer Science using MikroTik (RB951Ui-2HnD) and TP-Link (TL-WR841N) devices, and to apply the Decision Tree algorithm for network traffic classification. The methods include router configuration analysis, port scanning using Nmap (Zenmap), packet sniffing analysis with Wireshark, and network traffic classification using the Decision Tree algorithm implemented in RapidMiner Studio. Port scanning results indicate that from 1000 scanned ports, only 5 ports (0.5%) were detected as open. Wireshark packet capture over 5 minutes collected 6,708 packets, revealing the presence of unencrypted HTTP packets and TCP errors. The Decision Tree model achieved an accuracy of 86.05%, precision of 99.94%, and recall of 73.85% in classifying normal and anomalous traffic. This approach effectively provides an overview of LAN security conditions and can serve as a reference for improving network security in educational institutions.

The Landscape Image Classification Using Convolutional Neural Network on Intel Image Classification Datase

Winarnie Winarnie — 2026-07-31

Image classification is an important area of computer vision and artificial intelligence that enables computers to automatically recognize and categorize visual information. This research aims to develop a Convolutional Neural Network (CNN)-based image classification model for recognizing six categories of natural and urban landscapes using the Intel Image Classification dataset from Kaggle. The preprocessing stage included image resizing, data augmentation, and pixel normalization to improve model generalization and reduce overfitting. The dataset was divided into 80% training data and 20% testing data. The proposed CNN architecture consists of four convolutional layers, max-pooling layers, and three fully connected dense layers with ReLU and Softmax activation functions. The novelty of this study lies in the development of a lightweight CNN architecture that achieves competitive performance without relying on pretrained models or transfer learning approaches, making it suitable for deployment on resource-constrained devices. Experimental results show that the model achieved 85.85% training accuracy and 85.47% testing accuracy. Performance evaluation using precision, recall, F1-score, and confusion matrix indicates balanced classification performance across all classes. Furthermore, the trained model was successfully converted into TensorFlow SavedModel, TensorFlow Lite, and TensorFlow.js formats to support cross-platform deployment. The findings demonstrate that the proposed CNN model is effective, efficient, and suitable for real-world landscape image classification applications.

The Integrating Business Intelligence and Food Supply Chain Analytics to Support National Nutrition Programs (MBG): Evidence from Rice Production Dashboard in Indonesia

Uya Asy Syuura Anandri — 2026-07-31

This study provides the development and implementation of a food supply chain analytics-based Business Intelligence (BI) dashboard for assisting the Indonesian national nutrition programmes, specifically in the monitoring of rice production. Rice is the number one staple food and a key part of nutrition strategies implemented through mass interventions like the National Nutrition Fulfillment Program, therefore, guaranteeing data-driven decision making all along the rice value chain is crucial. The data for this research are secondary data covering rice production, harvested area, rice productivity 2020 – 2024 processed and visualised with the software Tableau Public. The proposed dashboard is a combination of various analytical views: such as temporal trend analysis (line graph), spatial distribution mapping (geographical visualization), comparative regional performance (bar chart) and proportional productivity assessment (donut chart). These visualisations allows stakeholders to discover production patterns and regional inequalities in an interactive and concise format, and possible supply risks. The findings show the efficiency of the BI dashboard in visualizing large amounts of agricultural data into actionable information, which can be used to guide and shape agriculture strategies and policy formulation. The system successfully identifies the major provincial differences in harvested area as well as the differences in production areas and over time, and is important for adopting food production to meet the operational requirements of the nutrition service unit. This research adds to the extensive research work about the parallel between Business Intelligence and public policies and its applications to data-driven agriculture and food supply chain analytics. The study highlights the opportunities of BI-based dashboards to improve transparency, efficiency and responsiveness in national level food systems to foster nutrition program sustainability and scale. This approach can be further enhanced by incorporating real-time data sources and predictive analytics in future studies to further improve the decision support process.

Application of CPM and PERT Methods in Residential Construction Project Scheduling: A Case Study

Rajainal Saragih — 2026-07-31

This study aims to analyze and optimize the scheduling of a residential construction project using the CPM, crashing techniques, and the PERT. A quantitative approach is employed by utilizing data obtained from observations and interviews conducted on a housing construction project in Bandar District, Simalungun Regency. CPM analysis is used to identify the critical path and determine the project duration, while crashing techniques are applied to obtain acceleration alternatives by considering additional costs. Furthermore, the PERT method is used to analyze uncertainty in project duration and to calculate the probability of project completion. The results show that the project duration under normal conditions is 59 days, with the critical path identified as A–B–D–E–G–H–J–O–P. After applying crashing, the project duration is reduced to 55 days with an additional cost of IDR 212,500. The PERT analysis indicates that the expected project duration is 44 days, with a completion probability of approximately 99% for a target duration of 49 days. These findings demonstrate that the combination of CPM, crashing, and PERT methods is effective in improving time and cost efficiency, while also providing a more reliable basis for decision-making under conditions of uncertainty.

A Multi-Ontology Approach for Deforestation Issue Analysis in Indonesian Digital Media

Dinar Munggaran Akhmad — 2026-07-31

The increasing volume of online news has made the identification of deforestation issues more challenging, particularly because conventional text classification methods rely mainly on statistical features and often fail to capture semantic relationships between terms. This study proposes a semantic text classification approach for Indonesian deforestation news by integrating DBpedia and a lexical ontology to enrich document representation before classification using the Support Vector Machine (SVM) algorithm. News articles were first preprocessed through case folding, tokenization, stop-word removal, and stemming. Semantic features were then generated through multi-ontology mapping and transformed into feature vectors for SVM classification. The proposed approach was evaluated using 91 testing documents with Accuracy, Precision, Recall, and F1-score as performance metrics. The results achieved an accuracy of 87.9%, with 88.5% precision, 87.8% recall, and an F1-score of 88.0%. These findings indicate that ontology-based semantic feature extraction improves document representation by capturing conceptual and lexical relationships that cannot be represented by statistical features alone. The proposed approach provides an effective and interpretable solution for classifying Indonesian deforestation news into four issue categories.

The Neural Network Based Multiple Linear Regression Model for Financial Profit Prediction

Sandy Budi Wirawan — 2026-07-31

Accurate financial forecasting plays a vital role in supporting business decision-making, particularly for organizations that depend on operational efficiency and strategic planning. CV. Surya Cipta Estetica Mandiri still manages its financial records manually, resulting in inaccurate estimations and limited predictive capabilities. This study proposes a profit prediction model using a Neural Network–based Multiple Linear Regression approach implemented through a Single Layer Perceptron (SLP) and integrated into a financial information system.

The dataset consists of 3,127 daily transaction records from January 2021 to April 2025, including date, category, total income, and total expense. The preprocessing phase includes handling missing values, label encoding, RobustScaler-based feature scaling, and an 80:20 train–test split. The model was developed using TensorFlow with a Sequential architecture, trained using 2,501 data points and evaluated using 626 data points.

The model demonstrates excellent predictive performance, achieving R² = 0.9988, MAPE = 1.03%, MAE = 258,080, RMSE = 893,381, and an overall accuracy of 98.97%. These results indicate that the SLP-based Multiple Linear Regression model provides stable and highly accurate profit predictions. Furthermore, the model can be expanded into a broader ERP system by integrating additional business modules such as finance, HR, and inventory management. Future research may incorporate external market variables, adaptive learning mechanisms, and non-linear algorithms such as Random Forest or Gradient Boosting to improve model robustness and predictive generalization.