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Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jalan Raya Pakuan PO. BOX 452, Bogor, Indonesia
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Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Published by Universitas Pakuan
ISSN : 16937554     EISSN : 26543990     DOI : https://doi.org/10.33751/komputasi.v22i2.29
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Mathematics
Komputasi is a journal that publishes scientific papers in the fields of computer science and mathematics. This journal, published by the Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor. This journal provides an opportunity for researchers or academics to submit papers in the field of computer science, as well as management policies related to all aspects of computers and their subdisciplines. The journal is published twice a year, is well-documented in book form, which includes a wide range of computer science and mathematics papers by authors from various backgrounds. In addition, we also have partners from local editors who graduated as professors from several universities who will review each article before it is published. Every article or paper published in this Journal will definitely be useful for all visitors and readers. Articles submitted to this journal will be reviewed by reviewers before being published by a blind review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 48 Documents
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Application of CPM and PERT Methods in Residential Construction Project Scheduling: A Case Study Rajainal Saragih; Hengki Mangiring Parulian Simarmata; July Antasari Br Sinaga; Gayus Simarmata; Andi Manalu
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.101

Abstract

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.
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
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.104

Abstract

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; Henry Saptono; Rusmanto
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.108

Abstract

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; Putri Ramdani; Maisan Dewi Puspa Khairani; Eka Ramadhani Putra
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.110

Abstract

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; Sulistyaningrum; Lucky Primanda Saputra; Sofa Machabba Haeta
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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; Hery Oktafiandi; Pebriyanti Panjaitan; M. Fajar Ramadhan; Yohanes Yohanes
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.112

Abstract

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; Lilis Indawati; Muh. Rasyid Ridha
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.113

Abstract

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.
A Multi-Ontology Approach for Deforestation Issue Analysis in Indonesian Digital Media Dinar Munggaran Akhmad; Ersa Resita; Carli Apriasnyah Hutagalung; Ryan Tsany Adelmar; Muhammad Dheki Akbar
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.118

Abstract

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.

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