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KOMPUTA : Jurnal Ilmiah Komputer dan Informatika
Published by Universitas Komputer Indonesia
ISSN : 20899033     EISSN : 27157849     DOI : 10.34010
Core Subject : Science,
Computer Science & IT
Jurnal Ilmiah KOMPUTA (Komputer dan Informatika), adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis di bidang kelimuan Komputer dan Informatika. Terbit dua kali dalam setahun pada bulan Maret dan Oktober.
Arjuna Subject : -
Articles 216 Documents
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Analisis Keamanan Website Kampus UNIPDU Melalui Metode Vulnerability Assessment (VA) dengan Menggunakan Tools Acunetix Syaifudin, Moh Rizki; Murtadho, Mohamad Ali; Wafa, Moh Shohibul; Masrur, Mukhamad
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.14693

Abstract

Amidst the rapid development of technology, website vulnerabilities are a major threat, opening up opportunities for hackers to hunt and steal important data. Web applications are a technological innovation that not only facilitates access to information on the Unipdu Jombang campus, but also functions as the main link in the information system, even though they have to face major challenges in maintaining its security. By using the Vulnerability Assessment (VA) approach that utilizes Acunetix technology, this study attempts to assess the weaknesses of the Unipdu Jombang campus website and offers suggestions for improving its security. The main domain of the website is the focus of the study, which uses automated testing methodology to find vulnerabilities that could be exploited. Many vulnerabilities were found by the test results, including the use of reverse proxy detected, using cloud services such as CloudFlare, and TLS/SSL certificates that are almost expired. Through reports from scans that comply with the OWASP Top 10 2021 guidelines on Acunetix tools, 2 groups of vulnerability categories were found, including: (A05) security misconfiguration and (A06) Vulnerable and Outdated Components. It is hoped that these efforts will improve data security and thwart various threats. The results of this study provide important information for Unipdu website developers, including the need to update SSL certificates and suggest scanning on internal versions of web applications without active WAF. These findings not only strengthen system security, but also help campuses maintain user trust while also being a guide for the development of more reliable and secure information systems in the future.
Evaluasi Website BBMKG Wilayah III Menggunakan User Experience Questionnaire (UEQ) Juniastra, Made Gde; Artha, I Gede Mony; Gunawan, I Made Agus Oka; Indrawan, Gede
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.14718

Abstract

The website of the Center for Meteorology, Climatology, and Geophysics (BBMKG) Region III serves as a tool for BBMKG Region III to fulfill its duty of providing meteorology, climatology, air quality, and geophysics information to the public. Continuous website evaluation needs to be carried out to improve website quality to provide optimal service to the community. One of the user experience evaluation method that can be used is the User Experience Questionnaire (UEQ), utilizing a quantitative survey approach in data collection. In this study, 25 respondents from various departments within BBMKG Region III participated. Benchmark results showed that five evaluation scales are in the good category namely Efficiency with a score of 1.55, Perspicuity with 1.83, Dependability with 1.49, Stimulation with 1.36, and Novelty with 1.35. Only one scale, Attractiveness, is in above average category with a score of 1.53. To increase the Attractiveness scale value, the author suggests refreshing the design/appearance of the website, one of which might be by using a web system development framework such as Laravel. It is hoped that by using this framework, the website's appearance will be more dynamic and attractive, and it will also be easier to update the design in the future. Based on the research results, none of the scales reached the excellent category, indicating that improvements in all aspects are necessary to achieve an excellent level.
Penerapan Simple Additive Weighting dalam Sistem Pendukung Keputusan Pemilihan Pelanggan Terbaik pada PT Fartan Energy Jayanti, Nila Rusiardi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.14753

Abstract

PT Fartan Energy operates in the marine service, oil and gas sectors. This company has existed for 19 years and has many partners/customers. To further strengthen the partner/customers who collaborate with this company, the author created a design for a Decision Support System to select customers at PT Fartan Energy using the Simple Additive Weighting (SAW) method. By creating this system, the aim is to select loyal partners who will be given rewards as a form of appreciation to strengthen cooperative relationships. The method used in the decision support system is Simple Additive Weighting. The SAW method combines several criteria and assigns weights to each criterion to evaluate the best customers. There are 4 criteria used in this research, namely shopping total, shopping activity, customer income, and customer address. In creating the SPK design, NetBeans software, was used the Java programming language and MySQL database. The output of this SPK is a final report in the form of who is selected as a loyal customer and will be given a reward. In this research there were 5 partners who were evaluated based on 4 assessment criteria. The rangking results show that alternative A3 got the highest score, namely the partner is Job Pertamina – Meco E&P Simenggaris with a final score 100, so it was decided as the best partner of PT Fartan Energy. The best partenrs will be given appreciation, so it is hoped that they can increase cooperation and also increase company profits.
Klasterisasi Negara Dunia Berdasarkan Data Sosioekonomi dan Demografi Tahun 2023 dengan PCA dan K-Means Marpaung, Dhea Romantika; Gunawan, Erin; Fa, Farrel Rio; Christianto, Albert
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15249

Abstract

The development of social, economic, and demographic factors is an important indicator for assessing the progress of a country. These factors reflect the quality of life, economic conditions, and population dynamics that can influence policies and development planning. Therefore, to better understand a country's conditions, it is important to cluster countries based on similar characteristics in these various aspects. The purpose of this study is to identify clusters of countries worldwide based on the analysis of socio-economic and demographic data for 2023 using Principal Component Analysis (PCA) and K-Means Clustering methods. This analysis examines the relationship between GDP, birth rate, death rate, population, and CO2 emissions. The results reveal three clusters with distinct characteristics. Cluster 0 shows high GDP with low infant mortality and controlled CO2 emissions. Cluster 1 shows lower GDP, high infant mortality, and challenges in the health and economic sectors. Cluster 2, which includes countries like China, India, and the US, has high GDP but faces high CO2 emission issues. These findings indicate the need for integrated policies to improve global well-being by considering economic, health, and environmental factors in a sustainable manner.
Analisis Perbandingan Metode DES (Double Exponential Smoothing) dan WMA (Weighted Moving Average) dalam Peramalan Penjualan Laptop Gunawan, Asrul; Hermawan, Arief; Avianto, Donny
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15314

Abstract

Rapid technological developments increase demand for electronic devices, especially laptops. Fluctuations in monthly sales are a challenge for companies in determining the optimal amount of inventory. The inability to predict market demand can disrupt inventory management and customer satisfaction. Therefore, accurate sales forecasting is essential for planning marketing and procurement strategies. This study compares two sales forecasting methods, namely Double Exponential Smoothing (DES) and Weighted Moving Average (WMA), to analyze the accuracy of each method. The results showed that the DES method has a better level of accuracy with an average MAPE value of 16.72%, compared to WMA which reached 21.22%. This study provides practical insights for companies in choosing the right forecasting method, in order to improve inventory management, product procurement strategies, and customer satisfaction
Optimasi Augmentasi Data Berbasis Synonym Replacement pada Klasifikasi Teks Berita Menggunakan Neural Network Huriah, Iffah Risma; Widianingrum, Amelia Ismania Sita; Azlina; Taslim
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15339

Abstract

In the digital era, online news has become one of the primary sources of information, encompassing various categories such as politics, technology, entertainment, and business. The increasing volume of news poses challenges in organizing and categorizing information into relevant categories. This study aims to enhance the accuracy of news text classification through a data augmentation approach based on synonym replacement. The methods employed include text preprocessing for data cleaning, augmentation using synonym replacement to improve data diversity, feature representation using TF-IDF and Word2Vec, and modeling with Neural Networks. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess performance. The results indicate that data augmentation can improve model accuracy by up to 95%, with balanced training and validation data distributions. The confusion matrix shows that most data can be correctly classified, although some errors occur in categories with similar features. This study demonstrates that synonym replacement-based data augmentation is effective in improving news text classification performance, particularly for datasets with limited training data.
Prediksi Temperatur Maksimum di Kota Tanjungpinang Menggunakan Model CNN-LSTM Nurfalinda; Fiani, Mia Al; Rathomi, Muhamad Radzi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15377

Abstract

The prediction of maximum temperature is important for supporting decision process related to public activities and reducing the consequences of climate change. The goal of this study is to analyze the performance of the CNN-LSTM hybrid method in forecasting maximum temperature in Tanjungpinang City by utilizing average humidity and rainfall as input variables. Historical weather data was obtained through the BMKG website, covering the period from January 1, 2022, to November 30, 2024, and was used as the research dataset. The CNN-LSTM model was developed by optimizing the advantages of CNN in recognizing spatial patterns and the capability of LSTM in capturing temporal patterns. The model was trained using an optimal configuration consisting of 128 CNN filters, a kernel size of 7, 200 LSTM units, a batch size of 16, and 120 epochs. Performance evaluation was conducted using two key metrics: Root Mean Squared Error (RMSE) of 1.65 and Mean Absolute Percentage Error (MAPE) of 4.19%. The findings indicate that the model can be used to predict maximum temperature based on available historical weather data. Additionally, the model has been implemented in a web-based platform that allows users to input historical data and select prediction periods ranging from 1, 3, 7, to 10 days ahead. The prediction results are presented in tables and graphical visualizations to facilitate users in understanding and evaluating the generated information.
Studi Pustaka: Optimalisasi Deteksi Malware melalui Integrasi Pembelajaran Mesin Heuristik dan Big Data untuk Keamanan Siber Supriyadi, Devi; Wahyudi, Bisyron; Rimbawa, Danang
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15595

Abstract

The increasingly complex and dynamic threat of malware drives the need for a more adaptive detection strategy than conventional signature-based methods. This study aims to evaluate the effectiveness of machine learning, heuristics, and big data approaches in detecting modern malware. The main problem raised is the limitation of traditional methods in identifying new malware variants, especially those that use obfuscation techniques such as polymorphism and metamorphism. Using a systematic literature study approach to the 2016-2024 literature from various reputable sources, this study compares the performance of each approach based on accuracy, efficiency, and resistance to adversarial attacks. The results of the analysis show that deep learning models such as the Convolutional Neural Network (CNN) have the highest detection accuracy, while heuristic methods excel in initial detection efficiency, and big data provides advantages in the scalability of real-time detection systems. This study concludes that the hybrid integration of these three approaches has the potential to create a malware detection system that is more adaptive and resilient to cyberattacks, although further empirical validation is still needed for real-world implementation.
Klasifikasi Penyakit Tanaman Padi Berdasarkan Kondisi Daun Menggunakan Compact Convolutional Transformers Nurdiana, Hestin; Lestari, Novi; Rusdiyanto, Rusdiyanto; Sobri, Ahmad
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15674

Abstract

Rice (Oryza sativa) is one of the world's main food crops as it serves as a staple food source for the majority of people in various countries, including Indonesia. Optimal rice productivity is highly dependent on plant health, particularly the condition of the leaves, which are susceptible to various diseases. Diseases affecting rice leaves can significantly reduce yields, making early detection and disease management crucial for farmers. Deep learning methods, such as Convolutional Neural Networks (CNNs), have demonstrated excellent performance in image pattern recognition, including plant disease classification based on leaf imagery. One of the latest advancements in this field is Compact Convolutional Transformers (CCT), which combine the strengths of CNNs in capturing local features with the ability of Transformers to understand global relationships between image features. The Compact Convolutional Transformers (CCT) method will be applied to classify rice plant diseases based on leaf images.  This study classifies four categories, namely Normal, Leaf smut, Brown spot, and Bacterial leaf blight. This technology is expected to assist farmers in detecting rice diseases automatically and more rapidly, ultimately enhancing productivity and harvest quality. The study has resulted in a reliable model, achieving an accuracy of 94% with a low loss.
Peningkatan Arsitektur Aplikasi Sistem Lelang Agunan Perbankan Menggunakan TOGAF ADM Setyawati, Febryana Nabilla; Abadi, Muhammad Djaka; Pratama, Andika Agus; Ernawati, Siti
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15715

Abstract

The collateral auction system in banking faces challenges in terms of scalability, data security, and technology integration. This study aims to analyze and enhance the application architecture of the collateral auction system at BNI46 using the TOGAF (The Open Group Architecture Framework). The target architecture is then designed based on the Architecture Development Method (ADM) phases in TOGAF, covering aspects of Business Architecture, Information Systems Architecture, and Technology Architecture. The research methodology includes observations, interviews, and literature studies to identify weaknesses in the existing system and business requirements. TOGAF is applied as a systematic approach to designing a more flexible, secure, and efficient architecture. Technologies such as containerization, Kubernetes, and a zero-trust security model are employed to improve system efficiency. The study results indicate that the implementation of TOGAF provides a structured approach to developing an architecture that is more flexible and business-oriented. With these improvements, the collateral auction system is expected to provide better services for customers, enhance the efficiency of asset management in banking, adapt to evolving business needs, and strengthen the bank's competitiveness in the digital era.

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