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Jurnal Ilmu Komputer dan Informatika
Published by Global Scients Publisher
ISSN : -     EISSN : 30639026     DOI : https://doi.org/10.62379/jiki
Core Subject : Science, Education, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics
Jurnal Ilmu Komputer dan Informatika (E-ISSN : 3063-9026 )adalah jurnal ilmiah yang diterbitkan oleh GLOBAL SCIENTS PUBLISHER. Jurnal Ilmiah Komputer dan Informatika diterbitkan secara berkala yaitu 4 kali dalam setahun (pada bulan januari, april, juli dan oktober) yang bertujuan untuk menyebarluaskan berbagai jenis hasil riset dibidang Komputer dan Informatika kepada publik. Saat ini Jurnal Ilmu Komputer dan Informatika menerima kiriman artikel hasil riset dibidang komputer dan informatika yang ditulis dalam Bahasa Indonesia .
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 37 Documents
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Strategi Pertahanan Keamanan Siber Berbiaya Rendah untuk UMKM: Tinjauan Literatur Pratama, Nugraha Adhi; Nugraha, Muhammad Rizki
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Digital transformation is driving Micro, Small, and Medium Enterprises (MSMEs) to adopt information technology in their business activities. However, reliance on digital systems increases cybersecurity risks, while limited resources are a major obstacle for MSMEs in implementing comprehensive security systems. This study reviewed 18 scientific articles related to low-cost cybersecurity defense strategies applicable to MSMEs. The review results indicate that MSMEs are vulnerable to cyber threats due to budget constraints, low security literacy, and weak information governance. A realistic strategy emphasizes three key aspects: people, processes, and simple technology, through education and training, basic security policies, consistent operational procedures, and the use of affordable technology. This study is expected to serve as a reference for MSMEs and researchers in designing effective, efficient, and sustainable cybersecurity strategies.
Analisis Komparatif Kinerja Lstm Dan Svr Dalam Prediksi Harga Emas Berjangka Buana, Aliman Fijar; Permana, Agung Nabawi; Zulfa, Hikmatul; Subarkah, Asuy; Kamalia, Antika Zahrotul
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

This study presents a comparative analysis of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) performance in predicting gold futures prices (XAU/USD). Using historical data from October 2020 to October 2025 (1,289 data points) covering the COVID-19 pandemic and post-pandemic periods, both models were evaluated under two scenarios: default parameters and optimized hyperparameters. Results showed significant performance differences without tuning, where LSTM achieved R² of 0.9874 while SVR produced negative R² of -6.3571. However, after hyperparameter optimization using 27 configurations for each model, both methods demonstrated excellent and comparable performance. Optimized SVR achieved R² of 0.9887, MAPE of 0.9402%, RMSE of $39.21, and MAE of $29.65, while optimized LSTM obtained R² of 0.9895, MAPE of 0.8996%, RMSE of $38.18, and MAE of $28.32. Paired t-test revealed no statistically significant difference between the two methods after tuning (p=0.0978 > 0.05), with both models exhibiting excellent generalization capabilities (R² gap < 0.005). These findings demonstrate that hyperparameter tuning is more critical than algorithm selection in achieving high prediction accuracy, suggesting that optimized SVR can serve as an efficient alternative to LSTM for applications with limited computational resources
Analisis Pola Cuaca di Provinsi Sumatera Utara Menggunakan Metode Clustering K-Means manalu, ester; Surbakti, Efrans; Sipayung, Sardo Pardingotan
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Weather is an important factor that influences various sectors of life, such as agriculture, transportation, and community activities. North Sumatra Province has diverse weather characteristics due to differences in geographical conditions; therefore, analytical methods are required to identify weather patterns based on historical data. This study aims to analyze weather patterns in North Sumatra Province using the K-Means clustering method. The data used consist of 50 daily weather records, including air temperature, humidity, and rainfall parameters.The research stages include data collection, data preprocessing, determination of the number of clusters, implementation of the K-Means algorithm, and analysis of the clustering results. The number of clusters used is K = 3 to represent different weather patterns. The clustering results indicate that the cluster representing clear to partly cloudy weather with low rainfall is the dominant cluster, accounting for 40% of the data, followed by the cluster representing humid weather with relatively lower temperatures at 36%, and the cluster representing rainy weather with high humidity at 24%. These results demonstrate that the K-Means algorithm can effectively group weather data based on the similarity of their characteristics. The information generated is expected to support decision-making related to activity planning and weather analysis in North Sumatra Province.
Analisis Klasifikasi Kualitas Udara Di DKI Jakarta Menggunakan Algoritma Decision Tree Hadi, Abdul; Hura, Bebi Kurniawan; Abulkhoir, Moh Azam; Abimur, Riski; Wati Hulu, Lestin Nurhadia; Ningsih, Windia; Deli, Indah Yani
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Air pollution is a serious environmental problem in urban areas, particularly in DKI Jakarta, due to increasing transportation activities, industrial development, and population density. This study aims to classify air quality in DKI Jakarta based on the Air Pollution Standard Index (ISPU) parameters using the Decision Tree algorithm with the CRISP-DM approach. The dataset consists of daily ISPU data from January 2024 to November 2025 obtained from the official Satu Data Jakarta platform, covering PM10, PM2.5, SO₂, CO, O₃, and NO₂ parameters. After data preprocessing, 2,864 records were used for modeling with an 80:20 split between training and testing data, implemented using RapidMiner. The evaluation results show that the Decision Tree model achieved a high classification performance with an accuracy of 99.13%, along with strong precision and recall across all air quality categories. The decision tree structure indicates that PM2.5, PM10, and NO₂ are the most influential attributes in determining air quality levels. These findings demonstrate that the Decision Tree algorithm is effective, accurate, and easily interpretable for air quality classification, and has the potential to support environmental monitoring and decision-making in the public health sector.
Analisis Kontribusi Pemain Sepak Bola Eropa Berbasis C4.5 Adyatma, Mochammad Eric; Adhiyasya, Rakha; Ryandhika, Rifaldi; Fadholi, Muhammad Farhan; Ajiansyah, Muhammad Rafly; Isak, Yorrel Jensek; Zalogo, Andrianus; Dakhi, Lisbet
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Modern football has evolved into a data-driven industry where statistical analysis is widely used to objectively evaluate player performance. This study aims to classify the level of goal contribution of top European football players using the C4.5 algorithm implemented through RapidMiner. The dataset is derived from player statistics of top European leagues in the 2022–2023 season, with key attributes including Shot on Target Percentage (SoT%), Shot-Creating Actions (SCA), standardized playing time (90s), and short and total passing accuracy. The research methodology consists of data selection, preprocessing, data transformation, data mining, and model evaluation. The C4.5 algorithm is applied using the Gini Index criterion with pruning techniques to prevent overfitting. Model validation is conducted using 10-Fold Cross Validation. The results show that the classification model achieves an accuracy of 90.67%, with SoT% identified as the most influential variable, followed by SCA and playing time. The generated decision tree provides clear and interpretable rules, making it useful as a decision-support tool for evaluating player contributions based on data-driven analysis.
Analisis Pengelompokan Minat Belajar Mahasiswa Menggunakan Algoritma K-Means simbolon, yoel; Giovani, Aritonang; Sipayung, Sardo Pardingotan
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

One of the main elements affecting students' academic success in higher education is their interest in learning. However, direct observation is frequently used to subjectively identify differences in students' learning interests, which could result in inaccurate assessments. Therefore, in order to objectively classify students according to their learning characteristics, a data-driven approach is needed. The purpose of this study is to analyze and categorize students' learning interest levels using the K-Means clustering algorithm. Thirty university students filled out a learning interest questionnaire with a Likert scale of 1 to 5. Attendance at lectures, classroom activity, timely completion of assignments, level of independent study, and interest in the course are among the variables examined. Three clusters—representing high, medium, and low learning interest levels—were created using the K-Means algorithm. Based on the final cluster centroids, the results show that the K-Means algorithm successfully divided the students into three clusters: 11 students with high learning interest, 12 students with moderate learning interest, and 7 students with low learning interest. These results offer an unbiased summary of students' learning environments and can be used as a foundation for creating more focused and efficient teaching methods in higher education.
Klasifikasi Penyakit Asma Menggunakan Algoritma Decision Tree Pada Rapidminer Ardhiyansyah, Pramudhitya; ., Abdurrazzaq; Alfiansyah, Afif; Hilmy Riwanto, Muhammad; Gultom, Sahdia; Shipa, Erna Grace; Ramdani Koswara, Mochamad Fauzi; Garamba, Yafianus
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Asthma is a disease characterized by chronic inflammation of the respiratory system with a relatively high recurrence rate in Indonesia. This condition highlights the need for a data-driven approach to support a more objective and systematic disease classification process. This study aims to classify asthma by applying the Decision Tree algorithm, which is implemented using RapidMiner software as an analytical tool. This research adopts the CRISP-DM framework as the research workflow, encompassing the stages of problem understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used is secondary data obtained from the Kaggle platform, with an initial total of 10,000 patient records. During the data preparation stage, data cleaning, transformation, feature selection, and class imbalance handling were performed, resulting in 4,866 data instances used for modeling. The evaluation results indicate that the Decision Tree model achieved an accuracy of 93.63%, with a precision value of 89.72% and a recall value of 98.56% for the asthma class. In addition to its strong performance, the resulting model is easily interpretable through clear decision rules, making it suitable as a decision-support tool for asthma disease classification.

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