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Jurnal Ilmu Komputer Ruru
Published by Yayasan Grace Berkat Anugerah
ISSN : -     EISSN : 30467314     DOI : https://doi.org/10.69688/jikr
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering
Jurnal Ilmu Komputer Ruru merupakan jurnal ilmiah dalam sistem informasi/teknologi informasi yang berisi literatur ilmiah tentang kajian penelitian murni dan terapan dalam sistem informasi/teknologi informasi dan tinjauan publik terhadap perkembangan teori, metode dan ilmu terapan yang terkait dengan subjek. Jurnal Ilmu Komputer Ruru (JIKR) diterbitkan oleh Yayasan Grace Berkat Anugerah. Redaksi mengajak peneliti, praktisi, dan mahasiswa untuk menulis perkembangan keilmuan di bidang yang berkaitan dengan sistem informasi/teknologi informasi. Jurnal Ilmu Komputer Ruru (JIKR) diterbitkan 2 (dua) kali setahun pada bulan Januari dan Juli. Jurnal ini berisi artikel penelitian dan kajian ilmiah.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 15 Documents
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PERBANDINGAN ALGORITMA C5.0 DAN REGRESI LINEAR UNTUK PREDIKSI KELULUSAN MAHASISWA Sijabat, Petti; Simangunsong, Agustina
Jurnal Ilmu Komputer Ruru Vol. 1 No. 2 (2024): Edisi Juli
Publisher : Yayasan Grace Berkat Anugerah

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

Abstract

Technological advances supported by human knowledge have a very good influence on data and information storage technology, including in predicting student graduation (Graduation Prediction) on time, by applying several existing algorithms. In this study, researchers used the C5.0 Algorithm and Linear Regression. The concept of the research is to compare two algorithms, namely C5.0 and Linear Regression to the case of graduating students on time. Based on the length of study, students who graduated correctly amounted to 651 (91%) with a male gender of 427 students and a female gender of 224 students while those who did not pass (late) correctly amounted to 64 (9%) with a male gender totaling 53 students and female gender totaling 11 students from 2017-2020. Comparison results The R2 score from the C5.0 algorithm reached 96.85% (training) and 93.72% (testing) while the R2 score from the Linear Regression reached 33.31% (training) and 40.30% (testing).
IMPLEMENTASI ALGORITMA DECISION TREE C4.5 DENGAN IMPROVISASI MEAN DAN MEDIAN PADA DATASET NUMERIK Bagus Sudirma
Jurnal Ilmu Komputer Ruru Vol. 2 No. 1 (2025): Edisi Januari
Publisher : Yayasan Grace Berkat Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/jikr.v2i1.17

Abstract

A decision tree algorithm or commonly called a decision tree is a classification method of data mining. The decision tree has one type of algorithm model, namely the C4.5 algorithm. The C4.5 decision tree algorithm is easy to understand because it has a tree-like structure in general. The C4.5 algorithm in handling quantitative data is often less efficient and effective. So to minimize information loss and time complexity, we can improvise the dataset on the numeric attributes when Preprocessing the data. Improvisation is done by using the mean and median on the numerical attributes to get a threshold value for implementing the C4.5 algorithm from the training data. Evaluation of the system used in this study uses a confusion matrix. Confusion matrix as a benchmark for testing the classification method using data testing. In this study, the dataset is partitioned into three scenarios. In scenario 1 with 70% training data and 20% testing data, the highest accuracy is 75%. The improvisation of the mean and median on the numerical attributes in the C4.5 algorithm can use in this scenario.
APLIKASI PENILAIAN KINERJA PERAWAT UNGGULAN MENGGUNAKAN PENDEKATAN SAW Sianturi, Fricles A
Jurnal Ilmu Komputer Ruru Vol. 1 No. 2 (2024): Edisi Juli
Publisher : Yayasan Grace Berkat Anugerah

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Abstract

Evaluating nurse performance is a critical aspect of human resource management in the healthcare sector. This study aims to develop a decision support system that assists management in determining the best-performing nurses using the Simple Additive Weighting (SAW) method. SAW is chosen for its simplicity and capability to handle various evaluation criteria. In this research, nurse performance data were collected from multiple sources, including supervisory reports, patient feedback, and peer assessments. Each evaluation criterion was assigned a weight according to its importance, and the nurse performance scores were calculated using the SAW formula. The process involves normalizing the criteria scores and then aggregating them to generate a composite performance score for each nurse, The main challenges addressed in this study include the subjectivity and inconsistency often associated with manual performance evaluations. By employing the SAW method, the study seeks to minimize biases and ensure a fair assessment of all nurses based on predefined and weighted criteria. This method was chosen because it can perform a ranking process based on criteria and weights and is able to determine the best alternative. System design with Context Diagram, HIPO, DAD and ERD. Application results are nurse reports, overall selection and selection of the best nurses. The results of the functionality test are proven to be successful and the Validity Test has been proven to be 100% valid.
PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST Asriyanik
Jurnal Ilmu Komputer Ruru Vol. 2 No. 1 (2025): Edisi Januari
Publisher : Yayasan Grace Berkat Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/jikr.v2i1.19

Abstract

With so many airlines competing with each other, airlines are competing to become the consumer/market's main choice, but to achieve this, there is no airline strategy that can predict the price of airline tickets according to market needs. To meet the needs of airlines, we need a way to determine the price of airline tickets according to market needs with the help of the influence of technology and information. This research method was carried out using Google Collaboratory as a media to create a data model with the Random Forest, Logistic Regression and Gradient Boosting Regressor algorithms. In this study, the model that produced the highest R2 value and the lowest RMSE was a random forest with an R2 value of 83.91% and an RMSE of $175.9. However, from the three models, Random Forest got a change in accuracy of 1.96% to 85.87. To assist in predicting the determination of flight fares, airline companies can more easily and be alert to determine flight fares that are in accordance with the market. Therefore, Random Forest can be declared better than Logistic Regression and Gradient Boosting models. The Random Forest model that has been created can be used to predict in real-time using Machine Learning.
OPTIMALISASI PENJUALAN KUE TRADISIONAL DJAJE DENGAN PENDEKATAN STP Sianturi, Ismali Marzuki
Jurnal Ilmu Komputer Ruru Vol. 1 No. 2 (2024): Edisi Juli
Publisher : Yayasan Grace Berkat Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/jikr.v1i2.20

Abstract

Djaje is a traditional cake business that has been established in 2017. The products sold by Djaje are various wet snacks. The initial strategy used was only offline, such as leaving cakes to resellers, but as the number of competitors increased, Djaje's sales decreased. This study uses a qualitative descriptive method by explaining the results obtained using analysis STP (Segmentation, Targeting, Positioning). The purpose of this study was to determine the results of implementing digital marketing strategies in improving traditional cakes. Results implementation of the implemented strategy by adjusting the content of photos, descriptions, posts, and advertisements using analysis STP (Segmentation, Targeting, Positioning) as a basis. By using various digital platforms to reach more potential consumers, Djaje has succeeded in making cake sales increase in sales from October 2021 to March 2022

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