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Jurnal Ilmu Komputer, Teknologi Dan Informasi
Published by CV. Graha Mitra Edukasi
ISSN : -     EISSN : 29630169     DOI : https://doi.org/10.62866/jurikti.v2i1
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Social Sciences Other
Jurnal Ilmu Komputer, Teknologi Dan Informasi, ini memiliki bidang kajian: 1. Manajemen Informatika, 2. Sistem Informasi, 3. Game Design, 4. Multimedia System, 5. Sistem Pembelajaran Berbasis Multimedia, 6. GIS, 7. Mobile Programming, 8. Database Design, 9. Network Programming, 10. Distributed System, 11. Data Mining, 12. Sistem Pakar, 13. Kriptografi, dan 14. Sistem Pendukung Keputusan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 31 Documents
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Penerapan Algoritma Double Data Encryption Standard Untuk Pengamanan File Citra Digital Laia, Feronika
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 2 No 2 (2024): Juli 2024
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v2i2.158

Abstract

Keamanan merupakan suatu hal yang sangat penting, karena berkaitan dengan kerahasiaan atau privasi. Jika suatu data hilang atau diubah oleh pihak yang tidak bertanggungjawab maka dapat merugikan pemilik data tersebut. Saat ini dimana masih banyak orang menngunakan file penting yang sebenarnya tidak boleh diketahui oleh orang lain,tetapi masih masih saja ada orang yang melakukan pencurian data atau file tersebut untuk kepentingan pribadinya, sehingga menimbulkan masalah pada file atau data tersebut. Seiring dengan berkembangnya teknologi yang semakin canggih maka faktor untuk menjaga kerahasiaan file atau data menjadi hal yang penting bagi orang yang tidak berkepentingan agar tdak dapat mengakses file atau data tersebut. Salah satu metode yang dapat digunakan untuk mengamankan sebuah data atau file adalah dengan menggunakan algoritma double data encryption standard, penggunaan algoritma ini data atau file akan dikuncigandakan dengan melakukan proses enkripsi dan deskripsi. Algoritma ini dapat mengenkripsi file, data, audio,video dan lain-lain. Dari hasil pengujian yang telah dilakukan mengenai pengamanan file citra menggunakan algoritma double data encryption standard ini, maka file ataupun data yang dimiliki akan sulit utuk diketahui dan untuk proses enkripsi dan deskripsi file yang besar akan memerlukan waktu yang lama , karena pada file citra dan data tersebut telah dilakukan penyandian sehingga tidak akan mudah terjadi penyadapan atau penyerangan pada file atau data tersebut sehingga terhindar dari hal-hal yang tidak diinginkan.
Classification of Types of Crimes Against Human Physique Using the K-Means Clustering Method Rizkah Fadillah; Fiqri Hidayat Rangkuti; Anggi Jaya Maulana Siregar; Rizky Ananda; Muhammad Syahrizal
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 3 No 1 (2025): Januari 2025
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v3i1.192

Abstract

Human physical crimes are unlawful acts and prohibited by the rule of law, which can harm or damage the body of others. This study aims to examine the number of groupings of types of crimes against human physique in 2019 to 2020 in all areas of East Nusa Tenggara province. To do this, we use the K-Means Clustering method to group the types of physical crimes against humans. The data used came from the Central Statistics Agency of East Nusa Tenggara province. The K-Means method is one of the non-hierarchical data clustering methods that seeks to partition data into the form of one or more clusters/groups. After the application of the K-Means algorithm in the grouping of types of crimes against human bodies in 2019 to 2020 in the East Nusa Tenggara region, there are 3 centroids, C1 for areas with low crimes, C2 for areas with moderate crimes and C3 for areas with high crimes. The initial centroid value is determined randomly and then for the next centroid is adjusted to the result of the calculation of the closest distance (minimum). The final results obtained are areas with low crime totaling 13 regions, namely East Sumba, Lembata, Sikka, Ende, Ngada, Manggarai, Rote Ndao, West Manggarai, Central Sumba, Southwest Sumba, Nagekeo, East Manggarai, and Sabu Raijua. There are 7 areas with moderate crimes, namely West Sumba, Kupang, South Central Timor, North Central Timor, Belu, Alor, and East Flores. As for the area with high crime, there is 1 area, namely Kupang City.
Sistem Pendukung Keputusan Pemilihan Security Officer Pada PT Kharisma Esa Unggul Menggunakan Metode VIKOR William Dhaniel Tampubolon
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 3 No 1 (2025): Januari 2025
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v3i1.193

Abstract

A security officer is a security unit or security group established by a business entity to ensure physical security in the environment where they are assigned. The duties of a security officer include maintaining safety and order in their work area and surroundings, covering physical security. Security officers also act as sources of information, protect, and serve the public and surrounding environment. A security officer is a unit or group of personnel formed by an institution or business entity to carry out physical security as part of organized self-managed security within the work environment. The issue that arises in selecting security officers at PT Kharisma Esa Unggul is that the company conducts basic evaluations, primarily assessing skills and applying subjective judgment, meaning the company relies on relative impressions, assumptions, feelings, or preferences. A Decision Support System (DSS) is a system that plays a role in supporting the decision-making process in an organization or company. Therefore, for selecting security officers, each alternative must have criteria, and each criterion should have a predetermined weight, followed by a resolution process using the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for ranking. In this study, the result of the VIKOR method calculation showed that Aziz Marbun (A2) achieved the highest score of 0. The calculated ranking score for selecting a security officer ranges from 0 to 1. This approach helps the company make security officer selections in an accurate, efficient, and precise manner.
Implementasi Algoritma Boldi Vigna Kompresi File Teks Dini Novelia
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 3 No 1 (2025): Januari 2025
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v3i1.194

Abstract

Data compression technology is the process of converting a set of data into a lower form of code. Compression techniques are usually used for data transmission processes, Data compression or data compression is a way in computer science to require smaller storage space, making it more efficient in storage. Boldi Vigna is an example of an algorithm for compression where both compress data by descending sorting each character. Implementation is the application or implementation, as an action to carry out the plans that have been made.
Implementasi Metode Felics Pada Kompresi Citra Format GIF Theresia Lumbantobing
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 3 No 1 (2025): Januari 2025
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v3i1.195

Abstract

At this time, the increasingly rapid development of science and technology makes two-dimensional image files very necessary, where the resulting image size is large, requiring very large storage media, and other problems arise when exchanging data and information which increasingly consumes time and bandwidth. Therefore, a solution to the problem above emerged, namely by data compression. Data compression or data compression is a way in computer science to require smaller storage space, so that storage is more efficient. This is a compression method designed for grayscale images. The FELICS method presents a simpler system for lossless image compression that runs very quickly and only loses minimal compression efficiency. Implementation is the application or implementation, as an action to carry out the plan that has been made.
Perancangan Aplikasi Edukasi Pembelajaran Tata Surya Pada Sekolah Dasar Menggunakan Metode ADDIE Iren Anggi Stevani Silaen
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 3 No 1 (2025): Januari 2025
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v3i1.196

Abstract

Learning is a process of interaction between students and educators and learning resources in a learning environment that includes teachers and students who exchange information. Learning is also a process to help students learn well. In the context of education, teachers teach a lesson so that students can learn and master the contents of the lesson to achieve a specified objective, can also influence changes in attitudes, and skills of a student, but this teaching process gives the impression of only one party's work, namely the teacher's work. ADDIE is an everyday term used to describe a systematic approach to learning development. In addition to the CBI, CAI, and other learning methods, the ADDIE method is also a method that is suitable for use in a learning system that will make it easier for teachers to deliver material. One way of computer-assisted learning is the application of the ADDIE method. The ADDIE method is one of the learning system design models that shows the basic stages of a simple and easy-to-learn learning system. In the computer-assisted learning process, where computers are used for the purpose of presenting learning in the form of tutorials, simulations, and games. This can really help students to be able to apply a learning system while playing which is very effective for the learning process for students.
Komparasi Algoritma Fitur Matching SIFT Dan AKAZE Untuk Pencocokan Fitur Wajah Berbasis Citra Galih Putra Pratama; Husin Fadhil Azizi; Tira Karel Agata; Muhammad Naufal
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.264

Abstract

The problem of matching facial features is an important challenge in biometric systems, especially due to variations in lighting, texture and facial details that affect the stability of keypoint detection. This research aims to compare the performance of the Scale-Invariant Feature Transform (SIFT) and Accelerated-KAZE (AKAZE) algorithms in the facial feature extraction and matching process to determine the trade-off between accuracy and computational efficiency. The dataset used comes from NIST with 393 training images and 341 validation images. Evaluation is carried out using the number of detected keypoints, number of matching keypoints, number of inliers and outliers, feature extraction time, as well as error metrics such as MSE, MAE, RMSE, and R². Experimental results show that SIFT produces better matching performance with a total of 934,763 keypoints detected, an average matching keypoint of 121.14, and the number of inliers of 116.95. In addition, SIFT produces lower MSE, MAE, and RMSE values ​​than AKAZE, indicating better feature matching consistency in facial images. However, AKAZE has higher computational efficiency with an average feature extraction time of 0.1699 seconds, faster than SIFT of 0.2928 seconds. The contribution of this research lies in the comparative analysis of the performance of SIFT and AKAZE in keypoint-based facial feature matching, so that it can be a reference in selecting algorithms according to application needs, both oriented towards accuracy and computational efficiency.
Komparasi Empat Kernel Support Vector Machine pada Klasifikasi Cyberbullying Twitter Berbahasa Indonesia Juni Ismail; Randi Sumitro
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.265

Abstract

Cyberbullying on the Twitter social media platform has emerged as a significant social problem in Indonesia, with adverse effects on the mental health and well-being of its victims. Given the enormous volume of daily tweets, automated detection of cyberbullying expressions has become an urgent necessity. This study aims to compare the performance of four kernel functions in the Support Vector Machine (SVM) algorithm namely Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid for cyberbullying classification on Indonesian-language tweets. The dataset used is a publicly available corpus of 13,169 annotated tweets released by Ibrohim and Budi in 2019. The preprocessing pipeline includes case folding, text cleaning, slang normalization using a colloquial dictionary, stopword removal, and stemming based on the Sastrawi library. Text features are extracted using Term Frequency–Inverse Document Frequency (TF-IDF) with a combination of unigrams and bigrams limited to the top 5,000 features. Model training is conducted on a stratified 80:20 split. Experimental results show that the RBF kernel achieves the highest performance with an accuracy of 0.8281 and an F1-score of 0.8269, slightly outperforming the Linear kernel (accuracy 0.8258; F1-score 0.8256). The Sigmoid kernel reaches an accuracy of 0.8204, while the Polynomial kernel records the lowest performance (accuracy 0.7674). The Linear kernel proves to be the most efficient option with the shortest training time (9.19 seconds) without significantly compromising accuracy. These findings can support the development of automated content moderation systems on Indonesian-language platforms.
Studi Literatur Review Penerapan Data Mining Untuk Prediksi Penyakit Jantung Menggunakan Naïve Bayes Anisa Rizki Septia; Hetty Rohayani
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.268

Abstract

Heart disease is one of the leading causes of global death, often difficult to detect early due to non-specific clinical symptoms. To overcome the limitations of manual diagnosis, the application of data mining techniques utilizing the Naïve Bayes algorithm presents an efficient and accurate computational solution. This study aims to analyze and map the effectiveness of Naïve Bayes implementation in predicting heart disease through a Systematic Literature Review (SLR) approach. The contribution of this study is to provide a comprehensive taxonomic guide regarding the influence of data geometry, preprocessing techniques, and the integration of feature selection methods on optimizing the performance of probabilistic models. The results of the literature review indicate that the model accuracy level varies between 58% and 91.80%, with the majority of performance stable in the range of 79%-91% which is deterministically influenced by the quality of data dimensionality reduction. Overall, the Naïve Bayes-based data mining process has proven to have great potential as a clinical decision support system in supporting early medical preventive measures.
Penerapan Metode Convolutional Neural Network pada Identifikasi Wajah Mahasiswa didalam Ruang Perkuliahan Rahman M. Abdullah; Mohamad Ilyas Abas; Syahrial Syahrial
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.271

Abstract

Manual student attendance systems still present several limitations, including the potential for data manipulation, human error, and low efficiency in large classroom environments. This study aims to implement the Convolutional Neural Network (CNN) method to simultaneously identify students’ faces within a classroom setting. The dataset consisted of 1,740 facial images collected from 58 students using a 2K Full HD webcam under varying capture angles and lighting conditions. The research stages included data collection, image preprocessing, data augmentation, CNN model training, and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score metrics. The developed CNN model, named FACENET V5, was designed using TensorFlow with three convolutional blocks, batch normalization, max pooling, dropout, and a softmax classifier. Experiments were conducted using image sizes of 100×100, 200×200, 300×300, and 400×400 pixels with several dataset split scenarios. The results demonstrated that the 100×100 image size with a 90:10 data split achieved the best performance, obtaining a validation accuracy of 98.28% and a loss value of 0.1127. Furthermore, FACENET V5 was compared with ResNet50V2, MobileNetV2, and VGG16. Comparative results indicated that FACENET V5 provided the most optimal performance in simultaneous student face recognition. This study confirms that CNN can be effectively implemented as an automated face recognition-based attendance system in academic environments.

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