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INDONESIA
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 225 Documents
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Evaluasi Usability Aplikasi Absensi Pegawai Menggunakan System Usability Scale : Studi Kasus di Pemerintah Provinsi Bali I Made Agus Oka Gunawan; I Gede Teguh Satya Dharma; I Wayan Rizky Wijaya
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): 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.v15i1.19367

Abstract

This study aims to evaluate the usability of a government employee attendance application using the System Usability Scale (SUS) method. A questionnaire-based survey involving 26 respondents was used in this study. Data were collected through a SUS questionnaire consisting of 10 statements with a five-point Likert scale, then analyzed using the SUS score calculation. This study also mapped SUS items into four usability aspects: effectiveness, efficiency, learnability, and user satisfaction. The results show that the application has a good level of usability and is in the Good category with a Grade B and Acceptable based on a SUS score of 74,62. The results of the measurement of four usability aspects show that the effectiveness aspect obtained a score of 3,13, the learnability aspect obtained a score of 3,03, the efficiency aspect obtained a score of 2,81 and the satisfaction aspect obtained a score of 2,79. These findings indicate that while the application is functionally sound, improvements to the user experience, based on usability, are still needed. The results of this study also provide an empirical basis that can be used as a reference in developing employee attendance applications that are more oriented towards user needs and experience.
Steganografi Pesan Teks Terkompresi Metode Huffman Coding ke dalam Citra Digital dengan Memanfaatkan Padding pada Citra Bitmap 24 bit Yosias Reinald Bunga Nawa; Arfan Yeheskiel Mauko; Clarissa Elfira Amos Pah
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): 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.v15i1.19856

Abstract

In the exchange of information over the Internet, message confidentiality is an important aspect because messages may be accessed by unauthorized parties. One technique used to protect message confidentiality is steganography, which conceals a message within another medium so that its existence remains unnoticed. The End of File (EoF) method is capable of preserving image quality; however, it may increase the file size after the embedding process. This study aims to address this limitation by utilizing the padding bits of 24-bit bitmap images as a message embedding medium and applying Huffman coding compression to improve storage efficiency. The system was tested using 15 text messages and 10 different 24-bit bitmap images. The results show that Huffman compression is effective for messages of certain lengths, whereas compression becomes less efficient for very short messages due to the overhead required to store the message header and Huffman code table. Fidelity testing produced an MSE value of 0 and an infinite PSNR value, indicating that the stego image is identical to the cover image because no pixel values are modified during the embedding process. This is possible because the secret data are stored in a memory area that does not contribute to the visual representation of the image. Furthermore, the embedded messages were successfully extracted and decompressed without any alteration when the stego images remained unmodified. However, robustness testing showed that the messages could not be recovered after the images were rotated by 90° or 180°, as changes in the bitmap data structure caused the loss of data stored in the padding bits.
Klasifikasi Citra X-Ray Dada untuk Deteksi COVID-19 Menggunakan Kombinasi Feature Embedding SqueezeNet dan Algoritma Machine Learning Hidayat Hidayat; Hadyan Ghazi Sumantri; Nanda Oktavian; Muhammad Virgi Firdaus
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): 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.v15i1.20589

Abstract

The rapid spread of Coronavirus Disease 2019 (COVID-19) has created an urgent need for accurate and rapid diagnostic methods to support clinical decision-making. Chest X-ray imaging has become one of the most widely used imaging modalities due to its availability, low cost, and ease of acquisition. However, manual interpretation of chest X-ray images is time-consuming and highly dependent on radiologists' expertise. This study aims to compare the performance of three supervised machine learning algorithms, namely Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), for classifying COVID-19 and normal chest X-ray images. Feature extraction was performed using the SqueezeNet image embedding method, which generated 1,000-dimensional numerical feature vectors. The dataset consisted of 288 chest X-ray images (144 COVID-19 and 144 normal), divided into 200 training images and 88 independent testing images. Model performance was evaluated using 5-fold cross-validation on the training set and an independent test set based on Area Under the Curve (AUC), Classification Accuracy (CA), Precision, Recall, F1-score, and Matthews Correlation Coefficient (MCC). The experimental results showed that all algorithms achieved an AUC above 98% and a CA above 94%. Among them, the SVM with a Radial Basis Function (RBF) kernel achieved the best performance, obtaining an AUC of 98.9%, CA of 97.0%, Precision of 97.1%, Recall of 97.5%, F1-score of 97.0%, and an MCC of 0.941. These findings demonstrate that the combination of SqueezeNet image embedding and SVM is an effective approach for chest X-ray image classification and has the potential to support the development of clinical decision support systems for COVID-19 diagnosis.
Model Platform Kolaboratif untuk Produksi dan MonetisasiKonten Video Pembelajaran di Perguruan Tinggi Andri Heryandi; Eddy Soeryanto Soegoto; Tri Utomo Wiganarto
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): 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.v15i1.20657

Abstract

Lecturers managing their own educational videos on YouTube face various challenges, such as time constraints, a lack of technical skills, and the absence of a structured monetization system. This study aims to propose a collaborative platform model that supports lecturers in the production, management, publication, monitoring, and monetization of educational video content. The Design Science Research (DSR) framework is applied, specifically focusing on the initial three stages: problem identification, formulation of solution objectives, and artifact design. The study's novelty lies in the integration of defined roles for lecturers and administrators, directly linked to the YouTube Data API and YouTube Analytics API to automate publication and performance monitoring. Quantitatively, the model utilizes YouTube analytics metrics—such as view count, watch time, and estimated revenue—as the basis for a measurable and proportional revenue-sharing formula. The scientific contribution of this research is the provision of a conceptual framework for the transparent, accountable, and efficient governance of digital content within higher education institutions. As a limitation, the study produces a conceptual model artifact that has not yet undergone the evaluation stage; therefore, further research is required.
Sistem Inventory Cerdas Dengan Integrasi Nixtla, Open AI dan Payment Gateway Eko Budi Setiawan; Difa Witsqa Raqina Dhiya
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 2 (2026): 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.v15i2.20710

Abstract

This study aims to enhance the efficiency of inventory management, order processing, and stock requirement forecasting for a distribution company. The primary challenge addressed is the reliance on manual record-keeping systems, which frequently lead to delays, recording errors, and difficulties in decision-making. The Software Development Life Cycle (SDLC) methodology was employed to plan, develop, test, and implement the software. The system incorporates intelligent inventory features leveraging the Nixtla and OpenAI APIs to generate stock requirement predictions, alongside a warehouse-integrated online ordering system that utilizes the Midtrans API as a payment gateway to streamline transactions and ensure automated payment verification. Black-box and user testing demonstrated that all functions operate according to specifications; the system accelerates inventory recording to a maximum of one minute, thereby increasing efficiency, simplifying order management, and assisting stock managers in making data-driven procurement decisions. Consequently, this research offers a viable solution and contributes to the integrated digitalization of distribution companies.

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