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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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Optimasi Protokol Komunikasi V2V untuk Lalu Lintas Perkotaan dan Jalan Raya dengan AODV Berbasis Learning Automata Sadiah, Hanna Halimatu; Bintoro, Ketut Bayu Yogha; Letsoin, Fita Sari; Bintoro, Ketut Bayu
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.v14i1.15837

Abstract

This research addresses optimizing communication efficiency in Vehicle-to-Vehicle (V2V) networks in urban and highway environments, focusing on the limitations of traditional routing protocols under varying traffic conditions. The study introduces an improved version of the AODV protocol, termed Learning Automata-based AODV (LA-AODV), designed to enhance data transmission reliability and reduce latency. In this approach, LA-AODV utilizes location and movement information to optimize communication paths, adaptively selecting the most reliable routes based on real-time traffic dynamics. The objective is to evaluate LA-AODV’s performance against AODV based on metrics such as packet delivery, jitter, and end-to-end delay. The study assesses protocols in dynamic urban and highway traffic settings through quantitative simulations. Results indicate that LA-AODV consistently outperforms AODV, reducing jitter by 15% and increasing packet delivery by 12% in urban scenarios while decreasing end-to-end delay by 10% on highways. These gains are achieved by LA-AODV’s enhanced route selection, which incorporates location-based decisions for optimal communication paths. The study’s findings substantiate the reliability of LA-AODV, which is a significant step forward in the field of V2V communication. This research provides a foundation for advancing next-generation V2V communication systems in urban and highway contexts, instilling confidence in the potential of LA-AODV to improve V2V communication efficiency.
Desain Sistem Penjadwalan Tenaga Keperawatan Rumah Sakit Menggunakan Algoritma Priority Scheduling Mardzuki, Tati Harihayati; Lubis, Riani; Abdulloh , Mahfudz
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.v14i1.15948

Abstract

Scheduling nursing staff in a hospital is a process that must be carried out every period to meet patient needs based on their services. The nursing staff scheduled in this study were nursing staff in the Outpatient Unit. The current schedule does not take into account the limitations of each nursing staff, such as nurses who are in advanced studies, pregnant, or are currently working as doctor's assistants. This has caused several complaints from several nurses, such as nurses whose pregnancy age is approaching 7-8 months getting an afternoon shift schedule more than four (4) times a month, which means it is not in accordance with the outpatient unit policy. The purpose of this study is to produce a scheduling system model by considering the limitations of nursing staff in the Outpatient Unit in the next period. The algorithm used in creating the nursing staff schedule is the priority scheduling algorithm, where this algorithm has been proven effective in managing the priority of the process sequence and has been applied to other information systems with satisfactory results. The results of the study indicate that the scheduling system model created can meet the needs of nursing staff in the Outpatient Unit
Perbandingan Algoritma Sobel, Kirsch, Laplacian Of Gaussian dan Canny Untuk Deteksi pada Citra Keretakan Dinding Verado, Kyan Dillan; Riti, Yosefina Finsensia; Mongkol, Nick Engelbert
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.15919

Abstract

The durability and structural soundness of building walls are matters of concern. The process of identifying cracks in building structures takes a lot of time and effort, and is also inefficient in terms of cost and accuracy because it relies on the subjective judgment of the supervisor. The use of edge detection in detecting cracks can improve the efficiency of the process. The four algorithms selected for this research are Sobel, Kirsch, LoG, and Canny algorithms. This study aims to analyze the best algorithm in detecting cracks in the walls of building structures, using the parameters of Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) values. The results of this study show that the Canny algorithm is the best algorithm of the four algorithms used to detect cracks and also avoid graffiti, with an MSE measurement value of 112.08 and a PSNR value of 27.66 on images that have cracks on the wall, and also has an MSE measurement value of 113.94 and a PSNR value of 27.61 on images that do not have cracks.
Model Sistem Digitalisasi Dokumen Akreditasi untuk Mendukung Proses Validasi Oleh Asesor Menggunakan Metode Ekstraksi Transformasi dan Load Setiyadi, Angga; Dwiguna Sumitra, Irfan; Hardyanto, Chrismika; Reza, Donny
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.16365

Abstract

This research aims to develop a university accreditation document digitization system that facilitates the validation process by assessors and increases efficiency, transparency, and security in document management. The developed system integrates a web-based Extract, Transform, and Load (ETL) process for accreditation data, starting with uploading Excel files to Google Drive, data extraction by the admin, and data transformation for structured structuring. Test results show that the digital system is able to accelerate processing time per document from 180 seconds to 45 seconds (a 75% improvement), accelerate document title searches from 90 seconds to 10 seconds (an 88.9% improvement), and increase data accuracy from 92% to 99% (+7 percentage points). These improvements were achieved through the implementation of automated searches, standardized data formats, and automatic validation before loading data into the database. The implementation of the Extract, Transform, and Load system has been proven to minimize manual errors, increase speed, and improve the accuracy and regularity of accreditation data, making it a representative solution for modern and standardized education data management.
Implementasi BERT dan SNA dalam Sistem Tanya Jawab untuk Survei Demam Berdarah Dengue Nadifah, Rofiatun; Syaifullah, Rindra; Almaasah Zatri, Nasywaa; Putri Permata, Regita
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.16488

Abstract

As a global public health challenge, Dengue Hemorrhagic Fever (DHF) necessitates an analysis of public understanding and perception. For controlling the spread of DHF, survey data is instrumental in uncovering these perceptions. This study was conducted to perform an in-depth analysis of the public's understanding of DHF by analyzing textual responses from surveys. The analyzed data consists of answers from 33 respondents to five key questions concerning the definition, symptoms, causes, transmission, and prevention of DHF. The methods used were semantic similarity analysis with a pre-trained BERT model, cosine similarity calculation, and Social Network Analysis (SNA) to identify key respondents as opinion leaders and to map the patterns of understanding dissemination. In this model, a highly consistent level of understanding was obtained, with an F1-Score for the "Very Similar" category reaching 0.94 and a highest cosine similarity value of 0.995.
Pendekatan Naive Bayes Campuran untuk Klasifikasi Email Spam dengan Metode Machine Learning Lainnya Aditya, Bintang; Kristy Wijaya, Marchello; Prabowo, Ary
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.17166

Abstract

Nowadays, email is a communication media that is often used in the digital era, with various advantages offered by email, accompanied by the rise of email spam which can disrupt the comfort of its users and accessibility on the email service provider platform. Using manual spam filtering techniques has proven to be very time-consuming and labor-intensive, so an alternative technique is needed that can perform sorting automatically using Machine Learning. This research aims to develop a form of spam detection model that uses a mixed Naive Bayes approach that combines various forms of TF-IDF feature representation with various statistical features that can calculate message length, number of capital letters, and various number of links, and compare its performance with various other algorithm approaches consisting of Support Vector Machine, Logistic Regression, and Random Forest, this study uses a public dataset containing examples of 5,572 emails containing important emails and spam emails combined. The evaluation form will be calculated using the metrics Accuracy, Precision, Recall, F1-Score, and Training Time. The results of the experiment explain that Naive Bayes with Mixture is able to produce an accuracy of 96.4% with advantages in calculating computational efficiency, but Random Forest has the highest accuracy level reaching 97.9%. So it shows that this research proves that Naive Bayes with various mixed approaches is worthy of being applied to an Email Spam detection system that requires high speed and efficiency.
Klasifikasi Jenis Burung Berdasarkan Suara Kicau Menggunakan Ekstraksi MFCC dan BiLSTM Janah, Roikhatul; Susanto, Eko Budi; Setiawan, Tri Agus
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.17694

Abstract

Automatic bird species classification based on chirping sounds has become an important solution to support conservation efforts for Indonesia's biodiversity, which comprises 1.835 bird species. This study proposes a classification system that combines Mel-Frequency Cepstral Coefficients (MFCC) feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) architecture to identify 10 commonly found Indonesian bird species. The research dataset utilized 750 bird sound recordings from the xeno-canto.org platform, segmented into 4-second duration clips and augmented to 3,750 samples through pitch shift and time stretch techniques. MFCC feature extraction with 40 coefficients was employed to represent the spectral characteristics of bird sounds, while the BiLSTM model was selected to capture complex bi-directional temporal dependencies in bird vocal signals. In the testing process, an 80:20 data split was performed for training and testing. Confusion matrix analysis confirms the model's capability to distinguish unique characteristics of each species with minimal error rates. Research results demonstrate that the system achieved a classification accuracy of 98%. The combination of MFCC and BiLSTM proves effective for automated and sustainable biodiversity monitoring and bird conservation applications in Indonesia.
Sistem Layanan Legalisir Online Terintegrasi dengan Payment Gateway dan Jasa Ekspedisi Permadi, Hermawan Budianto; Murtadho, Mohamad Ali; Wafa, Moh. Shohibul
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.16021

Abstract

Manual legalization of academic documents often leads to various issues such as long queues, slow processing times, and a lack of transparency in application status, especially for alumni who live far from campus. This makes it even more difficult for alumni due to transportation and accomodation issues. This study aims to design and develop an online legalization service system integrated with a payment gateway and delivery services to facilitate the legalization process for alumni. The system is developed using the Laravel framework and MySQL database, and integrates digital payment services via Midtrans and document delivery through Biteship as logistic aggregators. The result of this research is a web-based online legalization service system that allows alumni to submit legalization requests, make online payments, and choose document delivery options to their destination address. System testing shows that all features run well and can improve efficiency and user convenience in managing document legalization. Thus, this system can be a digital solution that supports faster, more transparent, and accessible academic administrative services.
Penerapan Metode Decision Tree Dalam Klasifikasi Status Gizi Balita MILLANO, FIDO; Kurniawan, Sandy; Gustriansyah, Rendra
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.16468

Abstract

Early childhood represents a crucial stage in human growth and development, making nutritional monitoring essential to prevent long-term health issues such as stunting. This research focuses on developing a nutritional status classification model for toddlers by applying the Decision Tree algorithm with four categories: severely stunted, stunted, normal, and tall. The dataset, obtained from Kaggle, contains 121,000 records and includes attributes such as age, gender, and height. The study was carried out through several phases, starting with data preprocessing to handle missing values, detect outliers, and balance class distribution, followed by model training in R-Studio, and performance evaluation using accuracy, precision, recall, and F1-score. The experimental results demonstrate that the model achieved an accuracy of 89.75%, precision of 89.74%, recall of 89.83%, and an F1-score of 89.78%. The novelty of this study lies in implementing a multi-class classification approach on a large and representative dataset, integrating oversampling and parameter optimization techniques to improve predictive performance, and conducting feature importance analysis that highlights the significant influence of height and age in determining nutritional status. Therefore, this work not only provides a reliable classification model but also contributes practical insights for developing early detection systems to support stunting prevention among toddlers.
Systematic Literature Review:  Penerapan Machine Learning dalam Diagnosis dan Prediksi Penyakit Diabetes Handayani, Oktavia Putri; Purwono; Ashari, Imam Ahmad; Ardianto, Rian
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (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.v14i2.16642

Abstract

Diabetes mellitus is a chronic disease with a growing global prevalence, posing significant challenges for healthcare systems worldwide. Machine Learning (ML) offers promising solutions for early diagnosis and prediction by analyzing complex medical data efficiently. This study adopts a Systematic Literature Review (SLR) method guided by the PRISMA protocol to analyze 15 open-access articles published between 2022 and 2025 from the ScienceDirect database. These studies explore the use of various ML algorithms—including Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN)—in diagnosing diabetes. The main objective is to evaluate the effectiveness, strengths, and limitations of each algorithm in clinical applications. The review highlights current trends, performance comparisons, and challenges in implementing ML models for diabetes diagnosis. The findings are expected to provide valuable insights for researchers and practitioners aiming to develop more accurate, efficient, and applicable ML-based diagnostic systems for improved diabetes management and early intervention.

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