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Kota yogyakarta,
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INDONESIA
Jurnal Buana Informatika
Published by Universitas Atma Jaya Yogyakarta
ISSN : 20872534     EISSN : 20897642     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 594 Documents
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Male Fertility Classification using Machine Learning and Oversampling Techniques Pradnya Sidhawara, Aloysius Gonzaga
Jurnal Buana Informatika Vol. 15 No. 01 (2024): Jurnal Buana Informatika, Volume 15, Nomor 01, April 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i1.8718

Abstract

Machine learning methods have been applied to male fertility diagnosis in recent years. Through early infertility case detection, this technology application offers potential benefits to the medical field. This study presents an experimental investigation that examines the prospect of using the oversampling technique and feature selection to enhance the performance of shallow classifiers to classify male fertility on the Fertility Dataset. Two oversampling techniques (SMOTE and ADASYN), two different scalers (MinMax and Standard), and two different feature selection methods (SelectKBest and SelectFromModel) were used to improve the performance of the classifier. The results show that the performance of machine learning models is better on the oversampled dataset than the original dataset. Random Forest performed best on the SMOTE test set with 90% accuracy, 89% and 100% Recall in Normal and Altered classes, respectively. Accidents or trauma, Age, and High Fevers features are selected by SelectKBest, and considered as factors that contribute to male fertility in prior studies.
Gamified Distance Learning Application Design for Enhanced Student Engagement and User Experience Putra Prakasa, Fedelis Brian; Samodra, Joseph Eric; Purnomo Sidhi, Thomas Adi
Jurnal Buana Informatika Vol. 15 No. 01 (2024): Jurnal Buana Informatika, Volume 15, Nomor 01, April 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i1.8737

Abstract

Distance Learning in Indonesia is one of the learning methods that began to be applied during the Covid-19 pandemic. Yet students face some obstacles, such as lack of motivation, struggling with operating learning devices, difficulty maintaining focus, and student engagement during the learning process. Gamification offers a solution to these problems by significantly enhancing user motivation and engagement, as it has been tested in research to have a profound impact. Therefore, this study aims to design a mobile application for Distance Learning by implementing gamification. It employs qualitative and quantitative data, including 32 students' responses from questionnaires like UEQ-S, utilized for testing user interface, and UES-SF, employed for testing gamification elements. By implementing gamification in this design, an engagement score of 83% was obtained, and the overall UEQ-S result was 1.89 in the Excellent category.
Penerapan Optical Character Recognition untuk Pengenalan Variasi Teks pada Media Presentasi Pembelajaran Nugraha, Kristian Adi
Jurnal Buana Informatika Vol. 15 No. 01 (2024): Jurnal Buana Informatika, Volume 15, Nomor 01, April 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Media digital merupakan bentuk utama media pembelajaran yang banyak digunakan untuk kegiatan belajar mengajar di kelas saat ini. Media pembelajaran digital umumnya tersimpan dalam bentuk citra karena memiliki unsur visual di dalamnya. Salah satu kelemahan data dalam bentuk citra adalah seluruh isi di dalamnya dianggap sebagai gambar, sementara pada media pembelajaran juga terdapat unsur teks di dalamnya. Oleh karena itu, dibutuhkan metode OCR untuk membaca teks di dalamnya agar media tersebut dapat diolah lebih lanjut, misalnya untuk keperluan kategorisasi (indexing) atau untuk dibaca pada sistem lain seperti chatbot. Umumnya, metode OCR digunakan untuk mengenali tulisan dengan bentuk yang seragam pada sebuah citra. Sedangkan pada media pembelajaran, teks di dalamnya memiliki variasi yang berbeda-beda. Penelitian ini mencoba menerapkan metode OCR dengan menggunakan Tesseract untuk menguji 30 data media pembelajaran yang memiliki berbagai macam variasi teks dalam sebuah citra. Hasil pengujian menunjukkan tingkat akurasi pengenalan teks yang cukup baik, yaitu sebesar 91,11%.
Mango and Banana Ripeness Detection based on Lightweight YOLOv8 Saragih, Raymond Erz; Purnajaya, Akhmad Rezki; Syafrinal, Ilwan; Pernando, Yonky; Yodi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Fruits like bananas and mangoes are harvested after reaching a specific ripeness stage. Traditionally, farmers rely on manual inspection to determine ripeness, a process that can be tedious, time-consuming, expensive, and subjective. This work proposes an automatic bananas and mangoes ripeness detector utilizing computer vision technology. The detected bananas and mangoes fall into two classes: ripe and unripe. The state-of-the-art YOLOv8 architecture serves as the core of the detector. Three YOLOv8 variants, YOLOv8n, YOLOv8s, and YOLOv8m, were investigated for their performance. Results show that YOLOv8s achieved the highest overall performance, 0.9991 recall, and a mean Average Precision (mAP) of 0.8897. While YOLOv8m achieved the highest precision of 0.9995, YOLOv8n is the most miniature model, making it suitable for deployment on devices with limited resources.
Identifikasi Kendaraan Beroda Menggunakan Algoritma YOLOv5 Michael
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

The importance of traffic density measurement in road planning has led to efforts in automation using object detection algorithms, particularly YOLO (You Only Look Once), which are replacing error-prone and time-consuming manual processes. However, challenges arise in dense traffic conditions, posing a challenge to vehicle detection accuracy. This research aims to compare the performance of vehicle detection between two YOLO approaches: multi-view layer detection and conventional detection, focusing on YOLOv5n, YOLOv5s, and YOLOv5m. The literature review encompasses Computer Vision, YOLO implementation, and related research to provide conceptual context. The research method details the steps of vehicle identification using YOLOv5, and the evaluation includes the performance of various YOLO variants and multi-view detection approaches. Thus, this study is expected to gain deeper insights into building an effective model and facilitating the selection of a suitable YOLO model for vehicle detection.
Analisis Sentimen Masyarakat terhadap Tayangan Televisi Nasional menggunakan Metode Deep Learning Bouchra, Ferhati; Suarjaya, I Made Agus Dwi; Rusjayanthi, Ni Kadek Dwi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Indonesia’s television industry faces fierce competition, particularly in chasing ratings and ad revenue. This has ultimately led to declining broadcast quality on some national TV stations. This research aims to understand perceptions towards content quality by focusing on public opinion through sentiment analysis of social media (Twitter) using Bi-LSTM and Word2Vec methods. The research involved data collection, preprocessing, vectorization, data splitting, model training and testing, evaluation to find the best model, sentiment data classification, and finally, sentiment data analysis. Using a dataset of 515,492 sentiment points, the model achieved an accuracy of 96.4%, precision of 72.1%, recall of 72.0%, and f1-score of 72.8%. Analysis of Twitter user sentiment leans towards neutral and positive perceptions. The results of the sentiment analysis of Twitter users tend to be neutral and positive. The results of the public satisfaction trend show a change in the pattern of public satisfaction with the quality of television station content.
Optimizing Book Delivery Routes Using Genetic Algorithms: Case Study of Erlangga Publisher Yogyakarta Branch Rosa, Damba Saputra; Setiyono, Asep; Santana, Yohanes Renaldi Rio; Mudjihartono, Paulus
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Optimizing Book Delivery Routes Using Genetic Algorithms: Case Study of Erlangga Publisher Yogyakarta Branch. This research aims to find the shortest route for book delivery using the Traveling Salesperson Problem (TSP) approach that is solved by a Genetic Algorithm (GA). The distance between the pair of locations will be known by using the longitude and latitude as the coordinates of the location (the place where books must be dropped and the trip continues). This network of the coordinates of locations is then viewed as TSP, which needs GA to solve the shortest path. Running the program for up to 100 iterations, this study resulted in the shortest route, 356 km in a whole route. Among the previous research, this research has its uniqueness, especially when the problem is viewed as a TSP, and when it comes to the crossover mechanism, it is quite rare. Moreover, the case of the Erlangga publisher is the first case that has used the GA.
Design and Implementation of Load Balancing for Quality of Service Improvement Widiasari, Indrastanti Ratna; Efendi, Rissal
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

At the Information Technology Faculty, Satya Wacana Christian University, load balancing systems are implemented where the web server serves 500 users. This is to prevent server overload or downtime during simultaneous access to the web server. Test results indicate significant differences in CPU usage, request time, and bandwidth between load balancing and single servers. The use of load balancing is more effective than relying on a single server, as evidenced by test results. The CPU usage with load balancing is significantly lower, with a difference of up to 45% compared to a single server. The request time with load balancing is also slightly better, with only 21.5ms compared to 42ms for a single server. However, the difference in bandwidth between load balancing and a single server is not very significant. The highest bandwidth recorded on a single server is 182kb/s, while with load balancing it reaches 165kb/s.
Prediksi Penyakit Batu Ginjal dengan Menerapkan Convolutional Neural Network Waluyo Poetro, Bagus Satrio; Mulyono, Sri; Vani Aulia Pramesti
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Kidney stones are a health problem that requires intensive treatment. If the disease is not treated quickly, it can lead to impaired kidney function and complications to other organs. Computerized Tomography Scan (CT Scan) with high resolution is used to scan the human body for disease diagnosis. The doctor will explain the diagnosis within a few days or one week. This research aims to create a prediction model for the classification of kidney stone disease through CT Scan images by applying the Convolutional Neural Network (CNN) method of DenseNet-121 architecture and deployment using Streamlit. The results of the model in this study with the application of CNN DenseNet-121 architecture are accuracy 98.18%, precision 96.36%, recall 100%, and F1-score 98.14%.
Sistem Penjadwalan Karyawan dengan Algoritma Genetika Fajarlestari, Maria Karmelia; Hardiyanti, Mawar
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Employee scheduling is a complex problem in Human Resource Management (HRM) that significantly impacts operational efficiency. This study develops an employee scheduling system using a genetic algorithm. The employee schedules are constructed by considering scheduling rules and various components such as the number of days, shifts, employee quality, and scheduling requests. The genetic algorithm, proven effective in solving various optimization problems, is employed to generate optimal schedules through the processes of selection, crossover, and mutation. The results indicate that the genetic algorithm can effectively produce employee schedules, with fitness values indicating improved schedule quality as iterations increase. The findings of this study are anticipated to be useful in HRM, aiming to improve both employee efficiency and satisfaction.

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