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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 602 Documents
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Implementasi Data Mining untuk Estimasi Produksi Cabai menggunakan Metode Exponential Smoothing Lintang Fauziyatu Azmi; Zahrotun, Lisna
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.v15i1.8333

Abstract

Cabai merupakan komoditas hortikultura yang banyak dibudidayakan dan berpengaruh pada fluktuasi ekonomi di Kabupaten Sleman. Dalam upaya menstabilkan fluktuasi harga dan pertumbuhan ekonomi di Kabupaten sleman, maka perlu dilakukan estimasi produksi cabai untuk periode ke depan. Estimasi produksi cabai yang dilakukan dalam penelitian ini menggunakan tiga jenis metode Exponential Smoothing dengan kombinasi parameter alpha, beta, dan gamma. Penelitian ini bertujuan untuk mengembangkan model estimasi produksi cabai dengan menggunakan Single, Double, dan Triple Exponential Smoothing. Hasil penelitian ini menunjukkan bahwa Triple Exponential Smoothing adalah metode yang paling tepat digunakan untuk mengestimasi produksi cabai di masa mendatang, dengan persentase tingkat error sebesar 6.5%.
Konfigurasi Model Prophet Untuk Prediksi Harga Saham Sektor Teknologi di Indonesia Yang Akurat Santoso, Ravelino Sebastian; Sari Dewi, Findra Kartika
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.v15i1.8634

Abstract

Saham merupakan salah satu instrumen investasi yang sedang ramai dan digemari oleh masyarakat muda Indonesia. Untuk dapat meramal harga saham, dapat dilakukan analisis teknikal dengan menerapkan machine learning. Namun, untuk dapat menggunakan machine learning, diperlukan implementasi algoritma yang membutuhkan waktu panjang serta keterampilan tinggi. Maka dari itu digunakanlah model Prophet, model machine learning yang mudah untuk dikembangkan. Pengembangan dilakukan dengan menyesuaikan karakteristik data saham yang merupakan data bertipe time series. Eksperimen dilakukan untuk menemukan konfigurasi yang perlu dilakukan terhadap model dalam menghasilkan peramalan yang paling akurat. Melalui penelitian yang dilakukan, hasil terbaik yang didapatkan adalah model Prophet yang menggunakan dataset paling banyak dan juga melalui hyperparameter tuning. Hal ini dapat dibuktikan dengan visualisasi yang ada serta nilai error yang rendah, dimana MAPE (Mean Absolute Percentage Error) mempunyai nilai error sebesar 15%.
Machine Learning for Environmental Health: Optimizing ConcaveLSTM for Air Quality Prediction Diqi, Mohammad; Hamzah; Ordiyasa, I Wayan; Wijaya, Nurhadi; Martin, Benedicto Reynaka Filio
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.v15i1.8707

Abstract

This study investigates the optimization of the ConcaveLSTM model for air quality prediction, focusing on the interplay between input sequence lengths and the number of LSTM units to enhance forecasting accuracy. Through the evaluation of various model configurations against performance metrics such as RMSE, MAE, MAPE, and R-squared, an optimal setup featuring 50 input steps and 300 neurons was identified, demonstrating superior predictive capabilities. The findings underscore the critical role of model parameter tuning in capturing temporal dependencies within environmental data. Despite limitations related to dataset representativeness and environmental variability, the research provides a solid foundation for future advancements in predictive environmental modeling. Recommendations include expanding dataset diversity, exploring hybrid models, and implementing real-time data integration to improve model generalizability and applicability in real-world scenarios.
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.
Mango and Banana Ripeness Detection based on Lightweight YOLOv8 Raymond Erz Saragih; Akhmad Rezki Purnajaya; Ilwan Syafrinal; Yonky Pernando; Yodi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.8895

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9086

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 Ferhati Bouchra; I Made Agus Dwi Suarjaya; Ni Kadek Dwi Rusjayanthi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9118

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.
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

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

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%.
Optimizing Book Delivery Routes Using Genetic Algorithms: Case Study of Erlangga Publisher Yogyakarta Branch Damba Saputra Rosa; Asep Setiyono; Yohanes Renaldi Rio Santana; Paulus Mudjihartono
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9371

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.

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