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All Journal Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) Jurnal TAM (Technology Acceptance Model) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues Jurnal TAM (Technology Acceptance Model)
Edi Faizal
Prodi Rekayasa Perangkat Lunak Aplikasi, FTI, Universitas Teknologi Digital Indonesia
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Model Rekomendasi Wisata dengan Pendekatan Collaborative Filtering Thomas Edyson Tarigan; Edi Faizal; Sumiyatun
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 21 No 2 (2023): Mei 2023
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v21i2.18

Abstract

Tingkat popularitas sebuah kawasan wisata ditandai dengan banyaknya ulasan, berita dan informasi yang tersebar. Perkembangan IT dan perubahan gaya hidup dalam penggunaan media digital bagi wisatawan dapat berakibat positif dan juga negatif. Tak jarang justru membuat kebingungan dalam menentukan pilihan terbaik sesuai keinginan dan kebutuhan. Salah satu cara yang dapat membantu wisatawan dalam memilih objek wisata adalah dengan menggunakan sistem rekomendasi (Recommender System/RSs). Penelitian ini akan mengembangkan model rekomendasi dengan pendekatan collaborative filtering. Teknik yang digunakan untuk memberikan rekomendasi menggabungkan konsep user base collaborative filtering (ub-cf). Penggunaan data dalam memberikan rekomendasi melibatkan data rating yang diberikan user terhadap objek wisata. Rekomendasi diberikan berdasarkan preferensi dan keinginan pengguna yang bersifat personal (personalized). Hasil penelitian ini berupa model yang dapat diimplementasikan pada sebuah aplikasi recommender system guna memudahkan wisatawan dalam memilih tempat wisata sesuai dengan preferensinya
ANALISIS KETUNTASAN PEMBELAJARAN ONLINE PADA MATAKULIAH BAHASA INGGRIS Edi Faizal; Andhina Ika Sunardi
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 19 No 2 (2021): Mei 2021
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v19i2.56

Abstract

The pandemic due to the corona virus at the end of 2019 (covid-19) has caused changes in the order of life, including education. Social and physical distancing in tackling the spread of Covid-19 affect the learning model. Starting from early childhood education to the university level applying an online learning model. This study analyzes the completeness of the online model of learning English. The analysis was carried out by using profile matching method on 430 student data from three universities in Yogyakarta. Completeness of learning is divided into 5 levels based on 4 components. The results showed that the level of completeness of online learning in all areas of higher education was 89.1%. The analysis of the results shows that the level of learning completeness of IT students is better than non-IT students, respectively 90.5% (IT) and 88.6% (Non-IT).
PERANCANGAN AUGMENTED REALITY SEBAGAI MEDIA PENGENALAN BENDA BUDAYA Sudarmanto; Edi Faizal
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 18 No 3 (2020): September 2020
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v18i3.60

Abstract

Augmented reality (AR) as a technology enabler is becoming very popular in various fields such as education, design, navigation, and medicine. The aim of AR is to develop technology that allows real-time integration of digital content created by computers with the real world. Until now the development and introduction of tourism, especially sites and cultural objects in the village of Pleret is still not optimal. On the other hand, learning models in the introduction of culture and cultural objects that are commonly used today still use textbooks containing text and still images. Students tend to prefer playing gadged or mobile phones rather than learning with conventional methods because they tend to be boring and unattractive. One solution to overcome these problems is to use learning media that can display 3D objects in real-time. The application of technology using AR is expected to be able to provide motivation and enthusiasm for learning, so that the learning process becomes fun and the results of guarded learning will increase. This research will design an augmented reality technology design for the introduction of cultural objects in Pleret Village as an interesting learning media. The results of this study are in the form of AR application design which can be an interesting alternative learning media for introducing cultural objects in addition to being a means of cultural preservation as well as being a promotional media.
ANANLISIS POTENSI WISATA DENGAN METODE SMART BERDASARKAN PENDEKATAN COMMUNITY- BASED TOURISM Sudarmanto; Edi Faizal
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 17 No 3 (2019): September 2019
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v17i3.70

Abstract

The application of the Smart Village concept involves many stakeholders and policies that accommodate all interests in the region. The development of tourist attractions in the village of Pleret is still not maximal because the priorities are not yet directed. This study will develop a computer-based system to support the analysis of priority tourism potential. The method of data collection uses questionnaires to obtain data sampling of several tourist objects. The analysis was carried out using the SMART method based on the criteria for the Community-Based Tourism approach. Based on the stages of the research that have been carried out and the results of system testing it can be concluded that, the developed system can be used to analyze tourism potential. The use of SMART method can determine the final result in the form of feasibility ranking based on the criteria value of an object. System testing shows that manual calculations and system calculations are the same, so that the system developed successfully and validly in analyzing input data and can be used as a tool to evaluate the potential of tourism objects.
A Machine Learning Perspective on Daisy and Dandelion Classification: Gaussian Naive Bayes with Sobel Suhendra, Christian Dwi; Najwaini, Effan; Maria, Eny; Faizal, Edi
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.112

Abstract

This study explores the classification of Daisy and Dandelion flowers using a Gaussian Naive Bayes classifier, enhanced by Sobel segmentation and Hu moment feature extraction. The research adopted a quantitative approach, utilizing a balanced dataset of Daisy and Dandelion images. The Sobel operator was employed for image segmentation, accentuating the floral features crucial for classification. Hu moments, known for their invariance to image transformations, were extracted as features. The Gaussian Naive Bayes algorithm was then applied, with its performance evaluated through a 5-fold cross-validation process. The results exhibited moderate accuracy, with the highest recorded at 60%, and precision peaking at 62.60%. These findings indicate a reasonable level of effectiveness in distinguishing between the two species, though variations in performance metrics suggested room for improvement. The study contributes to the field of botanical image classification by demonstrating the potential of integrating image processing techniques with machine learning for flower classification. However, it also highlights the limitations of the Gaussian Naive Bayes approach in handling complex image data. Future research directions include exploring more advanced machine learning algorithms and expanding the feature set to enhance classification accuracy. The practical implications of this research extend to ecological monitoring and agricultural studies, where efficient and accurate plant classification is vital
Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties Adi Pratama, I Putu; Jullev Atmadji, Ery Setiyawan; Purnamasar, Dwi Amalia; Faizal, Edi
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.124

Abstract

This study explores the application of a voting classifier, integrating Decision Tree, Logistic Regression, and Gaussian Naive Bayes models, for the multiclass classification of dry bean varieties. Utilizing a dataset comprising 13,611 images of dry bean grains, captured through a high-resolution computer vision system, we extracted 16 features to train and test the classifier. Through a rigorous 5-fold cross-validation process, we assessed the model's performance, focusing on accuracy, precision, recall, and F1-score metrics. The results demonstrated significant variability in the classifier's performance across different data subsets, with accuracy rates fluctuating between 31.23% and 96.73%. This variability highlights the classifier's potential under specific conditions while also indicating areas for improvement. The research contributes to the agricultural informatics field by showcasing the effectiveness and challenges of using ensemble learning methods for crop variety classification, a crucial task for enhancing agricultural productivity and food security. Recommendations for future research include exploring additional features to improve model generalization, extending the dataset for broader applicability, and comparing the voting classifier's performance with other ensemble methods or advanced machine learning models. This study underscores the importance of machine learning in advancing agricultural classification tasks, paving the way for more efficient and accurate crop sorting and grading processes.
Metode Modified Weighted Minkowski Untuk Pengembangan Sistem Penalaran Berbasis Kasus Faizal, Edi
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 7 No 1 (2017): March 2017
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (378.673 KB) | DOI: 10.33020/saintekom.v7i1.27

Abstract

Knowledge acquisition process is not easy, because of the different levels of expertise even though all true. Computer experts had tried other methods to resolve the problem of the acquisition, which is known as case-based reasoning. Representation of knowledge in CBR is a collection of previous case. This research focus is the application of CBR for diagnosing womb diseases. The level of similarity is calculated by using the modified weighted Minkowski. Methods of data collection are interviews, observation and study of literature. The test results show the system can be recognize the womb disease correctly is 94.44% (sensitivity), specitifity rate of 57.14%, PPV of 85.00% and 80.00% NPV. The system have an accuracy rate of 84.00% with an error rate of 16.00%.
Effectiveness Evaluation of the RandomForest Algorithm in Classifying CancerLips Data Siti Khomsah; Edi Faizal
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.84

Abstract

Lip cancer, though less commonly discussed, remains a significant concern in the realm of oncology. Early detection and diagnosis are paramount for improved patient outcomes. This research evaluated the effectiveness of the RandomForest algorithm in classifying the CancerLips dataset, a collection of lip images processed using the Canny segmentation method and described using Hu moments. Using a 5-fold cross-validation approach, the algorithm achieved an average accuracy of approximately 70.96%. The results highlight the potential of machine learning techniques, specifically RandomForest, in aiding lip cancer detection. However, the choice of preprocessing methods and feature extraction plays a crucial role in determining the outcome. The study underscores the need for further research, focusing on algorithm optimization and comparisons with other datasets or feature extraction methods, to enhance diagnostic precision in medical imaging.
Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset Azdy, Rezania Agramanisti; Syam, Rahmat Fuadi; Faizal, Edi; Sumiyatun, Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.96

Abstract

In this study, titled "Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset," we explore the effectiveness of the Bagging Meta-Estimator in diagnosing lung diseases, focusing on the challenges of imbalanced datasets. Utilizing a dataset segmented and characterized by Hu moments and encompassing categories of Normal, Bacterial Pneumonia, and Tuberculosis, the algorithm's performance was assessed through a 5-fold cross-validation. Results indicated moderate effectiveness with an average accuracy of 60.574%, precision of 60.749%, recall of 59.753%, and F1-Score of 59.416%, highlighting variable performance across folds. These findings suggest that while the Bagging Meta-Estimator has potential in medical imaging, further refinement is needed for consistent and reliable lung disease detection, especially in imbalanced datasets.
Geographic Information System untuk Pemetaan Wisata Budaya Iskandar, Edi; Kusjani, Adi; Faizal, Edi
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 22 No 3 (2024): September 2024
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v22i3.146

Abstract

Penelitian ini mengembangkan model pemetaan potensi wisata budaya berbasis  GIS di Kabupaten Bantul untuk mendukung pengembangan pariwisata berkelanjutan. Data primer dikumpulkan melalui survei lapangan dan wawancara dengan pemangku kepentingan, sedangkan data sekunder berasal dari dokumen resmi dan literatur ilmiah. Data dianalisis menggunakan perangkat lunak serta pustaka Python seperti Folium, GeoPandas, dan Shapely. Hasilnya divisualisasikan dalam peta interaktif yang menampilkan lokasi wisata budaya beserta informasi terkait. Peta interaktif yang dihasilkan memiliki akurasi tinggi, dengan 95% data koordinat dalam margin kesalahan kurang dari 10 meter. Waktu muat rata-rata peta adalah 3 detik, dengan peningkatan hingga 5 detik untuk peta dengan lebih dari 20 marker. Evaluasi oleh 25 pengguna menunjukkan tingkat kepuasan rata-rata 4,4 dari 5, dengan skor tertinggi untuk manfaat edukatif (4,5).
Co-Authors Alfian, Muhammad Arif Andhina Ika Sunardi Andhina Ika Sunardi Christian Dwi Suhendra, Christian Dwi Dedi Rosadi Edi Iskandar, Edi Effan Najwaini Gusti Ahmad Syaripudin Hera Wasiati I Putu Adi Pratama Iskandar, Edi Jati, Agam Saka Jullev Atmadji, Ery Setiyawan Khabib Mustofa Kurniyati, Nany Noor Maria, Eny Pulut Suryati Purnamasar, Dwi Amalia Rezania Agramanisti Azdy, Rezania Agramanisti Shimbun, Annisa Fikria Siti Khomsah, Siti Sri Redjeki Sri Setyowati Sudarmanto Sumiyatun Sumiyatun Syam, Rahmat Fuadi Thomas Edyson Tarigan Totok Suprawoto Totok Suprawoto Yagus Cahyadi Yasinta Marto Enga