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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Scientific Journal of Informatics
Siregar, Amril Mutoi
Teknik Informatika, Universitas Buana Perjuangan Karawang
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Journal : Scientific Journal of Informatics

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Classification Model of Public Sentiments About Electric Cars Using Machine Learning Romadoni, Nurul; Siregar, Amril Mutoi; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.1309

Abstract

Purpose: This research compared the accuracy level of six algorithms based on the ROC method and the Confusion Matrix evaluation on data regarding public sentiments towards electric cars. Methods: Data collection was conducted for data sourced from TikTok. Next, the data underwent text preprocessing (data cleaning and case folding) and text processing (stemming, tokenizing, stopword removal, word frequency, word relation, TF-IDF, scoring, and labeling). Modeling was then conducted using supervised (labeled) algorithms consisting of the Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, K-Neighbor, and Logistic Regression. Finally, an evaluation was conducted (confusion matrix and ROC). Result: The results revealed that the Decision Tree algorithm with the Confusion Matrix and ROC evaluation obtained the highest result of 87%. The algorithm with the lowest result is KNN, which has an accuracy of 56%. The classification result for the neutral sentiment has a percentage of 57.1%, followed by negative sentiment at 26.8% and positive sentiment at 16.1%. The KNN algorithm is suitable for large and low-dimensional data, SVM is suitable for data with many features and clear separation between classes, and Naive Bayes is efficient for large datasets with many low-quality features. Additionally, the Random Forest algorithm could overcome overfitting and unbalanced data. Logistic regression is also suitable for linear data without assuming a certain distribution. The Decision Tree algorithm is good for complex data as it provides a visual explanation of predictions. In this study, the Decision Tree algorithm obtained high results because it has the best characteristics and is a linear technique. Novelty: This study found that based on the ROC method and the Confusion Matrix evaluation conducted, the Decision Tree algorithm is more accurate than the other algorithms studied.
Comparison Model Optimal Machine Learning Model With Feature Extraction for Heart Attack Disease Classification Salsa Desmalia; Amril Mutoi Siregar; Kiki Ahmad Baihaqi; Tatang Rohana
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4561

Abstract

Purpose: The purpose of this study is to classify the number of people affected by heart disease and those not affected by heart disease based on various categories of heart attack causes. This study aims to urge people to take better care of their health and to serve as a reference for doctors to educate patients about the dangers of heart attacks. Methods: The model will be constructed via a machine learning methodology. The algorithms utilized in its development encompass the Support Vector Machine (SVM) algorithm, the K-Nearest Neighbor (k-NN) algorithm, and the Random Forest (RF) algorithm.  This study utilizes principal component analysis (PCA) as a means of extracting optimized features from the dataset, employing techniques for dimension reduction prior to modeling the data. Result: Cumulative explication of the concept of variance constitutes a foundational aspect of PCA (principal component analysis) within the scope of the current research, namely a dimensionality reduction technique employed in multivariate data analysis to facilitate model development, thereby enabling the creation of more optimal and comprehensive models. In this research, the dimensions of training data are incorporated during the process of model creation.   The results show KNN model exhibits the highest performance, with an accuracy of 86%, precision of 86%, recall of 91%, and F1-score of 88%. Furthermore, evaluation using the ROC metric also provides a relatively favorable value, 0.85. Novelty: Researchers used 1190 patient data sourced from Kaggle. Before modeling the algorithm, researchers conducted EDA & Preprocessing which includes missing values to find data that does not have information, then duplicate data to find duplicated data, there are 270 duplicated data, then the duplicated data is deleted so that the data becomes 737, then PCA implementation is carried out.  PCA is reducing features automatically without changing the data.
Co-Authors Abdul Mufti Ahmad Fauzi Ahmad Fauzi Alma Hidayanti Andri Juliyanto Anton Romadoni Junior ARIF, SITI NOVIANTI NURAINI Baihaqi, Kiki Ahmad Basuni, Nursela Bunga Tiara, Vira Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Faisal, Sutan Fariz Duta Nugraha Farkhina Dwi Utari Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Hanny Hikmayanti Handayani Hartono Wijaya, Sony Hilda Yulia Novita Indi Nurul Hassanah Indra Maulana, Indra Indra, Jamaludin Jaman, Jajam Haerul Jayidan, Zirji Juwita, Ayu Ratna Koirunnisa, Koirunnisa Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Lilis Kartika Lutfiah Adeliana Maulana Abdur Rofik Maulana, Ikhsan Mulya Cahya Ramadanty Murniasih nabila, putri Nahrowi Nahrowi Nahrowi Nofita Sari Nurani Khoerunnisa Permana, Tedi Pratama, Adi Rizky Pratama, Rio Priyatna, Bayu Rahmad Nahar Siregar Rahmat Rahmat Ramadhan, Naufal Cahya Rizqi Fahrozi Rohana, Tatang Romadoni, Nurul Salsa Desmalia Santi Arum Puspita Lestari Sekar Wuni Sinta Candra Dewi Sinung Suakanto SITI NURJANAH Siti Silvia Arifin Sony Hartono Wijaya Sony Hartono Wijaya Sutan Faisal Sutan Faisal Sutan Faisal Tatang Rohana Tia Astiyah Hasan Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Tukino, Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin