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All Journal Jurnal Rekayasa Sistem Industri PRISMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Conference on Innovation and Application of Science and Technology (CIASTECH) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jusikom: Jurnal Sistem Informasi Ilmu Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Scientific Journal of Informatics
Kusumaningrum, Dwi Sulistya
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Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Terhadap Ulasan Pengguna Aplikasi Mypertamina Menggunakan Confusion Matrix Syahril, Ade; Cahyana, Yana; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5639

Abstract

The large number of vehicles in Indonesia makes fuel oil (BBM) very important, especially for cars and motorbikes. The Indonesian government works closely with PT Pertamina Persero and requires transactions using the MyPertamina application to ensure that fuel subsidies are properly targeted. However, the MyPertamina app has received mixed feedback and criticism from users, such as complaints about frequent bugs, instability of the app during use and difficulties in the registration or login process. User feedback on the app has been both positive and negative. Users also provided their ratings and reviews on the Google Play Store. The purpose of this research is to analyse the opinions of MyPertamina application user comments and compare the accuracy of the Decision Tree and K-Nearest Neighbor algorithms. This research includes scraping, text preprocessing, weighting, algorithm implementation and evaluation. The data used was obtained from Google Play Store as much as 10,000 data based on the latest reviews, after data cleaning such as removing duplicate data and missing values obtained 8,072 reviews. The data is then grouped into positive classes (2,506 reviews) and negative classes (5,566 reviews), with more negative data. The classification results using the Decision Tree and K-NN methods, it is known that the Decision Tree method has a higher accuracy of 83%, while K-NN method is 58%. This finding indicates that the Decision Tree method is more effective in analysing user reviews of the MyPertamina application compared to the K-NN method.
Mengintegrasikan Prinsip Pembangunan Berkelanjutan dalam Pembelajaran Matematika untuk Merangsang Keterampilan Berkelanjutan pada Generasi Mendatang Kusumaningrum, Dwi Sulistya; Lestari, Santi Arum Puspita; Nurapriani, Fitria; Dwi Sulistya
Jurnal Rekayasa Sistem Industri Vol. 13 No. 1 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i1.7167.1-10

Abstract

This research constitutes a literature review employing a qualitative approach, analyzing scholarly articles, books, and other documents related to sustainable development. This article aims to summarize and analyze previous studies concerning sustainable development in the context of mathematics education, as well as strategies that can be employed to integrate the principles of sustainable education. Integrating the principles of sustainable development into education, including mathematics education, is crucial in fostering a more environmentally responsible society and promoting sustainability across all sectors. However, its implementation remains limited. Educators face various challenges, including a lack of time, resources, and understanding of sustainable education, along with a dearth of supportive teaching materials. The principles of sustainable development can serve as a framework for developing curricula and teaching practices that are more sustainable. Educators can select mathematical problems related to environmental or social issues, discuss relevant mathematical concepts in connection with these problems, and help students comprehend the impact of mathematical decisions on the environment and society. Integrating the principles of sustainable development into mathematics education not only aids in producing a generation with sustainable skills but also motivates students to learn mathematics in more engaging and meaningful ways. A learning approach centered around sustainable development can be an effective way to prepare students for a sustainable future. The article also underscores the necessity for curriculum development, training, and professional advancement for educators.
Analisis Perbandingan Algortima Support Vector Machine, Random Forest dan Naive Bayes Untuk Prediksi Penyakit Kanker Paru-Paru Rizky, Rendy Alfa; Fauzi, Ahmad; Kusumaningrum, Dwi Sulistya; Novita, Hilda Yulia
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9611

Abstract

The lungs are one of the vital organs responsible for the processes of respiration and blood circulation, with smoking habits being the primary factor contributing to the development of lung cancer. In Indonesia, the prevalence of this disease continues to increase, placing it eighth in the Southeast Asian region. Globally, lung cancer accounts for approximately 11.6% of all cancer cases and 18% of total cancer-related deaths.This study aims to analyze and compare the performance of Support Vector Machine (SVM), Random Forest, and Naïve Bayes algorithms in predicting lung cancer, as well as to determine the best-performing algorithm based on accuracy, precision, and recall metrics. The study utilizes the Lung Cancer Prediction dataset obtained from Kaggle, consisting of 309 instances and 16 attributes. The approach involves the implementation of three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The research process includes data collection, preprocessing, data transformation, feature selection, model development, and evaluation using a confusion matrix. The experimental results show that both SVM and Naïve Bayes achieve the same accuracy of 91.07%, while Random Forest obtains an accuracy of 89.28%. In terms of evaluation metrics, SVM demonstrates more consistent performance with a precision of 95% and recall of 93%, whereas Naïve Bayes shows a higher recall of 95% with a precision of 93%. On the other hand, Random Forest exhibits limitations in identifying non-cancer cases. Based on the overall results, SVM is considered the most optimal method as it provides a better balance of performance. This study indicates that machine learning has significant potential as a supporting tool for early detection of lung cancer in a more accurate and efficient manner.
Co-Authors Al Fathir Rizal Januar Baihaqi, Kiki Ahmad Dwi Sulistya Faisal, Sultan Faisal, Sutan Fauzi Ahmad Muda Hakim, Mirwan Abdurrahman Indra, Jamaludin Jamal, Malikil Lestari, Santi Arum Puspita Novita, Hilda Yulia Nurapriani, Fitria Ridwan Maulana, Ridwan Rizky, Rendy Alfa Rohana, Tatang Romadoni, Nurul Siregar, Amril Mutoi Syahril, Ade Vandelweiss, Dita Aura Yana Cahyana