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All Journal Jurnal Simetris Telematika Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pengembangan Rekayasa dan Teknologi Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Journal of Computer and Information Systems Ampera Jurnal Pengabdian Masyarakat Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mitra Masyarakat (JPMM) Tematik Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal DIMASTIK
Rastri Prathivi
Universitas Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Building of Informatics, Technology and Science

 1 
Perbandingan Algoritma Support Vector Machine dan Decision Tree untuk Klasifikasi Performa Perusahaan Utomo, Mario; Prathivi, Rastri
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5278

Abstract

The number of stock exchange investors in Indonesia reached 5.34 million by the end of December 2023. This figure is dominated by millennial generation investors, indicating a growing confidence in the fundamentals and economic prospects of the Indonesian capital market. However, the lack of financial literacy among this generation often results in ineffective and high-risk investments. Many millennials choose stocks based on short-term trends or recommendations that lack analysis. To address this issue, a more structured approach to stock selection is required. One method that can be employed is the classification of a company's performance based on its performance using various financial indicators and ratios. As the performance of a company affects the movement of its stock value, this research will compare Support Vector Machine and Decision Tree with the One Against All approach in classifying company performance. The features used for the classification of company performance consist of three financial ratios: profitability (ROA), liquidity (CR), and leverage (DER). The labels or targets in the classification are divided into three categories: normal, good, and unfavorable. This research will consider evaluations such as accuracy, cross validation, and confusion matrix. The results of the Support Vector Machine (SVM) algorithm demonstrated an accuracy of 86.67%, while the Decision Tree (DT) algorithm exhibited an accuracy of 93.33%. Consequently, the DT algorithm produced more accurate results than the SVM algorithm in classification. The number of stock exchange investors in Indonesia reached 5.34 million by the end of December 2023. This figure is dominated by millennial generation investors, indicating a growing confidence in the fundamentals and economic prospects of the Indonesian capital market. However, the lack of financial literacy among this generation often results in ineffective and high-risk investments. Many millennials choose stocks based on short-term trends or recommendations that lack analysis. To address this issue, a more structured approach to stock selection is required. One method that can be employed is the classification of a company's performance based on its performance using various financial indicators and ratios. As the performance of a company affects the movement of its stock value, this research will compare Support Vector Machine and Decision Tree with the One Against All approach in classifying company performance. The features used for the classification of company performance consist of three financial ratios: profitability (ROA), liquidity (CR), and leverage (DER). The labels or targets in the classification are divided into three categories: normal, good, and unfavorable. This research will consider evaluations such as accuracy, cross validation, and confusion matrix. The results of the Support Vector Machine (SVM) algorithm demonstrated an accuracy of 86.67%, while the Decision Tree (DT) algorithm exhibited an accuracy of 93.33%. Consequently, the DT algorithm produced more accurate results than the SVM algorithm in classification.
Implementasi K-Means Untuk Pengelompokan Makanan Cepat Saji Bagi Penderita Penyakit Obesitas Pradvenanta, Yoannes Dion; Prathivi, Rastri
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5279

Abstract

One in eight people in the world lives with obesity, a statistic that is worrying as it shows a significant increase compared to 1990. Obesity in adults has more than doubled, and obesity among adolescents and children has quadrupled. One factor in obesity is poor food quality. A lot of people who don't pay much attention to the quality of their food one of them is eating fast food because fast food consumption can be said to be good if the meal frequency is 1 time a week, if more than that and excess is said not good. Thus, there is a need for a fast food grouping model that helps obese people choose fast foods. The K-means algorithm is one of the ideal models for grouping fast foods. The results of the analysis using the elbow method show k=5, then consider three evaluations against the k=5 value: Sum Square Error (SSE), Silhouette Score, and Davies Bouldin Index (DBI). The results were data segmented taking into account the negative and positive nutrient content for obese patients. The data segmentation results found a fairly healthy cluster on label_0 with 244 data and an unhealthy cluster in label_2 with 25 data. From the cluster label_0, 244 of the data could be a healthy fast food choice for obesity patients
Komparasi Metode BERT, VADER, dan RoBERTa untuk Analisis Sentimen Masyarakat terhadap Keputusan Pasangan Nurdewanti, Debi Safa; Prathivi, Rastri
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6306

Abstract

This research discusses the phenomenon of childfree in Indonesia, which is increasingly being discussed in line with social and economic changes. Although a negative stigma is still attached to a couple's decision not to have children, public awareness of the childfree option continues to increase. This study aims to analyze public sentiment towards childfree decisions using three sentiment analysis methods, namely BERT, RoBERTa, and VADER. The analysis results show that the BERT method has the highest accuracy of 99%, signaling its ability to classify sentiment very accurately. In contrast, the RoBERTa and VADER methods show lower accuracy, at 50% and 41% respectively. Both methods had difficulty in distinguishing the sentiment classes, which resulted in many misclassifications. Evaluation using the confusion matrix shows that RoBERTa and VADER have a significant number of misclassifications, with RoBERTa having 9 FPs and 19 FNs, and VADER having 16 FPs and 84 FNs. Meanwhile, BERT has almost no errors in classification, with a total FP of 0 and FN of 1. These results confirm that the BERT method is superior for sentiment analysis of the childfree phenomenon compared to the RoBERTa and VADER methods. This research provides insight into how people view the childfree phenomenon and finds the best sentiment analysis method among the three methods.
Co-Authors Alauddin Maulana Hirzan Andrianto, Diky April Firman Daru Astrid Novita Putri, Astrid Atmoko Nugroho Cholil, Saifur Rohman Ciptandini, Madyantari Ipmas Dewanti, Tita Risa Edi Widodo Edi Widodo, Edi Elfitrianna, Ikka Ayu Febrian Wahyu Christanto Galet Guntoro Setiaji Hadi, Soiful Hanif, Mohammad Burhan Hetty Catur Ellyawati, Hetty Catur karunia, windy Lenny Margaretta Huizen Liu, Meifang Mar’atuzzulfa, Salma Nurdewanti, Debi Safa Pradvenanta, Yoannes Dion Qurinta Shinta Rahmaeda, Shafara Rastri Prathivi Rosidah, Nafiati Saifur Rohman Cholil Sara Sharifzadeh Siti Asmiatun, Siti Soiful Hadi Sri Handayani Susanto Susanto Titin Winarti Titis Handayani Tria Ardianita Tunggal, Anisa Utomo, Mario Victor Gayuh Utomo Windy Yunita Kurniawati, Yunita Zalzabila, Niken