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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jambura Journal of Mathematics Inferensi Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Mathematics, Computation and Statistics (JMATHCOS)
Mujiati Dwi Kartikasari
Department Of Statistics, Universitas Islam Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments Amimah Shabrina Putri Prasmono; Mujiati Dwi Kartikasari
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23591

Abstract

Childfree is a condition in which a person or couple decides not to have children in marriage. Childfree became popular in Indonesia when YouTuber and influencer Gita Savitri uploaded an Instagram story about it. This brought many pros and cons among the people towards the freedom to have children. Many TV broadcasts and YouTube videos cover this phenomenon. Several YouTube channels that broadcast this phenomenon are Menjadi Manusia and Analisa Channel. We collect YouTube comment data using web scraping techniques. From September 2021 to September 2022, 674 sample data points were obtained from two YouTube videos. Data is labelled (positive, negative, and neutral) using the Indonesian language lexicon approach as well as the Support Vector Machine (SVM) and Random Forest algorithms to determine the best model for classifying YouTube comments. The purpose of this research is to understand the public's perception of childfree and to compare the accuracy and AUC values of the two methods. Based on the results of the analysis, 128 comments are classified as positive, the remaining 39 comments are classified as neutral, and 503 comments are classified as negative. This shows that that the commentators on YouTube do not support or give a negative stigma to people who adhere to childfree. The solution to the balanced data problem for each sentiment class uses the random oversampling (ROS) approach. The RBF kernel SVM classification algorithm is a suitable method for classifying commentary data with an accuracy of 98.01% and an AUC of 98.58%, while the Random Forest algorithm only obtains an accuracy of 94.37% and an AUC of 95.87%.
Efektivitas Metode Hibrida ARIMA-MLP untuk Peramalan Nilai Tukar Petani Saffanah Nur Elvina Mulyawati; Mujiati Dwi Kartikasari
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23944

Abstract

The agricultural sector remains a crucial pillar of Indonesia's economy, making the most significant contribution. Still, the situation of farmers, primarily the elderly, indicates physical limitations and low income leading to high poverty levels, coupled with fluctuations in the Farmer Exchange Rate (FER) annually tending to decline in D.I. Yogyakarta, indicating losses due to increased production costs. This research aims to assess the effectiveness of the Hybrid Autoregressive Integrated Moving Average (ARIMA) – Multilayer Perceptron (MLP) method in forecasting NTP in D.I. Yogyakarta. This is based on the analysis of comparing the accuracy values of forecasts using Mean Absolute Percentage Error (MAPE) evaluation or through visualizing the forecast graphs generated between the ARIMA and Hybrid ARIMA-MLP methods. The combination (hybrid) of ARIMA and MLP methods addresses the complexity of time series, where ARIMA anticipates NTP changes by handling linear patterns. At the same time, MLP improves forecast accuracy by managing more complex patterns (both linear and nonlinear). Thus, it can provide more accurate information about the welfare development of farmers. The results show that the Hybrid ARIMA-MLP method is significantly better than the individual ARIMA method, with the obtained model being Hybrid ARIMA-MLP (12-5-10-2) and an accuracy of 99.993%.
Co-Authors Amimah Shabrina Putri Prasmono Galuh Syafa Azahra Hani’atul Maghfuroh Indri Noer Khoeriyah Kayyis Vatin Muthia Dishanur Izzati Rio Wahyu Saputra Rizka Fitria Herdiantini Saffanah Nur Elvina Mulyawati