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Pattimura Proceeding : Conference of Science and Technology
Published by Universitas Pattimura
ISSN : -     EISSN : 28293770     DOI : https://doi.org/10.30598/PattimuraSci.2021.KNMXX
Core Subject : Science, Education, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Physics
This journal is created to archieve collection of publications from a national or international seminar at Pattimura University for Science, Technology, and Its Applications
Arjuna Subject : Umum - Umum
Articles 179 Documents
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Modeling of Naïve Bayes and Decision Tree Algorithms to Analyze Sentiment Related to Jaklingko Public Transportation on Social Media X (Twitter) Purnamasari, Agustria; Agoestanto, Arief
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p67-78

Abstract

Technological developments have changed the way people access information and share opinions through the internet and social media such as X (Twitter). Public sentiment is now crucial in evaluating public services, especially in the era of advanced information technology. As a metropolitan city, public transportation in DKI Jakarta plays an important role in economic, business and government activities. The Jaklingko initiative launched by the DKI Jakarta Provincial Government aims to provide efficient and affordable public transportation. This research implements and compares Naive Bayes and Decision Tree classification methods to perform sentiment analysis of twitter users opinions regarding Jaklingko into positive and negative categories. Data was collected by crawling using tweet_harvest which obtained 6001 tweets about Jaklingko, then text preprocessing was carried out. Word weighting is done using the TF-IDF method to give value to each term in the document, and sentiment labeling is done using the Vader Lexicon library. The data is divided into training and testing data with a ratio of 80%:20% for the classification process. Evaluation of the method is done using confusion matrix. The results showed that the accuracy of Naive Bayes reached 84.9% and Decision Tree reached 84.2%. The wordcloud visualization depicts negative words including vehicle stoppage, bad driver attitude, and envy from people in other cities. Meanwhile, positive words included free system, useful programs, and user convenience. This research provides an in-depth understanding of public opinion towards Jaklingko, with potential implications for improving public transportation services in Jakarta.
Students Mathematical Critical Thinking Skills: Validity and Reliability with Winsteps and SPSS Vebiyanti, Dewi; Salsabila, Khalisha; Bilhaki, Raden Roafli; Faradhilla, Ayu; Alyani, Fitri
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p79-88

Abstract

It is of great importance to conduct validity and reliability testing in a study in order to ascertain the feasibility and effectiveness of the instruments employed. In this study, an analysis is required to ascertain the results of the validity and reliability tests on mathematical critical thinking skills utilising the SPSS and Winsteps applications. The research method employed was quantitative survey research, with a total of 133 high school students in the Jakarta and Depok areas comprising the sample population. The instrument designed to assess critical thinking skills comprises four indicators. The validity test on the mathematical critical thinking ability instrument with the SPSS application indicates that one value (value 11) is invalid, as evidenced by the Pearson correlation value. However, the remaining values meet the requirements for validity. The reliability value obtained through SPSS yielded a result of α = 0.75. The same results are observed when the Winsteps application is employed. One value (value 11) is identified as invalid, while the remaining values meet the criteria for the MNSQ outfit value, ZSTD outfit, and PTMEA-CORR. In addition, the reliability results obtained using Winsteps yielded a Cronbach's Alpha coefficient value of 0.87, indicating a high degree of reliability.
Binary Logistic Regression Modeling on Household Poverty Status in Bengkulu Province Sihombing, Esther Damayanti; Novianti, Pepi; Wahyuliani, Indah
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p89-100

Abstract

Binary logistic regression is a statistical method used to analyze the relationship between one or more predictor variables and a binary or dichotomous response variable. Poverty is an issue in every province in Indonesia. One of the provinces with a relatively high poverty rate is Bengkulu Province, ranking seventh in Indonesia with a poverty rate of 14.62%. The Central Bureau of Statistics of Bengkulu Province (2023) explains that efforts to reduce poverty must involve all levels of society. Various government programs and policies in various fields such as health, social, and other areas are continuously being implemented to reduce the number of households classified as poor. Identifying the characteristics of households in Bengkulu Province by poverty status is important to study, as it serves as a reference to ensure that government programs are implemented according to the target. One method that can be used to identify household characteristics is binary logistic regression. This study aims to model the poverty status of households in Bengkulu Province using binary logistic regression and to identify the factors that influence it. The data used are social and economic household data from March 2022. The response variable used is household poverty status (poor and not poor), while the predictor variables include the ownership of toilet facilities, the source of lighting, floor area, family size, and per capita calorie consumption. Modeling is done using binary logistic regression with simultaneous and partial parameter significance tests, as well as model fit tests. The analysis results show that the factors significantly influencing household poverty status in Bengkulu Province are the ownership of toilet facilities, the source of household lighting, floor area, family size, and per capita calorie consumption. The formed binary logistic regression model has a classification accuracy of 89.98% with a sensitivity of 18.34% and a specificity of 98.61%.
Comparison of Multiple Linear Regression and Random Forest Regression Models for House Price Prediction in Semarang City Using the CRISP-DM Method Sari, Fransisca Mulya; Sugiman, Sugiman
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p101-116

Abstract

The population density in Semarang City is increasing every year. This requires more potential land to build houses to accommodate the denser population. There are various kinds of house prices based on specifications in Semarang City. This requires the right prediction to get the desired house. This study implements and compares the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) models to predict house prices in Semarang City. The method used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining) as a data mining process. The data used in this research amounted to 9533 data with 8 variables obtained by web scraping. The data will go through a data preprocessing process then training the model. Next is the evaluation stage, which is carried out to measure the performance of the two models using evaluation metrics, namely R-Squared (prediction accuracy), MSE (Mean Squared Error), and RMSE (Root Mean Squared Error). The results of this study show that the MLR model obtained a prediction accuracy 61.1% with a training and testing data division ratio of 75%: 25%. While the RFR model produces a prediction accuracy 78.4% with a training and testing data division ratio of 90%: 10%. This shows that the RFR model is the best performing model. This research successfully applied the RFR model to the streamlit web framework. The final result of this research is a website that can be used by the public to predict house prices according to criteria in Semarang City.
Application of Max-Plus Algebra for Time Optimization of Wooden Furniture Production System “Berkah Usaha” Jepara Regency Khulda, Ifrikhatul; Isnarto, Isnarto
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p117-124

Abstract

This research aims to provide a periodic schedule on the Berkah Usaha wood furniture production system in Jepara using Max-Plus Algebra. The data required in the research is data on the flow and processing time of each project work unit. In this research, the main focuses on the Berkah Usaha wooden furniture dining table production system whose processing stages consist of raw material preparation, measurement, wood cutting, wood smoothing, measurement of puruses and holes, table floor manufacturing, puruses and holes manufacturing, table floor smoothing, assembling, sanding, and finishing. The techniques of data collection used in this research are interviews and field observations. Then, a directed graph and model of the Berkah Usaha wood furniture production system were compiled based on the flow data, processing time for each project work unit, and the rules for applying Max-Plus Algebra to the scheduling system. Analysis of the Max-Plus Algebra model of the Berkah Usaha wooden furniture production system using the Power Algorithm with Scilab software assisted. The results of the analysis obtained the production time period of the Berkah Usaha wooden furniture for 253 minutes and the optimal time to start production on each processing unit are 0, 11, 46, 92, 115, 117, 127, 137, 155, 195, 223 time units (in minutes).
Analysis of Big Data Literacy Skills of Prospective Mathematics Educators Through Case Method-Based Learning Setiawani, Susi; Prihandini, Rafiantika Megahnia
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p125-132

Abstract

The aim of this research is to analyze the big data literacy skills that a prospective mathematics educator needs to master. Specifically, the ability to read, analyze, make conclusions, and think based on data and information, especially large amounts of data, so that mathematics education students have a broad mindset, and can carry out new innovations. Kemmis and McTaggart's Classroom Action research model was used as the research method. Its stages have four, namely planning, action, observation, and reflection. The planning and action stages took the form of preparing and validating case method-based learning tools in the Operations Research course and were implemented in 3 classes. Lectures are divided into 3 stages: problem-based learning, case method learning from the business world, and case-based learning (Case Method). During the learning process, observation and reflection are carried out. Learning involving the business world, namely Astra Financial and an expert in data sciences, provides opportunities for students to process company big data. The data literacy ability indicators used are the ability to group data, check data, clean data, do qualitative coding, eliminate data, and develop models. The learning results show that students have carried out all big data processing steps in the very good category (> 80%), except for the developing model phase (65.38%). Mini research field studies at partner institutions for the application of operations research in the community. Learning completeness reached 83.11%, and learning activeness reached 82.14% in the very active category. Learning results show an increase in the Big Data Literacy skills of prospective educators and lecturers, even though mastery of big data processing software still needs to be improved.
Faktor-Faktor yang Mempengaruhi Preferensi Pemilih Usia Muda dalam Pemilihan Presiden 2024 di Kota Medan menggunakan Regresi Logistik Biner Husniyah, Nailuh; Tarigan, Enita Dewi Br.; Siringoringo, Yan Batara Putra
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p133-142

Abstract

This study aims to identify factors that influence young voters' preferences in the 2024 presidential election in Medan City. There are 12 independent variables used in this study, namely socio-culture, age, education, track record, policy, political issues, political interest, campaign, social media, trust, candidate quality, and debate results. The dependent variable is voter preference, with a value of 0 for not voting and 1 for voting. This study used binary logistic regression method with purposive sampling technique to collect data from 271 respondents of young voters in Medan City. The results show that the independent variables jointly affect the dependent variable, as indicated by the G-test value of 85.909 which is greater than the value of χ_((0,05;12))^2(21.026). Through the Wald test, it is known that there are 6 independent variables that have a significant effect on the dependent variable, namely socio-culture (9.011), age (11.339), track record (4.638), political issues (4.403), campaign (4.165) and debate results (13.901), where these values are greater than the value of χ_((0,05;1))^2(3.841). While the remaining 6 variables, namely education (1.203), policy (0.072), political interest (1.435), social media (0.128), trust (1.557), and candidate quality (1.891) have no significant effect on the dependent variable, because the Wald test values of the six variables are smaller than the value of χ_((0,05;1))^2(3.841).
Perbandingan Metode Kuadrat Terkecil dan Metode Rata-rata Bergerak dalam Peramalan Jumlah Pengunjung Wisata Panorama Geosite Hutaginjang Sumatera Utara Simaremare, Yehezekyel; Tarigan, Enita Dewi Br.
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p143-154

Abstract

The methods used in this research are the Least Square method and the Moving Average method. The use of this method is intended to compare which method is the most accurate and has the smallest forecasting error using the Mean Absolute Percentage Error (MAPE) method. The data used in this research is data on the number of visitors to the Panorama Geosite Hutaginjang tourist attraction from December 2021 to May 2024. The results of research and data analysis found that the MAPE values ​​for the Least Square method, Moving Average period 3 (MA(3)) and Moving Average period 5 (MA(5)) are 16.627% , 21.691% and 19.703%. From the MAPE values ​​obtained, it can be determined that the most accurate method is the Least Square method.
Penggunaan Konteks Budaya Ramayana Ballet Prambanan Untuk Soal Literasi Matematika Bagi Siswa SMP Gunawan, Monica Tiara; Julie, Hongki; Ruditho, M. Andy
Pattimura Proceeding Vol 5 No 1 (2024): Prosiding Konferensi Nasional matematika (KNM) XXII Tahun 2024
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ppcst.knmxxiiv5i1p155-172

Abstract

The purpose of this research is to develop and classify mathematical literacy questions using the context of Ramayana Ballet Prambanan Ballet for junior high school students. The type of research used is design research. The data collection methods used were observation, test, and interview. The instrument validation process used by researchers is expert validation and the technique used to test data validity is triangulation technique. The data analysis process used was reducing data, presenting data, and drawing conclusions. The results obtained were from the context of the Ramayana Ballet Prambanan Ballet could be made eight questions in the domain of numbers, data and probability analysis, algebra, measurement, and geometry for cognitive levels (1) knowing with a counting indicator, (2) applying with interpreting, applying/executing indicators, and (3) reasoning with a making justification indicator.

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