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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Juwara : Jurnal Wawasan dan Aksara
Lea, Lea
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Religion Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Learning Algorithms of SVR, DTR, RFR, and XGBoost (Case Study: Predictive Maintenance of Fuel Consumption) Parhusip, Hanna Arini; Lea, Lea; Trihandaru, Suryasatriya; Nugroho, Didit Budi; Santosa, Petrus Priyo; Hariadi, Adrianus Herry
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.85657

Abstract

The most complex aspect of predictive maintenance (PdM) for heavy vehicles is accurately forecasting fuel consumption as it is both critical and challenging to achieve optimal efficiency while minimizing expenses. Overfitting and failure to capture the existing data's linear relationships seem to remain the most persistent issues with traditional methods. In order to achieve this, the following techniques were analyzed to choose the best fuel consumption forecaster: Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFT), and XGBoost. The models were implemented and their performance measured using Mean Squared Error (MSE). The analysis revealed that SVR surpassed the others with a linear kernel (C=10) achieving the lowest MSE rates of 0.26, while DTR, RFR, and XGBoost earned significantly higher 3.375, 2.857, and 3.857 (MSEs). The other models lagged behind SVR because SVR was more effective in capturing linear relations and managing overfitting, a dominating issue with decision-tree based models. This points out another important aspect of predictive maintenance (PdM) : the appropriate machine learning technique plays a very important role in accurately predicting fuel consumption of heavy trucks, which improves precision and fuel efficiency.
Selection Dominant Features Using Principal Component Analysis for Predictive Maintenance of Heave Engines Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Heriadi, Adrianus Herry; Santosa, Petrus Priyo; Sardjono, Yohanes; Lea, Lea
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.22854

Abstract

This article aims to identify the dominant features that have a significant impact on the health of a heavy machine that relates to the digital infrastructure of a company. The importance of this research is that the authors define predictive maintenance based on Principal Component Analysis (PCA), which is the novelty of this article. The novel contribution of this research lies in the application of Principal Component Analysis (PCA) for predictive maintenance of heavy machinery, which has not been integrated into the Scheduled Oil Sampling (SOS) procedures. The recorded data are called Scheduled Oil Sampling (SOS) and historical data from an equipment called CoreDataQ, which works for recording many features from heavy machine activities. The data contain two sets data. The method is Principal Component Analysis (PCA). This method leads to obtain a maximum of 20 significant features on data based on SOS. The results have been confirmed and agreed upon by the manager who owned CoreDataQ to consider the selected dominant features for further related maintenance. 
Analisis Kemampuan Pemahaman Matematis Siswa pada Materi Turunan Fungsi Kelas XI SMA Lea, Lea; Mantili, Theresia Sukma; Christin, Etthy
Juwara: Jurnal Wawasan dan Aksara Vol. 2 No. 1 (2022)
Publisher : Yayasan Pendidikan dan Pengembangan Harapan Ananda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58740/juwara.v2i1.35

Abstract

Tujuan penelitian  adalah  menganalisis kemampuan pemahaman matematis siswa. Metode yang digunakan dalam penelitian ini yaitu metode deskriptif. Subjek penelitiannya adalah siswa kelas XI.instrumen yang digunakan dalam penelitian ini adalah tes kemampuan pemahaman matematis siswa yang berjumlah 3 soal sesuai dengan indikator pemahaman matematis siswa. Setelah pengumpulan data, peneliti menganalisis hasil pengerjaan soal kemampuan pemahaman matematis dan hasil wawancara untuk melihat kemampuan pemahaman matematis siswa. Berdasarkan analisis data dapat disimpulkan bahwa kemampuan pemahaman matematis siswa kelas XI SMA Kristen Filadelfia masih rendah yang ditunjukkan dengan terdapatnya banyak kekeliruan penyelesaian soal kemampuan pemahaman matematis khususnya pada indicator menyatakan ulang sebuah konsep dalam materi turunan fungsi, memberi contoh dan bukan contoh dari konsep, setra pada indikator menyajikan konsep dalam berbagai representasi matematis yang dinyatakan dalam materi turunan fungsi.  . Abstract  The purpose of this research is to analyze the students’ mathematical understanding ability. The method used in this research is descrivtive method. The research subjects were XI grade students. The instrument used in this study was a test of students’ mathematical understanding abilities, which consisted of 3 questions according to the indicators of students’ mathematical understanding. After colleting data, the researcher analyzed the results of working on the mathematical understanding ability and interview results to see the students’ mathematical understanding ability. Based on data analysis, it can be concluded that the mathematical understanding ability of grade XI students is still low, which is indicated by the presence of many errors in solving problems of mathematical understanding ability, especially on indicators of restating concepts in function derivative materials, providing examples and not examples of concept, and indicators presenting concepts in various mathematical representations expressed intrems of derivative function.
Co-Authors Christin, Etthy Didit Budi Nugroho Hanna Arini Parhusip Hariadi, Adrianus Herry Heriadi, Adrianus Herry Mantili, Theresia Sukma Santosa, Petrus Priyo Suryasatriya Trihandaru Yohanes Sardjono, Yohanes