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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications
Buulolo, Fatizanolo
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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ANALISIS PENGARUH PELAYANAN GRAB TERHADAP KEPUASAN PELANGGAN MENGGUNAKAN REGRESI LINEAR BERGANDA (STUDI KASUS: MAHASISWA UNIVERSITAS NEGERI MEDAN) Nadya, Fauza; Buulolo, Fatizanolo; Parinduri, Mutia Aini; Simamora, Jerry Misael; Nababan, Rodelta; Evelin, Shinta Kevin
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page87-98

Abstract

Dalam menghadapi kondisi perekonomian yang semakin maju di era globalisasi, perkembangan dunia usaha semakin pesat. Kesulitan dalam keadaan perekonomian mendorong munculnya persaingan di berbagai bidang kehidupan. Kesuksesan sebuah bisnis dapat dilihat dari tingkat kepuasan dari pelanggan atau customer dan pastinya dipengaruhi oleh beberapa faktor termasuk pelayanan. Karena itu, pada penelitian ini akan dilihat bagaimana pengaruh pelayanan Grab terhadap kepuasan pelanggan. Penelitian ini menggunakan pendekatan kuantitatif dengan studi kasus. Data didapatkan dengan menyebarkan kuesioner tanggal 10-21 November 2023 kepada mahasiswa di Universitas Negeri Medan yang diwakilkan ±10 orang per fakultas. Hasil penelitian penelitian ini diselesaikan dengan menggunakan program R. Model analisis regresi linear berganda yang dihasilkan . Untuk uji koefisien determinasi berganda adalah sebesar 0,8911, yang artinya secara simultan semua variabel independen berpengaruh terhadap variabel dependen (kepuasan). Analisis lainnya seperti uji multikolinieritas, uji autokorelasi, uji Durbin Watson, dan uji heteroskedastisita menunjukkan bahwa model regresi ini dapat diterima tanpa adanya masalah.
ENHANCING LQ45 STOCK PRICE FORECASTING USING LSTM MODEL Sinaga, Marlina Setia; Iskandar, Said; Manullang, Sudianto; Arnita, Arnita; Marpaung, Faridawaty; Buulolo, Fatizanolo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0427-0438

Abstract

Stocks listed in the LQ45 index represent companies with high liquidity, large market capitalization, and strong fundamentals, making them pivotal to the movements of the Indonesian capital market. This study selects eight LQ45-listed stocks from the energy and mining sectors, as well as the banking sector. Historical data spanning a 10-year period from February 28, 2015, to February 28, 2025. This research aims to mitigate the impact of stock market dynamics, a significant challenge for investor decision-making. The Long Short-Term Memory (LSTM) method was employed to forecast stock prices using four variables: opening, highest, lowest, and closing prices. The LSTM architecture was chosen because its gated memory cells can effectively capture long‑term dependencies and nonlinear patterns in financial time series, thereby aligning with the research objective of minimizing forecasting error under volatile market conditions. Evaluation results using the Mean Absolute Percentage Error (MAPE) showed prediction errors below 2.5%, indicating relatively low forecasting error. Root Mean Squared Error (RMSE) values varied depending on stock price volatility. Companies exhibiting higher stock prices, such as Indo Tambangraya Megah Tbk (ITMG), demonstrate larger RMSE values. For opening prices, predictive accuracy was notably strong, with MAPE values consistently below 1.26%. This suggests that opening prices, influenced by pre-market sentiment and historical data, are more stable and easier to predict compared to other price variables.
Co-Authors Arnita Arnita Evelin, Shinta Kevin Iskandar, Said Manullang, Sudianto Marpaung, Faridawaty Nababan, Rodelta Nadya, Fauza Parinduri, Mutia Aini Simamora, Jerry Misael Sinaga, Marlina Setia