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All Journal J@TI (TEKNIK INDUSTRI) Jurnal Rekavasi
Safira, Aretha
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Computer Science & IT Engineering Industrial & Manufacturing Engineering

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PENGEMBANGAN NEURAL NETWORK UNTUK PREDIKSI KUALITAS AIR Safira, Aretha; Sarudi As., L. M.; Puspitasari, Afifa; Normasari, Nur Mayke Eka; Rifai, Achmad Pratama
Jurnal Rekavasi Vol 10 No 2 (2022)
Publisher : Prodi Teknik Industri, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/rekavasi.v10i2.4014

Abstract

Research on artificial intelligence to determine water quality has been widely developed as a human endeavor toimprove the quality of life. This study employs an artificial neural network (ANN) to determine the optimalclassification model for determining the safety of water. This study uses existing Kaggle generic datasets. Numerouspreprocesses were performed on the dataset starting from cleaning the data from missing values and outliers toequalizing the weights of each parameter with the min-max scaler. This study compares the accuracy of ANN modelin various scenarios constructed with 10, 15, 20, and 30 neurons. Scaled Conjugate Gradient is implemented as thelearning algorithm for developing the prediction model. The obtained results of the experiments vary betweenscenarios. Overall accuracy increases when the number of neurons is between 10 and 20, and decreases when thenumber of neurons is between 20 and 30.
EFFECTS OF CONSUMERS’ SENSORY ATTRIBUTES ON THEIR WILLINGNESS TO PAY AND THE OPTIMUM PRICE FOR ICED COFFEE DRINKS Safira, Aretha; Masruroh, Nur Aini; Wijayanto, Titis
J@ti Undip: Jurnal Teknik Industri Vol 19, No 2 (2024): Mei 2024
Publisher : Departemen Teknik Industri, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jati.19.2.88-95

Abstract

The growing competition among coffee shops demands effective strategies, including the development of optimal pricing. An optimal pricing strategy must account for both changes in coffee ingredients and consumers' willingness to pay (WTP). This study investigated the factors influencing consumers' WTP and determined optimal prices through sensory evaluations of iced coffee. This study explored how demographic factors and sensory characteristics affect consumer WTP. This study involved direct consumer tastings, where participants provided subjective ratings of iced coffee and indicated their WTP. The coffee samples included variations in milk (white and black coffee) and sugar content (granulated sugar, palm sugar, and no sugar). To measure WTP, the Becker-DeGroot-Marschak (BDM) mechanism was employed, while a demand function was used to determine the optimal price. Stepwise backward logistic regression further analyzed the factors affecting WTP. The factors influencing willingness to pay were further analyzed using stepwise backward logistic regression. The findings reveal that optimal pricing varies, with iced coffee that includes both granulated sugar and milk commanding the highest WTP. Consumer WTP is significantly influenced by factors such as gender, frequency of coffee consumption, and individual taste preferences. There was a marked difference in WTP based on the amount of milk and sugar added, with coffee variations containing both granulated sugar and milk achieving the highest WTP. These results can serve as a valuable reference for coffee shops, helping them to determine the ideal product composition and pricing strategies to maximize revenue.
Co-Authors Achmad Pratama Rifai Nur Aini Masruroh Nur Mayke Eka Normasari Puspitasari, Afifa Sarudi As., L. M. Titis Wijayanto