Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal CogITo Smart Journal Riwayat: Educational Journal of History and Humanities
Derhass, Gerry Hudera
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : CogITo Smart Journal

 1 
Analysis Comparison of K-Nearest Neighbor, Multi-Layer Perceptron, and Decision Tree Algorithms in Diamond Price Prediction Kamila, Ahya Radiatul; Andry, Johanes Fernandes; Kusuma, Adi Wahyu Candra; Prasetyo, Eko Wahyu; Derhass, Gerry Hudera
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.532.298-311

Abstract

Diamond price predictions are essential due to the high demand for these gemstones, valued as investments and jewelry. Diamonds are expensive due to their rarity and extraction process. Their prices vary depending on key factors like the diamond's inherent value and secondary factors such as marketing costs, brand names, and market trends. These variations often confuse customers, potentially leading to investment losses. This research aims to help investors determine the true price of diamonds based solely on their intrinsic value, excluding secondary factors. A machine learning approach was utilized to predict diamond prices, focusing on primary determinants. Three models such as Multi-Layer Perceptron (MLP), Decision Tree, and K-Nearest Neighbor (KNN) were compared with manual hyperparameter tuning to identify the best performing algorithm. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Among the models, KNN demonstrated the best results, achieving MAPE, MAE, and MSE values of 1.1%, 0.00038, and 〖2.687 x 10〗^(-6) respectively. This study offers valuable insights for investors by accurately predicting diamond prices based on fundamental attributes, minimizing the impact of secondary factors.
Predictive Maintenance of Heavy Equipment Machines using Neural Network Based on Operational Data Ahya Radiatul Kamila; Derhass, Gerry Hudera; Andry, Johanes Fernandes; Lee, Francka Sakti; Budiyanto, Very; Anatasia, Velly
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.555.229-241

Abstract

Preventive maintenance is a routine maintenance strategy that aims to maximize equipment life cycle and prevent unplanned downtime which causes increased repair costs. When carrying out this maintenance, error in selecting machines need to be anticipated to avoid company losses. This research aims to reduce human error in machine selection for preventive maintenance using deep learning. The dataset used in this research is operational data of heavy equipment machine dataset from one of the palm oil companies in Indonesia with 9 independent features and 1 dependent feature. Dependent feature is a target feature contain two target classes representing effective and ineffective machines. The dataset in this study contains outlier, feature scales that are very different, and imbalanced data class. To handle outlier and standardise data scale, the Z-score method is used. Meanwhile, the over sampling method is used to handle imbalanced data classes. To obtain the best model performance, the number of epochs and two types of optimizers (adam&adamax) of neural network are selected. In selecting the number of epochs, experiments were carried out using 100 epochs. This research obtained the linearity relationship between the number of epochs and accuracy with the accuracy values using Adam and Adamax optimizers were 94.82% and 93.11% at the 100th epoch.
Co-Authors Ahya Radiatul Kamila Anatasia, Velly Budiyanto, Very EKO WAHYU PRASETYO Francka Sakti Lee Johanes Fernandes Andry Kamila, Ahya Radiatul Kusuma, Adi Wahyu Candra Surianto Surianto