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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abditani : Jurnal Pengabdian Masyarakat Majalah Ilmiah UNIKOM Jurnal Ilmiah Wahana Pendidikan

Ultach Enri

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Author-ID : 2138770

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Prediksi Harga Minyak Kelapa Sawit Menggunakan Linear Regression Dan Random Forest Yusuf Supriyanto; M. Ilhamsyah; Ultach Enri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 7 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Commodity export is an important activity because it can open up new market opportunities abroad. Besides being able to increase investment and foreign exchange for a country, palm oil is a plantation product that plays an important role in the Indonesian economy, palm oil is the largest foreign exchange earner. In palm oil exports, the volume tends to increase from 2016 to 2019 but when viewed from the export value of palm oil, it tends to fluctuate. Therefore it is necessary to predict the price of palm oil to help make commodity export decisions and also help palm oil investors in maximizing profits, in this study to predict the price of palm oil used data mining methods with the implementation of the Linear Regression and Random Forest algorithms using rapidminer, with data sharing scenarios training and testing is divided into three, namely 90:10, 80:20 and 70:30 to determine the performance of the algorithm. The data that will be used for research is historical data on palm oil prices taken from investing.com. From the results of the implementation of the algorithm obtained in the 90:10 data sharing scenario, the best algorithm is Random Forest with RMSE 25,106 results, in the second scenario with 80:20 data sharing the best algorithm is Linear Regression with RMSE 31,174, in the third scenario 70:30 Linear data sharing. regression has the best result with RMSE 30,227. then from the three scenarios, the Linear Regression algorithm gets the best performance

Penerapan Algoritma C4.5 dalam Klasifikasi Status Gizi Balita Hajar Izzatul Islam; Muhamad Khandava Mulyadien; Ultach Enri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 10 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Posyandu in Dawuan Barat determines the nutritional status of children by looking at the growth chart in KIA and calculating the z-score manually and then matching the results to the category table and threshold, this takes a long time and is at risk of being inaccurate. The formulation of the problem in this study is how to do classification of data mining to determine the nutritional status of toddlers and how the evaluation results from the classification model. This study uses the C4.5 algorithm with the CRISP-DM methodology to classify the nutritional status of toddlers and uses a confusion matrix to determine the accuracy, precision, recall and f1-score values of the classification model and then the model is implemented into an application. The evaluation results of the 3 model scenarios in this study stated that scenario 1 produced the best performance among other models with 90% accuracy and 87% value of precision, recall and f1-score

Algoritma K-Means Untuk Pengelompokan Bantuan Langsung Tunai (BLT) Muhamad Khandava Mulyadien; Ultach Enri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 12 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

The problem of poverty is a problem that often occurs in various regions in Indonesia, officials in West Dawuan Village continue to overcome these problems with various government assistance, one of which is Direct Cash Assistance (BLT). Often times in West Dawuan Village, there are obstacles in the distribution of the Direct Cash Aid funds received by parties who do not meet the criteria, there are residents who receive BLT successively and policy-making errors have a negative impact on the use of aid, especially in West Dawuan Village, Karawang. Using data mining clustering techniques, it is possible to group prospective beneficiaries according to the criteria to overcome these problems. This study uses the K-Means algorithm to classify BLT recipients in Dawuan Barat Village. The evaluation results from the resulting K-Means clustering model were then evaluated by using the SSE (Sum of Square Error) test. From the resulting K-Means clustering model, recommendations for prospective BLT recipients will be known according to the applicable criteria. With the help of the elbow method, the value of k=6 is obtained as the optimal k value and the results of the SSE cluster 6 test produce the best value among other clusters, which is 24551994078884.86. As a recommendation for BLT recipients, it can be done based on the best results from the SSE evaluation, namely cluster 6

Perbandingan Algoritma Backpropagation Neural Network dan Long Short-Term Memory dalam Memprediksi Harga Bitcoin Felix Andreas; Mikhael Mikhael; Ultach Enri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 12 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

In actual practice, Bitcoin is the decentralized currency that allows two individuals to transact without third-party intervention. However, due to its high volatility, it has been such an attraction to investors to gain profit. But, that also mean that high volatility can also bring disadvantage if someone predicts the increase or decrease of the price of Bitcoin incorrectly. The technical analysis which is often used to predict Bitcoin prices has a weakness, that is specifically depends on the users of technical indicators. Therefore, it is necessary to use the Data Mining algorithm as an alternative solution to predict Bitcoin prices. In this paper, the implemented algorithms to predict Bitcoin prices are Long Short-Term Memory (LSTM) and Backpropagation Neural Network. The final results using T-Test showed there is no significant difference between LSTM and Backpropagation in predicting the data test with an average RMSE value of 661.580 and 1.812.503, respectively. However Backpropagation has the advantage to predict new data (outside of the dataset) with an average RMSE value of 629.545, while the average RMSE value of the LSTM is 2.818.248.

Prediksi Harga Kartu Grafis NVIDIA Berdasarkan Pengaruh Harga Cryptocurrency Menggunakan Support Vector Regression Mohammad Nurfaizy Pangestu; Mohamad Jajuli; Ultach Enri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 17 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

The growing popularity of cryptocurrencies has caused the market demand for graphics cards to reach unusual heights for their efficient cryptomining capabilities. Graphics cards are not only used for crypto mining but also video editing, video streaming, and video games, this causes an unavailability of graphics card supply due to high demand, especially for cryptomining needs and leads to unusual prices increases which makes it difficult for graphics card consumers and miners to buy graphics cards at normal price. Therefore, it is necessary to predict the price of NVIDIA graphics cards based on the influence of cryptocurrency prices. The methodology used is KDD, and the algorithm used to make predictions is SVR because its ability to overcome the overfitting problem so it can produce more accurate predictions, besides that in this study the grid search algorithm is applied to determine optimal parameters. In this study, 6 graphics cards and 2 cryptocurrencies were used which produced the 6 best prediction models which were chosen based on the RMSE value. GTX 1050 has RMSE value of 0.2028, GTX 1050 Ti has RMSE value of 0.14564, GTX 1060 has an RMSE value of 0.07629, while in the RTX 30 series, RTX 3070 has an RMSE value of 0.03178, RTX 3080 has RMSE value of 0.0388, and RTX 3090 has RMSE of 0.06259. From these results, it can be stated that RTX 30 series has better accuracy than GTX 10 series in making predictions. RBF is better than linear which only excels on the GTX 1060.

ANALISIS SENTIMEN TERHADAP PERUBAHAN RUTE KRL COMMUTER JABODETABEK MENGGUNAKAN ALGORITME SUPPORT VECTOR MACHINE (SVM) Fauzapril Duta Sanubari; Ultach Enri; Susilawati Susilawati
Jurnal Ilmiah Wahana Pendidikan Vol 9 No 15 (2023): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.8206986

KRL Commuter Jabodetabek is one of the modes of public transportation that is an alternative choice for residents of the capital city of Jakarta and its surroundings to reduce congestion in the Jabodetabek area. However, since there was a change in the Jabodetabek Commuter KRL route on May 28, 2022, there have been pro and con opinions from among the public users of the Jabodetabek Commuter KRL public transportation mode. The data used in this research is tweet data from Twitter with the keyword 'krl route changes' with a time span between May 26, 2022 and February 28, 2023. This research uses the Knowledge Discovery in Database (KDD) method. The purpose of this study is to determine public sentiment towards changes in the Jabodetabek Commuter KRL route and to determine the performance evaluation value of Support Vector Machine (SVM) in analyzing public sentiment. This research compares 3 SVM kernels namely RBF kernel, Linear Kernel, and Polynomial Kernel with 3 dataset sharing scenarios (90:10, 80:20, and 70:30) and also compares the effect of using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to handle data imbalance. This research resulted in positive labels totaling 17, neutral labels totaling 184, and negative labels totaling 140. And the best accuracy value was obtained by RBF kernel and Polynomial kernel in scenario 2 (80:20) with the same value of 88.2%.

Co-Authors Fauzapril Duta Sanubari Felix Andreas Fery Anuar Ramadhan Putra Frido Firman Fadilah Hajar Izzatul Islam Ilhamsyah, M Isnin Apriyatin Ropikoh Lutfi Afifah, Lutfi Mikhael Mikhael Mohamad Jajuli Mohammad Nurfaizy Pangestu Muhamad Khandava Mulyadien Nina Sulistiyowati Nurcahyo W. Saputro Rijal Abdulhakim Susilawati Susilawati Yusuf Supriyanto

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