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All Journal Pilar Teknologi : Jurnal Penelitian Ilmu-ilmu Teknik The Indonesian Journal of Computer Science Research JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Windha Mega Pradnya Dhuhita
Universitas Amikom Yogyakarta
Computer Science & IT

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Sistem Pendukung Keputusan Penerimaan Programer Software House Menggunakan Metode Simple Additive Weighting (SAW) Wejo Triharseno; Windha Mega Pradnya Dhuhita; Adri Priadana
JURNAL PILAR TEKNOLOGI Jurnal Ilmiah Ilmu Ilmu Teknik Vol. 5 No. 1 (2020): JURNAL PILAR TEKNOLOGI
Publisher : LPPM Universitas Merdeka Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33319/piltek.v5i1.52

Abstract

Abstract— The software house business is one of the businesses that are developing in the 4.0 industrial revolution era, both globally and locally. There are many software houses that need programmers with various qualifications to be accepted as employees both contract and permanent employees. One of the problems that arises is the frequency of employees leaving (resigning) from the software house for various reasons such as meeting deadlines, difficulty in working in teams, and also limited ability of programmers (low-skilled). many components of test results to test the ability to make programs. This study aims to build a decision support system in the selection of software house employees who can rank the results of prospective applicants' tests quickly based on the weight of predetermined criteria. Decision support systems made in this study can process test score data, prospective programmer data, and criterion data. The software house programmer acceptance decision support system created in this study successfully implemented the SAW method and was able to display the results of the ranking ranking starting from the score with the highest value to the score with the lowest value. Keywords—: decision support systems; software house programmer acceptance; simpe additive weighting; SAW.
Perbandingan Algoritma Supervised Learning untuk Klasifikasi Judul Skripsi Berdasarkan Bidang Dosen Windha Mega Pradnya Dhuhita; Muhammad Farhan Khairul Amri Darmawan; Lasmita Triana; Nurrofiqi Ankisqiantari
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 2 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i2.4960

Abstract

At the level of education, especially for S1, the graduation requirement is to complete the thesis. In preparing the thesis, students are accompanied by a guidance lecturer who will direct and as a place to consult. The case is still there are students who are confused to take a thesis. There are several reasons that they do not have a title to be submitted, and are confused to choose a tutor who matches their title or theme. Sometimes on campus, students can get a mentor, but not in accordance with the field, and not in accordance with the theme of the thesis title. Therefore, in this study will make a classification of lecturer fields based on student titles. The data used as many as 1598 was taken from the campus of AMIKOM Yogyakarta University by adding some new data. With the lecturer in accordance with the field, it will be easier to guide students. This study conducted stages of labeling, text preprocessing, and word weighting or called TF-IDF (Term Frequency – Inverse Document Frequency). After that, the data split will be classified with naive bayes classifier (NBC), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) algorithms. The performance of the three algorithms is compared to find out the performance of the algorithm is good. The results showed the Support Vector Machine (SVM) algorithm performed better by producing an accuracy of 89.24%, while the Naive Bayes Classifier (NBC) algorithm produced an accuracy of 88.29%, and the K-Nearest Neighbor (KNN) algorithm with a k value of 18 produced an accuracy of 85.14%.
Analysis Of Lstm-Adamax Performance In Bitcoin Price Prediction Using RSI & MACD Indicators Windha Mega Pradnya Dhuhita; Yoga Imam Sumbadri
The Indonesian Journal of Computer Science Research Vol. 5 No. 1 (2026): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i1.247

Abstract

One digital asset that is difficult to predict due to its extreme volatility is Bitcoin. Bitcoin's value has been predicted using various techniques, ranging from technical analysis to artificial intelligence-based models. Long Short-Term Memory (LSTM) is an artificial neural network architecture capable of recognizing patterns in historical data and is often used for time series data prediction. This study explores the application of an optimized LSTM model with the Adamax algorithm combined with the technical indicators RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence) to predict Bitcoin prices based on historical data. The LSTM-Adamax model demonstrated strong performance, achieving an RMSE of 435.9, MAE of 284.5, and R² of 0.99947, indicating high accuracy and robustness in capturing price patterns. A comparative evaluation between the model with and without these indicators revealed a slight performance improvement when the technical indicators were used. The model was successfully implemented as a web application using Streamlit, allowing users to upload Bitcoin price data, configure prediction parameters, and visualize the results in real-time. The application also communicates the predicted price movement direction (up or down) and its magnitude. In conclusion, the integration of LSTM-Adamax with RSI and MACD proved effective in predicting Bitcoin prices based on time-series data, providing reliable predictions and user-friendly implementation through a web interface.
Co-Authors Adri Priadana Lasmita Triana Muhammad Farhan Khairul Amri Darmawan Nurrofiqi Ankisqiantari Wejo Triharseno Yoga Imam Sumbadri