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All Journal Proceedings of KNASTIK Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal ULTIMATICS Jurnal ULTIMA Computing Jurnal Informatika International Journal of Information Technology and Computer Science Applications (IJITCSA)
Willy Sudiarto Raharjo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Comparison of the Accuracy of Brown's and Holt's Double Exponential Smoothing in LQ45 Stock Price Forecasting Santosa, Raden Gunawan; Chrismanto, Antonius Rachmat; Raharjo, Willy Sudiarto
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 1 (2024): January - April 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i1.112

Abstract

As of May 2022, 787 stocks are listed on the Indonesia Stock Exchange (IDX), and the number of stock indices in Indonesia to date is 38. One interesting and important stock index is the LQ45 index. Because this index is a very important reference index for investors, this research data focuses on stocks in the LQ45 index. There are two essential things in the forecasting process: the data and the right forecasting method. Two forecasting methods that can be used are Brown and Holt's Double Exponential Smoothing (DES). This study examines two methods with the lowest accuracy error in forecasting the LQ45 stock price data. Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the error. The analysis methods used to compare the MAPE of the two methods are the F test for variance similarity, Boxplot, t-test to test paired means with different cases of variance, and Wilcoxon signed rank test to test paired means nonparametric statistics. The result is that the MAPE average with Holt's DES method is smaller than the average MAPE with Brown's DES method. This is supported by the t-test for paired means with different cases of variance and also supported by the Wilcoxon signed exact rank test. Meanwhile, the MAPE standard deviation with Holt's DES method is smaller than the MAPE standard deviation with Brown's DES method. This is supported by the F test to test the variance similarity and is visually supported by a Boxplot diagram. From this study, LQ45 stocks with the smallest MAPE value accuracy are ICBP stocks. In general, based on the MAPE value, Holt's DES method is better than Brown's DES method in predicting the prices of stocks in the LQ45 index.
LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method Santosa, Raden Gunawan; Chrismanto, Antonius Rachmat; Raharjo, Willy Sudiarto; Lukito, Yuan
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 2 (2024): May - August 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i2.150

Abstract

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.
Perancangan Aplikasi Time Management Untuk Mahasiswa Berbasis Gamification Sulistio, Gerry Susanto; Chrisantyo, Lukas; Raharjo, Willy Sudiarto
Jurnal Terapan Teknologi Informasi Vol 8 No 1 (2024): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2024.81.317

Abstract

Students commonly encounter challenges in time organization due to assignments and distractions, notably social media. To solve this problem, the application was meticulously crafted employing Design Thinking principles and incorporating gamification elements to bolster student motivation. During the testing phase, Task Scenario was utilized to measure effectiveness, while the System Usability Scale (SUS) was employed to measure user satisfaction. Test results revealed an effectiveness rate of 87,2%, and the SUS yielded an average score of 65.7%. These findings categorize the application within the level D (OK) and high marginal acceptability ranges. It can be inferred that the application still functions normally, and a considerable number of users are able to accept and use it effectively.
Redesain Remote Laboratorium Rumpun Mata Kuliah Jaringan Komputer FTI UKDW dengan Pendekatan Modular Narung, Olivia; Indriyanta, Gani; Raharjo, Willy Sudiarto
Jurnal Terapan Teknologi Informasi Vol 8 No 1 (2024): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2024.81.318

Abstract

The implementation of a remotely accessible network laboratory at FTI UKDW commenced in 2022, utilizing a VPN (Virtual Private Network) based remote system. Previous research identified several technical challenges in its implementation, coupled with periodic subscription cost constraints. Consequently, this study aims to redesign the system by adopting port forwarding as an alternative, with the expectation of optimizing the remote laboratory implementation while reducing maintenance costs. This research focuses on analyzing the performance quality of the redesigned remote laboratory using the port forwarding method. Additionally, the study aims to measure the level of comfort among students in using the remote network laboratory system through the collection of questionnaire data. The results of the research indicate that the redesign with the port forwarding method can be implemented as expected. Students expressed a high level of satisfaction, reaching 50%, with the majority finding it easy to access practical tools and considering the system user-friendly.
The MAPE Analysis of Arima (p,d,q) on LQ45 Stock Price to Determine Training Data Period Santosa, Raden Gunawan; Chrismanto, Antonius Rachmat; Lukito, Yuan; Raharjo, Willy Sudiarto
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.168

Abstract

Most of the research using the Arima (p,d,q) focused on the accuracy of prediction results. Unlike other research, this work examines the training data period suitable for modeling ARIMA (p,d,q) in stock prices. Due to the volatile movement of stocks, the number of training data is assumed to affect the LQ45 prediction results. This research used five kinds of training data, including daily data for up to 5 years. With these five types of data series, the Arima (p,d,q) was made for LQ45 stocks. The prediction was conducted for two months after obtaining the model 5 data series of LQ45 stocks. Two months of data were used for January and February 2021 prediction test data. The results of this prediction were compared with the test data to produce the MAPE value. Based on the observations and calculation results, the most suitable stock to use the Arima (p,d,q) was ASII. In 5 years, the stocks produced the lowest MAPE value of 0.05%. Relatively stable LQ45 stocks with no change in the Arima (p,d,q) using four consecutive data series were ACES, CTRA, INTP, MIKA, and TLKM. Based on the MAPE value analysis performed in this study, we concluded that the best period to use the Arima (p,d,q) for LQ45 stocks is two years, with a median error rate of only 6.0091%.
Optimasi Akurasi Koefisien Pajak Kendaraan Bermotor di Indonesia Menggunakan Metode Klasifikasi dan Regres Togatorop, Joiner Tennye Ariel; Chrismanto, Antonius Rachmat; Raharjo, Willy Sudiarto
Jurnal Terapan Teknologi Informasi Vol 9 No 1 (2025): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2025.91.389

Abstract

The growing awareness of the impact of motor vehicle emissions on the environment has encouraged Indonesia’s Ministry of Environment and Forestry to enforce emission testing regulations. These emission standards serve as a basis for calculating Motor Vehicle Tax (PKB). The Transportation Technology Research Center (BRIN) developed a tax coefficient prediction system to support this policy. Initial research utilized Orange Data Mining for machine learning analysis with algorithms like Random Forest, Neural Network, and AdaBoost. However, Orange Data Mining has limitations in flexibility, particularly in parameter tuning and preprocessing data, as well as inefficiencies in handling large datasets. This study adopts a more flexible approach, employing AutoML LazyPredict for quick identification of optimal models and GridSearchCV for hyperparameter optimization. The methodology involves two approaches: classification and regression. Classification employs models such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Extra Tree, and LightGBM, while regression utilizes Support Vector Regressor (SVR) optimized with GridSearchCV. Both approaches enable a comprehensive comparison and analysis of model performance. The results indicate that SVM and Decision Tree excelled in classification, achieving an accuracy of 100%. In regression, the models demonstrated high 16 performance with R² values ranging from 0.95 to 1.00, indicating exceptional predictive accuracy. Evaluations were conducted using metrics such as MAE, MSE, and R² for regression, along with accuracy scores and classification reports for classification tasks. This research underscores the effectiveness of machine learning model optimization, with both analyzed algorithms showing outstanding performance for classification and regression tasks.
Pengembangan Chat Bot Telegram Untuk Admisi UKDW Andersen; Raharjo, Willy Sudiarto; Nendya, Matahari Bhakti
Jurnal Terapan Teknologi Informasi Vol 9 No 1 (2025): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2025.91.397

Abstract

The Admissions and Promotion Office of Universitas Kristen Duta Wacana (UKDW) Yogyakarta serves as a service unit for the admission and registration of prospective students. Currently, information related to new student admissions is provided through physical services, direct contact, and social media. However, using social media requires human resources and time to respond to every inquiry from prospective students. To improve responsiveness, an automated system in the form of a Telegram Bot has been developed. This Telegram Bot is designed to provide fast and automated new student admission information services. Through the integration of AWS services, such as Lambda and DynamoDB, the bot can process user inputs in real-time and deliver relevant answers without manual intervention. This system is expected to support the Admissions Office in providing modern services to prospective students
Co-Authors Ahmad Ashari Aloysius Airlangga Bajuadji Andersen Aniek Prasetyaningsih Antonius Rachmat Chrismanto Bosman Tambunan Danny Aguswahyudi Dessy Sutanti Desyandri Desyandri Gani Indriyanta Karmela Saturnina Mega Wea Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lukas Chrisantyo Matahari Bhakti Nendya, Matahari Bhakti Narung, Olivia Pryadi, Melkhi Raden Gunawan Santosa Restyandito Rosalina Berliana Sulistio, Gerry Susanto Togatorop, Joiner Tennye Ariel Widi Hapsari Wulan Krista Waty Yuan Lukito