cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
I Putu Adi Pratama
Contact Email
putudipa@gmail.com
Phone
+6281236359112
Journal Mail Official
infoteks.organization@gmail.com
Editorial Address
Pogung Lor SIA XVII Sinduadi Mlati Sleman, Yogyakarta, Indonesia
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Published by Infoteks
ISSN : 26552183     EISSN : 26557290     DOI : 10.33173
Core Subject : Science,
Computer Science & IT
data analysis, natural language processing, artificial intelligence, neural networks, pattern recognition, image processing, genetic algorithm, bioinformatics/biomedical applications, biometrical application, content-based multimedia retrievals, augmented reality, virtual reality, information system, game mobile, dan IT bussiness incubation
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 149 Documents
 9   10   11   12   13 
Implementation of Web-Based Counseling System at SMK Negeri 1 Sukawati Welda, Welda; Kusuma, Aniek Suryanti; Junantara, Argi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.204

Abstract

State Vocational High School (SMK) Negeri 1 Sukawati, located in Gianyar Regency and renowned for its excellence in the field of visual arts, had a total of 533 students in the 2023/2024 academic year. As part of its efforts to enhance the quality of education, the school has implemented a Guidance and Counseling (BK) program aimed at helping students develop self-awareness, improve self-confidence, and behave in accordance with school regulations. One of the key components of this program is the student violation recording system. Currently, the process of recording violations is carried out manually using BK logbooks and Microsoft Excel, which is time-consuming and requires a high level of accuracy. This becomes a significant challenge considering the large number of students and the variety of infractions that need to be documented. This study aims to design and implement a web-based guidance and counseling information system to facilitate a more efficient and accurate method of recording student violations. By utilizing a web-based system, the recording process can be automated, data retrieval becomes easier, and student or parental summons can be generated automatically once certain violation thresholds are reached. The focus of this research is the development of a system that enables guidance counselors to report student violations more easily, contributing to improved student discipline. The implementation of this system is expected to enhance the efficiency of violation data management and support the school’s efforts in fostering better student discipline.
Support Vector Machine for Classifying Prostate Cancer Data B, Muslimin; Rachmadani, Budi; Rudito, Rudito
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.205

Abstract

Prostate cancer is one of the most prevalent cancers among men worldwide, making early detection and accurate classification essential for improving patient outcomes. This study investigates the application of Support Vector Machine (SVM) models for classifying prostate cancer using clinical and demographic data. Features such as prostate-specific antigen (PSA) levels, Gleason scores, tumor stage, and patient age were utilized to train and evaluate the model. Comprehensive preprocessing techniques, including handling missing values, feature normalization, and addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE), were employed to ensure robust model performance. The SVM model, optimized with a radial basis function (RBF) kernel, achieved an accuracy of 94.2%, with precision, recall, and F1-scores indicating reliable classification of both cancerous and non-cancerous cases. However, the results highlight challenges with the minority class, emphasizing the need for better handling of imbalanced datasets. Explainability techniques such as SHAP (Shapley Additive Explanations) were integrated to provide interpretable insights into the model’s predictions, with PSA levels and Gleason scores identified as the most influential features. This research demonstrates the potential of SVM in prostate cancer classification, providing a foundation for integrating machine learning models into clinical workflows for improved diagnostic precision and patient care.
Adaptive Operator and Scaling Factor Selection in Differential Evolution using Parametrized Reinforcement Learning Santiyuda, Kadek Gemilang; Sugiartawan, Putu; Santiago, Gede Agus; Ardriani, Ni Nengah Dita; Kafiyanna, Moch Ilham Nur
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.206

Abstract

Mutation strategy selection along with parameter settings are well known challenges in enhancing the performance of differential evolution (DE). In this paper, we propose to solve these problems as a parametrized action Markov decision process. A multi-pass deep Q-network (MP-DQN) is used as the reinforcement learning method in the parametrized action space. The architecture of MP-DQN comprises an actor network and a Q-network, both trained offline. The networks’ weights are trained based on the samples of states, actions and rewards collected on every DE iterations. We use 99 features to describe a state of DE and experiment on 4 reward definitions. A benchmark study is carried out with functions from CEC2005 to compare the performance of the proposed method to baseline DE methods without any parameter control, with random scaling factor, and to other DEs with adaptive operator selection methods, as well as to the two winners of CEC2005. The results show that DE with MP-DQN parameter control performs better than the baseline DE methods and obtains competitive results compared to the other methods.
Comparison Of the Accuracy of Decision Tree Algorithms C4.5 And C5.0 In Predicting Tuition Payment Delays at Mts. Al-Jabar Bali Dewi, Ni Wayan Jeri Kusuma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Delays in the payment of Educational Development Contributions (SPP) have become a major issue impacting financial management at MTs. Al-Jabar Bali, with approximately 60% of students experiencing payment delays each year. This study aims to compare the accuracy of Decision Tree algorithms C4.5 and C5.0 in predicting SPP payment delays. The research method adopts the CRISP-DM approach and is implemented using Python on the Google Colaboratory platform. The data used includes students’ payment histories, parents' occupations, and income. The models were evaluated using Accuracy, Precision, and Recall metrics. The results show that the C5.0 algorithm has higher accuracy (98%) compared to C4.5 (89%). The C5.0 algorithm is recommended as an effective predictive model to assist schools in making strategic financial management decisions.
Deep Learning Approach for USD to IDR Forecasting with LSTM Ardriani, Ni Nengah Dita; Sugiartawan, Putu; Santiago, Gede Agus; Darma Wandika, I Made Pranadata; Wiwahana Prasetya, I Made Irfan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.210

Abstract

This Research explores the use of Long Short-Term Memory (LSTM) networks for forecasting the USD to IDR exchange rate, with the goal of improving prediction accuracy in the volatile foreign exchange market. By leveraging historical data, including daily exchange rates and trading volume, the LSTM model captures long-term dependencies and patterns within the time series data. The results show that the LSTM model effectively predicts general trends and medium-term fluctuations, demonstrating its capacity to follow market dynamics. However, the model struggles with extreme volatility and sudden market shifts, particularly during unforeseen geopolitical or economic events. This limitation highlights the need for further enhancement through the incorporation of additional features, such as macroeconomic indicators, sentiment analysis, and real-time news data. Furthermore, the study suggests the potential benefits of combining LSTM with other machine learning techniques to create hybrid models that can better handle short-term fluctuations and extreme events. In conclusion, while LSTM shows promise for exchange rate forecasting, its performance can be improved by refining model parameters, incorporating diverse data sources, and exploring hybrid approaches. This research provides valuable insights for traders, investors, and policymakers seeking to make more informed decisions in the foreign exchange market.
LSTM Neural Network for Predicting Tourist Arrivals to Bali Erawati, Kadek Nonik; Sugiartawan, Putu; Ardriani, Ni Nengah Dita; Hartama, I Dewa Agung Bayu Mega; Frasetya, I Gusti Ngurah Hendra; Mahendra, I Gede Orka
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.211

Abstract

Tourism is a key pillar of Bali’s economy, contributing significantly to employment, cultural preservation, and income generation. Accurate forecasting of tourist arrivals is crucial for sustainable growth and resource optimization. This study applies Long Short-Term Memory (LSTM) neural networks to predict tourist arrivals in Bali, leveraging historical data and external factors such as global economic indicators, flight frequencies, cultural events, and environmental conditions. LSTM’s ability to model complex temporal dependencies and non-linear relationships offers significant advantages over traditional methods like ARIMA, especially in handling seasonal patterns and irregularities. The model was trained on a robust dataset, preprocessed to address missing values, outliers, and variability. Performance evaluation metrics, including RMSE, demonstrate high predictive accuracy during stable periods but highlight limitations in handling anomalies such as the COVID-19 pandemic. To address these challenges, recommendations include integrating additional external variables, employing hybrid models, and conducting scenario-based sensitivity analyses to enhance adaptability and robustness. The results highlight the practical utility of AI-driven forecasting tools in tourism management, providing actionable insights for policymakers and stakeholders to optimize planning, mitigate risks, and support sustainable development. This research contributes to the growing field of AI applications in tourism, promoting resilience and competitiveness in an increasingly dynamic global market.
Using Neural Networks for USD to IDR Exchange Rate Prediction Santiago, Gede Agus; Sugiartawan, Putu; Erawati, Kadek Nonik; Mahendra, I Gede Orka; Kumara, I Dewa Made Putra; Frasetya, I Gusti Ngurah Hendra
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.212

Abstract

Predicting the USD to IDR exchange rate is critical for financial markets, international trade, and economic policy. This research employs neural networks to model the complex and non-linear patterns inherent in time-series data. The methodology involves collecting historical daily exchange rate data, preprocessing to handle missing values, normalizing features, and transforming the data into a format suitable for modeling. The neural network architectures utilized include Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Model evaluation metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), indicate the neural networks’ effectiveness in capturing general trends with high accuracy, despite challenges during periods of high market volatility. Comparative analysis with traditional methods, such as ARIMA, highlights the superior ability of neural networks to manage non-linear relationships and long-term dependencies. This study provides valuable insights into developing advanced tools for exchange rate prediction, leveraging the power of machine learning. The results demonstrate the potential of neural networks in financial forecasting, with opportunities for improvement through integrating additional external factors and optimizing model architectures.
Comparison of ResNet CNN and Optimized Vision Transformer Model for Classification of Dried Moringa Leaf Quality Santiyuda, Kadek Gemilang; Febyanti, Putu Ayu; Wibawa, Gusti Putu Sutrisna; Welson, Samuel; Sutrisna, I Made Adi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.213

Abstract

The quality classification of dried Moringa leaves is an essential task in the agricultural and food processing industries due to its direct impact on product value and consumer acceptance. This study aims to compare the performance of a Convolutional Neural Network (CNN) based on ResNet architecture with an optimized Vision Transformer (ViT) model for automated classification of dried Moringa leaf quality. The methodology involved preprocessing and normalization of image data, followed by training and evaluation of both models under identical experimental settings. The ResNet CNN achieved an overall accuracy of 68%, showing strong performance in certain classes such as “A” (precision 0.78, recall 0.90) and “F” (precision 0.80, recall 1.00), but poor recognition of class “D.” Conversely, the optimized Vision Transformer model attained an accuracy of 60%, demonstrating robust classification for classes “C” (f1-score 0.77) and “D” (f1-score 0.79), though it struggled with class “E.” The findings indicate that while ResNet CNN yields higher overall accuracy, the Vision Transformer shows potential in handling complex visual variations with optimization. This study contributes to the development of AI-based agricultural quality assessment systems by providing comparative insights into deep learning architectures for image-based classification.
Optimizing Chili Price Prediction Using Machine Learning Classification Antara, I Gede Made Yudi; Sugiartawan, Putu; Ardriani, Ni Nengah Dita; Dewa, Hari Putra Maha; Widya Dharma, I Gusti Ngurah Adi; Satya, I Putu Adnya
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.214

Abstract

Optimizing chili price prediction is critical for agricultural stakeholders, enabling better decision-making in supply chain management, market strategies, and farming practices. This research focuses on leveraging machine learning classification models to improve the accuracy and reliability of chili price predictions. The research addresses the challenges of class imbalance, which often occurs due to the uneven representation of price fluctuations in datasets. Resampling techniques, including oversampling the minority class with Synthetic Minority Oversampling Technique (SMOTE) and undersampling the majority class, were employed to balance the dataset and enhance the model's sensitivity to less frequent price drops. Key predictive features such as weather conditions, market demand, transportation costs, and economic indicators were integrated into the models. Advanced classification algorithms like Random Forests and Gradient Boosted Trees were utilized, demonstrating their effectiveness in handling non-linear relationships and class imbalance. Regularization techniques and k-fold cross-validation were applied to prevent overfitting and ensure robust model performance across different data subsets.The results show significant improvements in precision, recall, and overall model accuracy, making the approach suitable for real-world applications. By optimizing machine learning models, this research provides actionable insights for stakeholders to manage price volatility effectively, supporting sustainable agricultural practices and market stability.
Comparison of ResNet CNN and Optimized Vision Transformer Model for Classification of Dried Moringa Leaf Quality Santiyuda, Kadek Gemilang; Febyanti, Putu Ayu; Wibawa, Gusti Putu Sutrisna; Welson, Samuel; Sutrisna, I Made Adi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 4 (2025): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.215

Abstract

The quality classification of dried Moringa leaves is an essential task in the agricultural and food processing industries due to its direct impact on product value and consumer acceptance. This study aims to compare the performance of a Convolutional Neural Network (CNN) based on ResNet architecture with an optimized Vision Transformer (ViT) model for automated classification of dried Moringa leaf quality. The methodology involved preprocessing and normalization of image data, followed by training and evaluation of both models under identical experimental settings. The ResNet CNN achieved an overall accuracy of 68%, showing strong performance in certain classes such as “A” (precision 0.78, recall 0.90) and “F” (precision 0.80, recall 1.00), but poor recognition of class “D.” Conversely, the optimized Vision Transformer model attained an accuracy of 60%, demonstrating robust classification for classes “C” (f1-score 0.77) and “D” (f1-score 0.79), though it struggled with class “E.” The findings indicate that while ResNet CNN yields higher overall accuracy, the Vision Transformer shows potential in handling complex visual variations with optimization. This study contributes to the development of AI-based agricultural quality assessment systems by providing comparative insights into deep learning architectures for image-based classification.

Page 11 of 15 | Total Record : 149

 9   10   11   12   13 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol 8 No 1 (2025): September Vol 7 No 4 (2025): June Vol 7 No 3 (2025): March Vol 7 No 2 (2024): December Vol 7 No 1 (2024): September Vol 6 No 4 (2024): June Vol 6 No 3 (2024): March Vol 6 No 2 (2023): December Vol 6 No 1 (2023): September Vol 5 No 4 (2023): June Vol 5 No 3 (2023): March Vol 5 No 2 (2022): December Vol 5 No 1 (2022): September Vol 4 No 4 (2022): June Vol 4 No 3 (2022): March Vol 4 No 2 (2021): December Vol 4 No 1 (2021): September Vol 3 No 4 (2021): June Vol 3 No 3 (2021): March Vol 3 No 2 (2020): December Vol 3 No 1 (2020): September Vol 2 No 4 (2020): June Vol 2 No 3 (2020): March Vol 2 No 2 (2019): December Vol 2 No 1 (2019): September Vol 1 No 4 (2019): June Vol 1 No 3 (2019): March Vol 1 No 2 (2018): December Vol 1 No 1 (2018): September More Issue