Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI)

Support Vector Machine for Classifying Prostate Cancer Data

2025-08-09T00:37:54+00:00

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.

Implementation of Web-Based Counseling System at SMK Negeri 1 Sukawati

2025-08-09T00:37:54+00:00

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.

Interactive Learning Media on Key Figures of Indonesian Independence Proclamation

2025-08-09T00:37:55+00:00

Education is a vital necessity in people's lives. One of the media that can be used is Interactive Learning Media, which can present material in visual form, as well as simulate material that is difficult to convey verbally. The author plans to provide material on Introduction to Key Figures Involved in the Proclamation of Independence, which is expected to facilitate students’ learning process. The research location is Jagapati Village, Abiansemal District, Badung Regency, Bali. The data collection methods used include interviews, observation, documentation, and literature review. The testing process involved both alpha and beta testing. The number of respondents was 32 people, consisting of 28 fifth-grade students, 2 subject matter experts in Social Studies for fifth grade at SD N 1 Jagapati, and 2 media experts. The results showed that the interactive learning media had a positive impact, with an evaluation score of 86% from teachers, indicating that the media is beneficial for teaching. The student evaluation score was 89.996%, showing a positive effect on student learning, while an evaluation score of 78% from university lecturers indicated that the media is suitable for use. It can therefore be concluded that Interactive Learning Media is highly practical for use by both students and teachers in the Social Studies learning process.

Adaptive Operator and Scaling Factor Selection in Differential Evolution using Parametrized Reinforcement Learning

2025-08-09T00:37:55+00:00

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

2025-08-09T00:37:55+00:00

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.