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J-Icon : Jurnal Komputer dan Informatika
Published by Universitas Nusa Cendana
ISSN : 23377631     EISSN : 26544091     DOI : -
Core Subject : Science,
Computer Science & IT
J-ICON : Jurnal Komputer dan Informatika focuses on the areas of computer sciences, artificial intelligence and expert systems, machine learning, information technology and computation, internet of things, mobile e-business, e-commerce, business intelligence, intelligent decision support systems, information systems, enterprise systems, management information systems and strategic information systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 208 Documents
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Optimasi Hiperparameter Model Pembelajaran Mesin Untuk Klasifikasi Data Survey Langit Efraim Kurniawan Dairo Kette
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 2 (2025): October 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i2.18493

Abstract

Discovering the optimal model in today's popularity of various machine learning applications remains an essential challenge. Besides data dependency, the performance of classification models is also affected by deciding on suitable algorithm with optimal hyperparameter settings. This study conducted a hyperparameter optimization process and compared the accuracy results by applying various classification models to the observation dataset. This study obtains data from the Sloan Digital Sky Survey Data Release 18 (SDSS-DR18) and Sloan Extension for Galactic Understanding and Exploration (SEGUE-IV). The SDSS-DR18 and SEGUE-IV provide observational data of space objects, such as stellar spectra with corresponding positions and magnitudes of galaxies or stars. The SDSS-DR18 dataset contains magnitude and redshift data of celestial objects with target features of stars, Quasi Stellar Objects (QSOs), and galaxies. The SEGUE-IV dataset contains equivalent-width parameters, inline indices, and other features to the radial velocity of the corresponding star spectrum. This study utilized several machine learning models, such as k-Nearest Neighbor (KNN), Gaussian-Naive Bayes, eXtreme Gradient Boosting (XGBoost), Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). This study utilized Bayesian, Grid, and Random-based approaches to find the optimal hyperparameters to maximize the performance of the classification model. This study proved that some classification models have improved accuracy scores through the Bayesian-based hyperparameter optimization settings. This study discovers the XGBoost model shows the highest classification results after hyperparameters optimization compared to other models for both datasets with an average accuracy of 99.10% and 95.11%, respectively.
A COMPARATIVE STUDY OF SUPERVISED FEATURE SELECTION METHODS FOR PREDICTING UANG KULIAH TUNGGAL (UKT) GROUPS Windy Chikita Cornia Putri; Wiyli Yustanti; Ervin Yohannes
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 2 (2025): October 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i2.23893

Abstract

The manual classification of Uang Kuliah Tunggal (UKT) groups at Indonesian public universities is laborious, subjective, and error-prone, especially given the explosion of socio-economic data captured via online admission portals. In this study, we evaluate five feature selection techniques Chi-Square filter, Random Forest importance, Recursive Feature Elimination, LASSO embedded selection, and Exploratory Factor Analysis on a dataset of 9,369 applicants described by 53 socio-economic variables. Six classifiers (Decision Tree, Random Forest, SVM-RBF, K-Nearest Neighbor, and Naïve Bayes) were tuned via stratified 5-fold cross-validation within an 80:20 train-test split. Performance was measured by accuracy, macro-F1, and training time, and differences in weighted-average accuracy across feature-selection scenarios were assessed using the Friedman test (χ² = 15.06, p = 0.010). Results show that reducing to 13 features via LASSO (weighted-average accuracy 0.730) or Chi-Square (0.678) significantly outperforms both the full feature baseline (0.624) and the EFA baseline (0.303), while cutting computational costs by over 40%. We conclude that supervised feature selection particularly LASSO and Chi-Square enables simpler, faster, and more transparent UKT prediction without sacrificing accuracy. The novelty of this study lies in comparing five feature-selection methods within a standardized preprocessing pipeline on real UKT data from UNESA, resulting in a 13-feature subset aligned with the current UKT policy. This finding is ready to be integrated into an automated UKT verification system to enhance decision accuracy and efficiency.
AGILE DEVELOPMENT IMPLEMENTATION ON VIDYAMEDIC HEALTHCARE INFORMATION SYSTEM BASED ON SCRUM FRAMEWORK Isep Lupti Nur; Mohammad Riza Nurtam; Daniel Nugraha
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 2 (2025): October 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i2.23586

Abstract

Efficient and integrated information systems are essential for managing healthcare services, including patient data, laboratory results, and radiology records. This study presents the development of Vidyamedic, a web-based healthcare information system designed to streamline healthcare data management. The system was developed using HTML, CSS, and JavaScript for the front end and PHP with the Laravel framework for the back end. The development process adopted the Scrum framework, an Agile methodology that supports iterative and adaptive system development. The project was completed over eight weeks, divided into four sprints. Each sprint spanned two weeks or 10 working days. A total of 14 development tasks were completed by a team of four members. Key Scrum activities included product backlog creation, sprint planning, and the use of burndown charts to monitor progress and identify performance trends during development. System validation was conducted through black-box testing to ensure that each feature operated according to user requirements, without evaluating the system's internal structure. The resulting application provided role-based dashboards tailored for physicians, laboratory administrators, radiology administrators, and queue administrators. The findings demonstrate that Agile methodologies, particularly Scrum, can be effectively applied in the healthcare sector to develop reliable and adaptable information systems that improve healthcare data management.
DEVELOPMENT OF A 360° VIRTUAL REALITY-BASED ANDROID APPLICATION FOR CAMPUS INTRODUCTION AT WASTUKANCANA COLLEGE OF TECHNOLOGY USING MDLC METHOD Rohmat Rohmat; Muhammad Rafi Muttaqin; Yusuf Muhyidin
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 2 (2025): October 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i2.23847

Abstract

This study aims to develop a 360 Virtual Tour application based on Android as an interactive medium to introduce the campus environment of Sekolah Tinggi Teknologi Wastukancana to new students. The application integrates Virtual Reality (VR) technology to provide an immersive exploration experience through 360 panoramic views. Key features include hotspot-based navigation between scenes, automatic audio narration using text-to-speech, and a stereoscopic display mode compatible with Google Cardboard. The development follows the Multimedia Development Life Cycle (MDLC) method, consisting of six stages: concept, design, material collecting, assembly, testing, and distribution. The application was implemented using Unity 2021.3.45 LTS and C# programming language, along with gyroscope sensor support to align the panorama with the user's viewing direction. Functional testing using the blackbox method was conducted with 177 test cases, all of which passed successfully with a 100% success rate. The APK file was distributed through GitHub and Google Sites for direct access by new students. Initial feedback indicated that the application is visually appealing, user-friendly, and capable of delivering a virtual tour experience that closely resembles the real campus environment. These results suggest that the application is effective as a digital campus introduction tool that is informative, practical, and innovative.
EVALUATION OF THE IMPLEMENTATION OF THE INDEPENDENT ACADEMIC INFORMATION SYSTEM (SIAMIR) AT STIKOM UYELINDO USING THE DELONE AND MCLEAN MODEL Semlinda Juszandri Bulan
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 2 (2025): October 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i2.24707

Abstract

Academic information systems are a crucial element in supporting the effectiveness and efficiency of academic management at universities. STIKOM Uyelindo uses an academic information system called SiAmir to serve the academic administration needs of students, lecturers, and staff. However, the success of this system's implementation needs to be systematically evaluated to ensure its continued improvement. This study aims to evaluate the implementation of SiAmir using the DeLone and McLean model, which consists of six variables: system quality, information quality, service quality, system usage, user satisfaction, and net benefits. Respondents in this study were 165 students and lecturers at STIKOM Uyelindo. The data analysis method used was Partial Least Squares (PLS) and was run using SmartPLS version 3.0 software. The results showed that six hypotheses were accepted and three were rejected. Information Quality and Service Quality significantly influenced User Usage and Satisfaction, while System Quality had no significant effect. User Satisfaction proved to be a key mediator with the strongest influence on Net Benefits, while Usage had no direct effect. The findings confirm that information system success is determined more by the quality of content and services than by technical aspects, with user satisfaction being the primary mediating factor. Practical implications suggest that organizations need to prioritize investments in information and service quality to maximize the benefits of information systems.
Perbandingan Kinerja Analisis Sentimen Ulasan Monumen Nasional (Monas) Menggunakan Model Deep Learning BERT dan Klasifikasi Machine Learning Multinomial Naïve Bayes. Riki Daniel Tanebeth
J-Icon : Jurnal Komputer dan Informatika Vol 14 No 1 (2026): March 2026
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v14i1.27042

Abstract

The National Monument (Monas), as an icon of Indonesian tourism, faces challenges in maintaining visitor satisfaction in the digital era. Online reviews on Google Maps serve as a crucial data source for understanding public perception. However, the large volume of data and the informal nature of review language hinder manual analysis. This study aims to analyze Monas visitor sentiment and compare the performance of conventional Machine Learning models with modern Deep Learning approaches. The method involves comparing the Multinomial Naïve Bayes algorithm using TF-IDF feature extraction against the IndoBERT (Bidirectional Encoder Representations from Transformers) model based on fine-tuning. The dataset consists of 1,110 visitor reviews from the 2023-2024 period. Experimental results demonstrate that the IndoBERT model significantly outperforms Naïve Bayes, achieving an accuracy of 93.5% and an F1-Score of 93.0%, while Naïve Bayes only reached 49.1% accuracy. Further aspect-based analysis reveals that although positive sentiment is dominant (49%), there are critical complaints regarding the digital ticketing system and elevator queues. This study recommends the implementation of transformer-based models for analyzing Indonesian tourism reviews and suggests improvements in queue management for Monas management.
PREDIKSI HARGA BERAS DI SUMBA TIMUR MENGGUNAKAN ALGORITMA NEURAL NETWORK Renol Bulu Manggal; Arini Aha Pekuwali; Raynesta Mikaela Indri Malo
J-Icon : Jurnal Komputer dan Informatika Vol 14 No 1 (2026): March 2026
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v14i1.27555

Abstract

Fluctuations in rice prices in East Sumba Regency are an important issue that directly affects farmers, traders, and consumers. Unstable price changes are influenced by weather conditions, supply availability, distribution, and market dynamics. Therefore, a prediction method is needed that can provide accurate estimates of rice prices as a basis for decision making. This study aims to predict the price of medium rice in East Sumba Regency using the Neural Network algorithm, specifically Long Short-Term Memory (LSTM), which is effective in modeling time series data. The data used are monthly rice price data for the period January 2021 to December 2025 obtained from Perum BULOG Waingapu Branch Office, with data processing and analysis carried out after all 2025 data became available. The research stages include data collection, data preprocessing, normalization using Min-Max Scaling, time series dataset formation, division of training and testing data, LSTM model training, and model performance evaluation. The evaluation was carried out using the Root Mean Square Error (RMSE) metric. The results show that the LSTM model is able to predict rice prices with an RMSE value of 360.91 Rp/Kg or around 3.35% of the average rice price. This value indicates that the prediction error of the model is relatively small, so the model can be said to have good prediction performance. Therefore, the developed LSTM model is considered feasible to be used as a tool for predicting rice prices and is expected to help farmers and traders in planning sales and become a consideration for the local government in maintaining rice price stability in East Sumba Regency.
Sistem Pendukung Keputusan untuk Menentukan Peminatan Mahasiswa Menggunakan Metode Fuzzy Tsukamoto dan TOPSIS Juan Keinan thimothi Paparang; Magdalena A. Ineke Pakereng
J-Icon : Jurnal Komputer dan Informatika Vol 14 No 1 (2026): March 2026
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v14i1.27447

Abstract

This study aims to design a decision support system that helps students choose a specialization that suits their abilities and interests, thus supporting timely graduation. The method used is a combination of Fuzzy and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). This study was conducted to obtain the most appropriate specialization recommendations for students, based on existing criteria. The criteria used in this study include academic scores of compulsory courses relevant to each specialization, skills, and programming abilities. The results of the study will provide specialization recommendations that are sorted by the highest preference value, so that students can see the choices of specializations that best suit their abilities and interests. In addition, these results are also expected to help students make the right decisions, as well as increase their chances of graduating on time.

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