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Jurnal Ilmu Komputer, Teknologi Dan Informasi
Published by CV. Graha Mitra Edukasi
ISSN : -     EISSN : 29630169     DOI : https://doi.org/10.62866/jurikti.v2i1
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Social Sciences Other
Jurnal Ilmu Komputer, Teknologi Dan Informasi, ini memiliki bidang kajian: 1. Manajemen Informatika, 2. Sistem Informasi, 3. Game Design, 4. Multimedia System, 5. Sistem Pembelajaran Berbasis Multimedia, 6. GIS, 7. Mobile Programming, 8. Database Design, 9. Network Programming, 10. Distributed System, 11. Data Mining, 12. Sistem Pakar, 13. Kriptografi, dan 14. Sistem Pendukung Keputusan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 44 Documents
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Peningkatan Akurasi Diagnosis Penyakit Ginjal Kronis melalui Integrasi Algoritma Naive Bayes dan Algoritma Genetika Eka Pandu Cynthia; Edi Ismanto
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.272

Abstract

Chronic Kidney Disease (CKD) is a significant global health challenge that necessitates early diagnosis to prevent severe organ failure. While machine learning techniques such as Naive Bayes (NB) have been widely implemented for medical classification, their performance is often hindered by redundant and irrelevant features within high-dimensional medical datasets. This study aims to address this limitation by reducing the dimensions of non-contributing medical attributes, thereby minimizing bias and improving classification accuracy. Consequently, this study proposes the integration of Genetic Algorithm (GA) as a feature selection method to optimize the performance of the Naive Bayes (NB) algorithm in diagnosing CKD. The dataset, sourced from the UCI Machine Learning Repository, consists of 400 samples and 24 clinical features. A genetic algorithm was employed to identify the optimal feature subset through a binary evolution mechanism, while NB served both as the classifier and the fitness evaluation function. The results demonstrate that GA successfully reduced the data dimensions by 50%, streamlining the initial 24 features into 12 highly discriminative ones. Evaluation using 10-Fold Cross-Validation revealed a significant increase in accuracy, rising from 92.50% using the standard NB to 98.50% with the integrated GA-NB model. Furthermore, the recall reached 98.40%, indicating the model's high capability in minimizing diagnostic errors for affected patients (false negatives). This research proves that GA-based feature selection effectively enhances diagnostic reliability and model efficiency, presenting substantial potential for implementation in clinical decision support systems for medical professionals.
Pengembangan Aplikasi Pemantauan dan Rekomendasi Kesehatan Lansia dengan Metode Content-Based Filtering dengan Sistem Notifikasi untuk Keluarga Fauzan Natsir; Redo Abeputra Sihombing; Andi Dwi Pangestu
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 3 No 2 (2025): Juli 2025
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v3i2.274

Abstract

The growth of the elderly population in Indonesia continues to increase significantly; however, access to adequate healthcare services remains a major challenge that has yet to be resolved. This situation is further compounded by the lack of continuous monitoring and the limited ability of families to detect changes in the health condition of elderly individuals at an early stage. This study aims to develop a web and mobile-based application that integrates real-time elderly health monitoring features with a health recommendation system using the Content-Based Filtering (CBF) method, equipped with an automatic notification system for family members as an effort to improve responsiveness to emergency conditions and significant health changes. The development method employed is the Prototype Model with an iterative approach, enabling continuous feature refinement based on user feedback at each development cycle. The CBF algorithm utilizes the comprehensive individual health profiles of elderly users, encompassing medical history, vital data, and daily lifestyle patterns, to generate personalized and relevant recommendations for each user. System testing was conducted using the Black Box Testing method for functional validation and the System Usability Scale (SUS) for evaluating the usability of the application interface. The test results indicate that all system functions operate in accordance with the defined specifications, with a weighted average SUS score of 75.9, which falls within the Good category. This application is expected to strengthen collaboration between families and healthcare professionals in efforts to improve the quality of life and overall well-being of the elderly in Indonesia in a sustainable manner.
Implementasi Autoregressive Integrated Moving Average untuk Prediksi Pasien Rumah Sakit Berbasis Dashboard Interaktif Amelia amel Contesa
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.304

Abstract

A major challenge in healthcare service management is the uncertainty of patient arrivals during each service period. Fluctuations in patient visits can affect the effectiveness of human resource planning, healthcare facility allocation, pharmaceutical inventory management, and the overall quality of services provided to the community. Therefore, an approach capable of accurately predicting the number of patients is required to support more effective decision-making processes. This study aims to implement the Autoregressive Integrated Moving Average (ARIMA) method to predict hospital patient visits and integrate the forecasting results into a web-based interactive dashboard. The dataset used in this study was obtained from a Kaggle repository containing daily patient visit records from healthcare facilities in India. The daily data were transformed into monthly data through a temporal aggregation process to generate a more stable time series suitable for forecasting modeling. The forecasting model employed was ARIMA (2,1,0), while the system was developed using Google Apps Script as the development platform and Chart.js as the data visualization component. The results indicate that the total number of patient visits during the observation period reached 11,934 patients, with an average of 442 patients per month. The ARIMA model predicted 727 patient visits for the subsequent period, indicating an increasing trend. The main contribution of this study lies in the integration of a forecasting model and an interactive dashboard within a single system capable of presenting statistical information, trend visualizations, and forecasting results in real time to support decision-making in healthcare management. The findings demonstrate that the proposed approach can serve as an effective data-driven tool for hospital operational planning and resource management.
Perancangan Sistem Informasi Administrasi Rukun Tetangga Berbasis Web Menggunakan Metode Scrum Miranty Yudistira; Dedy Setiawan; Ari Andrianti; Devi Listiani Safitri; Zikra Zana; M. Sakti Guruh Pratama
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.307

Abstract

The manual management of population administration at the Rukun Tetangga (RT) level causes significant inefficiencies, including slow data retrieval, physical document duplication, and a lack of transparency in social assistance distribution. This study aims to design and implement a web-based RT administration information system named BISAKELOLA at RT 14, Kenali Asam, Jambi City, which integrates resident data management and social assistance management into a single platform. The system was developed using the Scrum method through three iterative sprints, with data collected through direct observation, interviews, and literature review, and validated using Black Box Testing based on the Equivalence Partitioning technique. BISAKELOLA successfully centralized resident data, accelerated information retrieval, eliminated physical document duplication of family cards and identity cards, and digitalized the social assistance submission process into a more transparent workflow. All test scenarios across four main modules, namely authentication, resident data, social assistance, and account management, performed as expected without critical errors. Sprint reviews conducted at the end of each sprint proved effective in adaptively accommodating user requirement changes, resulting in a system that is functionally aligned with real community needs. BISAKELOLA proves to be an effective digital solution for modernizing RT administrative services and improving social assistance transparency, and is expected to serve as a replicable model for community-level digitalization across other RT/RW environments in Jambi City.
Penerapan Algoritma Naive Bayes Untuk Klasifikasi Kelayakan Penerima Beasiswa Berdasarkan Data Sosial Ekonomi Mahasiswa Kevin Rasi Dauly; Jaya Tata Hardinata; Togi Lumbantobing; Rahul Sinurat
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.323

Abstract

Scholarship programs are one of the efforts made by universities to improve educational quality while supporting students with limited economic conditions. The scholarship selection process generally considers various socio-economic factors, including parents' income, number of family dependents, Grade Point Average (GPA), ownership of the Indonesia Smart Card (KIP), and housing status. In practice, the selection process is still largely performed manually, which is time-consuming, prone to recording errors, and potentially less objective because it depends on the subjective judgment of the staff involved. This condition highlights the need for a computational approach that can support faster, more consistent, and measurable decision-making. This study aims to implement the Naive Bayes algorithm, a probability-based classification method that works based on Bayes' Theorem with the assumption of conditional independence among attributes, to classify scholarship eligibility based on students' socio-economic data. The research method consists of five main stages: data collection, data preprocessing (handling missing values, duplicate records, and categorical attribute transformation), dataset splitting using the Hold-Out Validation method with a composition of 80% training data and 20% testing data, model training using the Naive Bayes algorithm implemented with the Scikit-learn library, and performance evaluation using a Confusion Matrix. The dataset consists of 200 student records with seven attributes, namely parents' income, number of dependents, GPA, housing status, KIP ownership, semester level, and eligibility status. The results show that the Naive Bayes algorithm is able to classify scholarship eligibility with an Accuracy of 92.50%, Precision of 95.65%, Recall of 91.67%, and F1-Score of 93.62%. These results confirm that the Naive Bayes algorithm can be used as a decision support method to make the scholarship selection process faster, more objective, and more efficient than manual selection.
Analisis Faktor Penerimaan TikTok Go sebagai Layanan E-voucher Berbasis Modifikasi UTAUT2 dengan Metode PLS-SEM Amira Aurelia Salsabila; Daniel Arsa; Miranty Yudistira
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.330

Abstract

TikTok, as the fastest-growing social media platform in Indonesia, launched the TikTok Go service in February 2025. However, despite TikTok's massive user base in Indonesia, the market penetration of TikTok Go remains very low, at approximately 0.4% compared to 20% in the Chinese market. This low adoption rate potentially hinders service development, reduces platform revenue potential, limits incentives for content creators, and threatens the sustainability of partnerships with merchants. This phenomenon is supported by user review findings regarding various operational issues, such as technical glitches where barcodes fail to appear, data inconsistencies in product availability between merchants and the platform, and limited purchasing features in certain locations. This study aims to analyze the factors influencing user acceptance of TikTok Go by integrating a modified UTAUT 2 model. Data were collected through questionnaires from 180 respondents in Indonesia who have made at least two e-voucher purchases. The analysis was conducted using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to test the structural and measurement models. The results indicate that Facilitating Conditions and Price Value significantly influence Purchase Intention. Furthermore, Habit and Purchase Intention significantly affect Use Behavior, while Social Commerce Construct plays a crucial role in enhancing User Trust. These findings confirm that strengthening facility support, optimizing pricing strategies, and fostering user habits are key drivers for increasing service adoption. Consequently, this study provides strategic recommendations for platform management to optimize facility support, pricing strategies, and engagement programs to enhance TikTok Go service adoption in Indonesia.
Prediksi Harga Beras Tingkat Perdagangan Besar Menggunakan Algoritma Extreme Gradient Boosting Berbasis Indikator Ekonomi Makro Josua Jhon Radho Hutahaean
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.317

Abstract

Wholesale rice prices are an important indicator for food-price monitoring because their movements affect supply chains, distribution actors, and household purchasing power. This study develops an Indonesian wholesale rice-price forecasting model using Extreme Gradient Boosting (XGBoost) with macroeconomic indicators and temporal features. Official secondary data from Statistics Indonesia were collected from January 2018 to May 2026, comprising 101 monthly observations. The predictor variables include general inflation, rice production, harvested area, Farmers’ Terms of Trade for food crops, population, and the food Consumer Price Index. In addition, lag-1, lag-3, and a three-month moving-average feature were constructed from historical rice-price observations. The dataset was divided chronologically into 84 training observations and 17 testing observations. Hyperparameter optimization was performed using Grid Search with TimeSeriesSplit on the training data. XGBoost performance was compared with ARIMA(1,1,1) and Random Forest Regression using RMSE, MAE, MAPE, and R². The test results show that XGBoost achieved an RMSE of IDR 234.17/kg, an MAE of IDR 189.43/kg, a MAPE of 1.63%, and an R² of 0.9621. These results outperform ARIMA and Random Forest on the same test set. Feature-importance analysis indicates that the lag-1 price, three-month moving average, and Farmers’ Terms of Trade provide the largest predictive contributions. The findings indicate that multivariate XGBoost has potential as a prototype for data-driven wholesale rice-price monitoring.
Penerapan Algoritma Navi Bayes dalam Memprediksi Tingkat Resiko Penyakit Stroke Menggunakan Data Kesehatan Angel Ariski Simatupang
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.331

Abstract

Stroke is a non-communicable disease and one of the leading causes of death and long-term disability worldwide, making early detection essential through intelligent computational systems that can assist healthcare professionals in clinical decision-making. The main challenge addressed in this study is the complexity of multifactorial stroke risk factors and the imbalance of medical record datasets, which may affect the performance of classification models. This study implements the Gaussian Naive Bayes algorithm as the classification method, supported by comprehensive data preprocessing stages, including mean value imputation for missing data, label encoding to transform categorical variables into numerical values, Min-Max Scaling normalization, and classification modeling using RapidMiner. The objective of this research is to accurately predict the level of stroke risk based on patients' medical records in order to support clinical decision support systems for preventive healthcare and early intervention. The experimental results demonstrate that the proposed model achieved an overall accuracy of 90.70%, a precision of 72.08%, a recall of 78.89%, and an F1-score of 75.33%. Furthermore, the model successfully classified posterior probabilities into hierarchical risk categories, providing more interpretable information for identifying high-risk patients and supporting more effective and efficient clinical decision-making processes in preventive stroke management.
Analisis Sentimen Ulasan Pengguna Aplikasi Gojek di Google Play Store Menggunakan Metode Multinomial Naive Bayes dan Logistic Regression Nurul Hidayanah; Siska Fitriani; Icha Winadya Permadani; Ryan Randy Suryono
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.333

Abstract

The Gojek application has become one of the largest platforms providing transportation, food delivery, and digital payment services in Indonesia. Every day, thousands of users provide reviews in the form of criticisms, complaints, and compliments through the Google Play Store. However, the massive volume and unstructured nature of these reviews pose challenges for management in monitoring user satisfaction manually, objectively, and rapidly. The primary issue in analyzing this review data is the high level of class imbalance between the number of positive and negative reviews, where positive reviews frequently dominate significantly. This data imbalance becomes crucial as it tends to bias standard classification models and reduce their predictive accuracy toward the minority class (negative reviews), even though these negative reviews contain vital complaints necessary for system improvement. Therefore, this study aims to analyze the sentiment of Gojek users by comparing the performance of the Multinomial Naïve Bayes (MNB) and Logistic Regression (LR) classification algorithms, while simultaneously addressing the data imbalance issue. The research process encompasses text preprocessing stages, including cleansing, case folding, stopword removal, and stemming. Feature extraction is performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorizer methods based on Unigram and Bigram schemes. Data splitting utilizes a proportion of 80% training data and 20% testing data, where the Synthetic Minority Over-sampling Technique (SMOTE) is applied specifically to the training data to resolve the text category imbalance. The evaluation results are measured based on accuracy, precision, recall, and F1-score metrics. This research is expected to provide recommendations for the best algorithm for large-scale review text classification, as well as data-driven insights for Gojek developers to enhance service quality based on genuine user sentiments.
Komparasi Algoritma Naive Bayes, Random Forest, dan Decision Tree untuk Prediksi Penyakit Stroke Menggunakan Orange Data Mining Wulan Liviana Simbolon; David Ofel Gihon Purba; Ningsih Purba; Saudurma Sidabutar; Betharya Tampubolon; Jaya Tata Hardinata
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.359

Abstract

Stroke is one of the leading causes of death and long-term disability worldwide, making accurate prediction methods essential to support early detection and clinical decision-making. The problem addressed in this study is the lack of evidence regarding which classification algorithm provides the best performance for predicting stroke using the Healthcare Stroke Dataset. This study aims to compare the performance of the Naive Bayes, Random Forest, and Decision Tree algorithms using Orange Data Mining to identify the most effective predictive model. A quantitative approach with a comparative experimental design was employed. The dataset used in this research was the Healthcare Stroke Dataset obtained from Kaggle, consisting of 5,110 records with 12 attributes. The research process included data preprocessing using the Impute widget, feature selection using the Rank widget, classification model development, and model evaluation through 10-fold cross-validation. Performance was assessed using Accuracy, Area Under the Curve (AUC), Precision, Recall, F1-Score, Matthews Correlation Coefficient (MCC), Confusion Matrix, and Receiver Operating Characteristic (ROC) analysis. The results indicate that the Decision Tree algorithm achieved the highest accuracy of 95.1%, followed by Random Forest with 94.8%, while Naive Bayes achieved 92.4%. However, Naive Bayes obtained the highest AUC value of 0.804, demonstrating superior class discrimination capability on an imbalanced dataset. These findings suggest that algorithm selection should not rely solely on accuracy but also consider the model's ability to distinguish between classes consistently. This study contributes to providing recommendations for selecting appropriate classification algorithms to support the development of machine learning-based early stroke prediction systems

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