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KOMPUTA : Jurnal Ilmiah Komputer dan Informatika
Published by Universitas Komputer Indonesia
ISSN : 20899033     EISSN : 27157849     DOI : 10.34010
Core Subject : Science,
Computer Science & IT
Jurnal Ilmiah KOMPUTA (Komputer dan Informatika), adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis di bidang kelimuan Komputer dan Informatika. Terbit dua kali dalam setahun pada bulan Maret dan Oktober.
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Articles 221 Documents
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Implementasi Algoritma Neural Collaborative Filtering Menggunakan TensorFlow Sebagai Rekomendasi Buku Pada Aplikasi Praktikum Program Studi Sistem Informasi Fariz Aisyar Dafin, Ahmad; Irsyad, Akhmad; Rivani Ibrahim, Muhammad
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.16724

Abstract

Low literacy levels among students pose a significant challenge in supporting academic activities, especially in practical courses in the Information Systems Study Program. This study aims to develop a personalized and relevant book recommendation system using the Neural Collaborative Filtering (NCF) algorithm implemented in TensorFlow and deployed through FastAPI. The dataset used is Book-Crossing, containing over one million user-book interactions. The development follows the CRISP-DM methodology, covering business understanding, data preparation, modeling, and deployment. The NCF model utilizes embedding and dense layers to learn complex user-item interactions. Evaluation shows that the model achieves MAE of 0.3133 and MSE of 0.1531 on training data. The system was successfully deployed and validated through unit testing, capable of providing the top five book recommendations based on user input. The result demonstrates the effectiveness of deep learning approaches in enhancing student literacy through adaptive and integrated recommendation systems.
Prediksi Kelayakan Pemberian Kredit dengan Algoritma Backpropagation Agustin, Dhea Ayu; Febri; Arianto, Dede Brahma
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.17660

Abstract

One of problems in lending activities is credit risk due to errors in selecting debtors. In this study, the backpropagation algorithm will be used to develop a prediction calculation system that uses features such as age, gender, marital status, occupation, income, number of dependents, loan amount, time period, collateral, home ownership, and loan purpose to predict creditworthiness. To determine the accuracy level of built, a model evaluation was conducted. The model evaluation was carried out using a confusion matrix, but before that, the data used was separated by ratio of 80 : 20, namely 80% for training and 20% for testing. With the best hyperparameters from several hyperparameter tuning scenarios, the scenario used for implementation in the system is screnario model 5 with 2 hidden layers (50 and 25 neurons), ReLU activation function, learning rate 0.001, 500 epochs, batch size 64, adam optimizer, and early stopping, resulting in an accuracy of 98.18% and a f1 Score of 98.33%. These values are excelent amd show that system created can be used as a reference in predicting creditworthiness. In addition, these values show that the backpropagation model is free from overfitting.
Pengaruh Fitur Tambahan untuk Klasifikasi KepribadianMyers-Briggs Type Indicator (MBTI) Menggunakan SVM Widiastuti, Nelly Indriani; Dewi, Kania Evita; Sidik, Muhammad Abdul Rohman
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.17796

Abstract

This study examines the effect of adding metadata feature on the effectiveness of the Support Vector Machine (SVM) algorithm in classifying personality types based on the Myers–Briggs Type Indicator (MBTI) indicators, using data from Indonesian-language X (Twitter) posts as a representation of users' digital expressions. The developed model integrates two main feature categories: textual features extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) method, and metadata features that reflect users' social interaction patterns, such as the number of retweets, likes, followers, and publication time. These features are considered capable of representing user behaviour dynamics more comprehensively. After the dataset is cleaned, pre-processing, feature extraction, and encoding are performed. Classification is then performed using SVM. This study employed four systematically designed testing scenarios: two scenarios utilised pure text data, while the other two combined social metadata features. Each scenario was tested both before and after the hyperparameter tuning process to optimise model performance. The evaluation was conducted using accuracy and F1-score metrics to measure the accuracy and balance of the classification model. The results of the experiment showed that the combination of social media metadata features consistently improves classification performance, with accuracy increasing by 2–6% and F1-score by 2–8% compared to text-based models alone. These findings confirm that social media metadata contributes significantly to enriching feature representation, thereby improving the precision, generalisation, and stability of models in identifying the personality types of social media users.
Pengelompokan Mahasiswa Berdasarkan Capaian Pembelajaran Lulusan Menggunakan K-Means Clustering di Program Studi Teknik Informatika UNIKOM Agustia, Richi Dwi; Finandhita, Alif
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.17932

Abstract

The Outcome-Based Education (OBE) curriculum emphasizes learning outcomes as benchmarks of graduate competence. This study in the Informatics Engineering Program at UNIKOM aims to cluster students based on the fulfillment of Learning Outcomes (CPL) to more accurately identify graduate professional tendencies. The dataset consists of 336 OBE cohort students, 58 core courses, and 312,793 academic records. The K-Means clustering method was applied with preprocessing steps including removal of missing values, duplicates, and non-relevant general courses (MKDU). Cluster validity was evaluated using the Davies–Bouldin Index (DBI) and Silhouette Score. The results yielded four clusters: (1) hardware integration and technology consultancy, (2) basic programming and data management, (3) systemic and managerial competence in information systems, and (4) data analytics, business intelligence, and predictive modeling. Evaluation metrics (DBI = 1.18; Silhouette Score = 0.27) indicate reasonably valid clustering despite intra-cluster variation. This study provides a strategic contribution to curriculum development in the Informatics Engineering Study Program at UNIKOM, particularly in aligning graduate profiles with the demands of the digital technology-driven workforce.
Prediksi Curah Hujan Berbasis Regreasi PolinomialMenggunakan Data Historis Cuaca Diyani, Dela Putri; Sriwijayanti, Erlis Rahayu; Gustrianysah, Rendra
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.16331

Abstract

Rainfall is one of the most variable and difficult-to-predict climate factors, especially in tropical regions like Indonesia. This uncertainty can significantly impact various sectors such as agriculture, forestry, and disaster mitigation. This study aims to develop a rainfall prediction model based on polynomial regression using historical weather data from Southeast Sulawesi. The dataset includes average temperature, average humidity, and sunlight duration, obtained from BMKG and processed using linear interpolation to handle missing values. Polynomial regression was chosen due to its ability to capture non-linear relationships between weather variables and rainfall. Model evaluation using Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) resulted in values of 41.84, 4.67 and 4.85, respectively, indicating relatively low prediction error. Therefore, polynomial regression proves to be an effective, accurate, and computationally efficient method for short-term rainfall forecasting.
Analisis Stres Akademik pada Mahasiswa yang Bekerja dengan Menggunakan Metode Fuzzy Logic(Studi Kasus: Mahasiswa Prodi Sistem Informasi Unpam Kampus Serang) Stevanes, Stevanes; Septiani, Selly
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.17730

Abstract

Students who work are at risk of experiencing higher academic stress due to the dual burden of work and study. This study aims to analyze the level of academic stress among students in the Information Systems Program for working adults (Regular C Saturday) at UNPAM Serang using a fuzzy logic approach. The method used is the Mamdani Fuzzy Inference System through five main stages: fuzzification, rule base formation, inference process, defuzzification, and result interpretation. The testing was conducted through MATLAB application calculations and manual calculations, with accuracy evaluated using the Mean Absolute Percentage Error (MAPE). The test results showed that the difference in results between the manual method and MATLAB was very small, with a MAPE value of 0.843%, indicating that MATLAB has a very high accuracy rate in classifying academic stress into the categories of no stress, low stress, moderate stress, and high stress. These findings prove that fuzzy logic is effective for measuring psychological variables that are difficult to capture conventionally. Additionally, this approach has the potential to be developed as a tool for early detection of psychological conditions among students in higher education settings.
Model Blockchain Pada Rekam Medis Terdistribusi : Tinjauan Ontologi dan Epistemologi  Terhadap Integritas Data Sufa Atin; Hani Irmayanti; Andri Heryandi; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18639

Abstract

Data integrity is a fundamental epistemological foundation for ensuring the validity and reliability of clinical knowledge. However, conventional Electronic Medical Record (EMR) systems still face structural and trust-related challenges due to centralized data management. This approach often leads to information fragmentation, limited interoperability among healthcare facilities, and low patient autonomy in controlling access to medical data. Philosophically, these conditions expose the limitations of an epistemology grounded in single authority, which is vulnerable to data manipulation, opacity, and centralized failures.This study examines, from a philosophical perspective, the role of distributed EMR systems based on permissioned blockchain—particularly Hyperledger Fabric—in strengthening guarantees of medical record data integrity. The research applies a philosophical conceptual analysis combined with a systematic literature review on EMR systems, blockchain technology, and data integrity. The analysis highlights the epistemological limitations of conventional EMRs, the defining features of permissioned blockchain architectures, and the ontological and epistemological implications of distributed consensus mechanisms. The findings indicate that, ontologically, blockchain redefines medical record data as distributed truth that is persistent and resistant to manipulation. Epistemologically, trust shifts from single authority to cryptographic validation and collective consensus, marking a paradigm shift in the legitimation of clinical knowledge in the digital era.
Symantic Literatur Review : Artificial Intelligence dalam Telemedicine dan Remote Patient Monitoring Diana Effendi; Sri Nurhayati; Zainal Arifin Hasibuan; Bobi Kurniawan S.; Sri Supatmi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18702

Abstract

The development of telemedicine and Remote Patient Monitoring (RPM) is increasing along with the need for efficient, adaptive, and data-driven remote healthcare services. Artificial Intelligence (AI) plays a crucial role in strengthening these systems through predictive analysis, medical classification, and real-time patient monitoring. However, research on AI integration in telemedicine and RPM remains scattered and exhibits wide methodological variation, necessitating a systematic review to understand the consistency of findings and the direction of research development. This study conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol, analyzing 128 publications from 2020–2025 obtained from Scopus, PubMed, IEEE Xplore, and Google Scholar. This study combined SLR synthesis with bibliometric mapping (co-occurrence and thematic mapping) to highlight the evolution of themes and topical interrelationships more explicitly. Bibliometric analysis results show an increase in the number of publications from 12 articles in 2020 to 45 articles in 2024, a nearly fourfold increase, before stabilizing in 2025. Co-occurrence and thematic mapping findings reveal four main themes: telemedicine–AI, computational methods based on machine learning and deep learning, physiological monitoring, and human factors in clinical evaluation. The study also identifies several challenges, including data security, signal quality, model transparency, and healthcare worker readiness. Theoretically, the findings emphasize that AI integration in telemedicine–RPM needs to be understood as a socio-technical issue that demands human-centered evaluation. Policy-wise, strengthening data governance and clinical validation standards is necessary for more accountable and secure implementation. This study concludes that AI plays a central role in the development of telemedicine and RPM, but further studies are needed on service personalization, multimodal data integration, and large-scale clinical validation.
Implementasi Machine Learning berbasis Browser Extension untuk Deteksi URL Phishing menggunakan Logistic Regression Classification Suwarno Suwarno; Vincent Capricornness; Yefta Christian
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18882

Abstract

Phishing is an increasingly prevalent cyber threat that is difficult to counter with traditional methods such as blacklists, which often fail to swiftly recognize new phishing websites. This research addresses the issue by developing a machine learning-based phishing URL detection system implemented as a browser extension called PhishBonk, using Logistic Regression for classification. The development process encompassed the collection of a dataset consisting of 134,850 legitimate URLs and 100,945 phishing URLs, data preprocessing, URL feature extraction, model training and evaluation, and finally integrating the trained Logistic Regression model into the PhishBonk extension to enable automatic real-time detection. Experimental results demonstrate that the Logistic Regression model achieved an accuracy of approximately 99.53% in distinguishing phishing URLs from legitimate ones. Furthermore, a System Usability Scale (SUS) evaluation yielded an average score of 81%, indicating that the PhishBonk extension is user-friendly and well-received. These findings suggest that the proposed machine learning-based browser extension effectively provides real-time, accurate phishing detection while ensuring a positive user experience.
Analisis Penerimaan Pengguna Terhadap Aplikasi Pencari Makanan Non Halal di Kota Batam Menggunakan Metode TAM Li Cen; Willsen Austin; Tony Wibowo
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.19349

Abstract

In Batam City, despite its varied demographic makeup, the non-Muslim population encounters difficulties in finding verified non-halal food options due to scattered information. This study's main objective is to assess user acceptance of a non-halal food finder app using the Technology Acceptance Model (TAM) as its theoretical basis. The research examines the impact of Perceived Ease of Use and Perceived Usefulness on users' behavioral intentions and actual usage in Batam. A quantitative method was utilized, involving 400 non-Muslim participants chosen through Simple Random Sampling, with data analyzed using SPSS. The statistical findings reveal that all five hypotheses are significantly validated. Importantly, Perceived Ease of Use greatly influences Perceived Usefulness, and together, these factors enhance a positive Attitude Toward Using, which significantly boosts Behavioral Intention and Actual Usage. The study offers a dual contribution: theoretically, it broadens TAM applications to services aimed at minorities; practically, it provides a strategic solution to close information gaps for the non-Muslim community. Therefore, the study concludes that the application is highly pertinent and widely accepted.

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