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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
Arjuna Subject : -
Articles 20 Documents
 1   2 
Search results for , issue "Vol. 14 No. 2 (2025): MEY" : 20 Documents clear
The Role of Social Influence and Security Risk in Shaping Intention to Use Ride-Hailing in West Papua: A Theory of Planned Behavior Perspective Maswatu, Dewi Aulia Nurhayati; Inan, Dedi Iskandar; Juita, Ratna; Indra, Muhammad
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2333

Abstract

This study explores the adoption of ride-hailing services in West Papua, a developing region in Indonesia, where concerns about service performance and security risks influence user decisions. Guided by the Theory of Planned Behavior (TPB), the research examines how service performance, social influence, and perceived security risks affect users’ behavioral intention and actual usage. A total of 158 valid responses were collected through a quantitative survey, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that service effectiveness and social influence significantly influence behavioral intention, while efficiency and certainty do not. Additionally, certainty, effectiveness, and behavioral intention strongly affect user behavior. The model demonstrates moderate explanatory power with R² values of 0.561 for behavioral intention and 0.600 for user behavior. These results suggest that enhancing perceived service effectiveness and leveraging social influence can encourage adoption in regions with limited digital infrastructure. The study contributes to understanding technology acceptance in underdeveloped areas and offers practical insights for ride-hailing providers aiming to improve user trust and engagement.
Quantum Perceptron in Predicting the Number of Visitors to E-Commerce Websites in Indonesian Solikhun, Solikhun; Carissa Arishandy, Dinda; Batubara, Ela Roza; Poningsih
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2334

Abstract

In the current digital era, e-commerce has become the backbone of Indonesia's digital economy, which is experiencing rapid growth. However, competition in this industry is becoming increasingly fierce, indicating the importance of predicting the number of website visitors for an effective marketing strategy. Quantum Perceptron, the latest quantum computing innovation, promises a more accurate and efficient approach compared to conventional methods such as classical Perceptron. This research proposes the use of Quantum Perceptron to predict the number of visitors on large e-commerce platforms in Indonesia. The data used in the research is data on the number of e-commerce visitors obtained from the katadata.com website. Data from Shopee, Tokopedia, Lazada, Blibli, and Bukalapak were used to analyze and compare predictions with classical perceptron methods, showing the significant potential of Quantum Perceptron in supporting the development of more efficient business strategies. The research results show that the Quantum Perceptron algorithm can make predictions very well compared to the classical perceptron, proven by the Quantum Perceptron having a perfect accuracy of 100% with a total of 2 epochs while the classical perceptron has 100% accuracy with a total of 10 epochs. Quantum perceptron has better performance and shorter time, this can be seen from the smaller number of epochs.
A Comparative Study of EmberGen and Blender in Fire Explosion Simulations Arya Luthfi Mahadika; Ema Utami
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2335

Abstract

The advancement of visual effects (VFX) technology has intensified the need for efficient fire explosion simulations across film, gaming, and real-time applications. This study investigates and compares the performance of two prominent simulation tools—EmberGen and Blender—by focusing on processing time efficiency and simulation quality. The research specifically evaluates five critical simulation aspects: fire particle generation, smoke behavior, turbulence effects, light dispersion, and final rendering (finishing). A total of five professional VFX artists conducted five separate tests using each software, generating a comprehensive dataset for analysis. Results show that EmberGen achieves a 29.91% overall improvement in simulation speed compared to Blender, with significant gains in fire particle generation (38.5%), smoke simulation (42.3%), turbulence effects (15.7%), light dispersion (8.9%), and finishing (11.6%). These findings indicate that EmberGen is highly effective for real-time or rapid-turnaround projects, while Blender remains advantageous for detailed, high-fidelity simulations in cinematic contexts. The study concludes that software selection should be driven by project-specific demands, where EmberGen supports time-sensitive production workflows and Blender offers greater artistic control. This research underscores the critical need for aligning simulation tools with both creative goals and production efficiency, contributing to decision-making in VFX, animation pipelines, and educational training environments within the information systems and digital content domains.
SIMPELMAS: An Integrated Information System for Research and Community Service Using a Prototype Development Approach Muzid, Syafiul; Murti, Alif Catur; Triyanto, Wiwit Agus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2339

Abstract

The Institute for Research and Community Service (LPPM) plays a strategic role in coordinating academic research and community engagement activities. However, fragmented data management continues to hinder performance evaluations and strategic decision-making in many universities. This study aims to develop SIMPELMAS (Research and Community Service Management Information System), an integrated platform designed to streamline the management of human resources, research, and community service data. Using a prototype-based development methodology, SIMPELMAS was implemented and tested in Universitas Muria Kudus. The system achieved high success rates in various aspects: over 95% in functionality, 99–100% in security, and 80–85% in user satisfaction. Key features such as proposal submission, fund monitoring, and final reporting functioned optimally. Integration testing confirmed effective data synchronization, while user feedback highlighted the need for improvements in user experience, particularly for students and new users. This study contributes to the digital transformation of higher education by providing a replicable model for academic information systems that support real-time monitoring, transparency, and data-driven governance. While the system has met key eligibility standards, further enhancements in user interface and mobile responsiveness are recommended to ensure broader usability and adoption.
Sentiment Classification of Public Perception on LHKPN Using SVM and Naive Bayes Hermawan, Ahmad Rijal Hermawan; Hanif , Isa Faqihuddin
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2341

Abstract

The public’s perception of the State Officials’ Wealth Report (LHKPN) serves as a vital measure of confidence in the government's commitment to transparency and efforts to combat corruption.This research seeks to examine public sentiment as reflected on the social media platform X. A dataset comprising 1,200 tweets was gathered and processed through various text mining methods, such as case folding, data cleaning, tokenization, normalization, stemming, stopword elimination, and TF-IDF vectorization. The tweets were then manually annotated into two sentiment categories: positive and negative, with 77.3% of tweets labeled as positive and 22.7% as negative. Sentiment classification was conducted using two machine learning algorithms: Support Vector Machine (SVM) and Naive Bayes. The Naive Bayes algorithm recorded an accuracy of 86.66%, with a precision of 0.93, a recall score of 0.88, and an F1-score of 0.87. Conversely, the SVM model with a linear kernel demonstrated superior performance, achieving an accuracy rate of 93.33%, along with a precision of 0.93, recall of 0.98, and an F1-score of 0.95. To uncover frequently occurring topics, WordCloud visualizations were generated. These revealed that positive tweets often included words such as ‘lapor’ and ‘transparan’, while negative ones were more likely to contain terms like ‘bohong’ and ‘korupsi’. These findings indicate that public sentiment toward the LHKPN initiative is largely favorable, despite persistent concerns surrounding integrity and trustworthiness in asset reporting. This study highlights the effectiveness of sentiment analysis in gauging public opinion and informing future policy improvements.
Clustering Snack Products Based on Nutrition Facts Using SOM and K-Means for Diabetic Dietary Recommendation Maritza Adelia; Arum Handini Primandari
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2342

Abstract

The number of diabetics in Indonesia continues to rise, with Type II Diabetes Mellitus (DM) dominating 90% of cases. One of the main contributors is the excessive consumption of snack products high in Sugar, Salt, and Fat (SSF), which increases health risks, particularly for diabetics. However, the current nutrition facts provided in the product package is not easy to understand. Creating label for the product can make an effective information to assist people on buying decision. This study aims to segment snack products based on their nutritional facts, particularly focusing on their SSF content, to identify products that are potentially high-risk for diabetics. In this study, data on the nutritional facts of snack products were analyzed. Utilizing a hexagonal Self-Organizing Map (SOM) topology with a 5 × 9 grid, the best clustering method identified was k-means. This method yielded two clusters, with a silhouette index of 0.44, a Dunn index of 0.09, and a connectivity index of 11.14. The first cluster comprises 165 products that have low levels of total fat, saturated fat, sugar, and salt. In contrast, the second cluster consists of 46 products with high total fat and saturated fat content, and this cluster is of particular concern due to its elevated levels of these unhealthy fats. The segmentation results can serve as a reference for more intuitive food labeling, potentially improving consumer awareness and aiding in dietary decision-making, particularly for diabetics.
The Influence of Social Media on Student Learning Behavior and Its Effects on Academic Achievement Hamidah, Hamidah; okkita rizan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2344

Abstract

The advancement of digital technology, particularly social media, has significantly transformed students’ learning behavior. The rapid digital transformation has significantly reshaped higher education, particularly in how students engage with academic content. This study aims to examine how social media usage influences students' learning behavior and its impact on academic performance, using a case study at the Institute of Science and Business (ISB) Atma Luhur. A descriptive quantitative approach was adopted, involving 150 students from various study programs. Data was collected through an online questionnaire covering the frequency of social media usage, types of learning activities conducted via social platforms, and students’ Grade Point Averages (GPA). The results reveal a significant shift in students' learning patterns, where platforms like WhatsApp, YouTube, and Instagram are utilized for sharing materials, group discussions, and seeking references. However, uncontrolled use negatively affects concentration and time management. Regression analysis shows a moderate positive correlation between academic-oriented social media use and improved performance, while excessive non-academic use correlates negatively with achievement. These findings highlight the importance of digital literacy and time management in optimizing the educational benefits of social media. The study recommends institutional policies that promote productive social media use and digital learning skill development among students. The results of this study obtained an R value of 0.456. This shows that 45.6% has an influence on the use of social media on student learning behavior and its impact on academic achievement, the remaining 54.4% is influenced by other factors not included in this research model.
Comparative Analysis of Random Forest and Support Vector Machine for Sundanese Dialect Classification Using Speech Recognition Features Anshor, Abdull Halim; Wiyatno, Tri Ngudi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2347

Abstract

This study investigates the classification of West and South Sundanese dialects using Random Forest (RF) and Support Vector Machine (SVM). Using a dataset of 100 recordings with features extracted via Mel Frequency Cepstral Coefficient (MFCC), models were evaluated by accuracy, precision, recall, and F1-score. Results show RF achieved an accuracy of 93.33%, outperforming SVM's 73.33%. The analysis demonstrates that RF is more reliable in distinguishing dialectal features. This research contributes to regional speech recognition, supporting language preservation and improved dialectal analysis.
The Effect of SMOTE and Optuna Hyperparameter Optimization on TabNet Performance for Heart Disease Classification Wijayanto, Danang; Marco, Robert; Sidauruk, Acihmah; Sulistiyono, Mulia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2348

Abstract

Heart disease is a medical condition affecting the cardiovascular system, disrupting blood circulation and reducing cardiac function efficiency, which can lead to severe health complications. Early diagnosis of heart disease has become increasingly crucial as delayed detection can significantly impact patient outcomes and survival rates. While numerous studies have explored various approaches for heart disease classification, challenges related to data imbalance and improper parameter settings remain persistent issues that affect model performance. This research evaluated the effectiveness of combining TabNet with SMOTE and optuna hyperparameter optimization for heart disease classification. We conducted four experimental scenarios using a heart disease dataset with 303 instances: baseline TabNet, baseline TabNet with SMOTE, TabNet with Optuna, and TabNet with both SMOTE and Optuna. Results demonstrated that applying SMOTE alone to TabNet decreased model performance (accuracy from 85.24% to 77.04%, AUC from 0.89 to 0.83). However, when combining SMOTE with Optuna hyperparameter optimization, we achieved optimal performance with 90.16% accuracy, 93.33% precision, 87.50% recall, 90.32% F1-score, and 0.93 AUC. This represented a significant improvement over other configurations and several previous classification approaches. The integration of SMOTE with Optuna optimization  provided an effective framework for heart disease classification that outperformed traditional methods particularly in discriminative capability as evidenced by the superior AUC score.
Trend Analysis and Prediction of Violence Against Women and Children Cases in Jakarta Based on the Victim’s Education Level Using ARIMA and SARIMA Method Kurniawan, Zaqi; Tiaharyadini, Rizka; Wibowo, Arief; Rusdah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2349

Abstract

Violence against women and children remains a critical social issue in Jakarta, Indonesia, where densely populated urban areas often correlate with increased risks of domestic abuse. The urgency of addressing this problem lies in its direct impact on public health, education, and community well-being. This study uses time series prediction models to examine and anticipate trends in the number of reported incidents of violence against women and children in Jakarta. Using publicly accessible data from Jakarta Open Data and the National Commission for the Protection of Women and Children, we applied the ARIMA and SARIMA  Models. Key variables included in the dataset are the data period, education level, and total number of victims Using three performance indicators—MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error)—to assess model accuracy the ARIMA model performed better than the SARIMA model. SARIMA recorded an RMSE of 80.26, an MAE of 66.21, and an undefined MAPE because of zero values in the real data, while ARIMA specifically obtained an RMSE of 32.22, an MAE of 32.09, and a MAPE of 5.19%. These results suggest that the non-seasonal ARIMA model is more suitable for this dataset. The study contributes to policy planning and early intervention strategies by offering a data-driven approach to predicting trends in violence within urban contexts.

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