cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Fransiskus Panca Juniawan
Contact Email
Fransiskus Panca Juniawan
Phone
-
Journal Mail Official
fransiskus.pj@atmaluhur.ac.id
Editorial Address
-
Location
Kota pangkal pinang,
Kepulauan bangka belitung
INDONESIA
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 678 Documents
 64   65   66   67   68 
Forecasting Fuel Logistics Demand in Archipelagic Regions Using the ARIMA Methods: A Case Study of the Anambas Islands Regency Suswaini, Eka; Wibowo, Mochammad Agung; Jie, Ferry
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2543

Abstract

Indonesia as an archipelago faces complex logistical challenges, especially in the distribution of fuel oil (BBM) to remote areas. This research aims to forecast fuel logistics needs in the Anambas Islands Regency using the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting was carried out on three main aspects: fuel demand (Type 1 and Type 2) per sub-district, sea wave height, and number of vehicles by type. The results show that the three elements have a relatively stable pattern during the forecasting period until June 2025, with the dominant ARIMA model configurations (0,1,0) and (0,1,1). In these configurations, p = 0 indicates no autoregressive component, d = 1 represents first differencing to remove trends, and q = 0 or 1 reflects either the absence or presence of a simple moving-average term to smooth short-term fluctuations. These parameter combinations suggest that the time series data exhibit stable linear trends with limited short-term volatility. Fuel demand per sub-district shows a steady trend, sea waves are in the low to medium category, and the number of vehicles does not experience significant spikes. This stability supports efficient and predictive data-based fuel distribution planning. The research also recommends the integration of forecasting results into the development of an adaptive and sustainable decision-making system in the islands.
Unstacking the Stack: Synthesis of Optimization Strategies for Stacked Ensemble Models in Multi-Domain Contexts Widiyatmoko, Carolus Borromeus; Gernowo, Rahmat; Warsito, Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2545

Abstract

Stacked ensemble models (SEMs) remain widely used for integrating multiple learning algorithms into a single predictive system. However, SEMs continue to face challenges such as accuracy limitations, overfitting, high computational expenses, and limited interpretability. This study conducts a systematic review of 269 peer-reviewed papers published between 2020 and 2025, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure transparency and rigor in article selection. The review identifies key technical issues in SEM implementations and synthesizes their corresponding optimization strategies. To address these challenges, a method-engineering-based modular three-stage framework is proposed, consisting of pre-processing, processing, and post-processing phases. Each stage targets specific weaknesses by improving data quality, optimizing models and hyperparameters, and enhancing interpretability and adaptability. The framework provides a structured foundation that links SEM optimization approaches with their development stages, supporting the design of robust, efficient, and interpretable ensemble models for practical applications.
Spatial Recommendation Model for Internet Infrastructure Needs in Indonesia Using GIS Widjaja, Irena; Isnanto , R Rizal; Somantri , Maman; Nuraya , Sarah Nazly
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2546

Abstract

Indonesia faces a persistent digital divide between urban and rural areas despite significant progress in internet penetration. This disparity, driven by unequal infrastructure and socio-economic differences, highlights the need for a data-driven approach to infrastructure planning. This study proposes an integrated spatial recommendation model to identify and prioritize regions for internet infrastructure development. The methodology integrates multiple spatial analysis techniques, including optimized K-Means and DBSCAN clustering for socio-economic and infrastructure segmentation, Kernel Density Estimation (KDE) for identifying demand hotspots, and logistic regression and spatial regression as proxies for network accessibility and potential analysis. The analytical results are aggregated into a weighted composite score that classifies each region into high, medium, or low priority categories. The evaluation results show that the optimized K-Means model achieved a Silhouette Score of 0.631, DBSCAN scored 0.766, regression achieved R² = 0.68, and network accessibility classification reached an accuracy of 0.733 with an F1-score of 0.692. The final spatial map successfully identifies high-priority areas across provinces, revealing that infrastructure needs are locally distributed rather than regionally uniform. This framework provides an evidence-based decision-making tool for policymakers and telecommunication providers to guide targeted and equitable investments in digital infrastructure across Indonesia.
Optimized Machine Learning Approach for Malware Detection using Bayesian Optimization Wicaksana, Hilman Singgih; Huda, Khairul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2547

Abstract

 Information technology has facilitated innovation in numerous sectors and enhanced operational efficiency as a result of its rapid expansion. However, these technical innovations have also resulted in an increased likelihood of cyberattacks, particularly those that are initiated by malware.  Sophisticated evasion techniques, such as polymorphic and metamorphic transformations, are frequently implemented by contemporary malware, which significantly reduces the reliability of conventional detection methods. This investigation endeavors to evaluate and contrast the efficacy of a variety of machine learning algorithms that have been enhanced by Bayesian Optimization, a probabilistic method for hyperparameter tuning that effectively identifies the optimal model configurations. The study investigates five algorithms: Multilayer Perceptron, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Hist Gradient Boosting, using a supervised learning methodology with labeled data. The performance of each model was evaluated based on its accuracy, precision, recall, and F1-score, while its optimal parameters were meticulously fine-tuned. Additionally, experiments were implemented on a dataset consisting of 58,596 records that underwent rigorous cleansing and preprocessing. The Multilayer Perceptron demonstrated the highest performance, obtaining 99.97% across all evaluation measures, according to the results. These discoveries underscore the efficiency, precision, and adaptability of refined machine learning models in detecting malware in response to the evolution of cyber threats.
Integration of K-Means Clustering, Random Forest, and RFM Analysis for Optimizing Consumer Segmentation in Digital Advertising Strategies Ipmawati, Joang; Kusnawi, Kusnawi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2548

Abstract

In the era of data-driven marketing, accurate consumer segmentation is essential to improve the precision and impact of digital advertising. This study aims to produce more accurate consumer segmentation to support more targeted digital marketing strategies. The methods used include K-Means Clustering to group users based on digital behavior, RFM Analysis to evaluate user loyalty and interaction value with advertisements, and Random Forest to identify key factors influencing segmentation. The dataset includes demographic and behavioral information such as age, gender, income level, online duration, and interaction with digital ads. This dataset includes 200 user samples collected from public online advertising platforms. The results show that using five clusters (K=5) in K-Means Clustering yields optimal segmentation. RFM Analysis successfully categorizes users based on loyalty and engagement, while Random Forest identifies Click-Through Rate (CTR), Likes and Reactions, and Time Spent Online as the most influential variables in segmentation. This research contributes to improving the effectiveness of digital advertising campaigns and supports data-driven decision-making. The findings are significant for understanding consumer behavior patterns more deeply and for designing more efficient and relevant marketing strategies.
Toward an Adaptive IPO-Based Information Systems Framework for Customer Churn Management Syibli, Mohammad; Gernowo, Rahmat; Surarso, Bayu; Setiawan, Aldi; Setiabudi, Nur Andi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2552

Abstract

The high churn rate in the telecommunications industry remains a persistent challenge affecting customer retention, revenue stability, and long-term competitiveness. Despite extensive research, most customer churn management (CCM) studies in the telecom sector focus narrowly on improving model accuracy, overlooking organizational, strategic, and adaptive dimensions essential for effective management. This paper presents a systematic literature review (SLR) of academic publications from 2020 to 2025, analyzed through the Input–Process–Output (IPO) framework, to synthesize state-of-the-art developments in CCM from an Information Systems perspective. Twenty high-impact studies were coded across industries, emphasizing telecommunications, to examine data inputs, analytical processes, outputs, and feedback or retraining mechanisms. The findings reveal a strong bias toward predictive modelling using ensemble machine learning techniques (e.g., Random Forest, XGBoost, LightGBM) and limited exploration of explainable AI tools (SHAP, LIME), adaptive retraining, and business validation. This imbalance highlights the need for a holistic, adaptive framework integrating analytical intelligence with managerial decision-making. The study contributes by proposing a synthesized reference model and future research agenda for developing adaptive, information-systems-based churn management frameworks in the telecommunications industry.
Performance Evaluation of Cloud-Init as Deployment Automation, Virtual Machine, and LXC Container on Proxmox VE for AI LLM Deployment Jody, Jody; Riandhito, Febry Aryo; Yusuf, Rika; Saputra, Anggi; Riwurohi, Jan Everhard
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2562

Abstract

As Artificial Intelligence (AI) is used more and more in digital certification systems, it is important to create stable and efficient environments for the use of Large Language Models (LLMs). AI-based chatbots are very helpful for people who are taking online tests at professional certification schools and for people who are giving tests. However, it is still not clear where the best place is to run AI inference workloads because virtualization can use different amounts of resources and cost different amounts. This study aims to identify the optimal deployment environment by assessing Cloud Init, Virtual Machine (VM), and Linux Container (LXC) within the Proxmox Virtual Environment (VE). This environment tested Ollama and FastAPI on the same hardware (4 vCPU, 16 GB RAM, 32 GB SSD, 80 Mbps) and the Phi3:3.8b model. The study also checked the important numbers like CPU and memory usage, disk and network throughput, latency, and response time. The tests showed that LXC had the fastest disk speed (2.45 MB/s) and network speed (3.33 MB/s). VM had the longest response time (15.64 s) and the longest latency (6.89 ms). Cloud Init had mixed results: it made automation easier but less effective. These results show that the best way to use Cloud Init and LXC together for big certification systems is through hybrid orchestration. This is the best way to get a good balance between AI deployment that is flexible and fast. The methodology section provides a clearer description of the experimental process, including benchmark tools (Hey CLI, Sysbench, Prometheus), the number of test repetitions (three sessions per environment), and comparative data analysis methods to ensure result validity. Moreover, the conclusion emphasizes the scientific implications by explaining how Cloud Init’s automation capabilities can be combined with LXC’s performance efficiency to improve AI inference deployments in scalable and institutional environments.
Optimization Of Digital Marketing Utilization Based on E-Commerce to Enhance Sales and Marketing Larasati, Adinda Dwi; Diana, Anita
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2570

Abstract

This study aims to develop and optimize digital marketing strategies through an e-commerce–based system for the Charming Charlotte accessories business in order to enhance sales effectiveness and competitive advantage. The problems identified include the limited use of marketing media that rely solely on social media, the absence of a website capable of supporting online transactions, and manual order recording that increases the risk of data processing errors. This research employs the Business Model Canvas (BMC) to formulate the business model, utilizes Unified Modeling Language (UML) to model system processes, and develops an e-commerce website based on a Content Management System (CMS) using WordPress and WooCommerce integrated with Search Engine Optimization (SEO) techniques. The results indicate that the implementation of the e-commerce system effectively expands marketing reach, improves transaction management efficiency, provides more systematic sales reports, and enhances business visibility through search engine optimization. This research proves that the simultaneous integration of CMS, BMC, UML, and SEO is effective in strengthening digital marketing performance for small businesses.
Teacher Selection Analysis at Pangkalpinang Baptist School Using SAW Method Fitriyani, Fitriyani; Irawan, Devi; Alkodri, Ari Amir; Andrika, Yuyi; Mayasari, Melati Suci; Kirana, Chandra
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2574

Abstract

Selecting competent teachers plays a vital role in enhancing the quality of education at Pangkalpinang Baptist School. However, the use of subjective judgment in the recruitment process often leads to less effective decision-making. Therefore, this research aims to examine the teacher selection mechanism by applying the Simple Additive Weighting (SAW) method to strengthen the objectivity and precision of the selection decisions. The SAW approach assesses applicants using three main criteria: interview performance (30%), academic test results (35%), and micro teaching skills (35%).Each criterion receives specific weights according to importance levels, followed by calculations to determine candidates with the highest scores. Research results demonstrate that SAW method implementation provides more systematic and transparent decisions in teacher selection. The study evaluated 10 teacher candidates with Eka Sitompul achieving the highest score of 0.85, followed by Fandi Saputra and Vitta Natalia with 0.825 each. This method enables schools to conduct data-based selection, reducing subjectivity in recruitment processes and ensuring selected teaching staff possess competencies aligned with school requirements.
Gamification Design in Online Risk Management to Enhance Employee Awareness Firmansyah, Muhamad Dody; Jonathan, Jonathan; Wibowo, Tony
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Organizations increasingly require effective approaches to strengthen employee awareness of operational risks, yet conventional training methods often lack engagement and fail to support sustained learning. This study aimed to design and evaluate a gamified risk-management learning prototype that integrates interactive features with structured instructional content. The research focused on early-stage development and formative evaluation because existing workplace learning research has not sufficiently explored the feasibility of gamification in risk-related training. The study employed the first three stages of the 4D R&D model: Define, Design, and Develop to create the prototype, followed by a descriptive quantitative evaluation involving 82 participants. Data were collected using an online questionnaire adapted from validated instruments measuring usability, content clarity, interactivity, and user satisfaction. The results showed consistently high mean scores across all constructs, indicating positive user perceptions of the prototype’s interface, clarity of information, and engagement generated by gamified elements. Cronbach’s Alpha (0.985) confirmed excellent internal consistency, and correlation analysis demonstrated strong relationships among clarity, interactivity, and satisfaction. Overall, the findings suggest that gamification can serve as a feasible and engaging approach for risk-management learning in organizational contexts. The study provides early empirical evidence supporting further refinement, broader implementation, and more extensive testing of gamified learning systems for workplace risk awareness.

Page 67 of 68 | Total Record : 678

 64   65   66   67   68 

Filter by Year

2013 2026


Reset
Filter By Issues
All Issue Vol. 15 No. 02 (2026): MAY Vol. 15 No. 01 (2026): JANUARY Vol. 14 No. 4 (2025): NOVEMBER Vol. 14 No. 3 (2025): JULY Vol. 14 No. 2 (2025): MEY Vol. 14 No. 1 (2025): JANUARY Vol. 13 No. 3 (2024): NOVEMBER Vol 13, No 2 (2024): JULY Vol. 13 No. 2 (2024): JULY Vol. 13 No. 1 (2024): MARET Vol 13, No 1 (2024): MARET Vol 12, No 3 (2023): NOVEMBER Vol. 12 No. 3 (2023): NOVEMBER Vol 12, No 2 (2023): JULI Vol. 12 No. 2 (2023): JULI Vol 12, No 1 (2023): MARET Vol 11, No 3 (2022): NOVEMBER Vol 11, No 2 (2022): JULI Vol 11, No 1 (2022): MARET Vol 10, No 3 (2021): NOVEMBER Vol 10, No 2 (2021): JULI Vol 10, No 1 (2021): MARCH Vol 9, No 3 (2020): NOVEMBER0 Vol 9, No 2 (2020): JULI0 Vol 9, No 1 (2020): MARET0 Vol 9, No 3 (2020): NOVEMBER Vol 9, No 2 (2020): JULI Vol 9, No 1 (2020): MARET Vol 8, No 2 (2019): SEPTEMBER0 Vol 8, No 2 (2019): SEPTEMBER Vol 8, No 1 (2019): MARET Vol 7, No 2 (2018): September Vol 7, No 1 (2018): Maret Vol 6, No 2 (2017): September Vol 6, No 1 (2017): Maret Vol 5, No 2 (2016): September Vol 5, No 1 (2016): Maret Vol 4, No 2 (2015): September Vol 4, No 1 (2015): Maret Vol 3, No 2 (2014): September Vol 3, No 1 (2014): Maret Vol 2, No 1 (2013): Maret 2013 Vol 2, No 2 (2013): September More Issue