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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 669 Documents
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Fuel Logistics Demand Forecasting Model in the Islands Region with ARIMA Approachs 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). 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

The implementation of stacked ensemble models (SEMs) remains widespread because they combine multiple learning algorithms into one predictive system. SEM implementations continue to struggle with accuracy limitations and overfitting problems and high computational expenses and poor interpretability issues. This review examines 269 scholarly articles from 2020 to 2025 to determine the main technical problems and their associated optimization solutions. The research presents a method engineering-based modular three-stage system which includes pre-processing, processing, and post-processing phases. The three stages address particular weaknesses by improving data quality and features, optimizing models and their parameters, and enhancing interpretability and adaptability. The framework connects SEM pipeline phases to these strategies which enable context-specific reusable design for condition-aware implementation. This research provides a systematic framework to match SEM optimization approaches with development stages which helps develop strong ensemble models that are efficient and interpretable for practical use.
Recommendation Modeling for Fulfilling Internet Needs in Indonesia Based on Mapping Using Spatial Analysis and Geographic Information System (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 significant digital divide between urban and rural areas despite its growing internet penetration. This disparity, driven by uneven infrastructure and socio-economic factors, necessitates a strategic, data-driven approach to infrastructure development. This research proposes an integrated spatial recommendation model to identify and prioritize areas for internet infrastructure investment. The methodology combines several spatial analysis techniques, including K-Means and DBSCAN for clustering, Kernel Density Estimation (KDE) for demand hotspot analysis, and proxies for Network Analysis and Spatial Regression. These analytical outputs are integrated into a weighted composite score to classify regions into high, medium, and low priority tiers. The results demonstrate the model's ability to pinpoint specific, high-priority localities within different provinces, moving beyond broad regional assumptions. This framework provides an evidence-based tool for policymakers and telecommunication companies to guide targeted investments, ensuring resources are allocated more efficiently and equitably to bridge Indonesia's digital divide.
Optimized Machine Learning Approach for Malware Detection 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

The rapid evolution of information technology has created vast opportunities in multiple domains, yet it also brings critical challenges in the realm of cybersecurity, particularly with the growing frequency of malware attacks. Modern malware utilizes advanced evasion and spreading techniques, such as polymorphic and metamorphic transformations, which undermine the performance of conventional detection systems. This research aims to evaluate and compare the effectiveness of several machine learning algorithms optimized through hyperparameter tuning to determine the most accurate and reliable model for malware detection. The study applies a supervised learning approach using labeled data and examines five algorithms: Multilayer Perceptron, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Hist Gradient Boosting. Each model was fine-tuned to identify its optimal configuration, and performance was measured using accuracy, precision, recall, and F1-score. The experiments were conducted on a dataset comprising 58,596 records that had been thoroughly cleaned and preprocessed. The findings indicate that the Multilayer Perceptron achieved superior results, obtaining 99.97% across all evaluation metrics. These outcomes demonstrate the model’s strong potential for reliable malware detection and its suitability for integration into cybersecurity frameworks that demand fast response, high precision, and adaptability to evolving attack patterns.
Integration of K-Means Clustering, Random Forest, and RFM Analysis for Optimizing Consumer Segmentation in Digital Advertising Strategies Joang Ipmawati; 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. 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.
Unpacking Customer Churn Management through Information Systems Syibli, Mohammad; Gernowo, Rahmat; Surarso, Bayu; Setiawan, Aldi; Andi Setiabudi, Nur
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. This study aims to identify, classify, and synthesize the current state of customer churn management (CCM) research through a systematic lens. Despite growing interest in churn prediction, most studies focus narrowly on improving model accuracy, overlooking the organizational, strategic, and adaptive aspects essential for effective churn management. To address this gap, this paper conducts a systematic literature review (SLR) covering publications from 2020 to 2025, analyzed through the Input–Process–Output (IPO) framework. Twenty high-impact papers were coded across various industries to examine data inputs, modelling processes, output insights, and feedback or retraining mechanisms. The findings reveal a strong bias toward predictive modelling and performance metrics, with limited attention to adaptive retraining, business impact assessment, and decision-support feedback loops. This imbalance highlights the need for a more holistic approach to churn management that integrates analytical and managerial dimensions. The study concludes by proposing a synthesized reference model and future research agenda to guide the development of adaptive, information-systems-based churn management frameworks for dynamic business environments.
Performance Evaluation of Cloud-Init as Deployment Automation, Virtual Machine, and LXC Container on Proxmox VE for AI LLM Deployment Jody
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 Of Charming Charlotte Accessories Adinda Dwi Larasati; Anita Diana
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

Abstract—This study aims to optimize digital marketing strategies based on e-commerce for the Charming Charlotte accessories business to increase sales and competitiveness in the digital era. The main problem this business faces is the limited marketing media that rely only on social media platforms such as Instagram and WhatsApp, the lack of an official website that supports online transactions, and the manual recording of customer orders, which often causes errors in processing transaction data. In addition, sales reports are not presented systematically, making it difficult for the owner to make accurate business decisions. To solve these problems, this study develops an e-commerce website based on a Content Management System (CMS) using WordPress and WooCommerce, supported by Search Engine Optimization (SEO) techniques to improve website visibility on search engines. The study uses the Business Model Canvas (BMC) approach to design an integrated and effective digital business model. The results show that implementing the e-commerce system expands marketing reach, improves transaction efficiency, strengthens customer trust, and provides informative sales reports for management.
Teacher Selection Analysis at Pangkalpinang Baptist School Using SAW Method Fitriyani, Fitriyani; Devi Irawan; Ari Amir Alkodri; yuyi andrika; Melati Suci Mayasari; Chandra Kirana
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

Abstract— The selection of qualified teachers is a crucial factor in improving educational quality at Pangkalpinang Baptist School. Subjective selection processes often result in decision-making ineffectiveness. This study aims to analyze the teacher selection system by implementing the Simple Additive Weighting (SAW) method to improve objectivity and accuracy in the decision-making process. The SAW method evaluates candidates based on three criteria: interview performance (30%), academic tests (35%), and micro teaching abilities (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. Keywords— Teacher selection, SAW method, Simple Additive Weighting, decision making, education quality

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