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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 678 Documents
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Strategy for improving and empowering MSMEs through grouping using the AHC method Zahrotun, Lisna; Amanatullah, Yosyadi Rizkika; Linarti, Utaminingsih; Soleliza Jones, Anna Hendry
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2021

Abstract

The high number of migrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in Culinary and Handicrafts. The large amount of data collected by the Cooperative Office, which reached thousands, caused inas to have difficulties in determining what training was needed by MSMEs and also difficulties in choosing which MSMEs would receive training held by the Cooperative Office. In addition, the Yogyakarta Cooperatives and UMKM Office had difficulties in selecting which UMKM needed to receive these trainings. Grouping can be used as a strategy in selecting MSMEs and determining training according to their individual needs. The purpose of this study was to group SMEs using the Agglomerative Hierarchical Clustering Single Linkage method and its application to provide recommendations for MSME groups to the Yogyakarta Cooperative and MSME Office. The results of the recommendations for the number of groups can be used in providing implementation, design, and evaluation of the development and empowerment of MSME data in the City of Yogyakarta. This study uses the Agglomerative Hierarchical Clustering Single Linkage method. The stages in this research are Load Data, Cleaning Data, Data Selection, Transformation Data, Clustering Process with AHC single linkage, Silhouette Coefficient, and Knowledge Representation. This research resulted in 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Handicraft MSME data. The results of the silhouette score test in the Culinary Sector are included in the strong structure category with a value of 0.79 and the Crafts Sector is included in the Medium Structure category with a value of 0.615. From the number of these groups, recommendations were obtained for improving a service in increasing MSMEs, especially those with a turnover of less than 10 million, marketing purposes within the Yogyakarta area, and not having financial assistance from the government. The high number of immigrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in the Culinary and Crafts sector. The large number of MSMEs creates increasingly higher competitiveness. Apart from that, the large amount of data collected by the Department of Cooperatives and MSMEs, which reaches thousands, causes the Department to have difficulties in efforts to improve and empower these MSMEs. Grouping is one method that can be used as a strategy in mapping MSMEs, especially in efforts to improve and empower MSMEs through training conducted by the Department. The aim of this research is to group MSMEs using the Agglomerative Hierarchical Clustering (AHC) method in an effort to achieve strategies for improving and empowering MSMEs. The focus of this research is[a1]  MSMEs in the craft sector and MSMEs in the culinary sector. The results of this research provide 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Craft MSME data. The silhouette score test results in the Culinary Sector are in the strong structure category with a value of 0.79 and in the Crafts Sector are in the Medium Structure category with a value of 0.615. From the number of groups in the two MSMEs, strategies were obtained to improve and empower MSMEs, especially those with a turnover of less than 10 million, marketing objectives within the Yogyakarta area, and not having capital assistance from the government.  [a1]the result of the revision of the Abstract
Data-Driven Strategies for Fuel Distribution in Indonesia: A Case Study of PT Pertamina Patra Niaga Tiarazahra, Kania Lovia; Ambarwati, Rita
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2030

Abstract

Fuel oil or what is often referred to as BBM is one of the basic needs to drive all community activities. So the government appointed PT Pertamina as a single company which is a state-owned company to facilitate fuel needs for all levels of society. However, with increasing demand, the government formed a new policy to allow private companies to come in to meet all fuel demand. With this, PT Pertamina is no longer the only fuel supplier in Indonesia and must continue to develop mature strategies so that profits do not fade. One way is by examining sales data and predicting customer loyalty. The RFM method followed by the decision tree algorithm and k-means clustering is applied in this research, with the output being able to determine the level of customer loyalty, the level of salesman performance, as well as predicting the potential for customers to churn and its correlation with the salesman's skills. The data used as a reference for the research is sales transaction data obtained from PT Pertamina Patra Niaga Regional Jatimbalinus. And from the research, results showed that the majority of PT Pertamina Patra Niaga Regional Jatimbalinus customers are loyal customers. With a salesman, performance is divided into good performance and less good performance. This grouping is obtained based on the salesman's overall performance track record. As for customer churn predictions, it was found that there was 1 group of customers who were predicted to churn heavily, but this was not influenced by salesman performance, as evidenced by transaction track records in existing data
Analysis of Factors that Influence the Acceptance of Using Online Retail Applications: A Case Study of XYZ Wholesale and Retail Stores Inayah, Suci -; Sensuse, Dana Indra; Lusa, Sofian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2051

Abstract

E-commerce users in Indonesia continue to increase along with advances in digitalization. This causes a trend to occur where many offline shop entrepreneurs are responding to changes in consumer behavior by creating online shopping applications to maintain the existence of their business to be consistent with time progress. The purpose of this research is to find out what factors affect user acceptance of online retail applications used for online shopping at XYZ stores using the UTAUT2 acceptance model. In line with changes, case studies were conducted on grocery stores and retail stores that carried out digital innovation by creating online retail applications for their consumers. The research was conducted using a mixed method, data was collected through interviews with sources and using a questionnaire spread to 149 research sample consumers. The data processing technique uses PLS-SEM with SmartPLS tools. The research results show that 4 factors influence the use of online retail applications, including hedonic motivation, habit, behavioral intention, and application use. The results of this research can be used as material for management considerations to increase the excellence of the application so that user interest in online shopping using the application at XYZ store increases
Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method Pamungkas, Yuri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2117

Abstract

Biomedical research has become an essential entity in human life. However, finding trends related to research topics in the health sector contained in the repository is a challenging matter. In this study, we implemented topic modelling to analyze biomedical research trends using the LDA method. Topic modelling was carried out using data from 7000 articles from PubMed, which were processed with text processing such as lowercase, punctuation removal, tokenization, stop-word removal, and lemmatization. For topic modelling, the LDA with corpus conditions varied to 75% and 100% for validation. Alpha and beta parameters are also set with variations between 0.01, 0.31, 0.61, 0.91, symmetry, and asymmetry when the number of the corpus is changed. When the number of the corpus is 75%, the optimal number of topics is 7, with a coherence value of 0.52. Whereas when the number of the corpus is 100%, the optimal number of topics is 10 with a coherence value of 0.51. In addition, based on the results of article topic modelling, several topics are trending, including disease diagnosis, patient care, and genetic or cell research. Based on the classification of biomedical topics into seven categories, the optimal accuracy, precision, and recall values using the Random Forest algorithm were obtained, namely 85.57%, 87.36%, and 87.58%. The results of this study suggest that topic modelling using the LDA can be used to identify trends in biomedical research with high accuracy. This information can help stakeholders make informed decisions about the direction of future research.
Does The Lecturers’ Innovativeness Drive Online-Learning Adoption in Higher Education? A Study based on Extended TAM Purwandari, diah; Saparudin, Mohamad; Wulan, Mulyaning; Akbari, Deni Adha; Kania, Azzura
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2122

Abstract

Adoption and intention to use online learning is a developing area of education research. Despite a large body of research on online learning acceptance, more is needed to know about the factors that impact lecturers' intentions to continue utilizing online learning. The purpose of this study is to give empirical evidence regarding the acceptance of online learning. The proposed model is based on the technology acceptance model (TAM). Several hypotheses were created using the TAM Model, utilizing lecturers' personal innovativeness as an external varaibel. This study used structural equation modeling (SEM-PLS) to investigate technology use among 180 lecturers. The findings suggested that the proposed model accurately predicted the desire to continue using e-learning. Lecturers' innovativeness had a significant impact on perceived usefulness (PU), perceived ease of use (PEOU), and intention to continue using e-learning. Perceived usefulness was the most important factor influencing the intention to continue using e-learning. PEO had a significant influence on PU and PU was able to mediate the relationship between LPI and PEO with CI. However, PEO did not.
Project Management on Network and Security Development using the PMBOK Method Iqbal, Aldy Mercyano; Setiadi, Indra Tresna; Samidi, Samidi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2131

Abstract

HOSPITAL ABC is one of the companies that operates in the healthcare sector. In the construction of the hospitals, must be supported by the implementation of technology, especially network technology so that all operational devices in the hospital can be connected and communicate with each other, while still paying attention to the security factor on the network. Based on this, project management is needed in network and security development at the hospital to be built so that the development can be following the timeline and meet the expectations of all stakeholders. With the use of the PMBOK approach for network development and security in hospitals, every phase has very important activities and is related to other activities. Besides that, by using the PMBOK approach can also find out which parts are challenges in project implementation. In this project, the process of procuring/ordering devices, storing devices, and physically checking devices with BAST are critical parts and require more attention so that the project can run according to the specified time.
Comparative Analysis: Machine Learning Algorithms for TOC Prediction in Pharmaceutical Water Treatment Systems Mustapa, Dieki Rian; Tjahyanto, Aris
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2148

Abstract

Water quality is crucial in pharmaceutical production, where it serves as a solvent and raw material. Contamination with organic compounds poses a risk to product integrity and safety. TOC serves as a key indicator for assessing organic pollution levels in water. An increase in TOC signals potential issues with water treatment systems. Machine learning prediction of TOC values is essential for preemptive monitoring and maintenance. This study aimed to compare three different machine learning algorithms - Linear Regression (RL), Random Forest (RF), and multilayer perceptron (MLP) - for predicting Total Organic Carbon (TOC) in pharmaceutical water treatment systems. By utilizing a dataset covering various operational conditions of pharmaceutical water treatment systems, the research conducted a comprehensive analysis. Each algorithm underwent evaluation using performance metrics like coefficient of determination (R-squared), and prediction accuracy to assess their effectiveness in predicting TOC concentrations. A correlation coefficient approaching 1 (100%) signifies a strong relationship between model predictions and actual target values (accuracy prediction), while a smaller Mean Absolute Error (MAE) indicates higher accuracy in predicting target values. The study found that the results of the correlation coefficient in order from highest to lowest are the RF, MLP, and RL models with values of 95.04%, 93.11%, and 80.27%, respectively. Likewise, additional metrics for evaluation, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE), exhibit a ranking from lowest to highest values across RF, MLP, and RL models. RF has a higher prediction accuracy of the TOC than other models (95%) and lowest MAE (3.9). This research offers valuable insights into utilizing machine learning algorithms for TOC prediction within pharmaceutical water treatment to make informed decisions, improving water treatment systems and overall product quality.
EEG Signal Classification using K-Nearest Neighbor Method to Measure Impulsivity Level Ginting, Arico Sempana; Simanjuntak, Ruth Marsaulina; Lumbantoruan, Nurima; Sitanggang, Delima
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2154

Abstract

Impulsivity is the tendency to act without considering consequences or without careful planning. It involves a quick response to a stimulus without sufficient consideration of the consequences. Impulsivity needs to be measured and detected because it has a significant impact on various aspects of a person's life. The factors that influence the level of impulsivity include social environment, stress level, mental health, and genetic factors. Impulsivity can be divided into multiple components, such as reduced sensitivity to unfavorable behavioral outcomes, a disregard for long-term implications, and quick and spontaneous responses to stimuli. Electroencephalogram (EEG) studies can identify specific brain wave patterns such as, Alpha, Betha, Theta, and Gamma waves everything based on an individual brain's level of impulsivity. Signals from the brain are processed to extract specific features that reflect the user's intentions. EEG records brain activity without surgery, and this information is used for the diagnosis, monitoring, and treatment of neurological diseases, as well as scientific research on the brain and mind. K-Nearest Neighbor (KNN) is a classification algorithm that functions by utilizing several K nearest data values (its neighbors) as a reference to determine the class of new data. The K-Nearest Neighbors (KNN) algorithm is used for classification, clustering, and pattern recognition in EEG data where clustering is in 4 classifications (Impulsive, Not Impulsive, Potentially Impulsive, and Very Potentially Impulsive). This classification model shows high accuracy (Training Data: 94.7%, Testing: 91.3%, and Validation Data: 91.8%). This research shows that the KNN algorithm is effective for assessing the degree of impulsivity.
ResNet50-Based Deep Learning Architecture with Focal Loss Optimization for Automated Fruit Ripeness Classification Putri, Stefani Hardiyanti; Nasrullah, Nasrullah; Maulana, Fefi; Rahmayanti, Prilia; Maiyana, Efmi
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.v15i01.2449

Abstract

This study develops an Enhanced ResNet50 architecture with Focal Loss optimization for automated fruit ripeness classification. The research implements systematic modifications to the standard ResNet50 framework, incorporating attention mechanisms, strategic transfer learning with 20 trainable layers, and advanced class imbalance handling through Focal Loss function (α=[0.809, 1.904, 0.807], γ=2.0). The model processes RGB images (224×224×3) across three ripeness categories: Overripe, Ripe, and Unripe, utilizing the Kaggle Fruits Ripeness Classification Dataset containing 4,434 high-quality images. The Enhanced ResNet50 architecture achieves 97.22% classification accuracy with corresponding precision, recall, and F1-scores of 0.9722, demonstrating superior performance compared to standard ResNet50 (91.7%), VGG16 (89.2%), and EfficientNet-B0 (88.5%). The model exhibits efficient computational characteristics with 50-100ms inference time and 104.55 MB model size, while successfully addressing mild class imbalance (ratio 0.424) through systematic optimization techniques.
A Systematic Literature Review of Adaptive Machine Learning Approaches for Real-Time Fuel Efficiency Optimization in Open-Pit Mining Trucks Kusnawi; Mochamad Agung Wibowo; Ridwan Sanjaya
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.v15i01.2527

Abstract

Fuel consumption in open-pit mining operations is a significant operational cost, making fuel efficiency an important research topic. This project seeks to investigate the use of adaptive machine learning (ML) methodologies to improve real-time fuel efficiency in mining trucks. A Systematic Literature Review (SLR) was conducted following the PRISMA protocol to examine 47 peer-reviewed articles published from 2015 to 2025. Thematic synthesis and bibliometric analysis identified five dominant categories of machine learning, with deep learning and fuzzy logic being the most common. Many studies have examined adaptive energy regulation for varying terrain and loads; however, only 20% have included driver behavior, highlighting a significant research gap. Reinforcement learning and hybrid systems show significant potential for scheduling and control in dynamic environments; however, they face challenges in real-time applications due to factors such as edge computing and limited data integration. This review describes advances in fuel optimization research through the integration of artificial intelligence, control theory, and mining logistics, and proposes future goals including the development of simplified models for vehicle applications, empirical testing in industrial fleets, and the utilization of behavior and telemetry data to enhance contextual awareness in systems. Additionally, future research should focus on the real-time integration of driver behavior into adaptive ML models and the development of lightweight, deployable solutions tailored for industrial-scale applications in mining fleets.

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