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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
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Search results for , issue "Vol. 14 No. 3 (2025): JULY" : 20 Documents clear
A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) Indrisari, Emilda; Febiansyah, Hidayat; Adiwinoto, Bambang
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2366

Abstract

Stunting remains a serious public health issue in Indonesia, with persistently high prevalence and long-term impacts on children's physical and cognitive development. The growing need for data-driven early detection systems has encouraged the adoption of technologies such as machine learning (ML) to more effectively predict stunting prevalence. This study employed a Systematic Literature Review (SLR) to examine 20 scientific articles published between 2020 and 2024, focusing on the application of ML algorithms in stunting research. Literature was sourced from Scopus and Google Scholar, with inclusion criteria covering studies relevant to the Indonesian context or comparable global settings. The analysis focused on the algorithms used, data types, model performance, and implementation challenges. The findings indicate that Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) are the most frequently used algorithms, with prediction accuracy ranging from 72% to 99.92%. Dominant predictor variables include maternal education, economic status, sanitation, and spatial-temporal data. The main challenges include data imbalance, limited model interpretability, and a lack of external validation. In conclusion, machine learning holds strong potential to support predictive systems and data-driven policies for stunting prevention in Indonesia. This study recommends future research to focus on integrating spatial-temporal data, implementing Explainable AI (XAI), and conducting cross-regional validation to enhance model reliability and policy relevance.
Sentiment Analysis of User Reviews on the Game GTA V Using Support Vector Machine Saputra, Adika Kaka; Handayani, Maya Rini; Wibowo, Nur Cahyo Hendro; Umam, Khothibul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2368

Abstract

This study explores user sentiment toward the game Grand Theft Auto V (GTA V) by analyzing 101,540 user reviews collected from Steam and Kaggle. The reviews were processed using standard text preprocessing techniques including case folding, tokenization, stopword removal, and stemming. The TF-IDF method was used to convert text into numerical vectors, and sentiment classification was conducted using the Support Vector Machine (SVM) algorithm. The model was evaluated with accuracy, precision, recall, and F1-score as performance metrics. Results show that 88.8% of reviews are positive, while 11.2% are negative. The SVM model achieved an accuracy of 94.2% and an F1-score of 94.2%, indicating high reliability. Wordcloud analysis highlights key aspects valued by users such as graphics, story, and gameplay, while negative sentiment is often associated with technical issues like lag and bugs. This study demonstrates the effectiveness of combining TF-IDF and SVM for sentiment classification in the gaming domain, and it offers a scalable approach for understanding public opinion in digital platforms.
Analysis of Information System Quality on User Satisfaction of the Regional Financial Management Information System (SIPKD) Using the Delone & Mclean Model in the East Jakarta Administration Mayor's Government Ramadhan, Muhammad Ilham; Rizal, Erian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2376

Abstract

This study aims to evaluate the effect of information system quality on the level of user satisfaction of the Regional Financial Management Information System (SIPKD), with reference to the DeLone and McLean Model framework. The subject of this research is the State Civil Apparatus (ASN) who works within the East Jakarta Administrative City Government. The DeLone and McLean model is used as a basis for assessing how the quality of information, systems, and services affects usage intensity, user satisfaction, and individual performance. The approach used was quantitative, with data collection through distributing questionnaires to 100 respondents. Data analysis was conducted using the Structural Equation Modeling (SEM) method with the help of Partial Least Squares (SmartPLS) software. The results of the analysis show that information quality significantly affects user satisfaction, but does not show a significant effect on usage intensity. Meanwhile, system quality and service quality are proven to have a significant effect on usage intensity, but not on user satisfaction. Intensity of use has a positive and significant impact both on user satisfaction and on improving individual performance. In addition, user satisfaction is also proven to have a significant effect on individual performance.
Application of the Technology Readiness Index to Measure the Readiness of Personnel Information Systems for Village Employees in Tanjungmedar District Guntara, Agun; Fadil, Irfan; Supriadi, Fidi; Mulyana, Aa Agus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2378

Abstract

The development of information technology encourages village governments to adopt digital systems in improving services, including in the field of staffing. However, the readiness of village officials in using this information system is often a major challenge. This study aims to measure the level of readiness of village officials in Tanjungmedar District in adopting a personnel information system using the Technology Readiness Index (TRI) model. This model analyzes four psychological dimensions, namely optimism, innovativeness, discomfort, and insecurity. The study involved all village officials in nine villages in Tanjungmedar District with a total of 96 respondents. Data were collected through a Likert scale-based questionnaire and analyzed using validity, reliability, and TRI calculations. The research results showed a total TRI score of 3.38, which falls into the medium readiness index category. The variables of optimism and innovation received high scores, 1.02 and 1.03 respectively, while discomfort and insecurity scored lower, at 0.66 and 0.68. The research results indicate the need to improve aspects of comfort and security in the use of the information system. As a recommendation, technical training and system improvements are suggested to support optimal adaptation of information technology.
The Effect of the SMOTE Method on the Classification of Toddler Nutritional Status Using the Naïve Bayes Method Dewi Sartika; Florensia, Yesinta; Utari, Meylani
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2381

Abstract

The first five years of life are a golden age for growth and development, so fulfilling nutritional intake during this period is very important to avoid stunting or growth failure. The problem of stunting is still the focus of the government because it is related to nutrition which is one of the key aspects for the development of qualified resources as well as in national development. According to the report of the Ministry of Health in 2023, it was stated that the results of the 2023 Indonesian Health Survey showed that there had been a decreasing in the prevalence of stunting over the past 10 years but it had not been able to meet the target of the 2020-2024 National Medium-Term Development Plan of 14% in 2024. This study will classify the toddler’s nutritional status using the Naive Bayes method. This method uses a probability technique with Bayes' theorem which is based on the assumption of mutually independent and equal conditions. The calculation of the Naive Bayes probability in this study uses the Multinomial distribution because the data used is discrete data. The total numbers of toddlers’ nutritional status data obtained was 245 data, with 4 invalid data. Based on the data set owned, the number of samples for each class label had an unbalanced number. One method could be used to handle this unbalanced data is the random oversampling method, Synthetic Minority Oversampling (SMOTE). SMOTE will create synthetic data randomly to balance minority data samples. The analysis and testing results showed that in Multinomial Naive Bayes with the 10-cross validation technique, the g-means value obtained on the original data set was 44.98% while in the balanced data set the g-means value was 80.06%. In Multinomial Naive Bayes with the split validation technique, the g-means value obtained on the original data set was 44.20% while in the balanced data set was 80.06%. This showed that there was an increase in the g-means value of 35%. It can be stated that the SMOTE method effectively improves the overall capability of the Multinomial Naive Bayes model.
Comparative Analysis of RAG-Based Open-Source LLMs for Indonesian Banking Customer Service Optimization Using Simulated Data Lijaya, Hendra; Ho, Patricia; Santoso, Handri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2383

Abstract

In the digital era, banks face challenges in delivering fast, accurate, and efficient customer service, especially for frequently asked simple questions. This study evaluates the effectiveness of three open-source Large Language Models (LLMs), namely Gemma2-9B-Sahabat-AI, Qwen2.5-14B-Instruct, and Mistral-Nemo-Instruct in supporting a Retrieval-Augmented Generation (RAG) question-answering system for the banking sector. Using 12,000 synthetic billing documents indexed with intfloat/multilingual-e5-large-instruct embeddings (1024 dimensions), model performance was assessed via semantic similarity metrics, LLM-as-a-Judge scores (GPT-4o-mini and Gemini 2.0 Flash), and human validation Gemma2-9B-Sahabat-AI achieved the highest semantic similarity score (0.9627), followed by Mistral (0.9614) and Qwen2.5 (0.9284). In LLM-as-a-Judge evaluations, Qwen2.5 ranked highest on GPT-4o-mini (92.2), while Gemma2 led under Gemini 2.0 Flash (88.4). Human evaluators gave perfect scores for factual questions (1–10), but all models struggled with arithmetic in question 13. Gemma2’s average response time was 41 seconds, faster than Qwen2.5’s 72 seconds and Mistral’s 48 seconds, confirming Gemma2’s balanced performance in accuracy, speed, and computational efficiency. These findings underscore the potential of locally operated open-source LLMs for banking applications, ensuring privacy and regulatory compliance. However, limitations include reliance on synthetic data, a narrow question set, and lack of user diversity. Future research should involve broader queries, real user testing, and numeric reasoning modules to ensure robust and scalable deployment in real-world banking customer service environments.
Job Vacancy Recommendation System using JACCARD Method On Graph Database Riza, Saiful; Fuadi, Wahyu; Afrillia, Yesy
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2387

Abstract

In the rapidly evolving digital era, recommendation systems play a crucial role in helping users discover relevant information aligned with their preferences. PT Nirmala Satya Development, a company engaged in psychology and human resource development, faces challenges in utilizing big data consisting of 500 applicants, 500 job postings, and 500 job applications to generate accurate and relevant job recommendations. This study develops a job recommendation system using the Jaccard Coefficient method to measure similarity between users based on their job application history, implemented within a Neo4j graph database. The system models the relationships between entities through nodes and edges, allowing dynamic analysis using the Cypher Query Language. Testing on 237 users demonstrated that the majority received at least one relevant recommendation, with recall values often reaching 1.0, especially among users who had a single job target. The system achieved precision values ranging from 10% to 20%, which is considered acceptable given that ten recommendations are generated per user. The highest F1-score reached 0.33, although some users received F1 = 0 due to limited application history or unique preferences. Overall, the system effectively delivers personalized and efficient job recommendations, particularly for active users. This research also proves that combining the Jaccard Coefficient with a graph database structure is a powerful approach to representing and analyzing complex relationships between users and job postings in a modern recruitment platform.
Major Recommendation System for New Students at SMK Muhammadiyah 1 Lamongan with Naive Bayes Algorithm Muzaqi, Wildan Irsyad; Rohman, M. Ghofar; Reknadi, Danang Bagus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2390

Abstract

Students' majors in Vocational High Schools (SMK) are very important in determining the direction of their education and career, but the process carried out so far is often subjective and does not consider academic grades and interests objectively. To overcome this, this study develops a website-based major recommendation system at SMK Muhammadiyah 1 Lamongan using the Naive Bayes algorithm that is able to provide accurate major recommendations based on student data. This system is designed using a structured Waterfall Model software development method, starting from needs analysis, design, implementation, to testing. The Naive Bayes algorithm was chosen because of its simplicity and ability to work with relatively small datasets, such as new student data at the school. Of the total 675 student data collected, 60% or 405 data were used as training data to train the Naive Bayes algorithm, while the remaining 40% or 270 data were used as test data to measure the accuracy level of the recommendation system. The test results show that the system achieves an average accuracy of 90.91%, with precision above 0.73 for each major, recall above 0.80 except for the Office Management major which reaches 0.75, and an average F1 score of 81.72%. These findings indicate that the website-based major recommendation system with the Naive Bayes algorithm is effective and can help students determine majors that suit their potential and interests objectively and accurately, thus supporting a more precise and targeted major selection process.
User Opinion Mining on the Maxim Application Reviews Using BERT-Base Multilingual Uncased Safitri, Sindy Eka; Yuniarti, Wenty Dwi; Handayani, Maya Rini; Umam, Khothibul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2391

Abstract

Online transportation applications such as Maxim are increasingly used due to the convenience they offer in ordering services. As usage increases, the number of user reviews also grows, serving as a valuable source of information for evaluating customer satisfaction and service quality. Sentiment analysis of these reviews can help companies understand user perceptions and improve service quality. This study aims to analyze the sentiment of user reviews on the Maxim application using the BERT-Base Multilingual Uncased model. BERT was chosen for its ability to understand sentence context bidirectionally, and it has proven to outperform traditional models such as MultinomialNB and SVM in previous studies, with an accuracy of 75.6%. The dataset used consists of 10,000 user reviews with an imbalanced distribution: 4,000 negative, 2,000 neutral, and 4,000 positive reviews. The data was split into 90% training data (9,000 reviews) and 10% test data (1,000 reviews). From the 9,000 training data, 15% or 1,350 reviews were allocated as validation data, resulting in a final training set of 7,650 reviews. Evaluation results show that BERT is capable of classifying sentiment into three categories positive, neutral, and negative, with an accuracy of 94.7%. The highest F1-score was achieved in the positive class (0.9621), followed by the neutral class (0.9412), and the negative class (0.9246). The confusion matrix shows that most predictions match the actual labels. These findings indicate that BERT is an effective and reliable model for performing sentiment analysis on user reviews of online transportation applications such as Maxim.
Comparative Analysis of Random Forest and Logistic Regression Methods in Predicting Leukemia Blood Cancer Using Microscopic Blood Cell Images Banjarnahor, Jepri; Relungwangi, Galuh Wira
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2393

Abstract

Leukemia is one of the deadliest blood cancers that urgently requires early detection for effective treatment. However, conventional diagnosis methods are often subjective, time-consuming, and expensive, posing challenges especially in resource-constrained areas. This study presents a comprehensive comparative analysis of two widely-used machine learning algorithms - Random Forest (RF) and Logistic Regression (LR) - for leukemia prediction using an open-access dataset of 10,661 preprocessed microscopic blood cell images from Kaggle. The dataset was carefully partitioned into training (80%) and testing (20%) sets, with rigorous preprocessing including image normalization and feature extraction. Our evaluation incorporated multiple performance metrics: accuracy, sensitivity, specificity, and AUC. The results show that Random Forest's performance is superior with a classification accuracy of 85.23%, specificity of 0.9351, sensitivity of 0.6774, and AUC of 0.8881, significantly outperforming LR which achieved an accuracy of 78.11%, specificity of 0.8363, sensitivity of 0.6742, and AUC of 0.8120. These findings suggest that ensemble methods like RF are particularly well-suited for detecting one of the most deadly blood cancers, leukemia, due to their ability to handle complex feature interactions in medical imaging data. While both algorithms have potential as clinical decision support, future research can test deep learning techniques and larger datasets to improve the accuracy and reliability of the model.

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