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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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Comparison of CNN Architectures for Pre-Cancerous Cervical Lesion Classification Based on Colposopy Images Using IARC and AnnoCerv Datasets Sigit Prasetyo Noprianto; Siti Nurmaini; Dian Palupi Rini
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.v14i2.2361

Abstract

Cervical cancer represents a significant public health issue affecting women worldwide, and identifying the severity of lesions early on is crucial to selecting the right treatment. This research investigates and compares the effectiveness of various Convolutional Neural Network (CNN) models in classifying colposcopic images according to the severity of cervical lesions. The dataset used was obtained from the International Agency for Research on Cancer (IARC) and AnnoCerv, consisting of 452 colposcopy images categorized into four classes: Normal, CIN 1, CIN 2, and CIN 3. Five CNN architectures were evaluated: MobileNetV2, InceptionV3, Xception, VGG16, and DenseNet121. Experiments were conducted using default hyperparameters: batch size of 32, learning rate of 0.001, and 100 epochs. The results showed that MobileNetV2 achieved the highest accuracy at 67%, followed by DenseNet121 (60%), Xception (60%), InceptionV3 (55%), and VGG16 (42%). Based on these findings, MobileNetV2 is the most optimal model for classifying colposcopy images in this study. However, the study is limited by class imbalance and dataset size, which may affect model generalizability. Future work may explore ensemble learning techniques and larger, more diverse datasets for improved accuracy.
Implementation of Round Robin Algorithm in Public Transportation Scheduling System at Pakupatan Terminal in Serang City-Indonesia Darip, Mochammad
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.v14i2.2362

Abstract

Public transportation scheduling, particularly for city transit systems, is a critical factor in improving service efficiency and passenger comfort. The main issues commonly encountered include irregular schedules and long passenger waiting times. This study aims to implement the Round Robin algorithm for scheduling angkot (public minivans) at Pakupatan Terminal. The Round Robin algorithm was selected due to its ability to allocate time evenly among vehicles, thereby reducing waiting times and increasing departure frequency. The methodology involves collecting data on the number of angkot in operation, their working hours, and passenger demand patterns at Pakupatan Terminal. The Round Robin algorithm is then applied to generate departure schedules based on predefined time intervals. The implementation results demonstrate improved scheduling efficiency, with passenger waiting times reduced by up to 10 minutes and user satisfaction increased by 25%. Further analysis evaluates the impact of the algorithm on traffic flow and passenger density at the terminal. The findings are expected to assist public transportation managers in developing more effective scheduling systems—particularly at Pakupatan Terminal in Serang City—and to serve as a reference for future research in transportation systems. Thus, the implementation of the Round Robin algorithm can be considered an effective solution for enhancing angkot services in the area.
Road Damage Detection Using Yolov9-Based Imagery Azhari, Febrian Akbar; Rohana, Tatang; Baihaqi, Kiki Ahmad; Fauzi, Ahmad
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.v14i2.2377

Abstract

Road damage is one of the leading factors contributing to traffic accidents. Rapid identification and repair of damaged roads are crucial in road infrastructure management. This study aims to develop an effective method for detecting road damage, utilizing the YOLOv9 algorithm as a key component, such as cracks and potholes, using the Convolutional Neural Network (CNN) approach. YOLOv9 was chosen due to its efficient architecture, which enables real-time object detection, and its proven effectiveness in various object detection tasks. An annotated dataset of road images was used during the model training and testing process. The results show that the YOLOv9 model can accurately detect road damage. The model achieved a precision of 0.85 and a recall of 0.992 for pothole detection, and a precision of 0.94 for crack detection. Evaluation using mAP50 yielded a score of 0.96, while mAP50-95 reached 0.77, indicating strong detection and classification capability. A consistent decline in loss functions during training also signifies effective learning by the model. These findings suggest that YOLOv9 has the potential to be implemented in automated road damage detection systems, which can accelerate maintenance processes and enhance road user safety.
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.

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