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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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Comparative Study of KNN and SVM Methods for Analyzing College Major Consistency Based on High School Background Rizkyllah, Anabel Fiorenza; Meiriza, Allsela; Hardiyanti, Dinna Yunika
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2521

Abstract

Selecting a college major that aligns with students’ high school background is an essential factor in supporting academic achievement and career preparation. This study focuses on a comparative analysis of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithm ms in evaluating the consistency of college major selection. A dataset of 636 students was collected and processed for analysis. Model evaluation was performed using 5-Fold Cross Validation, in which the dataset was repeatedly partitioned into training and testing sets to ensure reliable and unbiased performance assessment. The results suggest that SVM demonstrates higher effectiveness, achieving average scores across precision, recall, F1-score, and accuracy of 85%. Meanwhile, KNN obtained average performance scores of 78%. These findings highlight that SVM provides better performance in analyzing the consistency between students’ high school majors and their chosen college majors. These findings also contribute to the development of decision support systems and counseling services to guide students in making more informed major choices.
Detection and Identification of Vehicle License Plates in Indonesia Transportation System Based on Deep Learning Using YOLOv11 and Easyocr Martadinata, Fendri; Firdaus, A; Amal, M.Ridho Tahsinul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2524

Abstract

The detection and identification of vehicle license plates in Indonesia still face significant challenges due to unstable environmental conditions during image capture, such as extreme lighting, varying angles of capture, physical damage to the plates, and diversity in design and font types. These conditions degrade the accuracy of existing recognition systems, especially if the model is not trained to handle such variability. In addition, the public's low understanding of license plate structure also hinders the optimal use of this information. This study aims to develop an accurate, adaptive license plate recognition system for real-world conditions that can interpret license plate information in real time. The model was created using the YOLOv11 algorithm for fast, high-precision plate detection, and EasyOCR for plate character identification. The dataset consisted of 709 images of two-wheeled (motorcycle) and four-wheeled (car) vehicle plates, collected from public datasets, the researchers' surroundings, and the campus area. Most of the data was collected through direct photography with cell phone cameras, reflecting real-world field conditions. The test results show that the YOLOv11 model has excellent detection performance, with mAP@50 of 94.2%, precision of 97.7%, and recall of 86.7%, while the EasyOCR method achieved a character recognition accuracy of 91.0%. The main innovation of this research is the application of a license plate recognition system to support intelligent transportation systems in campus environments, particularly for parking system implementation.
Performance Analysis of YOLOv8, YOLO11, and YOLOE in Detecting Patient Density under Complex Healthcare Conditions Ardiansyah, Ardiansyah; Syahputri, Rezyana Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2526

Abstract

Providing quality healthcare is a fundamental right of citizens as stipulated in the 1945 Constitution, making healthcare a national priority as outlined in the Ministry of Health's 2020-2024 Strategic Plan. However, high patient visitation rates can lead to overcrowding, impacting service efficiency and quality. Therefore, real-time patient monitoring technology is needed. Previous studies have shown promising results, but remain limited to ideal conditions for the machine. This study uses the YOLO algorithm to detect patient congestion in real healthcare facilities using CCTV footage from waiting rooms. This study uses three instance segmentation models — YOLOv8n-seg, YOLO11n-seg, and YOLOE-seg — that are tested on a custom dataset and compared with the official model. The results of training the custom dataset model are: YOLOv8n-seg Precision 96%, Recall 97%, mAP50 98%, mAP50-95 84%, and F1-score 97%. YOLO11n-seg precision 96%, Recall 97%, mAP50 98%, mAP50-95 84%, and F1-score 97%. and YOLOE-seg precision 96%, Recall 98%, mAP50 98%, mAP50-95 85%, and F1-score 97%. In addition, this study compared predictions with the official model, which found that all custom dataset models successfully detected objects with 100% density. In contrast, the official model correctly predicted density 70%-82% of the time. Therefore, this study concludes that models trained on custom datasets can improve the accuracy of patient density predictions, thereby enhancing the quality of real-time healthcare services.
IoT Botnet Detection Using Autoencoders and Decision Trees Susanto, Susanto; Arifin, M. Agus Syamsul; Wijaya, Harma Oktafia Lingga
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1693

Abstract

The use of IoT devices has grown rapidly, leading to an increase in cyber attacks that pose greater security and privacy threats than ever before. One such threat is botnet attacks on IoT devices. An IoT botnet is a group of Internet-connected IoT devices infected with malware and remotely controlled by an attacker. Machine learning techniques can be employed to detect botnet attacks. The use of machine learning-based detection methods has been shown to be effective in identifying cyber attacks. The performance of the detection system in machine learning can be improved by utilizing data reduction methods. The data reduction process in classification is used to overcome the problem of scalability and computation resources in the IoT. This paper proposes a detection system using the Autoencoder reduction method and the Decision tree classification method. The test results demonstrate that the Deep Autoencoder algorithm can reduce data and memory usage from 1.62 GB to 75.9 MB, while also improving the performance of decision tree classification, resulting in a high level of accuracy up to 100%. The Autoencoder approach in conjunction with the Decision Tree exhibits superior capabilities compared to previous studies.
Image Restoration Using Deep Learning Based Image Completion Chyan, Phie; Saptadi, Tri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1699

Abstract

Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the latest relying on artificial intelligence algorithms. This study aims to develop and implement an image completion model based on deep learning with the transfer learning method from the completion.net architecture. Using the Facesrub training dataset consisting of a collection of unique facial photos allows the model to understand facial attributes better. Compared to conventional image completion based on image patches, the method developed in this study can perform image filling in image gaps with more realistic results. Based on visual tests conducted on respondents, the results obtained enable respondents to understand all the information represented by the restored image, similar to the original image.
Early Detection of Alzheimer's Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization) Rosyida, Anistya; Sasongko, Theopilus Bayu
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1716

Abstract

Alzheimer's disease is a degenerative disease associated with memory loss, communication difficulties, mental health, thinking skills, and other psychological disorders that affect a person's daily activities. Alzheimer's disease is a disease that causes disability for people aged 70 years and over and is the seventh highest contributor to death in the world. However, until now there has not been found an effective treatment to cure Alzheimer's disease. Thus, early detection of Alzheimer's disease is very important so that sufferers of Alzheimer's disease can immediately receive intensive medical care so as to reduce the death rate from Alzheimer's disease. One method that can be used to detect Alzheimer's disease is by utilizing a machine learning algorithm model. The machine learning model in this study was carried out using the Decision Tree C4.5 algorithm classification method based on Binary Particle Swarm Optimization (BPSO). The C4.5 Decision Tree algorithm is used to classify Alzheimer's disease, while the BPSO algorithm is used to perform feature selection. By performing feature selection with the BPSO algorithm, the results show that the BPSO algorithm can improve accuracy and can increase the performance of the C4.5 algorithm in the Alzheimer's disease classification process. The results of the accuracy of the C4.5 algorithm using the BPSO feature selection are greater, namely 98.2% compared to the C4.5 algorithm without BPSO feature selection, which is only 96.4%. 
Determining Scholarship Recipients at STIT Prabumulih Using the AHP Method Christian, Andi; Ariansyah, Ariansyah; Wahyuni, Anggie Sri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1717

Abstract

In every educational institution, especially universities, there are lots of scholarships offered to students. Likewise with the Prabumulih College of Engineering (STIT Prabumulih) which has a scholarship program for its students by applying predetermined rules or criteria, for example, parents' income, parents' dependents, student achievement index scores, etc. Due to this, not all scholarship recipients who apply for scholarships will receive a scholarship. The problem faced by the campus today is in the process of winning scholarships. therefore a decision support system is needed that can assist in providing scholarship recipient recommendations. In this study the authors used the AHP method and the Expert Choice application. From the calculation results obtained by the specified criteria, the GPA of 0.389 is the highest priority weight compared to other criteria. Then, from the results of calculating student data or all alternatives, the total value of each student is obtained. It can be concluded that the one who can be recommended to get a UKT scholarship is Student A because it has the highest score, namely 16.6% of the total calculated.
Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm Sondakh, Debby Erce; Maringka, Raissa C; Ayorbaba, Ferlien P; Mangi, Joanne S. C. B. T.; Pungus, Stenly Richard
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1721

Abstract

As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.
Classification of Final Project Titles Using Bidirectional Long Short Term Memory at the Faculty of Engineering Nurul Jadid University Warda, Faridatul; Fajri, Fathorazi Nur; Tholib, Abu
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1723

Abstract

Every year, the Faculty of Engineering at Nurul Jadid University forms a committee to manage the process of students' final projects from the title selection stage to the final examination process until graduation. The process of selecting the final project title is still done manually, namely by checking the titles one by one, which takes a long time and allows errors because there is a lot of data to check, so human errors can also occur. Therefore, this research proposes to use the Bidirectional Long Short Term Memory (BiLSTM) method to classify the final project title based on its grade category. Several experiments were conducted to generate the most appropriate labels. The first experiment produced 4 labels and the second experiment produced 2 labels. From the results of several experiments, it was concluded that the second experiment had the best accuracy results with the 'good enough' and 'good' classes. The oversampling technique was then applied to overcome overlapping data, and the turning process was then performed on several parameters that could re-optimize the previous accuracy result of 75.24% to 91.15%. With a configuration of 10 random state parameters, using 64 batch sizes and 50 epochs. In addition, model adjustments were made to the hidden layer by adding a dropout layer and relu activation.
Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms Werdiningsih, Indah; Purwanti, Endah; Wira Aditya, Gede Rangga; Hidayat, Auliya Rakhman; Athallah, R. Sulthan Rafi; Sahar, Virda Adisty; Wibisono, Tio Satrio; Nura Somba, Darren Febriand
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1730

Abstract

The use of credit cards is increasing in today's digital era. This increase has resulted in many cases of fraud which have had a negative impact on credit card owners. To overcome this, many financial institutions have developed credit card fraud detection systems that can identify suspicious transactions. This study uses a classification method, namely random forest and decision tree to identify illegal transactions using a credit card, which then compares the results and attempts to create a model that can be useful for detecting fraud using a credit card that is more accurate and effective. The result of this study is that the accuracy provided by the Decision Tree Classifier is 0.98, while the accuracy provided by the Random Forest Classification is also 0.975. The conclusion obtained that the decision tree has a higher level of accuracy compared to the Random Forest Classification Algorithm, which is 98%. On the other hand, the Random Forest classification algorithm has a slightly lower level of accuracy compared to the Decision Tree classification algorithm, with an accuracy rate of 97.5%

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