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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 603 Documents
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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.
Implementation Of Mamdani Fuzzy Logic In The Assessment System Of Merdeka Belajar Kampus Merdeka (MBKM) Activities: Case Study Of Mathematics Study Program At Bangka Belitung University Aniska, Baiq Desy; Randa Trezenki; Afina Shabirah; Novia; Lianawati
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.v14i3.2394

Abstract

This study aims to apply Mamdani fuzzy logic in the assessment system of the Merdeka Belajar Kampus Merdeka (MBKM) activity to evaluate the effectiveness of student participation. This study was conducted on students of the Mathematics Study Program, Bangka Belitung University, class of 2021, involving four aspects of assessment: teamwork, basic skills, problem solving, and technological innovation. The method used is a descriptive quantitative approach with data processing using MATLAB software. Each input variable is converted into a fuzzy value through triangular and trapezoidal membership functions, followed by the preparation of if-then rules, Mamdani inference, and defuzzification using the centroid method. The results of the analysis show that the fuzzy system is able to produce an objective final assessment and in accordance with the assessor's policy, where students with dominant values ​​at a high level get a final score of 83.7 and are categorized as "good". This study proves that Mamdani fuzzy logic is effective as a tool in the evaluation process of MBKM based on soft skills and hard skills in a comprehensive and measurable manner.
Comparison of Linear Regression and Polynomial Regression for Predicting Rice Prices in Lhokseumawe City Muhammad Iqbal; Rozzi Kesuma Dinata; Rizki Suwanda
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.v14i3.2396

Abstract

Rice is a strategic food commodity in Indonesia, and its price fluctuations significantly impact inflation, economic stability, and poverty levels. Accurate price prediction is, therefore, essential for effective policymaking. The objective of this research is to develop a system for predicting the price of rice in Lhokseumawe City, employing a comparison of the accuracy of linear and polynomial regression models. To this end, daily price data from the Strategic Food Price Information Center (PIHPS) from 2020 to 2024 were utilized, with both models being implemented in Python. The findings indicate that 4th-order polynomial regression exhibited optimal performance, attaining a mean absolute percentage error (MAPE) of 1.85%, a mean absolute error (MAE) of 205.23, and a root mean squared error (RMSE) of 284.88. Conversely, the implementation of linear regression resulted in substantially elevated error metrics, with a mean absolute percentage error (MAPE) of 5.16%, a mean absolute error (MAE) of 553.91, and a root mean square error (RMSE) of 614.14. The findings indicate that 4th-order polynomial regression is a substantially more effective model for predicting rice prices in Lhokseumawe. The latter's superiority suggests that local rice price dynamics are characterized by significant non-linear patterns, rendering it a more robust tool for capturing data volatility and supporting data-driven policy.
Implementation of an IoT-based Threshold Method for a Food Hazardous Substance Detection Tool Malinda, Threa; Salamah, Irma; Anugraha, Nurhajar
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.v14i3.2397

Abstract

Food safety is a critical issue that has a direct impact on public health. Illegal addition of hazardous substances such as rhodamine B, melachite green, methanyl yellow, formalin, borax, and sodium hypochlorite are still commonly found in food products sold in the market. This research project aims to develop a tool for detecting hazardous substances in Internet of Things (IoT) based foods using a threshold method that refers to BPOM regulations. The threshold method refers to BPOM regulations. This system integrates two sensors: The TCS3200 sensor is used for RGB color analysis, and the HCHO sensor detects volatile compounds detecting volatile compounds. Test results show that this tool achieves 96.67% accuracy in identifying hazardous substances without producing false positives. The novelty of this research lies in combining both sensors into one system with real-time notification via Telegram. This research is novel because it combines both sensors into one system with real-time notifications via Telegram. It combines both sensors into a single system with real-time notifications via Telegram and ThingSpeak.
Technology Adoption Segmentation of MSMEs in Border Areas using TRI and Hierarchical Clustering Fadlan, Muhammad; Muhammad, Muhammad; Suprianto, Suprianto; Hadriansa, Hadriansa; Ilyas, Arifai
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.v14i3.2398

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in border areas such as Nunukan-Sebatik often face challenges in adopting modern technologies, which hinder their growth and competitiveness. This study employs a segmentation approach using agglomerative hierarchical clustering based on the Technology Readiness Index (TRI) to segment MSMEs in border areas and develop targeted strategies to accelerate technology adoption. A hierarchical clustering technique is applied to segment MSMEs according to their technology readiness levels. Data on technology readiness were collected through surveys, and the clustering results were analyzed to identify distinct MSME groups. The TRI score was 3.72, indicating a high level of technology readiness, which suggests that many MSMEs are open to technological innovation into their daily operations. The results also reveal that MSMEs in Nunukan-Sebatik can be grouped into two clusters based on hierarchical clustering:  Cluster 1, which consists of MSMEs that are more prepared and optimistic about technology adoption, and Cluster 2, which faces significant challenges. These findings highlight a digital readiness gap among MSMEs, where only a tiny portion (Cluster 1) is fully prepared, while the majority (Cluster 2) still encounters barriers to adoption.
Analysis of Public Sentiment Towards LGBT on Twitter Social Media using Naïve Bayes Method Yudhi Franata; Rizal; Rizki Suwanda
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.v14i3.2400

Abstract

The advancement of information technology and the widespread use of social media have provided a platform for individuals to express their views on various social issues, including those related to Lesbian, Gay, Bisexual, and Transgender (LGBT) topics. This study aims to assess public sentiment towards LGBT issues on Twitter by employing the Naïve Bayes classification algorithm. Relevant tweets were collected through web scraping based on specific LGBT-related keywords within a defined time frame. The collected data underwent several preprocessing stages, including data cleaning, tokenization, stopword removal, and stemming. The processed data were then categorized into three sentiment classes: positive, negative, and neutral. Naïve Bayes was chosen for its effectiveness and efficiency in handling large-scale textual data. The analysis revealed that negative sentiment toward LGBT issues was predominant, although a considerable portion of tweets expressed neutral and positive sentiments. These findings offer valuable insights for policymakers, social activists, and academics in understanding public perception and formulating more effective communication strategies related to LGBT discourse in Indonesia. The classification model achieved an accuracy of 57%, precision of 52%, recall of 100%, and an F1-score of 68%. While the Naïve Bayes approach proved capable in sentiment classification, the model's accuracy could be further enhanced through improved data preparation or the application of more advanced algorithms.
Unveiling Public Sentiment on Quarter Life Crisis: A Comparative Performance Evaluation of Support Vector Machine and Naïve Bayes Algorithms on Social Media X Data Septyorini, Talitha Dwi; Umam, Khothibul; Handayani, Maya Rini
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.v14i3.2405

Abstract

Quarter Life Crisis (QLC) is one of the psychological issues experienced by many young adults and is characterized by uncertainty, anxiety, and emotional distress. In the digital era, public opinion about QLC is increasingly expressed through social media, particularly platform X. This study seeks to classify public opinion related to the QLC into positive and negative sentiments by employing two computational classification models, namely Support Vector Machine (SVM) and Naïve Bayes (NB). Despite the growing discourse, there has been no study specifically comparing classification algorithms to analyze public sentiment on QLC. Data collection was conducted through crawling techniques on platform X from November 2024 to January 2025, resulting in a total of 1120 tweets. The data underwent preprocessing, lexicon-based sentiment labeling, and TF-IDF word weighting. After preprocessing, classification using SVM and NB was evaluated by accuracy, precision, recall, and F1-score. Results indicate that SVM achieved superior performance with an accuracy of 83%, outperforming NB, which recorded 74%. These outcomes demonstrate that the SVM algorithm demonstrates superior performance in analyzing public sentiment regarding QLC. This research contributes by providing empirical evidence regarding algorithm performance for sentiment analysis in mental health topics, offering recommendations for effective early detection strategies utilizing social media data.
Enhancing Review Processing in the Video Game Adaptation Domain through VADER and Rating-Based Labeling using SVM Sajmira, Danita Divka; Umam, Khothibul; Handayani, Maya Rini
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.v14i3.2409

Abstract

The adaptation of video games into films or television series has increasingly become a prominent trend in the entertainment sector, often eliciting diverse reactions from audiences.A prime example is The Last of Us, a video game adaptation series that generated substantial online discussions and sentiment, and serves as the specific case study in this research. Sentiment patterns found in audience reviews of The Last of Us on IMDb are analyzed using a domain-specific classification framework tailored to the language characteristics of entertainment media. A key issue addressed is the discrepancy between numerical ratings and the sentiment conveyed in review texts, which may lead to inconsistent labeling. The study employs a machine learning technique, Support Vector Machine (SVM), coupled with two distinct labeling methods: manual labeling based on IMDb ratings, and automatic labeling using the lexicon-driven VADER tool. A total of 2,017 English reviews of The Last of Us were gathered via web scraping from IMDb, followed by preprocessing, TF-IDF feature extraction, and hyperparameter optimization using RandomizedSearchCV. These results show that the SVM model trained on VADER-labeled data achieved an accuracy of 0.97, outperforming the model trained on manually labeled data at 0.79. Lexicon-based automatic labeling provides more consistent and reliable sentiment classification, particularly in specialized domains like video game adaptation reviews. Integrating VADER labeling with SVM enhances sentiment analysis effectiveness and offers practical value for media analytics, content creation, and audience insight research.

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