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All Journal Nusa: Jurnal Ilmu Bahasa dan Sastra Jurnal sistem informasi, Teknologi informasi dan komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Journal La Multiapp JURNAL ILMIAH GLOBAL EDUCATION Economicus: Jurnal Ekonomi dan Manajemen Jurnal Manajemen Informatika dan Bisnis Digital Wicara Jurnal Informatika: Jurnal Pengembangan IT
Khothibul Umam
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Identifikasi Polaritas Sikap Pengguna Aplikasi X terhadap Coretax di Indonesia Menggunakan Algoritma Naïve Bayes Prasilda, Dina Rahma; Yuniarti, Wenty Dwi; Handayani, Maya Rini; Umam, Khothibul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8548

Abstract

The Core Tax Administration System (Coretax) was launched by the Directorate General of Taxes (DGT) in January 2025 as a technology-based integrated tax system. While its initial goal was to improve tax efficiency and compliance, Coretax faced technical challenges, including system errors, slow processing speed, and criticism from the public. The main platform used to address these challenges is the X app (formerly known as Twitter). This research aims to understand the public's views and responses to Coretax's services by analyzing user sentiment patterns seen on social media. The research identifies the polarity of user attitudes by utilizing natural language processing (NLP) and Naïve Bayes algorithms, applied to a dataset of 1,628 tweets collected between January and March 2025. The analyzed data reflects a wide range of public reactions that include both positive and negative opinions towards the Coretax implementation, both in terms of functionality and ease of use. The results show that the model has an accuracy rate of 93.07%, a precision value of 95%, a recall value of 96%, and an F1-Score value of 96%. The results of this study are expected to be able to provide precise mapping related to changes in public opinion towards Coretax, so that it can be a valuable source of information for application developers, policy makers in the field of taxation, and analysis in the technology sector in responding to the needs and expectations of society in the digital era.
Digital Forensic Chatbot Using DeepSeek LLM and NER for Automated Electronic Evidence Investigation Qonita, Nuurun Najmi; Handayani, Maya Rini; Umam, Khothibul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4593

Abstract

The growing complexity of cybercrime necessitates efficient and accurate digital forensic tools for analyzing electronic evidence. This research presents an intelligent digital forensic chatbot powered by DeepSeek Large Language Model (LLM) and Named Entity Recognition (NER), designed to automate the analysis of various digital evidence, including system logs, emails, and image metadata. The chatbot is deployed on the Telegram platform, providing real-time interaction with investigators. The metric results show that the chatbot achieves a precision of 83.52%, a recall of 88.03%, and an F1-score of 85.71%. These results demonstrate the chatbot's effectiveness in accurately detecting forensic entities, significantly improving investigation efficiency. This study contributes to digital forensics by integrating LLM and NER for enhanced evidence analysis, offering a scalable and adaptive solution for automated cybercrime investigations. Future research may explore integrating anomaly detection and blockchain-based evidence integrity.
Dinamika Opini Publik Terkait Quarter Life Crisis Pada Media Sosisal X Menggunakan Support Vector Machine Septyorini, Talitha Dwi; Umam, Khothibul; Handayani, Maya Rini
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8648

Abstract

This study aims to analyze the dynamics of public opinion related to quarter life crisis on platform X through a sentiment analysis approach based on machine learning Support Vector Machine (SVM) algorithm is used to classify positive and negative sentiments from text data. A total of 6.312 tweets were collected with the keyword “quarter life crisis” from January 2024 to January 2025. The data was then processed through the stages of text cleaning, tokenization, stopword removal, stemming, and lexicon-based sentiment labeling. The classification process is carried out using SVM with a data division of 80% training and 20% test. The results showed an accuracy of 81.57% with a sentiment distribution of 59.3% negative and 40.7% positive. Implementation was done on Google Colab platform with evaluation using confusion matrix and classification report. The fingdings prove the effectiveness of SVM in analyzing psychosocial phenomena on social media and provide an empirical basis for the development of digital data-based mental health interventions. The machine learning pipeline optimized in this study can be used as a reference for other studies in analyzing psychological phenomena on social media
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.
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.
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.
Sentiment Classification of MyPertamina Reviews Using Naïve Bayes and Logistic Regression Dwi Yuni Saraswati; Handayani, Maya Rini; Umam, Khothibul; Mustofa, Mokhamad Iklil
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9723

Abstract

This research conducts a comparative evaluation of the effectiveness of the Naïve Bayes and Logistic Regression algorithms in mapping public perceptions of the MyPertamina application on the Google Play Store. The data consists of 2,000 user reviews obtained through a scraping technique. The research steps include labeling the reviews as positive or negative, followed by pre-processing and TF-IDF weighting. The dataset was systematically divided into two parts, with 80% allocated for model training and the remaining 20% for evaluation. The Naïve Bayes and Logistic Regression models were implemented using the Python programming language and evaluated based on accuracy, precision, recall, and F1-score metrics. The analysis shows that Logistic Regression achieved an accuracy of 86%, while Naïve Bayes achieved 81%. Logistic Regression demonstrated superior performance as it effectively captures linear relationships between features in TF-IDF representations and provides a more balanced outcome in terms of precision and recall. In contrast, Naïve Bayes is more influenced by high-frequency word distributions and does not account for feature correlations, which can limit its performance in certain contexts. Therefore, Logistic Regression is considered more suitable for sentiment classification tasks in this study. These findings emphasize the importance of selecting appropriate algorithms for sentiment analysis and suggest opportunities for future research using alternative methods to enhance predictive accuracy.
Implementasi Algoritma Random Forest dalam Klasifikasi Ulasan Pengunjung Mall Semarang untuk Pengambilan Keputusan Layanan Maizaliyanti, Annisa; Umam, Khothibul; Yuniarti, Wenty Dwi; Handayani, Maya Rini
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30379

Abstract

Visitor preferences for malls in Semarang are not optimal because bold reviews have not been utilized optimally in decision making. Our research aims to classify the sentiment of Google Maps reviews from 13 malls in Semarang with a total of 2,600 reviews. Labeling is done manually based on ratings, where ratings 1–3 are considered negative reviews and 4–5 as positive reviews. The classification method used is Random Forest because the ensemble approach (bagging) provides optimal results. The research process includes data collection, labeling, cleaning, data sharing, classification, and model evaluation. The data used is unbalanced and dominated by positive reviews, so the Synthetic Minority Over-sampling Technique (SMOTE) technique was applied. The overall accuracy before and after SMOTE remained the same at 84%. However, the model's performance in detecting negative reviews increased from 27% to 44% in recall and F1-score from 0.40 to 0.52, but these values ​​are still relatively low. Java Supermall Semarang is the mall with the best reviews, with a classification accuracy reaching 90%. This model is better at recognizing positive reviews, but less reliable for negative reviews. Therefore, its use as a decision-making preference needs to be done with caution. This research opens up opportunities for further development, including the use of other models such as BERT which are superior in understanding context and language in reviews.
DAMPAK COVID-19 TERHADAP PENDAPATAN PEDAGANG: (STUDI KASUS DI PASAR PETERONGAN JOMBANG) Lailatus Sa’adah; Khothibul Umam
Economicus : Jurnal Ekonomi dan Manajemen Vol. 11 No. 2 (2021): Juni: Economicus : Jurnal Ekonomi dan Manajemen
Publisher : Institut Teknologi dan Bisnis Dewantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47860/economicus.v11i2.71

Abstract

The formulation of the problem what is the impact felt by traders, especially the income of traders during the covid 19 outbreak This study aims to find out how the impact of covid 19 on the income of traders before the occurrence of covid 19, during the covid 19 pandemic and in the new normal, at the same time providing benefits for the government and traders so that they can solve a similar problem if it occurs in the future. The type used in this study is qualitative, the information collection techniques used are observation and interviews. The research population which is also the research sample is the traders of the Jombang Peterongan market, with the determination of the sample that the sample obtained is 100 respondents. And qualitative descriptive information analysis techniques using the Miles and Huberman model. The results of this study indicate that the income of Peterongan traditional market traders is sufficiently increased and stable so that they can meet their daily needs and support the traders 'economy and traders' income is also influenced by several factors, including the increasing number of regular customers and also the more crowded consumers who shop at the Peterongan market. . The Covid 19 pandemic has a very serious impact, the income of Peterongan Jombang traditional market traders has decreased very drastically, reaching 70% -80% experienced by traders and causing market closures. There was an increase in income in the normal era experienced by traders reaching 40% compared to the Covid 19 pandemic.
Co-Authors Azziizah, Almira Farradinda Dewi, Fiashintha Dina Wulan Yekti rahayu Dwi Yuni Saraswati Dwi Yuniarti, Wenty Endang Supriyati Fadhilah, Mohamad Agung Faruq, Muhammad Kholfan Fastabiqul Khusna Febrianto, Bagus Haryanti, Novia Heti Aprilianti Imtias, Muhamad Bunan Izzuddin, Zufar Wahyu Jinan, Muhammad Syifaaul Khoirotulmuadiba Purifyregalia Komariya, Siti Lailatus Sa’adah Laura Andri Retno Martini Limparu, Stella Lasverina Mahfudh, Adzhal Arwani Maizaliyanti, Annisa Malikhatul Ibriza Manurung, Gregorius Tri Hendrawan Masuzzahra, Tsaura Rafah Masy Ari Ulinuha Maya Rini Handayani Moh Muzakka, Moh Mokhamad Iklil Mustofa Muhammad Rafid Pratama Mustofa, Hery Mustofa, Mokhammad Iklil Nur Cahyo Hendro Wibowo Nur Cahyo Hendro Wibowo, Nur Cahyo Hendro Pramesti, Arfiana Diah Prasilda, Dina Rahma Puspita, Yonanda Dera Putri, Amiliya Putri, Indira Alifia Qonita, Nuurun Najmi Rahmadani, Nurul REDYANTO NOOR Rini Handayani, Maya Sabilillah, Muhsin Safitri, Sindy Eka Sajmira, Danita Divka Salmalina, Divana Taricha Sanjaya, Tegar Saputra, Adika Kaka Sa’adah, Lailatus Sa’adah, Siti Miftahus Septyorini, Talitha Dwi Siti Nur'aini Subowo, Moh Hadi Sukarjo Waluyo Syafiqa, Luthfia Rahmati Tambunan, Rieke Dinda Laila Tri Listyorini Wenty Dwi Yuniarti Wenty Dwi Yuniarti, Wenty Dwi Widyasari, Lia Widyasari, Sherly Gratia Yuniardi Fadilah Zaman, Farkhan Nuruz