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All Journal Jurnal Teknologi dan Manajemen Informatika Prosiding SNATIKA Vol 01 (2011) Record and Library Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Research and Technology Indonesian Journal of Artificial Intelligence and Data Mining JKTP: Jurnal Kajian Teknologi Pendidikan Jurnal Sisfokom (Sistem Informasi dan Komputer) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Terpadu JUSIM (Jurnal Sistem Informasi Musirawas) Buletin Ilmiah Sarjana Teknik Elektro JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Advanced in Information and Industrial Technology (JAIIT) SKANIKA: Sistem Komputer dan Teknik Informatika Teknika KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Data Science, Engineering, and Analytics (IJDASEA) DECODE: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Informatics, Electrical and Electronics Engineering Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Anik Vega Vitianingsih
UNITOMO, Surabaya
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Comparative Analysis of Deep Learning Methods for Predicting the Value of the Standard & Poor's Global Supply Chain Intelligence (S&P GSCI) Nickel Stock Index Rahmansyah, Ragada; Vitianingsih, Anik Vega; Hamidan, Rusdi; Lidya Maukar, Anastasia; Budi Suprio, Yoyon Arie
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36129

Abstract

The development of information technology has opened up new opportunities in stock market forecasting, especially in nickel commodities, which are increasingly strategic in the global energy transition. This study uses a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a Gated Recurrent Unit (GRU) to forecast the movement of the S&P GSCI Nickel stock index value. Yahoo Finance time series data for the years 2018–2024 are used in the dataset. The study's findings are used to evaluate each model's capacity to forecast changes in nickel stock prices. The RNN model is used in this study because it can work with sequential information, while LSTM works with three memory gates (input, forget, output), and GRU works with 2 gates, namely update and reset. Mean Absolute Percentage Error (MAPE) presents the results of open and closed variable forecasting errors with the lowest average for the RNN model of 2.08%, the LSTM model of 2.505%, and the GRU model of 1.505%. This study is expected to contribute to investor decision-making and the identification of the most accurate forecasting model for the nickel stock index
Sentiment Analysis of BCA Mobile App Reviews Using K-Nearest Neighbour and Support Vector Machine Algorithm Zandroto, Yosefin Yuniati; Vitianingsih, Anik Vega; Maukar, Anastasia Lidya; Hikmawati, Nina Kurnia; Hamidan, Rusdi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37773

Abstract

The rapid evolution of digital technology has significantly transformed the financial services landscape, especially in the realm of mobile banking. BCA Mobile stands among the most popular apps for digital banking in Indonesia. Despite its widespread adoption, user reviews reflect diverse viewpoints and sentiments about the app. The objective of this research is to examine the user sentiments regarding the BCA Mobile app, based on reviews sourced from the Google Play Store and App Store. Two classification models, namely Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN), are used in the analysis process. The collected review data undergoes several pre-processing stages and is labeled automatically using a Lexicon-Based technique. For feature weighting, the TF-IDF (Term Frequency-Inverse Document Frequency) approach is used.. Sentiment classification is then carried out using both K-NN and SVM, with performance evaluated through a matrix of confusion based on measurements like F1-score, recall, accuracy, and precision.  The findings show that the SVM algorithm outperforms K-NN in terms of performance, with an accuracy of 94%, while K-NN achieves an accuracy of 82%. This study offers valuable insights for BCA management in understanding user sentiment and enhancing service quality through the application of artificial intelligence
Small Object Detection in Medical Imaging Using Enhanced CNN Architectures for Early Disease Screening Zangana, Hewa Majeed; Omar, Marwan; Li, Shuai; Al-Karaki, Jamal N.; Vitianingsih, Anik Vega
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14015

Abstract

Early detection of subtle pathological features in medical images is critical for improving patient outcomes but remains challenging due to low contrast, small lesion size, and limited annotated data. The research contribution is a hybrid attention-enhanced CNN specifically tailored for small object detection across mammography, CT, and retinal fundus images. Our method integrates a ResNet-50 backbone with a modified Feature Pyramid Network, dilated convolutions for contextual scale expansion, and combined channel–spatial attention modules to preserve and amplify fine-grained features. We evaluate the model on public benchmarks (DDSM, LUNA16, IDRiD) using standardized preprocessing, extensive augmentation, and cross-validated training. Results show consistent gains in detection and localization: ECNN achieves an F1-score of 88.2% (95% CI: 87.4–89.0), mAP@0.5 of 86.8%, IoU of 78.6%, and a low false positives per image (FPPI = 0.12) versus baseline detectors. Ablation studies confirm the individual contributions of dilated convolutions, attention modules, and multi-scale fusion.However, these gains involve higher computational costs (≈2× training time and increased memory footprint), and limited dataset diversity suggests caution regarding generalizability. In conclusion, the proposed ECNN advances small-object sensitivity for early disease screening while highlighting the need for broader clinical validation and interpretability tools before deployment.
Comparative Analysis of SVM and NB Algorithms in Evaluating Public Sentiment on Supreme Court Rulings Maulidiana, Putri Dwi Rahayu; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Hermansyah, David
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2116

Abstract

The legal events that happened to Ferdy Sambo and the Supreme Court’s decision in the cassation triggered emotional reactions and various opinions among the public, especially on social media sites such as Xapps. Some comments reflect people’s concerns about fairness in the legal system. They doubted the integrity of legal institutions or believed that decisions were unfair or in line with vested interests. This research aims to analyze public perceptions of Supreme Court decisions. The research process includes data collection, preprocessing, labeling, weighting, classification using Support Vector Machine and Naïve Bayes, and performance evaluation using a confusion matrix. A dataset of 624 was taken from X apps using the Twitter scraping technique. The lexicon method is used for data labeling, dividing the data into positive, negative, and neutral classes. The analysis results show 46 tweets categorized as positive sentiment, 133 tweets categorized as negative sentiment, and 422 tweets categorized as neutral sentiment. Based on testing with a data ratio of 80:20, both SVM and NB methods show good performance. The SVM criteria showed an accuracy of 0.84, precision of 0.61, recall of 0.78, and f1-score of 0.66, while the NB criteria showed an accuracy of 0.73, precision of 0.37, recall of 0.57, and f1-score of 0.35.
Sentiment Analysis on Ajaib App Using the SVM Method Minggow, Lingua Franca Septha; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Rusdi, Jack Febrian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER (In Press)
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2402

Abstract

The rapid growth of investment applications has transformed trading accessibility, yet user dissatisfaction persists, particularly regarding transaction delays, technical issues, and inadequate customer support. This study addresses a research gap in sentiment analysis, specifically in the context of the Ajaib investment application, by employing a Support Vector Machine (SVM) model combined with lexicon-based labelling. The objective is to classify user-generated Google Play reviews into positive, negative, and neutral sentiments, providing actionable insights for service improvement. The research follows a structured methodology comprising data crawling, text pre-processing (cleaning, case folding, tokenization, stopword removal, and stemming), TF-IDF feature extraction, and supervised classification with SVM. Model validation utilises a 3×3 confusion matrix to calculate accuracy, precision, and recall, thereby ensuring a robust performance evaluation. Experimental results demonstrate that the SVM classifier achieves high accuracy in identifying sentiment polarity, highlighting its suitability for text-based sentiment analysis in the financial domain. The distinct contribution of this research is its implementation of SVM for sentiment classification for Ajaib, offering a replicable framework for leveraging user feedback to enhance digital investment platforms. These findings contribute to the development of automated sentiment analysis systems that support data-driven decision-making for improving customer satisfaction.
Sentiment Analysis on the FIFA U-20 World Cup in Argentina Using Support Vector Machine Warsito Sujatmiko, Achmad; Vitianingsih, Anik Vega; Kacung, Slamet; Cahyono, Dwi; Lidya Maukar, Anastasia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3973

Abstract

The decision made by FIFA regarding the selection of the soundtrack and the host country for the FIFA U-20 World Cup has sparked emotional reactions among the public and raised concerns about the event, especially on social media platform X. This is due to FIFA’s decision to choose a soundtrack not from the host country, Argentina, but from the previous host, Indonesia. FIFA should advocate for the creation of a soundtrack by the host country to reflect its distinctive characteristics or atmosphere. Concerns about the U-20 World Cup in Argentina have also been fueled by the country’s economic crisis, which is feared to affect the facilities and infrastructure for the young players representing their nations. This research focuses on filtering public responses to FIFA’s decisions regarding the soundtrack selection and the host country for the U-20 World Cup into positive, neutral, and negative categories using the Support Vector Machine (SVM) method. The research aims to provide policy recommendations regarding the host selection process and cultural representation in international sports events. Additionally, this study is expected to provide a deeper understanding of the preferences and values held by the public regarding international sports. The research steps include data collection, pre-processing, labeling, weighting, and classification using a Support Vector Machine. The data for this research were obtained through crawling on social media platform X, totaling 2400 data points. The performance evaluation of the SVM algorithm using a 50:50 ratio of training and testing data yielded an average accuracy of 85.71%, Precision of 85.98%, Recall of 85.71%, and F1-score of 85.58%.
Comparative Analysis of Naïve Bayes and K-NN Methods on Social Media Boycott Issue X Case Study: McDonald’s Azzahra, Morra Fatya Gisna Nourielda; Vitianingsih, Anik Vega; Cahyono, Dwi; Maukar, Anastasia Lidya; Badri, Fawaidul
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4956

Abstract

The boycott movement against McDonald’s, triggered by its alleged support for Israel during the conflict in Gaza, has generated significant public discourse, particularly on the social media platform X (formerly Twitter). This study investigates public sentiment regarding the boycott campaign by analyzing comments and reactions to related content. A total of 1,585 tweets were collected using techniques for web scraping and underwent a comprehensive pre-processing phase, encompassing cleaning, tokenization, filtering, and stemming. Sentiment categories, namely positive, neutral, and negative, are automatically assigned using a lexicon-based technique customized for the Indonesian language. Text data was transformed into numerical form through the Term Frequency-Inverse Document Frequency (TF-IDF) technique, followed by sentiment classification using two supervised machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Evaluation of both models was conducted using a confusion matrix and classification metrics. The results show that the dataset is highly imbalanced, with 93.5% of the tweets labelled as negative, 6.1% as neutral, and only 0.3% as positive. The K-NN model achieved better performance than Naïve Bayes (NB), with an accuracy of 93%, a precision of 31%, a recall of 33%, and an F1-score of 32%. On the other hand, the Naïve Bayes algorithm reached 39% accuracy, 33% precision, 29% recall, and an F1-score of 22%. These findings highlight the dominance of negative sentiment toward McDonald’s and demonstrate the efficacy of the K-NN algorithm in sentiment classification in unbalanced datasets. The insights from this study can inform public relations strategies and corporate reputation management in the face of socio-political controversies.
Co-Authors Abdul Rezha Efrat Najaf Achmad Choiron Ade Susianti, Febrina Ahmad, Sharifah Sakinah Syed Al-Karaki, Jamal N. Anastasia Lidya Maukar ANGGI FIRMANSYAH Azzahra, Morra Fatya Gisna Nourielda Badrussalam, Nanda Budi Suprio, Yoyon Arie Damayanti, Erika DWI CAHYONO Dwi Indrawan, Dwi Dwi Prasetyo, Septian Fardhan Maulana, Abelardi Fauzan, Rizky Fauzi, Ariq Ammar Fawaidul Badri Febrian Rusdi, Jack Firmansyah, Deden Fitri Ana Wati, Seftin Fitri, Anindo Saka Ghibran Jhi S, Moch Hamidan, Rusdi Hengki Suhartoyo, Hengki Hermansyah, David Hikmawati, Nina Kurnia Jazaudhi’fi, Ahmad Khusnaini, Geovandi Gamma KRISTIAWAN KRISTIAWAN Li, Shuai Lidya Maukar, Anastasia MARIFANI FITRI ARISA Maukar, Anastasia L Maukar, Anastasya Lidya Maulidiana, Putri Dwi Rahayu Miftakhul Wijayanti Akhmad, Miftakhul Wijayanti Minggow, Lingua Franca Septha Mudinillah, Adam Muzaki, Mochammad Rizki Omar, Marwan Pradana, Dwifa Yuda Pramisela, Intan Yosa Pramudita, Atanasia Pramudita, Krisna Eka Pujiono, Halim Puspitarini, Erri Wahyu Putra Selian, Rasyid Ihsan Putri, Jessica Ananda Putri, Natasya Kurnia Rahmansyah, Ragada Ramadhani, Illham Ratna Nur Tiara Shanty, Ratna Nur Tiara Rijal, Khaidar Ahsanur Riza , M. Syaiful Rusdi, Jack Febrian Salmanarrizqie, Ageng Sari, Dita Prawita Seftin Fitri Ana Wati Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Sufianto, Dani Suyanto Suyanto Suyanto Tiara Shanty, Ratna Nur Titus Kristanto Tri Adhi Wijaya, Tri Adhi Umam, Azizul Warsito Sujatmiko, Achmad Wati , Seftin Fitri Ana Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Wikaningrum, Anggit Wikanningrum , Anggit Yasin, Verdi Yoyon Arie Budi Suprio Yudi Kristyawan, Yudi Zandroto, Yosefin Yuniati Zangana, Hewa Majeed