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All Journal Jurnal Rekayasa Sistem Industri Jurnal Teknologi dan Manajemen Informatika SPEKTRUM INDUSTRI PROSIDING SEMINAR NASIONAL CENDEKIAWAN Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sistem dan Manajemen Industri RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Information Technology and Computer Science (JOINTECS) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA SMARTICS Journal Indonesian Journal of Artificial Intelligence and Data Mining JKTP: Jurnal Kajian Teknologi Pendidikan Jurnal Sisfokom (Sistem Informasi dan Komputer) Prosiding Seminar Nasional Pakar Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JUSIM (Jurnal Sistem Informasi Musirawas) ACADEMICS IN ACTION Journal of Community Empowerment Jurnal Sistem Teknik Industri Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Mulia International Journal in Science and Technical Journal of Industrial Engineering Zonasi: Jurnal Sistem Informasi International Journal of Industrial Research and Applied Engineering JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Teknika IDEAS: Journal of Management & Technology Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science Widya Teknik
Anastasia Lidya Maukar
Universitas Presiden
Humanities 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 Mathematics Mechanical Engineering Social Sciences Transportation Other

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SISTEM INFORMASI PERSEDIAAN STOK BERDASARKAN TURNOVER RATIO Muzaki, Mochammad Rizki; Vitianingsih, Anik Vega; Hamidan, Rusdi; Maukar, Anastasia Lidya; Wati, Seftin Fitri Ana
Journal of Information System Management (JOISM) Vol. 7 No. 1 (2025): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2025v7i1.2120

Abstract

Penelitian ini mengembangkan sistem informasi persediaan stok berdasarkan rasio perputaran (turnover ratio) untuk mengatasi pencatatan manual dan analisis intuitif yang tidak valid. Sistem dibangun menggunakan metode SDLC dan diuji dengan pendekatan black box testing dan uji penerimaan menggunakan system usability scale (SUS). Hasil pengujian menunjukkan bahwa 87% pengguna menyatakan proses input data berjalan lancar dan sesuai alur kerja. Fitur klasifikasi berdasarkan turnover ratio yaitu non-moving, slow-moving dan fast-moving sangat membantu dalam pengambilan keputusan logistik. Hasil penelitian menunjukkan nilai rata-rata skor SUS yaitu 75,33 dengan kategori “Good”. Sistem yang dibangun dalam penelitian ini mampu mendukung operasional gudang secara efektif dalam meningkatkan efisiensi pengelolaan stok, integrasi pemesanan dengan supplier, serta transparansi aktivitas melalui log sistem otomatis yang mengacu pada visualisasi dashboard dan fitur CRUD yang lengkap.
Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-Criteria Spatial Analysis Approach: Integration of AHP and WPM Methods Anik Vega Vitianingsih; Ullum, Choirul; Maukar, Anastasia Lidya; Yasin, Verdi; Wati, Seftin Fitri Ana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.4520

Abstract

Along with the increase in population and the acceleration of economic expansion, there has been a concomitant increase in the urgent requirement for additional property that can serve as a venue for a wide variety of community activities. It is not uncommon for large cities, which are the epicenter of urbanization, such as the city of Surabaya, to experience a sharp increase in the demand for land. One of the regions that has excellent accessibility is the Sidoarjo Regency, which is comparable to the City of Surabaya in this regard. The goal of this research is to use Web-GIS to conduct an analysis of spatial data to identify the land functions that are most suitable for use in residential areas. The Analytic Hierarchy Process (AHP) and the Weighted Product Model (WPM) are two of the methodologies that are included in the spatial data modeling method that uses multi-criteria decision making (MCDM). The parameters of the characteristics that are used are derived from data such as the distance to the city center, the distance to the market, the distance to the hospital, the distance to public transportation, the slope, the type of soil, and the amount of rainfall. The results of the spatial data modeling categorize the suitability of new residential land into categories of land that is not suitable for residential use and land that is acceptable for residential use. A K value of 0.27 is the result of the comparison test that was run between the two MCDM approaches using Cohen's Kappa coefficients.
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
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.
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 Achmad Aziz Wahdana Achmad Choiron Adi Saptari Agus Sasmito Agustinus Noertjahyana Ahmad Yanu Rokhim Anang Aris Widodo andini dwi arumsari Andira Andira Andira Andira Andira Andira Andira Taslim Andira, Andira ANGGI FIRMANSYAH Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Yuesti Anindo Saka Fitri Anindo Saka Fitri Apri Junaidi, Apri Arie Restu Wardhani Arizia Aulia Aziiza Arrosyadi, Laesa Qotrun Nada Arthur Silitonga Athina Sakina Ratum Avania Shinta Azzahra, Morra Fatya Gisna Nourielda Bella Chelsea Berliana Burhan Primanintyo Cakranegara, Pandu Adi Carolena Setephany Christian Setiadi Ciswondo Ciswondo Dewa Anggara Kesuma Dian Retno Sari Dewi DWI CAHYONO Fauzan, Rizky Fauzi, Ariq Ammar Fawaidul Badri Firmansyah, Deden Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marissa Gita Indah Marthasari Gunawan Hamidan, Rusdi Hashim, Ummi Rabah Helmi Indra Purnomo Hermansyah, David Herwan Yusmira Hikmawati, Nina Kurnia Husri Sidi Ineu Widaningsig Sosodoro Ineu Widaningsih Ineu Widaningsih Sosodoro Ineu Widaningsih Sosodoro Ineu Widaningsih Sosodoro, Ineu Widaningsih Intan Puspita Pribadi Intan Yosa Pramisela Jack Febrian Rusdi Jazid Rizkon Jean Hillary P Korua Jenifer Cafriaty Johan Krisnanto Runtuk Johan Runtuk Julius Mulyono Kacung Hariyono Kamalrudin, Massila Kevin Heryadi Yunior Kresna Arief Nugraha KRISTIAWAN KRISTIAWAN Luqman Hakim Mardiana Andarwati MARIFANI FITRI ARISA Mashudi Mashudi Maulidiana, Putri Dwi Rahayu Maurits Walalayo Mieke Wijayanti Minggow, Lingua Franca Septha Mochammad Syaiful Riza Mohamad Toha Mohd Syaiful Rizal Mucalinda Rupasari Mucalinda Rupasari Muhammad Afra Irwansyah Muzaki, Mochammad Rizki Nurhaba Djiha Octa Wendy Tanurahardja Oktavia Sunny Pramisela, Intan Yosa Pramudita, Atanasia Pramudita, Krisna Eka Puspitarini, Erri Wahyu Putri, Jessica Ananda Putri, Natasya Kurnia Putu Gede Ari Krismantoro Rachmad Ary Ramadhan Ramadhan, Rachmad Ary Rendy - Resza Adistya Pangestu Rhiza Adiprabowo Rhiza Adiprabowo, Rhiza Richki Hardi Rijal, Khaidar Ahsanur Rivaldo Tito Lamberto Da Silva Rusdi, Jack Febrian Salmanarrizqie, Ageng Seftin Fitri Ana Wati Seftin Fitri Ana Wati Shofa Ramadhina Sigit Sigalayan Siti Hajar Binti Mohtar Slamet Kacung Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Stefanus Setiady SUMARDI Susilo, Yunus Sutrisno Sutrisno Syahroni Wahyu Iriananda, Syahroni Wahyu Tantyo Edo Wicaksana Tubagus Mohammad Akhriza Ullum, Choirul Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Widiya Nur Permata Yana Hendriana Yasin, Verdi Yomara Oktafamero Yoyon Arie Budi Suprio Yudi Kristyawan, Yudi Yunus Susilo Yustian Zandroto, Yosefin Yuniati Zangana, Hewa Majeed