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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Teknologi dan Manajemen Informatika Prosiding SNATIKA Vol 01 (2011) Record and Library Journal Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 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) Jurnal Teknologi Sistem Informasi dan Aplikasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika JUSIM (Jurnal Sistem Informasi Musirawas) SPIRIT Building of Informatics, Technology and Science Journal of Information Systems and Informatics Buletin Ilmiah Sarjana Teknik Elektro Zonasi: Jurnal Sistem Informasi 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 INOVTEK Polbeng - Seri Informatika JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Anik Vega Vitianingsih
UNITOMO, Surabaya
Aerospace Engineering 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 Other

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Deteksi Notifikasi Suspend pada Aplikasi Ojek Online Menggunakan Metode MOORA Wijiono, Aditya Kusuma; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Pamudi, Pamudi
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.42159

Abstract

Online motorcycle taxi drivers often face the risk of account suspension due to violations of rules that are not always clear or understood by them. This ignorance can cause drivers to be unaware of actions that can lead to suspension, which can impact their income and reputation. To overcome this problem, this study proposes the use of the Multi-Objective Optimization based on Ratio Analysis (MOORA) method in detecting and providing early notification regarding potential suspension. The MOORA method is used to analyze various parameters related to violations, such as the frequency and type of violations, as well as the number of accumulated violation points. By processing this data, the developed system can predict the possibility of suspension and provide notification to the driver. The results of the application of the MOORA method show that this system is effective in providing accurate notifications and can help drivers avoid actions that have the potential to cause suspension. The application of this system has the potential to reduce the number of suspension cases and increase driver awareness of actions that must be avoided.
SISTEM PAKAR PENANGANAN PENYAKIT HIPERTENSI DENGAN TERAPI FARMAKOLOGI MENGGUNAKAN METODE FORWARD CHAINING Ramadhan, Prayudi Wahyu; Vitianingsih, Anik Vega; Kristyawan, Yudi; Suhartoyo, Hengki; Ana Wati, Seftin Fitri
SPIRIT Vol 16, No 1 (2024): SPIRIT
Publisher : LPPM ITB Yadika Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53567/spirit.v16i1.338

Abstract

Peningkatan tekanan darah sistolik dan diastolik merupakan hipertensi. Penderita penyakit hipertensi biasanya tidak menyadari bahwa dirinya menderita penyakit tersebut. Tindakan awal dan paling penting bagi individu yang telah mendapat diagnosis hipertensi adalah menurunkan tekanan darahnya melalui modifikasi gaya hidup dan pengobatan farmakologis. Keterlambatan pengobatan adalah masalah sosial saat ini, karena kondisi beberapa orang baru diperiksa setelah penyakitnya mencapai stadium lanjut. Pemeriksaan rutin diperlukan jika masalah muncul kembali dan kondisi kesehatan pasien memburuk. Namun, ada individu tertentu yang gagal melakukan pemeriksaan ini karena berbagai faktor, seperti jadwal yang padat dan biaya selangit yang terkait dengan penilaian tersebut. Dengan mengandalkan kemajuan teknologi, sekiranya penerapan pembuatan aplikasi sistem pakar penanganan penyakit hipertensi dengan terapi farmakologi memakai metode forward chaining berbasis website dapat membantu masyarakat dalam mengetahui gejala, jenis, dan penanganan penyakit hipertensi, serta menjadi media untuk konsultasi secara gratis. Dimana telah dibuktikan dengan 10 responden yang 94% rata rata menjawab sistem mudah digunakan.
Sistem Informasi Bimbingan Tugas Akhir Mahasiswa menggunakan Model SDLC Berbasis Iconix Process Ana Wati, Seftin Fitri; Fitri, Anindo Saka; Vitianingsih, Anik Vega; Najaf, Abdul Rezha Efrat; Maukar, Anastasia Lidya
Jurnal Sistem Informasi Bisnis Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss3pp224-236

Abstract

The final thesis assignment plays a crucial role in enabling students to meet the graduation requirements from college. However, the process of scheduling guidance for the final assignment between students and lecturers still relies on several common applications such as WhatsApp or email, which are not specifically designed for this purpose. The accumulation of incoming messages and various types of message information poses a challenge in the guidance process, leading to missed messages between students and lecturers and a lack of recorded information regarding the history of the process and guidance materials. These are some of the current issues. This paper aims to evaluate the functionality, quality, and reliability of the system by conducting black box testing on the application developed for the student final project guidance information system. This application uses the Iconix process-based SDLC (system development life cycle) model, covering student and lecturer profile information, guidance information, proposal submission, progress of the final thesis assignment, meeting schedule, guidance material, discussion forum, survey evaluation, and contact information. The SDLC model is employed in this research because it can effectively and efficiently achieve project targets, enhance software quality standards, and assist in better risk management and adaptation to change. The model comprises planning, needs analysis, design using the Iconix process, implementation, system testing, and maintenance. The Iconix process is utilized for system design modeling and analysis. Black box testing is performed on the system to verify that the system’s functional requirements are operating correctly. The findings of this research can serve as a control for management in the service and administration of final assignment guidance in higher education.
Sentiment Analysis on Indonesian National Football Team Naturalization using KNN and SVM Adharani, Salza Kartika; Kacung, Slamet; Vitianingsih, Anik Vega
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29653

Abstract

The naturalization of football players in Indonesia is largely viewed positively, with supporters highlighting its benefits for team performance, international competitiveness, and player development. While PSSI endorses naturalization to strengthen the national team, Liga Indonesia Baru (PT LIB) imposes limits to maintain fairness. The purpose of this research is to examine public sentiment toward the naturalization of Indonesian football players by analysing discussions on X and YouTube. This research analyses public sentiment toward the naturalization of Indonesian football players using a data and text mining approach based on 3,267 comments from X and YouTube between 2022 and 2024. The research process includes data collection, preprocessing, TFIDF, data labeling, and model training and evaluation. Two machine learning models, KNN and SVM, are implemented for classification, with SVM outperforming KNN in accuracy. Our results show that KNN achieved 76.71% accuracy (precision: 52%, recall: 56%, F1-score: 53%), while SVM RBF outperformed with 86.51% accuracy (precision: 59%, recall: 42%, F1-score: 26%). SMOTE and GridSearch effectively address the class imbalance and optimize model performance. Public sentiment is predominantly positive, highlighting enhanced team performance and global recognition. These insights assist PSSI and policymakers in making informed decisions regarding fairness, discrimination, and the governance of Indonesian football.
Sentiment Analysis of Cyberbullying Detection on Social Networks using the Sentistrenght Method Yunior, Kevin Heryadi; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Dwi Arumsari, Andini
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4226

Abstract

In today's swiftly changing digital realm, social media has emerged as a pervasive means of communication, yet it has also fostered the rise of cyberbullying, especially among young demographics. This research strives to develop an application that assesses public sentiment on Instagram regarding cyberbullying instances, categorizing sentiments as positive, negative, or neutral. Drawing data from Instagram accounts such as kumparandotcom, merdekadotcom, and okezonedotcom, the approach combines lexicon-based text labeling and sentiment analysis employing Sentistrength. Findings demonstrate the method's effectiveness, achieving accuracy, precision, and recall rates exceeding 85% while offering precise visualization of predictions. This study contributes to combatting cyberbullying, aiming to improve victims' mental well-being by providing clearer insights into social sentiment. The dataset comprises 4500 comments collected through web crawling, categorized into positive (735 entries), negative (2478 entries), and neutral (1288 entries) sentiments. The evaluation highlights the commendable performance of Sentistrength, achieving the highest accuracy at 93.85%.
Sentiment Analysis on Social Media Instagram of Depression Issues Using Naïve Bayes Method Voni Anggraeni Suwito Putri; Vitianingsih, Anik Vega; Rusdi Hamidan; Anastasia Lidya Maukar; Niken Titi Pratitis
INOVTEK Polbeng - Seri Informatika Vol. 9 No. 2 (2024): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/spchsk42

Abstract

The rapid expansion of the digital era has turned social media, especially Instagram, into a crucial source for examining public sentiment on mental health issues such as depression. Depression, a condition that adversely affects thoughts, behaviours, emotions, and overall mental well-being, is often less apparent than physical health problems, leading to delays in treatment. Low public awareness and societal stigma further aggravate these delays, making sufferers hesitant to seek professional help and more inclined to share their experiences online. This study aims to analyze public sentiment on Instagram concerning depression through the Naïve Bayes (NB) method. It involves developing an application that visualizes analysis reports via bar and pie charts, categorizing public comments on depression as neutral, positive, or negative. Data is sourced from Instagram using keywords related to depression and mental health, with lexicon-based methods for labelling and NB for sentiment classification. The findings show the effectiveness of this method, with the accuracy rate reaching 79%. The dataset consists of 1300 comments collected through web crawling. This evaluation displays the performance results of NB achieving an accuracy of 82.55%. The study aims to offer insights into public opinions on depression, provide datasets for future sentiment analysis research, and assess the NB method's effectiveness in managing complex sentiments on social media, ultimately aiming to improve public understanding and strategies for mental health intervention.
REPRESENTASI DATA HASIL ANALISA SPASIAL DAERAH RAWAN PENYAKIT CAMPAK MENGGUNAKAN METODE WEIGHT PRODUCT MODEL Vitianingsih, Anik Vega; Choiron, Achmad; Umam, Azizul; Cahyono, Dwi; Suyanto
Journal of Research and Technology Vol. 6 No. 1 (2020): JRT Volume 6 No 1 Jun 2020
Publisher : 2477 - 6165

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/jrt.v6i1.139

Abstract

Measles is a part of many diseases that occur in the tropics as happened in East Java. Measles disease data recorded in the Health Profile Book contains information on tabular data on the number of measles cases, the fatality rate of measles cases, and data that contain infant measles immunization. The purpose of the discussion of this paper is to represent spatial and attribute data resulting from spatial data processing in the spatial analysis process by Weight Product Model (WPM) methods and in the Multiple Attribute Decision Making (MADM). Data representation to determine areas prone to tropical diseases based on infant immunization status, nutritional status, epidemics, and PD3I. The results of the spatial data modeling will be represented into spatial data and attribute data obtained from the preferential value of Vi with the category of classification of tropical disease-prone areas with good, average, fair, and poor immunization status.
Performance Comparison of AHP and Saw Methods For Selection of Doc Broiler Chicken Suppliers Vitianingsih, Anik Vega; Krismantoro, Putu Gede Ari; Maukar, Anastasia Lidya; Aziiza, Arizia Aulia; Fitri, Anindo Saka
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 1 (2023): February 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i1.18634

Abstract

Choosing the most suitable day-old chick (DOC) broiler chicken supplier is currently one of the most important issues that must be addressed. This is because selecting the most suitable supplier can reduce the amount spent on purchases and the risk of sick chickens being delivered by the supplier. Another problem related to supplier selection that has been happening so far is the quality of products that are not following company standards or rejected products. The number of products provided does not match what was ordered by the company. The decision support system (DSS) can evaluate and select suppliers using multi-criteria characteristics related to the solutions offered based on parameters quality, price, delivery, supplier certificates, and death claims after the chickens have been delivered. The Analytical Hierarchy Process (AHP) and the Simple Additive Weighting (SAW) methods are used in this study as a comparison to produce the best-recommended accuracy value to get the best decision results based on ranking. The test results state that the AHP and SAW methods go well. The test was carried out using a dataset of the last ten months of history of purchasing docs broiler chicken from suppliers. The comparison of the results of the F1-score value between the AHP and SAW methods is 94% and 87%, respectively. The results state that the AHP method is superior as a system recommendation that can produce the best alternative supplier.
Emotion-Based Multi-Class Sentiment Analysis Of FirstMedia Customers Reviews Using SVM With Kernel Comparison Ongko, Bagus Kustiono; Vitianingsih, Anik Vega; Cahyono, Dwi; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15644

Abstract

The advancement of digital technology has made users increasingly reliant on online services, with user reviews serving as an essential resource for evaluating the quality of service provided by companies such as FirstMedia. However, these valuable data have not undergone comprehensive analysis to assess users’ emotional responses. This study aims to classify FirstMedia customers’ emotions into four categories (joy, sadness, anger, and neutral) and to evaluate the Support Vector Machine (SVM) method using four different kernel functions. Most existing studies primarily focus on polarity-based sentiment analysis and do not explicitly examine multi-emotion classification or kernel comparison in machine learning models. A total of 4,001 reviews were collected through web scraping from the Google Play Store and the X app and processed through several preprocessing steps. Emotion classification was conducted using the NRC Indonesian Emotion Lexicon, while word significance was determined using TF-IDF weighting. After preprocessing, 3,069 labeled reviews were retained and distributed as 1,065 neutral, 748 anger, 692 joy, and 564 sadness reviews, which were used for emotion classification. Model performance was evaluated using a hold-out validation scheme with an 80:20 train-test split and assessed through a confusion matrix. To address class imbalance, undersampling was applied, resulting in a balanced dataset for model training. The evaluation results show that the Linear kernel achieved the highest performance, with an accuracy of 82.63%, precision of 82.86%, recall of 82.63%, and an F1-score of 82.60%, outperforming the Gaussian, Polynomial, and Sigmoid kernels. This study demonstrates that multi-emotion sentiment analysis provides a more comprehensive understanding of user perceptions beyond conventional sentiment polarity, thereby supporting more informed evaluations of digital service quality.  
Sentiment Analysis Of NTB Syariah Bank Application Services using The Naïve Bayes and Support Vector Machine Methods Nabil, Muh; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16311

Abstract

This research analyzed user sentiment toward the NTB Syariah application using Support Vector Machine (SVM) and Naïve Bayes classification methods. A dataset comprising 814 reviews was obtained via web scraping, with 245 allocated for testing. Preprocessing encompassed cleaning, case folding, tokenization, filtering, and stemming, while sentiment labeling employed a lexicon-based approach integrated with TF-IDF weighting, categorizing reviews as positive, neutral, or negative. Model performance was assessed through accuracy, precision, recall, and F1-score metrics. Results demonstrated SVM's superior performance (accuracy: 92.65%; precision: 0.9327; recall: 0.9265; F1-score: 0.9149) compared to Naïve Bayes (accuracy: 84.49%; precision: 0.8415; recall: 0.8449; F1-score: 0.8005). SVM exhibited greater robustness in managing high-dimensional, complex, and moderately imbalanced datasets, delivering consistent cross-class sentiment classification. Conversely, Naïve Bayes remained computationally efficient and suitable for rapid implementation scenarios. These findings underscore machine learning's efficacy in sentiment analysis for digital banking platforms.
Co-Authors Abdul Rezha Efrat Najaf Achmad Choiron Ade Susianti, Febrina Adharani, Salza Kartika Agustinus Noertjahyana Ahmad, Sharifah Sakinah Syed Al-Karaki, Jamal N. Ana Wati, Seftin Fitri Anastasia Lidya Maukar ANGGI FIRMANSYAH Arumsari, Andini Dwi Arya Darmansyah, Mochammad Dzikri Ayomi, Jose Mario Aziiza, Arizia Aulia Azzahra, Morra Fatya Gisna Nourielda Badrussalam, Nanda Budi Suprio, Yoyon Arie Cahyono, Cahyono Kaelan Damayanti, Erika DWI CAHYONO Dwi Indrawan, Dwi Dwi Prasetyo, Septian Efendi, Kacung 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 Kacung Hariyono Khusnaini, Geovandi Gamma Krismantoro, Putu Gede Ari KRISTIAWAN KRISTIAWAN Li, Shuai Lidya Maukar, Anastasia Ma'rifani Fitri Arisa Maukar, Anastasia L Maukar, Anastasya Lidya Maulidiana, Putri Dwi Rahayu Miftakhul Wijayanti Akhmad, Miftakhul Wijayanti Minggow, Lingua Franca Septha Mudinillah, Adam Mustafa, Zulfikar Amirul Muzaki, Mochammad Rizki Nabil, Muh Niken Titi Pratitis Oktafamero, Yomara Omar, Marwan Ongko, Bagus Kustiono Pamudi Pamudi, Pamudi Pangestu, Resza Adistya 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 Ramadhan, Prayudi Wahyu Ramadhani, Illham Ratna Nur Tiara Shanty, Ratna Nur Tiara Rijal, Khaidar Ahsanur Riza , M. Syaiful Rizal, Moch Arif Samsul Rusdi Hamidan Rusdi, Jack Febrian Salmanarrizqie, Ageng Salsabilah, Azka Sari, Dita Prawita Seftin Fitri Ana Wati Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Slamet Winardi Sufianto, Dani Suyanto Suyanto Suyanto Tiara Shanty, Ratna Nur Titus Kristanto Tjatursari Widiartin Tri Adhi Wijaya, Tri Adhi Umam, Azizul Voni Anggraeni Suwito Putri Warsito Sujatmiko, Achmad Wati , Seftin Fitri Ana Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Wijiono, Aditya Kusuma Wikaningrum, Anggit Wikanningrum , Anggit Yasin, Verdi Yoyon Arie Budi Suprio Yudi Kristyawan, Yudi Yuliani, SY. Yunior, Kevin Heryadi Zandroto, Yosefin Yuniati Zangana, Hewa Majeed