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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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Sentiment Analysis of Brand Ambassador Influence on Product Buyer Interest Using KNN and SVM Putri, Natasya Kurnia; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Yasin, Verdi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29469

Abstract

In the dynamic marketing, companies usually use strategies involving celebrities or influencers to promote their products or brands. The currently popular strategy is using Korean boy bands as brand ambassadors. This collaboration certainly gets a lot of opinion responses through tweets on X app social media. This research aims to analyze sentiment to determine how the product buyer's interest responds to brand suitability, brand image management, and the influence of issues that arise in this collaboration. The research stages consist of data collection, pre-processing, labeling, weighting, and classification with K-Nearest Neighbor and Support Vector Machine and performance evaluation using a confusion matrix. The dataset used was 696 tweets taken using web scrapping techniques. This research uses the Lexicon-based method to divide the dataset into positive, negative, and neutral classes. The SVM method shows superior test results by achieving an accuracy rate of 83.34% compared to the KNN method, which produces an accuracy value of 71.2% in its calculations
Sistem Rekomendasi Pemilihan Komponen Komputer Menggunakan Metode AHP dan Profile Matching Salmanarrizqie, Ageng; Vitianingsih, Anik Vega; Kristyawan, Yudi; Maukar, Anastasia Lidya; Marisa, Fitri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7643

Abstract

Computers have become one of the technological tools that play a crucial role in enhancing society's productivity. Therefore, many desktop computer users assemble their own computers to achieve computer performance according to their preferences or needs. However, some people lack information about the variations, specifications, and capabilities of each computer component to be assembled. This research offers a recommendation system that is part of a decision support system (DSS) to assist users in providing recommendations for computer components that are being sought and needed based on brand, price, and specifications using the Analytic Hierarchy Process (AHP) and Profile Matching methods. Parameters are based on the processor, motherboard, graphics card (VGA), storage, RAM, power supply, and casing with priority categories based on specifications, price, and brand. Data weighting is done using the Analytic Hierarchy Process (AHP) method, while the Profile Matching method is used for ranking the weighting results. The research results show an accuracy rate of 60% using the Profile Matching method, while the AHP method achieves an accuracy rate of 57%.
Analisis Sistem Pembelajaran Daring Berbasis Gamification Collaboration untuk Mendukung Merdeka Belajar Menggunakan Octalysis Framework Maukar, Anastasia Lidya; Vitianingsih, Anik Vega; Marisa, Fitri; Pramudita, Atanasia; Putri, Jessica Ananda; Pramisela, Intan Yosa
Jurnal Teknologi dan Manajemen Informatika Vol. 8 No. 2 (2022): Desember 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i2.7855

Abstract

The COVID-19 pandemic has had a major impact on many things. Education, which is one of the important aspects in supporting human life, also felt the impact of the pandemic. Based on Circular Letter Number 4 of 2020 concerning the Implementation of Education Policies during the emergency period of the spread of COVID-19, the government has a policy that face-to-face education is not allowed to be carried out. Therefore, the world of education began to implement distance learning or e-Learning.  In addition to how to teach and learn, there are other things that need to be considered for the success of the process. Another thing is student learning motivation. Changes in the teaching and learning process also have an impact on student learning motivation. In this study, a questionnaire has been distributed to find out the core drive of student learning motivation. Based on a questionnaire that has been filled out by 167 respondents, the core drive that influences students' learning motivation is at a high level. This indicates that the level of student learning motivation during the COVID-19 pandemic is still high.
Penerapan Algoritma Klasifikasi pada Machine Learning untuk Deteksi Phishing: Application of Classification Algorithms in Machine Learning for Phishing Detection Fauzan, Rizky; Vitianingsih, Anik Vega; Cahyono, Dwi; Maukar, Anastasia Lidya; Suprio, Yoyon Arie Budi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1968

Abstract

Phishing merupakan salah satu bentuk kejahatan siber yang bertujuan mencuri informasi sensitif melalui metode penipuan, seperti situs web palsu yang menyerupai halaman resmi. Maka diperlukan sistem deteksi yang lebih akurat dan efisien untuk mengidentifikasi ancaman ini. Penelitian ini bertujuan untuk menganalisis penerapan algoritma klasifikasi dalam machine learning guna mendeteksi URL phishing. Algoritma yang digunakan dalam penelitian ini adalah Naïve Bayes, Random Forest, dan Decision Tree, yang diterapkan pada dataset yang dikumpulkan dari berbagai sumber. Dataset ini dianalisis menggunakan fitur berbasis Term Frequency - Inverse Document Frequency (TF-IDF) serta fitur numerik, seperti panjang URL, jumlah angka, karakter khusus, dan keberadaan kata kunci yang sering ditemukan dalam situs phishing. Evaluasi model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score untuk mengukur efektivitas sistem deteksi yang dikembangkan. Hasil eksperimen menunjukkan bahwa model Random Forest memiliki performa terbaik dengan akurasi mencapai 97,2%, diikuti oleh Decision Tree (96,3%), sementara Naïve Bayes memiliki akurasi lebih rendah (85,3%). Model Random Forest juga memiliki keseimbangan yang baik antara precision dan recall, sehingga lebih andal dalam mendeteksi URL phishing. Penggunaan algoritma Machine Learning terbukti dapat meningkatkan efektivitas deteksi phishing secara signifikan.
Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods Fauzi, Ariq Ammar; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Wati, Seftin Fiti Ana
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1198

Abstract

TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application.  This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data very accurately, while SVM is able to classify data on a large scale and is resistant to overfitting, while NB is able to classify text with clear probabilities. The Lexicon-based method as data labelling. The results of sentiment analysis from 1500 reviews with web scrapping show the classification of positive, negative, and neutral sentiments of 48, 726, and 646 data, respectively. Model performance in RF, SVM, and NB testing gets an accuracy value of 94%, 93.6%, and 77.8%, respectively. The RF model produces the best accuracy compared to other methods. The RF model produces the best accuracy compared to other methods. The results of sentiment analysis from 1500 user reviews allow developers to identify features that are often criticized or do not function properly in their application services.
Analisis dan Perancangan Sistem Informasi Manajemen Pegawai Menggunakan Metode Waterfall Berbasis Web Vitianingsih, Anik Vega; Fardhan Maulana, Abelardi; Kacung, Slamet; Lidya Maukar, Anastasia; Wati, Seftin Fitri Ana
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 5 No 2 (2024)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.5.2.237

Abstract

In today's digital era, the need for an employee management information system is increasingly urgent. Organizations must be able to overcome the complex challenges of managing their human resources to remain competitive in the ever-changing market. With the right Personnel Information System, organizations can optimize the management of personal, performance, and administrative information of their employees efficiently. One important aspect of a Personnel Information System is the mapping of workforce needs, which enables organizations to plan appropriate employee recruitment and development strategies. In addition, efficient scheduling is also a key focus, as proper placement and wise resource allocation can improve overall productivity. However, manually managing employee data is no longer sufficient in this digital age. Errors, delays, and loss of information often occur in manual processes, causing losses in terms of both time and finances. Therefore, the implementation of a robust and efficient Personnel Information System is a must. The Waterfall method, with its structured step-by-step approach, was able to provide clear guidance in the development of this system. A comprehensive analysis stage ensures that the needs of the organization are well understood, while the design stage guarantees that the system design meets the right specifications. With the results of this study, it is expected that organizations will be able to develop a Personnel Information System that suits their needs, improve the efficiency of human resource management, and optimize overall performance. Thus, the Personnel Information System is not only an administrative tool, but also one of the key factors in organizational success in this digital era.
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.
SISTEM PAKAR PENENTUAN POSISI PEMAIN SEPAK BOLA MENGGUNAKAN METODE FORWARD CHAINING Ramadhani, Illham; Kristyawan, Yudi; Vitianingsih, Anik Vega
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.6151

Abstract

Penelitian ini bertujuan mengembangkan sistem pakar untuk menentukan posisi ideal pemain sepak bola berdasarkan analisis keterampilan teknik, kondisi fisik, serta aspek mental pemain menggunakan metode forward chaining. Permasalahan yang diangkat adalah ketidaktepatan penempatan posisi pemain akibat penilaian subjektif pelatih tanpa dasar objektif. Sistem ini menggunakan data spesifik berupa 33 parameter keterampilan yang terbagi menjadi tiga kategori utama yaitu technical skill, physical skill, dan mental skill. Aturan (rule base) yang disusun mencocokkan kombinasi kemampuan dengan posisi ideal pemain dari kiper hingga penyerang. Sistem dikembangkan menggunakan pendekatan SDLC model Waterfall dan diimplementasikan berbasis web menggunakan framework Laravel. Hasil validasi menunjukkan tingkat akurasi sistem sebesar 81,8%, presisi 100%, dan F1-score 83,3%. Temuan ini menunjukkan bahwa sistem mampu memberikan rekomendasi posisi pemain secara objektif, logis, dan terstruktur. Namun demikian, sistem masih bergantung pada kelengkapan data input dan aturan yang disusun dalam basis pengetahuan. Pengembangan lebih lanjut dapat dilakukan dengan menambahkan metode fuzzy logic untuk menangani ketidakpastian dalam penilaian, serta memperluas cakupan sistem ke berbagai kelompok umur dan level kompetisi.
Recommendation System to Determine Achievement Students Using Naïve Bayes and Simple Additive Weighting (SAW) Methods Jazaudhi’fi, Ahmad; Vitianingsih, Anik Vega; Kristyawan, Yudi; Lidya Maukar, Anastasia; Yasin, Verdi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i1.19746

Abstract

Giving appreciation to outstanding students can motivate students to compete with each other in learning. MA Tanwirul Qulub Tanggungan often experiences difficulties in determining outstanding students due to There is no application that can assist school management in identifying outstanding students, the implementation is considered less than optimal. besides that, the determination of outstanding students is still based on report cards that are only ranked, and there are no criteria that refer to the K-13 curriculum. The purpose of this research is to offer a solution to create a recommendation system for selecting outstanding students using the parameters of midterm exams, final exams, assignments, attendance, attitude, extracurricular activities, organizations, and award certificates using decision support system techniques. Extracurricular grades are taken from Scouting activities only because students are generally required to participate in them. Naïve Bayes and Simple Additive Weighting methods are used in this research, where the Naïve Bayes method classifies the categories of outstanding students and not, while the SAW method is used for ranking. The contribution of this research has the potential to increase school efficiency in student assessment and support efforts to improve the quality of education by rewarding students appropriately. The validation test results of Naïve Bayes and SAW techniques get an accuracy value of 100%, which shows that the SAW method can produce the best alternative recommendations
Analysis of Restaurant Ordering Patterns Using Apriori Algorithm Marisa, Fitri; Badrussalam, Nanda; Ahmad, Sharifah Sakinah Syed; Vitianingsih, Anik Vega; Maukar, Anastasia L
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study implements the Apriori algorithm to analyze ordering patterns in home-based restaurants, specifically Dapur Mb Yani. Sales transaction data for three weeks shows that the Geprek Sambal Merah, Geprek Sambal Ijo, and Ayam Crispy menus are the most frequently ordered items, both individually and in combination. The combination of Geprek Sambal Merah, Ayam Crispy, and Es Teh has a high association value, making it a candidate for bundling promotions, while the strong relationship between Geprek Sambal Merah and Geprek Sambal Ijo opens up opportunities for special offers involving both menus. These results help restaurant managers design more effective promotional strategies, manage ingredient stocks efficiently, and improve customer experience. The application of the Apriori algorithm proves its relevance in supporting data-based decisions, especially for small businesses, as well as opening up opportunities for further development in the culinary industry.
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