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All Journal J@TI (TEKNIK INDUSTRI) JURNAL SISTEM INFORMASI BISNIS Jurnal Rekayasa Sistem Industri Jurnal Teknologi dan Manajemen Informatika SPEKTRUM INDUSTRI PROSIDING SEMINAR NASIONAL CENDEKIAWAN JOIV : International Journal on Informatics Visualization 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 Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) Prosiding Seminar Nasional Pakar Jurnal Teknologi Sistem Informasi dan Aplikasi 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 INOVTEK Polbeng - Seri Informatika 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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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
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 sentiment polarity classification, 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.
REDUCING SIX BIG LOSSES THROUGH TECHNICAL AND DIGITAL INTEGRATION TO IMPROVE OVERALL EQUIPMENT EFFECTIVENESS (OEE) ON A CARTONER MACHINE IN A PHARMACEUTICAL COMPANY Maukar, Anastasia Lidya; Hanum, Dinda Latifah
J@ti Undip: Jurnal Teknik Industri Vol 20, No 3 (2025): September 2025
Publisher : Departemen Teknik Industri, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jati.20.3.226-240

Abstract

Improving Overall Equipment Effectiveness (OEE) On Cartoner Machine Using Six Big Losses In Pharmaceutical Company.This research aims to improve the Overall Equipment Effectiveness (OEE) of the Cartoner machine at a pharmaceutical manufacturing facility in Cikarang, Indonesia. The main problem identified was low availability, primarily caused by frequent leaflet jams and delays in spare part replacement. Root cause analysis using Fishbone Diagrams and Pareto Charts revealed that leaflet jams contributed 67% of unplanned downtime.The study applied the Six Big Losses framework, focusing on downtime reduction. Two improvements were implemented. Technically, the suction system was redesigned to enhance leaflet handling and make a form for complaint to the supplier. Digitally, a Spare Parts Inventory Management System was developed using Microsoft Power Apps, enabling real-time stock monitoring, automated alerts, and faster spare part requests. After implementation, OEE improved from 75.36% to 81.90%, and availability increased from 76.94% to 83.62%. This research demonstrates that combining technical upgrades with digital tools can significantly reduce unplanned downtime and enhance production efficiency.  
DESAIN UI/UX APLIKASI PENJUALAN UMKM SABLON MENGGUNAKAN METODE DESIGN THINKING Oktafamero, Yomara; Wati, Seftin Fitri Ana; Fitri, Anindo Saka; Vitianingsih, Anik Vega; Maukar, Anastasia Lidya
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 3 (2024): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i3.21845

Abstract

Perkembangan teknologi yang kian hari makin pesat membantu banyak orang untuk melakukan kegiatan sehari-hari. Teknologi yang semakin pesat ini juga dimanfaatkan oleh para pelaku UMKM di Kabupaten Bogor, Provinsi Jawa Barat. Sablonkaos.id merupakan UMKM jasa sablon kaos yang berada di Kabupaten Bogor, Provinsi Jawa Barat. Pemilik sablonkaos.id dalam melakukan proses bisnisnya seperti pemasaran, pembuatan desain kaos, serta proses sablon kaos masih dilakukan sendiri tanpa adanya tenaga kerja lain. Tujuan daripada penelitian ini diharapkan perancangan UI/UX aplikasi penjualan UMKM sablon custom ini dapat menjadi rekomendasi bagi pemilik sablonkaos.id untuk dikembangkan ke tahap implementasi. Metode yang digunakan adalah design thinking yang memiliki lima tahapan yaitu empathize, define, ideate, prototype, dan test. Untuk mendapatkan feedback dari pengguna dilakukan usability testing menggunakan system usability scale (SUS). Hasil dari pengujian usability testing desain antarmuka aplikasi penjualan UMKM sablon yang diujikan kepada 11 responden mendapatkan nilai rata-rata sebesar 87.7 yang mana masuk ke dalam kateori acceptable.
Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices Pangestu, Resza Adistya; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Noertjahyana, Agustinus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28594

Abstract

Stock price prediction is a complex and important challenge for stock market participants. The difficulty of predicting stock prices is a major problem that requires an approach method in obtaining stock price predictions. This research proposes using machine learning with the Support Vector Regression (SVR) model and linear regression for stock price prediction—the dataset used in the daily Apple Inc historical data from 2018 to 2023. The hyperparameter tuning technique uses the Grid Search method with a value of k = 5, which will be tested on the SVR and Linear Regression methods to get the best prediction model based on the number of cost, epsilon, kernel, and intercept fit parameters. The test results show that the linear regression model with all hyperparameters k = 5 with the average taken performs best with a True intercept fit value. The resulting model can get an excellent error value, namely the RMSE value of 0.931231 and MSE of 0.879372. This finding confirms that the linear regression model in this configuration is a good choice for predicting stock prices.
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 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 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.
Product Label Design Training for MSMEs Producing Typical Lempuk Fish in Grati, Pasuruan Regency: Pelatihan Desain Label Produk UMKM Pengolah Ikan Lempuk Khas Grati Kabupaten Pasuruan Puspitarini*, Erri Wahyu; Maukar, Anastasia Lidya; Haryanto, Kurniawan Wahyu; Arifianto, Teguh; Rahardiyanto, Panca
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 6 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i6.16487

Abstract

The Lempuk fish, scientifically known as Gobiopterus spp., is a species of fish that exclusively inhabits the Ranu Grati lake in Pasuruan Regency. It is notable for its high protein content. PKK women utilise the processed Lempuk fish to create crispy lempuk. The issue at hand is that a significant number of these women lack the necessary expertise to create their own packaging label designs. Despite the items' excellent nutritious potential and pleasing taste. The objective of this Community Service is to enhance partners' proficiency in the design of processed products using Grati Typical Lempuk Fish. The design training method involves collaborative practice sessions followed by autonomous application utilising the Canva software. Design talents have shown a 60% boost. Partners utilise Canva to enhance their package labels, indicating their successful efforts in changing their product packaging design to transform them into processed lempuk items with a greater market value.
User Motivation Level Analysis of SME Collaboration Gamification Marisa, Fitri; Uda, Tonich; Maukar, Anastasia Lidya; Andarwati, Mardiana; Wardhani, Arie Restu; Handini, Mia
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.791

Abstract

One of the problems of SME is the low motivation to collaborate; the lack of research on exploring the motivation to collaborate is an issue that needs to be focused on solving. Objectives. This research aims to explore the level and type of motivation that influences SME's interest in collaborating to provide new insights for SME managers to apply appropriate treatment in developing collaborative activities. This study analyses six octalysis core drives that affect user interest in the use of SME collaboration gamification applications involving 293 SME respondents in the East Java Province. The research method is descriptive and quantitative, using Smart PLS, with a path analysis and analysis model. This study formulates six hypotheses to determine the effect of six core drives on using the collaborative gamification system. The results showed that the four constructs had a p-value less than 0.05 and a T-Statistic value greater than 1.96, while the other two constructs produced the opposite value. This finding reveals that four core drives (Epic Meaning, Development, Social Influence, and Avoidance) affect user interest in using collaborative gamification applications. In contrast, two core drives (Ownership and Unpredictable) do not affect it. The implication of this study is a recommendation for developers of collaboration-gamification systems to consider the results of this hypothesis, especially the role of core-drive catalysis as a reference in revising or developing collaborative gamification systems. Future work could apply the TAM model to analyze the technology acceptance rate of this system.
Co-Authors Abdul Rezha Efrat Najaf Achmad Aziz Wahdana Achmad Choiron Adi Saptari Agus Sasmito Agustinus Noertjahyana Ahmad Yanu Rokhim Ana Wati, Seftin Fitri Anang Aris Widodo 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 Yuesti Apri Junaidi, Apri Arie Restu Wardhani Arrosyadi, Laesa Qotrun Nada Arthur Silitonga Athina Sakina Ratum Avania Shinta Aziiza, Arizia Aulia Azzahra, Morra Fatya Gisna Nourielda Bella Chelsea Berliana Burhan Primanintyo Cahyono, Cahyono Kaelan 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 Fitri, Anindo Saka Gita Indah Marthasari Gunawan Hamidan, Rusdi Handini, Mia Hanum, Dinda Latifah Haryanto, Kurniawan Wahyu 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 Kamalrudin, Massila Kresna Arief Nugraha Krismantoro, Putu Gede Ari 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 Niken Titi Pratitis Nurhaba Djiha Octa Wendy Tanurahardja Oktafamero, Yomara Oktavia Sunny Pamudi Pamudi, Pamudi Pangestu, Resza Adistya Pramisela, Intan Yosa Pramudita, Atanasia Pramudita, Krisna Eka Puspitarini*, Erri Wahyu Puspitarini, Erri Wahyu Putri, Jessica Ananda Putri, Natasya Kurnia Rachmad Ary Ramadhan Rahardiyanto, Panca Ramadhan, Rachmad Ary Rendy - Rhiza Adiprabowo Rhiza Adiprabowo, Rhiza Richki Hardi Rijal, Khaidar Ahsanur Rivaldo Tito Lamberto Da Silva Rusdi Hamidan Rusdi, Jack Febrian Salmanarrizqie, Ageng Seftin Fitri Ana Wati Shofa Ramadhina Sigit Sigalayan Siti Hajar Binti Mohtar Slamet . Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Stefanus Setiady SUMARDI Susilo, Yunus Sutrisno Sutrisno Syahroni Wahyu Iriananda, Syahroni Wahyu Tantyo Edo Wicaksana TEGUH ARIFIANTO, TEGUH Tubagus Mohammad Akhriza Uda, Tonich Ullum, Choirul Voni Anggraeni Suwito Putri Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Widiya Nur Permata Wijiono, Aditya Kusuma Yana Hendriana Yasin, Verdi Yoyon Arie Budi Suprio Yudi Kristyawan, Yudi Yunus Susilo Yustian Zandroto, Yosefin Yuniati Zangana, Hewa Majeed