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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi SPIRIT JATI (Jurnal Mahasiswa Teknik Informatika) Informatics, Electrical and Electronics Engineering International Journal Of Computer, Network Security and Information System (IJCONSIST) ITIJ INOVTEK Polbeng - Seri Informatika
Fitri Ana Wati, Seftin
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Penerapan Metode Waterfall pada Sistem Manajemen Keuangan dan Absensi Karyawan Berbasis Web, Studi Kasus:Warung Jokodolog25 Surabaya Aminulloh Achri Biyanda, Margi; Vega Vitianingsih, Anik; Syahadiyanti, Litafira; Pamudi, Pamudi; Fitri Ana Wati, Seftin
SPIRIT Vol 17, No 1 (2025): SPIRIT
Publisher : LPPM ITB Yadika Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53567/spirit.v17i1.374

Abstract

Perkembangan teknologi informasi telah memberikan dampak signifikan dalam berbagai sektor, termasuk dunia bisnis. Namun, masih banyak usaha kecil dan menengah yang bergantung pada pencatatan manual dalam operasionalnya, seperti yang terjadi di Warung Jokodolog25 Surabaya. Pencatatan transaksi dan absensi karyawan yang dilakukan secara manual menimbulkan berbagai permasalahan, seperti efisiensi yang rendah, rentan terhadap kesalahan, serta keterlambatan dalam proses pengambilan keputusan. Oleh karena itu, penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi keuangan serta absensi karyawan berbasis web guna meningkatkan efisiensi dan akurasi pencatatan data. Penelitian ini menggunakan metode pengembangan perangkat lunak model Waterfall, yang terdiri dari tahapan analisis kebutuhan, desain sistem, implementasi, pengujian, dan pemeliharaan. Sistem dikembangkan menggunakan framework Laravel 11 dengan database MySQL untuk memastikan keamanan dan kecepatan akses data. Pengujian dilakukan dengan metode Black Box Testing guna memastikan setiap fitur berfungsi sesuai spesifikasi. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu mengotomatisasi pencatatan transaksi, manajemen keuangan, serta absensi karyawan secara real-time, sehingga meningkatkan efisiensi operasional Warung Jokodolog25.
Accuracy of Kidney Disease Expert System Based On Certainty Factor and Dempster Shafer Algorithm Agussalim; Astuti Triana, Nani; Maya Safitri, Eristya; Wulansari, Anita; Fitri Ana Wati, Seftin
IJCONSIST JOURNALS Vol 3 No 2 (2022): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v3i2.66

Abstract

The information technology developments today resulted in several innovations, including the existence of an Expert System. The systems help diagnose diseases without directly meeting with experts/doctors. Many researchers have proposed algorithms to improve the accuracy of the expert system to approach the diagnosis by experts/doctors, including certainty factor and dempster shader. This study compares the algorithm's accuracy with the results of expert diagnosis of kidney disease. The expert system was developed using UML and a web-based version. From the comparison results, the dempster shaver algorithm has an accuracy rate of 80%, while the certainty factor is 60% compared to expert diagnoses.
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.
Sentiment Analysis E-Wallet Application Services Using the Support Vector Machine and Long Short-Term Memory Methods Arya Darmansyah, Mochammad Dzikri; Vitianingsih, Anik Vega; Lidya Maukar, Anastasia; Yuliani, SY.; Fitri Ana Wati, Seftin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apedaz75

Abstract

The rapid growth of financial technology services in Indonesia has increased the volume of user reviews, yet their utilization for sentiment-based insights remains limited in the e-wallet sector. This study compares the effectiveness of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) in classifying the sentiment of 3,185 DANA e-wallet reviews collected from the Google Play Store and Instagram. The research process includes text preprocessing, lexicon-based labeling, and feature extraction using TF-IDF for SVM and word embeddings for LSTM. Model evaluation is conducted using a confusion matrix based on accuracy, precision, and recall, without inferential statistical testing. The results show that LSTM outperforms SVM, achieving an accuracy of 86.66%, a recall of 81.86%, and a precision of 82.09%, while the best SVM variant with an RBF kernel attains an accuracy of 84.93%. This study contributes by identifying key service-related factors influencing user satisfaction and dissatisfaction and by providing practical, sentiment-based insights to support service quality improvement. The novelty lies in the multi-platform analysis of Indonesian e-wallet reviews and the direct comparison of classical machine learning and deep learning approaches without statistical hypothesis testing. These findings confirm the effectiveness of deep learning for sentiment analysis of unstructured Indonesian text.
Co-Authors Abdul Rezha Efrat Najaf Achmad Hafidhin, Rizky Agussalim, Agussalim Agussalim, Agussalim Aminulloh Achri Biyanda, Margi Anik Vega Vitianingsih Apriyanti, Narti Arya Darmansyah, Mochammad Dzikri Astuti Triana, Nani Ayu Pangestuti, Roro Aziiza, Arizia Aulia Cahyo Wibowo, Nur DWI CAHYONO Efendi, Kacung Eka Damayanti, Natasya Fitri, Anindo Saka Ghibran Jhi S, Moch Lidya Maukar, Anastasia Lita Dwi Listyowati, Lita Dwi Maya Safitri, Eristya Nabil, Muh Ongko, Bagus Kustiono Pamudi Pamudi, Pamudi Permatasari, Reisa Pornama, Angga Pujiono, Halim Putra, Herlambang Haryo Rijaluddin, Khalid Riza , M. Syaiful Saka Fitri, Anindo Slamet Kacung, Slamet Suryo Widodo Vega Vitianingsih, Anik Wikanningrum , Anggit Wulansari, Anita Yuliani, SY.