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Atina, Vihi
Universitas Duta Bangsa Surakarta
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Performance Evaluation of Naive Bayes and SVM in Classifying Public Opinion toward Game-Based Learning Policy Septi Dwi Supriati; Hanifah Permatasari; Vihi Atina
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2319

Abstract

This study aims to analyze public sentiment toward the EDUBLOX program on Instagram comments using the Naive Bayes and Support Vector Machine (SVM) algorithms. The research process consisted of data collection, text preprocessing, manual sentiment labeling, feature extraction using TF-IDF, model training, and performance evaluation. The labeling process was conducted manually by two independent annotators based on predefined sentiment guidelines, and annotation reliability was evaluated using Cohen’s Kappa coefficient. The obtained Cohen’s Kappa value was (\kappa = 0.7907), indicating substantial agreement and good consistency between annotators. The dataset was divided into training and testing data using an 80:20 ratio. The evaluation process used confusion matrix metrics, including accuracy, precision, recall, and F1-score, while the McNemar test was applied to determine whether the performance difference between the two models was statistically significant. The results showed that the SVM model achieved a testing accuracy of 76.67%, marginally outperforming the Naive Bayes model with a testing accuracy of 75.29%. In addition, SVM demonstrated slightly better precision, recall, and F1-score values compared to Naive Bayes. However, the McNemar test produced a p-value of 0.1366, indicating that the performance difference between the two algorithms was not statistically significant. Therefore, both models can be considered to have relatively comparable classification capabilities, although SVM showed a slight numerical advantage in sentiment classification performance on Instagram comments related to the EDUBLOX program.
Evaluasi Kinerja Aparat Pengawas Internal Pemerintah (APIP) Berbasis Web Menggunakan Algoritma K-Means ibnu - salifi; Dwi Hartanti; Vihi Atina
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.95719

Abstract

Abstrak : Aparat Pengawas Internal Pemerintah (APIP) di Inspektorat Daerah Kabupaten Sragen memiliki peran penting dalam menjaga transparansi dan akuntabilitas pemerintah daerah. Namun, proses evaluasi kinerja yang masih manual dan subjektif mengakibatkan kesulitan dalam mengidentifikasi kinerja pegawai secara objektif dan efisien. Penelitian ini bertujuan untuk mengembangkan sistem evaluasi kinerja berbasis web yang memanfaatkan algoritma k-means untuk mengelompokkan pegawai APIP berdasarkan parameter kinerja tertentu, seperti budaya kerja BerAKHLAK, Sasaran Kinerja Pegawai (SKP), tingkat kehadiran, dan kompetensi. Metode penelitian menggunakan algoritma k-means untuk clustering data kinerja, dengan pendekatan pengembangan sistem model Waterfall. Data dikumpulkan melalui observasi, wawancara, dan kuesioner terhadap pegawai APIP di Inspektorat Daerah Kabupaten Sragen. Sistem yang dikembangkan bertujuan untuk menghadirkan alat bantu evaluasi yang tidak hanya cepat, tetapi juga akurat dan mendalam, memungkinkan pimpinan untuk memperoleh gambaran kinerja secara terstruktur dan berbasis data. Hasil penelitian menunjukkan bahwa sistem ini mampu mengelompokkan pegawai APIP ke dalam beberapa kategori kinerja yang lebih akurat yaitu menghasilkan cluster dengan 8 pegawai berkinerja baik, 15 pegawai berkinerja cukup, dan 19 pegawai dengan kinerja kurang. Implementasi sistem ini juga memberikan visualisasi data yang informatif, membantu dalam identifikasi potensi pengembangan individu maupun tim, serta menyusun strategi peningkatan kinerja secara menyeluruh. Sehingga sistem ini tidak hanya mempermudah proses evaluasi kinerja, tetapi juga memberikan dasar yang kuat bagi pengambilan keputusan dalam pengembangan kompetensi dan pelatihan pegawai. Kesimpulannya, penerapan algoritma k-means dalam evaluasi kinerja APIP terbukti efektif dalam meningkatkan kualitas pengawasan internal di lingkungan pemerintah daerah. Dengan demikian, sistem ini dapat menjadi model yang dapat direplikasi di berbagai instansi pemerintah lainnya untuk mendukung tata kelola yang lebih baik.====================================================Abstract : The Internal Government Supervisory Apparatus (APIP) at the Regional Inspectorate of Sragen Regency plays a critical role in maintaining transparency and accountability within the regional government. However, the manual and subjective performance evaluation process poses challenges in objectively and efficiently identifying employee performance. This study aims to develop a web-based performance evaluation system utilizing the k-means algorithm to cluster APIP employees based on specific performance parameters, such as the BerAKHLAK work culture, Employee Performance Targets (SKP), attendance rate, and competence. The research methodology employs the k-means algorithm for performance data clustering, using the Waterfall model for system development. Data was collected through observations, interviews, and questionnaires involving APIP employees at the Regional Inspectorate of Sragen Regency. The system is designed to provide a performance evaluation tool that is not only fast but also accurate and in-depth, enabling leadership to obtain structured and data-driven insights into employee performance. The research findings indicate that the system successfully categorizes APIP employees into several performance clusters, producing groups of 8 employees with good performance, 15 with average performance, and 19 with poor performance. The system implementation also provides informative data visualizations that aid in identifying individual and team development potential and devising comprehensive strategies for performance improvement. Thus, the system not only facilitates the performance evaluation process but also provides a robust basis for decision-making in developing competencies and training programs for employees. In conclusion, the application of the K-Means algorithm in evaluating APIP performance has proven effective in enhancing the quality of internal supervision within the regional government. Consequently, this system can serve as a replicable model for various other government agencies to support improved governance practices.
Sistem Rekomendasi Produk Umkm Menggunakan Content Based Filtering Pada Kampung Njawani Marobo; Vihi Atina; Dwi Hartanti
Jurnal DutaCom Vol. 19 No. 2
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/6p4ea121

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) di Kampung Njawani memiliki peran penting dalam mendukung perekonomian lokal melalui berbagai produk makanan, minuman, dan kerajinan. Namun, banyaknya variasi produk yang tersedia sering menyulitkan pengguna dalam menemukan produk yang sesuai dengan kebutuhan dan preferensi mereka. Kondisi ini menyebabkan proses pencarian produk menjadi kurang efisien serta berdampak pada rendahnya keterlihatan beberapa produk UMKM. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem rekomendasi produk UMKM Kampung Njawani berbasis web menggunakan metode Content-Based Filtering. Sitem dikembangkan dengan metode pengembangan perangkat lunak Extreme Programming (XP) yang menekankan proses pengembangan secara iteratif dan melibatkan pengguna secara langsung. Alur sistem dimulai dari pembuatan akun pengguna, pengisian preferensi melalui proses onboarding, hingga penyajian rekomendasi produk pada dashboard pengguna. Preferensi pengguna dan atribut produk direpresentasikan dalam bentuk dokumen teks, kemudian dilakukan pembobotan menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) dan pengukuran tingkat kemiripan menggunakan metode Cosine Similarity untuk menghasilkan skor kecocokan. Hasil penelitian menunjukan bahwa sistem mampu memberikan rekomendasi produk yang relevan sesuai dengan preferensi pengguna. Selain itu, sistem menyediakan dashboard admin yang menampilkan data rekomendasi dan skor kecocokan produk sebagai bahan analisis. Sistem ini diharapkan dapat membantu pengguna dalam menemukan produk UMKM secara efrektif serta mendukung pengembangan UMKM Kampung Njawani.
Evaluating U-Net Segmentation in Vgg16-Based Leukemia Classification: A Comparative Study Rizky Vera Oktarina; Afu Ichsan Pradana; Vihi Atina; Farahwahida Mohd
Jurnal Infokes Vol 16 No 2 (2026): Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan
Publisher : Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/5d37ge60

Abstract

Background: Leukemia is a malignant hematological disorder characterized by abnormal proliferation of white blood cells (WBCs), affecting over 474,000 patients globally each year, with manual diagnosis limited by inter-observer variability of up to 40%. Objective: This study evaluates whether U-Net-based WBC segmentation improves leukemia classification by comparing three configurations: VGG16 on original images, VGG16 with U-Net-predicted masks, and VGG16 with ground truth masks. Methods: A total of 3,256 ALL microscopic blood smear images were divided into training, validation, and testing sets (70:10:20). U-Net was trained using a combined Binary Cross-Entropy and Dice loss, and the segmentation outputs were used as inputs for VGG16 classification. Performance was evaluated using DSC, IoU, accuracy, precision, recall, F1-score, and AUC-ROC. Results: U-Net achieved a DSC of 0.7885 and an IoU of 0.7134. VGG16 with U-Net masks achieved 98.62% accuracy (AUC 0.9958), while VGG16 with ground truth masks achieved 98.31%. The baseline VGG16 achieved the highest performance with 99.69% accuracy (AUC 0.9999). Conclusion: Explicit segmentation did not improve classification performance for the dataset used in this study and may introduce unnecessary computational overhead when discriminative features are already preserved in the original images. Suggestion: Future studies should evaluate heterogeneous datasets and advanced segmentation architectures to further investigate the contribution of segmentation to leukemia classification.
Co-Authors Adri Surya Kusuma Afu Ichsan Pradana Agung Saputro Agustina Srirahayu Ahmad Bagus Prakoso Akbar Permana, Danny Aldi Wahyudi Arta Amad Tri Yanto Andi Saputro Andreas Sigit Andreas Anin Aliya Pahlevi, Khumaira Anisatul Farida Anjar Setiawan Aprilisa Arum Sari Ardi Lestari, Sofiana Arlin Govinda Putra Atmojo, Fernando Winantya Bagaskara, Ikrar Bagaskara, Mochammad Naufal Bagus Muhammad Latif Bambang Prasetyo Bawindra Surya, Lintang Chiva Olivia Bilah Dedi Nugroho Dimas Abimanyu Sutrisno Putro Dinita Christy Pratiwi Dwi Hartanti Dwi Hartanti Dwi Hartanti Eka P. Meravigliosi Eko Purwanto Eko Purwanto Ema Sagita Desylawati endra setiyawan Esti Suryani Esti Suryani Fajrin Fadhilah, Dayinta Fajrin, Shoffia Farahwahida Mohd FAULINDA ELY NASTITI Faulinda Ely Nastiti Hafids Sidiq, Muhammad Hartanti , Dwi Hartanti, Dwi Hasanah, Herliyani ibnu - salifi Imaduddin, Mohamad Indrastata, Ilham Buyung Infantono, Ardian Intan Oktaviani Janah, Selvi Miftakhul Joni Maulidar Kurnia Sari, Vena Lufti Puspitasari Marobo Maulidar, Joni Maulindar, Joni Meraldy Fiko Rastio Ajie Mohd, Farahwahida Muhamad Ridwan Muhammad Alwan Nurdin Muhammad Anugrah Putra Muhammad Dhafa Diar Ardhana Muhammad Fahmi Panwar Muhammad Frasha Candra Perdana Muhammad Hanif Hilmi Nailurrizqi, Adistya Nastiti, Faulinda Eli Niken Pratiwi, Niken Nugroho Arif Sudibyo Nur Arifin, Taufiq Nur Mahar Aji mahar Nurchim Nurchim Nurdin, Muhammad Alwan Nurlita, Catarina Ivanda Nurmalitasari Nurmalitasari Nurmalitasari Nurmalitasari Okta Ramma Saputri Oktaviani, Intan Pegi Hasyim Rosidi Permatasari, Hanifah Pipin Widyaningsih Pradana, Afu Ichsan Pradityo Utomo Pradityo Utomo Pramoedya Ananta Dzikri Pratiwi, Dinita Christy Purnama, Joel Adikurnia Purwanto, Eko Putra, Hasda Surya Putri, Della K. Putri, Desy Puspa Ragil Saputro, Abdullah Raharisti, Nur Arifah Ramadhan, Chandra Ratmini, Yuli Reza Aditya Angga Putra Reza Pradana, Areta Ridwan, Alfian Junior Rifan Amirul H, Muhammad Rifdah Azizah, Hani Rio Sandy Laksono Rizky Setiawan, Fadli Rizky Vera Oktarina Rudi Susanto Rusdiana Ekawati, Ratih Saputra, Dwi Bagus Saputro, Nurbagus Sejati, Ariya Putra Septi Dwi Supriati Setiawati, Neha Poetri Sihwi, Sari W. Sopingi Sopingi, Sopingi SRI SUMARLINDA Srisuk, Prattana Sulami, Atik Sulistiyo, Galih Suwandi, Djatmiko Tanwal Hu, Wupiwulang Taufiq NurHidayat Theo Santoso, Daniel Umi Salamah Umi Salamah Utomo, Dimas Cahyo Viona Putri Ardiana Vita Aryadi Wahyu Kurniawan, Christian Wibowo, Anita Carolina Wiharto Wiharto Wiharto Wijiyanto Wijiyanto Wijiyanto, Wijiyanto Yommy Adhiwira Yudha