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All Journal InComTech: Jurnal Telekomunikasi dan Komputer Nuansa Informatika
Syafrial Fachri Pane
Universitas Logistik dan Bisnis International
Computer Science & IT Electrical & Electronics Engineering Engineering

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Analisis Ketercapaian Vaksinasi Terhadap Penyebaran COVID-19 Menggunakan Machine Learning Syafrial Fachri Pane; Ferdy Berliano Putra; Gilang Romadhanu Tartila; Chandra Ahmad Rizki
InComTech : Jurnal Telekomunikasi dan Komputer Vol 12, No 3 (2022)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v12i3.15370

Abstract

Pada akhir tahun 2019 tepatnya bulan Desember Organisasi Kesehatan Dunia (WHO) mengatakan virus baru bernama virus COVID-19 ditemukan di Wuhan, Cina dengan cepat mempengaruhi masyarakat setempat. WHO sebagai organisasi kesehatan dunia menyetujui vaksinasi COVID-19 dan tersedia untuk seluruh masyarakat di dunia guna meningkatkan kekebalan tubuh manusia supaya tidak mudah terinfeksi oleh COVID-19. Untuk mengetahui ketercapaian vaksinasi diperlukan alat yang dapat bekerja secara otomatis dari pola data tanpa pemrograman eksplisit menggunakan Machine Learning (ML). Adapun data cakupan vaksinasi yang diprediksi adalah pada provinsi Jakarta dengan sumber melalui website satgas COVID-19 dengan parameter yang akan di uji adalah sasaran, belum vaksin, dosis 1, dosis 2, total vaksin diberikan kepada masyarakat. Pemodelan ML yang diusulkan adalah AdaBoost Regressor. Kinerja regressor ditentukan berdasarkan akar rata – rata keasalahan (RMSE) dan kesalahan mutlak (MAE). Nilai Akurasi yang didapatkan adalah 98% dengan nilai korelasi 99%. Berdasarkan berita dari kementrian kesehatan Indonesia dikatakan tercapainya vaksinasi jika sudah mencapai lebih kurang 80% untuk total vaksinasi yang sudah diberikan yaitu baik vaksinasi dosis 1 dan dosis 2. Total dosis vaksinasi yang sudah disebarkan pada masyarakat di provinsi DKI Jakarta dengan Kabupaten Adm. Kep. Seribu sebagai kota/kabupaten yang tertinggi untuk memberikan vaksinasi dosis 1 dan dosis 2 yang sudah mencapai 140% total dosis yang diberikan.
Hybrid PSO-Adaptive Boosting Regression for Employee Salary Prediction and Recommendation M. Amran Hakim Siregar; Bachtiar Ramadhan; Syafrial Fachri Pane
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.561

Abstract

Recommending appropriate employee salaries is important for supporting employee performance and data-driven managerial decisions. This study develops a hybrid machine learning model to recommend employee salaries and identify influential factors affecting monthly income. The dataset was obtained from Kaggle and consisted of 1,029 employee records with 34 variables covering company, personal, and demographic characteristics. Data preprocessing included categorical encoding, missing-value handling, duplicate checking, and outlier removal using the Interquartile Range method. The proposed approach combines Particle Swarm Optimization for variable optimization with an AdaBoost Regressor selected through TPOT Regression. Model performance was evaluated using R-Square and Mean Absolute Percentage Error. The PSO-AdaBoost Regressor achieved an R-Square value of 0.88 and a MAPE value of 0.22. Feature importance analysis identified Job Level as the most influential feature, with a score of 0.97156. The results were implemented in a Django-based web application
Web Based Task Management System Integrating the Pomodoro Technique with Productivity Analytics Syafrial Fachri Pane; Muhammad Baihaqi Siregar; Zidan Ardiansyah; Muhammad Amran Hakim Siregar
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.565

Abstract

Effective time management plays a crucial role in maintaining academic productivity among university students. However, increasing academic workloads and digital distractions often make it difficult for students to maintain consistent focus while completing their tasks. Although numerous task management and Pomodoro-based applications exist, most tools address either task organization or timed focus sessions in isolation, without integrating both functionalities into a unified productivity monitoring system. This research gap motivates the development of a system that combines structured time management with data-driven productivity analytics, which is not found in widely used tools such as Todoist or standard Pomodoro timers. This study proposes a web-based task management system that integrates the Pomodoro Technique with productivity analytics to support structured productivity monitoring. The proposed system was evaluated using activity data from recorded focus sessions comprising 120 Pomodoro sessions across a four-week observation period. The productivity aggregation model achieved a task completion rate of 78.3%, with an average daily focus duration of 142 minutes. The system successfully processed all recorded sessions without data discrepancies, and trend visualizations confirmed consistent productivity improvements over the observation period. The results show that the integrated Pomodoro–analytics approach effectively transforms recorded focus session data into measurable productivity indicators, achieving a 78.3% task completion rate and an average daily focus duration of 142 minutes across 120 sessions the integrated system into a scientifically measurable and structured productivity monitoring tool, distinguishing it from existing standalone task managers and Pomodoro applications through its unified analytics capability.
Co-Authors Bachtiar Ramadhan Chandra Ahmad Rizki Ferdy Berliano Putra Gilang Romadhanu Tartila M. Amran Hakim Siregar Muhammad Amran Hakim Siregar Muhammad Baihaqi Siregar Zidan Ardiansyah