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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pseudocode Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Informatika dan Teknik Elektro Terapan Seminar Nasional Informatika (SEMNASIF) International Journal of Artificial Intelligence Research IKRA-ITH Informatika : Jurnal Komputer dan Informatika INTECOMS: Journal of Information Technology and Computer Science JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Teknologi Informasi dan Pendidikan Syntax Idea JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Kajian Ilmiah Jurnal Computer Science and Information Technology (CoSciTech) Journal La Multiapp SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Mandiri IT Informatics and Computer Engineering Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Innovative: Journal Of Social Science Research Communautaire: Journal of Community Service Jurnal Medika: Medika Jurnal Informatika Dan Tekonologi Komputer
Vitri Tundjungsari
Universitas Esa Unggul, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Pengolahan Data Menggunakan Algoritma Untuk Sistem Pendukung Keputusan Karyawan Terbaik Bawiling, Hendry; Saputra, Indra; Nasir, Alfian; Tundjungsari, Vitri
Jurnal Kajian Ilmiah Vol. 26 No. 1 (2026): Januari 2026
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/pesfvt11

Abstract

Identifying high-performing employees is a critical component of human resource management, as it directly influences organizational productivity, work climate, service quality, and strategic goal achievement. However, conventional employee performance assessments often rely on subjective managerial judgment, making them vulnerable to personal bias and inconsistencies that can lead to dissatisfaction, decreased morale, and internal conflict. To address these challenges, Decision Support Systems (DSS) that employ data-processing algorithms have been increasingly adopted to enhance objectivity and accuracy in employee evaluation. This study conducts a Systematic Literature Review (SLR) of 25 scholarly publications published between 2017 and 2025 and indexed in nationally and internationally recognized databases. The analysis focuses on the types of algorithms applied, system development methodologies, and their relevance to optimizing the identification of top-performing employees. The findings indicate that multi-criteria decision-making methods, particularly the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW), are the most frequently used algorithms, followed by TOPSIS, PROMETHEE, MABAC, ELECTRE, Weighted Product, SMART, and hybrid approaches. In terms of system development, several studies did not explicitly specify their methodology, while others adopted structured approaches such as the System Development Life Cycle (SDLC) and Waterfall models. This review highlights methodological trends, identifies research gaps, and proposes potential directions for future studies on algorithm-based DSS applications in employee performance evaluation
Analisis Prediksi Probabilitas Otomatisasi Pekerjaan Tahun 2030 Menggunakan Algoritma Linear Regression Dan Gradient Boosting Nicholas Leonardo; Ahmad Zidane Arrasyid; Natagama, Muhammad Arif Billah; Hafidz Muhammad Dzaky; Vitri Tundjungsari
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.69-74

Abstract

The rapid development of artificial intelligence and automation is expected to significantly impact future employment. This study aims to predict job automation probability in 2030 using supervised learning methods. A public dataset containing job types, education levels, and automation probabilities was utilized. Linear Regression and XGBoost Regressor were employed to build and compare predictive models. The research process included data preprocessing, training–testing data split, model training, and performance evaluation using Root Mean Square Error (RMSE) and coefficient of determination (R²). Experimental results indicate that XGBoost outperforms Linear Regression by achieving lower RMSE and higher R² values. This study provides insights into automation risks and may support workforce skill development planning.
Optimalisasi Summarization Berita BBC dengan Metode BiLSTM-Transformer Rafael Austin; Andhika Dwi Rachmawanto; Michael Jeconiah Yonathan; M Naufal Arriz; Vitri Tundjungsari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10669

Abstract

The rapid growth of digital news, such as that from the BBC, presents challenges for readers in absorbing dense information within limited time. This research proposes an automated text summarization system using a hybrid BiLSTM Transformer architecture to produce concise yet contextually accurate summaries. The model integrates BiLSTM to capture local sequential relationships and Transformer’s self-attention mechanism to handle global context, overcoming the computational limitations of standalone Transformers. Utilizing a self-embedding approach, the system processes text in an unsupervised manner, making it suitable for datasets without ground truth summaries. Evaluation was conducted using 50 samples from the Xsum dataset and 25 live BBC news links, with performance measured via cosine similarity to assess contextual preservation. The results demonstrated a consistent average cosine similarity of 0.7959 for dataset samples and 0.7877 for new data. These findings indicate that the hybrid model effectively maintains semantic integrity and provides reliable summaries for complex news articles.
Analisis Perbandingan Metode K-Means dan Gaussian Mixture Model dalam Pengelompokan Playlist Musik Berbasis Fitur Audio Daniel Prasetiyo Dodi Darmawan; Christopher Mathew Putra; Samuel Yahya; Darren Jusman; Alfi Syahrian; Vitri Tundjungsari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10754

Abstract

Music streaming platforms rely on playlists as medium for users to store musical preferences and receive music recommendations based on the music stored. However, representing playlists as meaningful groups remains a major challenge due to the high diversity characteristics of music. In addition, the distribution of musical characteristics within playlists can vary significantly. This study aims to compare two clustering models with different approaches hard clustering using the K-Means method and soft clustering using the Gaussian Mixture Model (GMM). Playlists are represented as statistical aggregations of audio feature data from songs, such as energy, acousticness, and danceability. The hard clustering approach using K-Means produces compact and clearly separated clusters, while the Gaussian Mixture Model (GMM) generates clusters that capture playlist ambiguity, resulting in overlapping clusters due to its probabilistic nature. These differences have a direct impact on the implementation of the clustering results in downstream applications. This study emphasizes the importance of selecting an appropriate clustering method for further implementations, such as music recommendation systems, and provides insights into the trade-offs between interpretability and flexibility offered by both models.
Co-Authors A.A. Ketut Agung Cahyawan W Abdillah, Alfina Poetri Agung Ginanjar Agustian, Andri Ahmad Zidane Arrasyid Alfi Syahrian Amir, Arinal Dzikrul Haqqy Andhika Dwi Rachmawanto Andita, Ai Selpia Andreas Martinino Watratan Assifaa, Rr Nur Awaludin, Shafwan Bawiling, Hendry Capricorna, Epsilona Katiga Christopher Mathew Putra Daniel Prasetiyo Dodi Darmawan Darren Jusman Dzaky, Hafidz Muhammad Erlangga, Satrio Adhiyatama Evanza, Raihan Farrel Reyhan Putra Fikri, Mochammad Rabee Fathi Al Hafidz Muhammad Dzaky Ichwani, Arief Ilham, Muhammad Raihan Indra Saputra Irsyal, M.Dzikri Julianto, Eric Julio Arky Aquino Reyaan Kus Hendrawan Muiz M Naufal Arriz Martinino Watratan, Andreas Michael Jeconiah Yonathan Muflih, Rizky Muhamad Bahrul Ulum Mulyanto, Erwan Nasir, Alfian Natagama, Muhammad Arif Billah Nicholas Leonardo Ningrat, Sultan Wahyu Nizirwan Anwar Pamuji, Sonny Permatasari, Widya Indah Pratama, Ivan Febrian Prisdianto, Dendi Putratama, Maheswara Qomariyah Qomariyah Rafael Austin Rahmat, Ridwan Rino Sumiarno Rumi, Nur Ananda Rusman, Fericho Ryanto, Kamil Samuel Yahya Sinulingga, Samuel Mahesa Siti Fauziah Suhaeri Suhaeri Syafika Zalfanissa Dila Tambunan, Swandi Valentino Wijaya Widodo, Agung Mulyo Yahya, Ariel Rafael Yulhendri Yulhendri