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All Journal Teknika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Jurnal Informasi dan Komputer Idealis : Indonesia Journal Information System Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Publika Pengabdian Masyarakat NEAR: Jurnal Pengabdian kepada Masyarakat Jurnal PETISI (Pendidikan Teknologi Informasi)
Sushanty Saleh
Institut Informatika Dan Bisnis Darmajaya
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SiMoI New Method to Solve the Sparsity Problem in Collaborative Filtering Kurniawan, Hendra; Lestari, Sri; Saleh, Sushanty; Satrio, Rafli Banu
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1015

Abstract

Sparsity data is a major challenge in collaborative recommendation systems, characterized by the predominance of missing values within the user-item matrix. When a substantial portion of data is unavailable, the estimation process becomes hindered, and prediction accuracy declines due to limited usable information. To address this issue, this study introduces a novel method called SiMoI (Similarity, Mode, and Minimum Imputation), which is adaptively designed to handle high levels of sparsity. The SiMoI method combines user similarity with imputation strategies based on mode and minimum values. By leveraging subsets of the most informative users and items, the method efficiently fills missing entries while maintaining prediction stability. Evaluation was conducted using both real and synthetic datasets with varying sizes and degrees of sparsity, including an extreme scenario with 93.7% missing data. Experimental results show that SiMoI consistently produces more accurate predictions than baseline methods. Under high-sparsity conditions, SiMoI achieved an RMSE as low as 0.823, outperforming KNNI (0.947) and MEAN (1.021). Moreover, SiMoI demonstrated resilience across different data scales and sparsity distributions, indicating its flexibility and scalability in diverse contexts. These findings suggest that SiMoI is an effective and stable approach for addressing sparsity and holds strong potential for implementation in user-based recommendation systems, particularly in real-world scenarios where data availability is frequently limited.
Perbandingan Naïve Bayes dan Support Vector Machine Untuk Analisis Sentimen Pengguna Jobstreet Zai, Andica Parlindungan; Saleh, Sushanty
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 1 (2026): Jurnal IDEALIS Januari 2026 - in press
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i1.3663

Abstract

Meningkatnya penggunaan platform pencarian kerja digital seperti Jobstreet menghasilkan banyak ulasan pengguna yang dapat dimanfaatkan untuk memahami tingkat kepuasan dan pengalaman mereka terhadap layanan tersebut. Namun, jumlah ulasan yang besar membuat analisis manual menjadi tidak efektif sehingga diperlukan pendekatan melalui analisis sentimen. Penelitian ini bertujan untuk menganalisis 4835 data ulasan pengguna jobstreet yang dimana data diperoleh dari Google Play Store dengan pendekatan text mining, eksperimen ini berupaya membandingkan dua metode klasifikasi, yaitu Naïve Bayes dan Support Vector Machine (SVM), dalam mengelompokkan ulasan pengguna jobstreet kedalam sentimen positif dan negatif. Selain itu, penelitain ini juga menerapkan teknik Synthetic Minority Over-Sampling Tecnique (SMOTE) untuk menangani ketidakseimbangan kelas pada dataset ulasan, yang sering berdampak pada penurunan kemampuan model dalam mengenali sentimen dengan jumlah data yang lebih sedikit. Hasil evaluasi menunjukan bahwa algoritma SVM memberikan performa lebih stabil dengan akurasi meningkat dari 89% menjadi 91%, sedangkan algoritma Naïve Bayes meningkat dari 88% menjadi 90%. Penerapan teknik SMOTE efektif dalam mengoptimalkan performa model dalam mendeteksi sentimen negatif, yang ditunjukan oleh kenaikan recall pada kedua model, yaitu algoritma Naïve Bayes dari 85% menjadi 95% dan SVM dari 87% menjadi 93%. Secara keseluruhan, metode SVM menjadi algoritma yang lebih unggul untuk analisis sentimen ulasan pengguna Jobstreet karena memberikan hasil yang lebih konsisten pada berbagai metrik evaluasi.
Co-Authors - Nurjoko Agus, Isnandar Alan Bima Mismin Anggi Andriyadi Arman Suryadi Karim, Arman Suryadi Arnande, Muhammad Fivo Ayu Firdhayanti Azzahra, Layla Baehaqi Bobby Bachry Chendy Novia Basri DeppiLinda Dona Yulawati Dona Yuliawati Egi Safitri Endang Astitin Gita Suci Anggraini Halimah Halimah Halimah Yunus Handoyo Widi Nugroho Hasan Basri Hasan Basri Hendra Kurniawan Hernawati Hernawati Hery Yufrizal Hery Yufrizal Indera Indera Indera Irva, Rendi Eriyanta Kristian, Agustinus Linda Septarina Linda Septarina, Linda Lukmanul Hakim Melda Agarina Mutiara, Suci Neni Purwati Nevo Pratama Putra Nursiyanto Pebrina Swissia Pebrina Swissia Rini Nurlistiani Ruki Rizal Satrio, Rafli Banu Sifaul Khoiri Sri Handayani Sri Karnila Sri Lestari Suci Mutiara Suhendro Yusuf T.M Zaini TM Zaini Toni Fedrik Wasilah Wasilah Y. Suhendro Yuliawaati, Dona Zai, Andica Parlindungan Zaini, TM Zakaria, Asrul