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Open research questions in Data Mining and Machine Learning Applications

83 gap statements mined from Data Mining and Machine Learning Applications papers in our 4.5M-paper local library, which holds 327 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The lack of optimized machine learning models for the classification of recipients of Direct Cash Assistance. The high error rate in determining recipients of Direct Cash Assistance.

    Optimasi Model Machine Learning untuk Reduksi Kesalahan Klasifikasi Penerima BLT Berbasis Data Sosial-Ekonomi · 2026 · DOI

  • Future research can apply the study's approach to other villages or communities. Further research can explore the use of other data mining algorithms for health data analysis.

    PENERAPAN DATA MINING UNTUK CLUSTERING POTENSI KESEHATAN MASYARAKAT DESA MENGGUNAKAN ALGORITMA K-MEANS · 2026 · DOI

  • The research aims to address this gap by applying data mining techniques.

    PENERAPAN DATA MINING UNTUK CLUSTERING POTENSI KESEHATAN MASYARAKAT DESA MENGGUNAKAN ALGORITMA K-MEANS · 2026 · DOI

  • There is a need to identify the dominant factors that influence IPK. Prior studies have not provided a comprehensive view of the factors that influence IPK.

    Analisis Data Akademik Pada Faktor-Faktor yang Mempengaruhi IPK Mahasiswa Studi Kasus : Program Studi Sistem Informasi Universitas Mahakarya Asia · 2026 · DOI

  • Stunting is a complex problem affected by various factors. The prevalence of stunting in Nusa Tenggara Timur is high. There is a need to develop effective strategies to reduce stunting.

    Klasifikasi Stunting Balita Nusa Tenggara Timur Berdasarkan Faktor Sosial Ekonomi Kesehatan dengan Supervised Learning · 2026 · DOI

  • Further studies can explore the use of other supervised learning algorithms in classifying stunting. Further studies can investigate the impact of other factors on stunting.

    Klasifikasi Stunting Balita Nusa Tenggara Timur Berdasarkan Faktor Sosial Ekonomi Kesehatan dengan Supervised Learning · 2026 · DOI

  • The study focuses only on academic attributes, excluding non-academic factors. Future research could incorporate additional variables for a more holistic understanding.

    Analisis Klasterisasi Prestasi Siswa Dengan Metode K-Means di SMP Negeri 92 Jakarta · 2026 · DOI

  • Current evaluation methods lack objectivity and comprehensiveness. There is a need for methods that can identify distinct patterns in student achievement.

    Analisis Klasterisasi Prestasi Siswa Dengan Metode K-Means di SMP Negeri 92 Jakarta · 2026 · DOI

  • Future research can focus on improving the accuracy of diabetes prediction using the KNN algorithm. Future research can explore the use of other machine learning algorithms for diabetes prediction.

    Klasifikasi penyakit diabetes menggunakan metode K-Nearest Neighbors (KNN) berdasarkan data klinis · 2026 · DOI

  • There is a need for improved accuracy in diabetes prediction. The KNN algorithm has not been fully explored for diabetes prediction.

    Klasifikasi penyakit diabetes menggunakan metode K-Nearest Neighbors (KNN) berdasarkan data klinis · 2026 · DOI

  • There is a need for accurate population estimation to support data-driven decision-making by local governments. The existing methods may not provide reliable population estimates.

    Analisis Data Mining dalam Prediksi Pertumbuhan Penduduk Kabupaten Serdang Bedagai Menggunakan Regresi Linear Berganda · 2026 · DOI

  • The study highlights the need for a model for ordkonst in literature education. The paper outlines the limitations of prior research on ordkonst.

    Ordkonst som en del av litteraturundervisning – inlevelsefull läsning och kreativt skapande · 2026 · DOI

  • SUGGESTION Future research is recommended to compare ARIMA with other methods such as SARIMA, ARIMAX, GARCH, or machine learning methods to evaluate the model's ability to capture stock price volatility more broadly.

    PT Hexindo Adiperkasa TBK Stock Price Prediction for 2026-2027 Using the Autoregressive Integrated Moving Average (ARIMA) Method · 2026 · DOI

  • The study only considered a limited number of criteria. The sample size was limited to 30 respondents.

    Sistem Pendukung Keputusan Seleksi Penerima Beasiswa KIP Kuliah Menggunakan Metode SAW dengan Pembobotan Kriteria Akademik dan Kondisi Ekonomi Mahasiswa · 2026 · DOI

  • There is a lack of studies on the accuracy of extreme poverty data in Kota Bengkulu. The existing literature highlights the importance of accurate data for poverty reduction, but there is a need for more specific studies on the topic.

    Analisis Akurasi Data Kemiskinan Ekstrem di Kota Bengkulu · 2026 · DOI

  • The challenge is the degradation of moral values among teenagers. The challenge is the limited understanding of the transformative stages of character internalization. The challenge is the lack of effective character education programs.

    Internalisasi Karakter Sabar dan Tanggung Jawab Melalui Tartil Al-Qur’an: Studi Kasus di SMPN 21 Samarinda · 2026 · DOI

  • The study has a small sample size of 20 students. The study is limited to one school, SMPN 21 Samarinda. The study does not provide a comparison with other character education programs.

    Internalisasi Karakter Sabar dan Tanggung Jawab Melalui Tartil Al-Qur’an: Studi Kasus di SMPN 21 Samarinda · 2026 · DOI

  • There is a need for early prediction of stunting risk to provide targeted nutrition interventions in post-disaster areas. Current methods for predicting stunting risk may not be transparent or accountable.

    Model Explainable Machine Learning pada Aplikasi Nutrisiaga untuk Prediksi Risiko Stunting di Wilayah Pascabencana · 2026 · DOI

  • Limited functionality and user experience issues can lead to negative user sentiment. Integrating Google Classroom with other digital learning tools can be challenging.

    Analisis Sentimen Pengguna Google Classroom terhadap Efektivitas Pembelajaran Digital Menggunakan Naive Bayes · 2026 · DOI

  • The study only collects user reviews from Google Play Store, which may not be representative of all Google Classroom users. The study uses a limited dataset, which may not be generalizable to other contexts.

    Analisis Sentimen Pengguna Google Classroom terhadap Efektivitas Pembelajaran Digital Menggunakan Naive Bayes · 2026 · DOI

  • The study identifies a gap in the analysis of sentiment in online reviews. The study aims to fill this gap by evaluating the performance of machine learning methods for sentiment analysis.

    Klasifikasi Ulasan Pengguna Tiket Pesawat Online dengan Penanganan Ketidakseimbangan Data Menggunakan SMOTE dengan Machine Learning · 2026 · DOI

  • To evaluate the methods on multi-class datasets. To compare the performance of the methods with other techniques. To apply the methods to real-world applications.

    Komparasi Metode SMOTE-Tomek dan SMOTE-ENN untuk Mengatasi Data Imbalanced · 2026 · DOI

  • Penelitian sebelumnya belum secara spesifik menyoroti siswa dengan kemampuan akademik rendah pada jenjang menengah pertama. Penelitian ini secara khusus memfokuskan pada upaya guru dalam mengatasi kesulitan belajar matematika siswa berkemampuan rendah.

    UPAYA GURU DALAM MENGATASI KESULITAN BELAJAR MATEMATIKA SISWA BERKEMAMPUAN RENDAH DI MTS DARUL ILMI BANJARBARU · 2026 · DOI

  • Penelitian ini mengidentifikasi kesenjangan dalam metode analisis sentimen untuk ulasan pengguna aplikasi by.U. Penelitian ini mengidentifikasi kesenjangan dalam pembandingan metode pelabelan data.

    Analisis Sentimen Ulasan by.U dengan Pelabelan Rating dan Leksikon Menggunakan Multinomial Naïve Bayes · 2026 · DOI

  • Masalah-masalah ini memerlukan perhatian serius dari para pendidik. Penelitian ini bertujuan untuk mengisi kesenjangan tersebut dengan menganalisis bentuk kesalahan dan miskonsepsi siswa kelas IV sekolah dasar dalam materi penjumlahan dan pengurangan pecahan.

    Analisis Kesalahan Dan Miskonsepsi Siswa Kelas IV SD Dalam Penjumlahan Dan Pengurangan Pecahan · 2026 · DOI

Most-cited papers in Data Mining and Machine Learning Applications

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This page shows what the Data Mining and Machine Learning Applications literature already flags as unresolved. To narrow it to your specific question, search the Research Gap Finder: the search is free with a free account and lists the papers closest to your topic first. Unlocking that topic (50 credits, charged once) fills the comparison table from our 4.5M-paper local library and writes the gaps from its rows.

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Open questions raised by papers listed on this page include: (1) The lack of optimized machine learning models for the classification of recipients of Direct Cash Assistance (“Optimasi Model Machine Learning untuk Reduksi Kesalahan Klasifikasi Penerima…”, 2026); (2) Future research can apply the study's approach to other villages or communities (“PENERAPAN DATA MINING UNTUK CLUSTERING POTENSI KESEHATAN MASYARAKAT DESA…”, 2026); (3) There is a need to identify the dominant factors that influence IPK (“Analisis Data Akademik Pada Faktor-Faktor yang Mempengaruhi IPK Mahasiswa Studi…”, 2026); (4) There is a need to develop effective strategies to reduce stunting (“Klasifikasi Stunting Balita Nusa Tenggara Timur Berdasarkan Faktor Sosial…”, 2026); (5) Future research could incorporate additional variables for a more holistic understanding (“Analisis Klasterisasi Prestasi Siswa Dengan Metode K-Means di SMP Negeri 92…”, 2026). 83 gap statements have been mined from Data Mining and Machine Learning Applications papers in our 4.5M-paper local library, which holds 327 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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