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All Journal Marwah: Jurnal Perempuan, Agama dan Jender Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Malcom: Indonesian Journal of Machine Learning and Computer Science BATIK: Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural
Mustakim Mustakim
Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Membangun Desa Berbasiskan Teknologi Informasi Sebagai Sarana Pengetahuan Umum Kesehatan Masyarakat di Pulau Bengkalis Mustakim Mustakim; Mohammad Soleh; Syarfi Aziz
Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural Vol 1 No 3: BATIK Desember 2023
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/batik.v1i3.751

Abstract

Perkembangan teknologi informasi berkembang pesat yang dapat dirasakan sampai saat ini. Namun masih banyak daerah yang belum merasakan kemudahan yang diterima dari dampak teknologi informasi dibidang kesehatan seperti Kecamatan Bantan, Kabupaten Bengkalis, Provinsi Riau. Sebanyak 83% masyarakat Bantan telah mengenal terknologi internet dan dapat mengoprasikanya. Dinas Kesehatan Kabupaten Bengkalis mencatat terdapat 10-15 masyarakat Bantan setiap harinya berobat di Puskesmas dengan gejala penyakit ringan dan bertolak belakang degan kondisi desa yang banyak memiliki tanaman obat herbal. Dalam kaitanya dengan teknologi internet, Informasi kesehatan dan banyaknya tanaman obat herbal, serta SDM dapat dibangun sebuah sistem informasi untuk membantu masyarakat dalam mencari informasi terkait penyakit, tanaman obat serta memanfaatkan tanaman herbal dalam mengobati penyakit ringan. Hasil evaluasi yang dilakukan terdapat 10 responden dari masing-masing desa yang telah mengikuti sosialisasi sebanyak 90% bisa dengan baik menggunakan dan megoprasikan sistem yang dibangun. Dari hasil yang didapatkan dapat ditarik kesimpulan kegiatan yang dilakukan berhasil dan sistem informasi dapat dimanfaatkan dengan baik.
Implementation of Time Series Forecasting for Inflation Prediction in Indonesia Mustakim Mustakim; Wahyu Eka Putra; Hartono Hartono
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2172

Abstract

Inflation is a crucial macroeconomic indicator that reflects economic stability and influences sectors such as consumption, investment, and policy-making. This study aims to implement and compare three time series forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict inflation in Indonesia. The study utilizes monthly inflation data from Bank Indonesia (2003–2024) and Consumer Price Index (CPI) data from Statistics Indonesia (2003–2019). Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that SVR achieves the best performance in predicting inflation, with MAE of 1.53, MSE of 2.72, and RMSE of 1.64, demonstrating its effectiveness in capturing nonlinear patterns. Meanwhile, SARIMA provides the most accurate predictions for CPI, with MAE of 11.55, MSE of 191.76, and RMSE of 13.84. LSTM shows competitive performance but is less consistent compared to the other models. These findings highlight the importance of selecting appropriate models based on data characteristics to improve forecasting accuracy and support economic decision-making.
Enhancing Student Performance Classification Through Dimensionality Reduction and Feature Selection in Machine Learning Mustakim Mustakim; Windy Junita Sari; Fara Ulfa
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Education plays an important role in shaping the intellectual and character of the nation's next generation. However, poor student academic performance is a major challenge, especially regarding student retention and dropout risk. This study aims to evaluate the performance of machine learning algorithms, namely K-Nearest Neighbor (K-NN), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGB), and analyze the effect of dimensionality reduction using Principal Component Analysis (PCA) and feature selection with Recursive Feature Elimination (RFE) on student performance prediction accuracy. The research dataset consists of 395 student samples with demographic, social, and academic attributes. The results show that XGB has the best performance with 98.32% accuracy and can predict all classes with perfect 100% accuracy. LightGBM and K-NN achieved 94.87% and 93.88% accuracy, respectively. The best attributes affecting student performance were found in the “Highly Prioritized” category, including study time, family support, family, and health. Although PCA slightly degraded the model performance, feature selection with RFE significantly improved accuracy. This study concludes that proper algorithm selection and focus on relevant attributes can improve prediction accuracy and efficiency, making an important contribution to the development of more effective education prediction systems.
Co-Authors Ahmad Jaini Ahmad Sayuti Akhas Rahmadeyan Alfiah Alfiah Arianto Tarigan Assad Hidayat Azhiah Putri celsa bella Eki Saputra Elvia Andri Ervan Wahyudi Eva Normala Fara Ulfa Farhan Dio Pahlevi Gusni Rahayu H. Hartono Hamdani Asril Imaduddin Syukra Insanul Kamila Jeni Adhiva Kharisma, Oktaf Brillian Mohammad Soleh Muhammad Ridwan Muhammad Zakiy Fauzi Mulyana Widyastuti Nana Nurhaliza Naskah Naskah Nuryanti Nuryanti Okfalisa Okfalisa Puji Herwina Noprita Putri Anglenia Rahmatul Annisa Rice Novita Rifaldi Saputra Rika Taslim Rimelda Adha Risma Rustiyan Risma Rustiyan R Said Thaufik Rizaldi Salmiah Salmiah Siti Syahidatul Helma Syarfi Aziz Taufiq Qurahman Tri Juninda Trio Laksono Ulya Ramadhani Z Ummi Soleha Wahyu Eka Putra Windy Junita Sari Yoga Rizola Pratama Yogi Syafrialdi