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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pengabdian kepada Masyarakat Nusantara JURNAL REKAYASA INFORMASI SWADHARMA (JRIS)
Teguh Susyanto
Universitas Tiga Serangkai
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Social Sciences

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KLASIFIKASI PRESTASI AKADEMIK MAHASISWA MENGGUNAKAN METODE RANDOM FOREST Serly Sustiana Saputri; Dwi Remawati; Teguh Susyanto; Wawan Laksito Yuly Saptomo
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 6, No 2 (2026): JURNAL JRIS EDISI JULI 2026
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol6no2.1179

Abstract

This study aims to classify student academic achievement using the Random forest algorithm, utilizing the Student Performance dataset from Kaggle. The main attributes used as predictors include attendance rate, weekly study duration, and class engagement. The research methodology included data preprocessing with label encoding, an 80:20 split between training and test sets, and standardized model evaluation using accuracy, precision, recall, and F1-score. The results showed an accuracy of 31.03%. This low accuracy is due to the complexity of multi-class classification and imbalanced data distribution. This research contributes to mapping student learning behavior patterns and serves as a reference for developing more optimal academic prediction models.Penelitian ini bertujuan untuk mengklasifikasikan prestasi akademik mahasiswa menggunakan algoritma Random forest dengan memanfaatkan dataset Students Performance dari Kaggle. Atribut utama yang digunakan sebagai prediktor meliputi tingkat kehadiran, durasi belajar mingguan, dan keterlibatan di kelas. Metodologi penelitian mencakup pra-pemrosesan data dengan label encoding, pembagian data latih dan uji (80:20), serta evaluasi model yang diseragamkan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan nilai akurasi sebesar 31,03%. Rendahnya akurasi tersebut merupakan dampak dari kompleksitas klasifikasi multi-kelas dan distribusi data yang tidak seimbang (imbalanced data). Penelitian ini memberikan kontribusi dalam memetakan pola perilaku belajar mahasiswa serta menjadi referensi bagi pengembangan model prediksi akademik yang lebih optimal.
Otomasi Nutrisi Hidroponik Berbasis IoT untuk Greenhouse Mitra Soloraya melalui PjBL-OJT Ahmad Muhariya; Dziky Ridhwanullah; Yenny Rahmawati; Wawan Laksito Yuly Saptomo; Sapto Nugroho; Teguh Susyanto; Muhammad Hasbi; Saly Kurnia Octaviani; Ilham Fannani
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 2 (2026): Edisi Mei - Agustus
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i2.9243

Abstract

Kesenjangan antara penguasaan teori Internet of Things (IoT) dan implementasi perangkat keras di lapangan menjadi tantangan pendidikan tinggi dalam mencetak talenta pertanian modern. Merespons hal ini, Program Studi S1 Informatika Universitas Tiga Serangkai (UTS) menyelenggarakan Pelatihan dan On-the-Job Training (OJT) "Smart Farming Project Based Learning" berkolaborasi dengan Edutic dan Balai Pelatihan Vokasi dan Produktivitas (BPVP) Surakarta. Kegiatan ini bertujuan menyelesaikan kendala pencampuran nutrisi hidroponik manual yang rentan tidak presisi pada unit usaha mitra. Melalui pendekatan Project Based Learning (PjBL), 16 mahasiswa lintas program studi dilibatkan mulai dari perancangan hingga implementasi purwarupa Smart Nutrition System pada empat greenhouse mitra. Hasil kegiatan menunjukkan 100% peserta dinyatakan kompeten pada Uji Kompetensi (UJK) skema otomasi nutrisi, dan tiga dari empat purwarupa sistem berhasil diimplementasikan secara berkelanjutan di lokasi mitra. Kolaborasi antara kampus, industri, dan lembaga vokasi terbukti efektif mencetak talenta digital sekaligus mengakselerasi digitalisasi sistem hidroponik pada level UMKM/BUMDes.
Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling Iwan Ady Prabowo; Hendro Wijayanto; Teguh Susyanto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3421-3430

Abstract

Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Co-Authors Ahmad Muhariya Dwi Remawati Dziky Ridhwanullah Hendro Wijayanto Ilham Fannani Iwan Ady Prabowo Muhammad Hasbi Saly Kurnia Octaviani Sapto Nugroho Serly Sustiana Saputri Wawan Laksito Yuly Saptomo Yenny Rahmawati