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All Journal Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Arif, M Aidil
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Prediksi Pergerakan Harga Ethereum Menggunakan Machine Learning dengan Algoritma Random Forest dan XGBoost Girinata, I Made Candra; Styawan, Budi; Saputra, Arwin Wahyu; Arif, M Aidil; Dahur, Arnoldus Janssen
Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Vol 4 No 2 (2025): Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Publisher : STMIK Amika Soppeng

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70247/jumistik.v4i2.222

Abstract

ABSTRAK Perkembangan aset kripto yang pesat, khususnya Ethereum, menuntut adanya model prediksi harga yang akurat untuk mendukung strategi investasi dan manajemen risiko. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja dua algoritma machine learning ensemble, yaitu Random Forest (RF) dan XGBoost, dalam memprediksi harga harian Ethereum. Dataset historis ETH/USD sebanyak 3.423 observasi dari periode September 2016 hingga Juli 2025 diperoleh dari platform Bitfinex. Setelah melalui tahap pra-pemrosesan data dan rekayasa fitur temporal, dataset dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Model dievaluasi menggunakan metrik Root Mean Square Error (RMSE) dan Koefisien Determinasi (R²). Hasil eksperimen menunjukkan bahwa XGBoost secara signifikan mengungguli Random Forest, dengan nilai RMSE 134.63 dan R² 0.958. Sebagai perbandingan, Random Forest menghasilkan RMSE 208.45 dan R² 0.899. Temuan ini mengindikasikan bahwa mekanisme boosting pada XGBoost lebih efektif dalam menangkap kompleksitas dan volatilitas data pasar kripto. Kata kunci: Prediksi Harga, Ethereum, Machine Learning, XGBoost, Random Forest.
Analyzing ChatGPT Impact on Student Productivity in Information Technology Program at Politeknik Negeri Tanah Laut Hafizd, Khairul Anwar; Manalu, Mamed Rofendi; Arif, M Aidil
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9495

Abstract

The rapid development of generative artificial intelligence, particularly ChatGPT, has transformed the way students complete academic tasks, especially in the field of Information Technology. Despite its widespread adoption, concerns remain regarding its impact on students’ productivity and learning quality. This study aims to analyze the effect of ChatGPT usage on the productivity of students in the Information Technology Study Program at Politeknik Negeri Tanah Laut. A quantitative research approach with a survey method was employed. Data were collected through a Likert-scale questionnaire distributed to active students who had used ChatGPT for academic purposes. The collected data were analyzed using validity and reliability tests, followed by simple linear regression analysis to examine the effect of ChatGPT usage on student productivity. The results indicate that ChatGPT usage has a positive and significant effect on student productivity. Productivity improvements are mainly observed in task efficiency and timely task completion. However, the quality of academic outputs remains highly dependent on students’ ability to critically evaluate, verify, and further develop the outputs generated by ChatGPT. These findings suggest that ChatGPT functions effectively as an academic assistant rather than a substitute for critical thinking and independent learning. This study concludes that ChatGPT can be utilized as a supportive academic tool to enhance student productivity when used appropriately and responsibly, supported by adequate AI literacy and academic supervision. The findings are expected to provide empirical insights for higher education institutions in formulating policies and guidelines for the ethical and productive use of ChatGPT in academic activities.
Co-Authors Dahur, Arnoldus Janssen Girinata, I Made Candra Khairul Anwar Hafizd, Khairul Anwar Manalu, Mamed Rofendi Saputra, Arwin Wahyu Styawan, Budi