Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) bit-Tech
Basuki Rahmat
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Education

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Stacking Ensemble of XGBoost, LightGBM, and CatBoost for Green Economy Index Prediction Andini Fitriyah Salsabilah; Basuki Rahmat; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2530

Abstract

Indonesia faces persistent challenges in achieving sustainable development, particularly in harmonizing economic growth with environmental sustainability. The imbalance among economic, social, and environmental dimensions necessitates a comprehensive and reliable measurement tool to assess progress toward a green economy. The Green Economy Index (GEI), developed by the Ministry of National Development Planning (BAPPENAS), serves this function. However, limited data availability at the provincial level, such as in East Java, hampers accurate evaluation and informed policy formulation. This study aims to develop a machine learning-based predictive model for the GEI using a stacking ensemble approach that combines three powerful algorithms: XGBoost, LightGBM, and CatBoost. The model was built using relevant economic, social, and environmental indicators and evaluated on a holdout dataset to assess its predictive accuracy and generalizability. The results show that the stacking ensemble model achieved superior performance compared to the individual models, recording an RMSE of 0.0298, MAE of 0.0225, and the R² score of 0.9774. In comparison, CatBoost, XGBoost, and LightGBM individually performed with slightly lower accuracy. These findings confirm that the stacking ensemble approach is highly effective for predicting GEI values and offers a practical, data-driven solution for supporting sustainable development strategies at the regional level. The study concludes that such predictive tools can significantly enhance policy planning and monitoring of green economic growth, although further research is recommended to validate the model across other provinces.
Stres adalah masalah psikologis umum di kalangan Generasi Z, didorong oleh tekanan akademik, perbandingan sosial, dan paparan digital. Deteksi dini sangat penting untuk mencegah masalah kesehatan mental yang lebih parah seperti gangguan kecemasan, burnout Ananda Asa Firstha Affandi; Basuki Rahmat; Retno Mumpuni
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3375

Abstract

Stress is a common psychological issue among Generation Z, driven by academic pressure, social comparison, and digital exposure. Early detection is essential to prevent more severe mental health problems such as anxiety disorders, burnout, or depression. This study aims to optimize a web-based stress detection system using the Recursive Feature Elimination (RFE) method combined with the Random Forest algorithm. A dataset consisting of 500 psychological assessment records and 12 symptom features (G01 to G12) from A3M Consultant Surabaya was used as the basis for analysis. RFE successfully reduced the number of features to six key indicators, such as G01 (anxiety), G02 (emotional instability), G04 (restlessness), G08 (withdrawal), G09 (confusion), and G12 (suicidal thoughts) while maintaining high model accuracy. The baseline Random Forest using 12 features achieved 0.91 accuracy, while the RFE-optimized model with 6 selected features maintained a comparable accuracy of 0.90. The resulting model achieved an accuracy of approximately 0.90 based on Stratified K-Fold Cross Validation, showing consistent performance across folds. The optimized model was then integrated into a web application called “The Z Space,” which combines data driven predictions from Random Forest with rule- based reasoning using Forward Chaining. This hybrid approach ensures both interpretability and accuracy in determining stress levels. The findings highlight that RFE effectively reduces computational complexity without decreasing model performance, making it suitable for real time web implementation in stress detection systems for Generation Z.
RANCANG BANGUN SISTEM SMART TRAFFIC LIGHT BERBASIS IOT UNTUK MEMPRIORITASKAN KENDARAAN DARURAT MENGGUNAKAN FUZZY TYPE-2 Alfi Ramadhaniar; Basuki Rahmat; Henni Endah Wahanani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7790

Abstract

Kemacetan lalu lintas di area perkotaan menjadi salah satu hambat-an utama bagi kendaraan darurat yang membutuhkan akses cepat untuk menyelamatkan nyawa atau menuju tempat terjadinya in-siden. Penelitian ini bertujuan merancang dan mengimplementasi-kan prototipe sistem smart traffic light berbasis Internet of Things (IoT) yang dapat memprioritaskan kendaraan darurat dengan mendeteksi suara sirine menggunakan sensor suara KY-037. Sistem ini dikendalikan oleh mikrokontroler Arduino Uno R3 ditambah dengan WiFi ESP8266 dan menggunakan metode Fuzzy Logic Type-2 untuk menangani ketidakpastian tingkat intensitas suara yang diterima sensor, serta menentukan durasi lampu hijau pada arah kendaraan darurat menggunakan delaytime. Selain itu, sistem dilengkapi dengan fitur kontrol manual berbasis aplikasi Blynk IoT untuk memungkinkan intervensi lampu lalu lintas secara langsung dalam situasi tertentu. Hasil pengujian menunjukkan bahwa sistem prototipe mampu mengidentifikasi suara sirine kendaraan darurat dan merespon dengan mengubah sinyal lampu lalu lintas menjadi hijau untuk memprioritaskan laju kendaraan darurat, serta kembali ke mode normal setelah kendaraan melintas. Sistem ini diharapkan dapat meningkatkan efisiensi layanan darurat dan mengurangi dam-pak dari kendaraan darurat yang terjebak kemacetan pada jalur per-simpangan.
Co-Authors Alfi Ramadhaniar Ananda Asa Firstha Affandi Andini Fitriyah Salsabilah Eva Yulia Puspaningrum Henni Endah Wahanani Retno Mumpuni