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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Minfo Polgan (JMP) JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Mahkota Informatika
Nababan, Junerdi
Universitas Mahkota Tricom Unggul
Computer Science & IT Economics, Econometrics & Finance Education Library & Information Science Mathematics Social Sciences

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Journal : JOURNAL OF SCIENCE AND SOCIAL RESEARCH

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PERBANDINGAN PREDIKSI POLUSI UDARA MENGGUNAKAN LSTM DAN BILSTM Pratama, Andre; Sembiring, Asha; Nababan, Junerdi; Zarkasyi, Muhammad Imam; Rahayu, Novriza
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3596

Abstract

Abstract: Air pollution has become a serious problem in densely populated urban areas such as DKI Jakarta. To mitigate its negative impacts, an accurate air pollution prediction system is necessary. This study compares the performance of two deep learning models, Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), in predicting PM10 concentration using air quality data from DKI Jakarta between 2016 and 2019. The research process includes data collection and preprocessing, model training, and model evaluation. Both models were tested with various parameters such as the number of hidden neurons, dropout rate, epochs, and batch size. The results consistently show that BiLSTM outperforms LSTM, achieving lower Root Mean Square Error (RMSE) values across 54 testing scenarios. The best BiLSTM configuration, with 64 hidden neurons, 0.2 dropout rate, 50 epochs, and batch size 16, yielded an RMSE of 9.311401. Meanwhile, the best LSTM configuration, with 128 hidden neurons, 0.1 dropout rate, 100 epochs, and batch size 16, produced an RMSE of 9.330554. The advantage of BiLSTM lies in its ability to process data bidirectionally, making it more effective in capturing temporal patterns for air pollution prediction compared to LSTM. Keywords: air pollution prediction, pollutant, deep learning, LSTM, BiLSTM Abstrak: Pencemaran udara menjadi masalah serius di wilayah perkotaan padat seperti DKI Jakarta. Untuk mengurangi dampak negatifnya, diperlukan sistem prediksi polusi udara yang akurat. Penelitian ini membandingkan performa dua model deep learning, Long Short-Term Memory (LSTM) dan Bidirectional Long Short-Term Memory (BiLSTM), dalam memprediksi konsentrasi PM10 menggunakan data kualitas udara DKI Jakarta tahun 2016-2019. Proses penelitian mencakup pengumpulan dan praproses data, pelatihan model, serta evaluasi model. Kedua model diuji dengan berbagai parameter seperti jumlah hidden neuron, dropout rate, epoch, dan batch size. Hasil menunjukkan BiLSTM lebih unggul secara konsisten dengan nilai Root Mean Square Error (RMSE) lebih rendah melalui 54 skenario pengujian. Konfigurasi terbaik BiLSTM menggunakan 64 hidden neuron, dropout rate 0.2, 50 epoch, dan batch size 16 menghasilkan RMSE 9.311401. Sedangkan konfigurasi LSTM terbaik pada 128 hidden neuron, dropout rate 0.1, 100 epoch, dan batch size 16 menghasilkan RMSE 9.330554. Keunggulan BiLSTM terletak pada kemampuannya memproses data dua arah, sehingga lebih efektif dalam menangkap pola temporal untuk prediksi polusi udara dibandingkan LSTM.  Kata kunci: prediksi polusi udara, polutan, deep learning, LSTM, BiLSTM
PEMANFAATAN AI DALAM PEMBELAJARAN KEWIRAUSAHAAN DIGITAL UNTUK MENINGKATKAN INOVASI MAHASISWA UNIVERSITAS MAHKOTA TRICOM UNGGUL Theodora, Eka Martyna; Nababan, Junerdi; Kusnadi, Kusnadi; Gani, Petrus; Mipo, Mipo
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4009

Abstract

Abstract: This study aims to analyze the effect of the use of artificial intelligence (AI) in digital entrepreneurship learning on increasing student innovation. The background of this study is based on the rapid development of AI technology and its application in higher education, particularly in supporting student creativity and business idea development. This study uses a quantitative approach with the independent variable of AI Utilization and the dependent variable of Student Innovation. The research sample consisted of 60 students of Mahkota Tricom Unggul University selected through a purposive sampling technique. Data collection was conducted through a questionnaire and analyzed using SPSS version 25 with validity and reliability tests, as well as simple linear regression. The results showed that the research instrument was valid and reliable (Cronbach's Alpha X = 0.907; Y = 0.906). The regression results showed that the use of AI had a significant effect on student innovation with an R² value of 0.158 and a significance of p = 0.002. In conclusion, the use of AI has a positive contribution in encouraging student innovation, although some aspects still require a deeper practical approach. These findings can be the basis for the development of an adaptive and innovative technology-based entrepreneurship curriculum. Keywords: AI; entrepreneurship; digital; innovation; learning Abstrak: Penelitian ini bertujuan untuk menganalisis pengaruh pemanfaatan artificial intelligence (AI) dalam pembelajaran kewirausahaan digital terhadap peningkatan inovasi mahasiswa. Latar belakang penelitian ini didasarkan pada perkembangan pesat teknologi AI dan penerapannya dalam pendidikan tinggi, khususnya dalam mendukung kreativitas dan pengembangan ide bisnis mahasiswa. Penelitian ini menggunakan pendekatan kuantitatif dengan variabel independen Pemanfaatan AI dan variabel dependen Inovasi Mahasiswa. Sampel penelitian terdiri dari 60 mahasiswa Universitas Mahkota Tricom Unggul yang dipilih melalui teknik purposive sampling. Pengumpulan data dilakukan melalui kuesioner dan dianalisis menggunakan SPSS versi 25 dengan uji validitas, reliabilitas, serta regresi linear sederhana. Hasil penelitian menunjukkan bahwa instrumen penelitian valid dan reliabel (Cronbach's Alpha X=0.907; Y=0.906). Hasil regresi menunjukkan bahwa pemanfaatan AI berpengaruh signifikan terhadap inovasi mahasiswa dengan nilai R² sebesar 0.158 dan signifikansi p=0.002. Kesimpulannya, pemanfaatan AI memberikan kontribusi positif dalam mendorong inovasi mahasiswa, meskipun sebagian aspek masih memerlukan pendekatan praktis yang lebih dalam. Temuan ini dapat menjadi landasan bagi pengembangan kurikulum kewirausahaan berbasis teknologi yang adaptif dan inovatif. Kata kunci: AI; kewirausahaan; digital; inovasi; pembelajaran
Co-Authors Ahmad Rozy Andre Pratama Budi Hasian Daulay Cut Try Utari Edi Faisal Eka Tampubolon Ellanda Purwawijaya Firman Styono Frainskoy Rio Naibaho Gani, Petrus Ginting, Hendro Sutomo Kusnadi Mipo, Mipo Nidya Banuari Pasaribu, Sutrisno Arianto Pasaribu, Victor Patar Pratama, Andre Rahayu, Novriza Rambe, Aripin Ravel, Mutiha Aristo Richard Parlindungan Simanjuntak Salsabila, Nadiyah Shofa Sembiring, Asha Sinaga, Royanti Sutrisno Arianto Pasaribu Syahputra, Dinur Theodora, Eka Martyna Tulus Tulus Vera Wijaya Zakarias Situmorang Zarkasyi, Muhammad Imam