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All Journal REKAYASA SIGMA: Jurnal Pendidikan Matematika Knowledge Engineering and Data Science Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Konstruktivisme : Jurnal Pendidikan dan Pembelajaran
Faulina, Ria
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Physics Public Health Other

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Air Temperature Prediction System Using Long Short-Term Memory Algorithm Faulina, Ria; Nuramaliyah, Nuramaliyah; Safitri, Emeylia
Rekayasa Vol 17, No 3: Desember, 2024
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v17i3.28229

Abstract

Air temperature is a highly essential parameter in weather forecasting methods and a critical variable for predicting future weather patterns. An accurate temperature prediction system can assist individuals and organizations in preparing for activities heavily influenced by weather conditions. Therefore, developing a precise temperature prediction model requires a reliable and effective algorithm. In this study, the Long Short-Term Memory (LSTM) algorithm, a type of artificial neural network (Recurrent Neural Network - RNN), is implemented with time series data decomposition for variable input processing. LSTM is specifically designed to handle sequential data or time series data, such as weather data. Additionally, LSTM-GRU and LSTM-Conv1D models are utilized. The dataset used in this research comprises air temperature data provided by the Meteorology, Climatology, and Geophysics Agency (BMKG) in the DKI Jakarta region. Model evaluation is conducted using criteria for the smallest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiments show that the prediction system based on LSTM-GRU achieves the lowest MAE and RMSE values compared to LSTM and LSTM-Conv1D, across 10, 20, and 30-step predictions. It can be concluded that the LSTM-GRU algorithm provides the most accurate predictions compared to the LSTM and LSTM-Conv1D models for sequential temperature data, given sufficient data and a properly configured model. This is also graphically demonstrated by prediction results closely aligning with the actual data. 
PENGARUH MATEMATIS JUMLAH MAHASISWA UNIVERSITAS TERBUKA TERHADAP ANGKA PARTISIPASI KASAR PERGURUAN TINGGI TIAP PROVINSI DI INDONESIA Nuramaliyah, Nuramaliyah; Safitri, Emeylia; Faulina, Ria
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.830

Abstract

Education is a key determinant of the quality of human resources in Indonesia. One indicator used to measure educational participation in a region is the Gross Enrolment Ratio (GER). This study focuses on analyzing the influence of the socio-economic conditions of the population and the number students of Universitas Terbuka on the Gross Enrolment Ratio for higher education (GER-HE) in Indonesia. The aim of this research is to analyze the factors that influence the Gross Enrolment Ratio for higher education in several regions. This study uses a quantitative research design with a regression panel data approach. The study area covers all provinces in Indonesia, comprising 34 provinces. The data used in this study is secondary data obtained from the Badan Pusat Statistik (BPS) for the years 2018-2022, including data on GER-HR, poverty indicator, the number of higher education institutions, and expenditure per capita for each province. Additionally, data was sourced from DAAK-UT to obtain the number of Universitas Terbuka students for the years 2018-2022. Based on the results of the Fixed Effect Model (FEM) panel data regression with individual/cross section effects, the factors that influence the GER-HR value are the number of new UT students and per capita expenditure. The number of new UT students has a positive effect while per capita expenditure has a negative effect on GER-HE in Indonesia. Then for variables that have no effect are the number of universities, and per capita expenditure.
MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS Fitriana, Ika Nur Laily; Leviany, Fonda; Faulina, Ria; Nuramaliyah, Nuramaliyah; Safitri, Emeylia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.844

Abstract

The agricultural sector has an important role in national economic development in Indonesia. Based on data from the 2023 Agricultural Census from the Central Bureau of Statistics, it was found that the quantity and quality of the agricultural sector in various provinces in Indonesia still varies greatly. Hence, the suitable statistical methods are needed, namely cluster analysis, to group 38 provinces in Indonesia based on similar characteristics in the agricultural sector. Cluster analysis in this research uses the Self-organizing Maps (SOM) method. Before cluster analysis is carried out, Principal Component Analysis (PCA) is carried out to reduce the dimensions of the variables so that the data is easier to process and avoids the curse of dimensionality. The PCA results obtained 2 main components formed from 9 agricultural sector variables, which were then used as input data for clustering analysis with SOM. The results of clustering with SOM showed that the optimal number of provincial groups was 3 with a Davies-Boulden Index (DBI) value of 0.544 and a Silhouette of 0.623. The results of grouping the provinces can then be categorized into cluster 1 with a high average value of agricultural sector variables, cluster 2 with a medium average value of agricultural sector variables, and cluster 3 with a low average value of agricultural sector variables.
Pengembangan Media Pembelajaran Berbasis Android Dengan Menggunakan Articulate Storyline 3 pada Materi Trigonometri di SMKN 3 Bangkalan Rizki, Moch; Wijayanti, Rica; Faulina, Ria
Konstruktivisme : Jurnal Pendidikan dan Pembelajaran Vol 15 No 2 (2023): Juli 2023
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/konstruk.v15i2.2940

Abstract

Penelitian ini bertujuan untuk mengembangkan media pembelajaran dengan menggunakan Artciulate Storyline 3 pada materi trigonometri dan untuk mengetahui keefektifan penggunaan media pembelajaran Artciulate Storyline 3 pada materi trigonometri. Penelitian ini menggunakan model pengembangan Borg and Gall. Adapun Langkah-langkah yang digunakan, yaitu 1) potensi dan masalah, 2) pengumpulan data, 3) desain produk, 4) validasi desain, 5) revisi desain, 6) uji coba produk, 7) revisi produk, 8) uji coba pemakaian, 9) revisi produk, 10) produksi massal. Penelitian ini menggunakan tiga instrumen yang digunakan untuk memperoleh data, yaitu instrumen validasi ahli, angket respons siswa, dan tes. Instrumen validasi ahli terdiri dari tiga validasi, yaitu validasi media tampilan, validasi materi, dan validasi media information technology (IT). Hasil penelitian yang telah dilakukan menunjukkan bahwa pengembangan media pembelajaran Artciulate Storyline 3 pada materi trigonometri dinyatakan sangat efektif berdasarkan persentase keberhasilan hasil belajar siswa, yaitu 80%.
ANALISIS PERSEPSI GURU MATEMATIKA TINGKAT SMP -SMA SEDERAJAT DI KECAMATAN SOCAH TERHADAP PENGGUNAAN TIK UNTUK PEMBELAJARAN MATEMATIKA Priandini, Erlina Sulis; Faulina, Ria
SIGMA: JURNAL PENDIDIKAN MATEMATIKA Vol. 16 No. 1: Juni 2024
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/sigma.v16i1.14544

Abstract

Teknologi Informasi dan Teknologi (TIK) berperan penting bagi guru dalam segala aspek. Berbagai media TIK dapat digunakan untuk meningkatkan kemampuan, merangsang proses belajar siswa, dan memberikan peluang kepada peserta didik untuk mempelajari konsep secara lebih mendalam terhadap materi yang susah dipahami khususnya matematika. Sehingga peneliti ingin mengetahui persepsi guru matematika dalam penggunaan TIK, guna memberikan gambaran pentingnya TIK dalam proses pebelajaran. Jenis penelitian ini berupa mixed method dengan subjek penelitian 10 guru Matematika tingkat SMP dan 5 guru Matematika tingkat SMA sederajat di kecamatan Socah mengenai persepsi guru matematika dalam penggunaan TIK. Teknik pengumpulan data dimulai dari observasi, lalu penyebaran keisioner, dan pelaksanaan wawancara. Instrumen dalam penelitian ini berupa lembar angket dengan teknik analisis data menggunakan statistika deskriptif. Dari hasil penelitian diketahui bahwa persepsi guru terkait pemahaman TIK masuk dalam kategori cukup tinggi dengan rentang skor 75% -  < 100%. Hal ini dikuatkan pula dengan hasil wawancara dimana guru mampu menggunakan LCD serta membuat media pembelajaran digital sendiri. Namun kendala yang dialami yaitu sarana dan prasarana di sekolah belum memenuhi, seperti kurangnya LCD, komputer, serta didorong faktor siswa kurang paham dalam penggunaan TIK. Dapat disimpulkan bahwa persepsi guru matematika terhadap TIK dalam pembelajaran sangat penting untuk meningkatkan minat belajar siswa.
Pelatihan PELATIHAN PEMBUATAN MEDIA PEMBELAJARAN INTERAKTIF DENGAN MEMANFAATKAN TEKNOLOGI Liesdiani, Mety; Faulina, Ria; Aini, Nur
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 3 (2024): Volume 5 No. 3 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i3.16402

Abstract

Pendidikan merupakan kunci utama dalam menghasilkan sumber daya manusia yang berkualitas. Peranan guru dalam kegiatan belajar mengajar merupakan salah satu bagian utama dalam proses pendidikan. Masih banyak guru yang belum memanfaatkan teknologi dalam kegiatan belajar mengajar. Oleh sebab itu perlu adanya peningkatan kompetensi guru mengenai pemanfaatan teknologi melalui kegiatan Pengabdian Kepada Masyarakat (PKM). Tridarma perguruan tinggi merupakan wujud peran serta dosen dan mahasiswa, sebagai salah satu bentuk tridarma adalah PKM. Pada era industri 5.0 Guru dituntut untuk memiliki kemampuan lebih dalam memanfaatkan teknologi sebagai sumber pembelajaran. Pelatihan pemanfaatan pembelajaran interaktif dipilih sebagai media pendukung pembelajaran agar dapat menghasilkan suatu pembelajaran yang menarik dan tidak membosankan berbasis visual dan grafis. Kegiatan PKM dilaksanakan pada Sekolah Dasar Tlangoh di Kecamatan Tanjung Bumi. Artikel ini ditulis menggunakan pendekatan kualitatif dengan menggunakan data dan paparan yang bersumber dari hasil pelatihan Guru Serta bersumber dari kajian literature yang relevan. Dengan dilaksanakan kegiatan ini para guru memiliki kompetensi lebih dalam pemanfaatan teknologi dan telah berhasil membangun media pembelajaran sendiri. Selain itu kreativitas guru menjadi meningkat dengan adanya kegiatan ini.
Comparative Analysis of Deep Learning Algorithms for Predicting ENSO Based on Non-sequential Sampling Procedure Algorithms Faulina, Ria; Hasanah, Siti Hadijah; Nuramaliyah, Nuramaliyah; Fitriana, Ika Nur Laily
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN and LSTM models in predictive accuracy and robustness. Specifically, the proposed model achieved the lowest Mean Absolute Error (MAE) of 13.97 and Root Mean Square Error (RMSE) of 15.76 across multiple test samples. These findings indicate that the integration of convolution-based feature extraction and sequential memory learning effectively captures complex ENSO temporal patterns. The proposed approach offers a reliable computational framework for climate forecasting and may contribute to improved anticipatory planning in climate-sensitive decision-making contexts.
Co-Authors Fitriana, Ika Nur Laily Hasanah, Siti Hadijah Leviany, Fonda mety Liesdiani, mety Nur Aini Nuramaliyah, Nuramaliyah Priandini, Erlina Sulis Rica Wijayanti, Rica Rizki, Moch Safitri, Emeylia