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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Pengembangan Rekayasa dan Teknologi Variance : Journal of Statistics and Its Applications Buletin Ilmiah Sarjana Teknik Elektro Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Statistika dan Aplikasinya Journal Of Human And Education (JAHE) Journal of Data Insights Jurnal Pengabdian Masyarakat dan Riset Pendidikan Scientific Journal of Computer Science
Ariska Fitriyana Ningrum
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Evaluation of Deep Learning Optimizers for Predicting JISDOR Exchange Rates Using LSTM Networks: Evaluasi Pengoptimalan Deep Learning untuk Memprediksi Nilai Tukar JISDOR Menggunakan Jaringan LSTM Ariska Fitriyana Ningrum; Dannu Purwanto; Amelia Kusuma Wardani
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.726

Abstract

This research explores the application of four optimization algorithms—Adam, Nadam, RMSProp, and SGD—on a Long Short-Term Memory (LSTM) model to forecast the Jakarta Interbank Spot Dollar Rate (JISDOR). The volatile nature of exchange rate data, influenced by global and domestic economic dynamics, necessitates the use of models like LSTM that excel in capturing both short- and long-term dependencies. Performance was assessed using metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among the optimizers, Nadam proved to be the most effective, achieving the lowest RMSE of 62.767 and a MAPE of 0.003, indicating its capability in managing complex fluctuations in the dataset. Despite Nadam's promising results, opportunities for improvement remain, including the inclusion of additional input variables, fine-tuning model parameters, and expanding the training dataset. This study underscores the critical role of selecting appropriate optimization algorithms for enhancing the accuracy of LSTM models in forecasting volatile financial time-series data, particularly for currency exchange rates
Identification of Dominant Topics in Public Discussions on IKN using Latent Dirichlet Allocation (LDA) and BERTopic Ariska Fitriyana Ningrum; Florence Jean B. Talirongan; Diana May Glaiza G. Tangaro
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.19

Abstract

This study aims to analyze public opinion related to the relocation of Indonesia's National Capital City (IKN) through topic modeling on Twitter data. The two main approaches used are Latent Dirichlet Allocation (LDA) based on Bag of Words and BERTopic based on Transformer language model. LDA was chosen for its ability to identify topic distribution in large text collections, while BERTopic was used to overcome the limitations of LDA in capturing semantic meaning in short and informal texts such as tweets. The analysis was conducted on a collection of tweets discussing the relocation of IKN, with the aim of uncovering the main themes and public perceptions. The result of LDA showed three main topics in the public discussion, namely (1) political debate and nationalism related to the relocation, (2) policy implementation and project execution, and (3) economic justification and challenges facing Jakarta. Mean-while, BERTopic identified topics with more contextual representations, including aspects of investment, economic impact construction progress, and public perception. Dominant topics include urban relocation, investment in IKN, and socio-economic impacts. The novelty of study lies in the comparison of two topic modeling approaches in the context of social media sentiment analysis related to major public policy issues. These findings not only enrich the understanding of the narratives that develop in society, but also provide important insights for policy makers in responding to public opinion more appropriately and contextually.
Literasi Data Kependudukan melalui Infografis dan Monografi Desa Bagi Kader Posyandu : Pengabdian Tiani Wahyu Utami; Ariska Fitriyana Ningrum; Ali Imron; Sri Nurhidayah; Novia Yunanita
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5483

Abstract

Literasi data kependudukan menjadi sangat penting karena menyangkut kemampuan individu, organisasi, maupun pemerintah dalam memahami, menggunakan, dan mengelola informasi mengenai jumlah, karakteristik, serta dinamika penduduk. Bagi kader Posyandu, data kependudukan memiliki arti strategis karena mereka adalah ujung tombak layanan kesehatan dasar di tingkat masyarakat. Tanpa pencatatan yang baik, intervensi menjadi tidak terarah dan evaluasi program menjadi sulit dilakukan. Kelurahan Pedurungan Lor, khususnya RW 1, Kecamatan Pedurungan, Kota Semarang, dipilih sebagai lokasi kegiatan pengabdian masyarakat. Urgensi kegiatan pengabdian ini adalah mengingat pentingnya data sebagai dasar dalam pengambilan keputusan dan perencanaan kesehatan masyarakat, juga peningkatan kompetensi kader Posyandu dalam pengelolaan dan visualisasi data menjadi sangat mendesak. Oleh karena itu diperlukan kegiatan yang meningkatkan pengetahuan literasi data kependudukan dan melatih pembuatan infografis dan monografi desa. Evaluasi dilakukan dengan pre-post test terkait pemahaman literasi data dan pembuatan infografis. Berdasarkan kegiatan yang dilakukan menunjukkan hasil adanya peningkatan pemahaman literasi data dan pembuatan infografis melalui Canva, hal ini tercermin pada nilai rata-rata skor post test yang mengalami peningkatan yang signifikan.
Analisis Peramalan Suhu Permukaan Bumi di Kota Semarang Menggunakan Regresi Nonparametrik dengan Estimator Deret Fourier Berdasarkan Penalized Least Square (PLS) Ihsan Fathoni Amri; Tiani Wahyu Utami; Dannu Purwanto; Alwan Fadlurohman; Ariska Fitriyana Ningrum; Saeful Amri
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 10 No. 1 (2026): Mei (2026)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v10i1.14583

Abstract

Perubahan iklim global yang ditandai oleh peningkatan suhu permukaan menjadi isu penting, terutama di wilayah perkotaan dengan tingkat urbanisasi tinggi seperti Kota Semarang. Peningkatan suhu dapat memengaruhi kualitas lingkungan dan kenyamanan masyarakat, sehingga diperlukan pemodelan dan peramalan yang akurat untuk memahami pola perubahannya. Penelitian ini bertujuan membentuk model regresi nonparametrik menggunakan estimator deret Fourier dengan optimasi Penalized Least Square (PLS) serta meramalkan suhu permukaan di Kota Semarang. Parameter optimal ditentukan berdasarkan nilai Generalized Cross Validation (GCV) minimum. Hasil penelitian menunjukkan bahwa model terbaik diperoleh pada koefisien Fourier  dengan lambda optimal 0,00027 dan GCV minimum 0,81182. Model menghasilkan Mean Absolute Percentage Error (MAPE) sebesar 1,203717% dengan akurasi 98,7963%, yang termasuk kategori sangat baik. Hasil ini menunjukkan bahwa pendekatan deret Fourier berbasis PLS efektif dalam memodelkan dan meramalkan suhu permukaan di Kota Semarang.
Co-Authors Abdel Nasser Sharkawy Alfian Ma’arif Ali Imron Alwan Fadlurohman Amelia Kusuma Wardani Amri, Saeful Asno Azzawagama Firdaus Dannu Purwanto Dhani, Oktaviana Rahma Diana May Glaiza G. Tangaro Febi Anggun Lestari Fitri Diana Musa Florence Jean B. Talirongan Furizal, Furizal Ihsan Fathoni Amri Kariyamin, Kariyamin M. Al Haris Nakib, Arman Mohammad Novia Yunanita Oktaviana Rahma Dhani Prizka Rismawati Arum Rohim, Febrian Hikmah Nur Sitti Sahara Sri Nurhidayah Tiani Wahyu Utami Wardani, Amelia Kusuma Wijaya, Setiawan Ardi Yunanita, Novia Yusrin, Yusrin Zahra Aura Hisani