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All Journal Jurnal Bina Desa Unnes Journal of Mathematics
Riska Nur Fitrianingsih
Universitas Negeri Semarang
Education Mathematics Social Sciences

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Upaya Pencegahan Stunting dengan Pemanfaatan Kebun Gizi sebagai Inovasi dalam Peningkatan Gizi Anak di Desa Mojosari Fitria Ekarini; Mochamad Anjar Munggaran; Mushfiq Khamdani; Riska Nur Fitrianingsih; Sekar Ayu Putri Prameswari; Witoto Witoto
Jurnal Bina Desa Vol 6, No 2 (2024): Juni
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jbd.v6i2.49525

Abstract

Abstrak. Dalam mewujudkan pembangunan nasional pemerintah Indonesia melakukan berbagai program salah satunya penurunan angka stunting pada anak. Dengan adanya program pengabdian kepada masyarakat UNNES GIAT 6 melakukan kegiatan Pencegahan dan Penanggulangan Stunting di Desa Mojosari yang memiliki masalah stunting. Dari total 81 balita terdapat 17 balita (20,98%) terindikasi gizi kurang dan stunting. Mahasiswa UNNES GIAT 6 melakukan sosialisasi pencegahan stunting dan membuat kebun gizi dengan tujuan memenuhi gizi dan mencegah stunting. Metode dalam kegiatan melalui 4 tahap yaitu: Perencanaan, Persiapan, Pelaksanaan, dan Pendiseminasian. Dalam pelaksanaannya warga merespon baik dan membantu pembuatan kebun gizi. Kebun gizi tersebut ditanami bayam, kangkung, sawi, tomat, terong, kelor, daun bawang, dan seledri yang dibagi menjadi 7 bedeng. Hasil yang didapatkan dari kegiatan ini adalah tanaman yang ditanam sudah tumbuh dengan baik dan subur serta lahan yang kosong dapat dimanfaatkan dan kelolah dengan baikAbstract. To achieve national development, the Indonesian government implements various programs, one of which is aimed at reducing stunting rates in children. As part of this effort, UNNES GIAT 6 carried out a community service program focused on the prevention and management of stunting in Mojosari Village, which has a significant stunting issue. Out of 81 children under five, 17 (20.98%) were identified as undernourished and stunted. The UNNES GIAT 6 students conducted stunting prevention awareness sessions and established a nutrition garden to help meet nutritional needs and prevent stunting. The program was carried out in four stages: Planning, Preparation, Implementation, and Dissemination. The villagers responded positively and actively participated in the creation of the nutrition garden. The garden was planted with spinach, water spinach, mustard greens, tomatoes, eggplant, moringa, green onions, and celery, organized into seven plots. The results of this initiative showed that the plants grew well and healthily, and the previously unused land was effectively utilized and managed.Keywords: Stunting; Nutrition Garden; UNNES GIAT 6; Mojosari
Time Series Modeling of Stock Price Using CNN-BiLSTM with Attention Mechanism Riska Nur Fitrianingsih; Iqbal Kharisudin
Unnes Journal of Mathematics Vol. 13 No. 1 (2024): Unnes Journal of Mathematics Volume 1, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i1.13451

Abstract

Indonesia's capital market has experienced rapid development in recent years, marked by an increase in transaction value, the number of investors, and market capitalization. One of the sectors that has garnered attention is the telecommunications industry, which is rapidly growing alongside the increasing number of internet users and the public's demand for more advanced telecommunications services. PT Indosat Ooredoo Hutchison, as one of the leading telecommunications companies in Indonesia, has become an attractive investment choice for investors. However, the stock market is known for its fluctuating and irregular nature. Stock data has complex characteristics such as large data volume, ambiguous information, and non-linearity. Therefore, it is important for investors to understand stock price movements before making investments in order to reduce the risk of significant losses. One method that can be used to address that risk is by forecasting stock prices. Time series forecasting is a prediction about future values based on historical data. Statistical methods in forecasting allow for the identification of patterns and trends in historical data, as well as modeling the relationships between variables over time. One of the techniques that is becoming increasingly popular in forecasting is deep learning. In this study, a combination of \textit{Convolutional Neural Network} (CNN) and \textit{Bidirectional Long Short-Term Memory} (BiLSTM) with an attention mechanism is used. CNN excels at extracting data features, while BiLSTM is better at handling data with long time ranges. The addition of the attention mechanism allows the model to assign different weights to data features, enabling it to focus on the most relevant information. The combination of these three elements (CNN-BiLSTM with an attention mechanism) has the potential to yield higher prediction accuracy. To measure the accuracy of the forecasts, this study uses evaluation metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared (R²). The research results indicate that the CNN-BiLSTM model with an attention mechanism has proven to be the most superior model compared to other models in forecasting the stock price of PT Indosat Ooredoo Hutchison.
Co-Authors Ekarini, Fitria Iqbal Kharisudin Mochamad Anjar Munggaran Mushfiq Khamdani Sekar Ayu Putri Prameswari Witoto Witoto