SNM- 128 (Oral)
Rysfan -
(Universitas Riau)

Perbandingan Luas Yang Terbentuk Dari Trisektor Sisi Pada Sembarang Segiempat Nonkonveks

Pada umumnya banyak pembahasan mengenai trisektor sudut yaitu garis yang membagi sudut menjadi tiga bagian sama besar, namun belum banyak yang membahas trisektor sisi. Trisektor sisi adalah garis yang ditarik dari titik sudut yang membagi sisi depan sudut menjadi tiga bagian yang sama. Trisektor sisi ini sudah dikembangkan oleh beberapa penulis pada segitiga dan segiempat konveks. Namun, belum ada yang mengembangkan secara detail pada segiempat nonkonveks. Pada tulisan ini penulis akan menentukan perbandingan luas beberapa bangun yang terbentuk dari trisektor sisi pada segiempat nonkonveks dengan luas segiempat nonkonveks asli.

SNM- 249 (Participant)
Yayu Fitriyatul
(Universitas Mataram)

Participant

SNM- 146 (Participant)
Arni Nur Isma Saputri
(Universitas Mataram)

Participant

SNM- 135 (Participant)
Muksin Salim
(SMKN 2 Mataram )

Participant

SNM- 167 (Oral)
Yosef Gomgom Handayani
(University of Pamulang )

Prediksi Gempa Bumi Indonesia Menggunakan Simple Neural Network, CNN dan LSTM

This research was conducted to predict earthquakes using three types of machine learning methods, namely Simple Neural Network (SNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Earthquake prediction is crucial for mitigation and preparedness. Machine learning methods are used to predict earthquakes based on seismic data from the Meteorology, Climatology, and Geophysics Agency. The study utilizes a dataset from November 2008 to January 2023. The time and earthquake attribute data are transformed into appropriate formats for model training. The results indicate that LSTM and Residual LSTM are capable of predicting earthquakes effectively, especially when predicting all attributes simultaneously. CNN tends to be underfitted and requires better normalization. LSTM and Residual LSTM are the preferred choices for earthquake prediction in Indonesia when the dataset is sufficiently large. However, the right architecture in SNN can also yield good results. This study demonstrates that representing data in the form of arrays alone is not enough, and integrating other physical data with machine/deep learning architectures is key to more accurate predictions.

SNM- 137 (Oral)
Ika Siyam Pratiwi
(Universitas Pamulang)

Analisis Peramalan Kenaikan Penjualan Minyak Kelapa Sawit dengan Menggunakan Metode Time Series

Palm oil is a raw material that has various uses around the world. This crop has a productive period of about 25-30 years, and the right selection of seeds is very important because it will affect the productivity of the plantation in the next few decades. Oil palm fruits are called Fresh Fruit Bunches (FFB), and to harvest them, harvesters use dodos tools with long poles to cut fruit from tree trunks. Ready-to-harvest FFBs usually have a bright red color and fall to the ground in an amount of about 10-15 pieces. This research using time series analysis method is a statistical method used to process data collected from time to time. This method is used to create models that are used as the basis for forecasting. With time series analysis, we can understand the trend of data changing over time, whether it is caused by sentiment or other factors. This is important in the context of forecasting an increase in palm oil sales. In addition, in time series analysis, there is a stationary test used to determine whether the data used in the analysis has stationer properties or not, which is important in producing accurate forecasting.

SNM- ()
haritsah haritsah
(MTs Negeri 1 Lombok Tengah )

SNM- 154 (Participant)
Kurnia Ningsih
(Universitas Mataram )

Participant

SNM- 140 (Oral)
Husnul Khatimah
(ATKIP Taman Siswa Bima)

Pengembangan LKPD Etnomatematika dengan Pendekatan RME untuk Meningkatkan Numerasi Siswa

In implementing learning in schools, teachers still have difficulty developing teaching materials that can support the learning process, especially to improve students' mathematical literacy skills. Students' ability to understand learning material is also influenced by teaching practices and the tools used in learning. Learning mathematics linked to traditional games will make students learn while playing, but unfortunately not many teachers have developed learning tools related to ethnomathematics. This research is research into the development of student worksheets. RME-based ethnomathematics to improve elementary school students' mathematical literacy skills which aims to produce ethnomathematics worksheet products using the RME approach to improve students' numeracy literacy that are valid, practical and effective. This research is development research whose product is expected to improve the mathematical literacy skills of elementary school students. This research method uses the research and development (RnD) method which is often called the development method. The development model used in this research is the ADDIE development model. The ADDIE model consists of 5 stages, namely the analysis stage, design stage, development stage, implementation stage and evaluation stage. The instruments used in the research were validation sheets, student response questionnaires, teacher ability sheets in managing learning and learning outcomes tests (THB). The data analysis technique in research uses descriptive analysis. The research results show that RME-based ethnomathematics worksheets can improve students' numeracy literacy skills with effective student activities shown by each indicator observed according to a tolerance of 10%. Student responses to the LKPD were positive and reached above 80%. And classical learning completeness was achieved with a percentage of 95%. The teacher's ability to manage learning has good criteria with an observer assessment for each indicator of at least 3.

SNM- ()
I Made Yoga Arsana Putra
(Universitas Mataram )