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All Journal Jurnal Masyarakat Informatika Jurnal Pendidikan Fisika dan Keilmuan (JPFK) JTAM (Jurnal Teori dan Aplikasi Matematika) Majalah Ilmiah Matematika dan Statistika (MIMS) Journal of Soft Computing Exploration
Vianita, Etna
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Astronomy Computer Science & IT Education Mathematics Physics

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Identification of South Sumatra Province’s Local Wisdom as Science Literacy Objects Sari, Widya; Wiyono, Ketang; Setyawan, Dedy; Asiandu, Angga Puja; Sa’diyah, Khalimatus; Vianita, Etna; Septialti, Delita; Sutinah, Sutinah
Jurnal Pendidikan Fisika dan Keilmuan (JPFK) Vol 6, No 2 (2020)
Publisher : UNIVERISTAS PGRI MADIUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/jpfk.v6i2.8600

Abstract

The ability of Indonesian students to master physics is still low. Based on PISA in 2018, Indonesia ranked at 62th out of 72 participating countries. It was much lower than other Asian countries. Therefore, this problem should be overcome. One of the existing sources of physics literacy is local wisdom. The local wisdom can be used as a direct source of physics studies for students to increase their literacy skills. Therefore, in this paper, the authors discussed some local wisdoms in South Sumatra Province that potentially used as physics literacy objects for students in their school. South Sumatra has many local wisdoms potentially used as real objects in physics studies for students. Some of the local wisdom as otok-otok boat and roasted kemplang can be used as the object of heat transfer studies (thermal), and the traditional houses of Limas and Baghi can be used as media for learning force, mass, load and modulus of elasticity. The objects of literacy studies are sources of study that can be used by teachers in teaching physics. Through local wisdom-based literacy, it is certainly expected to improve Indonesia's PISA ranking. Not only to improve PISA ranking, but it is also increase the superiority of the nation's future generations.
PERAMALAN PRODUKSI KARET INDONESIA MENGGUNAKAN FUZZY TIME SERIES DUA FAKTOR ORDE TINGGI RELASI PANJANG BERDASARKAN RASIO INTERVAL Vianita, Etna; Tjahjana, Heru; Udjiani, Titi
Majalah Ilmiah Matematika dan Statistika Vol 22 No 1 (2022): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v22i1.30414

Abstract

The fuzzy time series method for forecasting continues to develop over time. This research discusses fuzzy time series, which considers two factors for high order using interval partitioning based on interval ratio with long relation construction for getting different accuracy in forecasting between combination method and existing method. The first step is the formation of the universe of speech. Second, divide the universe of discourse into several intervals using interval ratios. Third, fuzzification. Fourth, build fuzzy logic relations and fuzzy logic relation groups, and fifth, defuzzification. The previous methods would be compared with the fuzzy logic relation construction result. The simulation used Indonesian rubber production data for 2000-2020. The results and errors were tested using the average forecasting error rate (AFER). AFER value of the forecasting method is 1.863% obtained.Keywords: Forecasting, fuzzy time series, long relationMSC2020: 62M10, 62M20, 62M86, 03E72
Car insurance segmentation prediction based on the most influential features using random forest and stacking ensemble learning Vianita, Etna; Wibowo, Adi; Surarso, Bayu; Widodo, Aris Puji
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.39

Abstract

In addition to financial transaction services, the Bank also provides insurance services by conducting regular campaigns to attract new customers such as car insurance based on market segmentation, which is one of the main aspects of marketing used in financial services based on demographic data. One way to analyze the market is to predict the likely target market based on the campaign's target demographic data. Therefore, this study aims to find the best classification method for predicting campaign targets using historical data from 4000 customers of a bank in the United States. The market segmentation analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using important features for Random Forest. The ensemble learning used is a stacking model consisting of the basic model of Logistic Regression, Support Vector Classifier, Gradient Boosting, Extra Tree, Bagging, Adaboost, Gaussian Naive Bayes, MLP, XBoost, LGBM, KNeighbors, Decision Tree, and Random Forest. The accuracy results of the stacking model can exceed the accuracy of the basic model with an accuracy rate of 78.80%.
A Comparative Study of Machine Learning Models for Short-Term Load Forecasting Vianita, Etna; Tantyoko, Henri
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73130

Abstract

Short-Term Load Forecasting (STLF) was a critical task in power system operations, enabling efficient energy management and planning. This study presented a comparative analysis of five machine learning models namely XGBoost, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and LightGBM using real-world electricity demand data collected over a four-month period. Two modeling approaches were explored: one using only time-based features (hour, day of the week, month), and another incorporating historical lag features (lag_1, lag_2, lag_3) to capture temporal patterns. The results showed that MLP with lag features achieved the best performance (RMSE: 57.63, MAE: 34.54, MAPE: 0.22), highlighting its ability to model nonlinear and sequential dependencies. In contrast, SVR and LightGBM experienced performance degradation when lag features were added, suggesting sensitivity to feature dimensionality and data volume. These findings emphasized the importance of model-feature alignment and temporal context in improving forecasting accuracy. Future work could explore the integration of external variables such as weather and holidays, as well as the application of advanced deep learning architectures like LSTM or hybrid models to further enhance robustness and generalizability.
An Efficient Bidirectional Gated Recurrent Unit Approach for Student Study Duration Modeling and Timely Graduation Forecasting Purnama, Satriawan Rasyid; Tantyoko, Henri; Vianita, Etna
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.73275

Abstract

Delays in student graduation remain a persistent challenge in higher education, with approximately 28% of students requiring more than four years to complete their studies, exceeding the standard duration. This study addresses the issue by proposing a predictive model to estimate students’ graduation year using a Bidirectional Gated Recurrent Unit (BiGRU) neural network. The model is trained on a combination of academic and financial indicators, including Grade Point (GP) scores from the first to the fifth semester, cumulative Grade Point Average (GPA), and the single tuition fee tier (UKT). The integration of these features allows the model to learn temporal patterns in students’ academic progression and financial capacity. Empirical analysis reveals that students in the UKT 8 group consistently demonstrate superior academic performance, as evidenced by their higher average GPA across semesters, compared to students in lower UKT groups. The BiGRU model achieves a Mean Absolute Percentage Error (MAPE) of 9.5%, indicating high predictive accuracy. These findings highlight the potential of deep learning models, particularly BiGRU, in forecasting academic outcomes. Furthermore, the insights generated from this model can serve as a valuable tool for universities in formulating targeted academic interventions and policies aimed at promoting timely graduation and reducing dropout rates.
Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series Vianita, Etna
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.16910

Abstract

The improvement of plantation forecasting accuracy, particularly with regard to coffee production, was an essential aspect of earth observations for the purpose of informing plantation management alternatives. These decisions included strategic and tactical decisions on supply chain operations and financial decisions. Many research initiatives have used a variety of methodologies to the forecasting of plantation areas and related industries, such as coffee production. One of these methods was known as the fuzzy time series (FTS) technique. This  study combined ratio-interval and frequency density to get universe of discourse and partition followed by adopted weighted and modified that weighted. The first step was defined universe of discourse using ratio-interval algorithm. The second step was partition the universe of discourse using ratio-interval algorithm followed by frequency density partitioning. The third step was fuzzyfication. The fourth step built fuzzy logic relationship (FLR) and fuzzy logic relationship group (FLRG). The fifth step was adopted the modification weighted. The last step was defuzzyfication. The  models evaluated  by  average  forecasting  error  rate  (AFER)  and  compared  with  existing methods.  AFER  value  1.24%  for  proposed method.
Co-Authors Adi Wibowo Aris Puji Widodo Asiandu, Angga Puja Bayu Surarso Dedy Setyawan Ketang Wiyono Purnama, Satriawan Rasyid Redemtus Heru Tjahjana Sa’diyah, Khalimatus Septialti, Delita Sutinah Sutinah Tantyoko, Henri Titi Udjiani SRRM Widya Sari