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Unnes Journal of Mathematics
Published by Universitas Negeri Semarang
ISSN : 22526943     EISSN : 24605859     DOI : https://doi.org/10.15294/ujm
Core Subject : Education,
Mathematics
Unnes Journal of Mathematics (UJM) publishes research issues on mathematics and its apllication. The UJM processes manuscripts resulted from a research in mathematics and its application scope, which includes. The scopes include research in: 1. Algebra 2. Analysis 3. Discrete Mathematics and Graph Theory 3. Differential Equation 4. Geometry 5. Mathematics Computation, 6. Statistics.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 234 Documents
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PEMODELAN HARGA SAHAM MENGGUNAKAN METODE LONG SHORT TERM MEMORY Jannatun Khustia Lubis; Iqbal Kharisudin
Unnes Journal of Mathematics Vol 11 No 2 (2022)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v11i2.59758

Abstract

Penelitian ini bertujuan untuk mengetahui peramalan harga saham PT XL Axiata Tbk, PT Indosat Tbk, dan PT Telkom Indonesia Tbk menggunakan Long Short Term Memory (LSTM) dan Automatic ARIMA (auto ARIMA) melalui program python. Data harga penutupan saham mulai tanggal 01 Januari 2015 sampai 31 Desember 2020 dibagi menjadi dua, yaitu 80% data training dan 20% data testing. Data diproses menggunakan masing-masing metode secara terpisah. Tahapan penelitian pada metode LSTM yaitu, data preprocessing, build model, model selection, dan peramalan. Paramater fokus pada metode LSTM yaitu jumlah epoch dan jumlah neuron. Tahapan penelitian pada metode auto ARIMA yaitu, data preparation, build model, dan peramalan. Setelah kedua data diramalkan, data dibandingkan dengan metriks akurasi MSE dan RMSE. Pada penelitian ini, disimpulkan bahwa metode auto ARIMA lebih baik dibandingkan dengan metode LSTM untuk meramalkan saham PT XL Axiata Tbk dan PT Telkom Indonesia Tbk, serta metode LSTM lebih baik dibandingkan dengan metode auto ARIMA untuk meramalkan saham PT Indosat Tbk.
Penerapan Metode Multi Objective Goal Programming Berbantuan POM-QM Pada Perencanaan Produksi UMKM Dapur Bocil Nialis Septiyani; Rahayu Budhiati Veronica
Unnes Journal of Mathematics Vol 11 No 2 (2022)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v11i2.60982

Abstract

Perkembangan teknologi dan permasalahan pandemi menyebabkan sulitnya untuk mendapatkan pekerjaan. Hal ini, memicu masyarakat untuk membuka sebuah usaha. Dengan perkembangan seperti ini, dapat menyebabkan persaingan di dunia industri. Penelitian ini bertujuan untuk penerapan metode multi objective goal programming (MOGP) pada perencanaan produksi UMKM Dapur Bocil. Perencanaan produksi merupakan hal penting dan langkah awal yang perlu dilakukan oleh setiap usaha. Perencanaan produksi berhubungan dengan jumlah produksi, ketepatan waktu penyelesaian, pendapatan penjualan, dan biaya minimum. Metode MOGP dapat digunakan untuk menyelesaikan masalah yang mengandung satu atau lebih dari satu tujuan (goals). Penerapan untuk perencanaan produksi ini menggunakan software POM-QM. Dalam perancangan produksi UMKM Dapur Bocil ini menggunakan 4 variabel keputusan yaitu Dimsum Ayam, Dimsum Ayam Cabai, Dimsum Jamur, dan Dimsum Udang serta 7 goals yang akan dicapai. Berdasarkan hasil output POM-QM menunjukkan bahwa untuk mencapai goals yang telah dibuat, pemilik harus memproduksi 180 Dimsum Ayam, 60 Dimsum Ayam Cabai, 87 Dimsum Jamur, dan 33 Dimsum Udang. Hasil tersebut menunjukkan bahwa metode MOGP menghasilkan perencanaan produksi yang lebih menguntungkan dengan yang diterapkan sebelumnya di UMKM Dapur Bocil. Technological developments and the problems of the pandemic make it difficult to get a job. This triggers people to open a business. With developments like this, it can lead to competition in the industrial world. This study aims to apply the multi-objective goal programming (MOGP) method in the production planning of UMKM Dapur Bocil. Production planning is an important thing and the first step that needs to be done by every business. Production planning relates to the amount of production, timeliness of completion, sales revenue, and minimum costs. The MOGP method can be used to solve problems that contain one or more goals (goals). The application for this production planning uses POM-QM software. In designing the production of UMKM Dapur Bocil, 4 decision variables are used, namely Chicken Dimsum, Chili Chicken Dimsum, Mushroom Dimsum, and Shrimp Dimsum and 7 goals to be achieved. Based on the results of the POM-QM output, it shows that to achieve the goals that have been made, the owner must produce 180 Chicken Dimsum, 60 Chili Chicken Dimsum, 87 Mushroom Dimsum, and 33 Shrimp Dimsum. These results indicate that the MOGP method produces production planning that is more profitable than previously applied in UMKM Dapur Bocil.
PERBANDINGAN METODE ELMAN RECURRENT DAN RADIAL BASIS FUNCTION UNTUK PERAMALAN WISATAWAN Istiqomah Ambarwati; Scolastika Mariani
Unnes Journal of Mathematics Vol 11 No 2 (2022)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v11i2.61066

Abstract

Elman Recurent Neural Network (ERNN) adalah model jaringan syaraf yang memiliki minimal satu feedback loop, sedangkan Radial Basis Function Neural Network (RBFNN) adalah model JST yang mentransformasikan input secara nonlinier menggunakan fungsi aktivasi Gaussian pada lapisan tersembunyi sebelum diproses secara linier di lapisan output. Tujuan penelitian untuk memperoleh model terbaik dari ERNN dan RBFNN dengan fungsi pelatihan terbaik untuk peramalan jumlah kunjungan wisatawan mancanegara di Indonesia. Hasil ERNN model terbaik diperoleh pada arsitektur jaringan 2-15-1 dengan algoritma pelatihan gradient descent with momentum and adaptive learning rate dengan momentum 0,2 , learning rate 0,1 , nilai MSE dan MAPE pengujian sebesar 1,70E+10 dan 13,5869%, serta akurasi jaringan sebesar 86,4131%. Sedangkan hasil peramalan menggunakan RBFNN diperoleh model terbaik pada arsitektur jaringan 2-8-1 dengan algoritma pelatihan gradient descent with momentum and adaptive learning rate atau gradient descent with momentum dengan spread 1. Nilai MSE dan MAPE pengujian sebesar 1,26E-02 dan 6,7043%, serta akurasi jaringan sebesar 93,2957%. Model terbaik untuk peramalan jumlah kunjungan wisman adalah RBFNN (2-8-1). Elman Recurent Neural Network (ERNN) is a neural network model that has at least one feedback loop, while Radial Basis Function Neural Network (RBFNN) is an ANN model that transforms input nonlinearly using a Gaussian activation function in the hidden layer before being processed linearly in the output layer. The purpose of the study was to obtain the best model from ERNN and RBFNN with the best training fuction for forecasting the number of foreign tourist arrivals in Indonesia. The best ERNN model results are obtained on a 2-15-1 network architecture with a gradient descent with momentum and adaptive learning rate training algorithm with a momentum of 0.2, learning rate of 0.1, MSE and MAPE testing values 1.70E+10 and 13,5869%, and network accuracy of 86,4131%. While the results of forecasting using RBFNN obtained the best model on the network architecture 2-8-1 with a training algorithm gradient descent with momentum and adaptive learning rate or gradient descent with momentum with a spread of 1. The MSE and MAPE testing values 1.26E-02 and 6, 7043%, and network accuracy of 93.2957%. The best model for forecasting the number of foreign tourists visiting is RBFNN (2-8-1).
METODE GENETIC ALGORITHM - LONG SHORT-TERM MEMORY PADA PERAMALAN HARGA SAHAM Lathifatul Azizah; YL Sukestiyarno
Unnes Journal of Mathematics Vol 11 No 2 (2022)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v11i2.61067

Abstract

Pelatihan dan keberhasilan algoritma deep learning sangat dipengaruhi terhadap pemilihan hyperparameter yang tepat. Dalam penelitian ini akan dilakukan hybrid antara Genetic Algorithm dan Long Short-Term Memory untuk mencari model yang cocok dalam memprediksi harga saham Bank Rakyat Indonesia Tbk. Hybrid LSTM yang diintegrasikan dengan GA untuk menemukan window size, epoch, dan jumlah unit LSTM. Pemilihan algoritma untuk pengoptimalan menggunakan optimizers untuk mendapatkan model terbaik sehingga dapat ditemukan hyperparameter terbaik untuk peramalan data time series. Dari metode Genetic Algorithm – Long Short-Term Memory yang telah diterapkan menghasilkan, bahwa metode tersebut memiliki tingkat akurasi yang baik dengan nilai MAPE di bawah 10% di setiap optimizer yang digunakan. Tingkat kesalahan yang dihasilkan cukup rendah dengan nilai RMSE 93,03 sampai dengan 94,40 saat training dan testing. Kemudian hyperprameter terpilih yang dapat digunakan yaitu epoch sebesar 24, dengan neurons [4, 5, 2] dan window size 2, serta optimizer Adam. The training and success of deep learning algorithms is strongly influenced by the selection of the right hyperparameters. In this research, a hybrid between Genetic Algorithm and Long Short-Term Memory will be carried out to find a suitable model in predicting the stock price of Bank Rakyat Indonesia Tbk. Hybrid LSTM integrated with GA to find window size, epoch, and number of LSTM units. Algorithm selection for optimization uses optimizers to get the best model so that the best hyperparameters can be found for forecasting time series data. From the Genetic Algorithm – Long Short-Term Memory method that has been applied, it shows that this method has a good level of accuracy with MAPE values ​​below 10% in each optimizer used. The resulting error rate is quite low with an RMSE value of 93.03 to 94.40 during training and testing. Then the selected hyperparameter that can be used is epoch of 24, neurons [4, 5, 2], window size 2, and optimizer Adam.
Prediction of final grade in linear algebra course with multiple linear regression approach Andika Ellena Saufika Hakim Maharani; Siti Soraya; Gilang Primajati; Habib Ratu Perwira Negara; Ahmad Ahmad
Unnes Journal of Mathematics Vol 12 No 1 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v12i01.67448

Abstract

This article discusses the analysis of the final grade of Linear Algebra course with multiple linear regression approach. The study was conducted by collecting data on attendance, daily grades, and final grades from students of the Bumigora University Computer Science Program who took Linear Algebra courses in the odd semester of 2022/2023. Collected data were analyzed using multiple linear regression techniques. The purpose of this study is to determine the relationship between the variables that have a significant effect on student’s final grade and how to predict these variables using multiple linear regression models. The results of the analysis show that both independent variables, namely attendance and daily grades, have a significant impact on the dependent variable, namely student's final grade, with a significance value less than 0.05. The resulting multiple linear regression model can also be used to predict student’s final grade with an accuracy of 70.4%. Furthermore, the results of this analysis also show that daily grades has a greater influence than attendances in predicting final grades. The results of this study can provide useful information for lecturers in improving teaching and for students to improve their performance in the course.
Completion of CVRP model using sweep algorithm and guided local search algorithm for route optimization (case study: PT. Sumber Berkah Farmasi) Fajrie Novardhan Winadi
Unnes Journal of Mathematics Vol 12 No 1 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v12i1.65571

Abstract

Optimizing distribution costs can be done in various ways, one of these methods is determining the distribution route to be the optimal route. The optimal route has many indicators, one of these indicators is having the shortest route or the shortest distance traveled. Problems with optimizing routes and distribution costs occur at PT Sumber Berkah Farmasi. PT SBF is a sub-distributor of medicines to pharmacies in Central Java. The purpose of this research is to optimize distribution routes of PT SBF. Route optimization is carried out using the Sweep Algorithm which is calculated manually, followed by a comparison with the use of the Guided Local Search Algorithm from the Google OR-Tools library. The application of this algorithm requires data obtained from interviews, observations, and calculations using Google Maps to determine the distances between pharmacies. From the results of this study it can be concluded that the Guided Local Search Algorithm can generate route savings of 82 km or 23% and distribution cost savings of Rp. 27,306 or 26% of the route and initial distribution costs by the company. Meanwhile, the Sweep Algorithm resulted in route savings of 20 km or 5.6% and cost savings of Rp. 6,660 or 5,7% of the route and initial distribution costs by the company. Thus it can be concluded that the use of the Guided Local Search Algorithm can be used as an alternative in finding the optimal route for PT. Sumber Berkah Farmasi.
Optimizing the vehicle routing problem using the saving matrix method for LPG gas cylinder distribution routes PSO (case study : PT. Sukma Abadi in Cilacap Regency) Retno Ambar Fiyanti
Unnes Journal of Mathematics Vol 12 No 1 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v12i1.64545

Abstract

This study aims to determine the distribution route using the saving matrix method which will then be sorted using the nearest neighbor, nearest insert, and farthest insert methods. Sorting routes using the nearest neighbor, nearest insert, and farthest insert methods is calculated manually, then the effectiveness of using the three sorting methods is compared. Data collection was carried out by direct observation and interviews and using Google maps to find the distance from the depot to the base. From this study, it was found that the nearest neighbor and nearest insert method produced the same results and were more effective than the farthset insert method. The nearest neighbor or nearest insert method can optimize the distance by 16.46% and distribution costs can be reduced by 2.42% from conditions without optimization calculations. Thus it can be concluded that the nearest neighbor or nearest insert method can be used as an alternative for determining the distribution route for LPG gas cylinders by PT. Sukma Abadi, Cilacap Regency.
Application of fuzzy linear programming method in production optimization of Batik Tulis Lasem Sumber Rejeki Rif'atul Alawiyah; Isnarto Isnarto
Unnes Journal of Mathematics Vol 12 No 1 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v12i1.64970

Abstract

Production optimization is a way to regulate the use of company-owned resources. This study aims to determine: 1) Application of the Fuzzy Linear Programming method in determining the optimization of the production of Batik Tulis Lasem Sumber Rejeki. 2) Comparison of the results of the Fuzzy Linear Programming method and the original production in determining the optimization of the production of the company. 3) The response of the owner of Batik Tulis Lasem Sumber Rejeki related to research results. Data collection was carried out by observation and interviews with the owner. Furthermore, from the data, the constraint function and objective function are determined. The results of the settlement using Fuzzy Linear Programming (λ=0.51) get a maximum profit of IDR 10,400,000 if the number of one-color prime cotton hand-written batik produced is 83 sheets, one-color prime cotton hand-written batik is 15 sheets, two-color prime cotton hand-written batik 138 sheets, 12 sheets of two-color primis cotton written batik, 17 sheets of three-color prime cotton batik, and 4 sheets of three-color primis cotton written batik. Calculations made by the owner obtained a maximum profit of IDR 9,430,000 while using the Fuzzy Linear Programming method the maximum profit obtained was IDR 10,400,000..
Optimization of transportation problem using solver and production profit with interior point algorithm (case study at Jenang Menara Kudus) Rike Maulida
Unnes Journal of Mathematics Vol 12 No 1 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v12i1.67106

Abstract

Optimization of transportation problems and production profits is a goal to be achieved in this research. For the transportation problem, that is minimizing the cost of distributing goods from the place of origin to the destination with the help of the Solver program application, while for the production problem, that is maximizing profits with the Interior Point Algorithm with the help of the Matlab program application. Data collection was carried out by means of observation and direct interviews with the managers of the Menara Kudus Jenang Factory. In this study there were 8 types of processed Jenang Menara Kudus, namely Jenang Exclusive, Jenang Red Combination, Jenang Combination Black, Jenang Combination 500 gr, Jenang Large Mica Packaging, Jenang Small Mica Packaging, Jenang Refill 1 kg, Jenang Refill 0.5 kg. Observational data processed in this research are transportation cost data for each destination city, production capacity, production demand, raw material inventory, production composition, and production profit for each package. The results obtained in the optimization of transportation problems assisted by the Solver program to minimize transportation costs amounted to Rp. 1,433,500.00 while the transportation costs incurred by the Menara Kudus Jenang Factory before using the Solver program were Rp. 1,492,000.00. The calculation difference is IDR 58,500.00. The composition of the amount of each type of processed Jenang Menara Kudus is 800 packages of Jenang Exclusive, 600 packages of Jenang Combination Red, 460 packages Jenang Combination Black, 908 packages Jenang Combination 50 gr, 1000 packages Jenang Large Mika Packaging, 2540 packages Jenang Small Mika Packaging, 1,300 packages of 1 kg Jenang Refill, and 960 packages of 0.5 kg Jenang Refill, with the remaining raw materials being 38 kg of glutinous rice flour, 0 kg of palm sugar, 22 kg of granulated sugar, and 52 coconuts. Calculations in optimizing profits for Jenang Menara Kudus with the Interior Point Algorithm assisted by the application of the Matlab program amounted to Rp. 18,512,000.00 and calculations for maximizing profits carried out by the Jenang Menara Kudus Factory amounted to Rp. 18,420,000.00. So that it can be seen the difference before and after using the Matlab-assisted Interior Point Algorithm of IDR 92,000.00..
Comparison analysis of Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) methods on user reviews of the Google Maps application Riki Afiyanto Pratama; Scolastika Mariani
Unnes Journal of Mathematics Vol 12 No 1 (2023)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v12i1.67217

Abstract

Data is a collection of facts, where these facts can provide an overview of a situation. Data can be stored in various ways, for example, data about applications that are stored in a database server and have various types such as text, image numbers, and others. Google Maps is a free service from Google with functions like world maps which can be accessed using a browser or application. On the Google Play Store page, there are reviews and information about a product or application that is stored in the form of text, score, or something else. This research conducts sentiment analysis on user reviews of the Google Maps application in the Google Play Store. Sentiment analysis is carried out as a tool for classifying or categorizing information in the form of text into positive and negative categories or labels so that application developers can find out the advantages and disadvantages of their applications. The process for carrying out sentiment analysis is like doing text preprocessing and word weighting which aims to give value or weight to the words contained in a document. Then the classification method used in this research is the Naïve Bayes Classifier and K-Nearest Neighbor, then it will be visualized with a word cloud. The accuracy for the Naïve Bayes Classifier is 80.6%, while for the K-Nearest Neighbor it is 78.8%. Based on these results indicate that the Naïve Bayes Classifier method is better in classifying. Meanwhile, in visualizing with the word cloud, words that have negative labels, such as "point", "please", "accuracy", "location", and so on are obtained. Then for those with positive labels, they include "helpful", "good", "okay", "accurate", and so on.

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