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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Pangabdhi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ComTech: Computer, Mathematics and Engineering Applications Jurnal ABDINUS : Jurnal Pengabdian Nusantara G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) Jurnal Statistika dan Aplikasinya Elastisitas Jurnal Ekonomi Pembangunan Journal of Mathematics Education and Science Indonesian Community Journal Eksponensial Limits: Journal of Mathematics and Its Applications
Elly Pusporani
Universitas Airlangga
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Pengembangan Wisata Grojogan Londo melalui Lagu Kreasi sebagai Media Promosi Mardianto, M. Fariz Fadillah; Pusporani, Elly; Amelia, Dita; Rani, Lina Nugraha; Siregar, Naufal Ramadhan Al Akhwal; Simamora, Antonio Nikolas Manuel Bonar
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 8 No 2 (2024): Volume 8 Nomor 2 Tahun 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v8i2.19896

Abstract

Tourism villages are a form of implementation of community-based and sustainable tourism development. Grojogan Londo Tourism has its signature song entitled "Grojogan Londo" by Sodiq New Monata and has been watched 6,230 times on YouTube. In addition, there is a song composed by the Statistics Assisted Village Program Team at the University of Airlangga entitled “Ayang Londo”. Therefore, this study aims to measure respondents' assessment of the two Grojogan Londo Tourism promotional songs. Data was obtained through an online survey using Google Forms with 117 respondents and then analyzed descriptively, correlationally and comparatively. The method used is descriptive statistics, Rank Spearman correlation test, and Mann-Whitney test. The data support the hypothesis that the respondents' interest in the songs "Grojogan Londo" and "Ayang Londo" is different. In addition, 41.9% of respondents believe that music-based promotional media can influence tourists' interest in traveling to certain areas. Therefore, Grojogan Londo tourism promotion songs must be promoted more aggressively through various social media channels which are currently popular among the public, especially the millennial generation. This research contributes to expanding the literature and increasing knowledge about marketing media through songs and their influence on tourist interest which needs to be done as an effort to evaluate tourism development.
Peningkatan Kualitas UMKM Wonorejo Madiun Sebagai Dasar Pengembangan Wisata Budaya Mardianto, M. Fariz Fadillah; Amelia, Dita; Pusporani, Elly; Rani, Lina Nugraha; Siregar, Naufal Ramadhan Al Akhwal; Nurrohmah, Zidni ‘Ilmatun; Previan, Anggara Teguh; Putri, Ferdiana Friska Rahmana
Jurnal Ilmiah Pangabdhi Vol 10, No 2: Oktober 2024
Publisher : LPPM Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pangabdhi.v10i2.24394

Abstract

Wonorejo Village is one of the villages in Madiun Regency which has natural and cultural potential that can be integrated into local economic development through MSMEs and the tourism sector. MSME empowerment activities provide opportunities for local communities to create jobs and achieve Sustainable Development Goals (SDGs) in maintaining economic stability. By focusing on improving the quality of MSMEs and through appropriate mentoring activities, this village can achieve inclusive and sustainable economic development. This activity aims to provide counseling and assistance regarding digitalization of MSME marketing integrated with the potential of Wonorejo Village as a tourist village for Wonorejo Temple tourism. The method of community service consists of several stages. First, provide assistance regarding product design and marketing aspects. Second, regarding training in digitalizing MSMEs from creating social media for MSME products. Third, in the form of training in obtaining permits, business legality and halal certification. Fourth, in the form of mentoring and evaluation activities during the mentoring and training process. The results of this community service, namely counseling and assistance to MSMEs in Wonorejo Village, have a positive effect on increasing the products of MSMEs in Wonorejo Village. Recommendations and sustainability that can be given are increasing the role of the Wonorejo Village community in developing tourism because of the resources and quality of the village's MSMEs so that they are able to encourage success as a tourist village from the community service activities that have been carried out.
Prediction Analysis of Jakarta Composite Index Movement Using Support Vector Regression Method Marcelena Vicky Galena; Sediono Sediono; M. Fariz Fadillah Mardianto; Elly Pusporani
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.5879

Abstract

The JCI is an important indicator that reflects the performance of the Indonesian stock market. In recent times, the JCI has faced significant fluctuations due to complex factors, including global economic conditions and market sentiment, which make predicting its movements challenging. Good prediction is needed to support market stability and sustainable economic development as per SDGs point 8. This study applies a modern nonparametric regression method, namely Support Vector Regression (SVR), to predict a dataset in the form of weekly JCI data from the period April 2022 to October 2024 obtained from the investing.com website. The analysis shows that the SVR model with RBF kernel function provides the best performance, with MAPE of 1.43%, RMSE of 121.6196, and MAE of 104.65. The findings also reveal that the fluctuation pattern of the JCI cannot be fully explained based solely on historical data. External variables, such as global economic conditions and market sentiment, have a significant influence on the prediction results. Therefore, the SVR method can be utilized to optimize portfolio allocation based on weekly JCI predictions. In addition, the results of this study provide guidance for policymakers in designing proactive economic policies to mitigate market volatility and increase investor confidence.
Sentiment Analysis of Suicide on X Using Support Vector Machine and Naive Bayes Classifier Algorithms Mardianto, M. Fariz Fadillah; Pratama, Bagas Shata; Audilla, Marfa; Pusporani, Elly
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23742

Abstract

Background: The World Health Organization (WHO) defines health as a state of physical, mental, and social well-being, not just the absence of disease. Mental health, essential for overall well-being, is often neglected, leading to disorders like depression, a major cause of suicide. In Indonesia, suicide cases have surged, with 971 reported from January to October 2023. Objective: This study aims to analyze public sentiment regarding the rise in suicide cases in Indonesia using sentiment analysis methods, specifically Support Vector Machine (SVM) and Naive Bayes Classifier (NBC). The findings are expected to raise public awareness and provide policy recommendations to support mental health initiatives. Methods: One method used to understand public perception regarding the issue of suicide is text mining. This research employs text mining techniques with the Support Vector Machine (SVM) and Naive Bayes Classifier algorithms to analyze public sentiment related to suicide cases in Indonesia. Data was collected from tweets on social media platform X using crawling methods with snscrape and Python, totaling 1,175 tweets. Results: The results indicate that the Linear SVM model achieved higher accuracy than Naive Bayes in classifying tweet sentiments, with an accuracy rate of 80%. Conclusion: The SVM algorithm with a linear kernel achieved 80% accuracy and an identical ROC-AUC score. Word cloud visualization highlighted terms like "kill," "self," "depression," and "stress" as key negative sentiments. This study aims to raise public awareness and support better mental health policies in Indonesia.
Peningkatan Kompetensi dan Profesionalitas Warga Desa Tambaksawah Sidoarjo dalam Mengoperasikan Microsoft Excel Untuk Menuju Desa Yang Unggul dan Produktif Jannah, Sa'idah Zahrotul; Pusporani, Elly; Ana, Elly; Syahzaqi, Idrus; Makkiyah, Afifah Nur; Ramadhanti, Aulia; Ariyani, Azizah Dewi; Ramadhani, Azzah Nazhifa Wina; Trisa, Nadya Lovita Hana; Carista, Nashwa; Naura, Sheila Sevira Asteriska
I-Com: Indonesian Community Journal Vol 5 No 1 (2025): I-Com: Indonesian Community Journal (Maret 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i1.6272

Abstract

Tambaksawah Village, Sidoarjo, is an industrial village that has the potential to improve the quality of life. However, this village faces obstacles, such as the limited skills of village officials using Microsoft Excel and the minimal application of technology in village institutions. Community service programs using Microsoft Excel are useful in supporting village administration efficiency and encouraging productivity. The methods include on-site training, group mentoring, international certification, and certification exams. Training followed by mentoring is carried out for one month. The evaluation results show an increase in the average score from 50.4 to 60.16, which shows an increase the understanding of participants. Apart from 7 participants, only 5 participants succeeded in obtaining official certificates. This activity succeeded in improving participants' skills in operating Microsoft Excel, so it is hoped that this can be the first step to advancing technological capacity and can help in developing Tambaksawah Village.
Optimizing Brain Tumor MRI Classification with Transfer Learning: A Performance Comparison of Pre-Trained CNN Models Mardianto, M. Fariz Fadillah; Pusporani, Elly; Salsabila, Fatiha Nadia; Nitasari, Alfi Nur; Lu’lu’a, Na’imatul
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87377

Abstract

This study aims to classify brain MRI images into several types of brain tumors using the Convolutional Neural Network (CNN) approach with transfer learning. This method has the advantage of processing complex images in a shorter time than conventional CNN approaches. In this study, the data used was a public database from Kaggle, which consisted of four categories: glioma, meningioma, no tumor, and pituitary. Before entering the transfer learning process, data augmentation is carried out on the training data. Four pre-trained CNN models were used: VGG19, ResNet50, InceptionV3, and DenseNet121. The four models compared their ability to classify MRI images with several evaluation metrics: accuracy, precision, recall, and F1 score. The results of the performance comparison of the four pre-trained models show that the ResNet50 is the best model, with an accuracy of 98%. Meanwhile, VGG19, DenseNet121, and InceptionV3 produce 97%, 96%, and 95% accuracy, respectively. The ResNet50 architecture demonstrated superior performance in brain tumor classification, achieving 98% accuracy. It can be attributed to its residual learning structure, which efficiently manages complex MRI features.  Further research should concentrate on larger, more diverse datasets and advanced preprocessing techniques to enhance model generalizability.
Prediction of Nike’s Stock Price Based on the Best Time Series Modeling Sari, Adma Novita; Zuleika, Talitha; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21737

Abstract

Nike is one of the world's largest shoe, clothing, and sports equipment companies. The more modern the development of the era, the more diverse the fashion. Of course, investors can consider this when deciding whether to invest in Nike's brand shares. Stock prices constantly fluctuate up and down, so investors need to implement strategies to minimize losses in investing to achieve economic growth. This supports the Sustainable Development Goals (SDGs) in point 8 regarding the importance of sustainable economic growth and investment in infrastructure development to improve economic welfare. Investors can minimize losses by predicting or forecasting stock prices. Stock prices can be analyzed using specific methods. The update that will be brought in this study is the Nike brand stock price prediction for the 2020-2024 period using the best model from the time series method comparison conducted using classical nonparametric, which consists of the kernel estimator method and the Fourier series estimator method and modern nonparametric using the Support Vector Regression (SVR) method. Based on the analysis method, the best method is selected through the minimum MAPE value. A comparison of the results of Nike brand stock price predictions using several methods shows that the MAPE value of the Nike brand stock price data analysis is the minimum obtained using the kernel estimator approach, which is 1.564%. Thus, the kernel estimator approach predicts the Nike brand stock price much better. Predictions using the best methods can be recommendations and evaluations for economic actors to prepare better economic planning.
Application of Support Vector Regression in Time Series Analysis of Dior Stock Prices Sari, Adma Novita; Zuleika, Talitha; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.51-60

Abstract

Christian Dior (Dior) is a multinational company focusing on luxury goods, including fashion products, cosmetics, and accessories. In 2020–2024, Dior's share price will experience significant fluctuations influenced by financial performance, global market trends, etc. These fluctuations require investors to implement appropriate strategies to minimize the risk of losses and support sustainable economic growth. This step aligns with goal 8 of the Sustainable Development Goals (SDGs), emphasizing the importance of sustainable economic growth through investment and infrastructure development for economic prosperity. One of the effective methods for modeling and predicting stock prices is Support Vector Regression (SVR). By applying SVR using the Radial Basis Function (RBF) kernel, this study shows that the model can generate predictions with a MAPE value of 2.5864% on the test data. The SVR method is expected to provide accurate predictions, making it a helpful tool for investors and market analysts to make better investment decisions.
FOREIGN EXCHANGE RATE PREDICTION OF INDONESIA'S LARGEST TRADING PARTNER BASED ON VECTOR ERROR CORRECTION MODEL Mardianto, M. Fariz Fadillah; Farizi, Muhammad Fikry Al; Permana, Made Riyo Ary; Zah, Alfian Iqbal; Pusporani, Elly
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1705-1718

Abstract

Foreign exchange rates from the currencies of trading partners are a critical element in the development of Indonesia's economic landscape. As an active country in international trade, Indonesia's economic health is highly dependent on trade partnerships, movements, and interactions of foreign exchange rates from Indonesia's main trading partners. To achieve economic stability, Bank Indonesia intervenes in the foreign exchange market to keep the Rupiah exchange rate within a reasonable range. Indonesia is committed to achieving several points in the Sustainable Development Goals (SDGs), such as point 17, which emphasizes partnerships, and point 8, which underlines inclusive and sustainable economic growth. This commitment is an important factor in Indonesia's economic development. Therefore, it is necessary to predict the exchange rate value of Indonesia's largest trading partners considering these SDG aspects. In this study, the Vector Error Correction Model (VECM) was used to predict the foreign exchange rate of Indonesia's largest trading partners. The data used in this study is secondary data obtained from the investing.com webpage, comprising weekly data from January 2021 to November 2023. The foreign exchange rates of Indonesia's largest trading partners have a cointegration relationship, indicating long-term relationships and similarities in movements. The best model identified is VECM (1), with a very accurate MAPE value of 3.29%. The Impulse Response Function (IRF) analysis shows that the Chinese Yuan responds variably to different currencies, stabilizing over time. Variance Decomposition reveals that short-term fluctuations in the Chinese Yuan are primarily influenced by itself (87.89%) and significantly by the Singapore Dollar, South Korean Won, and Taiwan Dollar. The Granger Causality Test indicates that the Philippine Peso influences 11 other exchange rates, refining the VECM model and improving prediction accuracy. Indonesia is expected to build economic collaborations that can help achieve economic stability.
PREDICTION OF UNIT VALUE INDEX OF EXPORTS OF SITC 897 JEWELRY AND PRECIOUS GOODS GROUP IN INDONESIA Koesnadi, Grace Lucyana; Pratama, Bagas Shata; Ain, Dzuria Hilma Qurotu; Pusporani, Elly; Mardianto, M. Fariz Fadillah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2247-2262

Abstract

Export is an international trade activity that plays an important role in the economic progress in Indonesia. One of Indonesia's leading commodities that dominate the export market is jewelry. In export activities, the export unit value index is an important component that serves to describe the development of export commodity prices. This unit value index always changes every time and fluctuates. This research conducts a comparative analysis of the performance of parametric method, non-parametric method, and machine learning, specifically, ARIMA, Fourier series estimator, and Support Vector Regression (SVR). This study aims to evaluate the effectiveness of various methods in improving prediction accuracy for the unit value index of the SITC code 897 in Indonesia. The research data used is secondary data including monthly export unit value index data with SITC code 897 in Indonesia obtained from the Central Bureau of Statistics. The data divided into 90% training data and 10% testing data. The methods used in this analysis are ARIMA, Fourier series estimator, and SVR. The best model obtained from each method is ARIMA (1,1,1) with MAPE of 10.92%, Fourier series estimator with MAPE of 8.47%, and an SVR RBF kernel function with MAPE of 3.73%. The results of this study obtained the best method for predicting the unit value index of SITC code 897 is SVR with an RMSE value of 8.288 and very good prediction accuracy.
Co-Authors Ain, Dzuria Hilma Qurotu Alexandra, Victoria Anggia Alfredi Yoani Ana, Elly Ariyani, Azizah Dewi Audilla, Marfa Ayuning Dwis Cahyasari Ayuning Dwis Cahyasari Carista, Nashwa Christopher Andreas Diana Nurlaily Dita Amelia Dwitya, Shabrina Nareswari Elly Ana Fadillah Mardianto, M. Fariz Fajrina, Sofia Andika Nur Farida Nur Hayati Farizi, Muhammad Fikry Al Fauzi, Doni Muhammad Fidela Sahda Ilona Ramadhina Fitri, Marfa Audilla Fitriana Nur Afifa Grace Lucyana Koesnadi Haq, Affan Fayzul I Kadek Pasek Kusuma Adi Putra Ibrahim, Rahmat Agung Idrus Syahzaqi Indrasta, Irma Ayu Irhamah - Ismi, Ferissa Maulida Jannah, Sa'idah Zahrotul Karima, Sasy Okti Koesnadi, Grace Lucyana Lu'lu'a, Na'imatul Lu’lu’a, Na’imatul M. Fariz Fadillah Mardianto Makkiyah, Afifah Nur Marcel Laverda Subiyanto Marcelena Vicky Galena Mardianto, M. Fariz Fadillah Mardianto, Muhammad Fariz Fadillah Maula, Sugha Faiz Al Maulana, Bagas Melati, Adinda Tries Nabila Rahma Na’ifa, Ariza Naura, Sheila Sevira Asteriska Nitasari, Alfi Nur Nurrohmah, Zidni ‘Ilmatun Permana, Made Riyo Ary Pratama, Bagas Shata Previan, Anggara Teguh Putri, Farah Fauziah Putri, Ferdiana Friska Rahmana Putri, Refa Berliana Ramadhani, Azzah Nazhifa Wina Ramadhanti, Aulia Rani, Lina Nugraha Rasyid, Mochamad Riyanto, Aufa Muhammad Yogi Rohayah, Dewi Sa'idah Zahrotul Jannah Salsabila, Ailsa Shafa Salsabila, Fatiha Nadia Sangadji, Nurul Fajriah Deswani Sari, Adma Novita Sari, Adma Novita Sediono, Sediono Setiawan, Nicoletta Almira Dyah Simamora, Antonio Nikolas Manuel Bonar Siregar, Naufal Ramadhan Al Akhwal Siti Maghfirotul Ulyah Siti Qomariyah Steven Soewignjo Toha Saifudin Trisa, Nadya Lovita Hana Tsabita Amalia Shofa, Nayla Valida, Hanny Victoria, Deby Wieldyanisa, Ezha Easyfa Yuliati, Intan Yuniar, Muhammad Alvito Dzaky Putra Zah, Alfian Iqbal Zuleika, Talitha Zuleika, Talitha