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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jiko (Jurnal Informatika dan komputer) JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Pendidikan dan Konseling bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Advances in Data and Information Systems Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Studies in Learning and Teaching Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Bisnis Indonesia International Journal of Community Service International Journal of Data Science, Engineering, and Analytics (IJDASEA) Al Khidma: Jurnal Pengabdian Masyarakat Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Inkofar Kreatifitas: Jurnal Ilmiah Pendidikan Islam Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial Baitul Hikmah: Jurnal Ilmiah Keislaman STATISTIKA Kohesi: Jurnal Sains dan Teknologi Information Technology International Journal (ITIJ) Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia RAGAM: Journal of Statistics and Its Application Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Trimono, Trimono
Data Science Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
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Journal : Nusantara Science and Technology Proceedings

 1 
Stock Price Modeling with Geometric Brownian Motion and Value with Risk PT Ciputra Development TBK Amri Muhaimin; Trimono Trimono
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3329

Abstract

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing the company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns to Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016, until January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, the Value Risk Method of Monte Carlo Simulation with ? = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level ? = 5%, the Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.
Application of Google Data Studio for Data Visualization at SMK Tunas Bangsa Malang Trimono; Andreas Nugroho Sihananto; Muhammad Muharrom Al Haromainy; Edi Sugiyanto; Farkhan
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.33107

Abstract

The Department of Office Automation and Governance (OTKP) is one of the Vocational High School’s majors in Indonesia that focuses on office operations and information processing. One of the popular skill in information processing lately is data processing and visualization. In response of this trend, we propose a Google Data Studio training for Tunas Bangsa Vocational High School’s students from OTKP Majors. Google Data Studio is a free data analysis tool from Google. With this tool, users can not only display data with attractive and easy-to-understand visuals but also can process data from various sources on one worksheet. This service is mostly free, not limited to Google services such as Google Sheets but can be linked to other platforms, such as websites, applications or third party services. By the end of the training all participants have been able to use Google Data Studio for data visualization needed for offices in general.
Modelling of Return of S&P 500 Using the Non Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) Model Trimono, Trimono; Damaliana, Aviolla Terza; Putri, Irma Amanda
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4110

Abstract

ARIMA Box-Jenkins is one of the most popular forecasting methods. ARIMA modeling requires a non-heteroskedastic care that shows constant residual variants. Awake, meaning residual residue from heteroscedastic ARIMA modeling (not constant). To overcome the problem of residual heteroskedasticity ARIMA used modeling volatility that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH is used to model the ARIMA residual variant which means symmetric. Some financial data has an asymmetric nature caused by the influence of good news and bad news. To accommodate these asymmetric properties, we use the Non-Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) volatility model which is the development of the GARCH model. This research applies NGARCH model using S & P 500 share price index data from January 1, 2019, until July 31, 2023 during active day (Monday-Friday). The purpose of this study, to find the best model NGARCH. The best model generated for S & P 500 stock price index data is ARIMA (1,0,1) NGARCH (1,1) because it has small AIC.
Modelling of Return of S&P 500 Using the Non Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) Model Trimono Trimono; Aviolla Terza Damaliana; Irma Amanda Putri
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4110

Abstract

ARIMA Box-Jenkins is one of the most popular forecasting methods. ARIMA modeling requires a non-heteroskedastic care that shows constant residual variants. Awake, meaning residual residue from heteroscedastic ARIMA modeling (not constant). To overcome the problem of residual heteroskedasticity ARIMA used modeling volatility that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH is used to model the ARIMA residual variant which means symmetric. Some financial data has an asymmetric nature caused by the influence of good news and bad news. To accommodate these asymmetric properties, we use the Non-Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) volatility model which is the development of the GARCH model. This research applies NGARCH model using S & P 500 share price index data from January 1, 2019, until July 31, 2023 during active day (Monday-Friday). The purpose of this study, to find the best model NGARCH. The best model generated for S & P 500 stock price index data is ARIMA (1,0,1) NGARCH (1,1) because it has small AIC.
Forecasting The Number of Traffic Accidents in Purbalingga Regency on 2023 Using Time Series Model Trimono; Amri Muhaimin; Nabilah Selayanti
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4168

Abstract

Accident data from Satlantas Purbalingga Regency shows that in 2022 there is an increase in the number of traffic accidents in the Purbalingga Regency. In the future, the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant, residual independent test, residual normality test, and the smallest Mean Square Error value. According to data forecasting results the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So the necessary action from the relevant agencies to cope with the increasing number of traffic accidents in the Purbalingga Regency.
Stock Price Modeling with Geometric Brownian Motion and Value with Risk PT Ciputra Development TBK Amri Muhaimin; Trimono Trimono
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3329

Abstract

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing the company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns to Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016, until January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, the Value Risk Method of Monte Carlo Simulation with ? = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level ? = 5%, the Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.
Application of Google Data Studio for Data Visualization at SMK Tunas Bangsa Malang Trimono; Andreas Nugroho Sihananto; Muhammad Muharrom Al Haromainy; Edi Sugiyanto; Farkhan
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.33107

Abstract

The Department of Office Automation and Governance (OTKP) is one of the Vocational High School’s majors in Indonesia that focuses on office operations and information processing. One of the popular skill in information processing lately is data processing and visualization. In response of this trend, we propose a Google Data Studio training for Tunas Bangsa Vocational High School’s students from OTKP Majors. Google Data Studio is a free data analysis tool from Google. With this tool, users can not only display data with attractive and easy-to-understand visuals but also can process data from various sources on one worksheet. This service is mostly free, not limited to Google services such as Google Sheets but can be linked to other platforms, such as websites, applications or third party services. By the end of the training all participants have been able to use Google Data Studio for data visualization needed for offices in general.
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Ade Irma Agustian Adiwidyatma, Afdhal Reshanda Afidria, Zulfa Febi Aliya Dasa Pramesthi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Aurelia, Cenditya Ayu Aviolla Terza Damaliana Aviolla Terza Damaliana Awang, Wan Suryani Wan Azni Aisyah Azzahra, Adelia Ramadhina Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Desy Miftachul Ilmi Arifin Putri Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Dinda Putri Arnindi Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Faiz, Mochammad Abudrrochman Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Hendri Prabowo Herlina Herlina Hervrizal, Hervrizal I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Gusti Putu Asto Buditjahjanto Icha Rohmatul Jannah idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Kartika Maulida Hindrayani Kartika Maulida Hindrayani Kartini Kartini Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela M Zufar Irhab S Putra Maharani Ikaningtyas Maruddani, Di Asih Marwani, Arrum Mas'ad Mas'ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Milla Akbarany Baktiar Putri Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Muhammad Nasrudin Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Ningrum, Imelda Widya Ningtiyas, Rona Wulan Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putra, Andrawana Putri, Irma Amanda Putri, Nabila Rizky Amalia Putri, Nevia Desinta Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Ratna Yulistiani Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Riyantoko, Prismahardi Aji Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Saputra, Wahyu Syaifullah Jauharis Sekar Arum Melati Selly Rizkiyah Sihananto, Andreas Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Syukri Syukri Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wardah Ariij Adibah Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widayawati, Eny Widduro, Bagus Widison, Daffin Tanjiro Yuciana Wilandari yuliza, eva Zalfa Assyadida, Azizah