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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 Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) 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
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Journal : international journal of data science engineering and analytics ijdasea

 1 
Geometric Brownian Motion and Value at Risk For Analysis Stock Price Of Bumi Serpong Damai Ltd Trimono Trimono; Di Asih I Maruddani; Prisma Hardi Aji Riyantoko; I Gede Susrama Mas Diyasa
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 1 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1063.683 KB) | DOI: 10.33005/ijdasea.v1i1.3

Abstract

Investment is one of the activities that last actually attractive to the people of Indonesia. One of the most widely traded financial assets in the capital market is stocks. Stock prices frequently experience challenges to predict changes, so they can increase or decrease at any time. One method that can be applied to predict stock prices is GBM. Then, the risk can be measured using the VaR risk measure. The GBM model is determined to be accurate in predicting the stock price of BSDE.JK, with a MAPE value of 5.17%. By using VaR-HS and VaR CFE, the prediction of risk of loss at the 95% confidence level for the period 06/07/21 is -0.0597 and -0.0623
Negative Binomial Time Series Regression – Random Forest Ensemble in Intermittent Data Amri Muhaimin; Prismahardi Aji Riyantoko; Hendri Prabowo; Trimono Trimono
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.85 KB) | DOI: 10.33005/ijdasea.v1i2.10

Abstract

Intermittent dataset is a unique data that will be challenging to forecast. Because the data is containing a lot of zeros. The kind of intermittent data can be sales data and rainfall data. Because both sometimes no data recorded in a certain period. In this research, the model is created to overcome the problem. The approach that is used in this research is the ensemble method. Mostly the intermittent data comes from the Negative Binomial because the variance is over the mean. We use two datasets, which are rainfall and sales data. So, our approach is creating the base model from the time series regression with Negative Binomial based, and then we augmented the base model with a tree-based model which is random forest. Furthermore, we compare the result with the benchmark method which is The Croston method and Single Exponential Smoothing (SES). As the result, our approach can overcome the benchmark based on metric value by 1.79 and 7.18.
Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA (European Automobile Manufacturers Association) Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin; Kartika Maulida Hindrayani; Amri Muhaimin; Trimono
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1292.381 KB) | DOI: 10.33005/ijdasea.v1i2.12

Abstract

Time series is one of method to forecasting the data. The ACEA company has competition with opened the data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater, drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library which develop by Facebook team. We also use the other approach to making the data clean, or the data ready to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that the data not appearing the problem while we tried to forecast. In the other describe, we already get the results using univariate and multivariate Prophet method. The multivariate approach has presented the value of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.
Metric Comparison For Text Classification Amri Muhaimin; Tresna Maulana Fahrudin; Trimono; Prismahardi Aji Riyantoko; Kartika Maulida Hindrayani
Internasional Journal of Data Science, Engineering, and Anaylitics Vol. 2 No. 1 (2022): International Journal of Data Science, Engineering, and Analytics Vol 2, No 1,
Publisher : International Journal of Data Science, Engineering, and Analytics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v2i1.34

Abstract

Text classifications have been popular in recent years. To classify the text, the first step that needs to be done is to convert the text into some value. Some values that can be used, such as Term Frequencies, Inverse Document Frequencies, Term Frequencies – Inverse Document Frequencies, and Frequency of the word itself. This study aims to get which metric value is best in text classification. The method used is Naïve Bayes, Logistic Regression, and Random Forest. The evaluation score that is used is accuracy and Area Under Curve value. It comes out that some metric values produce similar evaluation scores. Another finding is that Random Forest is the best method among others, also the best metric for text classification is Term Frequencies – Inverse Document Frequencies.
Prediction of The Islamic Stock Price Index and Risk of Loss Using The Long Short-Term Memory (LSTM) and Value At Risk (VaR) Taufik, Ikbar Athallah; Trimono, Trimono; Muhaimin, Amri
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 4 No. 01 (2024): International Journal of Data Science, Engineering, and Analytics Vol 4, No 1,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v4i01.16

Abstract

Investment aims to increase the value of capital or earn additional income through asset growth, dividends or profits. One investment instrument that is in demand, especially among the Muslim community, is Islamic stocks, which are in accordance with Islamic principles that focus on a healthy economy. This research is focused on predicting Islamic stock prices using the Long Short-Term Memory (LSTM) method and measuring risk with Value at Risk (VaR) using the Cornish-Fisher Expansion (ECF) method. Stock price data from the food sector (PT Indofood), technology sector (Telkom Indonesia), and construction sector (Indocement) for the period 2018-2023 were analyzed. The results show that the ADAM model provides the best performance with the lowest prediction error rates for INTP and TLKM stocks (around 1.22%, 1.98%, and 1.41%). In addition, the SGD model shows limitations in accurate predictions with an error rate above 12%. VaR analysis reveals a slightly higher level of risk in INTP stocks, with a VaR value of around 2.85% at the 95% confidence level. Meanwhile, TLKM stock shows a lower level of risk, with a VaR of around 2.25% at the same confidence level. An in-depth understanding of the risk and growth characteristics of each stock, as well as the selection of the optimization model, are key in making wise investment decisions.
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Ade Irma Agustian Adelia Adelia, Adelia 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 Asfiani, Ilil Musyarof Aurelia, Cenditya Ayu Aviolla Terza Damaliana 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 Dwi Arman Prasetya Edi Sugiyanto Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Faiz, Mochammad Abudrrochman Faizi, Dandi Nur 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 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 Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Ningrum, Imelda Widya Ningtiyas, Rona Wulan Nova Auliyatul Maulidiyyah Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putra, Andrawana Putri, Irma Amanda Putri, Milla Akbarany Baktiar Putri, Nabila Rizky Amalia Putri, Nevia Desinta Putri, Shafira Amanda Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Ratna Yulistiani Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Riyantoko, Prismahardi Aji Rizkiyah, Selly Rizqin, Indira Zein Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Saputra, Wahyu Syaifullah Jauharis Sekar Arum Melati 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 Wardani, Ajeng Puspa Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widayawati, Eny Widduro, Bagus Widison, Daffin Tanjiro Yuciana Wilandari yuliza, eva Zalfa Assyadida, Azizah