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All Journal Statistika Jurnal Gaussian (JRAMB) Jurnal Riset Akuntansi Mercu Buana INFORMATIKA Jurnal Riset Sistem Informasi
Trimono Trimono
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Other

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PEMODELAN HARGA SAHAM DENGAN GEOMETRIC BROWNIAN MOTION DAN VALUE AT RISK PT CIPUTRA DEVELOPMENT Tbk Trimono Trimono; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.008 KB) | DOI: 10.14710/j.gauss.v6i2.16955

Abstract

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns 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 untuk January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, Value at Risk Method of Monte Carlo Simulation with α = 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 α = 5%, Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.. Keywords : Geometric Brownian Motion, Risk, Value at Risk, Backtesting
Valuasi Harga Saham PT Aneka Tambang Tbk sebagai Peraih IDX Best Blue 2016 Trimono Trimono; Di Asih I Maruddani
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 17, No 1 (2017)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v17i1.2579

Abstract

Menginvestasikan dan untuk membeli saham sebuah perusahaan merupakan salah satu bentuk investasi sektor finansial yang banyak diminati oleh para investor. Keuntungan investasi saham yang diperoleh, dapat dilihat dari nilai return saham. Harga saham adalah faktor utama yang berpengaruh terhadap nilai return saham. Namun, harga saham pada masa yang akan datang sering kali sulit untuk diprediksi. Geometric Brownian Motion (GBM) merupakan metode yang dapat digunakan untuk memprediksi harga saham jika diasumsikan return saham masa lalu berdistribusi normal. Jika dalam return saham masa lalu yang berdistribusi normal terdapat lompatan (jump), maka digunakan metode Jump Diffusion. Setelah diperoleh harga saham prediksi, dapat diukur nilai risiko investasinya. Hasil prediksi harga saham PT Aneka Tambang Tbk  periode 01/12/2016 sampai dengan 31/1/2017 dengan metode GBM, diperoleh nilai MAPE sebesar 11,01%. Berdasarkan nilai skewness dan kurtosis, dalam data return saham ANTM terdapat lompatan, sehingga harga saham ANTM lebih tepat dimodelkan dengan metode Jump Diffusion. Hasil prediksinya diperoleh nilai MAPE sebesar 1,95%. Metode Jump diffusion lebih tepat digunakan untuk prediksi, karena menghasilkan nilai MAPE yang lebih kecil. Untuk mengukur risiko investasi harga saham prediksi yang diperoleh dari model Jump Diffusion, digunakan metode VaR simulasi Monte Carlo dengan tingkat kepercayaan 95%. Dalam jangka waktu 1 hari setelah tanggal 25 Januari 2017 kerugian yang diterima tidak melebihi 5,617%. Berdasarkan uji backtesting, nilai VaR harga saham prediksi dengan metode Jump Diffusion pada taraf signifikansi 5% menghasilkan perhitungan yang akurat, karena tidak ditemukan adanya pelanggaran.Kata Kunci: Geometric Brownian Motion, Jump Diffusion Model, Value at Risk, Backtesting 
Prediksi harga saham PT. Astra agro lestari TBK dengan jump diffusion model Di Asih I Maruddani; Trimono Trimono
(JRAMB) Jurnal Riset Akuntansi Mercu Buana Vol 3, No 1: Mei 2017
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (223.161 KB) | DOI: 10.26486/jramb.v3i1.407

Abstract

Saham merupakan salah satu emiten yang paling banyak diperjualbelikan di pasar modal. Harga saham dan perubahannya merupakan dua indikator yang sering dijadikan bahan pertimbangan oleh para calon investor sebelum memutuskan untuk membeli saham suatu perusahaan. Harga saham hampir selalu mengalami perubahan, dan sulit diperkirakan bagaimana keadaannya pada periode yang akan datang. Terdapat berbagai metode yang dapat digunakan untuk memperikirakan harga saham pada periode yang akan datang. Diantaranya adalah pemodelan dengan Geometric Brownian Motion (GBM) dan pemodelan dengan GeometricBrownian Motion (GBM) dengan Jump. Metode GBM dapat memperediksi harga saham dengan baik apabila data return saham periode sebelumnya berdistribusi normal. Sedangkan jika pada data return saham periode sebelumnya memenuhi asumsi normalitas dan ditemukan adanya lompatan, maka digunakan metode Jump Diffusion. Prediksi harga saham AALI untuk periode 03/01/2017 sampai dengan 12/05/2017 dengan GBM menghasilkan akurasi peramalan yang baik, dengan nilai MAPE sebesar 11,26%. Prediksi harga saham AALI untuk periode 03/01/2017 sampai dengan 12/05/2017 dengan metode Jump Diffuison menghasilkan akurasi peramalan yang sangat baik, dengan nilai MAPE sebesar 2,60%. Berdasarkan nilai MAPE, model Jump Diffusion memberikan hasil yang lebih baik daripada model GBM.
ANALISIS JUMLAH TENAGA KESEHATAN DI JAWA TIMUR TAHUN 2022-2023 MENGGUNAKAN METODE RM MANOVA SATU ARAH Khufi, Rijal Apuila; Gunawan, Jovanka Vania; Nainggolan, Ester Yunita; Muhammad Nasrudin; Trimono Trimono
Jurnal Riset Sistem Informasi Vol. 2 No. 3 (2025): Juli : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/2hhgzp35

Abstract

In 2020, the COVID-19 pandemic had a significant impact on the healthcare sector in Indonesia, including the number and distribution of healthcare workers. Entering the recovery phase in 2022, various changes occurred in the healthcare employment system, which could affect the number of healthcare workers in different regions, including East Java Province. This study aims to analyze the differences in the number of healthcare workers in East Java between 2022 and 2023 using the Multivariate Analysis of Variance (MANOVA) method. In this study, multivariate normality was tested using Mardia's method, while the homogeneity of the covariance matrix was analyzed through Box's M test. The results of the tests indicated that the data met these assumptions, allowing MANOVA to be properly applied to identify the variables influencing the differences in the number of healthcare workers in East Java. These findings are expected to assist policymakers in designing a more effective distribution plan for healthcare workers.
SEGMENTASI WILAYAH INDONESIA BERDASARKAN IHK MENGGUNAKAN AHC DAN SPECTRAL CLUSTERING Shafira Amanda Putri; Tsabita Rosyidah Putri; Trimono Trimono; Muhammad Idhom
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1010

Abstract

Social and economic disparities between regions in Indonesia are still a serious problem, reflected in the high Gini Ratio and disparities in purchasing power. The Consumer Price Index (CPI) is an important indicator in measuring consumption patterns and price pressures between regions. This study aims to cluster 150 districts/cities based on consumption patterns through the CPI using the Agglomerative Hierarchical Clustering (AHC) and Spectral Clustering methods combined with Principal Component Analysis (PCA). The innovation lies in the comparison of the two clustering methods as well as the application of PCA to clarify the data structure. Evaluation with Silhouette Score and Davies-Bouldin Index showed that AHC gave the best results with four representative clusters (Silhouette: 0.76; DBI: 0.32), compared to Spectral Clustering with three clusters (Silhouette: 0.75; DBI: 0.54). Each cluster has different expenditure characteristics, useful for data-driven policy making. These results show that AHC is more effective in capturing interregional variations in consumption.
PREDIKSI HARGA PENUTUPAN SAHAM BBRI DENGAN MODEL HYBRID LSTM-XGBOOST Nabilah Selayanti; Dwi Amalia Putri; Trimono Trimono; Mohammad Idhom
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1011

Abstract

The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonlinear time series patterns and XGBoost's effectiveness in reducing prediction errors. The model leverages both historical data and feature extraction outputs from the LSTM model. Future stock price values are then predicted by XGBoost using this combined dataset. The Hybrid LSTM XGBoost model outperforms the individual base models in terms of prediction accuracy, achieving an RMSE of 117.89, MAE of 92.45, and MAPE of 2.21%.
PERBANDINGAN KINERJA GRU DAN SVR UNTUK PREDIKSI EMAS DI INDONESIA Mohammad Sufa Ammar Habibi; Arindra Harris Abdillah; Mohammad Idhom; Trimono Trimono
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1105

Abstract

Emas merupakan instrumen investasi yang banyak diminati di Indonesia, terutama saat terjadi ketidakstabilan ekonomi. Namun, volatilitas harga emas yang dipengaruhi oleh faktor makroekonomi domestik dan global membuat prediksinya menjadi tantangan tersendiri. Penelitian ini membandingkan kinerja dua model prediksi, yaitu Gated Recurrent Unit (GRU) dan Support Vector Regression (SVR), dalam meramalkan harga emas jangka pendek berdasarkan data historis harian periode 2020–2025 sebanyak 1.345 data. Data diolah melalui proses normalisasi dan pembentukan data sekuensial dengan jendela waktu 60 hari. Kedua model dievaluasi menggunakan metrik regresi seperti RMSE, MAE, MSE, dan R². Hasil menunjukkan bahwa model GRU lebih unggul dibandingkan SVR dalam menangkap pola non-linear dan temporal pada data deret waktu, serta menghasilkan prediksi yang lebih akurat. Harga emas pada 7 Mei 2025 diperkirakan sebesar Rp1.736.978. Temuan ini menunjukkan bahwa model deep learning seperti GRU memiliki potensi besar dalam analisis data keuangan dan dapat memberikan kontribusi praktis bagi investor, peneliti, dan pembuat kebijakan. Penelitian selanjutnya disarankan untuk mengintegrasikan variabel makroekonomi dan pendekatan hybrid guna meningkatkan akurasi prediksi.
PERBANDINGAN ALGORITMA K-PROTOTYPES DENGAN AGGLOMERATIVE CLUSTERING DALAM SEGMENTASI SISWA BERDASARKAN FAKTOR AKADEMIK DAN SOSIAL Muchamad Risqi; , Muhammad Nashif Farid; , Mohammad Idhom; Trimono Trimono
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/spkmfd39

Abstract

Student performance is affected by internal and external factors such as study time, absenteeism, tutoring, and parental support—factors often overlooked by traditional education methods. This study applies K-Prototypes and Agglomerative Clustering with Gower Distance to segment students using mixed-type data. Five key variables were analyzed: study time, absences, GPA, tutoring, and parental support. The Elbow Method was used to identify the optimal number of clusters, and Silhouette Score to evaluate performance. Results show K-Prototypes outperformed Agglomerative Clustering (0.332 vs 0.186). Three student segments emerged: active students with average GPA, low-risk learners, and high-achievers with minimal external support. These findings can inform more adaptive and data-driven academic interventions for education stakeholders.
Co-Authors , Mohammad Idhom , Muhammad Nashif Farid Arindra Harris Abdillah Cenditya Ayu Aurelia Di Asih I Maruddani Di Asih I Maruddani Dwi Amalia Putri Dwi Ispriyanti Gunawan, Jovanka Vania I Gede Susrama Susrama Mas Diyasa Khufi, Rijal Apuila Mohammad Idhom Mohammad Sufa Ammar Habibi Muchamad Risqi Muhammad Idhom Muhammad Nasrudin Nabilah Selayanti Nainggolan, Ester Yunita Shafira Amanda Putri Tsabita Rosyidah Putri