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All Journal Jurnal Ilmu Pertanian Indonesia Jurnal Manajemen dan Organisasi FORUM STATISTIKA DAN KOMPUTASI ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Indonesian Journal of Biotechnology Scientific Journal of Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Jurnal Jamu Indonesia Journal of Enterprise and Development (JED) ARRUS Journal of Mathematics and Applied Science Prosiding Seminar Nasional Official Statistics Xplore: Journal of Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Scientific Journal of Informatics Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications
Farit Mochamad Afendi
Biopharmaca Research Center, Bogor Agricultural University Department Of Statistic, Faculty Of Mathematics And Natural Sciences, Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Comparison of ARIMA and GRU Models for High-Frequency Time Series Forecasting. Ridwan, Mochamad; Sadik, Kusman; Afendi, Farit Mochamad
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.45965

Abstract

Purpose: The purpose of this research is to assess the efficacy of ARIMA and GRU models in forecasting high-frequency stock price data, specifically minute-level stock data from HIMBARA banks. In time series analysis, time series data exhibit interesting interdependence among observations. Despite its popularity in time series forecasting, the ARIMA model has limitations in capturing complicated nonlinear patterns. Forecasting high-frequency data is becoming more popular as technology advances and more high-frequency data becomes available.Methods: In this study, we compare the ARIMA and GRU models in forecasting minute-level stock prices of HIMBARA banks. The data used consists of 62,921 minute-level stock data points for each bank in the HIMBARA group, collected in the year 2022. The GRU model was chosen because it is capable of capturing complex nonlinear patterns in time series data. Each method's predicting performance is assessed using the Mean Absolute Percentage Error (MAPE) statistic.Results: In terms of forecasting accuracy, the GRU model outperforms the ARIMA model. The GRU model achieves a MAPE of 0.77% for BMRI stock, while the ARIMA model achieves a MAPE of 4.09%. The GRU model predicts a MAPE of 0.34% for BBRI stock, while the ARIMA model predicts a MAPE of 3.02%. For BBNI stock, the GRU model obtains a MAPE of 0.63%, while the ARIMA model achieves a MAPE of 1.52%. The GRU model achieves a MAPE of 0.58% for BBTN stock, while the ARIMA model achieves a MAPE of 6.2%.Novelty: In terms of minute-level time series data modeling, research in Indonesia has been limited. This study adds a new perspective to the discussion by comparing two modeling approaches: the traditional ARIMA model and the sophisticated deep learning GRU model, both of which are applied to high-frequency data. Beyond the present scope, there are several promising future directions to pursue, such as anticipating intraday stock fluctuations. This unexplored zone not only contributes to the field of financial modeling but also has the ability to uncover intricate patterns in minute-level data, an area that has not been extensively studied in the Indonesian context.
Perbandingan Metode GARCH, LSTM, GRU, dan CNN pada Peramalan Volatilitas Kurs Adeline Vinda Septiani; Farit Mochamad Afendi; Anang Kurnia
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3384

Abstract

Currency volatility is an important aspect of time series data analysis in economics and finance. This study aims to compare the performance of four methods: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), in predicting the volatility of the Rupiah against the US Dollar. The data used is daily exchange rates from January 2015 to March 2024. The evaluation is conducted by calculating the Root Mean Square Error (RMSE) and the percentage of actual values within a 95% confidence interval on training and testing data. The results indicate that LSTM achieves the lowest RMSE, with values of 5.30E-05 on training data and 2.50E-05 on testing data, demonstrating high accuracy in capturing non-linear patterns and long-term fluctuations. GRU records the highest percentage of actual values within the confidence interval, at 90.32% for training data and 91.72% for testing data, reflecting superior consistency compared to other methods. Meanwhile, GARCH shows competitive performance but lacks robustness on testing data. CNN exhibits the lowest performance, with high RMSE and a low percentage of data within the confidence interval. Overall, GRU emerges as the best method, offering an optimal balance between predictive accuracy and consistency, making it a reliable tool for modeling exchange rate volatility in high-volatility scenarios. Consequently, GRU is utilized for forecasting exchange rate volatility for the next 30 days. These findings contribute to the selection of appropriate methods for modeling exchange rate volatility, particularly amidst global market uncertainty.
Evaluasi Kinerja Metode CLARA dan FCM dalam Analisis Gerombol untuk Data Berjumlah Besar dengan Pencilan Indahwati Indahwati; Intan Juliana Panjaitan; Farit Mochamad Afendi
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 3 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i3.3118

Abstract

Analisis gerombol adalah suatu metode statistika yang mengidentifikasi gerombol objek berdasarkan karakteristik serupa. Masalah yang sering terjadi dalam analisis gerombol adalah keberadaan data pencilan. Keberadaan pencilan dapat mengakibatkan output yang tidak sesuai dengan gambaran yang sebenarnya, sehingga gerombol yang dihasilkan tidak merepresentasikan objek dengan tepat. Masalah lain yang dapat muncul dalam analisis gerombol adalah besarnya jumlah amatan, sehingga diperlukan metode analisis yang efisien dalam penggerombolan. Penelitian ini juga memperdalam tentang kinerja keduanya terhadap jarak antara pusat gerombol dan kondisi penggerombolan melalui kajian simulasi, dimana masing-masing faktor terdiri dari tiga level yang diobservasi.Metode Clustering Large Applications (CLARA) dan Fuzzy C-Means (FCM) adalah metode yang kekar terhadap pencilan dan mampu menganalisis dataset besar. Metode FCM menggunakan nilai pembobot (w) yang optimal untuk mencapai kekar terhadap pencilan. Metode CLARA memiliki sifat kekar dikarenakan menggunakan medoid sebagai pusat gerombol dan penggunaan jarak Manhattan dalam perhitungan jarak antara objek dan pusat gerombol. Metode tersebut akan dievaluasi menggunakan beberapa kriteria evaluasi kebaikan yaitu berdasarkan rasio simpangan baku dalam gerombol dan antar gerombol. Hasil analisis menunjukkan pengaruh signifikan pada masing-masing faktor dan interaksi antar faktor. Visualisasi menunjukkan bahwa peningkatan persentase pencilan mengurangi akurasi penggerombolan, sementara jumlah data yang lebih besar meningkatkan akurasi. Jarak yang lebih besar antara pusat gerombol dan kondisi gerombol yang terpisah menghasilkan rasio simpangan baku gerombol yang lebih kecil. Hasil penelitian menunjukkan bahwa metode FCM lebih efektif dalam menangani data dengan variasi yang signifikan.
Pengembangan Syariah Compliant Hotel: Hambatan & Inovasi Siti Nurfajar Octaviani; Mukhamad Najib; Farit Mochamad Afendi
Journal of Enterprise and Development (JED) Vol. 2 No. 2 (2020): Journal of Enterprise and Development (JED)
Publisher : Faculty of Islamic Economics and Business of Universitas Islam Negeri Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20414/jed.v2i2.2180

Abstract

Hotel Syariah memiliki peranan penting dalam pengembangan pariwisata syariah di Indonesia. Akan tetapi pelaku bisnis di bidang Hotel Syariah mendapatkan beberapa tantangan yang membuatnya sulit berkembang karena meniadakan unsur – unsur nonsyar’i, persepsi masyarakat yang menyamakan dengan hotel konvensional, dan fasilitas yang kurang menarik. Penelitian ini mencoba untuk mengurai tiga aspek (produk, pelayanan, dan pengelolaan) pengembangan Hotel Syariah guna menghadapi tantangan tesebut dengan metode ANP. Dapat dilihat pada aspek produk ruang ibadah menjadi prioritas utama yang harus diperhatikan, pada aspek pelayanan pemisahan layanan untuk tamu laki – laki dan tamu perempuan menjadi prioritas utama yang harus diperhatikan, dan pada aspek pengelolaan manajemen Sumber Daya Manusia menjadi prioritas utama yang harus diperhatikan. sedangkan aspek inovasi yang dianggap dapat menjadi solusi dari tantangan yang ada ialah adanya fasilitas hiburan, Herbal Bar, pusat belanja halal, dan interior yang bernuansa Islami.
ANALISIS PENGARUH DAERAH PEMASOK TERHADAP HARGA CABAI MERAH DI DKI JAKARTA MENGGUNAKAN VECTOR ERROR CORRECTION MODEL (VECM) Erwandi Erwandi; Farit Mochamad Afendi; Budi Waryanto
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.276

Abstract

This study aims to analyze the effect of red chili price and production in the supplier area on its prices in DKI Jakarta using the Vector Error Correction Model (VECM). The data used in this study are red chili price and average expenditure per month per capita in DKI Jakarta and red chili price and production in East Java, West Java, and Banten in the period January 2012 to July 2018. The model obtained was VECM (1) the price of red chili in DKI Jakarta. It showed that there was a long-term relationship (cointegration) on the first difference. The results the Forecast Error Variance Decomposition (FEVD) analysis showed that the contributions of the red chili price in DKI Jakarta and West Java, average monthly expense for red chili in DKI Jakarta, red chili production (West Java and Banten) are significant in explaining the behaviour of the red chili price change in DKI Jakarta. The results of the Impulse Response Function (IRF) analysis showed that the shock of the price of chili in DKI Jakarta and West Java in the previous month will increase the price of red chili in DKI Jakarta in the following month. Conversely, the shock of the average monthly expenditure of red chili in DKI Jakarta and red chili production (West Java and Banten) from the previous month will reduce the price of red chili in DKI Jakarta in the following month.
PENINGKATAN AKURASI KLASIFIKASI INTERAKSI FARMAKODINAMIK OBAT BERBASIS SELEKSI PASANGAN OBAT TAKBERINTERAKSI Hilma Mutiara Winata; Farit Mochamad Afendi; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.327

Abstract

Identifying the pharmacodynamics drug-drug interaction (PD DDI) is needed since it can cause side effects to patients. There are two measurements of drug interaction performance, namely the golden standard positive (GSP) which is the drug pairs that interact pharmacodynamics and golden standard negative (GSN), which is a drug pairs that do not interact. The selection of GSN in the previous which studies were only selected randomly from a list of drug pairs that do not interact. The random selection is feared to contain drug pairs that actually interact but have not been recorded. Therefore, in this study the determination of GSN was carried out by, first, grouping drug pairs included in the GSP using the DP-Clus algorithm with certain values of density and cluster properties. Then the drugs in different group would be paired and only the drug pairs in the GSN list are selected. It was found that our new proposed classification method increases the AUC value compared to the results obtained by random selection of GSN.
PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK Hasnita Hasnita; Farit Mochamad Afendi; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.328

Abstract

One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.
METODE ANALISIS DISKRIMINAN KUADRAT TERKECIL PARSIAL UNTUK KLASIFIKASI SEGMEN LOYALITAS KONSUMEN SUSU PERTUMBUHAN Herdina Kuswari; Farit Mochamad Afendi; Khairil Anwar Notodiputro
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i2.586

Abstract

Consumer segmentation is the process of dividing consumers into different segments based on consumer characteristics, making it easier for companies to develop marketing strategies. The segmentation is carried out based on consumer loyalty using the RFM (Recency, Frequency, Monetary) approach a number of 7753 members of a nutritional product loyalty program is considered in the analysis. Partial least square discriminant analysis classification modeling is built using the results of consumer segmentation being the a response variable. The model is not good enough based on the AUC (Area Under Curve) value of the ROC (Relative Operating Characteristic) curve that quite low for each segment. The explanatory variables that have high contribution to the model is X5, X9, and X2 with VIP (Variable Importance in the Projection) values more than 1.
Comparison of Chi-Square Automatic Interaction Detector (CHAID) and Random Forest Methods in the Classification of Household Poverty Status in Central Java: Perbandingan Metode Chi-Square Automatic Interaction Detector (CHAID) dan Random Forest dalam Klasifikasi Status Kemiskinan Rumah Tangga di Jawa Tengah Fatkhul Izzati; Mohammad Masjkur; Farit Mochamad Afendi
Indonesian Journal of Statistics and Applications Vol 8 No 1 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i1p1-13

Abstract

Central Java was in second position as the province with the highest number of poor people in Indonesia in March 2020. Poverty alleviation efforts have been carried out, but many are still not yet on target. The purpose of this study was to model the classification of household poverty status in Central Java using CHAID and random forest methods and compare the two methods. The data used in this study is data from the 2020 National Socioeconomic Survey (SUSENAS) conducted by the Central Bureau of Statistics (BPS) for Central Java. The number of poor households is much less than non-poor households. Therefore, Synthetic Minority Oversampling Technique (SMOTE) was performed to handle unbalanced data. The random forest method produced better classification performance than the CHAID method with accuracy, sensitivity, specificity, and AUC of 93,95%, 98,43%, 89,92%, and 0,9417, respectively. The important variables that build the random forest model are the floor area of the house, the age of the head of the household, cooking fuel, the place for the final disposal of feces, and ownership of the place to defecate.
Analysis Of Stock Market, Mining Commodity, Exchange Rate, And Energy Sector Stock Index Using Vector Error Correction Model: Analisis Bursa Saham, Komoditas Pertambangan, Kurs, Dan Indeks Saham Sektor Energi Menggunakan Vector Error Correction Model Melati; Pika Silvianti; Farit Mochamad Afendi
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i1p44-55

Abstract

Energy Sector is one of the sectors that has a significant impact on the overall economic growth of a country. Economic growth is always linked to energy consumption, as increasing economic development leads to higher energy demand. Therefore, this study aims to analyze the factors influencing the energy sector stock index in Indonesia using Vector Error Correction Model (VECM). The data used include the energy sector stock index, crude oil prices, coal prices, gas prices, Nikkei Index, Shanghai Index, Dow Jones Index, and exchange rates from January 2021 to March 2023. VECM analysis results indicate that in the short term, crude oil prices and coal prices have a significant impact on the energy sector stock index. In the long term, significant factors are coal prices, gas prices, Nikkei Index, and exchange rates. The Impulse Response Function (IRF) analysis reveals that shocks to the energy sector stock index, crude oil prices, and coal prices can increase the energy sector stock index. Conversely, shocks to the Nikkei Index can decrease the energy sector stock index. The Forecast Error Variance Decomposition (FEVD) results demonstrate that the contributions of the energy sector stock index, crude oil prices, coal prices, and gas prices are significant in explaining the behavior of changes in the energy sector stock index.
Co-Authors . Indahwati . Sutoro Aam Alamudi Abd. Rasyid Syamsuri Abialam Koesnandy H Adeline Vinda Septiani Agus Mohamad Soleh Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Aki Hirai Anang Kurnia Anggraini Sukmawati Annisa Malik Aqmar, Nurzatil Bagus Sartono Budi Susetyo Budi Waryanto Budi Waryanto Budi Waryanto Cici Suhaeni Dairul Fuhron Dian Ayuningtyas Eka Setiawaty Erwandi Erwandi fatimah Fatimah Fatkhul Izzati Febie Tri Lestari Fitrianto, Anwar H S, Rahmat Handayani, Vitri Aprilla Handayani, Vitri Aprilla Hari Wijayanto Hari Wijayanto Hasibuan, Rafika Aufa Hasnita Hasnita Herdina Kuswari Hiroki Takahashi I Made Sumertajaya Ikhlasul Amalia Rahmi Indahwati Indahwati Indahwati Intan Juliana Panjaitan Isnan Mulia Itasia Dina Sulvianti Kensuke Nakamura Khairil Anwar Notodiputro Kusman Sadik Latifah Kosim Darusman Maya Deanti Maysarah Sabariah Kudadiri Md. Altaf-Ul-Amin . Melati Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muchlishah Rosyadah Muhammad Ali Umar Mukhamad Najib Nadhif Nursyahban Nur Hikmah Nur Janah Nur Jannah Nurul Qomariasih Octaviani, Siti Nurfajar Panjaitan, Intan Juliana Pika Silvianti Pika Silvianti Pika Silvianti Puspita, Novi Qomariasih, Nurul Rifqi Aulya Rahman Rizal Bakri Rossi Azmatul Barro Rosyadah, Muchlishah Rudi Heryanto Safitri, Wa Ode Rahmalia Septaningsih, Dewi Anggraini Septanti Kusuma Dwi Arini Shigehiko Kanaya Siti Nurfajar Octaviani Sulistiyani . Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Usman, Muhammad Syafiuddin Widhiyanti Nugraheni Widya Putri Nurmawati Winata, Hilma Mutiara Wisnu Ananta Kusuma Zana Aprillia