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All Journal Jurnal Ilmu Pertanian Indonesia Jurnal Manajemen dan Organisasi FORUM STATISTIKA DAN KOMPUTASI ComEngApp : Computer Engineering and Applications Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi 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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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 : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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 : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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 : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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 : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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.
PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS Safitri, Wa Ode Rahmalia; Mochamad Afendi, Farit; Susetyo, Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1625-1636

Abstract

Time series forecasting often faces challenges in producing reliable predictions due to inherent uncertainty in dynamic systems. While point predictions are commonly used, they may not adequately capture this uncertainty, especially in financial systems where forecasting accuracy directly impacts decision-making. Prediction intervals offer a solution by providing a range of likely outcomes rather than single-point estimates. This study implements multivariate time series forecasting using gradient boosting algorithms (XGBoost, CatBoost, and LightGBM) to predict cash flow patterns in banking transactions, focusing on constructing reliable prediction intervals. Using transaction data from Bank Rakyat Indonesia (BRI), the research analyzes both office and e-channel transactions with different lag structures based on Granger Causality tests. Model performance was evaluated using RMSLE, MAE, and MAPE metrics, with RMSLE chosen as primary due to its ability to handle skewed distributions. LightGBM achieved best performance for office cash-in transactions (RMSLE: 0.2395), while CatBoost outperformed others for office cash-out (RMSLE: 0.2848), e-channel cash-in (RMSLE: 0.3946), and e-channel cash-out (RMSLE: 0.4221). For prediction intervals, two methods were compared: Residual Bootstrap with 500 samples and Quantile Regression. Residual Bootstrap generally produced coverage probabilities closer to the 80% level (i.e., 10–90% prediction interval), especially for office transactions, while maintaining narrower interval widths. In contrast, Quantile Regression tended to generate wider intervals and often overestimated uncertainty, resulting in overly high coverage in some cases. However, both methods showed clear limitations when applied to e-channel transactions, particularly for cash-in e-channel, where coverage probabilities fell below 50% due to high volatility and irregular transaction patterns. Unlike previous work focused only on point forecasts, this study offers insights into forecast uncertainty by evaluating how well each method quantifies, providing practical guidance for financial institutions aiming to improve risk management through interval-based forecasting.
Performance Evaluation of ARDL Model Stacked with Boosted Ridge Regression on Time Series Data with Multicollinearity: Evaluasi Kinerja Estimasi Model ARDL stacked with Boosted Ridge Regression pada Data Deret Waktu dengan Multikolinearitas Dalimunthe, Amir Abduljabbar; Soleh, Agus Mohamad; Afendi, Farit Mochamad
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, 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.v9i1p136-144

Abstract

Time series data plays a vital role in financial and economic study. Two commonly applied models for such data are Vector Autoregression (VAR) and Autoregressive Distributed Lags (ARDL). Nonetheless, interdependence among explanatory variables often leads to multicollinearity, posing challenges for model reliability. This study investigates the effectiveness of the ARDL model integrated with boosted ridge regression as a method to mitigate multicollinearity. Due to limitations in available empirical data, simulation data will be generated to support the analysis. The research consists of two stages: synthetic data generation and analysis on simulated data. Results suggest that ARDL performs well under various multicollinearity conditions, particularly when the training set is sufficiently large and model structure is correctly specified. For smaller training sets, the ARDL Ridge variant demonstrates improved predictive performance.
The Implementation of the Fuzzy C-Means Method in Handling Outlier Data in the 2021 Village Potential Data of Bengkulu Province Panjaitan, Intan Juliana; Indahwati, Indahwati; Afendi, Farit Mochamad
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12274

Abstract

Clustering groups aims to ensure similarity within clusters and disparity between them. The research evaluated the Fuzzy C-Means method’s effectiveness in clustering large datasets containing outliers, focusing on the 2021 Village Potential data from Bengkulu Province. The dataset, comprising 1,514 observations from villages and urban villages, provided a comprehensive resource for understanding regional development. Outliers, a common challenge in cluster analysis, were detected using univariate and multivariate methods, revealing substantial variability. PCA was applied, improving clustering quality to address multicollinearity among variables. In the results, the fuzzifier (w) parameter in the FCM method plays a crucial role in controlling the degree of membership for data points in clusters, which can potentially reduce the impact of outliers, enhancing clustering robustness and accuracy. The FCM method effectively produces clusters with high intra-cluster homogeneity and inter-cluster heterogeneity. Using the Elbow method, three optimal clusters are identified. Cluster 1, dominated by villages in Bengkulu City, is the most advanced, with superior infrastructure and services, but the fewest villages business units, necessitating economic empowerment. Cluster 2, comprising villages in North Bengkulu Regency, demonstrates moderate development but suffers from poor transportation access, requiring improvements to support socio-economic activities. Cluster 3, dominated by villages in Kaur Regency, is the least developed, with limited basic services and infrastructure, highlighting the need for substantial investments in governance and essential services. These findings provide actionable insights for village development in Bengkulu Province, supporting targeted policies tailored to each cluster’s unique characteristics.
Pengembangan Syariah Compliant Hotel: Hambatan & Inovasi Octaviani, Siti Nurfajar; Najib, Mukhamad; Afendi, Farit Mochamad
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.
COMPARISON OF SARIMA, SVR, AND GA-SVR METHODS FOR FORECASTING THE NUMBER OF RAINY DAYS IN BENGKULU CITY Puspita, Novi; Afendi, Farit Mochamad; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (624.009 KB) | DOI: 10.30598/barekengvol16iss1pp353-360

Abstract

The number of rainy days is a calculation of the rainy days that occur in one month. In recent years, there has been a decrease in rainy days in some parts of Indonesia. One of the areas at risk of quite a high decreasing number of rainy days is the Bengkulu City area. The decrease in the number of rainy days is one of the impacts caused by climate change. The community will feel the impact of climate change-related to the season, especially those working in the agricultural sector. In compiling the planting calendar, it is necessary to consider the seasons to estimate water availability. This study aimed to forecast the data on the number of rainy days in Bengkulu City in the period January 2000 to December 2020 using the Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Genetic Algorithm Support Vector Regression (GA-SVR) methods. The criteria for selecting the best model used was Mean Absolute Deviation (MAD). The MAD value in the SARIMA method was 4,16, 5,07 in the SVR model, and 3,67 in the GA-SVR model. Based on these results, it can be concluded that the GA-SVR model is the best model for forecasting the number of rainy days in Bengkulu City.
Performance Analysis of Machine Learning Models using RFE Feature Selection and Bayesian Optimization in Imbalanced Data Classification with Shap-Based Explanations Aqmar, Nurzatil; Wijayanto, Hari; Mochamad Afendi, Farit
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.31459

Abstract

Purpose: This research aims to evaluates the performance of Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) models integrated with Recursive Feature Elimination (RFE) for feature selection, Bayesian Optimization (BO) for hyperparameter tuning, and three imbalanced data handling techniques Random Undersampling (RUS), Random Oversampling (ROS), and SMOTENC. Identifying key determinants of household food insecurity in Papua using SHAP for transparent feature interpretation. Methods: The research used 2022 SUSENAS data from Papua Province. Exploring data composition and variable characteristics, and aggregating individual data into household data. Data were split using random sampling (80% training, 20% testing). Eighteen experimental scenarios were created by combining feature selection or no feature selection, three imbalance handling methods, and default or hyperparameter tuning. RF and LightGBM were evaluated over 50 iterations using accuracy, sensitivity, specificity, and G-Mean, with SHAP applied to the best-performing models for interpretability. Result: LightGBM achieved the highest accuracy and stability, particularly when combined with SMOTENC and RFE+BO. RF showed better performance in maintaining G-Mean when paired with RUS, with the highest G-Mean (0.756) obtained by RF + BO + RUS. Three-way ANOVA proved that model type, imbalance handling, feature selection, and their interaction significantly affected the G-Mean value. SHAP analysis shows that health, financial, and educational limitations can increase the risk of food insecurity. Novelty: This research offers a new integration between feature selection, hyperparameter tuning, and imbalanced data handling within an interpretable machine learning framework, thereby providing a robust solution for food vulnerability classification on imbalanced datasets.
Co-Authors . Indahwati . Sutoro Aam Alamudi Abd. Rasyid Syamsuri Agus Mohamad Soleh Agus Santoso Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Aki Hirai Anang Kurnia Anggraini Sukmawati Annisa Malik Apino, Ezi Aqmar, Nurzatil Bagus Sartono Budi Susetyo Budi Waryanto Budi Waryanto Budi Waryanto Cici Suhaeni Dairul Fuhron Dalimunthe, Amir Abduljabbar Dian Ayuningtyas Eka Setiawaty Erwandi Erwandi fatimah Fatimah 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 Heri Retnawati Hiroki Takahashi I Made Sumertajaya Ikhlasul Amalia Rahmi Indahwati Indahwati Indahwati Isnan Mulia Itasia Dina Sulvianti Izzati, Fatkhul Kensuke Nakamura Khairil Anwar Notodiputro Koesnandy H, Abialam Kusman Sadik Latifah Kosim Darusman M. Rafi 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 Pardede, Timbul Pika Silvianti Pika Silvianti Pika Silvianti Puspita, Novi Qomariasih, Nurul Rifqi Aulya Rahman Risnawati, I'lmisukma Rizal Bakri Rossi Azmatul Barro Rosyada, Munaya Nikma Rosyadah, Muchlishah Rudi Heryanto Safitri, Wa Ode Rahmalia Septaningsih, Dewi Anggraini Septanti Kusuma Dwi Arini Septiani, Adeline Vinda Shigehiko Kanaya 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