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All Journal International Journal of Electrical and Computer Engineering MATEMATIKA SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Jurnal Matematika Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Mahasiswa Pasca Sarjana Proceeding SENDI_U Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) Jurnal Matematika: MANTIK Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL INSTEK (Informatika Sains dan Teknologi) Gema Wiralodra Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration International Journal of Social Service and Research Interdisciplinary Social Studies Jurnal Sosial dan Sains
Juwanda, Farikhin
Departemen Matematika FSM UNDIP
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Singh's Fuzzy Time Series Forecasting Modification Based on Interval Ratio Erikha Feriyanto; Farikhin Farikhin; Nikken Prima Puspita
Jurnal sosial dan sains Vol. 4 No. 3 (2024): Junral Sosial dan Sains
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsosains.v4i3.1248

Abstract

Background: One forecasting method that is often used is time series forecasting. The development of applied mathematics has encouraged new mathematical findings that led to the birth of new branches of mathematics, one of which is fuzzy. Purpose: The objectives of the study, namely forecasting, fuzzy set, time series, fuzzy time series, fuzzy time series Singh, interval ratio and measurement of accuracy level. Method: This research method applies Chen's fuzzy time series in the section of determining the universe of talk you to the fuzzification of historical data and in the part of forecasting results obtained through a heuristic approach by building three forecasting rules, namely Rule 2.1, Rule 2.2, and Rule 2.3 to obtain better results and affect very small AFER values. As well as making modifications to the interval partition section using interval ratios to be able to reflect data variations. Results: Based on the calculation of AFER values for order 2, order 3, and order 4 respectively obtained at 1.06389%, 0.689368%, and 0.711947%. Therefore, it can be said, Singh's fuzzy time series forecasting method based on the ratio of 3rd-order intervals is better than that of 2nd-order and 4th-order. Conclusion: Based on the results of research and discussion that has been carried out, it can be concluded that Singh's fuzzy time series forecasting method has the same algorithm as fuzzy time series forecasting. Singh's fuzzy time series forecasting method based on interval ratios applies fuzzy time series and Singh forecasting. Singh's fuzzy time series forecasting modification accuracy rate based on interval ratios produces excellent forecasting values according to evaluator average forecasting error rate (AFER).
The forecasting of palm oil based on fuzzy time series-two factor Wulandari, Ratri; Surarso, Bayu; Irawanto, Bambang; Farikhin, Farikhin
Journal of Soft Computing Exploration Vol. 2 No. 1 (2021): March 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Palm oil is a vegetable oil obtained from the mesocarp fruit of the palm tree, generally, from the species, Elaeis guineensis, and slightly from the species Elaeis oleifera and Attalea maripa. Palm oil is naturally red due to its high alpha and beta-carotenoid content. Palm kernel oil is different from palm kernel oil produced from the same fruit core. Planning for palm oil production is necessary because it greatly affects to the level of the country’s economy. Forecasting can reduce uncertainty in planning. Forecasting used in the palm oil problem is two-factor forecasting using the Kumar method with uama factors in the form of palm oil production and supporting factors in the form of land area. The forecasting is evaluated using AFER and MSE, from the acquisition of AFER value of 1.212% <10%, then the forecasting has very good criteria.
PENENTUAN PENERIMA BEASISWA BIDIKMISI MENGGUNAKAN METODE FUZZY SIMPLE ADDITIVE WEIGHTING DAN ELIMINATION AND CHOICE TRANSLATION REALITY Donny Irawan Mustaba; Budi Warsito; Farikhin
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 9 No 2 (2024): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v9i2.49111

Abstract

Beasiswa bidikmisi, yang kini dikenal sebagai beasiswa KIP-K, ditujukan untuk calon mahasiswa yang kurang mampu. Oleh karena itu, beasiswa ini harus tepat sasaran agar benar-benar bermanfaat bagi yang membutuhkan. Dalam upaya memastikan ketepatan sasaran tersebut, proses seleksi calon penerima beasiswa perlu dilakukan dengan baik, salah satunya dengan menggunakan metode ilmiah. Penelitian ini menguji proses seleksi penerima beasiswa dengan dua metode ilmiah, yaitu Fuzzy Simple Additive Weighting (Fuzzy-SAW) dan Elimination and Choice Translation Reality (ELECTRE). Kedua metode ini dipilih karena banyak penelitian telah menggunakannya, namun belum ada yang membandingkannya. Hasil penelitian menunjukkan bahwa kedua metode menghasilkan peringkat pertama dan terakhir yang sama, meskipun urutan lainnya berbeda. Tingkat kecocokan total dari peringkat keseluruhan adalah 21,3% dengan selisih peringkat antara 1 hingga 5 atau sebesar 1,7%. Untuk perankingan dengan data yang banyak, metode Fuzzy-SAW lebih direkomendasikan karena prosesnya lebih sederhana dan mudah diterapkan.
Academic Performance Prediction Using Supervised Learning Algorithms in University Admission Gufroni, Acep Irham; Purwanto, Purwanto; Farikhin, Farikhin
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2974

Abstract

Each educational institution has designed an academic system with the aim of providing as perfect learning as possible to students. The quality of good students is influenced by various factors, one of which is the available academic system. Previous research has shown that the quality of a student, which can be called academic achievement, can be determined through historical data on the student admission process. This research aims to process one of the admission processes previously implemented in Indonesian state universities using the National Selection for State University Entrance (SNMPTN) data, combined with Cumulative Achievement Index (GPA) data, so that it can be processed using a machine learning model. The algorithm used to create the model is a Supervised Learning Classification algorithm, which includes a Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). The research was carried out in three schemes based on the percentages of training data and test data. The results obtained show that DT produces the highest accuracy and precision values, with an accuracy value of 0.79 and a precision value of 0.56, respectively. The XGB produces the highest recall and f1-score values, with a recall value of 0.35 and an f1-score value of 0.36. The model with the highest f1-score can be selected as the best model, namely, the model with the XGB algorithm on a 70%-30% train-test data scheme. The resulting model achieved a success rate of 77%.
STOCK PRICE FORECASTING USING FUZZY C-MEANS AND TYPE-2 FUZZY TIME SERIES Satriani, Rineka Brylian Akbar; Farikhin, Farikhin; Surarso, Bayu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1365-1378

Abstract

Stock prices have unstable movements, so forecasting is needed to decide to invest appropriately according to the strategy. Fuzzy Time Series (FTS) uses fuzzy sets to forecast future time series values using historical data. However, interval partitioning in FTS needs to be considered as it can affect the forecasting results. FCM is applied to solve the problem of interval assignment in the universe of discourse. It allows the evaluation of the distribution of historical data and forming intervals of different sizes. Type 2 Fuzzy Time Series (T2FTS) is an extension of FTS to improve forecasting performance and refine fuzzy relationships. This research aims to improve forecasting accuracy using the Fuzzy C-Means (FCM)-T2FTS combination. This research uses daily data on BBRI stock prices from January 2023 to May 2024, with the variables used being close, high, and low prices. The results showed that determining the interval length using unequal length is more efficient than fixed interval length and can improve model performance, demonstrated from the MAPE values of T2FTS and FCM-T2FTS, which are 2.09% and 1.97%, respectively, the difference between the two MAPEs, is 0.12%. Hence, FCM-T2FTS is 12% more efficient than T2FTS. Therefore, FCM-T2FTS can improve forecasting accuracy.
Comparison of Matrix Decomposition in Null Space-Based LDA Method Usman, Carissa Devina; Farikhin; Titi Udjiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5637

Abstract

Problems with small sample sizes and high dimensionality are common in pattern recognition. Almost all machine learning algorithms degrade in high-dimensional data, so that singularities in the scatter matrices, the main problem of the Linear Discriminant Analysis (LDA) technique, might result. A null space-based LDA (NLDA) has been conceived to address the singularity issue. NLDA aims to maximize the distance between classes in the null space of the within-class scatter matrix. In the first research, the NLDA method was performed by computing the eigenvalue decomposition and singular value decomposition (SVD). This research led to several new implementations of the NLDA method that use other matrix decompositions. The new implementations include NLDA using Cholesky decomposition and NLDA using QR decomposition. This paper compares the performance of three NLDA methods that use different matrix decompositions, namely, SVD, Cholesky decomposition, and QR decomposition. Two sets of data were used in the experiments that used three different NLDA algorithms. To determine the performance of the NLDA methods, the classification accuracy of the three methods was measured using the confusion matrix. The results show that the NLDA method using SVD has the best performance when compared to the other two methods, achieving a precision of 77.8% accuracy for the Colon dataset and a precision of 98.8% accuracy for the TKI-resistance dataset.
Lagrange Method with Mean Semivariance Approach in Forming an Optimal Portfolio Wardani, Novita Koes; Farikhin, Farikhin; Udjiani S.R.R.M, Titi
International Journal of Social Service and Research Vol. 5 No. 6 (2025): International Journal of Social Service and Research
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/ijssr.v5i6.1242

Abstract

Investment decisions involve the allocation of resources in the present with the expectation of gaining future returns, but they are inherently accompanied by unavoidable risks. These risks need to be managed through the construction of an optimal portfolio. The main issue examined in this study is the limitation of the Mean-Variance approach in addressing downside risk, which is particularly relevant under unstable market conditions. Therefore, an alternative approach, Mean-Semivariance, is integrated with the Lagrange method to obtain a more effective portfolio optimization solution. This study aims to construct a mathematical model for an optimal portfolio that explicitly accounts for downside risk. The model is formulated through an objective function and a system of constraints solved using the Lagrange multiplier method. The results indicate that the Mean-Semivariance approach yields more conservative portfolio weights compared to the Mean-Variance approach. Risk evaluation using Value at Risk (VaR) and Conditional Value at Risk (CVaR) shows that the portfolio optimized through the Mean-Semivariance approach provides better protection against extreme loss potential. Thus, this approach can be relied upon as a more responsive portfolio optimization strategy toward negative risk under volatile market dynamics.
Stacked Random Forest-LightGBM for Web Attack Classification Pradana, Fadli Dony; Farikhin, Farikhin; Warsito , Budi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4950

Abstract

The rapid expansion of web services in the digital era has intensified exposure to increasingly complex and imbalanced cyber threats. This study proposes a stacking hybrid ensemble framework for web attack classification, integrating Random Forest as the base learner and LightGBM as the meta-learner, enhanced by the SMOTE technique for data balancing. The Web Attack subset of the CICIDS-2017 dataset serves as a case study, with a focus on detecting minority attacks such as SQL Injection, XSS, and Brute Force. The preprocessing pipeline includes data cleaning, removal of irrelevant features, normalization, extreme value imputation, and ANOVA F-test-based feature selection. Evaluation results indicate that the proposed model outperforms baseline models in both multiclass classification (98.7% accuracy, 0.634 macro F1-score) and binary classification (99.41% accuracy, 99.47% F1-score), while maintaining high sensitivity to minority classes. These results contribute to informatics and cybersecurity scholarship through a generalizable stacking baseline and well-specified evaluation procedures for web-attack detection, facilitating replicability, fair comparison, and dataset-agnostic insights.
Application of the geometric Brownian motion model in West Texas Intermediate crude oil price prediction Pangestika, Vidya Dwi; Farikhin, Farikhin; Udjiani , Titi
Gema Wiralodra Vol. 14 No. 3 (2023): Gema Wiralodra
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/gw.v14i3.546

Abstract

Crude oil is one of the primary commodities in the global economy. Crude oil prices are among the most complex and challenging to model because of the irregular, non-linear, non-stationary fluctuations in crude oil prices and their high volatility. It is essential to predict crude oil commodity prices to reduce the negative impact of fluctuations in crude oil commodity prices. Several mathematical models can be used to forecast crude oil commodity prices. One model that can be used is the Geometric Brownian Motion model, also known as the Wiener process. In this research, predictions for WTI (West Texas Intermediate) crude oil in 2022 were carried out using the Geometric Brownian Motion model. The results of this research are predictions of crude oil prices for July 2023 with iterations of 100, 200, and 1000, respectively, producing MAPE values of 6.092415%, 7.364198%, and 7.276606%.
Dilated Convolutional Neural Network for Skin Cancer Classification Based on Image Data Khasanah, Uswatun; Surarso, Bayu; Farikhin, Farikhin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 1 (2023): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i1.11667

Abstract

Skin cancer is a disorder of cell growth in the skin. Skin cancer has a big impact, causing physical disabilities that can be seen directly and high treatment costs. In addition, skin cancer also causes death if nor treated properly. Generally, dermatologists diagnose the presence of skin cancer in the human body by using the Biopsy process. In this study, the Dilated Convolutional Neural Network method was used to classify skin cancer image data. Dilated Convolutional Neural Network method is a development method of the Convolutional Neural Network method by modifying the dilation factors. The Dilated Convolutional Neural Network method is divided into two stages, including feature extraction and fully connected layer. The data used in this study is HAM1000 dataset. The data are dermoscopic image datasets which consists of 10015 images data from 7 types of skin cancer. This study conducted several experimental scenarios of changes in the value of d, which are 2,4,6, and 8 to get the optimal results. The parameters used in this study are epoch = 100, minibatch size = 8, learning rate = 0.1, and dropout = 0.5. The best results in this study were obtained with value of d=2 with the value of accuracy is 85.67% and the sensitivity is 65.48%.
Co-Authors A. Haris A. Rusgiyono Acep Irham Gufroni Adi Ariyo Munandar Adi Suliantoro Ahmad Abdul Chamid Ahmad Lubis Ghozali Aprilia, Maita Aris Sugiharto Arnelli Arnelli B. Raharjo Bambang Irawanto Bambang Irawanto Bambang Subeno Bayu Surarso Bayu Surarso Beta Noranita Bibit Waluyo Aji Budi Warsito Carolin Carolin Catur Edi Widodo D. Ispriyanti Didik Setiyo Widodo Dinar Mutiara Kusumo Nugraheni Djuwandi Djuwandi DONNY IRAWAN MUSTABA Dwinta Rahmallah Pulukadang, Dwinta Rahmallah E. Setiawati Erikha Feriyanto Erlin Dwi Endarwati, Erlin Dwi Esti Wijayanti, Esti F. Ariyanto Faozi, Safik Fauzi, Irza Nur Feriyanto, Erikha Ferry Jie, Ferry Fitika Andraini H. Sutanto Heny Maslahah, Heny I. Marhaendrajaya Iswahyudi Joko Suprayitno J. E. Suseno Kartono . Keszya Wabang Kusworo Kusworo Laily Rahmania, Laily LM Fajar Israwan, LM Fajar M. Izzati M. Nur Madani, Faiq Mansur Mansur Meryta Febrilian Fatimah, Meryta Febrilian Mustafid Mustafid Neza Zhevira Septiani Nikken Prima Puspita Nikken Prima Puspita Nur Khasanah Oky Dwi Nurhayati Pangestika, Vidya Dwi Pradana, Fadli Dony Prantiastio Prastio, Wahyu Tedi Priyono Priyono Purwanto Purwanto R. Hariyati R. Hastuti Rachmat Gernowo Ratri Wulandari Retno Kusumaningrum Rezki Kurniati, Rezki Rinta Kridalukmana Robertus Heri Sulistyo Utomo S. Tana Safik Faozi, Safik Satriani, Rineka Brylian Akbar Siti Khabibah Siti Khabibah Sri Wahyuni Sugito Sugito Suhartono Suhartono Sunarsih . Suparti Suparti T. Windarti Titi Udjiani SRRM Toni Prahasto Udjiani , Titi Udjiani S.R.R.M, Titi Usman, Carissa Devina Uswatun Khasanah W. H. Rahmanto Wardani, Novita Koes Wardianto, Wardianto Warsito , Budi Wicaksono, Mahad Wyne Mumtaazah Putri Yosza Dasril Yully Estiningsih Z. Muhlisin