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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Lingua Jurnal Bahasa dan Sastra Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Karya Pendidikan Matematika Journal of ICT Research and Applications Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOIN (Jurnal Online Informatika) Jurnal Matematika: MANTIK JMPM: Jurnal Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Indonesian Journal of Electrical Engineering and Computer Science Milang Journal of Mathematics and Its Applications Jurnal Matematika Integratif MILANG Journal of Mathematics and Its Applications
Bib Paruhum Silalahi
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Bogor Agricultural University, Darmaga Campus, Jalan Meranti, Wing 20 Level 5, Bogor 16680,
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

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Price Model with Generalized Wiener Process for Life Insurance Company Portfolio Optimization using Mean Absolute Deviation Putra, Hilman Yusupi Dwi; Silalahi, Bib Paruhum; Budiarti, Retno
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.23093

Abstract

The Financial Services Authority (OJK) has issued Regulation of the Financial Services Authority of the Republic of Indonesia Number 5 Year 2023. Article 11 paragraph 1d explains the limitations of assets allowed in the form of investment, investment in the form of shares listed on the stock exchange for each issuer is a maximum of 10% of the total investment and a maximum of 40% of the total investment. The investment manager of a life insurance company needs to adjust its investment portfolio. In 1991, Konno and Yamazaki proposed an approach to the portfolio selection problem with Mean Absolute Deviation (MAD) model. This model can be solved using linear programming, effectively solving high-dimensional portfolio optimization problems. Another problem in stock portfolio formation is that the ever-changing financial markets demand the development of models to understand and forecast stock price behavior. One method that has been widely used to model stock price movements is the generalized Wiener Process. The generalized Wiener process provides a framework that can accommodate the stochastic nature of stock price changes, thus allowing portfolio managers to be more sensitive to unanticipated market fluctuations. The stock price change model using the Generalized Wiener Process is very good at predicting stock price changes. The results of this stock price prediction can then be used to find the optimal portfolio using the MAD model. The portfolio optimization problem with the MAD model can be solved using linear programming to obtain the optimal stock portfolio for life insurance companies. 
ONE-DIMENSIONAL CUTTING STOCK PROBLEM THAT MINIMIZES THE NUMBER OF DIFFERENT PATTERNS Silalahi, Bib Paruhum; Hanum, Farida; Setyawan, Fajar; Supriyo, Prapto Tri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.926 KB) | DOI: 10.30598/barekengvol16iss3pp805-814

Abstract

Cutting stock problem (CSP) is a problem of cutting an object into several smaller objects to fulfill the existing demand with a minimum unused object remaining. Besides minimizing the remaining of the object, sometimes there is another additional problem in CSP, namely minimizing the number of different cutting patterns. This happens because there is a setup cost for each pattern. This study shows a way to obtain a minimum number of different patterns in the cutting stock problem (CSP). An example problem is modeled in linear programming and then solved by a column generation algorithm using the Lingo 18.0 software.
SOME CONSTRUCTION OF 8N-DIMENSIONAL PERFECT MAGIC CUBE WITH ARITHMETIC SEQUENCE Mu'min, Ulil Albab; Silalahi, Bib Paruhum; Guritman, Sugi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0565-0578

Abstract

A magic square whose dimensions are expanded is called a magic cube. A magic cube whose properties are expanded is called a perfect magic cube. The perfect magic cube problem is how to arrange numbers in an cube (matrix) such that the sum of rows, columns, pillars, diagonals (planes and spaces) produces a magic constant of the cube. In this paper, it will be studied how to construct a perfect magic cube of order for whose entries contain an arithmetic sequence with the difference which is set to find specific patterns, and the algorithm for constructing a perfect magic cube is then implemented into programming language to solve large orders.
PARTICLE SWARM OPTIMIZATION FOR CUTTING ALUMINUM STOCK AND ITS COMPARISON WITH THE EXACT METHOD Silalahi, Bib Paruhum; Aminah, Siti; Mayyani, Hidayatul; Aman, Amril
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2791-2802

Abstract

The Cutting Stock Problem (CSP) is a common challenge in many industries, involving the optimization of material cutting to minimize waste while meeting customer demands. Various methods can be used to address this issue. This paper applies the heuristic Particle Swarm Optimization (PSO) method to solve CSP in the case of one-dimensional aluminum roll cutting. First, we identify feasible cutting pattern combinations. A mathematical model and constraints are then formulated based on these patterns. Next, the PSO algorithm is employed to determine the optimal combination of cutting patterns, minimizing material waste. The results yield the optimal aluminum roller cutting pattern. Furthermore, we compare the results between the PSO method and the exact method.
Enhancing Adaptive Particle Swarm Optimization Based on Human Social Learning with Human Learning Strategies for the Traveling Salesman Problem Qomah, Yusti; Silalahi, Bib Paruhum; Bakhtiar, Toni
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.31466

Abstract

Particle Swarm Optimization (PSO) is a widely used metaheuristic approach for solving optimization problems. Recent developments in this field involve the adaptation of human learning behaviors to enhance algorithmic performance. One such adaptation is the Adaptive Particle Swarm Optimization based on Human Social Learning (APSO-HSL), a variant of PSO that incorporates human inspired learning strategies. This study aims to enhance the performance of APSO-HSL on the Traveling Salesman Problem (TSP) by incorporating additional human learning strategies. The proposed algorithm, named Modified Adaptive Particle Swarm Optimization–Human Learning Strategies (MAPSO-HLS), integrates learning mechanisms from Human Learning Optimization (HLO), including individual, random, and social learning. This research is classified as applied research and algorithmic experimentation, focusing on the development and modification of a metaheuristic algorithm to solve a well-known combinatorial optimization problem. Benchmark datasets from the Traveling Salesman Problem Library (TSPLIB) are used for evaluation, and all computations and experiments are implemented in Python. The performance of MAPSO-HLS is compared with the exact method in terms of shortest distance and computation time. The results of the study indicate that the MAPSO-HLS algorithm is capable of producing TSP solutions with low total distance deviation, below 10%, compared to exact solutions across all tested datasets. This reflects a high level of solution accuracy. In addition, MAPSO-HLS demonstrates better time efficiency than the exact ILP method, particularly for datasets with a large number of cities. The integration of human learning strategies within the adaptive PSO framework provides significant advantages in terms of both efficiency and effectiveness in solving TSP.
Robust Optimization of Vaccine Distribution Problem with Demand Uncertainty Fikri, Faiqul; Silalahi, Bib Paruhum; Jaharuddin, Jaharuddin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20035

Abstract

This study proposes a multi objective optimization model for vaccine distribution problems using the Maximum Covering Location Problem (MCLP) model. The objective function of the MCLP model in this study is to maximize the fulfillment of vaccine demand for each priority group at each demand point. In practice, the MCLP model requires data on the amount of demand at each demand point, which in reality can be influenced by many factors so that the value is uncertain. This problem makes the optimization model to be uncertain linear problem (ULP). The robust optimization approach converts ULP into a single deterministic problem called Robust Counterpart (RC) by assuming the demand quantity parameter in the constraint function is in the set of uncertainty boxes, so that a robust counterpart to the model is obtained. Numerical simulations are carried out using available data. It is found that the optimal value in the robust counterpart model is not better than the deterministic model but is more resistant to changes in parameter values. This causes the robust counterpart model to be more reliable in overcoming uncertain vaccine distribution problems in real life. This research is limited to solving the problem of vaccine distribution at a certain time and only assumes that the uncertainty of the number of requests is within a specified range so that it can be developed by assuming that the number of demand is dynamic.
PENERAPAN ALGORITMA GENETIKA DENGAN METODE ROULETTE WHEEL DAN REPLACEMENT PADA OPTIMASI OMZET Mayyani, Hidayatul; Nurbaiti, Marisa; Supriyo, Prapto Tri; Aman, Amril; Silalahi, Bib Paruhum
MILANG Journal of Mathematics and Its Applications Vol. 19 No. 2 (2023): MILANG Journal of Mathematics and Its Applications
Publisher : School of Data Science, Mathematics and Informatics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.19.2.153-172

Abstract

Perhitungan masalah memaksimumkan omzet serta analisis yang tepat terhadap proses produksi diperlukan untuk meningkatkan pendapatan perusahaan. Permasalahan memaksimumkan omzet ini dapat diselesaikan dengan algoritma genetika. Terdapat banyak metode seleksi dalam algoritma genetika, dua di antaranya ialah roulette wheel dan replacement. Penelitian dilakukan untuk mencari metode seleksi terbaik berdasarkan rata-rata nilai fitness yang dihasilkan. Penelitian ini ditinjau berdasarkan tiga kasus yang berbeda dalam membandingkan kedua metode seleksi yang diuji, kasus pertama menggunakan ukuran populasi 10 dan banyak generasi juga 10, kasus kedua menggunakan ukuran populasi 25 dan banyak generasi 10, sedangkan kasus ketiga menggunakan ukuran populasi 10 dan banyak generasi 50. Ketiga kasus tersebut menggunakan parameter tetap yaitu crossover rate 0,8 dan mutation rate 0,1. Dari penelitian ini didapatkan bahwa metode replacement lebih baik dari metode roulette wheel.
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Buono, Agus; Priandana, Karlisa; Paruhum Silalahi, Bib; Sri Hendrastuti, Elisabeth
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Simulated Annealing Algorithm for Determining Travelling Salesman Problem Solution and Its Comparison with Branch and Bound Method Silalahi, Bib Paruhum; Sahara, Farahdila; Hanum, Farida; Mayyani, Hidayatul
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 3 (2022): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i3.8481

Abstract

Travelling Salesman Problem (TSP) is a problem where a person must visit some places, starting from one city and then moving on to the next city with the conditions that the places visited can only be passed precisely once and then back to the starting city. TSP is an NP-hard, an important problem in operations research. TSP problems can be solved by an exact method or an approximation method, namely the metaheuristic method. This research aims to solve the TSP problem with an approximation method called the Simulated Annealing (SA), and then compare the results of this approximation method with the exact Branch and Bound method. The results indicated that the SA method could accomplish TSP problems. However, like other metaheuristic methods, SA only accomplishes it using an approach to get good results. Still, it cannot be determined that SA has the most optimal results, but the time needed by the SA method is faster than the Branch and Bound method. In case I, the percentage difference between the distance generated using the SA method with the B-and-B method is 0%, in case II it is 7% and in case III it is 8%.  
Asymptotic Distribution of an Estimator for Variance Function of a Compound Periodic Poisson Process with Power Function Trend Utama, Muhammad Wiranadi; Mangku, I Wayan; Silalahi, Bib Paruhum
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.10213

Abstract

In this paper, an asymptotic distribution of the estimator for the variance function of a compound periodic Poisson process with power function trend is discussed. The periodic component of this intensity function is not assumed to have a certain parametric form, except it is a periodic function with known period. The slope of power function trend is assumed to be positive, but its value is unknown. The objectives of this research are to modify the existing variance function estimator and to determine its asymptotic distribution. This research begins by modifying the formulation of the variance function estimator. After the variance function is obtained, the research is continued by determining the asymptotic distribution of the variance function estimator of the compound periodic Poisson process with a power function trend. The first result is modification of existing estimator so that its asymptotic distribution can be determined. The main result is asymptotic normality of the estimator of variance function of a compound periodic Poisson process with power function trend.
Co-Authors Agung Prajuhana Putra Agus Buono Amril Aman Amril Aman Annisa, Winda Nur Brigitha, Nabilah Diar Danny Dimas Sulistio Elisabeth Sri Hendrastuti Fadhilah Syafria Fahren Bukhari Faisal Muhammad Faisal Muhammad, Faisal Farida Hanum Fikri, Faiqul Franki Yusuf Bisilisin I Gede Arta Wibawa I Wayan Mangku Inggih Permana Jaharuddin Julio Adisantoso Karlisa Priandana Khoerul Fatihin Marisa Nurbaiti Mayyani, Hidayatul Mu'min, Ulil Albab Mutia Atika Ngakan Nyoman Kutha Krisnawijaya Novanto, Mohamad Nur Ahzan, Zulkaidah Nur Aliatiningtyas Nurbaiti, Marisa Nurul Fathiah Pertiwi, Silviana Eka Prapto Tri Supriyo Putra, Hilman Yusupi Dwi Qomah, Yusti Rachmadini, Haliza Suci Rafika Husnia Munfa'ati Retno Budiarti Sahara, Farahdila Saputra, T Murdani Setyawan, Fajar Siti Aminah Sri Hendrastuti, Elisabeth Sri Nurdiati Sugi Guritman Sumanto, Sumanto Syeiva Nurul Desylvia Toni Bakhtiar Ulil Albab Mu'min Utama, Muhammad Wiranadi Yasin Yusuf