Article Per Year (5 Year)
p-Index From 2021 - 2026
3.138
P-Index
This Author published in this journals
All Journal JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN Jurnal Manajemen dan Agribisnis Jurnal Keuangan dan Perbankan TELKOMNIKA (Telecommunication Computing Electronics and Control) SEMIRATA 2015 BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics International Journal of Industrial Optimization (IJIO) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Milang Journal of Mathematics and Its Applications Jurnal Matematika Integratif Jurnal Ekonomi dan Pembangunan Indonesia MILANG Journal of Mathematics and Its Applications
Toni Bakhtiar
Departemen Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor
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 Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
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.
PENJADWALAN TENAGA SUKARELAWAN DI KABUPATEN PURWOREJO MENGGUNAKAN INTEGER PROGRAMMING Amriyah, Amanatul; Bakhtiar, Toni; Hanum, Farida; Supriyo, Prapto Tri; Mayyani, Hidayatul
MILANG Journal of Mathematics and Its Applications Vol. 18 No. 2 (2022): 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.18.2.155-167

Abstract

Kabupaten Purworejo termasuk salah satu kabupaten rawan bencana di Jawa Tengah. Daerah rawan bencana membutuhkan banyak tenaga sukarelawan ketika bencana terjadi. Saat ini, sukarelawan di Kabupaten Purworejo memiliki latar belakang profesi yang beragam dan masih terbatas jumlahnya. Keberagaman profesi dan keterbatasan jumlah tersebut membutuhkan pengaturan yang baik sehingga sukarelawan dapat bekerja secara optimal. Penelitian ini membahas penjadwalan tenaga sukarelawan di Kabupaten Purworejo yang dimodelkan menggunakan integer programming. Fungsi objektif masalah ini adalah memaksimumkan preferensi sukarelawan terhadap sektor kerja, shift waktu, dan jarak antara lokasi domisili sukarelawan dengan titik lokasi bencana. Penyelesaian masalah ini menggunakan software LINGO 17.0 menghasilkan jadwal sukarelawan selama satu periode yang memenuhi semua kendala dan memaksimumkan preferensi sukarelawan.
PENENTUAN RUTE DISTRIBUSI RASTRA MENGGUNAKAN ALGORITMA GENETIKA Syakina, Lana; Bakhtiar, Toni; Hanum, Farida; Supriyo, Prapto Tri
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.19.2.97-115

Abstract

Proses distribusi produk yang dilakukan oleh produsen untuk memenuhi kebutuhan konsumen sering kali melibatkan penyelesaian masalah perutean kendaraan. Vehicle routing problem (VRP) dapat digunakan untuk menentukan rute dan alokasi kendaraan agar biaya distribusi minimum. Penelitian ini membahas masalah penentuan rute distribusi beras untuk keluarga sejahtera (rastra) dari gudang penyimpanan Perum Bulog di Kabupaten Ponorogo ke beberapa desa/kelurahan. Periode pendistribusian yang diambil dalam penelitian ini hanya satu dari dua belas periode yang tersedia. Terdapat desa/kelurahan dengan permintaan yang melebihi kapasitas kendaraan, sehingga memerlukan distribusi terpisah. VRP dapat diselesaikan menggunakan metode eksak maupun heuristik. Dalam penelitian ini, metode heuristik yang digunakan adalah algoritma genetika dengan solusi awal diperoleh dari metode nearest neighbour untuk distribusi beras di Perum Bulog. Dari hasil implementasi diperoleh rute kendaraan yang meminimumkan biaya distribusi dan memenuhi semua kendala yang ada menggunakan algorima genetika dan diberikan pula hasil perbandingannya dengan solusi dari metode eksak.
PERENCANAAN PRODUKSI MENGGUNAKAN FUZZY GOAL PROGRAMMING: STUDI KASUS PRODUKSI JILBAB PADA KONVEKSI RUMAHAN Suhairi, Rafif; Supriyo, Prapto Tri; Mayyani, Hidayatul; Bakhtiar, Toni; Julianto, Mochamad Tito
MILANG Journal of Mathematics and Its Applications Vol. 20 No. 1 (2024): 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.20.1.43-53

Abstract

Perencanaan produksi adalah salah satu strategi untuk menghadapi berbagai permasalahan dalam proses produksi. Tujuan yang ingin dicapai dalam perencanaan produksi cukup bervariasi. Seringkali tujuan-tujuan tersebut bersifat samar sehingga mengandung ketidakpastian atau fuzzy. Penelitian ini bertujuan memodelkan masalah perencanaan produksi sebagai model fuzzy goal programming (FGP). Ketidakpastian pada fungsi tujuan diselesaikan menggunakan fungsi keanggotaan fuzzy. Fungsi keanggotaan fuzzy dibangun dengan refererensi batas optimal dari masing-masing fungsi tujuan dan batas toleransi yang diinginkan oleh pembuat keputusan. Model FGP diformulasikan dengan pendekatan max-min Bellman-Zadeh. Model diimplementasikan menggunakan bantuan software LINGO pada industri rumahan konveksi jilbab di Kebon Jeruk, Jakarta Barat dengan fungsi tujuan meminimumkan waktu produksi dan memaksimumkan keuntungan. Hasil implementasi menghasilkan tingkat produksi jilbab yang mengoptimalkan fungsi tujuan, yaitu meminimumkan waktu produksi dan memaksimumkan keuntungan. Minimum waktu produksi yang diperoleh 2105 menit, sedangkan maksimum keuntungan yang diperoleh yaitu Rp 3.460.295,68.
Optimalitas Rute pada Pengiriman Multiperjalanan dengan Armada Kendaraan Listrik Heterogen Salsabilla, Kamilia; Bakhtiar, Toni; Hanum, Farida
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23993

Abstract

Electric vehicles are emerging as a key trend in sustainable mobility, mitigating emissions, and reducing dependence on fossil fuels. The challenge in optimizing route modeling lies in some limitations, such as battery range, charging time, and the diversity of electric vehicle types. This article explores the optimality of routes in a multiple-trip distribution system using a heterogeneous fleet of electric vehicles. The electric vehicle routing problem is formulated as a mixed-integer linear programming aiming to find the most cost-efficient optimal route. A notable feature of the model allows electric vehicle fleets to undertake additional travel to complete distribution tasks, i.e., multiple trips. The model is implemented in two illustrative examples involving the delivery of goods using homogeneous and heterogeneous electric vehicle fleets characterized by loading and battery capacities. Each case includes one depot, 8 and 10 customers, and 2 battery swapping stations, solved using the branch-and-bound method through Lingo 18.0. Simulation results indicate that battery capacity and the presence of battery swapping stations significantly influence the routes selection.
Co-Authors Amalia, Imas Saumi Amanatul Amriyah Amril Aman Amriyah, Amanatul Annisa, Winda Nur Bambang Juanda Bib Paruhum Silalahi Bustanul Arifin Cahyona, Dwizani Vinoma Dedi Budiman Hakim Endy Jeri Suswono Erliza Noor Farhana Zahrotunnisa Farida Hanum Farida Hanum Farida Hanum Ferry Syarifuddin JAENAL EFFENDI Jaharuddin Kinanti, Yoanda Astri Ayu Kurniawan, Benny Robby Lana Syakina Luciana, Shelen Maya Widyastiti Mayyani, Hidayatul Mochamad Tito Julianto Nunung Nuryartono Nur Azizah Paian Sianturi Permana, Rendy Candra Prapto Tri Supriyo Qomah, Yusti Rachman, Muhammad Ibrahim Rafif Suhairi Rahma Nurlailawati Salsabilla, Kamilia Sugema, Iman Suhairi, Rafif Syakina, Lana Tan, Elfina