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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer UNEJ e-Proceeding Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTAM (Jurnal Teori dan Aplikasi Matematika) Contemporary Mathematics and Applications (ConMathA) Tensor: Pure and Applied Mathematics Journal InEJ: Indonesian Engagement Journal Moneter : Jurnal Keuangan dan Perbankan
Herry Suprajitno
Departemen Matematika Fakultas Sains Dan Teknologi, Universitas Airlangga
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Materials Science & Nanotechnology Mathematics Social Sciences

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Grasshopper Optimizaton Algorithm (GOA) untuk Menyelesaikan Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) Anatasia Naomi; Asri Bekti Pratiwi; Herry Suprajitno
Tensor: Pure and Applied Mathematics Journal Vol 3 No 2 (2022): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol3iss2pp73-84

Abstract

The purpose of this paper is to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) using the Grasshopper Optimization Algorithm (GOA). Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a problem of forming routes that serve each customer, by delivering and retrieving simultaneously. The purpose of VRPSPD is to minimize the total mileage to serve all customers with the limit that each customer is served exactly once, and the vehicle load does not exceed its maximum capacity. Grasshopper Optimization Algorithm (GOA) is an algorithm inspired by nature by mimicking the living behavior of grasshopper swarms in search of food sources. GOA has several main stages, namely initialization of parameters, determination of target grasshoppers, calculating the coefficient of decline, calculating the distance between grasshoppers, and calculating the new position of the grasshoppers. Implementation of the GOA program to complete VRPSPD using the C++ programming language using 3 types of data, data with 13 customers, 22 customers, and 100 customers. Based on the results of the running program, it can be concluded that the more iterations and the number of populations, the solution obtained tends to be better.
Cuckoo Search Algorithm untuk Menyelesaikan Bi-Objective Permutation Flowshop Scheduling Problem Asri Bekti Pratiwi; Herry Suprajitno; Siti Sarah
Tensor: Pure and Applied Mathematics Journal Vol 2 No 1 (2021): Tensor : Pure And Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol2iss1pp5-12

Abstract

Tujuan dari penelitian ini adalah menyelesaikan permasalahan Bi-objective Permutation Flowshop Scheduling Problem (BPFSP) menggunakan Cuckoo Search Algorithm (CSA). BPFSP memiliki lebih dari satu fungsi tujuan yaitu meminimalkan makespan dan total tardiness. Program penerapan CSA untuk menyelesaikan BPFSP diimplementasikan dalam kasus dengan tiga jenis data yaitu data kecil dengan 5-pekerjaan 4-mesin, data sedang dengan 20-pekerjaan 10-mesin, dan data besar 50-pekerjaan 20-mesin dengan penggunaan beberapa nilai parameter yang bervariasi diantaranya maksimum iterasi, banyaknya sarang serta probabilitas pergantian sarang. Berdasarkan hasil running pada ketiga jenis data diperoleh bahwa semakin banyak jumlah sarang serta iterasi maka akan memberikan nilai fungsi tujuan BPFSP yang cenderung lebih baik. Sebaliknya, nilai fungsi tujuan BPFSP akan cenderung lebih baik jika nilai probabilitas pergantian sarang semakin kecil.
Enhancing Spare Parts Management through Support Vector Regression: A Case Study in the Service and Maintenance Industry Riwayadi, Eko; Suprajitno, Herry; Miswanto, Miswanto
Moneter: Jurnal Keuangan dan Perbankan Vol. 12 No. 2 (2024): JULI
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/moneter.v12i2.866

Abstract

This study investigates the effectiveness of Support Vector Regression (SVR) in accurately predicting spare part usage within the service and maintenance industry, with a specific focus on coolers and freezers. Leveraging historical data from Hutama spanning January 2019 to December 2023, the SVR model successfully forecasts future spare parts demand with remarkable precision. Through rigorous parameter tuning using the grid search with optimization method, the SVR model achieves optimal performance, yielding reliable predictions with MAPE 8.55% and RMSE 9.43. Despite its effectiveness, limitations include the study's narrow focus on coolers and freezers within the service and maintenance sector, potential influences of external factors on prediction accuracy, and assumptions regarding the linear or nonlinear patterns in spare parts usage data.
Optimasi Bobot K-Means Clustering untuk Mengatasi Missing Value dengan Menggunakan Algoritma Genetica Khotimah, Bain Khusnul; Syarief, Muhammad; Miswanto, Miswanto; Suprajitno, Herry
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 4: Agustus 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021844912

Abstract

Nilai yang hilang membutuhkan preprosesing dengan teknik imputasi untuk menghasilkan data yang lengkap. Proses imputasi membutuhkan initial bobot yang sesuai, karena data yang dihasilkan adalah data pengganti. Pemilihan nilai bobot yang optimal dan kesesuaian nilai K pada metode K-Means Imputation (KMI) merupakan masalah besar, sehingga menimbulkan error semakin meningkat. Model gabungan algoritma genetika (GA) dan KMI atau yang dikenal GAKMI digunakan untuk menentukan bobot optimal pada setiap cluster data yang mengandung nilai yang hilang. Algoritma genetika digunakan untuk memilih bobot dengan menggunakan pengkodean bilangan riel pada kromosom. Model hybrid GA dan KMI dengan pengelompokan menggunakan jumlah jarak Euclidian setiap titik data dari pusat clusternya. Pengukuran kinerja algoritma menggunakan fungsi kebugaran optimal dengan nilai MSE terkecil. Hasil percobaan data hepatitis menunjukkan bahwa GA efisien dalam menemukan nilai bobot awal optimal dari ruang pencarian yang besar. Hasil perhitungan menggunakan nilai MSE =0.044 pada K=3 dan replika ke-5 menunjukkan kinerja GAKMI menghasilkan tingkat kesalahan yang rendah untuk data hepatitis dengan atribut campuran. Hasil penelitian dengan menggunakan pengujian tingkat imputasi menunjukkan algoritma GAKMI menghasilkan nilai r = 0.526 lebih tinggi dibandingkan dengan metode lainnya. Penelitian ini menunjukkan GAKMI menghasilkan nilai r yang lebih tinggi dibandingkan metode imputasi lainnya sehingga dianggap paling baik dibandingkan teknik imputasi secara umum.  AbstractMissing values require preprocessing techniques as imputation to produce complete data. Complete data imputation results require the appropriate initial weights, because the resulting data is replacement data. The choice of the optimal weighting value and the suitability of the network nodes in the K-Means Imputation (KMI) method are big problems, causing increasing errors. The combined model of Genetic Algorithm (GA) and KMI is used to determine the optimal weights for each data cluster containing missing values. Genetic algorithm is used to select weights by using real number coding on chromosomes. GA is applied to the KMI using clustering calculated using the sum of the Euclidean distances of each data point from the center of the cluster. Performance measurement algorithms using the fitness function optimally with the smallest MSE value. The results of the hepatitis data experiment show that GA is efficient in finding the optimal initial weight value from a large search space. The results of calculations using the MSE value = 0.04 for K = 3 and the 5th replication. So, GAKMI resulted in a low error rate for mixed data. The results of research using imputation level testing performed GAKMI  produced r = 0.526 higher than the other methods. Thus, the higher the r value, the best for the imputation technique.
Best Architecture Recommendations of ANN Backpropagation Based on Combination of Learning Rate, Momentum, and Number of Hidden Layers Syaharuddin, Syaharuddin; Fatmawati, Fatmawati; Suprajitno, Herry
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.v6i3.8524

Abstract

This article discusses the results of research on the combination of learning rate values, momentum, and the number of neurons in the hidden layer of the ANN Backpropagation (ANN-BP) architecture using meta-analysis. This study aims to find out the most recommended values at each learning rate and momentum interval, namely [0.1], as well as the number of neurons in the hidden layer used during the data training process. We conducted a meta-analysis of the use of learning rate, momentum, and number of neurons in the hidden layer of ANN-BP. The eligibility data criteria of 63 data include a learning rate of 44 complete data, the momentum of 30 complete data, and the number of neurons in the hidden layer of 45 complete data. The results of the data analysis showed that the learning rate value was recommended at intervals of 0.1-0.2 with a RE model value of 0.938 (very high), the momentum at intervals of 0.7-0.9 with RE model values of 0.925 (very high), and the number of neurons in the input layer that was smaller than the number of neurons in the hidden layer with a RE model value of 0.932 (very high). This recommendation is obtained from the results of data analysis using JASP by looking at the effect size of the accuracy level of research sample data.
Co-Authors Anatasia Naomi Asri Bekti Pratiwi, Asri Bekti Atika Dwi Hanun Amalia Auli Damayanti Bain Khusnul Khotimah Bella Pristianisa Subari Derby Prayogo Samdean Edi Winarko Fatmawati Fatmawati Fatmawati, Fatmawati Fatmawati, Fatmawati Ihda Septiyafi Kiswara A. Santoso Laula Ika Setya Rahman Miswanto Miswanto Muhammad Syarief, Muhammad Piping Prabawati Quinn Nathania PJY Riwayadi, Eko Siti Sarah Syaharuddin Syaharuddin Syaharuddin Syaharuddin Syaharuddin Yayuk Wahyuni Zeni Murtafiani