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A combination of TDM and KSAM to determine initial feasible solution of transportation problems Muhammad Sam'an; Ifriza, Yahya Nur
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 | DOI: 10.52465/joscex.v2i1.16

In case of the Transportation Problem (TP), it was found that TP had equal the smallest so that the existing methods will be generated two or more IFS values. The newly developed algorithm is generated through a combination of Total Difference Method (TDM) and Karagul-Sahin Approximation Method (KSAM) algorithm, is capable to determine the initial feasible solution of TP. Based on the numerical illustration of TP example to evaluate the performance of the new proposed algorithm. The computational performances have been compared to the existing methods (TDM1 and KSAM) and the results shown this algorithm achieved better performance than the existing methods for TP example.

Three stages algorithm for finding optimal solution of balanced triangular fuzzy transportation problems Muhammad Sam'an
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 | DOI: 10.52465/joscex.v2i1.24

In the literature, the fuzzy optimal solution of balanced triangular fuzzy transportation problem is negative fuzzy number. This is contrary to the constraints that must be non-negative. Therefore, the three stages algorithm is proposed to overcome this problem. The proposed algorithm consist of segregated method with segregating triangular fuzzy parameters into three crisp parameters. This method avoids the ranking technique. Next, total difference method is used to get initial basic feasible solution (IBFS) value based on segregating triangular fuzzy parameters. While, modified distribution algorithm is used to determine optimal solution based on IBFS velue. In order to illustrate the proposed algorithm is given the numerical example and based on the result comparison, the proposed algorithm equality to the two existing algorithms and better then the one existing algorithm. The proposed algorithm can solve in the fuzzy decision-making problems and can also be extended to an unbalanced fuzzy transportation problem.

Solusi Optimal Masalah Transportasi Fuzzy Penuh Menggunakan Total Integral Ranking dan Ranking Score Method Muhammad Sam'an
Jurnal Pendidikan Matematika (Kudus) Vol 1, No 2 (2018): Jurnal Pendidikan Matematika (Kudus)
Publisher : Institut Agama Islam Negeri (IAIN) Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jmtk.v1i2.4144

Abstract.Masalah transportasi fuzzy penuh merupakan masalah transportasi dimana biaya transportasi, jumlah persediaan, jumlah permintaan dan variabel keputusan dinyatakan dalam bentuk bilangan fuzzy. Untuk memecahkan masalah transportasi fuzzy tersebut, parameter bilangan fuzzy harus diubah ke bilangan crisp yang disebut metode perangkingan bilangan fuzzy. Pada tulisan ini diberikan masalah transportasi fuzzy yang diselesaikan menggunakan algoritma transportasi fuzzy dengan metode perangkingan yang berbeda yaitu total integral ranking dan ranking score method. Algoritma Transportasi Fuzzy dengan perankingan Total integral Ranking menghasilkan solusi dan nilai optimal fuzzy yang lebih besar dibandingankan menggunakan Algoritma Transportasi Fuzzy dengan perankingan menggunakan Ranking Score Methode. Namun itersai yang diilakukan pada Algoritma Transportasi Fuzzy dengan perankingan Total integral Ranking lebih cepat dibandingkan Algoritma Transportasi Fuzzy dengan perankingan Ranking Score Method.

Triangular Fuzzy Time Series for Two Factors High-order based on Interval Variations A. Nafis Haikal; Etna Vianita; Muhammad Sam'an; Bayu Surarso; Susilo Hariyanto
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.v6i3.8627

Fuzzy time series (FTS) firstly introduced by Song and Chissom has been developed to forecast such as enrollment data, stock index, air pollution, etc. In forecasting FTS data several authors define universe of discourse using coefficient values with any integer or real number as a substitute. This study focuses on interval variation in order to get better evaluation. Coefficient values analyzed and compared in unequal partition intervals and equal partition intervals with base and triangular fuzzy membership functions applied in two factors high-order. The study implemented in the Shen-hu stock index data. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 0.28% for Shen-hu stock index daily data. Based on the result, this research can be used as a reference to determine the better interval and degree membership value in the fuzzy time series.

Pelatihan Kecerdasan Artifisial (KA) kepada Guru SD di Kabupaten Blora Jawa Tengah untuk Peningkatan Kemampuan di Bidang Digital Muhammad Munsarif; Samsudi Raharjo; Muhammad Sam'an
Jurnal Surya Masyarakat Vol 5, No 1 (2022): November 2022
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.5.1.2022.101-105

In this era of digitalization, the Government is trying from an early age to introduce programming concepts and Artificial Intelligence, which in the future can be used to support the learning process of elementary school education. This training aims to provide new knowledge about how coding programs are included in the subject matter of elementary school children. The teachers are given lessons on basic things suitable to be applied in class and immediately put into practice their projects. With this method, teachers learn to adapt to the new model, where the material will be taught to students in class. Activities are carried out offline at the Arra Cepu Hotel. Stages of Training through Presentations, Videos, and Quizzes. In this activity, the teachers gain knowledge and skills in coding programs for beginners; pre-test and post-test will be used as a measure to assess it. In the future, teachers will experiment with coding lessons with students. An early introduction to programming and artificial intelligence for elementary school teachers is expected to positively impact students, mainly in their ease of receiving and understanding a lesson.

An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending Much Aziz Muslim; Yosza Dasril; Muhammad Sam'an; Yahya Nur Ifriza
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1002-1011

Internet finance and big data technology are booming in the world. The launch of peer to peer (P2P) lending platforms is a sign and a great opportunity for entrepreneurs to easily increase their capital injection. However, this great opportunity has a high risk of impacting the sustainability and security development of the platform. One way to minimize loan risk is to predict the possibility of loan default. Hence, this study aims to find the best predictive model for predicting loan default of P2P Lending Club dataset. An improved light gradient boosting machine (LightGBM) via features selection by using swarm algorithms i.e. Ant colony optimization (ACO) and bee colony optimization (BCO) to the prediction analysis process. The best feature selection process is selected 6 out of 18 features. The synthetic minority oversampling technique (SMOTE) method is also provided to solve the unbalance class problem in the dataset, then a series of operations such as data cleaning and dimension reduction are performed. The experimental results prove that the LightGBM algorithm has been successfully improved. This success is shown by the prediction accuracy of LightGBM+ACO is 95.64%, LighGBM+BCO is 94.70% and LightGBM is 94.38%. This success also demonstrates outstanding performance in predicting loan default and strong generalizations.

The cross-association relation based on intervals ratio in fuzzy time series Etna Vianita; Muhammad Sam'an; A. Nafis Haikal; Ina Salamatul Mufaricha; Redemtus Heru Tjahjana; Titi Udjiani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2040-2051

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modified steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross-association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals.

Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning Muhammad Munsarif; Muhammad Sam’an; Safuan Safuan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3927

Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.

Pelatihan Kecerdasan Artifisial (KA) kepada Guru SD di Kabupaten Blora Jawa Tengah untuk Peningkatan Kemampuan di Bidang Digital Muhammad Munsarif; Samsudi Raharjo; Muhammad Sam'an
Jurnal Surya Masyarakat Vol 5, No 1 (2022): November 2022
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.5.1.2022.101-105

In this era of digitalization, the Government is trying from an early age to introduce programming concepts and Artificial Intelligence, which in the future can be used to support the learning process of elementary school education. This training aims to provide new knowledge about how coding programs are included in the subject matter of elementary school children. The teachers are given lessons on basic things suitable to be applied in class and immediately put into practice their projects. With this method, teachers learn to adapt to the new model, where the material will be taught to students in class. Activities are carried out offline at the Arra Cepu Hotel. Stages of Training through Presentations, Videos, and Quizzes. In this activity, the teachers gain knowledge and skills in coding programs for beginners; pre-test and post-test will be used as a measure to assess it. In the future, teachers will experiment with coding lessons with students. An early introduction to programming and artificial intelligence for elementary school teachers is expected to positively impact students, mainly in their ease of receiving and understanding a lesson.

Peningkatan Kompetensi Guru Madrasah Ibtidayah Duren dan Sabilul Huda Bandungan melalui Pelatihan Pembelajaran Berbasis Teknologi Informasi Ahmad Ilham; Akhmad Fathurrohman; Muhammad Sam'an; Safuan Safuan; Muhammad Munsarif; Luqman Assaffat; Asdani Kindarto; Arfido Ramadhani; Juyus Muhammad Adinullhaq; Febrianto Febrianto; Irvan Nurmantoro; Yevi Alviatul Ardhani; Nova Ariyanto
Jurnal Surya Masyarakat Vol 5, No 2 (2023): Mei 2023
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.5.2.2023.264-269

Madrasah Ibtidaiyah (MI) Duren Village and Sabilul Huda Jimbaran Bandungan District Semarang Regency want to produce quality graduates. However, the competence of teachers is still conventional learning aids so the learning process is not optimal. To answer this problem, the Department of Informatics, Faculty of Engineering at Universitas Muhammadiyah Semarang, Indonesia proposed information technology-based learning training activities for madrasah teachers. The purpose program is to strengthen human resources for teachers in MI Desa Duren and Sabilul Huda Jimbaran. The proposed program is divided into three learning schemes, (1) interactive presentation media, (2) online classroom learning, and (3) online learning evaluation. The results of this program are that the participants proved to be able to produce effective, elaborative, and interactive teaching materials based on information technology so that students are not bored and enthusiastic about following lessons in the classroom. It can be cancluded the program with the theme "Strengthening Teacher Competencies Through Information Technology-Based Learning Training" can overcome problems in Madrasah Ibtidaiyah (MI) Duren Village and Sabilul Huda Jimbaran Bandungan District Semarang Regency.

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