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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EKSAKTA: Journal of Sciences and Data Analysis Katalogis International Journal of Advances in Intelligent Informatics Al-Ahwal: Jurnal Hukum Keluarga Islam Prosiding Seminar Nasional INDOCOMPAC Asy-Syir'ah: Jurnal Ilmu Syari'ah dan Hukum JOIN (Jurnal Online Informatika) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Riset Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Dialog Jurnal Berdaya Mandiri Indonesian Journal of Electrical Engineering and Computer Science Mathematics and Applications (MAp) Journal Enthusiastic : International Journal of Applied Statistics and Data Science MAQASIDI Bulletin of Applied Mathematics and Mathematics Education Bulletin of Social Informatics Theory and Application Al-Hukama: The Indonesian Journal of Islamic Family Law Jurnal Akuakultur SEBATIN
Muhammad Muhajir, Muhammad
Department Of Statistics, Universitas Islam Indonesia
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K-affinity propagation clustering algorithm for the classification of part-time workers using the internet Novendri Isra Asriny; Muhammad Muhajir; Devi Andrian
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp464-472

Abstract

There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.
Random Forest Method Approach to Customer Classification Based on Non-Performing Loan in Micro Business Muhammad Muhajir; Julia Widiastuti
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.842

Abstract

This study aims to classify potential customers’ characteristics based on non- performing loans through the random forest method. This research uses data obtained from Syariah Mandiri Bank branch in Jambi, which includes data on micro-financing customers in years 2016–2020. The random forest method is used for analysis. The novelty of this work is that, unlike existing researches that used other soft-computing methods, we employ Random Forest method, specifically using an imbalanced class sampling technique. The obtained results show that credit risk can be estimated by taking into account factors such as age, monthly installments, margin, price of insurance, loan principal, occupation, and long installments. The research results indicate that the sensitivity, precision, and G-mean value increase compared to using the original data. Random forest with oversampling technique has the high Area Under the ROC Curve score that is equal to 66.69%.
APPLICATION OF FUZZY PRINCIPAL COMPONENT ANALYSIS FOR THE BEST ALTERNATIVE TOURIST ATTRACTION IN YOGYAKARTA Aditya Rizq Herlandy Karjawan; Muhammad Muhajir
MAp (Mathematics and Applications) Journal Vol 5, No 1 (2023)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/map.v5i1.6046

Abstract

One of the regions in Indonesia that depends on the tourism sector is the Special Region of Yogyakarta Province. The Province of the Special Region of Yogyakarta itself every year becomes a tourist destination province for vacations, many tourists do not know which places are the best choices for tourists. Therefore, this research aims to make it easier for tourists to obtain information on the best tourist spots in the Province of the Special Region of Yogyakarta. Based on the data obtained from the results of distributing online questionnaires during June 2022 – July 2022. The results of the study using the Fuzzy Principal Component Analysis method, it was found that the best tourist attraction is the Prambanan Temple tourist attraction with a PCA score of 0.677057 which means that Prambanan Temple becomes a tourist attraction. The most sought after by tourist visitors, followed by the Malioboro Street Area with a PCA score of 0.48146 and Parangtirtis Beach with a PCA score of 0.369162. The results of this study are expected to help tourists determine
COMPARISON OF ACCURACY BETWEEN NEURAL NETWORK AND REGRESSION MODELS IN FORECASTING Hermansah Hermansah; Muhammad Muhajir
MAp (Mathematics and Applications) Journal Vol 5, No 1 (2023)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/map.v5i1.5949

Abstract

This research discusses the forecasting of the median value of owner occupied homes (MEDV) using all the other continuous variables available in the Boston dataset. The Boston dataset is a collection of data about housing values in the suburbs of Boston. The used method is Feed Forward Neural Network (FFNN) and the multiple linear regression method as a comparison. The result of the research indicates that the FFNN method is better than multiple linear regression in forecasting the median value of owner occupied homes (MEDV) using all the other continuous variables available in the Boston dataset. It is proven that the MSE and MAPE value of using the FFNN method is 15.7518 and 0.14563, whereas the value of multiple linear regression is 31.2630 and 0.21040. Based on this result, the research can be concluded that the FFNN method has the smaller MSE value, the result of the forecasting is more accurate.
Improving the term weighting log entropy of latent dirichlet allocation Muhammad Muhajir; Dedi Rosadi; Danardono Danardono
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp455-462

Abstract

The process of analyzing textual data involves the utilization of topic modeling techniques to uncover latent subjects within documents. The presence of numerous short texts in the Indonesian language poses additional challenges in the field of topic modeling. This study presents a substantial enhancement to the term weighting log entropy (TWLE) approach within the latent dirichlet allocation (LDA) framework, specifically tailored for topic modeling of Indonesian short texts. This work places significant emphasis on the utilization of LDA for word weighting. The research endeavor aimed to enhance the coherence and interpretability of an Indonesian topic model through the integration of local and global weights. Local Weight focuses on the distinct characteristics of each document, whereas global weight examines the broader perspective of the entire corpus of documents. The objective was to enhance the effectiveness of LDA themes by this amalgamation. The TWLE model of LDA was found to be more informative and effective than the TF-IDF LDA when compared with short Indonesian text. This work improves topic modeling in brief Indonesian compositions. Transfer learning for NLP and Indonesian language adaptation helps improve subject analysis knowledge and precision, this could boost NLP and topic modeling in Indonesian.
Application of rule association with algorithm apriori of disaster residental fires Muhajir, Muhammad; Gusmayyeni, Gusmayyeni; Sari, Redita Anggita; Rahmatika, Tusriana
Bulletin of Social Informatics Theory and Application Vol. 1 No. 2 (2017)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v1i2.27

Abstract

Data mining is a technique of decision making by means of extracting information based on historical data of existing data in a large database. One of technique in data mining, is association rule algorithm where the method is searching for a set of items that frequently appear together. This study will use data association rule mining method for data processing Fire Disaster settlements in Indonesia because we want to know what information is often occur together in the event of fire disaster settlement. From the analysis associative relationship, event's pattern that occur from residential fires in Indonesia which the data is from the beginning of January 2015 to June 2015, support the highest value that the event of catastrophic fires in settlements in the afternoon resulted in broken homes with a value of 0.8148148 support and confident value of 1.0292398.
PENGELOMPOKAN STUNTING MENGGUNAKAN METODE K-MEDOIDS DI DAERAH ISTIMEWA YOGYAKARTA (DIY) Cahyani, Amalia Rizki; Muhajir, Muhammad; Puspita, Ersa Riga; Pratiwi, Lathifah Aliya
MAp (Mathematics and Applications) Journal Vol 6, No 1 (2024)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/map.v6i1.8523

Abstract

Stunting, or the condition of short stature in toddlers, is a problem caused by prolonged insufficient nutrition intake. This issue arises from inadequate feeding practices that do not meet the nutritional needs of a toddler. Stunting can begin during fetal development and becomes apparent around the age of two. Several factors contribute to stunting, including high-risk levels, lack of adequate housing, lack of proper sanitation facilities, lack of access to safe drinking water, and inadequate family income. This study employs k-medoids cluster analysis to identify the grouping of sub-districts in the Yogyakarta Special Region (DIY Province) based on stunting risk factors. The research findings indicate that Cluster 1 has the highest rates of stunting and lack of family income, Cluster 4 has the highest instances of inadequate access to safe drinking water and housing, and Cluster 5 has the highest rates of inadequate sanitation facilities.
PENERAPAN TEKNOLOGI MESIN PEMOTONG SINGKONG UNTUK MENINGKATKAN PRODUKTIVITAS UMKM DI DUSUN MIRI, POLOKARTO, SUKOHARJO Guritno, Muhammad Wahyu Suryo; Muhajir, Muhammad; Saputra, Nurirwan
Jurnal Berdaya Mandiri Vol. 6 No. 2 (2024): JURNAL BERDAYA MANDIRI (JBM)
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jbm.v6i2.6425

Abstract

This research aims to design and develop an efficient and effective cassava-cutting machine for use by MSME players in Miri, Bulu, Polokarto, Sukoharjo Regency. Cassava is one of the important natural resources in the region, and cutting cassava manually often takes significant time and effort. The research method used is a field survey to identify the needs and problems faced by MSMEs in Miri sub-village. The results of this survey became the basis for designing a cassava-cutting machine that suits local conditions. This machine is designed by considering aspects of ergonomics, efficiency, and user safety. The results showed that the designed cassava cutting machine can cut cassava quickly and accurately, reducing the time and effort required in the cutting process. In addition, this cassava-cutting machine has the potential to increase the productivity of MSMEs. With the use of this machine, it is expected to reduce heavy manual labor and allow MSME players to focus on other more value-added activities. This research is a positive contribution to supporting the development of MSMEs in Miri sub-village and can be a model for the development of similar machines in other areas. This cassava-cutting machine is expected to provide significant benefits for MSME actors.  Keyword: Cassava, Cutting Machine, MSMEs
Random Forest Method Approach to Customer Classification Based on Non-Performing Loan in Micro Business Muhajir, Muhammad; Widiastuti, Julia
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.842

Abstract

This study aims to classify potential customers’ characteristics based on non- performing loans through the random forest method. This research uses data obtained from Syariah Mandiri Bank branch in Jambi, which includes data on micro-financing customers in years 2016–2020. The random forest method is used for analysis. The novelty of this work is that, unlike existing researches that used other soft-computing methods, we employ Random Forest method, specifically using an imbalanced class sampling technique. The obtained results show that credit risk can be estimated by taking into account factors such as age, monthly installments, margin, price of insurance, loan principal, occupation, and long installments. The research results indicate that the sensitivity, precision, and G-mean value increase compared to using the original data. Random forest with oversampling technique has the high Area Under the ROC Curve score that is equal to 66.69%.
Identifying malaria disease through red-blood microscopic image with XGBoost and random forest methods Fajriyah, Rohmatul; Muhajir, Muhammad; Abdullah, Ahmad Hussain; Ayu, Devina Gilar; Rahman, Iqbal Fathur
Bulletin of Applied Mathematics and Mathematics Education Vol. 4 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v4i2.11740

Abstract

Blood cells that flow in the human body provide information to diagnose a disease. The information provided can be obtained through images of these blood cells using image processing techniques. Malaria is a very deadly disease and can affect everyone. Patients with malaria will experience anaemia because the red blood cells or erythrocytes are contaminated with plasmodium. This study offers an alternative solution to malaria disease identification through the image classification of red blood cells, by applying image processing and image classification methods with XGBoost and random forest. The research was conducted using the R programming language in RStudio and Python. The accuracy of XGBoost and random forest methods were 71.26% and 77.58%, respectively. Therefore, the random forest provided a better optimal classification model with higher accuracy. The model is used to build an application which is R web-based, RShiny. In practice, this application can be used by health workers in classifying patients based on red blood cell images such that the health centre would be easier to manage the existing patients.
Co-Authors Abdullah, Ahmad Hussain Aditya Rizq Herlandy Karjawan Alhaidar, Nihrul Bahi Ali, Asriyanti Annisa Ayunda Permata Sari Ayu, Devina Gilar Baehaqi Berky Rian Efanna Cahyani, Amalia Rizki Canas Rodrigues, Paulo Danardono Danardono Danardono, Danardono Dedi Rosadi Devi Andrian Fatahillah, Zahrul Guritno, Muhammad Wahyu Suryo Gusmayyeni, Gusmayyeni Henni Syawal Hermansah Hermansah Hermansah Iesje Lukistyowati Inggrid Septa Narendra Jaka Nugraha Julia Widiastuti Makhtum, Ahmad Rohiqim Muhaimin Muhaimin Muhammad Fadli Kamil Mustain, Riefyal Arshyza Novendri Isra Asriny Nurirwan Saputra, Nurirwan Pandri Ferdias Pandri Ferdias, Pandri Pratiwi, Lathifah Aliya Primananda, Dhea Laksmita Arsya Puspita, Ersa Riga Qurratul Uyun, Qurratul Rachmad Febrian Rafendra Agustianda Putra Rahmadi Yotenka Rahman, Iqbal Fathur Rahmatika, Tusriana Riefyal Arshyza Mustain Rohmatul Fajriyah Rosadi , Dedi Sari, Redita Anggita Syamsud Dhuha Uswatun Hasanah Widiastuti, Julia Wulandari, Kanth Yayi Diyah Palupi Zetta Nillawati Reyka Putri