Article Per Year (5 Year)
p-Index From 2021 - 2026
4.221
P-Index
This Author published in this journals
All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Science and Technology: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science
Jazman, Muhammad
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Social Sciences Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : building of informatics technology and science

 1 
Analisis Kepuasan Pengguna Sistem Informasi E-Campus Menggunakan Metode E-Servqual Dan Model Kano Nurhadi, Muhammad; Hamzah, Muhammad Luthfi; Ahsyar, Tengku Khairil; Jazman, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2691

Abstract

This study aims to identify the dimensions and service attributes that need to be improved, as well as improvements related to the quality of services provided by the e-campus system to students at UIN Mahmud Yunus Batusangkar. It also aims to identify the quality of services provided by the e-campus system to students. in order to provide advice or consideration to improve the quality of the e-campus system to the campus or developer, which in turn will produce a solution to improve the quality of the e-campus system for user satisfaction. The procedures used include 5 stages are planning, gathering information processing information analysis and the latest reporting review. The research results created calculations using e-servqual procedures of 0.92 ≤ 1, showing that the quality of the services provided could be said to be unable to fulfill students' expectations. If the level of performance is met, it will bring about a very large increase in user satisfaction. But if not fulfilled, it will reduce the level of satisfaction. Whereas the canoe model gets eight attributes included in the must-be category, eight one-dimensional attributes and six attractive attributes. The ranking level obtained is a source of attributes that are prioritized to be improved. Every attribute that has a very large negative value will also be less effective in achieving user satisfaction
Analisis Loyalitas Pelanggan Berdasarkan Model LRFM Menggunakan Metode K-Means Putri, Runi Aulia; Jazman, Muhammad; Syaifullah, Syaifullah; Rahmawita, Medyantiwi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6565

Abstract

In the era of intense competition in the beauty industry, it is important for companies to understand customer behavior and identify loyal customer segments. Ths study aims to analyze customer loyalty at the Lanona Skincare Beauty clinic using the LRFM (Length, Recency, Frequency, Monetary) model with the K-Means Clustering method. Beauty clinics have not implemented CRM as part of theur business strategy. There is ineffective marketing strategies. Customer transaction data from April to October 2023 was collected and analyzed to determine customer value based on LRFM parameters. The analysis results show that K-Means is effetive in grouping cutomers until the best three clusters are obtained. Cluster 1 with a results of 0,620 is the most loyal customers, cluster 2 with a results of 0,100 is grouped into new inactive customers and cluster 3 with a results of 0,353 is high frequency customers but low revenue contribution. The proposed marketing strategies for each cluster include rewarding an improving communication to maintain customers loyalty. This research provides valuable insights for Lanona Skincare Beauty Clinic in creating a more focused and succesfull marketing plan to increase customer happiness and loyalty.
Analisis Sentimen Pada Ulasan Aplikasi Bank Syariah Indonesia Mobile Menggunakan Support Vector Machine dan Naïve Bayes Aqilla, Nabila Fadia; Jazman, Muhammad; Syaifullah, Syaifullah; Rahmawita, Medyantiwi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6567

Abstract

The internet plays a crucial role in facilitating various human activities, including in the field of electronic banking services, which encompasses various financial services such as ATMs, internet banking, SMS banking, and mobile banking. All of these aim to enhance service quality with a focus on security, convenience, and effectiveness. BSI is one of the banks offering mobile banking services. Based on user reviews, the BSI Mobile app often experiences technical issues such as bugs and transaction failures. To assess the level of satisfaction with the app, the researcher uses sentiment analysis methods. This method also helps potential customers identify aspects that need improvement or development in the products and services to enhance their quality. The study employs Support Vector Machine (SVM) and Naïve Bayes algorithms. The test results show that the Naïve Bayes algorithm achieves an accuracy of 74.37%, recall of 74.37%, precision of 75.46%, and an F1-score of 74.5%. Meanwhile, the SVM algorithm achieves an accuracy of 77.39%, precision of 77.8%, recall of 77.39%, and an F1-score of 77.38%. These findings indicate that SVM performs better in sentiment classification tasks compared to Naïve Bayes. With its superior performance, SVM is the more suitable algorithm for analyzing user perceptions of the BSI Mobile app. Therefore, the findings of this study can contribute to the development of more innovative digital service strategies and enhance competitiveness in the digital era.
Analisis Sentimen Masyarakat Menggunakan Algoritma Long Short Term Memory (LSTM) Pada Ulasan Aplikasi Halodoc Yulianti, Nelvi; Afdal, M; Jazman, Muhammad; Megawati, Megawati; Anofrizen, Anofrizen
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7243

Abstract

Halodoc is a digital healthcare platform that provides users with convenient access to medical services online. This study aims to analyze public sentiment toward the Halodoc application based on 1,416 user reviews collected during the period from July to September 2024. The reviews are categorized into three sentiment classes: positive, negative, and neutral, using the Long Short-Term Memory (LSTM) algorithm. Prior to classification, the Word2Vec technique is applied to transform the words in the reviews into numerical vector representations for processing by the model. The analysis revealed that a portion of the reviews expressed negative sentiments, mainly concerning delays in medication delivery and slow responses from customer service. Model performance evaluation shows that the implementation of the LSTM algorithm optimized with the Adam (Adaptive Moment Estimation) optimizer and a dropout rate of 0.2 achieved the highest accuracy of 89.40% and an F1-score of 88.63%. These results indicate that the model performs very well in classifying sentiments and can be used as a useful tool for understanding user satisfaction with the Halodoc application.
Integrating Support Vector Machines and Geospatial Analysis for Enhanced Tuberculosis Case Detection and Spatial Mapping Jannah, Miftahul; Jazman, Muhammad; Afdal, M; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7158

Abstract

Tuberculosis (TB) remains a significant global health problem, with Indonesia ranking third in the world in terms of TB burden. Riau Province recorded 13,007 notified TB cases in 2022 with a Case Notification Rate (CNR) of 138 per 100,000 population, still far from the national target. This study aims to develop a TB case classification system using Support Vector Machine (SVM) integrated with geospatial analysis to identify TB positive cases from screening data and visualize their spatial distribution in Riau Province. The research data was sourced from the Tuberculosis Information System (SITB) of the Riau Provincial Health Office for the period January-December 2024, covering 350 samples with demographic information, clinical symptoms, and patient risk factors. The research process includes data collection, preprocessing with Min-Max and Z-Score methods, feature extraction, modeling with SVM using various kernels (RBF, Linear, Polynomial, and Sigmoid), and geospatial visualization using Google Earth Engine (GEE). The results showed that the SVM model with Linear kernel achieved the highest accuracy of 80%, sensitivity of 100%, and specificity of 80% in detecting TB cases. Geospatial analysis successfully identified clusters of TB cases in several districts in Riau Province, with Pekanbaru City (112 cases) and Rokan Hulu (89 cases) as the main hotspots. The integration of machine learning and geospatial analysis proved effective in improving TB detection and providing a comprehensive understanding of disease spread patterns in Riau Province.
Co-Authors Adam, A Ahsyar, T. Khairil Ahsyar, Tengku Khairil Am, Andri Nofiar Angraini Angraini Anisa Nirmala, Fitri Anmi, Fauzi Hidayatul Anofrizen Anofrizen Aprinastya, Rachell Aqilla, Nabila Fadia Arif Marsal Ariyansir, Sigit Butar Butar, Febiola Siska Dermawan, Tri Dharmawan, Muhammad Rizky Eki Saputra Fahmi Rifai Fandi, Ridho Arif Febi Nur Salisah Fenty Kurnia Oktorina Fina Nasari Fitri Hamdi, Fathan Surya Hamzah, Muhammad Luthfi Herliza, Herliza Inggih Permana Khairil Ahsyar, Tengku Kusuma, Gathot Hanyokro M. Afdal MAULANA, BIMA Maulana, Rizki Azli Megawati - Miftahul Jannah Mona Fronita, Mona Muhammad Naufal, Muhammad Muhammad Ridwan Muttakin, Fitriani Novita Sari Nurhadi, Muhammad Permata, Nur Fitria Mita Prananda, Yaldri Oktra Praniffa, Anisya Caty Pribadi, Antoni Putra, Adhytia Pratama Putri, Runi Aulia Rahman, Eman Rahmawita, Medyantiwi Raihan, Muhammad Renaldi, Rengga Rianda, Rahmad Didho Rice Novita Rika Aryati Rozanda, Nesdi Evrilyan Saputra , Eki Sari, Gusmelia Puspita Setiawati, Elsa Siregar, Syafril Syaifullah Syaifullah Syaifullah Yulianti, Nelvi Zarnelly Zarnelly