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All Journal Jurnal Pendidikan Indonesia Jurnal Informatika dan Teknik Elektro Terapan Creative Information Technology Journal INTEGER: Journal of Information Technology Best Journal (Biology Education, Sains and Technology) Jurnal Computer Science and Information Technology (CoSciTech) Journal of Soft Computing Exploration Jurnal Informatika Terpadu International Journal of Community Service Journal of Innovation Research and Knowledge Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Jurnal Riset Rumpun Ilmu Kesehatan (JURRIKES) Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research Indonesian Vocational Research Journal Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi IJENSET
Mufti Ari Bianto
Universitas Muhammadiyah Lamongan
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Penerapan K-Means Clustering pada Pengelompokan Pelamar di Sistem Rekrutmen Berbasis Web Fiqrul Labib Abdullah; Mufti Ari Bianto; Vico Tegar Rawo Wijoyo; Ahmad Abdullah Fahmi
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1498

Abstract

In an increasingly competitive world of work, companies are required to have a fast, efficient, and objective employee selection process to get the best candidates. However, the high number of job applicants often causes the administrative selection process to be slow, inefficient, and prone to subjective errors in assessment. Therefore, a technology-based solution is needed that is able to systematically classify job applicants based on relevant criteria. This study proposes the application of the K-Means Clustering method to group job applicants based on three main variables, namely last education, work experience, and selection test scores. A total of 20 applicant data were analyzed using the K-Means algorithm with the stages of initial centroid initialization, Euclidean distance calculation, and iteration until the convergence point was reached. The results of the grouping resulted in three main categories: prioritized, considered, and doubtful applicants. Each group has its own characteristics that can help the HRD team in compiling a more selective and accurate list of candidates. This system is implemented in the form of a web-based recruitment platform that makes it easier for companies to conduct early selection automatically, structured, and data-based. The use of this method also increases accuracy and transparency in decision-making and reduces the potential for bias that often occurs in manual selection. These findings prove that K-Means Clustering is an effective and applicable method to support strategic decision-making in the field of human resources, especially in the early stages of employee selection. Additionally, this method can be easily adapted to the needs of other companies that have different selection criteria, making it flexible and widely applicable. The potential for the development of this system is also open to integration with other technologies such as machine learning or big data analytics in the future.
Decision support system for selecting outstanding students using simple addictive weighting (SAW) and rank order centroid (ROC) methods Sholikha, Hidayatin; Ardiansyah, Hery; Bianto, Mufti Ari
Journal of Soft Computing Exploration Vol. 6 No. 3 (2025): September 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i3.574

Abstract

Selecting outstanding students is essential in fostering appreciation and motivation within the school environment. Nevertheless, many educational institutions continue to use manual assessment methods, which are often subjective and inefficient. This research focuses on the development of a web-based decision support system designed to assist in the selection process at a in Indonesia. The system integrates the Simple Additive Weighting (SAW) technique to generate student rankings based on preference scores, while the Rank Order Centroid (ROC) method is applied to assign weight values to the evaluation criteria, including academic performance, attendance, behavior, and extracurricular involvement. Data for this study were collected through interviews, direct observation, and student records. The application was developed using PHP for the backend, MySQL for database handling, and Bootstrap for the user interface design. The system’s functionality was verified using black box testing, which confirmed that all features operated correctly. Additionally, the system was evaluated against the manual selection process conducted by the school, and the results showed an accuracy level of 80% in matching student rankings. This system proves to be a practical and structured solution for enhancing the transparency and objectivity of student achievement evaluations.
Sistem Stok Barang Berbasis Rfm Dengan Mempertimbangkan Kebiasaan Konsumen Dan Barang Slow-Moving Di Usaha Konter Wijayanti, Lusi Salsabilla; Bianto, Mufti Ari; Zamani, Sevian Nadi; A, Dwi Putra
Innovative: Journal Of Social Science Research Vol. 5 No. 4 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i4.20978

Abstract

Mobile phone accessory stores, particularly those selling accessories and spare parts, face challenges in managing stock accumulation, especially with slow-moving items that are rarely purchased. This study aims to implement the RFM (Recency, Frequency, Monetary) method to segment customers based on their transaction behavior and relate it to product movement. Through this analysis, store owners can identify which products are fast-moving or slow-moving, and which customers actively contribute to inventory turnover. The segmentation results support better decision-making in promotional strategies, restocking the right products, and managing discounts for rarely sold items. This research shows that the RFM approach is not only useful for customer management, but also effective in enabling a more adaptive and efficient inventory management system.
Recommendation Implementation of a Digital Book Recommendation System Using Item-Based Collaborative Filtering in a University Library Application.: Item-Based Collaborative Filtering, recommendation system, digital library, Pearson correlation, MAE. Mutsna, Mutsna; Mufti Ari Bianto; M. Cahyo Kriswantoro
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.10011

Abstract

This study implements the Item-Based Collaborative Filtering (IBCF) method for a digital book recommendation system within a web-based library application. The system accommodates two user types (administrator and student) with features for managing physical/digital books, barcode-based borrowing, and ebook rating functionality. The similarity matrix was calculated using Pearson Correlation based on student ratings, with predictions evaluated via Mean Absolute Error (MAE) to measure accuracy. Evaluation results show an MAE of [your MAE value], indicating a low level of prediction error. Book recommendations are displayed on the student dashboard based on highest ratings, enhancing user experience in reading material selection. This implementation demonstrates IBCF's effectiveness for limited datasets within a university library context.
A Car Booking Method Using K-Means : Case Study: Car Rental Tsalits Wildan Hamid; Mufti Ari Bianto
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.1055

Abstract

This study discusses the application of the K-Means Clustering algorithm in the car rental ordering system. The objective is to help group booking data based on certain patterns such as car type, booking frequency, and rental duration. The clustering results are expected to improve service efficiency and help companies better understand customer preferences. The research was conducted using historical car rental booking data from a rental company. The results show that the K-Means method can successfully cluster booking data into several useful clusters for business decision-making. This extended paper also explores theoretical concepts of clustering, related studies, limitations of the method, and potential future enhancements such as integrating predictive analytics. It highlights the importance of transforming large volumes of raw booking data into actionable business intelligence to support marketing strategies, fleet management, and customer segmentation.  
Implementasi Sistem Pemesanan Hotel Menggunakan Algoritma Haversine untuk Optimalisasi Rekomendasi Lokasi Adhani, Hamka Lukmanul Hakim; Bianto, Mufti Ari; Pratama, Alif Nanda; Hidayah, Septina Alfiani
Jurnal Informatika Terpadu Vol 11 No 2 (2025): September, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v11i2.2030

Abstract

A location-based lodging recommendation system helps users find nearby hotels efficiently through a web-based platform. The system utilizes the Haversine algorithm to calculate the distance between the user's location and the hotel by automatically retrieving coordinates via the Geolocation API. Calculated distances are compared with hotel data stored in a MySQL database, and the results are displayed on a web interface integrated with the Google Maps API. Testing was conducted on six hotels with distances ranging from 6.73 km to 23.97 km, and results were compared with Google Maps estimates. The system achieved an average distance difference of 0.0183 km, with an accuracy rate of 99.83%. These findings indicate that the Haversine algorithm provides highly accurate distance estimations and is reliable for location-based hotel recommendation systems.
Integrasi Metode Hybrid Recommendation dan Random Forest Regression untuk Optimasi Prediksi Durasi Menginap pada Sistem Pemesanan Kos Berbasis Web Mufti Ari Bianto; Hanif Azhar Ramadhan; Ardian Hudi Ramadhani; Tsalits Wildan Hamid
JURNAL RISET RUMPUN ILMU TEKNIK Vol. 4 No. 3 (2025): Desember : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v4i3.6591

Abstract

This study proposes the integration of a Hybrid Recommendation method (combining Content-Based and Collaborative Filtering) with Random Forest Regression (RFR) to improve the accuracy of stay duration prediction in web-based boarding house booking systems. The main issue in online boarding booking systems is the inaccuracy of predicting user stay duration, affecting room allocation efficiency and customer satisfaction. The dataset was sourced from the hotel sector due to its attribute similarities and data validity. The research process includes data preprocessing (missing value imputation, normalization, and one-hot encoding), temporal and contextual feature engineering, hybrid recommendation system construction with CBF and CF score weighting, and RFR model training optimized through Grid Search and 10-fold cross-validation. Evaluation was conducted using MAE, RMSE, R² metrics, as well as recommendation metrics such as Precision@5, Recall@5, and Mean Reciprocal Rank (MRR). Results show that this integrated model achieved an R² of 0.7239 and an MAE of 1.0537 days, as well as a Precision@5 of 0.9636. This integration proves effective in improving prediction accuracy and recommendation relevance and contributes to the development of AI-based intelligent systems in the accommodation domain.
Sistem Diagnostik Mata Digital berbasis Optoscope untuk Deteksi Dini Gangguan Penglihatan secara Akurat dan Efisien Mufti Ari Bianto; Mohammad Huda Adi Sanjaya; Yuni Furoida Maknuna; Adam Rizky Al’insani
JURNAL RISET RUMPUN ILMU KESEHATAN Vol. 4 No. 3 (2025): Desember : Jurnal Riset Rumpun Ilmu Kesehatan
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrikes.v4i3.6586

Abstract

The development of digital technology has had a significant impact on the health sector, including eye examination services. This study discusses the development and implementation of a Digital Optoscope-based Eye Diagnostic application designed using a web platform (HTML). This application provides two main features: visual acuity testing and astigmatism testing, which can be accessed independently by users through digital devices such as laptops, tablets, or mobile phones. Users only need to follow the visual instructions presented interactively on the screen, then perform the test according to the procedure. The testing method is carried out by displaying font size settings according to the Snellen chart standard and radial astigmatism patterns. The results of each test session are automatically recorded and can be saved for further analysis. In addition, users can perform repeated tests to improve the accuracy of self-diagnosis, and the system will provide lens type recommendations based on the measurement results obtained. The trial results show that this application is able to provide convenience, efficiency, and initial accuracy in the process of examining vision disorders. This is very useful, especially for people in remote areas or with limited access to professional eye health services. Thus, the Digital Optoscope-based Eye Diagnostic application has the potential to be an innovative solution for the early digital detection of vision disorders. This study recommends further development, particularly in clinical validation, testing on a wider sample size, and integration with electronic medical record systems to enhance its benefits in comprehensive public health services. Furthermore, collaboration with medical professionals is crucial to ensure diagnostic accuracy. With this approach, the app is expected to become a reliable tool for continuous eye health monitoring.
APPLICATION OF THE SIMPLE MOVING AVERAGE METHOD FOR FARMING FISH PRICE FORECASTING SYSTEMS Mubaarok, Ahmad Husni; Bianto, Mufti Ari; Saputra, Bagus Dwi
Indonesian Journal of Engineering, Science and Technology Vol. 1 No. 1 (2024): VOL. 01 NO. 01 (JUNE 2024)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v1i1.713

Abstract

Price is one of the important things that needs to be considered as a determining factor for profit or loss on product sales as a result of price fluctuations which are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to overcome the problem of price fluctuations is to forecast the entry price of fish. Forecasting is the art or science of predicting future events using past data. The purpose of this study is to apply the simple moving average method to estimate the price of farmed fish. The simple moving average method uses a number of actual demand data to generate forecast values for future requests. This method has two special properties, namely to make forecasts that require historical data over a certain period of time, the longer the moving average, the smoother the moving average will be. This study uses data on fish prices (milkfish and tilapia) daily for January 2023. The results show that the Simple moving average produces a very accurate forecast with a MAPE percentage for milkfish of 2% and tilapia of 1.97%.
CLEAN WATER RECOMMENDATION SYSTEM BASED ON WATER QUALITY WITH TURBIDITY AND TDS (TOTAL DISSOLVE SOLID) SENSORS BASED ON INTERNET OF THINGS (IOT) Arbiansyah, Lutfi; Bianto, Mufti Ari; Ardiansyah, Heri
Indonesian Journal of Engineering, Science and Technology Vol. 1 No. 2 (2024): VOL. 01 NO. 02 (DECEMBER 2024)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v1i2.1014

Abstract

Residents of Khayangan Residence Cepu typically use water from natural sources such as rivers, lakes, and wells, often unaware of the potential dangers posed by contaminated water. To address this, a detection system is proposed to monitor and provide real-time information on water quality using Turbidity and Total Dissolved Solids (TDS) sensors. The system is developed using the Waterfall methodology, which ensures a structured and systematic approach, with each stage of development completed before proceeding to the next. This minimizes errors and enhances the accuracy of the final system. The IoT-based system utilizes Turbidity and TDS sensors connected to an ESP32 microcontroller, which processes data every 3 seconds and displays it on a website. The system measures water quality, with recorded values of PPM at 276, TDS at 0.34, and Turbidity at 16.08. This real-time monitoring system provides a straightforward process for assessing water quality in the housing complex, ensuring that residents have access to safe and clean water. The aim is to empower residents to make informed decisions about water use, thereby enhancing efficiency and safety in daily water consumption. Keywords-  ESP32; Turbidity Sensor; and TDS Sensor
Co-Authors A, Dwi Putra Adam Rizky Al’insani Adhani, Hamka Lukmanul Hakim AF Suryaning Ati MZ Ahmad Abdullah Fahmi Arbiansyah, Lutfi Ardian Hudi Ramadhani Aris Widodo Badriyatul Musyarofah Bagus Dwi Jauhari Bagus Dwi Saputra Bufa Al badi Eko Handoyo Fiqrul Labib Abdullah Hanif Azhar Ramadhan Heri Ardiansyah Hidayah, Septina Alfiani Kusrini Kusrini Kusrini, Kusrini M. Cahyo Kriswantoro Mala Rosa Aprillya Mohammad Hisyam Romadhon Mohammad Huda Adi Sanjaya Mubaarok, Ahmad Husni Muhammad Syaifuddin Alamsyah Mutsna, Mutsna Pratama, Alif Nanda Ramadhan, Bayu Putra Rifki Noor Rohman Rofiatun Solekha Ryky Ardiansyah Salasun Nisah SAPUTRA, BAGUS DWI Setiyowati, Putri Ayu Ika Sholikha, Hidayatin Sudarmawan Sudarmawan Tsalits Wildan Hamid Uswatun Chasanah Vico Tegar Rawo Wijoyo Wijayanti, Lusi Salsabilla Yuni Furoida Maknuna Zafi, Ali Zamani, Sevian Nadi