Article Per Year (5 Year)
p-Index From 2021 - 2026
4.399
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL Just TI (Jurnal Sains Terapan Teknologi Informasi) TEPIAN Buletin Poltanesa Jurnal Info Sains : Informatika dan Sains JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) ETAM: Jurnal Pengabdian kepada Masyarakat Adopsi Teknologi dan Sistem Informasi
Muslimin B, Muslimin
Politeknik Pertanian Negeri Samarinda
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Other

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 37 Documents
Search

 1   2   3   4 
Perancangan Aplikasi Multi Criteria Decision Making Dalam Penerimaan Beasiswa Kepada Dosen Studi Lanjut STMIK Balikpapan Menggunakan Metode SAW B, Muslimin; Sumardi, Sumardi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 2 No 4 (2020): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (818.951 KB) | DOI: 10.33173/jsikti.86

Abstract

Interests and number of STMIK Balikpapan new student enrollments are increasing every year. The balance of the ratio of lecturers to students is one of the most important components in improving the quality and teaching and learning process of a university. Avoiding shortages in the number of lecturers can be realized by providing scholarship programs to alumni and teaching assistants. This study aims to build a multi criteria decision making application that can assist the Head of HRD in the process of receiving scholarships to advanced and effective study lecturers. The multi criteria decision making application developed in this study uses the SAW method. The implementation of the SAW method includes the process of evaluating the weighting of criteria, evaluating alternative weights, the matrix process, the results of decision making preferences, resulting in the weighting and ranking of each alternative candidate for the scholarship recipient. The results of the evaluation of multi-criteria application decision making in the study are expected to produce modeling with a high degree of accuracy. The results of the analysis carried out can provide alternative recommendations for prospective scholarship recipients to advanced study lecturers in STMIK Balikpapan
Hypertension Risk Prediction Using GRU-Based Neural Network with Adam Optimization B, Muslimin; Racmadhani, Budi; Rudito, Rudito
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 2 (2023): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.258

Abstract

Hypertension remains one of the most prevalent chronic conditions worldwide and continues to be a major contributor to cardiovascular morbidity and mortality. Early identification of individuals at high risk is essential, yet conventional screening approaches often rely on periodic clinical examinations that may overlook subtle lifestyle or behavioral indicators. This study aims to address this challenge by developing a predictive model that estimates hypertension risk using a GRU-based neural network enhanced with the Adam optimization algorithm. The motivation for using this approach stems from the ability of GRU networks to capture nonlinear feature interactions and the effectiveness of Adam in improving training stability and convergence. The proposed system incorporates a structured preprocessing pipeline, feature scaling, and a sequential model architecture to classify individuals into hypertension and non-hypertension groups. The results show that the model achieves strong predictive performance, supported by accuracy trends, loss reduction patterns, and confusion matrix analysis that collectively demonstrate consistent learning behavior. The evaluation indicates that the GRU classifier successfully recognizes relevant health attributes such as stress levels, salt intake, age, sleep duration, and heart rate. Future research may explore expanded datasets, additional health indicators, or hybrid architectures to further enhance accuracy and improve clinical applicability. Overall, this work contributes an interpretable and efficient approach for health risk prediction and supports the development of intelligent digital health monitoring systems.
KNN-Based Prediction Model for Assessing Hypertension Risk from Lifestyle Features B, Muslimin; Rowa, Heruzulkifli
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.265

Abstract

Hypertension is one of the most common chronic conditions associated with serious cardiovascular complications, and its prevalence continues to rise due to the influence of lifestyle related factors, motivating the use of data driven approaches for early risk identification. Although various machine learning models have been applied in health analytics, many still face challenges in processing heterogeneous lifestyle attributes, which limits their ability to accurately detect individuals at risk. This study addresses that gap by implementing the K Nearest Neighbors algorithm to predict hypertension using a dataset of 1,985 records containing variables such as age, salt intake, stress score, sleep duration, body mass index, family history, medication use, physical activity, and smoking status. The motivation for selecting KNN lies in its simplicity, adaptability, and strong performance in classification tasks involving structured health data. The contribution of this research includes the development of a lifestyle based hypertension prediction model supported by a preprocessing pipeline and optimized hyperparameters, enabling effective handling of mixed numerical and categorical features. The model is evaluated using accuracy, precision, recall, f1 score, and confusion matrix visualization, achieving an accuracy of 85 percent with balanced performance across both classes, showing that KNN offers reliable generalization for this dataset. Future work involves comparing KNN with ensemble or deep learning models, exploring feature selection techniques, and expanding dataset diversity to improve model robustness and applicability for real world digital health solutions.
Decision Support System Selection Cocoa Seed Using Web-Based AHP Hybrid WP Method Andarias Liku; Muslimin B; Yuanita Yuanita
TEPIAN Vol. 1 No. 4 (2020): December 2020
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v1i4.197

Abstract

Cocoa Plant (Theobroma cacao L) is one of the plantation commodities that has an important role in the Indonesian economy. One of the cocoa-producing regions in Indonesia is East Kalimantan, the expanse of land in KALTIM Province is still a lot that is not optimally cultivated so that if utilized for cocoa crops, it will have a positive impact on the regional economy. The first step that should be taken by cocoa farmers is the need to use superior cocoa planting materials (seeds). But in cocoa seed selection there are still many who use manual systems so that this kind of thing takes a long time and is less efficient, so a system is needed that is the System supporting cocoa seed selection decisions using AHP hybrid WP method based on the web. This aims to make it easier for cocoa farmers to efficiently select cocoa seeds. This study aims to calculate and create a system that can manage cocoa seed selection by applying modeling of hybrid WP AHP method that can be accessed by many people. The method used is the AHP Hybrid WP Method which is the merging of two methods namely the AHP method and the WP method. The AHP method is used to evaluate the weight value of the criteria, while the WP method is used to evaluate alternative values so that the two methods get a ranking decision. This application can calculate and process cocoa seedlings to produce the best cocoa seed sequence and accessible to many in need.
Decision Support System for Selection of Superior Crystal Guava Seeds with SMART Method Yuliyana; Muslimin B; Suci Ramadhani
TEPIAN Vol. 3 No. 3 (2022): September 2022
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v3i3.890

Abstract

Crystal guava is one of the horticultural plants that play a role in meeting food needs, the horticultural sector is also able to contribute to domestic income. The purpose of this study was to produce a Decision Support System for the Selection of Crystal Guava. By using the PHP programming language and the database using MySQL and using the waterfall model for system development and Unified Modeling Language (UML) for system design. In this study, the data collection techniques used were literature study, observation and interviews. The result of this research is a decision support system is made to determine the proper selection of Crystal Guava Seeds. Users can input alternative data, view criteria data. Then the system will find a solution using the SMART method. After the decision is obtained, the system will display the final result of the calculation.
Predicting USD to IDR Exchange Rates with Decision Trees Muslimin B; Syafei Karim; Asep Nurhuda
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.235

Abstract

Predicting currency exchange rates is a complex challenge due to the numerous factors influencing market fluctuations. This study explores the application of decision trees to predict the USD to IDR exchange rate, leveraging historical data and key economic indicators. Decision trees, known for their ability to model non-linear relationships, offer an interpretable approach to understanding the factors driving exchange rate movements. The study demonstrates that decision trees can successfully capture the patterns in the data, providing a foundation for accurate predictions. However, the volatility and unpredictability of exchange rates, driven by geopolitical events, market sentiment, and macroeconomic shifts, highlight the limitations of the model. While decision trees provide a valuable starting point, the research suggests that combining them with advanced methods, such as ensemble techniques (random forests or gradient boosting) or time-series models (ARIMA or LSTM), could improve forecasting accuracy. Incorporating a wider range of features, including macroeconomic indicators and market sentiment analysis, further enhances the model's robustness. The findings underscore the need for hybrid approaches that combine the strengths of multiple models to better capture the dynamic and complex nature of financial markets. This research contributes to the broader understanding of exchange rate prediction and offers practical insights for businesses and financial institutions seeking to make informed decisions.
Development of Accrual-Based Accounting Information System for Financial Planning Muslimin B; Budi Racmadhani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 3 (2026): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.281

Abstract

The increasing complexity of financial management in small and medium-sized enterprises (SMEs) requires the implementation of robust accounting systems. While accrual accounting provides more accurate financial insights by recognizing revenues and expenses when incurred, many SMEs still rely on cash-based accounting, hindering their financial decision-making. This research aims to develop an accrual-based accounting information system tailored for SMEs, integrating essential features such as cost control and forecasting. The proposed system automates key processes, from transaction entry to report generation, offering a comprehensive solution to enhance financial transparency and decision-making. The system is evaluated through real-world data simulations to assess its effectiveness in improving reporting accuracy and forecasting capabilities. The results demonstrate that the system improves financial planning and resource allocation, providing valuable insights for SMEs. Future work will focus on scaling the system for larger enterprises and incorporating machine learning techniques to improve financial forecasting and anomaly detection.
Co-Authors Afak, Richa Rachmawati Andarias Liku annafi franz, annafi Annafi’ Franz Bin Idris, Nasruddin Budi Racmadhani Eko Junirianto Husmul Beze, Husmul Ida Maratul Khamidah Imron Jamal Jamal Jamal Jamal Karim, Syafei Maria, Eny Mauko, Arfan Moch. Rizky Yuliansyah Moch. Rizky Yuliansyah Noviana Nurhuda, Asep Puput Misliyana PUTU SUGIARTAWAN Rachmadani, Budi Racmadhani, Budi Rahmawati Riza Putra, Emil Rowa, Heruzulkifli Rudito Rudito Rudito Selvia Rani Sri Murdhani, I Dewa Ayu Suci Ramadhani Suci Ramadhani Suci Ramadhani Suci Ramadhani Suci Ramadhani, Suci Sumardi . Suswanto Suswanto Taruk, Medi Wahyuni Eka Sari Yuanita Yuanita Yuliyana Yunike Andrayani Yunike Andrayani