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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
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Penerapan Sistem Informasi Akuntansi untuk Meningkatkan Penjualan pada UD. Cahaya Goa Mampu B, Muslimin; Riza Putra, Emil; Bin Idris, Nasruddin; Jamal, Jamal
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1032

Abstract

Cahaya Gowa Mampu is a Micro, Small, and Medium Enterprise (MSME) engaged in the sale of agricultural products and the rental of farming equipment, located in Sekaduyan Taka Village, Nunukan City. This study aims to improve sales transaction management, accurate stock inventory recording, agricultural equipment rentals, and transparent financial reporting. The research employs the waterfall methodology, including interviews and data analysis on sales transactions, stock management, and equipment rentals. The findings indicate that implementing an accounting information system at UD. Cahaya Gowa Mampu enhances recording accuracy, reduces transaction time, improves sales transparency, optimizes inventory and rental management, and increases monthly sales and equipment rental summaries. The adoption of digital information technology through an accounting information system enhances operational efficiency, improves customer service, and enables real-time monitoring of periodic financial reports.
Support Vector Machine for Classifying Prostate Cancer Data B, Muslimin; Rachmadani, Budi; Rudito, Rudito
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.205

Abstract

Prostate cancer is one of the most prevalent cancers among men worldwide, making early detection and accurate classification essential for improving patient outcomes. This study investigates the application of Support Vector Machine (SVM) models for classifying prostate cancer using clinical and demographic data. Features such as prostate-specific antigen (PSA) levels, Gleason scores, tumor stage, and patient age were utilized to train and evaluate the model. Comprehensive preprocessing techniques, including handling missing values, feature normalization, and addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE), were employed to ensure robust model performance. The SVM model, optimized with a radial basis function (RBF) kernel, achieved an accuracy of 94.2%, with precision, recall, and F1-scores indicating reliable classification of both cancerous and non-cancerous cases. However, the results highlight challenges with the minority class, emphasizing the need for better handling of imbalanced datasets. Explainability techniques such as SHAP (Shapley Additive Explanations) were integrated to provide interpretable insights into the model’s predictions, with PSA levels and Gleason scores identified as the most influential features. This research demonstrates the potential of SVM in prostate cancer classification, providing a foundation for integrating machine learning models into clinical workflows for improved diagnostic precision and patient care.
Prostate Cancer Detection Using Gradient Boosting Machines Effectively MusliminB, Muslimin; Karim, Syafei; Nurhuda, Asep
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.107742

Abstract

Prostate cancer remains a leading cause of cancer-related deaths among men globally, emphasizing the critical need for accurate diagnostic tools. This study investigates the application of Gradient Boosting Machines (GBMs) for prostate cancer detection using a dataset with key tumor characteristics such as radius, texture, area, and symmetry. Data preprocessing included normalization, missing value handling, and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. The GBM model demonstrated an accuracy of 75%, with high precision (82%) and recall (88%) for malignant cases, underscoring its potential as a reliable diagnostic tool. However, the model's performance for benign cases was limited by severe class imbalance, reflected in a precision of 33% and recall of 25%. Interpretability was enhanced using SHAP values, identifying key predictors like tumor perimeter and compactness. While GBMs show promise in prostate cancer diagnostics, future research should incorporate multimodal data, advanced balancing techniques, and rigorous validation frameworks to enhance generalizability and fairness. This study highlights the value of machine learning in healthcare, contributing to improved diagnostic accuracy and patient outcomes.
Predicting USD to IDR Exchange Rates with Decision Trees B, Muslimin; Karim, Syafei; Nurhuda, Asep
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.
Decision Support System for Selection of Superior Crystal Guava Seeds with SMART Method Yuliyana; B, Muslimin; Ramadhani, Suci
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.
Metode Dea untuk Benchmarking Organisasi Ramadhani, Suci; B, Muslimin; Maria, Eny
Poltanesa Vol 23 No 1 (2022): Juni 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i1.1291

Abstract

Kinerja tiap organisasi perlu dievaluasi secara berkala dalam proses benchmarking. Proses benchmarking ini diharapkan dapat melakukan perbaikan kinerja tiap organisasi sehingga tiap organisasi yang inefficient dapat menjadi efficient. Pada dasarnya proses benchmarking ini dilakukan dengan mengukur input yang diberikan dengan output yang dihasilkan dan perbandingan dengan bagian lain yang ada. Salah satu metode yang dapat digunakan adalah Data Envelopment Analysis (DEA) yang merupakan pendekatan non-parametrik yang berbasis program linear (linear programming) yang digunakan untuk mengukur efisiensi relatif dari setiap Decision Making Unit yang melibatkan penggunaan input-input tertentu untuk menghasilkan output-output tertentu. Dikatakan suatu organisasi efisien jika nilai efficiency adalah sebesar 1 dan jika lebih kecil dari 1 maka dikatakan tidak efisien. Hasil benchmarking diharapkan dapat meningkatkatkan kinerja organisasi. Melalui penelitian diperoleh hasil bahwa Data Envelopment Analysis (DEA) dapat melakukan kegiatan benchmarking dengan baik.
LightGBM-Based Classification of Customer Feedback in Restaurant X Sri Murdhani, I Dewa Ayu; B, Muslimin
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 4 (2024): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.236

Abstract

This research aims to classify customer feedback from Restaurant X using the LightGBM model to enhance service quality and customer satisfaction amidst growing industry competition. Customer feedback, collected through surveys and online platforms, is analyzed to uncover patterns and trends related to various aspects of the dining experience. The methodology encompasses data collection, preprocessing, model training, and evaluation. LightGBM, renowned for its efficiency and accuracy with large datasets, serves as the primary tool for building a robust classification model. Analysis reveals that key features such as food quality, service, and cleanliness significantly influence customer satisfaction. The model demonstrates high classification accuracy, providing actionable insights for Restaurant X management. These insights enable targeted strategies for improving specific areas of service, fostering better customer experiences and driving loyalty. The research underscores the importance of leveraging advanced machine learning models like LightGBM for data-driven decision-making in the restaurant industry.
Forecasting USD to IDR Exchange Rates Using Prophet Time-Series Model B, Muslimin; Afak, Richa Rachmawati; Racmadhani, Budi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 4 (2024): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.237

Abstract

This study evaluates the effectiveness of the Prophet time-series model in forecasting USD to IDR exchange rates using a historical dataset of 2812 daily records, including opening and closing prices, highs, lows, and percentage changes. Data preprocessing steps, such as handling missing values and standardizing numeric fields, were performed to ensure data quality. Prophet, developed by Facebook, was chosen for its capability to model seasonality, irregular patterns, and external regressors, outperforming traditional models like ARIMA. The model's performance was validated using error metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), demonstrating its predictive accuracy. Comparative analysis with ARIMA confirmed Prophet’s superior ability in capturing complex patterns in volatile financial data. The inclusion of external factors such as inflation rates and global economic indicators further improved the forecast accuracy. The results provide valuable insights for policymakers, investors, and financial analysts, supporting more informed decision-making and risk management strategies. This research highlights the importance of proper data preprocessing and advanced forecasting techniques for improving currency prediction accuracy, especially in emerging markets like Indonesia. Future research could explore hybrid models combining Prophet with machine learning techniques for enhanced forecasting capabilities.
Random Forest Methodology for Analyzing Diabetes Risk Factors B, Muslimin; Karim, Syafei; Nurhuda, Asep
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 4 (2023): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.248

Abstract

Diabetes is a chronic disease posing significant health challenges globally, with rising prevalence due to genetic, lifestyle, and environmental factors. This research employs the Random Forest methodology to analyze diabetes risk factors and predict outcomes using a dataset of 768 patient records. Key attributes such as glucose levels, BMI, blood pressure, and age were evaluated to uncover their contribution to diabetes risk. The study achieved an overall accuracy of 72%, with glucose emerging as the most influential predictor, followed by BMI and age. While the model showed strong performance in identifying non-diabetic cases, moderate precision and recall for diabetic cases highlighted the impact of class imbalance. Feature importance analysis provided actionable insights, emphasizing glucose and BMI monitoring in diabetes management. Despite its strengths, challenges such as class imbalance and feature redundancy were noted, suggesting the need for oversampling techniques, additional variables, and advanced feature engineering. These findings demonstrate the utility of Random Forest in healthcare analytics, supporting predictive and preventive care strategies. Future research should focus on integrating lifestyle factors, expanding datasets, and exploring advanced machine learning models to enhance predictive accuracy and real-world applicability.
Efficient Wine Quality Prediction and Classification Using LightGBM Model B, Muslimin
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.253

Abstract

This study develops an efficient machine learning model using the Light Gradient Boosting Machine (LightGBM) algorithm to predict and classify wine quality based on physicochemical properties. The dataset used in this research consists of multiple chemical attributes, including alcohol content, acidity levels, sulphates, and phenolic compounds, which collectively influence wine quality. The preprocessing stage involved data cleaning, outlier treatment, feature scaling, and handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). Feature selection was conducted using mutual information and recursive feature elimination to identify the most influential predictors. The optimized LightGBM model achieved superior performance with 100% accuracy, precision, recall, and F1-score across all quality classes, outperforming traditional algorithms such as Random Forest, SVM, and Logistic Regression. Feature importance analysis revealed that Proline, Flavanoids, and Magnesium were the most significant attributes contributing to wine classification. These findings demonstrate that LightGBM is a robust and scalable solution for wine quality prediction, offering an efficient, data-driven alternative to traditional sensory evaluations. The proposed model can enhance quality control processes in the wine industry by providing accurate and interpretable insights into the chemical determinants of wine quality.
Co-Authors Afak, Richa Rachmawati Andarias Liku annafi franz, annafi Annafi’ Franz Bin Idris, Nasruddin 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 Yuliyana Yunike Andrayani Yunike Andrayani