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All Journal Jurnal Computech & Bisnis (e-Journal) Nuansa Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Technologia: Jurnal Ilmiah Journal of Empowerment Community Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian dan Edukasi Sekolah (Jubaedah) International Journal of Quantitative Research and Modeling Joutica : Journal of Informatic Unisla Formosa Journal of Applied Sciences (FJAS) Paradigma International Journal of Ethno-Sciences and Education Research Journal Of Information System And Artificial Intelligence Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) International Journal of Computer Technology and Science International Journal of Information Engineering and Science Journal of Computing Innovations and Emerging Technologies Bulletin of Intelligent Machines and Algorithms Jurnal Informatika
Budiman
Universitas Informatika Dan Bisnis Indonesia, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Transportation Other

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ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS Kaunang, Valencia Claudia Jennifer; Alamsyah, Nur; Nursyanti, Reni; Budiman, Budiman; Danestiara, Venia R; Setiana, Elia
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6647

Abstract

Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems. 
Optimization of Human Development Index in Indonesia Using Decision Tree C4.5, Support Vector Machine Algorithm, K-Nearest Neighbors, Naïve Bayes, and Extreme Gradient Boosting Ramadhan, Ilham; Budiman, Budiman; Alamsyah, Nur
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12253

Abstract

The Human Development Index (HDI) is a measure of human development achievement based on quality of life indicators such as Life Expectancy (LE), Mean Years of Schooling (MYS), Expected Years of Schooling (EYS), and Adjusted Per Capita Expenditure (AECE). HDI describes how people access development outcomes through income, health, and education. The determination of development programs implemented by local governments must be based on district/city priorities based on their HDI categories and must be right on target. Therefore, a decision system is needed that can accurately determine the HDI category in each district/city in Indonesia, using machine learning models such as Decision Tree C4.5, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, and Extreme Gradient Boosting (XGBoost). Machine learning models will be used to classify the HDI in Indonesia in 2022 and determine the performance of the most optimal model in classification. This research uses the CRISP-DM method with secondary data from the Central Statistics Agency (BPS) as much as 548 data. The analysis results show that the Decision Tree C4.5 models have an accuracy of 0.86, KNN of 0.95, Naïve Bayes of 0.90, XGBoost of 0.93, and SVM provides the most optimal results with an accuracy of 0.97. UHH, RLS, and HLS variables significantly influence changes in HDI values in Indonesian regions based on the Chi-square, Pearson Correlation, Spearman, and Kendal test results. 
Digital Marketing Strategy Optimization Using Support Vector Machine Algorithm AlFauzi, Ihsan; Budiman, Budiman; Alamsyah, Nur
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12257

Abstract

Information and communication technology (ICT) is essential in rapidly disseminating information. This research discusses the influence of ICT use in marketing promotions through TV, radio, and social media and compares the performance of several classification algorithms in processing the promotion data. The dataset is from Kaggle, with promotional attributes on TV, radio, and social media. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is used. Algorithms tested include Naive Bayes, K-Nearest Neighbor, Support Vector Machine (SVM), Random Forest, and XGBoost. The results showed that SVM had the best performance with 80% accuracy, followed by KNN (79%), Naive Bayes (77%), XGBoost (77%), and Random Forest (76%). SVM provided the most accurate and consistent predictions in marketing promotion classification. This research concludes that the optimal utilisation of ICT and the application of appropriate classification algorithms can increase the effectiveness of marketing promotions in the digital era.
Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection Budiman Budiman; Reni Nursyanti; R Yadi Rakhman Alamsyah; Imannudin Akbar
International Journal of Quantitative Research and Modeling Vol. 1 No. 3 (2020): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v1i3.72

Abstract

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.
Information System Security Audit SIMKA(Sistem Informasi Kearsipan) at Badan Pendapatan Daerah Jawa Barat Kota Bandung III Using COBIT 5 Framework and Standard IS0/IEC 27002 Suci Fitriani Setiawan; Titan Parama Yoga; Budiman Budiman
International Journal of Quantitative Research and Modeling Vol. 4 No. 3 (2023): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i3.499

Abstract

One of the main problems for an agency or a company is the security of information systems. High security is needed to maintain the confidentiality and misuse of information within the organization. To improve the security of business operations and the quality of information technology resources, it is necessary to evaluate to op Badan Pendapatan Daerah Jawa Barat Kota Bandung III, namely SIMKA BAPENDA whose function is to collect data on PKB (pajak kendaraan bermotor) and BBNKB (bea balik nama kendaraan bermotor) which manage the data computerized. The purpose of this study is to carry out a security audit of SIMKA BAPENDA at the Badan Pendapatan Daerah Jawa Barat Kota Bandung III using the COBIT 5 framework and ISO/IEC 27002 to document audit findings of the information system audit of the Badan Pendapatan Daerah Jawa Barat Kota Bandung III to make a report on the audit results. Based on the results of research that has been done through interviews and questionnaires using framework and using the APO13 and DSS05 sub domains, the results show that the Capability Existing is at level 1 while Capability Level is level 3 so the Capability Gap is 2.
An LSTM-Based Approach for Short-Term Solar Power Forecasting with Diurnal and Intra-Day Variability Darsiti Darsiti; Tarsinah Sumarni; Fahmi Abdullah; Budiman
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 2 (2026): BIMA January 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i2.7

Abstract

The increasing penetration of solar photovoltaic (PV) systems into modern power grids demands accurate, reliable short-term power forecasting to ensure operational stability and efficient energy management. However, solar power generation exhibits strong nonlinearity, non-stationarity, and pronounced temporal dependencies, driven by diurnal cycles and rapid environmental variations, which pose significant challenges for conventional forecasting approaches. This study aims to develop an efficient Long Short-Term Memory (LSTM)-based framework for short-term DC power prediction that effectively captures the temporal dynamics of solar power generation while maintaining low computational complexity. The proposed approach utilizes historical power and operational data collected from two utility-scale solar PV plants in India. A comprehensive time-series preprocessing pipeline is applied, including temporal feature extraction, categorical transformation, and Min–Max normalization. Multiple LSTM architectures with varying numbers of hidden units are systematically evaluated to identify an optimal balance between model complexity and predictive performance. Model training is conducted using the Adam optimizer with exponential learning rate decay and early stopping to prevent overfitting. Experimental results demonstrate that the proposed LSTM model with a 25–50 unit configuration achieves the best performance, yielding a test Mean Squared Error of 51.92 and a prediction error of only 0.36%. Visual and quantitative analyses confirm that the model accurately reconstructs diurnal patterns and intra-day fluctuations, with strong generalization capability on unseen data. The findings indicate that a carefully configured LSTM can deliver high forecasting accuracy without relying on complex hybrid architectures or additional weather data, making it suitable for practical solar energy management applications.
Machine Learning Based Cervical Cancer Risk Prediction with SHAP-Driven Feature Interpretation Fachrizal Ardiansyah; Raka Deny Abdi Putra; Budiman
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i3.16

Abstract

Cervical cancer remains a critical public health problem, particularly in developing countries where early detection is often limited. This study presents a machine learning–based approach for cervical cancer risk prediction that emphasizes both predictive accuracy and interpretability. Several supervised algorithms, namely K-Nearest Neighbors, Random Forest, XGBoost, and CatBoost, were evaluated using the Cervical Cancer (Risk Factors) dataset from the UCI Machine Learning Repository following comprehensive data preprocessing and systematic hyperparameter optimization. The experimental results show that CatBoost achieved the best overall performance, with an optimized accuracy of 97.01% and improved sensitivity in detecting high-risk cases, supported by stable k-fold cross-validation results. To enhance clinical transparency, explainable artificial intelligence was incorporated via SHAP, revealing that key predictors such as the Schiller test, age, and reproductive factors played dominant roles in the model’s decisions. These findings demonstrate that the proposed framework is not only accurate and stable but also interpretable and clinically relevant, making it well-suited to support early detection and decision-making in cervical cancer screening, especially in resource-limited healthcare settings.
Comparative Analysis of Machine Learning Regression Models for Paddy Yield Prediction Chery Cardinawati Sitohang; Fitri Kinkin; Budiman
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i3.17

Abstract

Accurate paddy yield prediction is essential to support food security, agricultural planning, and data-driven decision-making. The increasing availability of agricultural data has encouraged the adoption of machine learning approaches to overcome the limitations of conventional yield estimation methods. This study presents a comparative analysis of five regression-based machine learning algorithms—Linear Regression, K-Nearest Neighbors Regressor, Decision Tree Regressor, Random Forest Regressor, and Support Vector Regression—for paddy yield prediction. The experiments were conducted using the Paddy dataset from the UCI Machine Learning Repository, which consists of 2,789 samples and 45 variables (44 input features and 1 target variable). The dataset was preprocessed through data cleaning, feature standardization, and an 80:20 train–test split. Model performance was evaluated using Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and the coefficient of determination (R²). Experimental results show that Linear Regression achieved the best overall performance with an R² value of 0.9896 and an RMSE of 942.09, indicating strong predictive accuracy and stability. Despite its simplicity, Linear Regression outperformed more complex models, suggesting that the underlying relationships between input variables and paddy yield in the dataset are predominantly linear. These findings highlight the importance of systematic model evaluation and demonstrate that simpler regression models can remain effective and interpretable for practical paddy yield prediction and agricultural decision support systems.
YOLO26n-Based Apple Leaf Disease Detection for Precision Agriculture Using Lightweight Deep Learning and Object Detection Darsiti Darsiti; Budiman; Dhika Wdiyanto; Tarsinah Sumarni
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.22

Abstract

Early detection of apple leaf diseases is a critical factor in supporting agricultural productivity and minimizing losses caused by plant disease outbreaks. However, manual identification processes still have limitations in terms of accuracy, consistency, and time efficiency. This study aims to develop an apple leaf disease detection model based on object detection using YOLO26n to identify four main classes: Apple__BlackRot, Apple__CedarRust, Apple__Healthy, and Apple__Scab. The dataset was obtained from Kaggle in YOLO format, consisting of 2,754 training images and 687 validation images. The study employs a transfer learning approach with various data augmentation techniques, such as mosaic, mixup, copy-paste, rotation, translation, and HSV transformation, to enhance the model’s generalization ability. Evaluation was conducted using the Precision, Recall, mAP50, and mAP50-95 metrics. The results showed that the YOLO26n model achieved a Precision of 0.968, a Recall of 0.887, an mAP50 of 0.958, and an mAP50-95 of 0.880. The best performance was achieved on the Apple__BlackRot class with an mAP50-95 value of 0.987. The inference results also show that the model is capable of accurately localizing diseases through bounding boxes with a high level of confidence. These findings indicate that YOLO26n has great potential as an efficient and accurate lightweight model for the implementation of real-time precision agriculture-based plant disease detection systems.
An Analysis of the Impact of Zoom on Online Learning Using the Technology Acceptance Model Zatin Niqotaini; Budiman Budiman; Fahreja Ramadhan; Artika Arista; Esa Prakasa; Arafat Febriandirza; Nur Alamsyah; Rezza Novian Noor Rochmat; Henki Bayu Seta
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.30

Abstract

This study aims to analyze the effect of using the ZOOM application at the University of Informatics and Business Indonesia (UNIBI) using the Technology Acceptance Model (TAM) approach, which is often used by some researchers to examine user acceptance of technology. This research is quantitative using descriptive method. The data analysis technique was carried out using SEM (Structural Equation Model) with AMOS (Analysis of Moment Structure) software. The population in this study were UNIBI students. Determination of the sample is carried out by proportional sampling, which is a proportional sampling method based on sub-populations. The results of this study prove that only 4 hypotheses are accepted from a total of 6 hypotheses proposed. The following is the percentage of the influence of each variable: a) Perceived Ease of Use (PEOU) is 28%, b) Perceived Usefulness (PU) is 74%, c) Attitude Toward Using (ATU) is 57%, d) Behavioral Intention to Use (BITU) is 65%, and e) Actual system usage (AU) is 75%. This proves that the use of the ZOOM application as an online learning medium cannot be fully explained by the Technology Acceptance Model.
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Ahmad Fauzi Ramadhan Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Alif Januantara Prima Amos Duan Nugroho Anto Widianto Arafat Febriandirza Ari Rizki Fauzi Artika Arista Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Chery Cardinawati Sitohang Danestiara, Venia R Dani Rizky Zaelani Darsiti . Dhika Wdiyanto Dirham Triyadi Dirham Triyadi Erpurini, Wala Esa Prakasa Fachrizal Ardiansyah Fahmi Abdullah Fahreja Ramadhan Fauzi Ramadhan, Ahmad Fikri Rizqillah Hasani Fitri Kinkin Gelar, Trisna Gunthur Bayu Wibisono Habibi, Chairul Hamzah, Encep Hani Fitria Rahmani Hasan Nuraripin Henki Bayu Seta Hernawan, Kartika Nursyabanita Ilham Ramadhan Ismi Nur Muhamad Jennifer Kaunang, Valencia Claudia Karlina, Nichi Hana Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Muhammad Noerhadi Muhammad Rizki Ramadhan Nasution, Vani Maharani Niqotaini, Zatin NUR ALAMSYAH Nur Alamsyah Nur Alamsyah Nur Alamsyah, Nur Nursyanti, Reni PARAMA YOGA, TITAN R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah R. Yadi Rakhman Alamsyah Raka Deny Abdi Putra Rakhman Alamsyah, Rd. Yadi Rd. Yadi Rakhman Alamsyah Rd. Zidni Rizan Al-Zhahir Yanuar Reni Nursyanti Reni Nursyanti Reni Nursyanti Reynaldy Gimnastiar Rezza Novian Noor Rochmat Rijwan Rijwan S.W. Manurip, Atanasius Angga Sardjono Setiana, Elia Silvana Anggraeni, Zulmeida Sophian Ramadhan Suci Fitriani Setiawan Tarsinah Sumarni Tiara Permata Hati Titan Parama Titan Parama Yoga Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Venia Restreva Danestiara Wulandari Wulandari Yoga Rizki Rahmawan Zein Suna Arfigan Said