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All Journal Dinamik JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknoinfo JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Tekno Kompak Progresif: Jurnal Ilmiah Komputer Jurnal Pengabdian Kepada Masyarakat Ungu ( Abdi Ke Ungu) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Sains Teknologi dan Sistem Informasi Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Sains dan Teknologi Journal of Engineering and Information Technology for Community Service Journal of Artificial Intelligence and Technology Information
Erliyan Redy Susanto
Universitas Teknokrat Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Optimasi Model Prediktif untuk Deteksi Dini Penyakit Hati Kronis melalui Seleksi Fitur dan Teknik Oversampling berbasis Machine Learning Agvina Maharani; Erliyan Redy Susanto
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10537

Abstract

Chronic liver disease is one of the leading causes of global morbidity and mortality, including in Indonesia. Early detection is essential to prevent disease progression to cirrhosis and hepatocellular carcinoma; however, clinical diagnosis is often delayed due to non-specific early symptoms and limited access to invasive diagnostic procedures. This study aims to develop and optimize a machine learning–based predictive model for early detection of chronic liver disease using clinical patient data. The Liver Cirrhosis dataset obtained from Kaggle was utilized, with preprocessing steps including missing value imputation, categorical variable encoding, feature selection using SelectKBest, and class imbalance handling through the Synthetic Minority Oversampling Technique (SMOTE). Three classification algorithms—Support Vector Machine (SVM), Random Forest, and XGBoost—were evaluated under a binary classification scheme of early-stage and advanced-stage disease. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the integration of feature selection and SMOTE improves model performance, particularly in enhancing sensitivity toward advanced-stage cases. XGBoost achieved the best overall performance based on AUC-ROC values, while Random Forest demonstrated a favorable balance between predictive performance and computational efficiency. This approach shows strong potential as a clinical decision support tool for early screening of chronic liver disease.
Klasifikasi Tipe Anemia berdasarkan Parameter CBC menggunakan Metode SVM Gilang Febrianto; Erliyan Redy Susanto
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10586

Abstract

The precision medicine paradigm is shifting conventional diagnosis toward AI-based systems. However, the literature on Support Vector Machines (SVM) with systematic hyperparameter optimization for the multi-class classification of anemia (Iron Deficiency/ID, Iron Deficiency Anemia/IDA, Normal) remains limited. This study develops an optimized SVM model for identifying three categories of anemia using a Kaggle dataset (1,000 samples, 6 features: Hemoglobin, RDW, MCV, Age, Gender, Anemia Type). The methods include data preprocessing, an 80:20 stratified split, and hyperparameter optimization via 5-fold cross-validation (CV) grid search with exploration of kernels (linear, RBF, poly) and the C parameter (0.1, 1, 10, 100). Results show that an SVM with a linear kernel and C=100 achieves 100% accuracy on the test data, with perfect precision, recall, F1-score, and ROC AUC (1.000) for all classes, as well as a cross-validation mean accuracy of 99% (±1%). Feature analysis identifies Hemoglobin as the dominant predictor, followed by RDW, MCV, Age, and Gender. The study’s contributions include an SVM benchmark framework for anemia classification, a demonstration of the effectiveness of hyperparameter optimization, and the strengthening of the Indonesian medical informatics literature in the application of machine learning to metabolic diseases, which accelerates the digital transformation of clinical hematology practice.
Analisis Implementasi Sistem Keamanan Basis Data Berbasis Role-Based Access Control (RBAC) pada Aplikasi Enterprise Resource Planning M Sahyudi; Erliyan Redy Susanto
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): April 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i1.3997

Abstract

Role-Based Access Control (RBAC) has become the main approach in improving data security in various information systems. This study analyzes the implementation of RBAC in the context of Enterprise Resource Planning (ERP) applications and cloud-based, mobile, and multi-domain systems. Using a systematic literature review (SLR) methodology, this study synthesizes findings from various studies to evaluate the effectiveness of RBAC in addressing challenges such as data privacy, regulatory compliance, and access policy complexity. The results show that the integration of intelligent technologies, such as machine learning (decision tree and random forest algorithms) for user behavior analysis, natural language processing for policy interpretation, and blockchain to record access activities with a security increase of up to 37%, can increase the flexibility and efficiency of RBAC, especially in detecting anomalies and managing dynamic policies. In addition, automation in RBAC deployments has been proven to reduce operational costs by 42% and management time by up to 65% compared to traditional manual approaches. However, RBAC implementation also faces significant challenges, including the need to adapt to complex regulations and the dynamics of a multi-domain environment. This research makes a theoretical contribution by expanding the understanding of the role of RBAC in modern data security management and offering practical recommendations for optimizing RBAC implementation. Thus, RBAC has proven to be a relevant and reliable model in answering data security needs in the digital era. 
Hybrid XGBoost-SVM Model untuk Sistem Pendukung Keputusan dalam Prediksi Penyakit Diabetes Muhammad Surono; Muhammad Fadli; Dian Sri Purwanti; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5410

Abstract

Diabetes is a chronic disease that continues to rise globally each year, requiring early detection for more effective prevention. This study develops an artificial intelligence-based decision support system for diabetes prediction using a Hybrid XGBoost-SVM model. The model combines the Support Vector Machine (SVM), known for its interpretability, with XGBoost (XGB), which enhances accuracy through boosting techniques. The study utilizes the Pima Indians Diabetes Dataset, undergoing preprocessing, normalization, data splitting, and model training. The evaluation compares accuracy, precision, recall, and F1-score across the three models. Experimental results indicate that XGBoost and SVM both achieve an accuracy of 75%. However, the Hybrid XGBoost-SVM model provides consistently improved performance, achieving the highest accuracy (77%), along with increased precision (70%) and F1-score (65%). Although the numerical improvement in accuracy appears relatively small, this enhancement is significant in the medical context, especially due to improved precision and balanced classification. This study concludes that the Hybrid XGBoost-SVM approach offers a more optimal and reliable alternative in decision support systems for diabetes prediction. Future research can explore other model combinations, such as Stacking or Weighted Voting, to enhance predictive performance further.
Deteksi Dini Stroke Menggunakan Machine Learning Kevinda Sari; Muhammad Fadli; Muhammad Fahmi Fudholi; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.5590

Abstract

Stroke is one of the leading causes of death and disability worldwide. Early detection of stroke risk is crucial to prevent more severe complications. This study aims to develop a stroke prediction model based on machine learning using an open dataset from Kaggle containing patients' medical and demographic information. Four machine learning algorithms were utilized and compared: AdaBoost, Gradient Boosting, LightGBM, and XGBoost. Data preprocessing steps included missing value imputation, categorical variable encoding, numerical feature normalization, and class balancing using the SMOTEENN method. Additionally, feature selection was performed using the Extra Trees algorithm to enhance model performance. The results showed that the XGBoost model delivered the best performance, achieving an accuracy of 97.16%, an F1-score of 97.49%, and an AUC of 99.75%. This model proved to be effective in detecting stroke cases and holds potential for integration into clinical decision support systems. The study concludes that a combination of modern boosting algorithms and optimal preprocessing techniques can yield a reliable stroke prediction system suitable for implementation in digital healthcare contexts.
Rancang Bangun Modul Kontrol Berbasis PID untuk Pengaturan Kecepatan dan Posisi Motor DC Menggunakan STM32 dan Rotary Encoder Doris Juarsa; Muhammad Fadli; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.6081

Abstract

Precision control of DC motors in industrial and robotics applications is often compromised by external loads and the limitations of open-loop systems, which cause instability in rotational speed and angular position. This study aims to design and build a PID-based intelligent control module for integrated DC motor speed and position control using an STM32F103C8T6 microcontroller and a rotary encoder as feedback. This system is designed as a closed-loop system to reduce the error between the setpoint and the actual value. Tests were conducted under no-load and with-load conditions at various speed setpoints (10–30 RPM) and angular changes (slow and fast). The results show that the system is able to stabilize motor performance with an average speed error of −0.3033 and 0.2766 RPM (no-load) for Motors A and B, and 0.2633 and 3.47 RPM (with-load). For angular position control, the average errors were 0.69° and 0.895° (without load), and 0.475° and 0.335° (with load). These findings demonstrate the effectiveness of the PID-based intelligent control module in improving system accuracy and stability. This system offers a compact and practical solution for industrial automation and robotics applications requiring precise motor control.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KEPALA BAGIAN MEKANIK MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) Renny Fatimah Az Zahra; Erliyan Redy Susanto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5528

Abstract

Proses penentuan kepala bagian merupakan salah satu aspek krusial dalam sebuah perusahaan. Kesuksesan perusahaan bergantung pada kemampuan kepala bagian untuk memimpin dan mengelola timnya. oleh karena itu diperlukan sistem yang terstruktur dan objektif dalam memilih kepala bagian yang tepat. PT. Hasil Sinar Baru Sentosa sebelumnya masih menggunakan sistem penilaian yang subjektif untuk menentukan kepala bagian mekanik. Hal ini menimbulkan beberapa masalah, salah satunya adalah ketidakakuratan dalam pemilihan. Untuk mengatasi masalah tersebut digunakan metode perhitungan yang lebih objektif menggunakan Sistem Pendukung Keputusan (SPK) dengan metode Analytical Hierarchy Process (AHP). Penelitian ini bertujuan untuk memberikan pendekatan sistematis dan terstruktur dalam pengambilan keputusan dengan cara menentukan kriteria yang relevan. Terdapat Empat kriteria yang digunakan dalam penelitian ini, yaitu pendidikan, pengalaman kerja, absensi, dan kepemimpinan, kriteria-kriteria tersebut dipilih berdasarkan kebutuhan perusahaan. Hasil penelitian ini menunjukkan bahwa metode AHP dapat digunakan untuk membantu perusahaan dalam memilih kepala bagian mekanik yang tepat. Implementasi metode AHP di PT. Sinar Baru Sentosa menghasilkan beberapa keuntungan, seperti meningkatkan objektivitas dan menghasilkan peringkat yang sesuai. Kebaharuan penelitian ini terletak pada penggunaan metode AHP untuk mengubah proses penentuan kepala bagian yang sebelumnya subjektif menjadi objektif, hal ini membantu perusahaan dalam membuat panduan pengambilan keputusan yang lebih terarah.
Pendekatan Machine Learning Untuk Prediksi Motif Batik Indonesia Wahyu Marzian Putra; Erliyan Redy Susanto; Neneng Neneng
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 3 (2026): Volume 4 Number 3 September 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i3.270

Abstract

Batik merupakan warisan budaya tak benda Indonesia yang diakui UNESCO sejak tahun 2009. Perkembangan teknologi kecerdasan buatan, khususnya deep learning, membuka peluang otomatisasi klasifikasi visual batik. Namun, klasifikasi otomatis batik Indonesia masih menghadapi tantangan utama, yaitu ketidakseimbangan kelas pada dataset (imbalanced dataset) dan kemiripan visual antarkelas yang tinggi . Penelitian ini mengembangkan sistem klasifikasi 20 jenis batik Indonesia menggunakan model YOLOv11 yang diintegrasikan dengan teknik Synthetic Minority Over-sampling Technique (SMOTE) serta skema 5-Fold Stratified Cross-Validation guna memperoleh evaluasi yang objektif. Dataset terdiri atas 983 gambar yang diakuisisi dari repositori Kaggle. SMOTE diterapkan pada representasi fitur piksel berdimensi tinggi (d = 12.288) untuk menyeimbangkan distribusi antarkelas tanpa mengorbankan informasi kelas mayoritas. Model YOLOv11 dilatih pada setiap lipatan dengan konfigurasi hyperparameter terkontrol menggunakan pengoptimasian AdamW. Hasil penelitian menunjukkan nilai mean Average Precision (mAP) keseluruhan sebesar 0,8251 dan rata-rata mAP per lipatan sebesar 0,8788 ± 0,0264, dengan konsistensi metrik yang tinggi di seluruh lipatan validasi. Analisis matriks kebingungan mengindikasikan bahwa misclassification cenderung terjadi antara kelas batik yang memiliki kemiripan visual tinggi. Kerangka kerja yang diusulkan berpotensi diimplementasikan sebagai sistem identifikasi batik otomatis untuk mendukung pelestarian warisan budaya, autentifikasi produk pada platform e-commerce, serta dokumentasi koleksi museum di era digital.
Co-Authors ., Rusliyawati Adi Sucipto, Adi Agus Wantoro Agvina Maharani Ajeng Savitri Puspaningrum Alita, Debby Andi Nurkholis Apri Candra Widyawati Ari Sulistiawati Ari Sulistyawati Bayu Dwi Juniansyah Damayanti, Damayanti Dian Pratiwi Dian Sri Purwanti Dimas Eko Putro Dimas Farian Savero Doris Juarsa Ebi Supriyadi Fadli, Muhammad Fahri Aulia Hasbi Feby Amanda Fenty ariany Ferdy Ramadhan Fitrah Amalia Rahmadianti Galuh Eka Saputra Geri Marizki Gilang Febrianto Gita Rahmawati Helma Nopijani Heidy Heni Sulistiani Hironimus Edit Kristanto Icha Winadya Permadani Ikhu Muhibatul Umaya Iqbal Isnsan Athok Jhonnry Frengky Bire Logo Juvenski Juvenski Kevinda Sari Komang Anita M Sahyudi M. Ghufroni An'ars Mia Melinda Muhamad Fitratullah Muhamad Taufik Adha Pratama Muhammad Aldhi Septianto Muhammad Fadli Muhammad Fahmi Fudholi Muhammad Surono Neneng Neneng Nirwana Hendrastuty Priandika, Adhie Thyo Raka Sulistyo Rendy Pratama Renny Fatimah Az Zahra Rohmat Indra Borman S. Samsugi Setiawansyah Setiawansyah sodikin sodikin sodikin Stya Bagus Pujiatma Styawati Styawati Tien Yulianti Tria Setyani Ulum, Faruk Wahyu Marzian Putra Wahyudi, Agung Deni Wawan Kurniawan Wisnu Mukti Dwiyudho Putro Yulia Indriani Zahara Yunita