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All Journal Bulletin of Electrical Engineering and Informatics Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Universitas Batanghari Jambi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Aisyah Journal of Informatics and Electrical Engineering Journal of Information Systems and Informatics Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Respati Jurnal Abdi Insani JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Computer System and Informatics (JoSYC) Jurnal Graha Pengabdian Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA SENADA : Semangat Nasional Dalam MengabdI Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia Bulletin of Network Engineer and Informatics (BUFNETS) SENADA : Semangat Nasional Dalam Mengabdi TECHNOVATAR Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Jurnal Teknik AMATA Jurnal TAM (Technology Acceptance Model)
Kusnawi Kusnawi
Universitas Amikom Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Random Search Optimization Using Random Forest Algorithm For Liver Disease Prediction RIYAN BAYU SATRIYA; Kusnawi Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15468679

Abstract

The liver is a vital human organ with complex and diverse functions. One of the diseases that affect the liver is hepatitis or liver disease. Early detection is crucial to enable more effective intervention and slow the progression of the disease. However, diagnosing liver disease often faces challenges, especially in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the Random Forest algorithm using the Random Search method for liver disease detection. The Random Forest algorithm is applied as the primary model in this research, while hyperparameter optimization is performed using the Random Search method to enhance model performance. The results show that the Random Forest model without optimization achieves an accuracy of 93%. After hyperparameter optimization, the model's accuracy increases to 94%. In conclusion, applying hyperparameter optimization using the Random Search method successfully improves the performance of the Random Forest model. The resulting model provides more accurate predictions.
AI Web-based Computer Service Management System at PUSCOM Muhammad Irvan Shandika; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16280893

Abstract

This research aims to develop a web-based computer service management system with artificial intelligence (AI) integration at PUSCOM to address challenges in manual service management, such as customer data recording, service status tracking, and report generation. The problems faced by PUSCOM include potential data errors, loss of physical documents, and delays in performance evaluation due to manual processes. The research method used is the Agile SDLC approach, covering problem identification, data collection through interviews and documentation, functional and non-functional requirements analysis, system modeling using UML, NoSQL Firebase database design, interface design, implementation using Next.js and Javascript, and AI chatbot integration using Vercel AI SDK with the Google Gemini model. The research results demonstrate the successful development of a system capable of automating data recording, facilitating online service registration, managing products, and providing an AI chatbot to assist admins in report generation and real-time damage analysis. This system is proven to enhance operational efficiency, reduce manual errors, and support strategic decision-making at PUSCOM, contributing to improved service quality and customer satisfaction.
COMPARISON OF BAYESIAN MARKOV CHAIN MONTE CARLO AND MACHINE LEARNING ALGORITHMS FOR STUDENT CUMULATIVE GRADE POINT AVERAGE PREDICTION WITH FEATURE ENGINEERING Husni Hidayat Malik; Indra Surya Permana; Alva Hendi Muhammad; Kusnawi Kusnawi
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/vfk6qw80

Abstract

Prediksi Indeks Prestasi Kumulatif (IPK) merupakan salah satu tantangan penting dalam manajemen akademik perguruan tinggi. Penelitian ini mengusulkan penerapan algoritma Markov Chain Monte Carlo (MCMC) berbasis inferensi Bayesian untuk memprediksi IPK mahasiswa berdasarkan Indeks Prestasi Semester (IPS) 1-8, dilengkapi dengan tiga fitur rekayasa: rata-rata IPS, variabilitas IPS (standar deviasi), dan tren IPS. Keunggulan utama pendekatan Bayesian adalah kemampuannya menghasilkan kuantifikasi ketidakpastian (uncertainty quantification) berupa interval kepercayaan 95% untuk setiap prediksi, yang tidak dapat dilakukan metode machine learning konvensional. Model MCMC dibandingkan secara komprehensif dengan tujuh algoritma machine learning: Random Forest, XGBoost, Gradient Boosting, SVM, KNN, AdaBoost, dan Bagging, menggunakan dataset 543 mahasiswa dari Institut Teknologi dan Kesehatan Mahardika periode 2017-2021. Evaluasi dilakukan melalui train-test split (80:20) dan 5-fold cross-validation menggunakan metrik RMSE, MAE, MAPE, dan R². Hasil pada data testing menunjukkan MCMC Bayesian memperoleh RMSE 0.0792 dan R² 0.867, berada pada peringkat kedua setelah Gradient Boosting. Namun pada evaluasi cross-validation yang lebih robust, MCMC Bayesian unggul dengan RMSE terendah 0.0832±0.0162 dan R² tertinggi 0.848. Temuan ini menunjukkan MCMC Bayesian tidak hanya kompetitif dari sisi akurasi, tetapi juga memberikan nilai tambah unik berupa estimasi ketidakpastian prediksi yang sangat berguna untuk sistem peringatan dini akademik.   Predicting Cumulative Achievement Index (GPA) is a key challenge in higher education academic management. This study proposes the application of Markov Chain Monte Carlo (MCMC) Bayesian inference for predicting student GPA based on Semester Achievement Index (IPS) from semesters 1-8, enriched with three engineered features: IPS mean, IPS variability (standard deviation), and IPS trend. The primary advantage of the Bayesian approach is its ability to produce uncertainty quantification in the form of 95% credible intervals for each prediction, which conventional machine learning methods cannot provide. The MCMC model was comprehensively compared against seven machine learning algorithms: Random Forest, XGBoost, Gradient Boosting, SVM, KNN, AdaBoost, and Bagging, using a dataset of 543 students from Institut Teknologi dan Kesehatan Mahardika for the period 2017-2021. Evaluation was conducted through an 80:20 train-test split and 5-fold cross-validation using RMSE, MAE, MAPE, and R² metrics. Test set results show MCMC Bayesian achieved RMSE 0.0792 and R² 0.867, ranking second after Gradient Boosting. However, in the more robust cross-validation evaluation, MCMC Bayesian outperformed all competitors with the lowest RMSE of 0.0832±0.0162 and highest R² of 0.848. These findings demonstrate that MCMC Bayesian is not only competitive in accuracy but also provides the unique value of prediction uncertainty estimation, which is highly useful for early academic warning systems
Interpretable Feature-Scenario Analysis for Ethereum Transaction Anomaly Detection Using Random Forest and XGBoost Indana Zulfa; Kusnawi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1727

Abstract

The substantial and imbalanced volume of Ethereum transactions presents significant challenges for anomaly detection, especially when labels serve as proxies for execution errors rather than confirmed fraud. An interpretable feature-scenario framework was developed utilizing Logistic Regression, Random Forest, and XGBoost on 4,604,555 unique transactions. The isError attribute was employed as a proxy anomaly label. Data splitting occurred prior to address encoding; encoders were trained exclusively on the training set, unseen wallets were assigned a reserved code, and Random Under Sampling (RUS) was applied solely to training data. Evaluation incorporated both an imbalanced random test set and future-block validation. Among 920,911 random-test transactions (3.59% anomalies), Random Forest, excluding the Hour feature and without resampling, achieved optimal operational performance: 0.8204 precision, 0.6716 recall, 0.7386 F1-score, 0.7820 PR-AUC, 0.7338 MCC, and a 0.0055 false-positive rate. Application of RUS increased recall to 0.9035 but reduced precision to 0.3088, resulting in 69.12% of 96,703 alerts being false positives. Future-block validation further reduced PR-AUC to 0.0177 and MCC to 0.0678, indicating a substantial distribution shift. SHAP identified destination-wallet encoding and BlockHeight as the most influential model features, while LIME provided local, non-causal explanations. The primary contribution is an interpretable feature-scenario and validation framework; however, verified malicious labels and dynamic graph representations are still required for operational deployment.
A Modular Agroclimatic Feature Engineering Framework for Country-Level Crop Yield Prediction Using XGBoost and LightGBM Ledyvia Audiz Coranov; Kusnawi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1762

Abstract

Accurate crop yield prediction is essential for supporting food-security assessment, agricultural planning, and policy-level decision-making. This study proposes a modular agroclimatic feature engineering framework for country-level crop yield prediction using XGBoost and LightGBM. The proposed framework integrates climate indicators, climate–pesticide interactions, temporal descriptors, composite agroclimatic indices, and nonlinear transformations to improve predictive representation while controlling data leakage. Experiments were conducted on 28,151 country–crop–year observations from 98 countries covering 1990–2013. To evaluate temporal and geographic generalization, models were assessed using time-based validation, Random KFold, GroupKFold by country, bootstrap confidence intervals, and held-out-country evaluation. Results show that LightGBM with the S3 feature configuration achieved the best temporal prediction performance, obtaining an R² of 0.9492 and RMSE of 21,257 hg/ha. However, held-out-country evaluation revealed lower transferability, with the best configuration achieving R² of 0.6694, highlighting the challenge of geographic generalization. The findings demonstrate that engineered agroclimatic features can significantly improve country-level crop yield prediction, but model performance depends strongly on the validation setting. This framework provides a reliable benchmark for leakage-controlled agricultural machine learning research rather than direct farm-level operational forecasting.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Aldi Yogie Pramono Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri Arief Maehendrayuga Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Christa Putri Rahayu Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dharma Kusumah, Prema Adhitya Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fajar Abdillah, Moh Fajar Aji Prayoga Hakiki, Muhammad Ridhwan Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Husni Hidayat Malik Indana Zulfa Indra Surya Permana Irawanto, Indra Joang Ipmawati Joang Ipmawati Joang Ipmawati Juventania Sheva Mellany Karisma Septa Kresna Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini, Kusirini Ledyvia Audiz Coranov M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Melcior Paitin Kanoena Mochamad Agung Wibowo Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Olajuwon, Sayyid Muh. Raziq Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pebri Antara Pitaloka, Nadhira Triadha Prastyo, Rahmat Pringandana, Cokorda Gde Lanang Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Raffa Nur Listiawan Dhito Eka Santoso RAMADHAN, SYAIFUL Ridwan Sanjaya Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun RIYAN BAYU SATRIYA Rizal Khadarusman Rodney Maringka Rohim, Ni’matur saifulloh Saifulloh, saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sentoso, Thedjo Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Sudirman, San Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko, Taryoko Tegar Wirawan Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Wibowo , Mochamad Agung Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yudha Bagas Pattimura Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri